Price Action Pattern Breakout Strategy: Wedge,Triangle,ChannelIntroducing the Price Action Pattern Breakout Strategy: Wedge,Triangle,Channel 💹🚀
The "Price Action Pattern Breakout Strategy: Wedge, Triangle, Channel" is a dynamic and automated trading strategy that excels in recognizing and capitalizing on breakout opportunities within the realm of powerful price action patterns. It is finely tuned to achieve exceptional precision in detecting three distinct pattern types: Wedge, Triangle, and Channel. This diversity equips you to confidently navigate a wide range of market scenarios and opportunities.
This strategy automates trade entries and exits upon confirmed pattern breakouts, this eliminates human errors in correctly recognizing patterns and prevents emotional decisions. This strategy is designed to work across different time frames, making it suitable for both short-term and long-term traders. Whether you're a day trader, swing trader, or investor, this strategy provides the flexibility you need to thrive in diverse market conditions.
💎 How it Works:
▶️ In this strategy, three price action patterns have been utilized, one of which is the "Wedge" pattern. The Wedge pattern has consistently demonstrated a high level of credibility, typically resulting in sharp and rapid price movements following a confirmed breakout from this pattern. This characteristic makes the Wedge pattern highly noteworthy in our strategy. The second pattern is the "Triangle" pattern, which, depending on its formation, whether ascending or descending, can indicate a strong continuation or reversal of the trend. The last pattern is the "Channel" pattern. The reason for using the Channel pattern is its versatility in various market conditions and its tendency to produce reliable results.
In the snapshot below, you can observe the types of patterns that this strategy is capable of identifying at a glance:
▶️ This strategy employs two types of targeting systems: Fixed Targets and Trailing Targets.
Fixed Targets is the default targeting system of the strategy, incorporating two primary targets: TP1 (Target Point 1) and TP2 (Target Point 2). These targets are thoughtfully adjusted in alignment with specific rules for each pattern. With Fixed Targets, you have the flexibility to designate the position size percentage for your exits at TP1 and TP2. For instance, should you opt to allocate 60% of your position size to TP1, as soon as the price triggers the first take profit level, 60% of your initial position is gracefully closed, leaving the remaining 40% to exit the trade upon reaching TP2.
Trailing Targets represent the strategy's alternative targeting system. With this system, the trailing stop becomes active once the price reaches the specified trigger point. The strategy then exits the trade based on the defined offset percentage and price retracement from the trailing limit.
▶️ This strategy relies on a single type of stop loss, determined by previous pivot points and adjusted based on the trade's direction, whether long or short, placing the stop loss above or below the prior pivot. This stop loss approach has demonstrated reliability when used alongside price action patterns.
In addition to this fixed stop loss, you can specify a percentage buffer, offering protection against potential stop hunting due to market fluctuations. This buffer helps protect your positions from sudden price swings. For example, selecting a 1% buffer means your stop loss will be positioned 1% higher or lower concerning the last pivot, depending on your trade's direction. This added layer of security ensures your trades remain resilient and less vulnerable to market volatility.
▶️ A practical feature of this strategy is the "Risk-Free" option. Once activated, it continuously monitors price movements, and as soon as the price progresses in the trade's direction and surpasses the designated Risk-Free Trigger Point in percentage, the stop loss is dynamically shifted from its initial position to the entry price, effectively making the trade "risk-free." This means that if the trade doesn't go as expected, we exit at the entry point, incurring neither profit nor loss from the trade.
Additionally, you have the flexibility to fine-tune the modified stop loss, positioning it slightly above or below the entry price through the configuration of a specified percentage. This allows for effective consideration of commission fees in your trading strategy.
▶️ Risk management is a crucial concept in trading, playing a significant role in a trader's long-term success. This strategy introduces a unique feature called "Fixed Loss Position Sizing", where upon activation, you can limit the risk exposure to a specified percentage of your capital per trade. Set your preferred risk percentage along with the intended leverage. The strategy independently considers your available capital and designated leverage, determining the position size before executing any trade.
In the case of a stop loss, your loss is limited to the specified risk percentage. For instance, with a $1000 account and a 1% risk set, the strategy adjusts each trade's size to ensure a maximum loss of $10 if the stop loss is triggered. Enabling this feature will ensure disciplined risk management, aligning potential losses precisely with your predetermined risk percentage, contingent upon your total available capital.
▶️ Another feature of this strategy is a sophisticated mechanism called "Loss Compensation". When enabled, Loss Compensation dynamically adjusts the position size after a loss, aiming to recover from previous losses in subsequent trades. This adaptive mechanism continually modifies the position size to mitigate the impact of consecutive losses until reaching a user-defined limit for consecutive loss compensations.
The feature's configurability allows users to set the maximum number of consecutive losses to compensate for and also includes an option to factor in trading fees from prior trades into the compensation calculation. Loss Compensation operates in conjunction with the 'Fixed Loss Position Sizing' setting, ensuring that once losses are sufficiently compensated, subsequent entries revert to the predefined configurations within the 'Fixed Loss Position Sizing' settings.
This advanced tool ensures a stable risk management approach by changing trade sizes dynamically according to past results during consecutive loss periods.
▶️ This strategy incorporates a feature known as the "Counter-Pattern Breakout", altering its approach to wedge, triangle, and channel pattern breakouts. Normally, the strategy relies on standard pattern signals to determine whether to enter long or short positions based on breakout directions.
For example, in an ascending channel or a rising wedge pattern, the strategy typically seeks a short position opportunity upon a confirmed breakout in the lower line, and breakouts from the upper line are disregarded by the strategy. But with this feature enabled, strategy disregards the conventional pattern signals, seizing breakouts from upper or lower lines to open corresponding positions. For instance, in the ascending channel or the rising wedge pattern example, the strategy might enter a long position if the upper line breaks or a short position if the lower line breaks.
This introduces a more adaptive and opportunistic trading style, allowing you to capitalize on price movements, irrespective of the typical signal direction indicated by the pattern.
▶️ This strategy is fully compatible with third-party trading bots, allowing for easy connectivity to popular trading platforms. By leveraging the TradingView webhook functionality, you can effortlessly link the strategy to your preferred bot and receive accurate signals for position entry and exit. The strategy provides all the necessary alert message fields, ensuring a smooth and user-friendly trading experience. With this integration, you can automate the execution of trades, saving time and effort while enjoying the benefits of this powerful strategy.
⚙️ How to Use & Configure User Settings:
To fully utilize the "Price Action Pattern Breakout Strategy: Wedge, Triangle, Channel," it's essential to consider and comprehend the following steps. They play a crucial role in enhancing its functionality and achieving its utmost potential outcomes:
1. General Strategy Settings:
Enable Dark Mode if using a dark TradingView theme for improved chart visibility.
Select the Strategy's Trade Direction: Long, Short, or Both.
Choose Pattern Recognition Accuracy: High for precise recognition but fewer positions, Low for more positions with slightly less accuracy.
Enable 'Prevent New Entry on Opposite Signal While In Position' to avoid new trades if the opposite signal occurs.
Switch to Indicator Mode if solely using the strategy as an indicator or in combination with other strategies.
2. Pattern and Pivot Configuration:
Consider configuring the Number of Patterns and Pivot Lookback Lengths. Here, you can personalize the pivot lookback lengths for wedge, triangle, and channel patterns across eight different settings on your chart. For lower time frames, consider larger lengths to reduce chart noise. Alternatively, to maintain clarity on your chart, you can disable multiple patterns with different lengths while ensuring at least one pattern remains enabled.
Note that enabling more patterns doesn't always equate to increased potential profit. Sometimes, fewer patterns result in greater profit potential, and vice versa. Experiment with lengths and the number of patterns to determine the most profitable and optimal outcome for your trading symbol and timeframe.
3. Targeting System Selection:
Choose between 'Fixed Targets' or 'Trailing Targets' for your targeting system.
'Fixed Targets' is the default setting, operational when 'Trailing Targets' are turned off.
Set the TP1 Position Size as a percentage, defining the size for TP1, and the rest exits at TP2.
Optionally activate 'Skip Entry if TP1 is Passed' to bypass entering positions if the price has exceeded TP1.
Alternatively, opt for the 'Trailing Target' for dynamic exits based on trigger points and offsets. Note that this option disables fixed targets.
4. Stop Loss Configuration:
Determine the number of candles to consider for stop loss placement based on the last pivot.
Optionally add a percentage to the stop loss to create a buffer against market fluctuations, guarding your positions from sudden price swings.
5. Risk Management Configuration:
You can activate the 'Risk-Free' feature, making your trades risk-free by moving the stop loss to the entry price upon reaching a specified trigger point.
You have the possibility to enable 'Fixed Loss Position Sizing' to limit risk to a percentage of total capital per trade, ensuring prudent risk management.
You can employ 'Use Real-Time Balance for Each Entry' to precisely calculate fixed loss position sizing according to the real-time balance for every entry.
The 'Loss Compensation' feature can be activated to automatically adjust trade sizes during consecutive losses and compensate for prior incurred losses.
Loss compensation continues adjusting trade sizes until it reaches the defined limit of consecutive losses specified in the 'Maximum Consecutive Losses To Compensate' field.
You can factor in commission fees by specifying a percentage in the 'Include Trading Fees in Compensation (%)' field, providing an option for more accurate loss compensation calculations.
You have the option to enable 'Limit Compensation to Real-Time Balance' to prevent consecutive loss compensation from exceeding your current real-time account balance.
It's important to note that for the 'Loss Compensation' feature to operate, the 'Fixed Loss Position Sizing' must be enabled.
6. Counter-Pattern Breakout Configuration:
In this section you have the option to enable the "Counter-Pattern Breakout" feature to adjust the strategy's approach to wedge, triangle, and channel pattern breakouts. Once enabled, the strategy disregards traditional pattern signals and capitalizes on breakouts from either the upper or lower lines, initiating corresponding positions accordingly.
Choose between 'Fixed Target' or 'Trailing Target' for your targeting system. If you opt for the 'Fixed Target', set a specific target point as a percentage, serving as the default target for counter-pattern breakouts. Alternatively, choose the 'Trailing Target' for dynamic exits based on trigger points and offsets. Do keep in mind that selecting the 'Trailing Target' option disables the fixed target setting.
Keep in mind that for standard, non-counter-pattern breakouts, the target point settings in their respective sections remain applicable, distinct from the settings configured for targeting within this section.
Note that the stop loss configurations are shared across standard pattern and counter-pattern breakouts and can be adjusted within the stop loss section.
7. Info Tables:
In the info tables section, you can show or hide different tables on the charts. This includes the backtest table, the current balance table displaying available funds, and a table showcasing Maximum Consecutive Wins or Losses. Choose which to display according to your preferences and specific needs.
8.Date & Time Range Filter:
Utilize the Date & Time Range filter feature to precisely select a start and end date, including time, to filter data within the chosen range.
When connecting this strategy to a trading bot for automated trades, ensure to set the start date and time to the intended initiation moment to avoid undesired outcomes as this directly affects the real-time balance calculations of the strategy.
8. Integration with Third-Party Bots:
To automate trading, leverage the strategy's compatibility with third-party trading bots. Seamlessly integrate the strategy into well-known trading platforms by using alert message fields to input commands from third-party trading bots, enabling automated trade execution for both long and short positions.
By furnishing these adjustable settings, the strategy empowers you to personalize it according to your unique requirements, thereby bolstering the adaptability and efficacy of your trading approach.
🔐 Source Code Protection:
The 'Price Action Pattern Breakout Strategy: Wedge, Triangle, Channel' source code is engineered for precision, reliability, and effectiveness. Its original and innovative design warrants protection and restricted access, preserving the strategy's exclusivity. Safeguarding the code maintains the strategy's integrity and distinctiveness, providing users with a competitive advantage in their trading endeavors.
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Strategy Developer ToolSolar Strategies: Strategy Developer Tool Complete Guide
This guide provides full explanation of the intended purpose of our script along with individual explanation of each input and the logic behind them coupled with general knowledge which we find useful in using our tool regarding elements of risk and strategy. Use this information wisely and understand we are not providing financial advise, this is a learning tool meant to help advance traders knowledge of the markets and their strategies which are formed as such.
Basics
Before getting into the specifics of how to use our strategy developer tool, it's important to understand a few basic fundamental things about it. The purpose of the tool is to allow the user to optimize a strategy through back testing with our strategy tracker and 50+ user inputs. The way you optimize your strategy depends on a couple things:
The state of the current and recent previous market.
The timeframe you trade on.
The types of trades you prefer. (swings, scalps, etc.)
How much risk you are willing to take on.
Risk Basics:
Going off the last bullet point on the list above, risk plays a huge part in how you optimize your strategy, with that being said here are a few general rules of risk as they relate to trades:
The more trades you take on, the more risk you are opening your strategy up to.
If done correctly, more trades will often result in more profit with slightly lower accuracy, and more risk.
The less trades you take on, the easier it is to have higher accuracy because ideally by rooting out the losing trades, you are left with fewer overall trades but mostly winning trades.
Less trades with higher accuracy often result in less profit but will 100% be less risky than the opposite. (More trades, less accurate, more profit, MORE RISK)
Input Basics:
More trades, less trades, more risk, less risk, what does this all mean as it relates to our tool?
The 50+ user inputs that allow you to optimize and create your strategy all effect when the script takes a trade.
Many of the inputs are essentially conditions. By changing these inputs, what you are doing is changing how specific the conditions need to be in order to take a trade.
This is how the inputs tie into the bullet point list above regarding risk and the number of trades you take on. By raising or lowering certain inputs, you are making the conditions more or less specific on when to trade.
Making conditions more specific will allow for less trades to be taken and will often result in a higher win rate, and less associated risk.
Making conditions less specific will allow for more trades to be taken and depending on the state of the market, could result in more profit being realized, but at the same time opens you up to more risk because you are stating a more general set of conditions in order to take a trade.
How does it work?
Our strategy developer tool is based on two simple factors in order to identify specific areas in the market deemed good for trade. They are as follows:
Directional momentum to identify when a move might happen.
A confirmation of the desired move.
Indicators:
The tool gets its information on these two factors from two custom built indicators which are hard coded into the script. These two indicators and the inputs which affect them can be found labeled with Indicator 1 or Indicator 2 in the tool's settings.
When the conditions are met based on the factors of both indicators, it then decides your stop losses and take profits using pivot points.
Indicator 1 is the momentum indicator.
Indicator 2 looks for confirmation of the move.
Hedges:
Since nothing is ever certain when trading, our tool also aims to minimize potential loss before it can happen by incorporating hedges when a signal prints in the opposite direction of the trade you are currently in.
To identify when to hedge, the candles will appear with the opposite color of your original trade. Candles, while in a long trade, appear as green and candles while in a short trade appear as red. While in a long trade the only time red candles will appear is when a hedge occurs and vice versa for shorts.
Example: If you just took a long trade based on a long signal that the script gave off, but a short signal prints off while you are in the long, you are directed to sell half your long position and enter that half into a short position. Since there is now more uncertainty in the long because of the short signal, minimizing your position size and having a smaller position in the opposite direction allows you to cover your bases if the trade moves against you. If it doesn’t move against you and ends up going long as originally intended, you are not to lose any money, likely a small profit or break even when all is said and done.
In order to give the hedges a greater change of hitting, the take profits are smaller than a normal trade, this way even if your hedge wasn’t necessary and the original trade does not move against you, it's likely that your hedge will still win, and you can just consider it a small scalp to further your profits on the original trade.
Doubles:
Besides minimizing loss, we also aim to maximize the potential gain. When a second signal prints off in the direction of the trade you are currently already in, the tool directs you to double your position size.
The signal for doubling is a label with “2x” written inside.
The logic here is similar to hedging but in the opposite way. Just as a signal in the opposite direction creates uncertainty, a signal in the same direction indicates more certainty hence doubling your position size.
Example: If you are currently in a long position and you get a second long signal, you would then double your existing position since two long signals printing off before the first one has a chance to play out indicates a stronger chance of movement in the intended direction of your trade.
User Inputs
Upon opening the tools settings tab, you will find all the user inputs which can then be modified to fit your desired strategy. In this section of our guides, you will find individual explanations and use cases for each input so you can correctly use them to your best advantage.
Strategy Tracker Table:
By ticking this input on, the strategy tracker table will be visible to the user. (Default is on)
Indicator 1 Greater Than: Long:
By ticking this input on, you are adding a condition the script will then look for in order to take a long. (Default is on)
This condition is that an average of indicator 1, which searches for momentum, must fall above a certain level, which is determined in the next input.
The purpose of this is to ensure that the average momentum is not too low because this would indicate prolonged downwards movement on the timeframe of the market being observed, making a long position riskier.
Indicator 1 Greater Than Input: Long:
This input correlates to the previous input directly above.
If Indicator 1 Greater Than: Long is ticked on, then one of the conditions in order to take a long position will be that the average of indicator 1 must fall above the level which you set in this input.
max level 100, min level 0
Indicator 1 Less Than: Long
By ticking this input on, you are adding a condition the script will then look for in order to take a long position. (Default is on)
This condition is that an average of indicator 1, which searches for momentum, must fall below a certain level, which is determined in the next input.
The purpose of this is to ensure that the average momentum is not too high, because this would indicate a prior significant upwards movement or trend on the timeframe of the market being observed.
Taking a long position while the average momentum is at higher levels exposes the risk of longing as the market has started to pull back from a peak or when the market has just reached a peak.
Indicator 1 Less Than Input: Long
This input correlates to the previous input directly above.
If Indicator 1 Less Than: Long is ticked on, then one of the conditions in order to take a long position will be that the average of indicator 1 must fall below the level which you set in this input.
max level 100, min level 0
Indicator 1 Greater Than: Short
By ticking this input on, you are adding a condition the script will then look for in order to take a short. (Default is on)
This condition is that an average of indicator 1, which searches for momentum, must fall above a certain level, which is determined in the next input.
The purpose of this is to ensure that the average momentum is not too low because this would indicate prolonged downwards movement or trend on the timeframe of the market being observed.
Taking a short position while the average momentum is at lower levels exposes the risk of shorting as the market has started to recover from a bottom or when the market has just reached a bottom.
Indicator 1 Greater Than Input: Short
This input correlates to the previous input directly above.
If Indicator 1 Greater Than: Short is ticked on, then one of the conditions in order to take a short position will be that the average of indicator 1 must fall above the level which you set in this input.
max level 100, min level 0
Indicator 1 Less Than: Short
By ticking this input on, you are adding a condition the script will then look for in order to take a short position. (Default is on)
This condition is that an average of indicator 1, which searches for momentum, must fall below a certain level, which is determined in the next input.
The purpose of this is to ensure that the average momentum is not too high, because this would indicate a prior significant upwards movement or trend on the timeframe of the market being observed.
Taking a short position while the average momentum is at higher levels exposes the risk of shorting as the market is currently in a strong uptrend.
Indicator 1 Less Than: Short
This input correlates to the previous input directly above.
If Indicator 1 Less Than: Short is ticked on, then one of the conditions in order to take a short position will be that the average of indicator 1 must fall below the level which you set in this input.
max level 100, min level 0
Summary of Input Group: Indicator 1 Greater/Less Than Long/Short
This grouping of inputs is best used as a filter of sorts, much like many of the other inputs which are also essentially filters of the market to find areas ripe for trade. Specifically, however, this group of inputs is especially powerful because if used correctly, it can specify a range for the average momentum to fall in when looking for either long or short trades. Think of it like a sweet spot where the average is not too high nor too low. In combination with the numerous other inputs which will shortly be explained, this sweet spot can be a great indication. Keep in mind that once you find a working range, this will not last forever. Conditions in the market are ever changing and as such your inputs, in this case the range the average momentum must fall in, will also need to change with the market conditions.
Bars Since Crossover:
This input simply describes a crossover of the momentum indicator (indicator 1) and its average.
In the category How does it work? Two main factors are discussed, the first being directional momentum to determine when an upwards move might happen. The crossover correlated to this input is the directional momentum as mentioned earlier.
As also mentioned in How does it work? The second factor is a confirmation of the desired upwards move. This confirmation is a crossover of the current price and indicator 2 which will be further addressed later on.
What's important to understand about the two key factors at play in regard to Bars Since Crossover is that this input is determining a condition which looks for a certain number of bars prior to the confirmation of indicator 2 which the crossover of momentum and its average has happened on indicator 1.
Example: Bars Since Crossover input is set to 10. This means that the crossover of momentum and its average from indicator 1 must be within 10 bars prior to the confirmation from indicator 2. If this happens then this condition is met for a long position.
Bars Since Crossunder:
This input simply describes a crossunder of the momentum indicator (indicator 1) and its average.
In the category How does it work? Two main factors are discussed, the first being directional momentum to determine when a downwards move might happen. The crossunder correlated to this input is the directional momentum as mentioned earlier.
As also mentioned in How does it work? The second factor is a confirmation of the desired downwards move. This confirmation is a crossunder of the current price and indicator 2 which will be further addressed later on.
What's important to understand about the two key factors at play in regard to Bars Since Crossunder is that this input is determining a condition which looks for a certain number of bars prior to the confirmation of indicator 2 which the crossunder of momentum and its average has happened on indicator 1.
Example: Bars Since Crossunder input is set to 10. This means that the crossunder of momentum and its average from indicator 1 must be within 10 bars prior to the confirmation from indicator 2. If this happens then this condition is met for a short position.
Summary of Input Group: Bars Since Crossover/Crossunder
These two inputs can have a large effect on the types of trades being taken and the risk which your strategy opens up to. The idea is that in order for the two key factors described in How does it work? to be correlated and therefore indicate a strong directional move, the two events must happen within a somewhat small period of time. If the period of time between the two events taking place is too large, then it's riskier for your strategy due to a delay in directional momentum and the necessary confirmation. It's important to note that this “small period of time” is relative to the security you're trading and the timeframe its being trades on. Small could mean 5 bars in some cases or 20 bars in others, this is why our custom back tester exists. So that the process of optimization on different securities and different timeframes is smooth and only requires adjustments to inputs then your own analysis of the back test results.
Indicator 1 Input Long
Defines how strong the upwards momentum needs to be in order to take a long position.
When optimizing your strategy, this input is likely to have some of the most effect on when the script takes a long position.
The reasoning for this is because the level you set for this input is the level which indicator 1 must close above following the crossover of its average.
Example: Indicator 1 Input Long set to 50, this means that when the momentum crosses over its average from indicator 1, upon the close of this crossover the momentum must be above the level 50 in order for this condition to be met to take a long position.
The higher the level, the stronger the upwards momentum must be, and therefore by using higher levels for this input, the script will search for stronger directional moves leaving less chance for the trade to move against you.
Indicator 1 Input Short
Defines how strong the downwards momentum needs to be in order to take a short position.
When optimizing your strategy, this input is likely to have some of the most effect on when the script takes a short position.
The reasoning for this is because the level you set for this input is the level which indicator 1 must close below following the crossunder of its average.
Example: Indicator 1 Input Short set to 40, this means that when the momentum crosses under its average from indicator 1, upon the close of this crossunder the momentum must be below the level 40 in order for this condition to be met to take a short position.
The lower the level, the stronger the downwards momentum must be, and therefore by using lower levels for this input, the script will search for stronger directional moves leaving less chance for the trade to move against you.
Summary of Input Group: Indicator 1 Input Long/Short
These two inputs are so important to your strategy because at the end of the day no matter how you set it up, it's still a momentum-based strategy. With that being said the level of momentum or the strength needed in order to take trades is of course going to be a key decider in the successfulness of the strategy. When optimizing these two inputs make sure to take into account what the overall market conditions are, meaning if it’s a bull market maybe make the momentum needed to take a long slightly less comparatively to the amount needed to take a short, in other words make long conditions less specific and short conditions more specific. Slight variations of this input can have very big effects, even changing it by 1 or 2 can make a major difference. In might even be good to consider starting optimization with these inputs and then work the rest of the strategy out from there. A lot could be said about these inputs and more docs will be added in order to further explain more strategy approaches revolving around them, for now don’t hesitate to ask any questions.
Indicator 2 Red
This input is used as a sort of chop filter at its base level, however if used correctly it can be a much broader filter for what areas of the market you want to trade in.
Indicator 2 shows as either red or green and is used as a confirmation when price crosses over it following the crossover of momentum and its average from indicator 1 to take a long position.
If ticked on, Indicator 2 Red states a condition in order for the script to take a long position. (Default is on)
The condition is that upon the crossover of the current price and Indicator 2, 10 bars ago indicator 2 must have been red.
The reason for this input is because the current color of indicator 2 upon the crossover must also be red. However, this condition is hard coded in and cannot be changed by any input.
This is because the type of trade being targeted is that of a type of reversal or continuation.
If indicator 2 showed green 10 bars ago and is currently red this would indicate that a top was just reached, and price is reversing downwards making this not a good area to take a long.
Another scenario if indicator 2 showed green 10 bars ago and is currently red is that there is currently a sideways trend going on or otherwise known as chop, also not an ideal area to take a long
However, if 10 bars ago indicator 2 was red and it's currently red this would indicate a more prolonged pullback.
If all conditions are met and we know that price has been pulling back, now we can enter a long with more knowledge pointing to price reversing upwards from a downwards trend, or continuing its upwards trend after a pullback.
Indicator 2 Green
This input is used as a sort of chop filter at its base level, however if used correctly it can be a much broader filter for what areas of the market you want to trade in.
Indicator 2 shows as either red or green and is used as a confirmation when price crosses under it following the crossunder of momentum and its average from indicator 1 to take a short position.
If ticked on, Indicator 2 Green states a condition in order for the script to take a short position. (Default is on)
The condition is that upon the crossunder of the current price and Indicator 2, 10 bars ago indicator 2 must have been green.
The reason for this input is because the current color of indicator 2 upon the crossunder must also be green. However, this condition is hard coded in and cannot be changed by any input.
This is because the type of trade being targeted is that of a type of reversal or continuation.
If indicator 2 showed red 10 bars ago and is currently green this would indicate that a bottom was just reached, and price is reversing upwards making this not a good area to take a short.
Another scenario if indicator 2 showed red 10 bars ago and is currently green is that there is currently a sideways trend going on or otherwise known as chop, also not an ideal area to take a short.
However, if 10 bars ago indicator 2 was green and it's currently green this would indicate a more prolonged upwards movement.
If all conditions are met and we know that price has been moving up, now we can enter a short with more knowledge pointing to price reversing downwards from an upwards trend, or continuing its downwards trend after a bounce up.
Summary of Input Group: Indicator 2 Red/Green
Similar to Indicator 1 Greater/Less Than Long/Short, the goal of these inputs is to try to get a picture of what the previous recent market has been doing. By getting this picture it's easier to find different areas of the market more ideal for trades. Different from Indicator 1 Greater/Less Than Long/Short though, Indicator 2 Red/Green is directly correlated to the price action in the market rather than the momentum. By switching these on or off you are setting more or less specific conditions for taking trades. Some markets require this extra condition to lower your risk in your strategy, however others may not.
Pivot Low
This input is used to define the number of bars the script will look back to grab a pivot low when taking a long position.
This pivot low is then used to set the stop loss when entering a long position.
This input is very important and optimizing it correctly can be extremely crucial to your strategies success.
The Strategy Developer tool uses a 1:1 risk to reward ratio when setting your first take profit point, so when the script looks back to get a pivot low based on the input you set, it will then set your first take profit at an equal ratio to the stop loss found from the pivot low.
The goal in optimizing this input is to give enough lookback to find real pivot points where price has reversed off of, but not to give too much lookback where its grabbing previous pivot points unrelated to the current move of momentum the script is giving a long signal from.
Consider the type of trades you're looking for in your strategy and what timeframe you are trying to trade on.
Longer swing trades which aim to catch bigger moves in the market, possibly on higher time frames, may require a further lookback in order to get your take profits in the correct positioning to catch the desired move, and not exit early before the trade has fully played out.
Shorter scalp trades may aim to catch smaller moves and therefore you don’t want to allow for too much risk by having a large stop loss and large take profits as a result.
Pivot Low 2
Pivot low 2 can be thought of as a backup lookback in order to get the correct pivot low.
In an input which will be discussed shortly called Pivot Low Minimum, you can set a minimum percentage for your pivot low to be, if the pivot low does not meet the minimum then the script will look to Pivot Low 2’s input to use as a bar lookback in order to get the correct pivot low.
This input is used because you might find a Pivot Low input that works well for the majority of the trades in your back tested strategy, however, there will always be outliers and when this Pivot Low input falls short of getting the correct level to put your stop losses at, Pivot Low 2 is used.
Pivot Low 2’s input should always be higher than Pivot Low’s input, that way you can allow the script to look back further in time to find the correct level when the minimum is not met.
Pivot High
This input is used to define the number of bars the script will look back to grab a pivot high when taking a short position.
This pivot high is then used to set the stop loss when entering a short position.
This input is very important and optimizing it correctly can be extremely crucial to your strategies success.
The Strategy Developer tool uses a 1:1 risk to reward ratio when setting your first take profit point, so when the script looks back to get a pivot high based on the input you set, it will then set your first take profit at an equal ratio to the stop loss found from the pivot high.
The goal in optimizing this input is to give enough lookback to find real pivot points where price has reversed off of, but not to give too much lookback where its grabbing previous pivot points unrelated to the current move of momentum the script is giving a short signal from.
Consider the type of trades you're looking for in your strategy and what timeframe you are trying to trade on.
Longer swing trades which aim to catch bigger moves in the market, possibly on higher time frames, may require a further lookback in order to get your take profits in the correct positioning to catch the desired move, and not exit early before the trade has fully played out.
Shorter scalp trades may aim to catch smaller moves and therefore you don’t want to allow for too much risk by having a large stop loss and large take profits as a result.
Pivot High 2
Pivot high 2 can be thought of as a backup lookback in order to get the correct pivot high.
In an input which will be discussed shortly called Pivot High Minimum, you can set a minimum percentage for your pivot high to be, if the pivot high does not meet the minimum then the script will look to Pivot High 2’s input to use as a bar lookback in order to get the correct pivot high.
This input is used because you might find a Pivot High input that works well for the majority of the trades in your back tested strategy, however, there will always be outliers and when this Pivot High input falls short of getting the correct level to put your stop losses at, Pivot High 2 is used.
Pivot High 2’s input should always be higher than Pivot High’s input, that way you can allow the script to look back further in time to find the correct level when the minimum is not met.
Pivot Low Risk Tolerance
This input is very important in managing the risk associated with your strategy.
Pivot Low Risk Tolerance is defining a maximum percentage the pivot low can be away from your entry.
Since the pivot low that’s found is assigned to your stop loss and directly affects the placement of your take profits when taking a long position, making sure the pivot low isn’t too far down is crucial.
Depending on the types of trades you're aiming to take, the timeframe you choose to trade on, and the leverage you use in your strategy, you may want to assign a higher risk tolerance or a lower one.
Example: Pivot Low Risk Tolerance input set to 3, this means that when all other conditions are met in order to take a long position, when searching for the pivot low in order to set a stop loss, if the script finds the pivot low is greater than 3% away from the entry point, it will not take the trade.
Pivot High Risk Tolerance
This input is very important in managing the risk associated with your strategy.
Pivot High Risk Tolerance is defining a maximum percentage the pivot high can be away from your entry.
Since the pivot high that’s found is assigned to your stop loss and directly affects the placement of your take profits when taking a short position, making sure the pivot high isn’t too far up is crucial.
Depending on the types of trades you're aiming to take, the timeframe you choose to trade on, and the leverage you use in your strategy, you may want to assign a higher risk tolerance or a lower one.
Example: Pivot High Risk Tolerance input set to 3, this means that when all other conditions are met in order to take a short position, when searching for the pivot high in order to set a stop loss, if the script finds the pivot high is greater than 3% away from the entry point, it will not take the trade.
Pivot Low Minimum
Sometimes when searching for the pivot low, the script's defined lookback may not be enough to find the proper pivot point.
This can cause improper placement of stop losses and take profits and may cause trades to be exited early before they can fully play out in your favor.
Pivot Low Minimum is an input used to combat this problem, when the script finds a pivot low that does not meet the minimum percentage away from the entry point, it will then turn to Pivot Low 2 input in order to gain a further lookback and grab the correct pivot point to set your stop loss and take profits with.
When reading and setting this input, understand that setting it to 1 means there is no minimum, setting it to 0.9 would mean the minimum is a 10% difference between the pivot low and your entry point.
Think of it in terms of decimals and their equivalent percentage, 0.1 is equal to 10%, 0.01 is equal to 1%.
Whatever percentage you want to set for a minimum, convert it to a decimal, then simply subtract it from 1.
Example: Say you desire a 1.5% minimum pivot low and as a result an equivalent stop loss of 1.5% below your long entry and furthermore a take profit 1.5% above your long entry since the script uses a 1:1 ratio. Converting 1.5% to a decimal would give you 0.015, then subtracting it from 1 would give you 0.985, this would be the input assigned to Pivot Low Minimum.
Pivot High Minimum
Sometimes when searching for the pivot high, the script's defined lookback may not be enough to find the proper pivot point.
This can cause improper placement of stop losses and take profits and may cause trades to be exited early before they can fully play out in your favor.
Pivot High Minimum is an input used to combat this problem, when the script finds a pivot high that does not meet the minimum percentage away from the entry point, it will then turn to Pivot High 2 input in order to gain a further lookback and grab the correct pivot point to set your stop loss and take profits with.
When reading and setting this input, understand that setting it to 1 means there is no minimum, setting it to 0.9 would mean the minimum is a 10% difference between the pivot high and your entry point.
Think of it in terms of decimals and their equivalent percentage, 0.1 is equal to 10%, 0.01 is equal to 1%.
Whatever percentage you want to set for a minimum, convert it to a decimal, then simply subtract it from 1.
Example: Say you desire a 1.5% minimum pivot high and as a result an equivalent stop loss of 1.5% above your short entry and furthermore a take profit 1.5% below your short entry since the script uses a 1:1 ratio. Converting 1.5% to a decimal would give you 0.015, then subtracting it from 1 would give you 0.985, this would be the input assigned to Pivot High Minimum.
Summary of Input Group: Pivot Low/High - Pivot Low/High 2 – Pivot Low/High Risk Tolerance – Pivot Low/High Minimum
The first key takeaway from all these inputs is that your stop losses and take profits will be directly affected through optimizing any of them. The second key takeaway is that these inputs are crucial in managing the risk in your strategy, and while this has been said many times throughout the guide for various inputs, when it comes to stop losses and take profits it is especially true. Having a stop loss which is too high opens up the possibility for much bigger losses, and as a result your take profits will also be too high, minimizing the chance of any of them being hit. Having a stop loss which is too low increases the chance that your trade will get stopped out preemptively, before the trade can mature and move in your favor because remember that trades will not always move immediately in the intended direction, a good amount of patience is often involved in creating consistent successful trades and a successful strategy as such. On the same note, too low of a stop loss could also mean you are missing out on unrealized profit since your take profits are a direct result of the stop loss which is found. When optimizing your pivot low/high risk tolerance, think not about how much you are willing to lose on a single trade, but how much your portfolio can actually afford to lose not just on a single trade but multiple trades, sometimes even in a row. Obviously, the goal in creating a strategy is that you avoid losing trades and especially multiple in a row, however, there are many things that can’t be accounted for. The only way to manage this unaccounted risk is to use proper risk management and not open yourself up to big losses even in the worst most unlikely scenarios. Even if you don’t lose multiple trades in a row, ask yourself, could I afford to lose multiple trades with the risk tolerance I have set if everything were to go to $hit, (hopefully it would not), but in the off chance it did, instead of beating yourself up over what you did wrong, you’ll be patting yourself on the back for what you did right.
TP2-4 Long Placement
The first thing to understand about the take profit placement is that our system of stop losses and take profits uses a 1:1 risk to reward ratio for the first stop loss and first take profit.
This means that if your stop loss falls 2% below your long entry, your first take profit will be 2% above your long entry, hence 1:1.
As for take profits 2-4, they are just extensions of that ratio. This means that if TP2 Long Placement is set to 1.5, the ratio for your second take profit is 1:1.5.
Using the same percentage from the second bullet point being 2%, we can now gather that with a 1:1.5 ratio our second take profit would be at 3% above our long entry.
The same applies for the rest of the take profits, meaning whatever the take profit is set at regardless of which one, apply that number to the second placeholder of the ratio.
Example: First stop loss falls 2% below long entry. TP2 Long Placement input set to 1.5; risk to reward ratio is 1:1.5; corresponding percentage would be a 3% gain. TP3 Long Placement input set to 2; risk to reward ratio is 1:2; corresponding percentage would be a 4% gain. TP4 Long Placement input set to 2.5; risk to reward ratio is 1:2.5; corresponding percentage would be a 5% gain.
The next key thing to understand about the trailing take profits system is the position size being sold at each take profit and therefore how the strategy tracker calculates your strategy's profit.
At the first take profit, 50% of your position is being calculated as sold, locking in good profits off the bat.
At TP2, 20% of your position is being calculated as sold, leaving a remaining 30% open to gain more profit.
At TP3, another 20% of your position is being calculated as sold, leaving 10% to collect any additional possible gains.
At TP4 the remaining 10% of your position is sold and the trade will be fully closed out.
SL2-4 Long Placement
Our system of trailing stop losses is completely similar to that of our trailing take profits.
Just like the trailing take profits, the inputs for stop losses 2-4 are also used as the second placeholders in the risk to reward ratio.
This may be confusing since generally stop losses are associated with a loss on your position, however, the only stop loss which results in a loss on your position is the first one, not stop losses 2-4.
This is because once your first take profit is hit on your long, your stop loss will automatically move up to the price equivalent to the ratio which you set using these inputs that lies in profit.
Example: Since your first take profit will always be at a 1:1 risk to reward ratio with your stop loss, your second take profit could be at a 1:0.8 ratio. So, to clarify, once your first take profit is hit at a 1:1, your original first stop loss will now be moved up in profits to just below your first take profit at a 1:0.8 risk to reward ratio. This only happens AFTER the first take profit is hit.
For stop losses 3 and 4, the same logic is true, once TP2 is hit, your second stop loss will now be moved up to the placement of SL3 which will fall somewhere below TP2. Once TP3 is hit, your third stop loss will now be moved up to the placement of SL4 which will fall somewhere below TP3. If stop loss 4 does not get hit, then the only thing left to happen is for TP4 to hit and the trade will fully close out.
The one major difference between our system of trailing stop losses and take profits is that no matter what stop loss is hit, the entire remainder of your position will be calculated as sold.
So, if your first take profit hits and sells 50% of your long position, but the trade does not continue upwards and moves down to your second stop loss, the remaining 50% of your position will be calculated as sold.
The same applies to SL3 and SL4, so at SL3 the remaining 30% of your position will be calculated as sold, and at SL4 the remaining 10% will be calculated as sold.
Your trailing stop loss placement is dependent on what types of trades you desire. For shorter scalps on smaller timeframes, it's recommended to place each stop loss just below each corresponding take profit for long trades.
This way you leave just enough room for the trade to continue upwards if there is enough momentum, but you don’t open yourself up to losing your unrealized profit if it does not make this continuation.
If you desire longer swing trades on higher timeframes, it might be a good idea to leave more room in between the take profit and corresponding stop loss.
This way you leave more room for the trade to mature and move in your favor since when trading longer moves, often they will not shoot straight up but rather have a series of small pullbacks throughout the more general upwards trend.
Note that when a long trade is first entered the only stop loss and take profit in play are your original stop loss found by the pivot low which would result in a loss, and the first take profit at a 1:1 risk to reward ratio from that pivot low.
TP2-4 Short Placement
The first thing to understand about the take profit placement is that our system of stop losses and take profits uses a 1:1 risk to reward ratio for the first stop loss and first take profit.
This means that if your stop loss falls 2% above your short entry, your first take profit will be 2% below your short entry, hence, 1:1.
As for take profits 2-4, they are just extensions of that ratio. This means that if TP2 Short Placement is set to 1.5, the ratio for your second take profit is 1:1.5.
Using the same percentage from the second bullet point being 2%, we can now gather that with a 1:1.5 ratio our second take profit would be at 3% below our short entry.
The same applies for the rest of the take profits, meaning whatever the take profit is set at regardless of which one, apply that number to the second placeholder of the ratio.
Example: First stop loss falls 2% above short entry. TP2 Short Placement input set to 1.5; risk to reward ratio is 1:1.5; corresponding percentage would be a 3% gain. TP3 Short Placement input set to 2; risk to reward ratio is 1:2; corresponding percentage would be a 4% gain. TP4 Short Placement input set to 2.5; risk to reward ratio is 1:2.5; corresponding percentage would be a 5% gain.
The next key thing to understand about the trailing take profits system is the position size being sold at each take profit and therefore how the strategy tracker calculates your strategy's profit.
At the first take profit, 50% of your position is being calculated as sold, locking in good profits off the bat.
At TP2, 20% of your position is being calculated as sold, leaving a remaining 30% open to gain more profit.
At TP3, another 20% of your position is being calculated as sold, leaving 10% to collect any additional possible gains.
At TP4 the remaining 10% of your position is sold and the trade will be fully closed out.
SL2-4 Short Placement
Our system of trailing stop losses is completely similar to that of our trailing take profits.
Just like the trailing take profits, the inputs for stop losses 2-4 are also used as the second placeholders in the risk to reward ratio.
This may be confusing since generally stop losses are associated with a loss on your position, however, the only stop loss which results in a loss on your position is the first one, not stop losses 2-4.
This is because once your first take profit is hit on your short, your stop loss will automatically move down to the price equivalent to the ratio which you set using these inputs that lies in profit.
Example: Since your first take profit will always be at a 1:1 risk to reward ratio with your stop loss, your second take profit could be at a 1:0.8 ratio. So, to clarify, once your first take profit is hit at a 1:1, your original first stop loss will now be moved down in profits to just below your first take profit at a 1:0.8 risk to reward ratio. This only happens AFTER the first take profit is hit.
For stop losses 3 and 4, the same logic is true, once TP2 is hit, your second stop loss will now be moved down to the placement of SL3 which will fall somewhere above TP2. Once TP3 is hit, your third stop loss will now be moved down to the placement of SL4 which will fall somewhere above TP3. If stop loss 4 does not get hit, then the only thing left to happen is for TP4 to hit and the trade will fully close out.
The one major difference between our system of trailing stop losses and take profits is that no matter what stop loss is hit, the entire remainder of your position will be calculated as sold.
So, if your first take profit hits and sells 50% of your short position, but the trade does not continue downwards and moves up to your second stop loss, the remaining 50% of your position will be calculated as sold.
The same applies to SL3 and SL4, so at SL3 the remaining 30% of your position will be calculated as sold, and at SL4 the remaining 10% will be calculated as sold.
Your trailing stop loss placement is dependent on what types of trades you desire. For shorter scalps on smaller timeframes, it's recommended to place each stop loss just above each corresponding take profit for short trades.
This way you leave just enough room for the trade to continue downwards if there is enough momentum, but you don’t open yourself up to losing your unrealized profit if it does not make this continuation.
If you desire longer swing trades on higher timeframes, it might be a good idea to leave more room in between the take profit and corresponding stop loss.
This way you leave more room for the trade to mature and move in your favor since when trading longer moves, often they will not shoot straight down but rather have a series of small bounces throughout the more general downwards trend.
Note that when a short trade is first entered the only stop loss and take profit in play are your original stop loss found by the pivot high which would result in a loss, and the first take profit at a 1:1 risk to reward ratio from that pivot high.
Summary of Take Profit/Stop Loss Placement:
Correctly placed take profits and stop losses are essential in having a successful strategy and proper risk management. With that being said there are also many ways in which to use this system. Deciding how to set them up is really just a matter of determining the trading style you aim to succeed with. Once this has been determined, the placement of take profits and stop losses should be easier to configure. However, if there is any confusion on either of these topics as the ratios and corresponding TP/SL can get confusing, please do not hesitate to ask further questions in our discord!
Leverage Long
Leverage Long input simply defines the leverage used in your long positions, and is used in calculating the profit in Strategy Tracker
A rundown of risk associated with using leverage will not be given here since it should assume that if you're using leverage, you should already understand the risks.
If you are not using any leverage, then set Leverage Long input to 1.
Long Position Size
This input defines the position size you are using in your long trades.
This input is also used in calculating profit in Strategy Tracker.
Long Hedge Position Size
This input is used to define the position size of long hedge positions.
This input is also used in calculating profit in Strategy Tracker.
Important: Your Long Hedge Position Size should always be half of your Long Position Size for accurate profit calculation.
Double Long Position Size
This input is used to define the position size when in a double long.
This input is also used in calculating profit in Strategy Tracker
Important: Your Double Long Position Size should always be double your Long Position Size for accurate profit calculation.
Short Position Size
This input defines the position size you are using in your short trades.
This input is also used in calculating profit in Strategy Tracker.
Short Hedge Position Size
This input is used to define the position size of short hedge positions.
This input is also used in calculating profit in Strategy Tracker.
Important: Your Short Hedge Position Size should always be half of your Short Position Size for accurate profit calculation.
Double Short Position Size
This input is used to define the position size when in a double short.
This input is also used in calculating profit in Strategy Tracker
Important: Your Double Short Position Size should always be double your Short Position Size for accurate profit calculation.
A Message From the Developer PLEASE READ!!!
If you have made it this far in the guide, I applaud you and thank you for sticking with it as I know there is a lot of information here! This is not an exaggeration when I say there are hundreds of millions of possible variations that could be applied throughout all the inputs which is why I much prefer to call this a tool rather than an algorithm. Algorithm is a loaded word in my opinion as it comes with an implication of guarantee in the trades being made. This is not meant to discourage anybody from taking trades based off the tool which is also why I provided the option for automated alerts which through third party software can turn into automated trades; if you have the confidence in your strategy by all means I encourage you to trade it, automated or not. Just please understand that it's highly recommended to also apply your own knowledge and analysis before taking a trade as historical back testing data has its limitations and cannot always account for current market conditions. The real applicability does not fall in what the back tester window is saying you would have made or how accurate your strategy would have been, it's within the sheer number of markets and scenarios this tool can be used in and the information you can get which a human just can’t comprehend all at once; its literally endless. I urge all of you to be creative and think outside the box about what you can do with such a powerful tool at your fingertips. After all this is the reason why so many inputs were provided. Another main goal of this project was to give users a better understanding of risk management. It can be hard to manage your risk when it’s all kept in your head, but when you can modify your strategy to better manage your risk by simply optimizing a few inputs, it’s a lot easier to comprehend and actually apply when trading. The last thing I want to say is have fun working through the possible learning curve in using this tool, it may be a process but enjoy it because the one thing I can guarantee is that you will come out the other side a better trader than before!
Cyatophilum Swing Trader [ALERTSETUP]This is an indicator for swing trading which allows you to build your own strategies, backtest and alert. This version is the alertsetup which allows to create automated alerts hosted on TradingView servers that will trigger in form of emails, SMS, webhooks, notifications, and more. The backtest version can be found in my profile scripts page.
The particularity of this indicator is that it contains several indicators, including a custom one, that you can choose in a drop down list, as well as a trailing stop loss and take profit system.
The current indicators are :
CYATO AI: a custom indicator inspired by Donchian Channels that will catch each big trend and important reversal points .
The indicator has two major "bands" or channels and two minor bands. The major bands are bigger and are always displayed.
When price reaches a major band, acting as a support/resistance, it will either bounce on it or break through it. This is how "tops" and "bottoms", and breakouts are caught.
The minor bands are used to catch smaller moves inside the major bands. A combination of volume, momentum and price action is used to calculate the signals.
Advantages of this indicator: it should catch top and bottoms better than other swing trade indicators.
Cons of this indicator: Some minor moves might be ignored. Sometimes the script will catch a fakeout due to the Bands design.
Best timeframes to use it : 2H~4H
Sample:
Other indicators available:
SARMA: A combination of Parabolic Stop and Reverse and Exponential Moving Average (20 and 40) .
SAR: Regular Parabolic Stop and Reverse .
QQE: An indicator based on Quantitative Qualitative Estimation .
SUPERTREND: A reversal indicator based on Average True Range .
CHANNELS: The classic Donchian Channels .
More indicators might be added in the future.
About the signals: each entry (long & short) is calculated at bar close to avoid repainting. Exits (SL & TP) can either be intra-bar or at bar close using the Exit alert type parameter.
STOP LOSS SYSTEM
The base indicators listed above can be used with or without TP/SL.
TP and SL can be both turned on and off and configured for both directions.
The system can be configured with 3 parameters as follows:
Stop Loss Base % Price: Starting Value for LONG/SHORT stop loss
Trailing Stop % Price to Trigger First parameter related to the trailing stop loss. Percentage of price movement in the right direction required to make the stop loss line move.
Trailing Stop % Price Movement: Second parameter related to the trailing stop loss. Percentage for the stop loss trailing movement.
Another option is the "Reverse order on Stop Loss". Use this if you want the strategy to trigger a reverse order when a stop loss is hit.
TAKE PROFIT SYSTEM
The system can be configured with 2 parameters as follows:
Take Profit %: Take profit value in percentage of price.
Trailing Profit Deviation %: Percent deviation for the trailing take profit.
Combining indicators and Take Profit/Stop Loss
One thing to note is that if a reversal signal triggers during a trade, the trade will be closed before SL or TP is reached.
Indeed, the base indicators are reversal indicators, they will trigger long/short signals to follow the trend.
It is possible to use a takeprofit without stop loss, like in this example, knowing that the signal will reverse if the trade goes badly.
The base indicators settings can be changed in the "Advanced Parameters" section.
Configuration used for this snapshot:
ALERTS DEFINITION
Each alert correspond to the labels on chart.
01. LONG ENTRY (BUY) : Long alert
02. LONG STOP LOSS : Long stop loss event
03. LONG TAKE PROFIT : Long take profit event
04. SHORT ENTRY (SELL) : Short alert
05. SHORT STOP LOSS : Short stop loss event
06. SHORT TAKE PROFIT : Short take profit event
07. LONG EXIT : Long exit alert. Triggers on both Stop loss and Take Profit
08. SHORT EXIT : Short exit alert. Triggers on both Stop loss and Take Profit
09. ALL TAKE PROFITS : Long and Short Take Profits. Both directions.
10. ALL STOP LOSSES : Long and Short Stop Losses. Both directions.
11. ALL EXITS : Long and Short exits alert. Stop Loss and Take Profit both Long and Short.
Use the link below to obtain access to this indicator.
Cracking Cryptocurrency - Bottom Feeder Strategy TesterBottom Feeder - Strategy Tester
The Bottom Feeder is designed to algorithmically detect significantly oversold conditions in price that represent profitable buying opportunities. Combining this with it’s unique Stop and Target System, the Bottom Feeder is designed to return consistent return with minimal draw down. Whether used as a Market Bottom Detector or as a system for executing safe, profitable mean reversion trades, the Bottom Feeder is a powerful tool in any trader’s arsenal.
Bottom Feeder was designed to be used on BTCUSD, however it is also effective on other USD/USDT pairs. One will have to check the individual pair they wish to trade with the Strategy Tester to simulate performance.
Strategy displayed is from 2018-2021 on **Conservative Mode** with Percent of Equity (30%) enabled.
Options
Let’s go through the input options one by one, so that you are able to comfortably navigate all that this indicator has to offer. The link below will display a picture of the layout of the settings for your convenience.
For the sake of simplicity, let’s note now that all settings marked **Conservative Mode** will not work in Aggressive Mode.
Mode : Determines how aggressively Bottom Feeder generates a buy signal. In Conservative Mode, trades can only be opened once per candle and the stop and target will update as new signals appear. In Aggressive Mode, a separate trade is opened each time Bottom Feeder signals, which may be multiple times within one Daily candle.
Position Sizing Strategy : Determines what Risk Management system you will deploy when trading Bottom Feeder. Your options are “Percent of Equity” and “Distance to Stop Loss”. If Percent of Equity is selected, a trade size will be equal to a percentage of your equity, pursuant to the value in the ‘Percent of Equity’ box. If Distance to Stop Loss is selected, then your Position Size will be determined based off the distance to your stop loss and the value in the ‘Risk Percentage’ box.
Percent Of Equity : Determines what percentage of your equity will be allocated to each trade when ‘Position Sizing Strategy’ is enabled.
Risk Percentage : Determines the size of each trade if ‘Distance to Stop Loss’ strategy is enabled. This value reflects what percent of your account you will lose per trade if the trade hits your stop loss.
Plot Target and Stop Loss : Toggles on/off the visualized take profit and stop losses on the chart.
**Conservative Mode** TP Multiplier : This is an input box, it requires a float value. That is, it can accept either a whole number integer or a number with a decimal. This number will determine your Take Profit target. It will take whatever number is entered into this box and multiply the Average True Range against it to determine your Take Profit.
**Conservative Mode** SL Multiplier : See above - this will modify your Stop Loss Value.
**Conservative Mode** Average or Median True Range : This is a drop-down option, the two options are Average True Range or Median True Range. If Average True Range is selected, then this indicator will use the Average True Range calculation, that is, the average of a historical set of True Range values to determine the Average True Range value for Target and Stop Loss calculation. If Median True Range is selected, it will not take an average and will instead take the Median value of your historical look back period.
**Conservative Mode** True Range Length : This is an input that requires an integer. This will represent your historical lookback period for Average/Median True Range calculation.
**Conservative Mode** True Range Smoothing : This is a drop-down with the following options: Exponential Moving Average ( EMA ), Simple Moving Average ( SMA ), Weighted Moving Average ( WMA ), Relative Moving Average (RMA). This will determine the smoothing type for calculating the Average True Range if it is selected. Note: if Median True Range is selected above, this option will not have any effect as there is no smoothing for a Median value.
**Conservative Mode** Custom True Range Value? : This is a true/false option that is false by default. If enabled, it will override the Average/Median True Range calculation in favor of a users custom True Range value to be input below.
**Conservative Mode** Custom True Range Value : This is an input box that requires a float value. If Custom True Range is enabled this is where a user will input their desired custom True Range value for Target and Stop Loss calculation.
From Month/Day/Year to Month/Day/Year : This sets the Time Frame of your backtest for the Bottom Feeder Strategy. It will run FROM the date selected TO the date selected.
Stop and Target Description
Because Bottom Feeder is designed only to scalp the various market bottoms that can appear over time in the market and not to identify trends or to trade ranges, it’s imperative that the indicator notify us not just to when to enter our trades, but when to exit! In the service of that, CC Bottom Feeder has a built in Stop and Target system that tracks and displays the stop loss and take profit levels of each individual open trade, whether in Aggressive or Conservative Mode.
Conservative Mode Targeting: In Conservative Mode, Bottom Feeder signals are aggregated into a compound trade. The signal will appear as a green label pointing up below a candle, and will appear upon a candle close. If Bottom Feeder then generates another signal the stop loss and target price will be updated. The process will continue until the aggregated trade completes in either direction. On a trade with multiple signals, a larger position is slowly entered into upon each buy signal.
Aggressive Mode Targeting: In Aggressive Mode, Bottom Feeder signals are individually displayed as they are generated, regardless of how many signals are generated on any single candle. If Bottom Feeder continues to signal, each individual open trade will have their own stop loss and target that will be displayed on the chart until the individual trade completes in either direction. As opposed to a large compound position, aggressive mode represents a higher number of independent signals with their own stop and target levels.
Stop losses and targets are designed to be hard, not soft. That is, they are intended to be stop market orders, not mental stop losses. If price wicks through the target or stop, it will activate.
NAS Oracle AlgoThe NAS Oracle Algo is a powerful and versatile daily trading indicator designed to provide clear, automated support and resistance levels for both long and short trading strategies. By calculating a dynamic range based on the previous day's price action, it projects key entry points, stop-losses, and up to six profit targets onto your chart, giving you a complete roadmap for the trading day.
Key Features:
Dual-Sided Strategy: Generates independent levels for BUY and SELL setups, making it effective for both directional and range-bound markets.
Customizable Reference Point: Choose between using the current day's "Open" or the previous day's "Pre Close" as the base for all calculations.
Comprehensive Levels:
Entry Level: The price level to execute a trade.
Stop Loss: A predefined level to limit potential losses.
Profit Targets (1-6): Six incremental take-profit levels, allowing for partial profit-taking strategies.
Multiple Display Options:
Visual Levels & Labels: Clean horizontal lines and text labels are drawn directly on the chart for easy price reference.
Information Table: A highly customizable data table that summarizes all key levels, which can be positioned at the Top or Bottom of the chart and resized.
Flexible Configuration: Toggle the visibility of levels and choose to show either 3 or 6 profit targets to suit your trading style and avoid chart clutter.
How to Use:
Add the Indicator: Apply the "NAS Oracle Algo" to your chart. It works best on daily and intraday timeframes.
Configure Settings: In the indicator's settings, choose your preferred Option (Open/Pre Close), toggle levels and the table on/off, and adjust their position and size.
Interpret the Signals:
BUY Setup: When the price moves above the green "Buy Above" level, consider a long entry.
Stop Loss: Place your stop loss at the BUY_SL level.
Take Profit: Scale out of your position at the six progressively higher target levels (T1 to T6).
SELL Setup: When the price moves below the red "Sell Below" level, consider a short entry.
Stop Loss: Place your stop loss at the SELL_SL level.
Manipulation Model [FB]GENERAL OVERVIEW:
The Manipulation Model indicator is a complete rule-based system that identifies and confirms setups from the Funded Brothers Manipulation Model. It detects large impulsive candles, called Manipulation Candles and Almost Manipulation Candles, that form around key market levels such as session highs/lows, daily, weekly, and monthly levels, or higher timeframe Fair Value Gaps (FVGs). Using this structure, the indicator automatically marks long, short, bulltrap, and beartrap setups with predefined entry, stop loss, and take profit areas.
This indicator was developed by Flux Charts in collaboration with the Funded Brothers.
ATTRIBUTION NOTICE:
This indicator incorporates concepts and source code from the indicator “MCs with Alerts” authored by @hamza_xau on TradingView. We have received full written permission from the original author to use and commercialize this code within this invite-only script.
Original script: MCs with Alerts:
What is the purpose of the indicator?:
The indicator automates detection of the Manipulation Model trading strategy setups by combining candle structure, key levels, session timing, and higher timeframe Fair Value Gaps. It removes discretion by enforcing fixed conditions for valid signals and automatically managing entry, stop-loss, and take-profit logic.
What is the theory behind the indicator?:
The indicator is built on how price interacts with major reference points such as session highs and lows, or daily and weekly levels. These levels are commonly referenced in technical analysis as areas where price previously reversed or consolidated. Manipulation Candles identify moments when price breaks past these reference points on both sides of the prior candle before closing firmly in one direction. When these candles form near higher timeframe Fair Value Gaps, it reflects price reacting inside an area that previously showed directional imbalance. The higher timeframe EMA filter aligns all detected setups with the broader market trend, allowing only signals that match the dominant direction.
MANIPULATION MODEL FEATURES:
Manipulation Candlesticks
Almost Manipulation Candlesticks
Higher Timeframe Fair Value Gaps
Sessions
Key Levels
Signals
Dashboard
Alerts
MANIPULATION CANDLESTICKS:
Manipulation Candlesticks (MCs) are candles that sweep both sides of the previous candle’s range and close outside of it. In the Manipulation Model indicator, these candles form the foundation for the long/short setups. Once one forms, the indicator checks its position relative to sessions, key levels, and higher timeframe Fair Value Gaps to determine if a valid setup exists.
🔹What is a Manipulation Candlestick?
A Manipulation Candlestick (MC) is defined by structure rather than size. It forms when price takes out both the high and low of the previous candle, then closes outside that range.
A bullish Manipulation Candle occurs when price sweeps below the previous candle’s low and then closes above the previous candle’s high.
A bearish Manipulation Candle occurs when price sweeps above the previous candle’s high and then closes below the previous candle’s low.
🔹How to interpret and use Manipulation Candlesticks:
Manipulation Candlesticks show where price made a strong one-sided move after taking both sides of the previous candle’s range. When one forms, it marks an area where buyers or sellers were likely trapped as price moved aggressively in one direction.
A bullish MC shows strong buying after a false move lower. Price sweeps below the prior low, takes out the prior high, and closes above the previous range, confirming buyers are in control.
A bearish MC shows strong selling after a false move higher than the previous candle’s high. Price sweeps above the prior high, drops below the prior low, and closes beneath the previous range, confirming sellers are dominant.
🔹How Manipulation Candlesticks are identified:
The indicator confirms Manipulation Candles using three filters once a candle closes:
Sweep Condition:
Price must take both sides of the previous candle’s range, moving above its high and below its low, before closing outside that range.
Directional Close:
A bullish MC must close above the previous high, and a bearish MC must close below the previous low.
Wick Confirmation:
A bullish MC must have a smaller upper wick (high - close) than lower wick (open - low), and a bearish MC must have a smaller lower wick (close - low) than upper wick (high - open).
Once these conditions are met at candle close, it is confirmed as a bullish or bearish Manipulation Candle.
🔹Bullish Manipulation Candle
A bullish Manipulation Candle forms when price sweeps below the previous candle’s low, then breaks above its high, and closes above it. The lower wick must be larger than the upper wick, showing little pullback as price pushed upward and confirming strong buying pressure.
🔹Bearish Manipulation Candle
A bearish Manipulation Candle forms when price sweeps above the previous candle’s high, then drops below its low, and closes beneath it. The upper wick must be larger than the lower wick, showing little pullback as price moved downward and confirming strong selling pressure.
🔹Manipulation Candle Visuals
When the indicator detects a Manipulation Candle, it automatically changes the candle’s color on the chart. Both bullish and bearish Manipulation Candles use the same color. Users can change this color in the settings by adjusting the “Manipulation Candlestick” option found under the “Style Customization” section.
The candle coloring feature can also be turned off entirely, which only removes the visual highlight from the chart and does not affect the signals or any of the indicator’s underlying logic that uses Manipulation Candlesticks.
ALMOST MANIPULATION CANDLESTICKS:
Almost Manipulation Candlesticks (AMCs) are similar to Manipulation Candles, except they close inside the previous candle’s range instead of outside it. In the Manipulation Model indicator, these candles help identify when price is showing the same sweeping behavior but hasn’t yet confirmed full displacement. They act as early warnings that a manipulation event may be developing. Just like Manipulation Candles, the indicator checks an AMC’s position relative to sessions, key levels, and higher timeframe Fair Value Gaps to determine if a valid setup exists.
🔹What is an Almost Manipulation Candlestick?
An Almost Manipulation Candlestick (AMC) forms when price sweeps both the high and low of the previous candle and closes inside that candle’s range.
A bullish AMC occurs when price sweeps below the previous low, moves above the previous high, and closes within the previous candle’s body instead of above it.
A bearish AMC occurs when price sweeps above the previous high, drops below the previous low, and closes within the previous candle’s body instead of beneath it.
🔹How to Interpret and Use Almost Manipulation Candlesticks:
Almost Manipulation Candles highlight hesitation or early signs of manipulation.
A bullish AMC indicates buyers pushed price up after sweeping lower, but price did not close decisively above the prior high.
A bearish AMC indicates sellers pushed price down after sweeping higher, but price did not close decisively below the prior low.
🔹How Almost Manipulation Candlesticks are identified:
The indicator confirms Almost Manipulation Candles using the same sweep and wick logic as Manipulation Candles, except the candle’s close must remain inside the previous candle’s range:
Sweep Condition:
Price must take both sides of the previous candle’s range, moving above its high and below its low.
Candle Close Location:
The candle’s close must stay within the prior candle’s range.
Wick Confirmation:
For a bullish AMC, the lower wick must be larger than the upper wick. For a bearish AMC, the upper wick must be larger than the lower wick.
Once these conditions are met at candle close, it is confirmed as a bullish or bearish Almost Manipulation Candle.
🔹Bullish Almost Manipulation Candle
A bullish AMC forms when price sweeps below the previous candle’s low, moves above the prior candle’s high, and closes back inside the previous candle’s range. The lower wick must be larger than the upper wick, showing that buyers defended lower prices but the move did not close decisively upward.
🔹Bearish Almost Manipulation Candle
A bearish AMC forms when price sweeps above the previous candle’s high, drops below the previous candle’s low, and closes back inside the previous candle’s range. The upper wick must be larger than the lower wick, showing that sellers rejected higher prices but the candle did not close decisively lower.
🔹Almost Manipulation Candle Visuals
When the indicator detects an Almost Manipulation Candle, it automatically changes the candle’s color on the chart. Both bullish and bearish Almost Manipulation Candles use the same color. Users can change this color in the settings by adjusting the “Almost Manipulation Candlestick” option found under the “Style Customization” section.
The candle coloring feature can also be turned off entirely, which only removes the visual highlight from the chart and does not affect the signals or any of the indicator’s underlying logic that uses Almost Manipulation Candlesticks.
HIGHER TIMEFRAME FAIR VALUE GAPS:
The Manipulation Model indicator automatically plots Fair Value Gaps from two user-selected higher timeframes.
🔹What is a Fair Value Gap?:
A Fair Value Gap (FVG) is an area where the market’s perception of fair value suddenly changes. On your chart, it appears as a three-candle pattern: a large candle in the middle, with smaller candles on each side that don’t fully overlap it. A bullish FVG forms when a bullish candle is between two smaller bullish/bearish candles, where the first and third candles’ wicks don’t overlap each other at all. A bearish FVG forms when a bearish candle is between two smaller bullish/bearish candles, where the first and third candles’ wicks don’t overlap each other at all.
Bullish & Bearish FVGs:
🔹Why are Fair Value Gaps important?:
Fair Value Gaps (FVGs) show where price moved so quickly that one side of the market never got a chance to trade. They represent sudden shifts in what traders believe something is worth, where “fair value” changed. When a large candle drives straight through an area without overlap from the candles before and after it, it means buyers or sellers were so aggressive that the market skipped that price zone entirely.
These gaps matter because they mark the moment when confidence in price changes. If price rallies and never pulls back, it signals that traders accept the new higher prices as fair and are willing to keep buying there. The same logic applies in reverse for bearish gaps. They tell you where the market re-priced aggressively and where value was last accepted.
🔹How are Fair Value Gaps used?:
Higher Timeframe FVGs are used as a confluence for all setups within the Manipulation Model indicator. The indicator automatically detects and plots these imbalances from the chosen higher timeframe onto the current chart. When a Manipulation or Almost Manipulation Candle forms near or inside a higher timeframe Fair Value Gap, it adds context to the setup. They are not trade signals by themselves but act as a supporting element that contextualizes setups.
🔹When are Higher Timeframe Fair Value Gaps mitigated?
A Higher Timeframe Fair Value Gap is considered mitigated when the selected higher timeframe closes above the gap for a bearish FVG or below the gap for a bullish FVG.
🔹Higher Timeframe FVG Settings:
Timeframe 1 / Timeframe 2:
Select up to two higher timeframes to use for Fair Value Gaps. Disabling either one removes it visually from the chart but does not affect signal generation. However, the timeframes you select will be used for signal generation logic.
For example, if you select the 1-hour and 4-hour timeframes, then the 1-hour and 4-hour FVGs will be used for signal generation logic, which is explained in the signals section below.
Combine Zones:
When enabled, overlapping FVGs on the same higher timeframe are merged into a single zone. This keeps the chart clean and prevents duplicate zones from displaying.
Midline:
Adds a center line through each higher timeframe FVG.
Labels:
Displays a “ FVG” label beside each zone. This helps users see which timeframe the FVG is detected from.
Color Customization:
Each timeframe has separate color settings for bullish and bearish FVGs. Users can adjust these colors independently for both timeframes to fit their chart layout.
FVG Display Limit:
Controls how many higher timeframe FVGs are shown at once. Only the nearest X active gaps to current price will appear, helping maintain a clear view of relevant imbalances.
SESSIONS:
The Manipulation Model indicator includes six customizable trading sessions: Asia, London, NY AM, NYSE, London Close, and NY PM. All session times and visuals are fully user-configurable. Each session has adjustable start and end times that can be set to match your preferred schedule. Users can also customize visuals for each session, including the color, opacity, and visibility of session zones.
Session highs and lows are automatically tracked and used within the indicator’s signal logic. When a Manipulation or Almost Manipulation Candle forms near a session high or low, it is recognized within the indicator’s signal detection.
Default times used for each session (in EST):
Asia: 20:00 - 00:00
London: 02:00 - 05:00
NY AM: 08:00 - 09:30
NYSE: 09:30 - 10:00
London Close: 10:00 - 11:00
NY PM: 11:00 - 14:00
🔹Session Settings:
Session Boxes:
Each session has a box that outlines its active time window. These boxes can be toggled on or off independently. When active, they visually separate each part of the trading day. Users can adjust the color and opacity of each session box.
Session Highs/Lows:
Every session can display its own high and low as horizontal lines. Users can customize the line style for session highs/lows, choosing between solid, dashed, or dotted. The color of the lines will match the same color used for the session box.
Labels and Price Display:
Labels can be toggled on for all session highs and lows. Users can adjust label color, text size, and choose whether to show the price next to the label. Users can adjust the text size, choosing between tiny, small, normal, large, and huge.
Extend Levels:
When enabled, each session’s high and low levels can be extended forward by a set number of bars.
Session Titles:
Titles for each enabled session (e.g., “Asia,” “London,” “NY AM”) can be displayed directly on the chart.
Show Last:
The “Show Last” setting allows you to choose how many recent sessions of each type appear on the chart. For example, if you only have the Asia session enabled and have this setting set to 2, the recent two Asia sessions will be displayed.
🔹Sessions Used
Under the “Sessions Used” section in the settings, users can choose which sessions are active for signal generation. Only sessions enabled here will produce signals. For example, if you want setups to form only during the London session, turn off all other sessions in this section.
Disabling a session under the main Sessions section only hides its visuals (boxes, lines, or labels). It does not impact signal detection or logic. However, changing a session’s start and end time in either section will affect signals, since signals are tied to the exact session windows defined by the user. This distinction ensures you have full control over what’s displayed visually versus what contributes to active trade signal logic.
Please Note: Signals are only detected and plotted on your chart during sessions. Signals can not be detected outside of session time windows.
KEY LEVELS:
The Manipulation Model indicator includes 10 key market levels that outline important structural price areas across daily, weekly, and monthly timeframes. These levels include the Daily Open, Previous Day High/Low, Weekly Open, Previous Week High/Low, Monthly Open, Previous Month High/Low, and Midnight Open. The levels can be enabled or disabled and customized in color and line style. These levels are used for the indicator’s signal logic.
🔹Daily Open
The Daily Open marks where the current trading day began.
🔹Previous Day High/Low
The Previous Day High (PDH) marks the highest price reached during the previous regular trading session. It shows where buyers pushed price to its highest point before the market closed. This value is automatically pulled from the daily chart and projected forward onto intraday timeframes.
The Previous Day Low (PDL) marks the lowest price reached during the previous regular trading session. It shows where selling pressure reached its lowest point before buyers stepped in. Like the PDH, this level is retrieved from the prior day’s data and extended into the current session.
🔹Weekly Open
The Weekly Open marks the first price of the current trading week.
🔹Previous Week High/Low
The Previous Week High (PWH) marks the highest price reached during the previous trading week. It shows where buying pressure reached its peak before the weekly close. This value is automatically pulled from the weekly chart and extended forward into the current week for easy reference on intraday timeframes.
The Previous Week Low (PWL) marks the lowest price reached during the previous trading week. It shows where sellers pushed price to its lowest point before buyers regained control. Like the PWH, this level is sourced from the prior week’s data and projected onto the current week’s chart.
🔹Monthly Open
The Monthly Open marks the opening price of the current month.
🔹Previous Month High/Low
The Previous Month High (PMH) marks the highest price reached during the previous calendar month. It represents the point at which buyers achieved the strongest push before the monthly close. This level is automatically retrieved from the monthly chart and extended into the new month on all lower timeframes.
The Previous Month Low (PML) marks the lowest price reached during the previous calendar month. It shows where selling pressure was strongest before buyers stepped back in. Like the PMH, this value is pulled from the prior month’s data and extended into the new month on all lower timeframes.
🔹Midnight Open
The Midnight Open marks the first price of the trading day at 00:00 EST.
🔹Customization Options:
Users can fully customize the appearance of all key levels, including the following:
Daily Levels: Daily Open, PDH, and PDL
Weekly Levels: Weekly Open, PWH, and PWL
Monthly Levels: Monthly Open, PMH, and PML
Midnight Open
Color Settings:
Each group of levels (Daily, Weekly, Monthly) shares a single color for the Open, High, and Low lines. For example, the Daily Open, PDH, and PDL all use the same color. Colors can be changed for each group, but not for individual levels within the same group.
Line Style:
Users can select a global line style, choosing between solid, dashed, or dotted, for all Daily, Weekly, and Monthly levels. This style applies to all levels within those groups. For example, the Weekly Open, PWH, and PWL must all share the same line style.
The Midnight Open has its own independent line style setting and can use a different style from the other key levels.
Show Labels:
When enabled, text labels appear to the right of each key level. Users can adjust label color, but only one label color is applied to all levels for consistency.
🔹Key Levels Used:
Under the “Key Levels Used” section, users can choose which Key Levels and Session Levels (Session Highs/Lows) are factored into signal generation. Only levels enabled here are considered within the logic that confirms setups.
Users can choose between the following levels:
Daily Open
Previous Day High/Low
Weekly Open
Previous Week High/Low
Monthly Open
Previous Month High/Low
Asia Session High/Low
London Session High/Low
NY AM Session High/Low
NY Lunch Session High/Low
NY PM Session High/Low
London Close Session High/Low
Midnight Open
For example, if you only want to see setups that form using the Daily and Weekly levels, you should only enable the Daily Open, Previous Day High/Low, Weekly Open, and Previous Week High/Low.
Disabling a level in the main “Key Levels” section only hides its visuals, while disabling it in “Key Levels Used” removes it entirely from the signal logic. Adjusting or removing any level in this section directly affects how setups are detected since the indicator references these levels when confirming Long, Short, Bulltrap, and Beartrap setups.
SIGNALS:
The Manipulation Model indicator automatically identifies Long, Short, Bulltrap, and Beartrap setups based on the interaction between Manipulation Candles (MCs), Almost Manipulation Candles (AMCs), and two main entry conditions: Key Levels and Fair Value Gaps (FVGs).
Each signal type uses the structure of a Manipulation or Almost Manipulation Candle as its foundation. When one of these candles forms and aligns with the entry conditions, the indicator automatically plots labels for an entry, stop loss (SL), and take profit (TP). Every signal follows a mechanical set of rules and is marked in real time. Once confirmed on a candle close, the signal remains fixed on the chart and does not repaint.
🔹Higher Timeframe Bias Filter
Before a signal is generated, the indicator automatically determines directional bias using the 50-period Exponential Moving Average (EMA) on the 1-hour timeframe.
If price is above the 50 EMA, only bullish setups are allowed.
If price is below the 50 EMA, only bearish setups are allowed.
🔹Stop Loss and Take Profit Logic:
For every setup, the stop loss is placed at the low of the Manipulation or Almost Manipulation Candle for bullish setups, and at the high for bearish setups. The take profit is automatically calculated at a 1:1 risk-to-reward ratio relative to that distance.
Users can adjust both the SL Multiplier and TP Multiplier in the settings, under the “General Configuration” section, to extend or contract these levels. For example, increasing the TP Multiplier to 1.5 sets the take profit at 1.5x the distance between the entry and stop loss.
🔹Signal Input Settings:
Candle Type:
Choose which candle type is used to generate signals. Options include:
Manipulation Candle (MC) only
Almost Manipulation Candle (AMC) only
Both (signals are generated from either candle type)
Entry Method:
Determines whether signals are generated based on:
Key Levels only
Fair Value Gaps only
Both (signals are generated from Key Levels AND Fair Value Gaps)
Setup Types:
You can enable or disable specific setup types. Only the selected setup types will appear on your chart:
Long Setups
Short Setups
Bulltrap Setups
Beartrap Setups
🔹Long Setup – Manipulation Candle + Key Level:
A long setup forms when a bullish Manipulation Candle touches a toggled-on key level under the “Key Levels Used” section and closes above it during a toggled-on session from the “Sessions Used” section. After the candle closes and price is above the 1-hour 50 EMA, the indicator marks:
Entry: At the close of the bullish Manipulation Candle
Stop Loss: At the low of the same candle
Take Profit: Equal distance above the entry, based on TP multiplier
In this example, a bullish MC touches the PDH during the London Session and closes above the level:
🔹Short Setup – Manipulation Candle + Key Level
A short setup forms when a bearish Manipulation Candle touches a toggled-on key level under the “Key Levels Used” section and closes below it during a toggled-on session from the “Sessions Used” section. After the candle closes and price is below the 1-hour 50 EMA, the indicator marks:
Entry: At the close of the bearish Manipulation Candle
Stop Loss: At the high of the same candle
Take Profit: Equal distance below the entry, based on the TP Multiplier
In this example, a bearish MC touches the Daily Open during the NY AM Session and closes below the level:
🔹Trap Confirmation Settings
Two settings control how bulltrap and beartrap setups are confirmed once a Manipulation or Almost Manipulation Candle forms.
Candles Between Confirmation:
This setting defines the maximum number of candles allowed between the initial Manipulation Candle and the confirmation candle that closes back in the opposite direction.
For example, if this value is set to 2, the confirmation candle must appear within two bars of the Manipulation Candle for the setup to remain valid. If too many candles form in between, the bull/bear trap setup is ignored.
Trap Wick-to-Body Ratio:
This input measures the ratio of the confirmation candle’s wick size to its body size for bulltrap and beartrap setups. Lower values require a larger body compared to the wick, meaning the confirmation candle must close more decisively. If the ratio is above the threshold set by the user, the confirmation candle for a bulltrap/beartrap setup is considered valid.
For example, if the wick is 10 points and the body is 10 points, the ratio is 1.0 (10 / 10). If the wick is 10 points and the body is 20 points, the ratio is 0.5 (10 / 20).
🔹Beartrap Setup – Manipulation Candle + Key Level
A beartrap setup forms when a bearish Manipulation Candle touches a toggled-on key level under the “Key Levels Used” section. The candle does not need to close above or below the level, it only needs to touch it. After this bearish MC forms, a confirmation candle must close back above the MC’s high during an enabled session under the “Sessions Used” section. The sweep or initial touch can occur before or outside the session, but the confirmation candle must close within an active session window.
To confirm the setup, the following conditions must be met:
The confirmation candle must close within the limit set by the Candles Between Confirmation input.
Its wick-to-body ratio must be less than or equal to the Trap Wick-to-Body Ratio input
Once these conditions are met and price is above the 1-hour 50 EMA, the indicator marks:
Entry: At the close of the confirmation candle
Stop Loss: At the low of the confirmation candle
Take Profit: Equal distance above the entry, measured 1:1 from the candle’s body and scaled by the TP Multiplier
In this example, a bearish Manipulation Candle touches the Daily Open level before price reverses and a confirmation candle closes above it. The confirmation candle occurs during the Asia Session, has a strong body with minimal wicks, meeting the Trap Wick-to-Body Ratio requirement, and it forms just two candles after the bearish MC which is within the limit set by the Candles Between Confirmation input.
🔹Bulltrap Setup – Manipulation Candle + Key Level
A bulltrap setup forms when a bullish Manipulation Candle touches a toggled-on key level under the “Key Levels Used” section. The MC does not need to close above or below the level, it only needs to touch it. After this bullish MC forms, a confirmation candle must close back below the MC’s low during an enabled session under the “Sessions Used” section. The initial key level touch from the MC can occur before or outside the session, but the confirmation candle must close within an active session window.
To confirm the setup, the following conditions must be met:
The confirmation candle must close within the limit set by the Candles Between Confirmation input.
Its wick-to-body ratio must be less than or equal to the Trap Wick-to-Body Ratio input.
Once these conditions are met and price is below the 1-hour 50 EMA, the indicator marks:
Entry: At the close of the confirmation candle
Stop Loss: At the high of the confirmation candle
Take Profit: Equal distance below the entry, measured 1:1 from the candle’s body and scaled by the TP Multiplier
In this example, a bullish Manipulation Candle touches the Daily Open level before price reverses and a confirmation candle closes below it. The confirmation candle forms during the NY AM Session, has a strong body with minimal wicks that meet the Trap Wick-to-Body Ratio requirement, and it appears two candles after the bullish MC which is within the limit defined by the Candles Between Confirmation input.
🔹Long Setup – Almost Manipulation Candle + Key Level
A long setup forms when a bullish Almost Manipulation Candle (AMC) touches a toggled-on key level under the “Key Levels Used” section and closes above it during a toggled-on session from the “Sessions Used” section. After the candle closes and price is above the 1-hour 50 EMA, the indicator marks:
Entry: At the close of the bullish Almost Manipulation Candle
Stop Loss: At the low of the same candle
Take Profit: Equal distance above the entry, based on the TP Multiplier
In this example, a bullish AMC touches the Daily Open during the NYSE Session and closes above the level.
🔹Short Setup – Almost Manipulation Candle + Key Level
A short setup forms when a bearish Almost Manipulation Candle (AMC) touches a toggled-on key level under the “Key Levels Used” section and closes below it during a toggled-on session from the “Sessions Used” section. After the candle closes and price is below the 1-hour 50 EMA, the indicator marks:
Entry: At the close of the bearish Almost Manipulation Candle
Stop Loss: At the high of the same candle
Take Profit: Equal distance below the entry, based on the TP Multiplier
In this example, a bearish AMC touches the Midnight Open during the NY AM Session and closes below the level.
🔹Beartrap Setup – Almost Manipulation Candle + Key Level
A beartrap setup forms when a bearish Almost Manipulation Candle (AMC) touches a toggled-on key level under the “Key Levels Used” section. The candle does not need to close above or below the level, it only needs to touch it. After this bearish AMC forms, a confirmation candle must close back above the AMC’s high during an enabled session under the “Sessions Used” section. The initial touch can occur before or outside the session, but the confirmation candle must close within an active session window.
To confirm the setup, the following conditions must be met:
The confirmation candle must close within the limit set by the Candles Between Confirmation input.
Its wick-to-body ratio must be less than or equal to the Trap Wick-to-Body Ratio input.
Once these conditions are met and price is above the 1-hour 50 EMA, the indicator marks:
Entry: At the close of the confirmation candle
Stop Loss: At the low of the confirmation candle
Take Profit: Equal distance above the entry, measured 1:1 from the candle’s body and scaled by the TP Multiplier
In this example, a bearish AMC touches the Midnight Open before price reverses and a confirmation candle closes above it. The confirmation candle forms during the London Session, has a large body with minimal wicks that meet the Trap Wick-to-Body Ratio requirement, and appears seven candles after the bearish AMC which is within the Candles Between Confirmation limit (10 by default).
🔹Bulltrap Setup – Almost Manipulation Candle + Key Level
A bulltrap setup forms when a bullish AMC touches a toggled-on key level under the “Key Levels Used” section. The candle does not need to close above or below the level; it only needs to touch it. After this bullish AMC forms, a confirmation candle must close back below the AMC’s low during an enabled session under the “Sessions Used” section. The initial touch can occur before or outside the session, but the confirmation candle must close within an active session window.
To confirm the setup, the following conditions must be met:
The confirmation candle must close within the limit set by the Candles Between Confirmation input.
Its wick-to-body ratio must be less than or equal to the Trap Wick-to-Body Ratio input.
Once these conditions are met and price is below the 1-hour 50 EMA, the indicator marks:
Entry: At the close of the confirmation candle
Stop Loss: At the high of the confirmation candle
Take Profit: Equal distance below the entry, measured 1:1 from the candle’s body and scaled by the TP Multiplier
In this example, a bullish AMC touches the NY Lunch Session Low before price reverses and a confirmation candle closes below it. The confirmation candle forms during the Asia Session, has a strong body with minimal wicks that meet the Trap Wick-to-Body Ratio requirement, and appears six candles after the bullish AMC which is within the Candles Between Confirmation limit.
🔹Long Setup – Manipulation Candle + Fair Value Gap
A long setup forms when a bullish Manipulation Candle touches a bullish higher timeframe Fair Value Gap (FVG) from one of the two higher timeframe inputs under the “Fair Value Gaps” section. The candle must close during an enabled session under the “Sessions Used” section. After the candle closes and price is above the 1-hour 50 EMA, the indicator marks:
Entry: At the close of the bullish Manipulation Candle
Stop Loss: At the low of the same candle
Take Profit: Equal distance above the entry, scaled by the TP Multiplier
In this example, a bullish MC taps into a bullish 1-hour FVG during the Asia Session.
🔹Short Setup – Manipulation Candle + Fair Value Gap
A short setup forms when a bearish Manipulation Candle touches a bearish higher timeframe FVG from one of the two selected higher timeframe inputs under the “Fair Value Gaps” section. The candle must also close during an enabled session under the “Sessions Used” section. After the candle closes and price is below the 1-hour 50 EMA, the indicator marks:
Entry: At the close of the bearish Manipulation Candle
Stop Loss: At the high of the same candle
Take Profit: Equal distance below the entry, scaled by the TP Multiplier
In this example, a bearish MC taps a bearish 1-hour FVG during the Asia Session.
🔹Beartrap Setup – Manipulation Candle + Fair Value Gap
A beartrap setup forms when a bearish Manipulation Candle touches a bullish or bearish higher timeframe FVG from one of the two higher timeframe inputs under the “Higher Timeframe FVG Settings” section. After the bearish MC forms, price must reverse and a confirmation candle must close above the bearish MC’s high during an enabled session under the “Sessions Used” section. The initial touch of the FVG can occur before or outside the session, but the confirmation candle must close within an active session window.
To confirm the setup, the following conditions must be met:
The confirmation candle must close within the limit set by the Candles Between Confirmation input.
Its wick-to-body ratio must be less than or equal to the Trap Wick-to-Body Ratio input.
Once these conditions are met and price is above the 1-hour 50 EMA, the indicator marks:
Entry: At the close of the confirmation candle
Stop Loss: At the low of the confirmation candle
Take Profit: Equal distance above the entry, measured 1:1 from the candle’s body and scaled by the TP Multiplier
In this example, a bearish MC taps a 1-hour bearish FVG, price reverses, and a confirmation candle closes above the bearish MC’s high. The confirmation candle forms during the London Session, has a strong body with minimal wicks that meet the Trap Wick-to-Body Ratio requirement, and appears two candles after the bearish MC which is within the Candles Between Confirmation limit.
🔹Bulltrap Setup – Manipulation Candle + Fair Value Gap
A bulltrap setup forms when a bullish MC touches a bearish or bullish higher timeframeFVG from one of the two higher timeframe inputs under the “Higher Timeframe FVG Settings” section. After the bullish MC forms, price must reverse and a confirmation candle must close below the MC’s low during an enabled session under the “Sessions Used” section. The initial touch of the FVG can occur before or outside the session, but the confirmation candle must close within an active session window.
To confirm the setup, the following conditions must be met:
The confirmation candle must close within the limit set by the Candles Between Confirmation input.
Its wick-to-body ratio must be less than or equal to the Trap Wick-to-Body Ratio input.
Once these conditions are met and price is below the 1-hour 50 EMA, the indicator marks:
Entry: At the close of the confirmation candle
Stop Loss: At the high of the confirmation candle
Take Profit: Equal distance below the entry, measured 1:1 from the candle’s body and scaled by the TP Multiplier
In this example, a bullish MC taps a 4-hour bearish FVG, price reverses, and a confirmation candle closes below the bullish MC’s low. The confirmation candle forms during the NY PM Session, has a strong body with minimal wicks that meet the Trap Wick-to-Body Ratio requirement, and appears six candles after the bullish MC which is within the Candles Between Confirmation limit.
🔹Long Setup – Almost Manipulation Candle + Fair Value Gap
A long setup forms when a bullish AMC touches a bullish higher timeframe FVG from one of the two higher timeframe inputs under the “Fair Value Gaps” section. The candle must close during an enabled session under the “Sessions Used” section. After the candle closes and price is above the 1-hour 50 EMA, the indicator marks:
Entry: At the close of the bullish AMC
Stop Loss: At the low of the same candle
Take Profit: Equal distance above the entry, scaled by the TP Multiplier
In this example, a bullish AMC taps into a bullish 1-hour FVG during the London Session.
🔹Short Setup – Almost Manipulation Candle + Fair Value Gap
A short setup forms when a bearish AMC touches a bearish higher timeframe FVG from one of the two selected higher timeframe inputs under the “Fair Value Gaps” section. The candle must also close during an enabled session under the “Sessions Used” section. After the candle closes and price is below the 1-hour 50 EMA, the indicator marks:
Entry: At the close of the bearish AMC
Stop Loss: At the high of the same candle
Take Profit: Equal distance below the entry, scaled by the TP Multiplier
In this example, a bearish AMC taps a bearish 1-hour FVG during the NY PM Session.
🔹Beartrap Setup – Almost Manipulation Candle + Fair Value Gap
A beartrap setup forms when a bearish AMC touches a bullish or bearish higher timeframe FVG from one of the two higher timeframe inputs under the “Higher Timeframe FVG Settings” section. After the bearish AMC forms, price must reverse and a confirmation candle must close above the bearish AMC’s high during an enabled session under the “Sessions Used” section. The initial touch of the FVG can occur before or outside the session, but the confirmation candle must close within an active session window.
To confirm the setup, the following conditions must be met:
The confirmation candle must close within the limit set by the Candles Between Confirmation input.
Its wick-to-body ratio must be less than or equal to the Trap Wick-to-Body Ratio input.
Once these conditions are met and price is above the 1-hour 50 EMA, the indicator marks:
Entry: At the close of the confirmation candle
Stop Loss: At the low of the confirmation candle
Take Profit: Equal distance above the entry, measured 1:1 from the candle’s body and scaled by the TP Multiplier
In this example, a bearish AMC taps a 4-hour bearish FVG, price reverses, and a confirmation candle closes above the bearish AMC’s high. The confirmation candle forms during the NY PM Session, has a strong body with minimal wicks that meet the Trap Wick-to-Body Ratio requirement, and appears seven candles after the bearish AMC, which is within the Candles Between Confirmation limit.
🔹Bulltrap Setup – Almost Manipulation Candle + Fair Value Gap
A bulltrap setup forms when a bullish AMC touches a bearish or bullish higher timeframe FVG from one of the two higher timeframe inputs under the “Higher Timeframe FVG Settings” section. After the bullish AMC forms, price must reverse and a confirmation candle must close below the AMC’s low during an enabled session under the “Sessions Used” section. The initial touch of the FVG can occur before or outside the session, but the confirmation candle must close within an active session window.
To confirm the setup, the following conditions must be met:
The confirmation candle must close within the limit set by the Candles Between Confirmation input.
Its wick-to-body ratio must be less than or equal to the Trap Wick-to-Body Ratio input.
Once these conditions are met and price is below the 1-hour 50 EMA, the indicator marks:
Entry: At the close of the confirmation candle
Stop Loss: At the high of the confirmation candle
Take Profit: Equal distance below the entry, measured 1:1 from the candle’s body and scaled by the TP Multiplier
In this example, a bullish AMC taps a 1-hour bullish FVG, price reverses, and a confirmation candle closes below the bullish AMC’s low. The confirmation candle forms during the Asia Session, has a strong body with minimal wicks that meet the Trap Wick-to-Body Ratio requirement, and appears six candles after the bullish AMC, which is within the Candles Between Confirmation limit.
🔹Signal Style Customization
The Manipulation Model indicator provides full visual customization for all signal elements, allowing users to easily adjust the appearance of entry, stop loss, and take profit labels.
Label Colors:
Users can customize the label color for Long Setups (Long and Beartrap) and Short Setups (Short and Bulltrap).
Long and Beartrap setups share the same label color.
Short and Bulltrap setups share the same label color.
Label text color can also be customized and applied globally to all signal labels.
Stop Loss (SL) and Take Profit (TP) Labels:
The SL and TP label colors can be customized independently.
Users can toggle SL Labels and TP Labels on or off. When turned off, the corresponding labels are hidden, but their levels remain active on the chart.
Entry, Stop Loss, and Take Profit Lines:
Each of these lines can be individually toggled on or off.
Entry Line: Marks the entry price level.
Stop Loss Line: Displays the SL level derived from each setup’s logic.
Take Profit Line: Displays the TP level calculated using the Take Profit Multiplier setting.
Users can also toggle the labels for each line on or off and adjust the color for each line type independently.
WIN RATE DASHBOARD:
The Win Rate Dashboard gives traders a quick way to see the recent performance of their enabled setups. It automatically calculates and displays win rates for each signal type turned on under the “General Configuration” section, based on the sessions and key levels currently active in the settings.
The dashboard updates in real time, showing both the win rate percentage and total trade count for all enabled signal types combined. It looks back at a set number of bars to calculate results, providing a simple performance snapshot directly on your chart.
How It Works:
When a signal triggers, the indicator tracks whether price first reaches the Take Profit (TP) or Stop Loss (SL) level.
A winning trade is recorded when the take profit is hit before the stop loss.
A losing trade is recorded when the stop loss is hit before the take profit.
The win rate = (Winning Trades / Total Trades) x 100
🔹Dashboard Customization:
Users can adjust the dashboard’s appearance with the following settings:
Background Color
Frame Color
Border Color
Text Color
You can also toggle the dashboard on or off from the settings menu. It appears in the top-right corner of the chart by default and its position cannot be changed.
🔹Disclaimer:
The Win Rate Dashboard provides historical performance data based on the signals and conditions you’ve enabled. These results are calculated from past bars and are not indicative of future performance or profitability.
ALERTS:
The Manipulation Model indicator includes full alert functionality powered by AnyAlert(), allowing users to receive notifications for all major setups and level breaks in real time.
Users can choose exactly which alerts they want to receive under the “Alerts” section of the settings. Once your preferred alerts are toggled on, you can create a TradingView alert using the AnyAlert() condition. This will automatically trigger alerts for all selected events as they occur on your chart.
Available Alerts:
Long Setup
Short Setup
Bulltrap Setup
Beartrap Setup
Manipulation Candle
Almost Manipulation Candle
Previous Day High/Low Break
Current Day Open Break
Previous Week High/Low Break
Current Week Open Break
Previous Month High/Low Break
Current Month Open Break
Asia Session High/Low Break
London Session High/Low Break
NY AM Session High/Low Break
NYSE Session High/Low Break
London Close Session High/Low Break
NY PM Session High/Low Break
Midnight Open Break
To receive alerts:
Open the alert creation window in TradingView
Select “Manipulation Model ” as the condition
Choose AnyAlert() from the dropdown
Create the alert
IMPORTANT NOTES:
TradingView has limitations when running features on multiple timeframes, which can result in the following restriction:
Computation Error:
The computation of using MTF features is very intensive on TradingView. This can sometimes cause calculation timeouts. When this occurs, simply force the recalculation by modifying one indicator’s settings or by removing the indicator and adding it to your chart again.
UNIQUENESS:
The Manipulation Model is unique because every setup type is fully rule-based and tied to strict structural logic. Traders can control exactly how signals form by selecting which candle types are used, which key levels and sessions are active, and whether entries trigger from Key Levels, Fair Value Gaps, or both. All setups use objective rules for confirmation, wick-to-body ratio, and higher timeframe bias. The indicator also provides full customization for visuals, alerts, and trade parameters like TP and SL multipliers. A built-in Win Rate Dashboard tracks real-time performance for all enabled setup types based on the user’s active sessions and signal filters. Together, these features make it a complete, mechanical implementation of the Funded Brothers Manipulation Model and it works across all asset classes including stocks, crypto, forex, and futures.
Hellenic EMA Matrix - PremiumHellenic EMA Matrix - Alpha Omega Premium
Complete User Guide
Table of Contents
Introduction
Indicator Philosophy
Mathematical Constants
EMA Types
Settings
Trading Signals
Visualization
Usage Strategies
FAQ
Introduction
Hellenic EMA Matrix is a premium indicator based on mathematical constants of nature: Phi (Phi - Golden Ratio), Pi (Pi), e (Euler's number). The indicator uses these universal constants to create dynamic EMAs that adapt to the natural rhythms of the market.
Key Features:
6 EMA types based on mathematical constants
Premium visualization with Neon Glow and Gradient Clouds
Automatic Fast/Mid/Slow EMA sorting
STRONG signals for powerful trends
Pulsing Ribbon Bar for instant trend assessment
Works on all timeframes (M1 - MN)
Indicator Philosophy
Why Mathematical Constants?
Traditional EMAs use arbitrary periods (9, 21, 50, 200). Hellenic Matrix goes further, using universal mathematical constants found in nature:
Phi (1.618) - Golden Ratio: galaxy spirals, seashells, human body proportions
Pi (3.14159) - Pi: circles, waves, cycles
e (2.71828) - Natural logarithm base: exponential growth, radioactive decay
Markets are also a natural system composed of millions of participants. Using mathematical constants allows tuning into the natural rhythms of market cycles.
Mathematical Constants
Phi (Phi) - Golden Ratio
Phi = 1.618033988749895
Properties:
Phi² = Phi + 1 = 2.618
Phi³ = 4.236
Phi⁴ = 6.854
Application: Ideal for trending movements and Fibonacci corrections
Pi (Pi) - Pi Number
Pi = 3.141592653589793
Properties:
2Pi = 6.283 (full circle)
3Pi = 9.425
4Pi = 12.566
Application: Excellent for cyclical markets and wave structures
e (Euler) - Euler's Number
e = 2.718281828459045
Properties:
e² = 7.389
e³ = 20.085
e⁴ = 54.598
Application: Suitable for exponential movements and volatile markets
EMA Types
1. Phi (Phi) - Golden Ratio EMA
Description: EMA based on the golden ratio
Period Formula:
Period = Phi^n × Base Multiplier
Parameters:
Phi Power Level (1-8): Power of Phi
Phi¹ = 1.618 → ~16 period (with Base=10)
Phi² = 2.618 → ~26 period
Phi³ = 4.236 → ~42 period (recommended)
Phi⁴ = 6.854 → ~69 period
Recommendations:
Phi² or Phi³ for day trading
Phi⁴ or Phi⁵ for swing trading
Works excellently as Fast EMA
2. Pi (Pi) - Circular EMA
Description: EMA based on Pi for cyclical movements
Period Formula:
Period = Pi × Multiple × Base Multiplier
Parameters:
Pi Multiple (1-10): Pi multiplier
1Pi = 3.14 → ~31 period (with Base=10)
2Pi = 6.28 → ~63 period (recommended)
3Pi = 9.42 → ~94 period
Recommendations:
2Pi ideal as Mid or Slow EMA
Excellently identifies cycles and waves
Use on volatile markets (crypto, forex)
3. e (Euler) - Natural EMA
Description: EMA based on natural logarithm
Period Formula:
Period = e^n × Base Multiplier
Parameters:
e Power Level (1-6): Power of e
e¹ = 2.718 → ~27 period (with Base=10)
e² = 7.389 → ~74 period (recommended)
e³ = 20.085 → ~201 period
Recommendations:
e² works excellently as Slow EMA
Ideal for stocks and indices
Filters noise well on lower timeframes
4. Delta (Delta) - Adaptive EMA
Description: Adaptive EMA that changes period based on volatility
Period Formula:
Period = Base Period × (1 + (Volatility - 1) × Factor)
Parameters:
Delta Base Period (5-200): Base period (default 20)
Delta Volatility Sensitivity (0.5-5.0): Volatility sensitivity (default 2.0)
How it works:
During low volatility → period decreases → EMA reacts faster
During high volatility → period increases → EMA smooths noise
Recommendations:
Works excellently on news and sharp movements
Use as Fast EMA for quick adaptation
Sensitivity 2.0-3.0 for crypto, 1.0-2.0 for stocks
5. Sigma (Sigma) - Composite EMA
Description: Composite EMA combining multiple active EMAs
Composition Methods:
Weighted Average (default):
Sigma = (Phi + Pi + e + Delta) / 4
Simple average of all active EMAs
Geometric Mean:
Sigma = fourth_root(Phi × Pi × e × Delta)
Geometric mean (more conservative)
Harmonic Mean:
Sigma = 4 / (1/Phi + 1/Pi + 1/e + 1/Delta)
Harmonic mean (more weight to smaller values)
Recommendations:
Enable for additional confirmation
Use as Mid EMA
Weighted Average - most universal method
6. Lambda (Lambda) - Wave EMA
Description: Wave EMA with sinusoidal period modulation
Period Formula:
Period = Base Period × (1 + Amplitude × sin(2Pi × bar / Frequency))
Parameters:
Lambda Base Period (10-200): Base period
Lambda Wave Amplitude (0.1-2.0): Wave amplitude
Lambda Wave Frequency (10-200): Wave frequency in bars
How it works:
Period pulsates sinusoidally
Creates wave effect following market cycles
Recommendations:
Experimental EMA for advanced users
Works well on cyclical markets
Frequency = 50 for day trading, 100+ for swing
Settings
Matrix Core Settings
Base Multiplier (1-100)
Multiplies all EMA periods
Base = 1: Very fast EMAs (Phi³ = 4, 2Pi = 6, e² = 7)
Base = 10: Standard (Phi³ = 42, 2Pi = 63, e² = 74)
Base = 20: Slow EMAs (Phi³ = 85, 2Pi = 126, e² = 148)
Recommendations by timeframe:
M1-M5: Base = 5-10
M15-H1: Base = 10-15 (recommended)
H4-D1: Base = 15-25
W1-MN: Base = 25-50
Matrix Source
Data source selection for EMA calculation:
close - closing price (standard)
open - opening price
high - high
low - low
hl2 - (high + low) / 2
hlc3 - (high + low + close) / 3
ohlc4 - (open + high + low + close) / 4
When to change:
hlc3 or ohlc4 for smoother signals
high for aggressive longs
low for aggressive shorts
Manual EMA Selection
Critically important setting! Determines which EMAs are used for signal generation.
Use Manual Fast/Slow/Mid Selection
Enabled (default): You select EMAs manually
Disabled: Automatic selection by periods
Fast EMA
Fast EMA - reacts first to price changes
Recommendations:
Phi Golden (recommended) - universal choice
Delta Adaptive - for volatile markets
Must be fastest (smallest period)
Slow EMA
Slow EMA - determines main trend
Recommendations:
Pi Circular (recommended) - excellent trend filter
e Natural - for smoother trend
Must be slowest (largest period)
Mid EMA
Mid EMA - additional signal filter
Recommendations:
e Natural (recommended) - excellent middle level
Pi Circular - alternative
None - for more frequent signals (only 2 EMAs)
IMPORTANT: The indicator automatically sorts selected EMAs by their actual periods:
Fast = EMA with smallest period
Mid = EMA with middle period
Slow = EMA with largest period
Therefore, you can select any combination - the indicator will arrange them correctly!
Premium Visualization
Neon Glow
Enable Neon Glow for EMAs - adds glowing effect around EMA lines
Glow Strength:
Light - subtle glow
Medium (recommended) - optimal balance
Strong - bright glow (may be too bright)
Effect: 2 glow layers around each EMA for 3D effect
Gradient Clouds
Enable Gradient Clouds - fills space between EMAs with gradient
Parameters:
Cloud Transparency (85-98): Cloud transparency
95-97 (recommended)
Higher = more transparent
Dynamic Cloud Intensity - automatically changes transparency based on EMA distance
Cloud Colors:
Phi-Pi Cloud:
Blue - when Pi above Phi (bullish)
Gold - when Phi above Pi (bearish)
Pi-e Cloud:
Green - when e above Pi (bullish)
Blue - when Pi above e (bearish)
2 layers for volumetric effect
Pulsing Ribbon Bar
Enable Pulsing Indicator Bar - pulsing strip at bottom/top of chart
Parameters:
Ribbon Position: Top / Bottom (recommended)
Pulse Speed: Slow / Medium (recommended) / Fast
Symbols and colors:
Green filled square - STRONG BULLISH
Pink filled square - STRONG BEARISH
Blue hollow square - Bullish (regular)
Red hollow square - Bearish (regular)
Purple rectangle - Neutral
Effect: Pulsation with sinusoid for living market feel
Signal Bar Highlights
Enable Signal Bar Highlights - highlights bars with signals
Parameters:
Highlight Transparency (88-96): Highlight transparency
Highlight Style:
Light Fill (recommended) - bar background fill
Thin Line - bar outline only
Highlights:
Golden Cross - green
Death Cross - pink
STRONG BUY - green
STRONG SELL - pink
Show Greek Labels
Shows Greek alphabet letters on last bar:
Phi - Phi EMA (gold)
Pi - Pi EMA (blue)
e - Euler EMA (green)
Delta - Delta EMA (purple)
Sigma - Sigma EMA (pink)
When to use: For education or presentations
Show Old Background
Old background style (not recommended):
Green background - STRONG BULLISH
Pink background - STRONG BEARISH
Blue background - Bullish
Red background - Bearish
Not recommended - use new Gradient Clouds and Pulsing Bar
Info Table
Show Info Table - table with indicator information
Parameters:
Position: Top Left / Top Right (recommended) / Bottom Left / Bottom Right
Size: Tiny / Small (recommended) / Normal / Large
Table contents:
EMA list - periods and current values of all active EMAs
Effects - active visual effects
TREND - current trend state:
STRONG UP - strong bullish
STRONG DOWN - strong bearish
Bullish - regular bullish
Bearish - regular bearish
Neutral - neutral
Momentum % - percentage deviation of price from Fast EMA
Setup - current Fast/Slow/Mid configuration
Trading Signals
Show Golden/Death Cross
Golden Cross - Fast EMA crosses Slow EMA from below (bullish signal) Death Cross - Fast EMA crosses Slow EMA from above (bearish signal)
Symbols:
Yellow dot "GC" below - Golden Cross
Dark red dot "DC" above - Death Cross
Show STRONG Signals
STRONG BUY and STRONG SELL - the most powerful indicator signals
Conditions for STRONG BULLISH:
EMA Alignment: Fast > Mid > Slow (all EMAs aligned)
Trend: Fast > Slow (clear uptrend)
Distance: EMAs separated by minimum 0.15%
Price Position: Price above Fast EMA
Fast Slope: Fast EMA rising
Slow Slope: Slow EMA rising
Mid Trending: Mid EMA also rising (if enabled)
Conditions for STRONG BEARISH:
Same but in reverse
Visual display:
Green label "STRONG BUY" below bar
Pink label "STRONG SELL" above bar
Difference from Golden/Death Cross:
Golden/Death Cross = crossing moment (1 bar)
STRONG signal = sustained trend (lasts several bars)
IMPORTANT: After fixes, STRONG signals now:
Work on all timeframes (M1 to MN)
Don't break on small retracements
Work with any Fast/Mid/Slow combination
Automatically adapt thanks to EMA sorting
Show Stop Loss/Take Profit
Automatic SL/TP level calculation on STRONG signal
Parameters:
Stop Loss (ATR) (0.5-5.0): ATR multiplier for stop loss
1.5 (recommended) - standard
1.0 - tight stop
2.0-3.0 - wide stop
Take Profit R:R (1.0-5.0): Risk/reward ratio
2.0 (recommended) - standard (risk 1.5 ATR, profit 3.0 ATR)
1.5 - conservative
3.0-5.0 - aggressive
Formulas:
LONG:
Stop Loss = Entry - (ATR × Stop Loss ATR)
Take Profit = Entry + (ATR × Stop Loss ATR × Take Profit R:R)
SHORT:
Stop Loss = Entry + (ATR × Stop Loss ATR)
Take Profit = Entry - (ATR × Stop Loss ATR × Take Profit R:R)
Visualization:
Red X - Stop Loss
Green X - Take Profit
Levels remain active while STRONG signal persists
Trading Signals
Signal Types
1. Golden Cross
Description: Fast EMA crosses Slow EMA from below
Signal: Beginning of bullish trend
How to trade:
ENTRY: On bar close with Golden Cross
STOP: Below local low or below Slow EMA
TARGET: Next resistance level or 2:1 R:R
Strengths:
Simple and clear
Works well on trending markets
Clear entry point
Weaknesses:
Lags (signal after movement starts)
Many false signals in ranging markets
May be late on fast moves
Optimal timeframes: H1, H4, D1
2. Death Cross
Description: Fast EMA crosses Slow EMA from above
Signal: Beginning of bearish trend
How to trade:
ENTRY: On bar close with Death Cross
STOP: Above local high or above Slow EMA
TARGET: Next support level or 2:1 R:R
Application: Mirror of Golden Cross
3. STRONG BUY
Description: All EMAs aligned + trend + all EMAs rising
Signal: Powerful bullish trend
How to trade:
ENTRY: On bar close with STRONG BUY or on pullback to Fast EMA
STOP: Below Fast EMA or automatic SL (if enabled)
TARGET: Automatic TP (if enabled) or by levels
TRAILING: Follow Fast EMA
Entry strategies:
Aggressive: Enter immediately on signal
Conservative: Wait for pullback to Fast EMA, then enter on bounce
Pyramiding: Add positions on pullbacks to Mid EMA
Position management:
Hold while STRONG signal active
Exit on STRONG SELL or Death Cross appearance
Move stop behind Fast EMA
Strengths:
Most reliable indicator signal
Doesn't break on pullbacks
Catches large moves
Works on all timeframes
Weaknesses:
Appears less frequently than other signals
Requires confirmation (multiple conditions)
Optimal timeframes: All (M5 - D1)
4. STRONG SELL
Description: All EMAs aligned down + downtrend + all EMAs falling
Signal: Powerful bearish trend
How to trade: Mirror of STRONG BUY
Visual Signals
Pulsing Ribbon Bar
Quick market assessment at a glance:
Symbol Color State
Filled square Green STRONG BULLISH
Filled square Pink STRONG BEARISH
Hollow square Blue Bullish
Hollow square Red Bearish
Rectangle Purple Neutral
Pulsation: Sinusoidal, creates living effect
Signal Bar Highlights
Bars with signals are highlighted:
Green highlight: STRONG BUY or Golden Cross
Pink highlight: STRONG SELL or Death Cross
Gradient Clouds
Colored space between EMAs shows trend strength:
Wide clouds - strong trend
Narrow clouds - weak trend or consolidation
Color change - trend change
Info Table
Quick reference in corner:
TREND: Current state (STRONG UP, Bullish, Neutral, Bearish, STRONG DOWN)
Momentum %: Movement strength
Effects: Active visual effects
Setup: Fast/Slow/Mid configuration
Usage Strategies
Strategy 1: "Golden Trailing"
Idea: Follow STRONG signals using Fast EMA as trailing stop
Settings:
Fast: Phi Golden (Phi³)
Mid: Pi Circular (2Pi)
Slow: e Natural (e²)
Base Multiplier: 10
Timeframe: H1, H4
Entry rules:
Wait for STRONG BUY
Enter on bar close or on pullback to Fast EMA
Stop below Fast EMA
Management:
Hold position while STRONG signal active
Move stop behind Fast EMA daily
Exit on STRONG SELL or Death Cross
Take Profit:
Partially close at +2R
Trail remainder until exit signal
For whom: Swing traders, trend followers
Pros:
Catches large moves
Simple rules
Emotionally comfortable
Cons:
Requires patience
Possible extended drawdowns on pullbacks
Strategy 2: "Scalping Bounces"
Idea: Scalp bounces from Fast EMA during STRONG trend
Settings:
Fast: Delta Adaptive (Base 15, Sensitivity 2.0)
Mid: Phi Golden (Phi²)
Slow: Pi Circular (2Pi)
Base Multiplier: 5
Timeframe: M5, M15
Entry rules:
STRONG signal must be active
Wait for price pullback to Fast EMA
Enter on bounce (candle closes above/below Fast EMA)
Stop behind local extreme (15-20 pips)
Take Profit:
+1.5R or to Mid EMA
Or to next level
For whom: Active day traders
Pros:
Many signals
Clear entry point
Quick profits
Cons:
Requires constant monitoring
Not all bounces work
Requires discipline for frequent trading
Strategy 3: "Triple Filter"
Idea: Enter only when all 3 EMAs and price perfectly aligned
Settings:
Fast: Phi Golden (Phi³)
Mid: e Natural (e²)
Slow: Pi Circular (3Pi)
Base Multiplier: 15
Timeframe: H4, D1
Entry rules (LONG):
STRONG BUY active
Price above all three EMAs
Fast > Mid > Slow (all aligned)
All EMAs rising (slope up)
Gradient Clouds wide and bright
Entry:
On bar close meeting all conditions
Or on next pullback to Fast EMA
Stop:
Below Mid EMA or -1.5 ATR
Take Profit:
First target: +3R
Second target: next major level
Trailing: Mid EMA
For whom: Conservative swing traders, investors
Pros:
Very reliable signals
Minimum false entries
Large profit potential
Cons:
Rare signals (2-5 per month)
Requires patience
Strategy 4: "Adaptive Scalper"
Idea: Use only Delta Adaptive EMA for quick volatility reaction
Settings:
Fast: Delta Adaptive (Base 10, Sensitivity 3.0)
Mid: None
Slow: Delta Adaptive (Base 30, Sensitivity 2.0)
Base Multiplier: 3
Timeframe: M1, M5
Feature: Two different Delta EMAs with different settings
Entry rules:
Golden Cross between two Delta EMAs
Both Delta EMAs must be rising/falling
Enter on next bar
Stop:
10-15 pips or below Slow Delta EMA
Take Profit:
+1R to +2R
Or Death Cross
For whom: Scalpers on cryptocurrencies and forex
Pros:
Instant volatility adaptation
Many signals on volatile markets
Quick results
Cons:
Much noise on calm markets
Requires fast execution
High commissions may eat profits
Strategy 5: "Cyclical Trader"
Idea: Use Pi and Lambda for trading cyclical markets
Settings:
Fast: Pi Circular (1Pi)
Mid: Lambda Wave (Base 30, Amplitude 0.5, Frequency 50)
Slow: Pi Circular (3Pi)
Base Multiplier: 10
Timeframe: H1, H4
Entry rules:
STRONG signal active
Lambda Wave EMA synchronized with trend
Enter on bounce from Lambda Wave
For whom: Traders of cyclical assets (some altcoins, commodities)
Pros:
Catches cyclical movements
Lambda Wave provides additional entry points
Cons:
More complex to configure
Not for all markets
Lambda Wave may give false signals
Strategy 6: "Multi-Timeframe Confirmation"
Idea: Use multiple timeframes for confirmation
Scheme:
Higher TF (D1): Determine trend direction (STRONG signal)
Middle TF (H4): Wait for STRONG signal in same direction
Lower TF (M15): Look for entry point (Golden Cross or bounce from Fast EMA)
Settings for all TFs:
Fast: Phi Golden (Phi³)
Mid: e Natural (e²)
Slow: Pi Circular (2Pi)
Base Multiplier: 10
Rules:
All 3 TFs must show one trend
Entry on lower TF
Stop by lower TF
Target by higher TF
For whom: Serious traders and investors
Pros:
Maximum reliability
Large profit targets
Minimum false signals
Cons:
Rare setups
Requires analysis of multiple charts
Experience needed
Practical Tips
DOs
Use STRONG signals as primary - they're most reliable
Let signals develop - don't exit on first pullback
Use trailing stop - follow Fast EMA
Combine with levels - S/R, Fibonacci, volumes
Test on demo before real
Adjust Base Multiplier for your timeframe
Enable visual effects - they help see the picture
Use Info Table - quick situation assessment
Watch Pulsing Bar - instant state indicator
Trust auto-sorting of Fast/Mid/Slow
DON'Ts
Don't trade against STRONG signal - trend is your friend
Don't ignore Mid EMA - it adds reliability
Don't use too small Base Multiplier on higher TFs
Don't enter on Golden Cross in range - check for trend
Don't change settings during open position
Don't forget risk management - 1-2% per trade
Don't trade all signals in row - choose best ones
Don't use indicator in isolation - combine with Price Action
Don't set too tight stops - let trade breathe
Don't over-optimize - simplicity = reliability
Optimal Settings by Asset
US Stocks (SPY, AAPL, TSLA)
Recommendation:
Fast: Phi Golden (Phi³)
Mid: e Natural (e²)
Slow: Pi Circular (2Pi)
Base: 10-15
Timeframe: H4, D1
Features:
Use on daily for swing
STRONG signals very reliable
Works well on trending stocks
Forex (EUR/USD, GBP/USD)
Recommendation:
Fast: Delta Adaptive (Base 15, Sens 2.0)
Mid: Phi Golden (Phi²)
Slow: Pi Circular (2Pi)
Base: 8-12
Timeframe: M15, H1, H4
Features:
Delta Adaptive works excellently on news
Many signals on M15-H1
Consider spreads
Cryptocurrencies (BTC, ETH, altcoins)
Recommendation:
Fast: Delta Adaptive (Base 10, Sens 3.0)
Mid: Pi Circular (2Pi)
Slow: e Natural (e²)
Base: 5-10
Timeframe: M5, M15, H1
Features:
High volatility - adaptation needed
STRONG signals can last days
Be careful with scalping on M1-M5
Commodities (Gold, Oil)
Recommendation:
Fast: Pi Circular (1Pi)
Mid: Phi Golden (Phi³)
Slow: Pi Circular (3Pi)
Base: 12-18
Timeframe: H4, D1
Features:
Pi works excellently on cyclical commodities
Gold responds especially well to Phi
Oil volatile - use wide stops
Indices (S&P500, Nasdaq, DAX)
Recommendation:
Fast: Phi Golden (Phi³)
Mid: e Natural (e²)
Slow: Pi Circular (2Pi)
Base: 15-20
Timeframe: H4, D1, W1
Features:
Very trending instruments
STRONG signals last weeks
Good for position trading
Alerts
The indicator supports 6 alert types:
1. Golden Cross
Message: "Hellenic Matrix: GOLDEN CROSS - Fast EMA crossed above Slow EMA - Bullish trend starting!"
When: Fast EMA crosses Slow EMA from below
2. Death Cross
Message: "Hellenic Matrix: DEATH CROSS - Fast EMA crossed below Slow EMA - Bearish trend starting!"
When: Fast EMA crosses Slow EMA from above
3. STRONG BULLISH
Message: "Hellenic Matrix: STRONG BULLISH SIGNAL - All EMAs aligned for powerful uptrend!"
When: All conditions for STRONG BUY met (first bar)
4. STRONG BEARISH
Message: "Hellenic Matrix: STRONG BEARISH SIGNAL - All EMAs aligned for powerful downtrend!"
When: All conditions for STRONG SELL met (first bar)
5. Bullish Ribbon
Message: "Hellenic Matrix: BULLISH RIBBON - EMAs aligned for uptrend"
When: EMAs aligned bullish + price above Fast EMA (less strict condition)
6. Bearish Ribbon
Message: "Hellenic Matrix: BEARISH RIBBON - EMAs aligned for downtrend"
When: EMAs aligned bearish + price below Fast EMA (less strict condition)
How to Set Up Alerts:
Open indicator on chart
Click on three dots next to indicator name
Select "Create Alert"
In "Condition" field select needed alert:
Golden Cross
Death Cross
STRONG BULLISH
STRONG BEARISH
Bullish Ribbon
Bearish Ribbon
Configure notification method:
Pop-up in browser
Email
SMS (in Premium accounts)
Push notifications in mobile app
Webhook (for automation)
Select frequency:
Once Per Bar Close (recommended) - once on bar close
Once Per Bar - during bar formation
Only Once - only first time
Click "Create"
Tip: Create separate alerts for different timeframes and instruments
FAQ
1. Why don't STRONG signals appear?
Possible reasons:
Incorrect Fast/Mid/Slow order
Solution: Indicator automatically sorts EMAs by periods, but ensure selected EMAs have different periods
Base Multiplier too large
Solution: Reduce Base to 5-10 on lower timeframes
Market in range
Solution: STRONG signals appear only in trends - this is normal
Too strict EMA settings
Solution: Try classic combination: Phi³ / Pi×2 / e² with Base=10
Mid EMA too close to Fast or Slow
Solution: Select Mid EMA with period between Fast and Slow
2. How often should STRONG signals appear?
Normal frequency:
M1-M5: 5-15 signals per day (very active markets)
M15-H1: 2-8 signals per day
H4: 3-10 signals per week
D1: 2-5 signals per month
W1: 2-6 signals per year
If too many signals - market very volatile or Base too small
If too few signals - market in range or Base too large
4. What are the best settings for beginners?
Universal "out of the box" settings:
Matrix Core:
Base Multiplier: 10
Source: close
Phi Golden: Enabled, Power = 3
Pi Circular: Enabled, Multiple = 2
e Natural: Enabled, Power = 2
Delta Adaptive: Enabled, Base = 20, Sensitivity = 2.0
Manual Selection:
Fast: Phi Golden
Mid: e Natural
Slow: Pi Circular
Visualization:
Gradient Clouds: ON
Neon Glow: ON (Medium)
Pulsing Bar: ON (Medium)
Signal Highlights: ON (Light Fill)
Table: ON (Top Right, Small)
Signals:
Golden/Death Cross: ON
STRONG Signals: ON
Stop Loss: OFF (while learning)
Timeframe for learning: H1 or H4
5. Can I use only one EMA?
No, minimum 2 EMAs (Fast and Slow) for signal generation.
Mid EMA is optional:
With Mid EMA = more reliable but rarer signals
Without Mid EMA = more signals but less strict filtering
Recommendation: Start with 3 EMAs (Fast/Mid/Slow), then experiment
6. Does the indicator work on cryptocurrencies?
Yes, works excellently! Especially good on:
Bitcoin (BTC)
Ethereum (ETH)
Major altcoins (SOL, BNB, XRP)
Recommended settings for crypto:
Fast: Delta Adaptive (Base 10-15, Sensitivity 2.5-3.0)
Mid: Pi Circular (2Pi)
Slow: e Natural (e²)
Base: 5-10
Timeframe: M15, H1, H4
Crypto market features:
High volatility → use Delta Adaptive
24/7 trading → set alerts
Sharp movements → wide stops
7. Can I trade only with this indicator?
Technically yes, but NOT recommended.
Best approach - combine with:
Price Action - support/resistance levels, candle patterns
Volume - movement strength confirmation
Fibonacci - retracement and extension levels
RSI/MACD - divergences and overbought/oversold
Fundamental analysis - news, company reports
Hellenic Matrix:
Excellently determines trend and its strength
Provides clear entry/exit points
Doesn't consider fundamentals
Doesn't see major levels
8. Why do Gradient Clouds change color?
Color depends on EMA order:
Phi-Pi Cloud:
Blue - Pi EMA above Phi EMA (bullish alignment)
Gold - Phi EMA above Pi EMA (bearish alignment)
Pi-e Cloud:
Green - e EMA above Pi EMA (bullish alignment)
Blue - Pi EMA above e EMA (bearish alignment)
Color change = EMA order change = possible trend change
9. What is Momentum % in the table?
Momentum % = percentage deviation of price from Fast EMA
Formula:
Momentum = ((Close - Fast EMA) / Fast EMA) × 100
Interpretation:
+0.5% to +2% - normal bullish momentum
+2% to +5% - strong bullish momentum
+5% and above - overheating (correction possible)
-0.5% to -2% - normal bearish momentum
-2% to -5% - strong bearish momentum
-5% and below - oversold (bounce possible)
Usage:
Monitor momentum during STRONG signals
Large momentum = don't enter (wait for pullback)
Small momentum = good entry point
10. How to configure for scalping?
Settings for scalping (M1-M5):
Base Multiplier: 3-5
Source: close or hlc3 (smoother)
Fast: Delta Adaptive (Base 8-12, Sensitivity 3.0)
Mid: None (for more signals)
Slow: Phi Golden (Phi²) or Pi Circular (1Pi)
Visualization:
- Gradient Clouds: ON (helps see strength)
- Neon Glow: OFF (doesn't clutter chart)
- Pulsing Bar: ON (quick assessment)
- Signal Highlights: ON
Signals:
- Golden/Death Cross: ON
- STRONG Signals: ON
- Stop Loss: ON (1.0-1.5 ATR, R:R 1.5-2.0)
Scalping rules:
Trade only STRONG signals
Enter on bounce from Fast EMA
Tight stops (10-20 pips)
Quick take profit (+1R to +2R)
Don't hold through news
11. How to configure for long-term investing?
Settings for investing (D1-W1):
Base Multiplier: 20-30
Source: close
Fast: Phi Golden (Phi³ or Phi⁴)
Mid: e Natural (e²)
Slow: Pi Circular (3Pi or 4Pi)
Visualization:
- Gradient Clouds: ON
- Neon Glow: ON (Medium)
- Everything else - to taste
Signals:
- Golden/Death Cross: ON
- STRONG Signals: ON
- Stop Loss: OFF (use percentage stop)
Investing rules:
Enter only on STRONG signals
Hold while STRONG active (weeks/months)
Stop below Slow EMA or -10%
Take profit: by company targets or +50-100%
Ignore short-term pullbacks
12. What if indicator slows down chart?
Indicator is optimized, but if it slows:
Disable unnecessary visual effects:
Neon Glow: OFF (saves 8 plots)
Gradient Clouds: ON but low quality
Lambda Wave EMA: OFF (if not using)
Reduce number of active EMAs:
Sigma Composite: OFF
Lambda Wave: OFF
Leave only Phi, Pi, e, Delta
Simplify settings:
Pulsing Bar: OFF
Greek Labels: OFF
Info Table: smaller size
13. Can I use on different timeframes simultaneously?
Yes! Multi-timeframe analysis is very powerful:
Classic scheme:
Higher TF (D1, W1) - determine global trend
Wait for STRONG signal
This is our trading direction
Middle TF (H4, H1) - look for confirmation
STRONG signal in same direction
Precise entry zone
Lower TF (M15, M5) - entry point
Golden Cross or bounce from Fast EMA
Precise stop loss
Example:
W1: STRONG BUY active (global uptrend)
H4: STRONG BUY appeared (confirmation)
M15: Wait for Golden Cross or bounce from Fast EMA → ENTRY
Advantages:
Maximum reliability
Clear timeframe hierarchy
Large targets
14. How does indicator work on news?
Delta Adaptive EMA adapts excellently to news:
Before news:
Low volatility → Delta EMA becomes fast → pulls to price
During news:
Sharp volatility spike → Delta EMA slows → filters noise
After news:
Volatility normalizes → Delta EMA returns to normal
Recommendations:
Don't trade at news release moment (spreads widen)
Wait for STRONG signal after news (2-5 bars)
Use Delta Adaptive as Fast EMA for quick reaction
Widen stops by 50-100% during important news
Advanced Techniques
Technique 1: "Divergences with EMA"
Idea: Look for discrepancies between price and Fast EMA
Bullish divergence:
Price makes lower low
Fast EMA makes higher low
= Possible reversal up
Bearish divergence:
Price makes higher high
Fast EMA makes lower high
= Possible reversal down
How to trade:
Find divergence
Wait for STRONG signal in divergence direction
Enter on confirmation
Technique 2: "EMA Tunnel"
Idea: Use space between Fast and Slow EMA as "tunnel"
Rules:
Wide tunnel - strong trend, hold position
Narrow tunnel - weak trend or consolidation, caution
Tunnel narrowing - trend weakening, prepare to exit
Tunnel widening - trend strengthening, can add
Visually: Gradient Clouds show this automatically!
Trading:
Enter on STRONG signal (tunnel starts widening)
Hold while tunnel wide
Exit when tunnel starts narrowing
Technique 3: "Wave Analysis with Lambda"
Idea: Lambda Wave EMA creates sinusoid matching market cycles
Setup:
Lambda Base Period: 30
Lambda Wave Amplitude: 0.5
Lambda Wave Frequency: 50 (adjusted to asset cycle)
How to find correct Frequency:
Look at historical cycles (distance between local highs)
Average distance = your Frequency
Example: if highs every 40-60 bars, set Frequency = 50
Trading:
Enter when Lambda Wave at bottom of sinusoid (growth potential)
Exit when Lambda Wave at top (fall potential)
Combine with STRONG signals
Technique 4: "Cluster Analysis"
Idea: When all EMAs gather in narrow cluster = powerful breakout soon
Cluster signs:
All EMAs (Phi, Pi, e, Delta) within 0.5-1% of each other
Gradient Clouds almost invisible
Price jumping around all EMAs
Trading:
Identify cluster (all EMAs close)
Determine breakout direction (where more volume, higher TFs direction)
Wait for breakout and STRONG signal
Enter on confirmation
Target = cluster size × 3-5
This is very powerful technique for big moves!
Technique 5: "Sigma as Dynamic Level"
Idea: Sigma Composite EMA = average of all EMAs = magnetic level
Usage:
Enable Sigma Composite (Weighted Average)
Sigma works as dynamic support/resistance
Price often returns to Sigma before trend continuation
Trading:
In trend: Enter on bounces from Sigma
In range: Fade moves from Sigma (trade return to Sigma)
On breakout: Sigma becomes support/resistance
Risk Management
Basic Rules
1. Position Size
Conservative: 1% of capital per trade
Moderate: 2% of capital per trade (recommended)
Aggressive: 3-5% (only for experienced)
Calculation formula:
Lot Size = (Capital × Risk%) / (Stop in pips × Pip value)
2. Risk/Reward Ratio
Minimum: 1:1.5
Standard: 1:2 (recommended)
Optimal: 1:3
Aggressive: 1:5+
3. Maximum Drawdown
Daily: -3% to -5%
Weekly: -7% to -10%
Monthly: -15% to -20%
Upon reaching limit → STOP trading until end of period
Position Management Strategies
1. Fixed Stop
Method:
Stop below/above Fast EMA or local extreme
DON'T move stop against position
Can move to breakeven
For whom: Beginners, conservative traders
2. Trailing by Fast EMA
Method:
Each day (or bar) move stop to Fast EMA level
Position closes when price breaks Fast EMA
Advantages:
Stay in trend as long as possible
Automatically exit on reversal
For whom: Trend followers, swing traders
3. Partial Exit
Method:
50% of position close at +2R
50% hold with trailing by Mid EMA or Slow EMA
Advantages:
Lock profit
Leave position for big move
Psychologically comfortable
For whom: Universal method (recommended)
4. Pyramiding
Method:
First entry on STRONG signal (50% of planned position)
Add 25% on pullback to Fast EMA
Add another 25% on pullback to Mid EMA
Overall stop below Slow EMA
Advantages:
Average entry price
Reduce risk
Increase profit in strong trends
Caution:
Works only in trends
In range leads to losses
For whom: Experienced traders
Trading Psychology
Correct Mindset
1. Indicator is a tool, not holy grail
Indicator shows probability, not guarantee
There will be losing trades - this is normal
Important is series statistics, not one trade
2. Trust the system
If STRONG signal appeared - enter
Don't search for "perfect" moment
Follow trading plan
3. Patience
STRONG signals don't appear every day
Better miss signal than enter against trend
Quality over quantity
4. Discipline
Always set stop loss
Don't move stop against position
Don't increase risk after losses
Beginner Mistakes
1. "I know better than indicator"
Indicator says STRONG BUY, but you think "too high, will wait for pullback"
Result: miss profitable move
Solution: Trust signals or don't use indicator
2. "Will reverse now for sure"
Trading against STRONG trend
Result: stops, stops, stops
Solution: Trend is your friend, trade with trend
3. "Will hold a bit more"
Don't exit when STRONG signal disappears
Greed eats profit
Solution: If signal gone - exit!
4. "I'll recover"
After losses double risk
Result: huge losses
Solution: Fixed % risk ALWAYS
5. "I don't like this signal"
Skip signals because of "feeling"
Result: inconsistency, no statistics
Solution: Trade ALL signals or clearly define filters
Trading Journal
What to Record
For each trade:
1. Entry/exit date and time
2. Instrument and timeframe
3. Signal type
Golden Cross
STRONG BUY
STRONG SELL
Death Cross
4. Indicator settings
Fast/Mid/Slow EMA
Base Multiplier
Other parameters
5. Chart screenshot
Entry moment
Exit moment
6. Trade parameters
Position size
Stop loss
Take Profit
R:R
7. Result
Profit/Loss in $
Profit/Loss in %
Profit/Loss in R
8. Notes
What was right
What was wrong
Emotions during trade
Lessons
Journal Analysis
Analyze weekly:
1. Win Rate
Win Rate = (Profitable trades / All trades) × 100%
Good: 50-60%
Excellent: 60-70%
Exceptional: 70%+
2. Average R
Average R = Sum of all R / Number of trades
Good: +0.5R
Excellent: +1.0R
Exceptional: +1.5R+
3. Profit Factor
Profit Factor = Total profit / Total losses
Good: 1.5+
Excellent: 2.0+
Exceptional: 3.0+
4. Maximum Drawdown
Track consecutive losses
If more than 5 in row - stop, check system
5. Best/Worst Trades
What was common in best trades? (do more)
What was common in worst trades? (avoid)
Pre-Trade Checklist
Technical Analysis
STRONG signal active (BUY or SELL)
All EMAs properly aligned (Fast > Mid > Slow or reverse)
Price on correct side of Fast EMA
Gradient Clouds confirm trend
Pulsing Bar shows STRONG state
Momentum % in normal range (not overheated)
No close strong levels against direction
Higher timeframe doesn't contradict
Risk Management
Position size calculated (1-2% risk)
Stop loss set
Take profit calculated (minimum 1:2)
R:R satisfactory
Daily/weekly risk limit not exceeded
No other open correlated positions
Fundamental Analysis
No important news in coming hours
Market session appropriate (liquidity)
No contradicting fundamentals
Understand why asset is moving
Psychology
Calm and thinking clearly
No emotions from previous trades
Ready to accept loss at stop
Following trading plan
Not revenging market for past losses
If at least one point is NO - think twice before entering!
Learning Roadmap
Week 1: Familiarization
Goals:
Install and configure indicator
Study all EMA types
Understand visualization
Tasks:
Add indicator to chart
Test all Fast/Mid/Slow settings
Play with Base Multiplier on different timeframes
Observe Gradient Clouds and Pulsing Bar
Study Info Table
Result: Comfort with indicator interface
Week 2: Signals
Goals:
Learn to recognize all signal types
Understand difference between Golden Cross and STRONG
Tasks:
Find 10 Golden Cross examples in history
Find 10 STRONG BUY examples in history
Compare their results (which worked better)
Set up alerts
Get 5 real alerts
Result: Understanding signals
Week 3: Demo Trading
Goals:
Start trading signals on demo account
Gather statistics
Tasks:
Open demo account
Trade ONLY STRONG signals
Keep journal (minimum 20 trades)
Don't change indicator settings
Strictly follow stop losses
Result: 20+ documented trades
Week 4: Analysis
Goals:
Analyze demo trading results
Optimize approach
Tasks:
Calculate win rate and average R
Find patterns in profitable trades
Find patterns in losing trades
Adjust approach (not indicator!)
Write trading plan
Result: Trading plan on 1 page
Month 2: Improvement
Goals:
Deepen understanding
Add additional techniques
Tasks:
Study multi-timeframe analysis
Test combinations with Price Action
Try advanced techniques (divergences, tunnels)
Continue demo trading (minimum 50 trades)
Achieve stable profitability on demo
Result: Win rate 55%+ and Profit Factor 1.5+
Month 3: Real Trading
Goals:
Transition to real account
Maintain discipline
Tasks:
Open small real account
Trade minimum lots
Strictly follow trading plan
DON'T increase risk
Focus on process, not profit
Result: Psychological comfort on real
Month 4+: Scaling
Goals:
Increase account
Become consistently profitable
Tasks:
With 60%+ win rate can increase risk to 2%
Upon doubling account can add capital
Continue keeping journal
Periodically review and improve strategy
Share experience with community
Result: Stable profitability month after month
Additional Resources
Recommended Reading
Technical Analysis:
"Technical Analysis of Financial Markets" - John Murphy
"Trading in the Zone" - Mark Douglas (psychology)
"Market Wizards" - Jack Schwager (trader interviews)
EMA and Moving Averages:
"Moving Averages 101" - Steve Burns
Articles on Investopedia about EMA
Risk Management:
"The Mathematics of Money Management" - Ralph Vince
"Trade Your Way to Financial Freedom" - Van K. Tharp
Trading Journals:
Edgewonk (paid, very powerful)
Tradervue (free version + premium)
Excel/Google Sheets (free)
Screeners:
TradingView Stock Screener
Finviz (stocks)
CoinMarketCap (crypto)
Conclusion
Hellenic EMA Matrix is a powerful tool based on universal mathematical constants of nature. The indicator combines:
Mathematical elegance - Phi, Pi, e instead of arbitrary numbers
Premium visualization - Neon Glow, Gradient Clouds, Pulsing Bar
Reliable signals - STRONG BUY/SELL work on all timeframes
Flexibility - 6 EMA types, adaptation to any trading style
Automation - auto-sorting EMAs, SL/TP calculation, alerts
Key Success Principles:
Simplicity - start with basic settings (Phi/Pi/e, Base=10)
Discipline - follow STRONG signals strictly
Patience - wait for quality setups
Risk Management - 1-2% per trade, ALWAYS
Journal - document every trade
Learning - constantly improve skills
Remember:
Indicator shows probability, not guarantee
Important is series statistics, not one trade
Psychology more important than technique
Quality more important than quantity
Process more important than result
Acknowledgments
Thank you for using Hellenic EMA Matrix - Alpha Omega Premium!
The indicator was created with love for mathematics, markets, and beautiful visualization.
Wishing you profitable trading!
Guide Version: 1.0
Date: 2025
Compatibility: Pine Script v6, TradingView
"In the simplicity of mathematical constants lies the complexity of market movements"
Tight Entry Trend Engine Strategy═══════════════════════════════════════
TIGHT ENTRY TREND ENGINE
═══════════════════════════════════════
A breakout-based trend-following system designed to capture explosive
moves by entering at precise resistance/support breakouts with minimal
entry risk and massive profit potential.
⚠️ LOW WIN RATE, HIGH REWARD SYSTEM ⚠️
This is NOT a high win-rate strategy. Expect 25-35% winners, but
when it hits, winners are typically 10X+ larger than losers.
═══════════════════════════════════════
🎯 WHAT THIS SYSTEM DOES
═══════════════════════════════════════
The Tight Entry Trend Engine identifies powerful breakout opportunities
by detecting when price breaks through established trendlines with
confirmation from higher timeframe trends:
1. DYNAMIC TRENDLINE DETECTION (3 BANKS)
• Automatically draws support and resistance trendlines
• 3 separate "banks" capture short-term, medium-term, and long-term levels
• Each bank has configurable parameters (required pivot touch count,
angle limits, lengths)
2. BREAKOUT ENTRY TIMING
• Enters LONG when price breaks ABOVE resistance trendlines
• Enters SHORT when price breaks BELOW support trendlines
• Entry Alert occurs at the exact moment of breakout = "tight entry"
• Stop-loss placed just below/above the broken trendline (configurable)
3. HIGHER TIMEFRAME TREND FILTER
• Uses Hull Moving Average (HMA) on higher timeframe for trend following
• Auto-adjusts HTF based on your chart timeframe
• Optional filters prevent entries against major trend
• Optional "overextension" filter avoids buying parabolic moves
4. VOLATILITY-ADAPTIVE RISK MANAGEMENT
• Stop-loss calculated using Average True Range (ATR)
• Tighter stops = better R:R
• Profit targets adjust dynamically with volatility
• Breakeven stop moves automatically when in profit
• Extended profit targets when far from HTF trend
═══════════════════════════════════════
📊 HOW IT WORKS (METHODOLOGY)
═══════════════════════════════════════
STEP 1: TRENDLINE FORMATION
The system continuously scans for pivot highs and pivot lows to
construct trendlines. You control:
BANK 1 (Short-Term):
- Pivot Length: How many bars to look back for swing points
- Min Touches: How many pivots needed to form a line (default: 3)
- Max Length: How far back lines can reach (default: 180 bars)
- Angle Limits: Maximum steepness allowed for valid trendlines
- Tolerance: How close pivots must align to form horizontal lines
BANK 2 (Medium-Term):
- Slightly longer pivot periods for more significant levels
- Captures medium-term trend structure
- Default Max Length: 200 bars
BANK 3 (Long-Term):
- Focuses on major support/resistance zones
- Often uses horizontal levels (angled lines disabled by default)
- Default Max Length: 300 bars
The system draws RESISTANCE lines (red) above price and SUPPORT
lines (green) below price. These adapt in real-time as new pivots form.
STEP 2: BREAKOUT DETECTION
LONG SIGNALS:
- Price closes above a resistance trendline
- Higher timeframe trend is up (optional filter)
- Price not overextended from HTF trend (optional filter)
- No position currently open
SHORT SIGNALS:
- Price closes below a support trendline
- Higher timeframe trend is down (optional filter)
- Price not overextended from HTF trend (optional filter)
- No position currently open
The "tight" aspect: Because you're entering right at the trendline
break, your stop-loss can be placed very close (just below the
broken resistance for longs), creating exceptional risk/reward ratios.
STEP 3: POSITION SIZING
Choose between:
- Fixed $ Risk Per Trade: Risk same dollar amount every trade
- % Risk Per Trade: Risk percentage of current equity
Position size automatically calculated based on:
- Your risk amount
- Distance to stop-loss (ATR-based)
- Works with stocks, futures, crypto (auto-adjusts for contract multipliers)
STEP 4: EXIT MANAGEMENT
Multiple exit methods working together:
- PROFIT TARGET: Exits when profit reaches 100x your risk
- EXTENDED PROFIT: Earlier exit (80R) when very far from HTF trend
- STOP LOSS: Fixed ATR-based stop below entry
- HTF TREND EXIT: Exits when price crosses below HTF trend with profit
- BREAKEVEN PULLBACK: Exits if profit drops below 0.6R after reaching breakeven
- PARTIAL PROFITS: Optional - take partial profits at specified R-multiple
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🔧 KEY COMPONENTS EXPLAINED
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HULL MOVING AVERAGE (HMA)
A smoothed moving average that reduces lag compared to traditional
MAs. The system uses HMA on a higher timeframe to determine the
dominant trend direction. You can choose:
- Auto HTF: System picks appropriate HTF based on your chart timeframe
- Manual HTF: You specify the higher timeframe
AVERAGE TRUE RANGE (ATR)
Measures current market volatility. Used for:
- Stop-loss distance (tighter when volatility low)
- Profit targets (larger when volatility high)
- Position sizing (smaller positions in volatile conditions)
- Breakeven trigger distance
TRENDLINE ANGLE FILTERING
Each trendline bank has angle limits to ensure quality:
- Resistance lines: Max downward/upward slope allowed
- Support lines: Max downward/upward slope allowed
- Angles automatically adjust based on current volatility
- Prevents overly steep/unreliable trendlines
SENSITIVITY CONTROL
One master slider adjusts multiple parameters:
- Trendline detection sensitivity
- HTF MA length
- Exit timing
- Auto-adjusts for daily+ timeframes (60% increase)
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⚙️ WHAT YOU SEE ON YOUR CHART
═══════════════════════════════════════
TRENDLINES:
✓ Red resistance lines above price
✓ Green support lines below price
✓ Orange broken lines (past breakouts)
✓ Lines extend to show current levels
HTF TREND:
✓ Thick colored line showing higher timeframe trend
✓ Color gradient: Red (bearish) → Orange → Yellow → Green (bullish)
✓ 250-bar smoothed curve for visual clarity
ENTRY/EXIT SIGNALS:
✓ Small green dot below bar = Long entry
✓ Small red dot above bar = Short entry
✓ Small red dot above = Long exit
✓ Small black dot below = Short exit
OPTIONAL DETAILED LABELS:
✓ Bank number that triggered entry (Bank 1, 2, or 3)
✓ Exit reason (Profit Target, Stop Loss, HTF Exit, etc.)
✓ Partial profit notifications
POSITION TRACKING:
✓ Yellow dashed line at entry price (extends right)
✓ Green/red fill showing current profit/loss zone
✓ Lime arrows at top = Currently in long position
✓ Red arrows at bottom = Currently in short position
✓ Gray background = No position (flat)
STATS TABLE (Top Right):
✓ Current position (LONG/SHORT/FLAT)
✓ Risk per trade ($ or %)
✓ Entry price
✓ Unrealized P/L in dollars
✓ P/L in R-multiples (how many R's profit/loss)
✓ Average winner/loser R ($ mode) OR CAGR (% mode)
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📈 OPTIMAL USAGE
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BEST ASSETS:
- NASDAQ:QQQ on 1-hour (reg) chart ⭐ (PRIMARY OPTIMIZATION)
- Strong trending stocks: NVDA, AAPL, TSLA, MSFT, GOOGL, AMZN
- High volatility tech stocks
- Crypto: BTC, ETH
- Any liquid asset with clear trends and momentum (GOLD)
AVOID:
- Low volatility stocks
- Ranging/choppy markets
- Penny stocks or illiquid assets
- Assets without clear directional movement
BEST TIMEFRAMES:
- PRIMARY: 1-hour charts (optimal for QQQ)
- ALSO EXCELLENT: 2H, 4H, 8H
- WORKS: 15min, 30min (only momentum leaders, more noise)
- WORKS WITH ADJUSTMENTS: 1D, 2D (decrease trendline pivot lengths)
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📊 BACKTEST RESULTS (QQQ 1H (Reg hours), 1999-2024)
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The system showed on NASDAQ:QQQ 1-hour timeframe (regular hours):
- Total Return: 1,100,000%+ over 24 years
- Total Trades: 500+
- Win Rate: ~20-24% (LOW - this is by design!)
- Average Winner: 8-15% gain
- Average Loser: 2-4% loss
- Win/Loss Ratio: 10:1 (winners much bigger than losers)
- Profit Factor: 3+
- Max Drawdown: 45-50%
- Risk per trade: 3% of capital
KEY INSIGHT: This is a LOW WIN RATE, HIGH REWARD system. You will
lose more trades than you win, but the few winners are so large
they more than compensate for many small losses.
IMPORTANT: These are backtested results using optimal parameters
on historical data. Real trading results will vary based on:
- Your execution and timing
- Slippage and commissions
- Your emotional discipline
- Market conditions during your trading period
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🎓 WHO IS THIS FOR?
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IDEAL FOR:
✓ Swing traders comfortable holding winners for longer period
✓ Part-time traders (1H = check 2-3x per day)
✓ Traders seeking exceptional risk/reward ratios
✓ Those comfortable with low win rates if winners are huge
✓ Technical analysis enthusiasts
✓ Breakout traders
✓ Trend followers
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🚀 GETTING STARTED - STEP BY STEP
═══════════════════════════════════════
STEP 1: APPLY TO YOUR CHART
- Search "Tight Entry Trend Engine" in indicators
- Click to apply to your chart
- Trendlines and HTF line will appear immediately
STEP 2: CHOOSE YOUR SETTINGS
For BEGINNERS - Use These Settings First:
1. Trade Direction & Filters:
• ENABLE LONGS: ✓ ON
• ENABLE SHORTS: ✗ OFF (start with longs only)
• Sensitivity: 1.0 (default)
• HTF Trend Entry Filter: ✓ ON (safer entries)
• Block Entries When Overextended: ✓ ON (avoid parabolic tops)
2. Position Sizing & Risk:
• Position Sizing: "Per Risk"
• RISK Type: "$ Per Trade"
• Risk Amount: $200 (or 1-3% of your account)
3. Visual Settings:
• Show Support Lines: ✗ OFF (unless trading shorts)
• Show Detailed Entry/Exit Labels: ✓ ON
• Show Stats Table: ✓ ON
• Show Entry Line & P/L Fill: ✓ ON
4. Leave everything else at DEFAULT for now
STEP 3: UNDERSTAND WHAT YOU SEE
When trendlines appear:
- RED lines above = Resistance (watch for price breaking UP through these)
- GREEN lines below = Support (watch for price breaking DOWN)
- When price breaks a red line = Potential LONG entry
- When price breaks a green line = Potential SHORT entry
The HTF trend line (thick colored):
- Green/lime = Strong uptrend (favorable for longs)
- Red = Strong downtrend (favorable for shorts if enabled)
- Orange/yellow = Transitioning
STEP 4: OBSERVE SIGNALS
- Small GREEN dot below bar = System entered LONG
- Small RED dot above bar = System exited LONG
- Check the label to see which "Bank" triggered (Bank 1, 2, or 3)
- Watch the yellow entry line and colored fill show your P/L
STEP 5: PAPER TRADE FIRST
- Use TradingView's paper trading feature
- Watch how signals perform on YOUR chosen asset
- Understand the win rate will be LOW (20-35%)
- Verify that winners are indeed much larger than losers
- Test for at least 20-30 signals before going live
STEP 6: OPTIMIZE FOR YOUR ASSET (OPTIONAL)
If default settings aren't working well:
For FASTER signals (more trades):
- Reduce Pivot Length 1 to 3-4
- Reduce Max Length 1 to 120-150
- Increase Sensitivity to 1.2-1.5
For SLOWER signals (higher quality):
- Increase Pivot Length 1 to 7-10
- Increase Max Length 1 to 250+
- Decrease Sensitivity to 0.7-0.9
For DAILY timeframes:
- Increase all Pivot Lengths by 30-50%
- Increase all Max Lengths significantly
- Sensitivity: 0.6-0.8
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⚙️ ADVANCED SETTINGS EXPLAINED
═══════════════════════════════════════
TRENDLINE BANK SETTINGS:
Each bank (1, 2, 3) has these parameters:
- Min Touches: Minimum pivots to form a line
- Lower (2) = More lines, earlier detection
- Higher (4+) = Fewer lines, higher quality
- Pivot Length: Lookback for swing points
- Lower (3-5) = Reacts to recent price action
- Higher (10+) = Only major swing points
- Max Length: How old a trendline can be
- Shorter (100-150) = Only recent lines
- Longer (300+) = Include historical levels
- Tolerance: Alignment strictness for horizontal lines
- Lower (3.0-3.5) = Very strict horizontal
- Higher (4.5+) = More forgiving alignment
- Allow Angled Lines: Enable diagonal trendlines
- ON = Catches sloped support/resistance
- OFF = Only horizontal levels
- Angle Limits: Maximum steepness allowed
- Lower (1-2) = Only gentle slopes
- Higher (4-6) = Accept steeper angles
- Automatically adjusts for volatility
ATR MULTIPLIERS:
- STOP LOSS ATR (0.6): Distance to stop-loss
- Lower (0.4-0.5) = Tighter stops, stopped out more
- Higher (0.8-1.0) = Wider stops, more room
- PROFIT TARGET ATR (100): Main profit target
- This is 100x your risk = 10,000% R:R
- Lower (50-80) = Take profits sooner
- Higher (120+) = Let winners run longer
- BREAKEVEN ATR (40): When to move stop to breakeven
- Lower (20-30) = Protect profits earlier
- Higher (60+) = Give more room before protecting
HIGHER TIMEFRAME:
- Auto HTF: Automatically selects appropriate HTF
- 5min chart → uses 2H
- 15-30min → uses 6H
- 1-4H → uses 2D
- Daily → uses 4D
- HTF MA Length (300): HMA period for trend
- Lower (150-250) = More responsive
- Higher (400-500) = Smoother, less whipsaw
- HTF Trend Following Exit: Exits when crossing HTF
- ON = Additional exit method
- OFF = Rely only on profit targets/stops
- HTF Trend Entry Filter: Only trade with HTF trend
- ON = Safer, fewer signals
- OFF = More aggressive, more signals
- Block Entries When Overextended: Prevents chasing
- ON = Avoids parabolic tops/bottoms
- OFF = Enter all breakouts regardless
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💡 TRADING PHILOSOPHY & EXPECTATIONS
═══════════════════════════════════════
This system is built on one core principle:
"ACCEPT SMALL, FREQUENT LOSSES TO CAPTURE RARE, MASSIVE WINS"
What this means:
- You WILL lose 65%-75% of your trades
- Most losses will be small (1-2R)
- Some winners hit 80R+
- Over time, math works in your favour
Recovery StrategyDescription:
The Recovery Strategy is a long-only trading system designed to capitalize on significant price drops from recent highs. It enters a position when the price falls 10% or more from the highest high over a 6-month lookback period and adds positions on further 2% drops, up to a maximum of 5 positions. Each trade is held for 6 months before exiting, regardless of profit or loss. The strategy uses margin to amplify position sizes, with a default leverage of 5:1 (20% margin requirement). All key parameters are customizable via inputs, allowing flexibility for different assets and timeframes. Visual markers indicate recent highs for reference.
How It Works:
Entry: Buys when the closing price drops 10% or more from the recent high (highest high in the lookback period, default 126 bars ~6 months). If already in a position, additional buys occur on further 2% drops (e.g., 12%, 14%, 16%, 18%), up to 5 positions (pyramiding).
Exit: Each trade exits after its own holding period (default 126 bars ~6 months), regardless of profit or loss. No stop loss or take-profit is used.
Margin: Uses leverage to control larger positions (default 20% margin, 5:1 leverage). The order size is a percentage of equity (default 100%), adjustable via inputs.
Visualization: Displays blue markers (without text) at new recent highs to highlight reference levels.
Inputs:
Lookback Period for High Peak (bars): Number of bars to look back for the recent high (default: 126, ~6 months on daily charts).
Initial Drop Percentage to Buy (%): Percentage drop from recent high to trigger the first buy (default: 10.0%).
Additional Drop Percentage to Buy (%): Further drop percentage to add positions (default: 2.0%).
Holding Period (bars): Number of bars to hold each position before selling (default: 126, ~6 months).
Order Size (% of Equity): Percentage of equity used per trade (default: 100%).
Margin for Long Positions (%): Percentage of position value covered by equity (default: 20%, equivalent to 5:1 leverage).
Usage:
Timeframe: Designed for daily charts (126 bars ~6 months). Adjust Lookback Period and Holding Period for other timeframes (e.g., 1008 hours for hourly charts, assuming 8 trading hours/day).
Assets: Suitable for stocks, ETFs, or other assets with significant price volatility. Test thoroughly on your chosen asset.
Settings: Customize inputs in the strategy settings to match your risk tolerance and market conditions. For example, lower Margin for Long Positions (e.g., to 10% for 10:1 leverage) to increase position sizes, but beware of higher risk.
Backtesting: Use TradingView’s Strategy Tester to evaluate performance. Check the “List of Trades” for skipped trades due to insufficient equity or margin requirements.
Risks and Considerations:
No Stop Loss: The strategy holds trades for the full 6 months without a stop loss, exposing it to significant drawdowns in prolonged downtrends.
Margin Risk: Leverage (default 5:1) amplifies both profits and losses. Ensure sufficient equity to cover margin requirements to avoid skipped trades or simulated margin calls.
Pyramiding: Up to 5 positions can be open simultaneously, increasing exposure. Adjust pyramiding in the code if fewer positions are desired (e.g., change to pyramiding=3).
Market Conditions: Performance depends on price drops and recoveries. Test on historical data to assess effectiveness in your market.
Broker Emulator: TradingView’s paper trading simulates margin but does not execute real margin trading. Results may differ in live trading due to broker-specific margin rules.
How to Use:
Add the strategy to your chart in TradingView.
Adjust input parameters in the settings panel to suit your asset, timeframe, and risk preferences.
Run a backtest in the Strategy Tester to evaluate performance.
Monitor open positions and margin levels in the Trading Panel to manage risk.
For live trading, consult your broker’s margin requirements and leverage policies, as TradingView’s simulation may not match real-world conditions.
Disclaimer:
This strategy is for educational purposes only and does not constitute financial advice. Trading involves significant risk, especially with leverage and no stop loss. Always backtest thoroughly and consult a financial advisor before using any strategy in live trading.
Game Theory Trading StrategyGame Theory Trading Strategy: Explanation and Working Logic
This Pine Script (version 5) code implements a trading strategy named "Game Theory Trading Strategy" in TradingView. Unlike the previous indicator, this is a full-fledged strategy with automated entry/exit rules, risk management, and backtesting capabilities. It uses Game Theory principles to analyze market behavior, focusing on herd behavior, institutional flows, liquidity traps, and Nash equilibrium to generate buy (long) and sell (short) signals. Below, I'll explain the strategy's purpose, working logic, key components, and usage tips in detail.
1. General Description
Purpose: The strategy identifies high-probability trading opportunities by combining Game Theory concepts (herd behavior, contrarian signals, Nash equilibrium) with technical analysis (RSI, volume, momentum). It aims to exploit market inefficiencies caused by retail herd behavior, institutional flows, and liquidity traps. The strategy is designed for automated trading with defined risk management (stop-loss/take-profit) and position sizing based on market conditions.
Key Features:
Herd Behavior Detection: Identifies retail panic buying/selling using RSI and volume spikes.
Liquidity Traps: Detects stop-loss hunting zones where price breaks recent highs/lows but reverses.
Institutional Flow Analysis: Tracks high-volume institutional activity via Accumulation/Distribution and volume spikes.
Nash Equilibrium: Uses statistical price bands to assess whether the market is in equilibrium or deviated (overbought/oversold).
Risk Management: Configurable stop-loss (SL) and take-profit (TP) percentages, dynamic position sizing based on Game Theory (minimax principle).
Visualization: Displays Nash bands, signals, background colors, and two tables (Game Theory status and backtest results).
Backtesting: Tracks performance metrics like win rate, profit factor, max drawdown, and Sharpe ratio.
Strategy Settings:
Initial capital: $10,000.
Pyramiding: Up to 3 positions.
Position size: 10% of equity (default_qty_value=10).
Configurable inputs for RSI, volume, liquidity, institutional flow, Nash equilibrium, and risk management.
Warning: This is a strategy, not just an indicator. It executes trades automatically in TradingView's Strategy Tester. Always backtest thoroughly and use proper risk management before live trading.
2. Working Logic (Step by Step)
The strategy processes each bar (candle) to generate signals, manage positions, and update performance metrics. Here's how it works:
a. Input Parameters
The inputs are grouped for clarity:
Herd Behavior (🐑):
RSI Period (14): For overbought/oversold detection.
Volume MA Period (20): To calculate average volume for spike detection.
Herd Threshold (2.0): Volume multiplier for detecting herd activity.
Liquidity Analysis (💧):
Liquidity Lookback (50): Bars to check for recent highs/lows.
Liquidity Sensitivity (1.5): Volume multiplier for trap detection.
Institutional Flow (🏦):
Institutional Volume Multiplier (2.5): For detecting large volume spikes.
Institutional MA Period (21): For Accumulation/Distribution smoothing.
Nash Equilibrium (⚖️):
Nash Period (100): For calculating price mean and standard deviation.
Nash Deviation (0.02): Multiplier for equilibrium bands.
Risk Management (🛡️):
Use Stop-Loss (true): Enables SL at 2% below/above entry price.
Use Take-Profit (true): Enables TP at 5% above/below entry price.
b. Herd Behavior Detection
RSI (14): Checks for extreme conditions:
Overbought: RSI > 70 (potential herd buying).
Oversold: RSI < 30 (potential herd selling).
Volume Spike: Volume > SMA(20) x 2.0 (herd_threshold).
Momentum: Price change over 10 bars (close - close ) compared to its SMA(20).
Herd Signals:
Herd Buying: RSI > 70 + volume spike + positive momentum = Retail buying frenzy (red background).
Herd Selling: RSI < 30 + volume spike + negative momentum = Retail selling panic (green background).
c. Liquidity Trap Detection
Recent Highs/Lows: Calculated over 50 bars (liquidity_lookback).
Psychological Levels: Nearest round numbers (e.g., $100, $110) as potential stop-loss zones.
Trap Conditions:
Up Trap: Price breaks recent high, closes below it, with a volume spike (volume > SMA x 1.5).
Down Trap: Price breaks recent low, closes above it, with a volume spike.
Visualization: Traps are marked with small red/green crosses above/below bars.
d. Institutional Flow Analysis
Volume Check: Volume > SMA(20) x 2.5 (inst_volume_mult) = Institutional activity.
Accumulation/Distribution (AD):
Formula: ((close - low) - (high - close)) / (high - low) * volume, cumulated over time.
Smoothed with SMA(21) (inst_ma_length).
Accumulation: AD > MA + high volume = Institutions buying.
Distribution: AD < MA + high volume = Institutions selling.
Smart Money Index: (close - open) / (high - low) * volume, smoothed with SMA(20). Positive = Smart money buying.
e. Nash Equilibrium
Calculation:
Price mean: SMA(100) (nash_period).
Standard deviation: stdev(100).
Upper Nash: Mean + StdDev x 0.02 (nash_deviation).
Lower Nash: Mean - StdDev x 0.02.
Conditions:
Near Equilibrium: Price between upper and lower Nash bands (stable market).
Above Nash: Price > upper band (overbought, sell potential).
Below Nash: Price < lower band (oversold, buy potential).
Visualization: Orange line (mean), red/green lines (upper/lower bands).
f. Game Theory Signals
The strategy generates three types of signals, combined into long/short triggers:
Contrarian Signals:
Buy: Herd selling + (accumulation or down trap) = Go against retail panic.
Sell: Herd buying + (distribution or up trap).
Momentum Signals:
Buy: Below Nash + positive smart money + no herd buying.
Sell: Above Nash + negative smart money + no herd selling.
Nash Reversion Signals:
Buy: Below Nash + rising close (close > close ) + volume > MA.
Sell: Above Nash + falling close + volume > MA.
Final Signals:
Long Signal: Contrarian buy OR momentum buy OR Nash reversion buy.
Short Signal: Contrarian sell OR momentum sell OR Nash reversion sell.
g. Position Management
Position Sizing (Minimax Principle):
Default: 1.0 (10% of equity).
In Nash equilibrium: Reduced to 0.5 (conservative).
During institutional volume: Increased to 1.5 (aggressive).
Entries:
Long: If long_signal is true and no existing long position (strategy.position_size <= 0).
Short: If short_signal is true and no existing short position (strategy.position_size >= 0).
Exits:
Stop-Loss: If use_sl=true, set at 2% below/above entry price.
Take-Profit: If use_tp=true, set at 5% above/below entry price.
Pyramiding: Up to 3 concurrent positions allowed.
h. Visualization
Nash Bands: Orange (mean), red (upper), green (lower).
Background Colors:
Herd buying: Red (90% transparency).
Herd selling: Green.
Institutional volume: Blue.
Signals:
Contrarian buy/sell: Green/red triangles below/above bars.
Liquidity traps: Red/green crosses above/below bars.
Tables:
Game Theory Table (Top-Right):
Herd Behavior: Buying frenzy, selling panic, or normal.
Institutional Flow: Accumulation, distribution, or neutral.
Nash Equilibrium: In equilibrium, above, or below.
Liquidity Status: Trap detected or safe.
Position Suggestion: Long (green), Short (red), or Wait (gray).
Backtest Table (Bottom-Right):
Total Trades: Number of closed trades.
Win Rate: Percentage of winning trades.
Net Profit/Loss: In USD, colored green/red.
Profit Factor: Gross profit / gross loss.
Max Drawdown: Peak-to-trough equity drop (%).
Win/Loss Trades: Number of winning/losing trades.
Risk/Reward Ratio: Simplified Sharpe ratio (returns / drawdown).
Avg Win/Loss Ratio: Average win per trade / average loss per trade.
Last Update: Current time.
i. Backtesting Metrics
Tracks:
Total trades, winning/losing trades.
Win rate (%).
Net profit ($).
Profit factor (gross profit / gross loss).
Max drawdown (%).
Simplified Sharpe ratio (returns / drawdown).
Average win/loss ratio.
Updates metrics on each closed trade.
Displays a label on the last bar with backtest period, total trades, win rate, and net profit.
j. Alerts
No explicit alertconditions defined, but you can add them for long_signal and short_signal (e.g., alertcondition(long_signal, "GT Long Entry", "Long Signal Detected!")).
Use TradingView's alert system with Strategy Tester outputs.
3. Usage Tips
Timeframe: Best for H1-D1 timeframes. Shorter frames (M1-M15) may produce noisy signals.
Settings:
Risk Management: Adjust sl_percent (e.g., 1% for volatile markets) and tp_percent (e.g., 3% for scalping).
Herd Threshold: Increase to 2.5 for stricter herd detection in choppy markets.
Liquidity Lookback: Reduce to 20 for faster markets (e.g., crypto).
Nash Period: Increase to 200 for longer-term analysis.
Backtesting:
Use TradingView's Strategy Tester to evaluate performance.
Check win rate (>50%), profit factor (>1.5), and max drawdown (<20%) for viability.
Test on different assets/timeframes to ensure robustness.
Live Trading:
Start with a demo account.
Combine with other indicators (e.g., EMAs, support/resistance) for confirmation.
Monitor liquidity traps and institutional flow for context.
Risk Management:
Always use SL/TP to limit losses.
Adjust position_size for risk tolerance (e.g., 5% of equity for conservative trading).
Avoid over-leveraging (pyramiding=3 can amplify risk).
Troubleshooting:
If no trades are executed, check signal conditions (e.g., lower herd_threshold or liquidity_sensitivity).
Ensure sufficient historical data for Nash and liquidity calculations.
If tables overlap, adjust position.top_right/bottom_right coordinates.
4. Key Differences from the Previous Indicator
Indicator vs. Strategy: The previous code was an indicator (VP + Game Theory Integrated Strategy) focused on visualization and alerts. This is a strategy with automated entries/exits and backtesting.
Volume Profile: Absent in this strategy, making it lighter but less focused on high-volume zones.
Wick Analysis: Not included here, unlike the previous indicator's heavy reliance on wick patterns.
Backtesting: This strategy includes detailed performance metrics and a backtest table, absent in the indicator.
Simpler Signals: Focuses on Game Theory signals (contrarian, momentum, Nash reversion) without the "Power/Ultra Power" hierarchy.
Risk Management: Explicit SL/TP and dynamic position sizing, not present in the indicator.
5. Conclusion
The "Game Theory Trading Strategy" is a sophisticated system leveraging herd behavior, institutional flows, liquidity traps, and Nash equilibrium to trade market inefficiencies. It’s designed for traders who understand Game Theory principles and want automated execution with robust risk management. However, it requires thorough backtesting and parameter optimization for specific markets (e.g., forex, crypto, stocks). The backtest table and visual aids make it easy to monitor performance, but always combine with other analysis tools and proper capital management.
If you need help with backtesting, adding alerts, or optimizing parameters, let me know!
Portfolio Tracker ARJO (V-01)Portfolio Tracker ARJO (V-01)
This indicator is a user-friendly portfolio tracking tool designed for TradingView charts. It overlays a customizable table on your chart to monitor up to 15 stocks or symbols in your portfolio. It calculates real-time metrics like current market price (CMP), gains/losses, and stoploss breaches, helping you stay on top of your investments without switching between multiple charts. The table uses color-coding for quick visual insights: green for profits, red for losses, and highlights breached stoplosses in red for alerts. It also shows portfolio-wide totals for overall performance.
Key Features
Supports up to 15 Symbols: Enter stock tickers (e.g., NSE:RELIANCE or BSE:TCS) with details like buy price, date, units, and stoploss.
Symbol: The stock ticker and description.
Buy Date: When you purchased it.
Units: Number of shares/units held.
Buy Price: Your entry price.
Stop Loss: Your set stoploss level (highlighted in red if breached by CMP).
CMP: Current market price (fetched from the chart's timeframe).
% Gain/Loss: Percentage change from buy price (color-coded: green for positive, red for negative).
Gain/Loss: Total monetary gain/loss based on units.
Optional Timeframe Columns: Toggle to show % change over 1 Week (1W), 1 Month (1M), 3 Months (3M), and 6 Months (6M) for historical performance.
Portfolio Summary: At the top of the table, see total % gain/loss and absolute gain/loss for your entire portfolio.
Visual Customizations: Adjust table position (e.g., Top Right), size, colors for positive/negative values, and intensity cutoff for gradients.
Benchmark Index-Based Header: The title row's background color reflects NIFTY's weekly trend (green if above 10-week SMA, red if below) for market context.
Benchmark Index-Based Header: The title row's background color reflects NIFTY's weekly trend (green if above 10-week SMA, red if below) for market context.
How to Use It: Step-by-Step Guide
Add the Indicator to Your Chart: Search for "Portfolio Tracker ARJO (V-01)" in TradingView's indicator library and add it to any chart (preferably Daily timeframe for accuracy).
Input Your Portfolio Symbols:
Open the indicator settings (gear icon).
In the "Symbol 1" to "Symbol 15" groups, fill in:
Symbol: Enter the ticker (e.g., NSE:INFY).
Year/Month/Day: Select your buy date (e.g., 2024-07-01).
Buy Price: Your purchase price per unit.
Stoploss: Your exit price if things go south.
Units: How many shares you own.
Only fill what you need—leave extras blank. The table auto-adjusts to show only entered symbols.
Customize the Table (Optional):
In "Table settings":
Choose position (e.g., Top Right) and size (% of chart).
Toggle "Show Timeframe Columns" to add 1W/1M/3M/6M performance.
In "Color settings":
Pick colors for positive (green) and negative (red) cells.
Set "Color intensity cutoff (%)" to control how strong the colors get (e.g., 10% means changes above 10% max out the color).
Interpret the Table on Your Chart:
The table appears overlaid—scan rows for each symbol's stats.
Look at colors: Greener = better gains; redder = bigger losses.
Check CMP cell: Red means stoploss breached—consider selling!
Portfolio Gain/Loss at the top gives a quick overall health check.
For Best Results:
Use on a Daily chart to avoid CMP errors (the script will warn if on Weekly/Monthly).
Refresh the chart or wait for a new bar if data doesn't update immediately.
For Indian stocks, prefix with NSE: or BSE: (e.g., BSE:RELIANCE).
This is for tracking only—not trading signals. Combine with your strategy.
If no symbols show, ensure inputs are valid (e.g., buy price > 0, valid date).
Finally, this tool makes it quite easy for beginners to track their portfolios, while also giving advanced traders powerful and customizable insights. I'd love to hear your feedback—happy trading!
Neuracap Gap AnalysisThe Neuracap Gap Analysis indicator is a comprehensive tool designed to identify and track price gaps, special candlestick patterns, and high-volume breakout signals. It combines multiple trading strategies into one powerful indicator for gap trading, pattern recognition, and momentum analysis.
🎯 What This Indicator Does
1. Gap Detection & Tracking
Automatically identifies price gaps (up and down)
Tracks gap fills with visual boxes that extend until closed
Manages gap history with customizable limits
Color-coded visualization (Green = Gap Up, Red = Gap Down)
2. Upside Tasuki Gap Pattern
Identifies the bullish continuation pattern
Colors candles yellow when pattern is detected
Confirms trend continuation signals
3. Episodic Pivot Detection
High-volume breakout identification
EMA filter ensures signals only in uptrends
Strong momentum confirmation
Fuchsia-colored candles with arrow markers
🔍 How to Use for Trading
📈 Gap Trading Strategy
Gap Up Trading:
Wait for gap up (green box appears)
Check volume - Higher volume = stronger signal
Entry options:
Aggressive: Enter at market open
Conservative: Wait for pullback to gap level
Stop loss: Below the gap fill level
Target: Previous resistance or 2:1 risk/reward
Gap Down Trading:
Identify gap down (red box appears)
Look for bounce opportunities
Entry: When price shows reversal signs
Stop: Below recent lows
Target: Gap fill level
💫 Tasuki Gap Strategy
Yellow candle indicates bullish continuation
Confirms uptrend is likely to continue
Entry: On next candle after pattern
Stop: Below the gap low
Target: Next resistance level
🚀 Episodic Pivot Strategy
Fuchsia candle + arrow = High probability breakout
All conditions met:
Price above EMA 20, 50, 200
High volume (2x+ average)
Strong price move (4%+)
Entry: At close or next open
Stop: Below EMA 20 or recent swing low
Target: Measured move or next resistance
📊 Reading the Visual Signals
Gap Boxes
🟢 Green Box: Gap up - potential bullish continuation
🔴 Red Box: Gap down - potential bounce or bearish continuation
Box extends until gap is filled
Box disappears when gap closes
Candle Colors
🟡 Yellow: Tasuki gap pattern (bullish continuation)
🟪 Fuchsia: Episodic pivot (high-volume breakout)
⬜ Normal: No special pattern detected
Arrows & Markers
⬆️ Triangle Arrow: Episodic pivot confirmation
💡 Trading Tips & Best Practices
✅ Do's
Combine with trend analysis - Trade gaps in direction of trend
Check volume - Higher volume = more reliable signals
Use multiple timeframes - Confirm on higher timeframes
Risk management - Always set stop losses
Wait for confirmation - Don't chase, let signals develop
❌ Don'ts
Don't trade all gaps - Focus on high-quality setups
Avoid low volume - Weak volume = unreliable signals
Don't ignore trend - Counter-trend trading is risky
Don't overtrade - Quality over quantity
Don't ignore context - Consider market conditions
⚠️ Risk Management
Position sizing: Risk 1-2% per trade
Stop losses: Always define before entry
Target levels: Set realistic profit targets
Market conditions: Avoid trading in choppy markets
📈 Performance Optimization
For Conservative Traders:
Increase minimum gap size to 1%
Set volume multiplier to 3.0x
Only trade episodic pivots in strong uptrends
Wait for gap fill confirmation
For Aggressive Traders:
Decrease minimum gap size to 0.3%
Set volume multiplier to 1.5x
Trade both gap types
Enter on pattern confirmation
🚨 Alert Setup
The indicator provides alerts for:
Gap Up Detected
Gap Down Detected
Upside Tasuki Gap
Episodic Pivot
Recommended: Enable all alerts and filter manually based on your strategy.
📝 Summary
This indicator excels at identifying high-probability trading opportunities through gap analysis, pattern recognition, and momentum confirmation. Use it as part of a complete trading system with proper risk management for best results.
EMA 12/26 With ATR Volatility StoplossThe EMA 12/26 With ATR Volatility Stoploss
The EMA 12/26 With ATR Volatility Stoploss strategy is a meticulously designed systematic trading approach tailored for navigating financial markets through technical analysis. By integrating the Exponential Moving Average (EMA) and Average True Range (ATR) indicators, the strategy aims to identify optimal entry and exit points for trades while prioritizing disciplined risk management. At its core, it is a trend-following system that seeks to capitalize on price momentum, employing volatility-adjusted stop-loss mechanisms and dynamic position sizing to align with predefined risk parameters. Additionally, it offers traders the flexibility to manage profits either by compounding returns or preserving initial capital, making it adaptable to diverse trading philosophies. This essay provides a comprehensive exploration of the strategy’s underlying concepts, key components, strengths, limitations, and practical applications, without delving into its technical code.
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Core Philosophy and Objectives
The EMA 12/26 With ATR Volatility Stoploss strategy is built on the premise of capturing short- to medium-term price trends with a high degree of automation and consistency. It leverages the crossover of two EMAs—a fast EMA (12-period) and a slow EMA (26-period)—to generate buy and sell signals, which indicate potential trend reversals or continuations. To mitigate the inherent risks of trading, the strategy incorporates the ATR indicator to set stop-loss levels that adapt to market volatility, ensuring that losses remain within acceptable bounds. Furthermore, it calculates position sizes based on a user-defined risk percentage, safeguarding capital while optimizing trade exposure.
A distinctive feature of the strategy is its dual profit management modes:
SnowBall (Compound Profit): Profits from successful trades are reinvested into the capital base, allowing for progressively larger position sizes and potential exponential portfolio growth.
ZeroRisk (Fixed Equity): Profits are withdrawn, and trades are executed using only the initial capital, prioritizing capital preservation and minimizing exposure to market downturns.
This duality caters to both aggressive traders seeking growth and conservative traders focused on stability, positioning the strategy as a versatile tool for various market environments.
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Key Components of the Strategy
1. EMA-Based Signal Generation
The strategy’s trend-following mechanism hinges on the interaction between the Fast EMA (12-period) and Slow EMA (26-period). EMAs are preferred over simple moving averages because they assign greater weight to recent price data, enabling quicker responses to market shifts. The key signals are:
Buy Signal: Triggered when the Fast EMA crosses above the Slow EMA, suggesting the onset of an uptrend or bullish momentum.
Sell Signal: Occurs when the Fast EMA crosses below the Slow EMA, indicating a potential downtrend or the end of a bullish phase.
To enhance signal reliability, the strategy employs an Anchor Point EMA (AP EMA), a short-period EMA (e.g., 2 days) that smooths the input price data before calculating the primary EMAs. This preprocessing reduces noise from short-term price fluctuations, improving the accuracy of trend detection. Additionally, users can opt for a Consolidated EMA (e.g., 18-period) to display a single trend line instead of both EMAs, simplifying chart analysis while retaining trend insights.
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2. Volatility-Adjusted Risk Management with ATR
Risk management is a cornerstone of the strategy, achieved through the use of the Average True Range (ATR), which quantifies market volatility by measuring the average price range over a specified period (e.g., 10 days). The ATR informs the placement of stop-loss levels, which are set at a multiple of the ATR (e.g., 2x ATR) below the entry price for long positions. This approach ensures that stop losses are proportionate to current market conditions—wider during high volatility to avoid premature exits, and narrower during low volatility to protect profits.
For example, if a stock’s ATR is $1 and the multiplier is 2, the stop loss for a buy at $100 would be set at $98. This dynamic adjustment enhances the strategy’s adaptability, preventing stop-outs from normal market noise while capping potential losses.
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3. Dynamic Position Sizing
The strategy calculates position sizes to align with a user-defined Risk Per Trade, typically expressed as a percentage of capital (e.g., 2%). The position size is determined by:
The available capital, which varies depending on whether SnowBall or ZeroRisk mode is selected.
The distance between the entry price and the ATR-based stop-loss level, which represents the per-unit risk.
The desired risk percentage, ensuring that the maximum loss per trade does not exceed the specified threshold.
For instance, with a $1,000 capital, a 2% risk per trade ($20), and a stop-loss distance equivalent to 5% of the entry price, the strategy computes the number of units (shares or contracts) to ensure the total loss, if the stop loss is hit, equals $20. To prevent over-leveraging, the strategy includes checks to ensure that the position’s dollar value does not exceed available capital. If it does, the position size is scaled down to fit within the capital constraints, maintaining financial discipline.
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4. Flexible Capital Management
The strategy’s dual profit management modes—SnowBall and ZeroRisk—offer traders strategic flexibility:
SnowBall Mode: By compounding profits, traders can increase their capital base, leading to larger position sizes over time. This is ideal for those with a long-term growth mindset, as it harnesses the power of exponential returns.
ZeroRisk Mode: By withdrawing profits and trading solely with the initial capital, traders protect their gains and limit exposure to market volatility. This conservative approach suits those prioritizing stability over aggressive growth.
These options allow traders to tailor the strategy to their risk tolerance, financial goals, and market outlook, enhancing its applicability across different trading styles.
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5. Time-Based Trade Filtering
To optimize performance and relevance, the strategy includes an option to restrict trading to a specific time range (e.g., from 2018 onward). This feature enables traders to focus on periods with favorable market conditions, avoid historically volatile or unreliable data, or align the strategy with their backtesting objectives. By confining trades to a defined timeframe, the strategy ensures that performance metrics reflect the intended market context.
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Strengths of the Strategy
The EMA 12/26 With ATR Volatility Stoploss strategy offers several compelling advantages:
Systematic and Objective: By adhering to predefined rules, the strategy eliminates emotional biases, ensuring consistent execution across market conditions.
Robust Risk Controls: The combination of ATR-based stop losses and risk-based position sizing caps losses at user-defined levels, fostering capital preservation.
Customizability: Traders can adjust parameters such as EMA periods, ATR multipliers, and risk percentages, tailoring the strategy to specific markets or preferences.
Volatility Adaptation: Stop losses that scale with market volatility enhance the strategy’s resilience, accommodating both calm and turbulent market phases.
Enhanced Visualization: The use of color-coded EMAs (green for bullish, red for bearish) and background shading provides intuitive visual cues, simplifying trend and trade status identification.
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Limitations and Considerations
Despite its strengths, the strategy has inherent limitations that traders must address:
False Signals in Range-Bound Markets: EMA crossovers may generate misleading signals in sideways or choppy markets, leading to whipsaws and unprofitable trades.
Signal Lag: As lagging indicators, EMAs may delay entry or exit signals, causing traders to miss rapid trend shifts or enter trades late.
Overfitting Risk: Excessive optimization of parameters to fit historical data can impair the strategy’s performance in live markets, as past patterns may not persist.
Impact of High Volatility: In extremely volatile markets, wider stop losses may result in larger losses than anticipated, challenging risk management assumptions.
Data Reliability: The strategy’s effectiveness depends on accurate, continuous price data, and discrepancies or gaps can undermine signal accuracy.
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Practical Applications
The EMA 12/26 With ATR Volatility Stoploss strategy is versatile, applicable to diverse markets such as stocks, forex, commodities, and cryptocurrencies, particularly in trending environments. To maximize its potential, traders should adopt a rigorous implementation process:
Backtesting: Evaluate the strategy’s historical performance across various market conditions to assess its robustness and identify optimal parameter settings.
Forward Testing: Deploy the strategy in a demo account to validate its real-time performance, ensuring it aligns with live market dynamics before risking capital.
Ongoing Monitoring: Continuously track trade outcomes, analyze performance metrics, and refine parameters to adapt to evolving market conditions.
Additionally, traders should consider market-specific factors, such as liquidity and volatility, when applying the strategy. For instance, highly liquid markets like forex may require tighter ATR multipliers, while less liquid markets like small-cap stocks may benefit from wider stop losses.
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Conclusion
The EMA 12/26 With ATR Volatility Stoploss strategy is a sophisticated, systematic trading framework that blends trend-following precision with disciplined risk management. By leveraging EMA crossovers for signal generation, ATR-based stop losses for volatility adjustment, and dynamic position sizing for risk control, it offers a balanced approach to capturing market trends while safeguarding capital. Its flexibility—evident in customizable parameters and dual profit management modes—makes it suitable for traders with varying risk appetites and objectives. However, its limitations, such as susceptibility to false signals and signal lag, necessitate thorough testing and prudent application. Through rigorous backtesting, forward testing, and continuous refinement, traders can harness this strategy to achieve consistent, risk-adjusted returns in trending markets, establishing it as a valuable tool in the arsenal of systematic trading.
Sideways Scalper Peak and BottomUnderstanding the Indicator
This indicator is designed to identify potential peaks (tops) and bottoms (bottoms) within a market, which can be particularly useful in a sideways or range-bound market where price oscillates between support and resistance levels without a clear trend. Here's how it works:
RSI (Relative Strength Index): Measures the speed and change of price movements to identify overbought (above 70) and oversold (below 30) conditions. In a sideways market, RSI can help signal when the price might be due for a reversal within its range.
Moving Averages (MAs): The Fast MA and Slow MA provide a sense of the short-term and longer-term average price movements. In a sideways market, these can help confirm if the price is at the upper or lower extremes of its range.
Volume Spike: Looks for significant increases in trading volume, which might indicate a stronger move or a potential reversal point when combined with other conditions.
Divergence: RSI divergence occurs when the price makes a new high or low, but the RSI does not, suggesting momentum is weakening, which can be a precursor to a reversal.
How to Use in a Sideways Market
Identify the Range: First, visually identify the upper resistance and lower support levels of the sideways market on your chart. This indicator can help you spot these levels more precisely by signaling potential peaks and bottoms.
Peak Signal :
When to Look: When the price approaches the upper part of the range.
Conditions: The indicator will give a 'Peak' signal when:
RSI is over 70, indicating overbought conditions.
There's bearish divergence (price makes a higher high, but RSI doesn't).
Volume spikes, suggesting strong selling interest.
Price is above both Fast MA and Slow MA, indicating it's at a potentially high point in the range.
Action: This signal suggests that the price might be at or near the top of its range and could reverse downwards. A trader might consider selling or shorting here, expecting the price to move towards the lower part of the range.
Bottom Signal:
When to Look: When the price approaches the lower part of the range.
Conditions: The indicator will give a 'Bottom' signal when:
RSI is below 30, indicating oversold conditions.
There's bullish divergence (price makes a lower low, but RSI doesn't).
Volume spikes, suggesting strong buying interest.
Price is below both Fast MA and Slow MA, indicating it's at a potentially low point in the range.
Action: This signal suggests that the price might be at or near the bottom of its range and could reverse upwards. A trader might consider buying here, expecting the price to move towards the upper part of the range.
Confirmation: In a sideways market, false signals can occur due to the lack of a strong trend. Always look for confirmation:
Volume Confirmation: A significant volume spike can add confidence to the signal.
Price Action: Look for price action like candlestick patterns (e.g., doji, engulfing patterns) that confirm the reversal.
Time Frame: Consider using this indicator on multiple time frames. A signal on a shorter time frame (like 15m or 1h) might be confirmed by similar conditions on a longer time frame (4h or daily).
Risk Management: Since this is designed for scalping in a sideways market:
Set Tight Stop-Losses: Due to the quick nature of reversals in range-bound markets, place stop-losses close to your entry to minimize loss.
Take Profit Levels: Set profit targets near the opposite end of the range or use a trailing stop to capture as much of the move as possible before it reverses again.
Practice: Before trading with real money, practice with this indicator on historical data or in a paper trading environment to understand how it behaves in different sideways market scenarios.
Key Points for New Traders
Patience: Wait for all conditions to align before taking a trade. Sideways markets require patience as the price might hover around these levels for a while.
Not All Signals Are Equal: Sometimes, even with all conditions met, the market might not reverse immediately. Look for additional context or confirmation.
Continuous Learning: Understand that this indicator, like any tool, isn't foolproof. Learn from each trade, whether it's a win or a loss, and adjust your strategy accordingly.
By following these guidelines
Strategy: Candlestick Wick Analysis with Volume Conditions
This strategy focuses on analyzing the wicks (or shadows) of candlesticks to identify potential trading opportunities based on candlestick structure and volume. Based on these criteria, it places stop orders at the extremities of the wicks when certain conditions are met, thus increasing the chances of capturing significant price movements.
Trading Criteria
Volume Conditions:
The strategy checks if the volume of the current candle is higher than that of the previous three candles. This ensures that the observed price movement is supported by significant volume, increasing the probability that the price will continue in the same direction.
Wick Analysis:
Upper Wick:
If the upper wick of a candle represents more than 90% of its body size and is longer than the lower wick, this indicates that the price tested a resistance level before pulling back.
Order Placement: In this case, a Buy Stop order is placed at the upper extremity of the wick. This means that if the price rises back to this level, the order will be triggered, and the trader will take a buy position.
SL Management: A stop-loss is then placed below the lowest point of the same candle. This protects the trader by limiting losses if the price falls back after the order is triggered.
Lower Wick:
If the lower wick of a candle is longer than the upper wick and represents more than 90% of its body size, this indicates that the price tested a support level before rising.
Order Placement: In this case, a Sell Stop order is placed at the lower extremity of the wick. Thus, if the price drops back to this level, the order will be triggered, and the trader will take a sell position.
SL Management: A stop-loss is then placed above the highest point of the same candle. This ensures risk management by limiting losses if the price rebounds upward after the order is triggered.
Strategy Advantages
Responsiveness to Price Movements: The strategy is designed to detect significant price movements based on the market's reaction around support and resistance levels. By placing stop orders directly at the wick extremities, it allows capturing strong movements in the direction indicated by the candles.
Securing Positions: Using stop-losses positioned just above or below key levels (wicks) provides better risk management. If the market doesn't move as expected, the position is automatically closed with a limited loss.
Clear Visual Indicators: Symbols are displayed on the chart at the points where orders have been placed, making it easier to understand trading decisions. This helps to quickly identify the support or resistance levels tested by the price, as well as potential entry points.
Conclusion
The strategy is based on the idea that large wicks signal areas where buyers or sellers have tested significant price levels before temporarily retreating. By placing stop orders at the extremities of these wicks, the strategy allows capturing price movements when they confirm, while limiting risks through strategically placed stop-losses. It thus offers a balanced approach between capturing potential profit and managing risk.
This description emphasizes the idea of capturing significant market movements with stop orders while providing a clear explanation of the logic and risk management. It’s tailored for publication on TradingView and highlights the robustness of the strategy.
Turtle Trading Strategy@lihexieThe full implementation of the Turtle Trading Rules (as distinct from the various truncated versions circulating within the community) is now ready.
This trading strategy script distinguishes itself from all currently publicly available Turtle trading systems on Tradingview by comprehensively embodying the rules for entries, exits, position management, and profit and loss controls.
Market Selection:
Trade in highly liquid markets such as forex, commodity futures, and stock index futures.
Entry Strategies:
Model 1: Buy when the price breaks above the highest point of the last 20 trading days; Sell when the price drops below the lowest point of the last 20 trading days. When an entry opportunity arises, if the previous trade was profitable, skip the current breakout opportunity and refrain from entering.
Model 2: Buy when the price breaks above the highest point of the last 55 trading days; Sell when the price drops below the lowest point of the last 55 trading days.
Position Sizing:
Determine the size of each position based on the price volatility (ATR) to ensure that the risk of each trade does not exceed 2% of the account balance.
Exit Strategies:
1. Use a fixed stop-loss point to limit losses: Close long positions when the price falls below the lowest point of the last 10 trading days.
2. Trailing stop-loss: Once a position is profitable, adjust the stop-loss point to protect profits.
Pyramiding Rules:
Unit Doubling: Increase position size by one unit every time the price moves forward by n (default is 0.5) units of ATR, up to a maximum of 4 units, while also raising the stop-loss point to below the ATR value at the level of additional entries.
海龟交易法则的完整实现（区别于当前社区各种有阉割海龟交易系统代码）
本策略脚本区别于Tradingview目前公开的所有的海龟交易系统，完整的实现了海龟交易法则中入场、出场、仓位管理，止盈止损的规则。
市场选择：
选择流动性高的市场进行交易，如外汇、商品期货和股指期货等。
入市策略：
模式1：当价格突破过去20个交易日的高点时，买入；当价格跌破过去20个交易日的低点时，卖出。当出现入场机会时，如果上一笔交易是盈利的，那么跳过当前突破的机会，不进行入场。
模式2：当价格突破过去55个交易日的高点时，买入；当价格跌破过去55个交易日的低点时，卖出。
头寸规模：
根据价格波动性（ATR）来确定每个头寸的大小, 使每笔交易的风险不超过账户余额的2%。
退出策略：
1. 使用一个固定的止损点来限制损失：当多头头寸的价格跌破过去10个交易日的低点时，平仓止损。
2. 跟踪止损：一旦头寸盈利，移动止损点以保护利润。
加仓规则：
单位加倍：每当价格向前n（默认是0.5）个单位的ATR移动时，就增加一个单位的头寸大小（默认最大头寸数量是4个），同时将止损点提升至加仓点位的ATR值以下。
Rate of Change StrategyRate of Change Strategy :
INTRODUCTION :
This strategy is based on the Rate of Change indicator. It compares the current price with that of a user-defined period of time ago. This makes it easy to spot trends and even speculative bubbles. The strategy is long term and very risky, which is why we've added a Stop Loss. There's also a money management method that allows you to reinvest part of your profits or reduce the size of your orders in the event of substantial losses.
RATE OF CHANGE (ROC) :
As explained above, the ROC is used to situate the current price compared to that of a certain period of time ago. The formula for calculating ROC in relation to the previous year is as follows :
ROC (365) = (close/close (365) - 1) * 100
With this formula we can find out how many percent the change in the current price is compared with 365 days ago, and thus assess the trend.
PARAMETERS :
ROC Length : Length of the ROC to be calculated. The current price is compared with that of the selected length ago.
ROC Bubble Signal : ROC value indicating that we are in a bubble. This value varies enormously depending on the financial product. For example, in the equity market, a bubble exists when ROC = 40, whereas in cryptocurrencies, a bubble exists when ROC = 150.
Stop Loss (in %) : Stop Loss value in percentage. This is the maximum trade value percentage that can be lost in a single trade.
Fixed Ratio : This is the amount of gain or loss at which the order quantity is changed. The default is 400, which means that for each $400 gain or loss, the order size is increased or decreased by an amount chosen by the user.
Increasing Order Amount : This is the amount to be added to or subtracted from orders when the fixed ratio is reached. The default is $200, which means that for every $400 gain, $200 is reinvested in the strategy. On the other hand, for every $400 loss, the order size is reduced by $200.
Initial capital : $1000
Fees : Interactive Broker fees apply to this strategy. They are set at 0.18% of the trade value.
Slippage : 3 ticks or $0.03 per trade. Corresponds to the latency time between the moment the signal is received and the moment the order is executed by the broker.
Important : A bot has been used to test the different parameters and determine which ones maximize return while limiting drawdown. This strategy is the most optimal on BITSTAMP:BTCUSD in 1D timeframe with the following parameters :
ROC Length = 365
ROC Bubble Signal = 180
Stop Loss (in %) = 6
LONG CONDITION :
We are in a LONG position if ROC (365) > 0 for at least two days. This allows us to limit noise and irrelevant signals to ensure that the ROC remains positive.
SHORT CONDITION :
We are in a SHORT position if ROC (365) < 0 for at least two days. We also open a SHORT position when the speculative bubble is about to burst. If ROC (365) > 180, we're in a bubble. If the bubble has been in existence for at least a week and the ROC falls back below this threshold, we can expect the asset to return to reasonable prices, and thus a downward trend. So we're opening a SHORT position to take advantage of this upcoming decline.
EXIT RULES FOR WINNING TRADE :
The strategy is self-regulating. We don't exit a LONG trade until a SHORT signal has arrived, and vice versa. So, to exit a winning position, you have to wait for the entry signal of the opposite position.
RISK MANAGEMENT :
This strategy is very risky, and we can easily end up on the wrong side of the trade. That's why we're going to manage our risk with a Stop Loss, limiting our losses as a percentage of the trade's value. By default, this percentage is set at 6%. Each trade will therefore take a maximum loss of 6%.
If the SL has been triggered, it probably means we were on the wrong side. This is why we change the direction of the trade when a SL is triggered. For example, if we were SHORT and lost 6% of the trade value, the strategy will close this losing trade and open a long position without taking into account the ROC value. This allows us to be in position all the time and not miss the best opportunities.
MONEY MANAGEMENT :
The fixed ratio method was used to manage our gains and losses. For each gain of an amount equal to the value of the fixed ratio, we increase the order size by a value defined by the user in the "Increasing order amount" parameter. Similarly, each time we lose an amount equal to the value of the fixed ratio, we decrease the order size by the same user-defined value. This strategy increases both performance and drawdown.
NOTE :
Please note that the strategy is backtested from 2017-01-01. As the timeframe is 1D, this strategy is a medium/long-term strategy. That's why only 34 trades were closed. Be careful, as the test sample is small and performance may not necessarily reflect what may happen in the future.
Enjoy the strategy and don't forget to take the trade :)
Cracking Cryptocurrency - Bottom FeederThe Bottom Feeder
The Bottom Feeder is designed to algorithmically detect significantly oversold conditions in price that represent profitable buying opportunities. Combining this with it’s unique Stop and Target System, the Bottom Feeder is designed to return consistent return with minimal draw down. Whether used as a Market Bottom Detector or as a system for executing safe, profitable mean reversion trades, the Bottom Feeder is a powerful tool in any trader’s arsenal.
Bottom Feeder was designed to be used on BTCUSD , however it is also effective on other USD/USDT pairs. One will have to check the individual pair they wish to trade with the Strategy Tester to simulate performance.
Options
Let’s go through the input options one by one, so that you are able to comfortably navigate all that this indicator has to offer. The link below will display a picture of the layout of the settings for your convenience.
For the sake of simplicity, let’s note now that all settings marked **Conservative Mode** will not work in Aggressive Mode.
Mode: Determines how aggressively Bottom Feeder generates a buy signal. In Conservative Mode, trades can only be opened once per candle and the stop and target will update as new signals appear. In Aggressive Mode, a separate trade is opened each time Bottom Feeder signals, which may be multiple times within one Daily candle.
Plot Target and Stop Loss: Toggles on/off the visualized take profit and stop losses on the chart.
**Conservative Mode** TP Multiplier: This is an input box, it requires a float value. That is, it can accept either a whole number integer or a number with a decimal. This number will determine your Take Profit target. It will take whatever number is entered into this box and multiply the Average True Range against it to determine your Take Profit.
**Conservative Mode** SL Multiplier: See above - this will modify your Stop Loss Value.
**Conservative Mode** Average or Median True Range: This is a drop-down option, the two options are Average True Range or Median True Range. If Average True Range is selected, then this indicator will use the Average True Range calculation, that is, the average of a historical set of True Range values to determine the Average True Range value for Target and Stop Loss calculation. If Median True Range is selected, it will not take an average and will instead take the Median value of your historical look back period.
**Conservative Mode** True Range Length: This is an input that requires an integer. This will represent your historical look back period for Average/Median True Range calculation.
**Conservative Mode** True Range Smoothing: This is a drop-down with the following options: Exponential Moving Average ( EMA ), Simple Moving Average ( SMA ), Weighted Moving Average ( WMA ), Relative Moving Average (RMA). This will determine the smoothing type for calculating the Average True Range if it is selected. Note: if Median True Range is selected above, this option will not have any effect as there is no smoothing for a Median value.
**Conservative Mode** Custom True Range Value?: This is a true/false option that is false by default. If enabled, it will override the Average/Median True Range calculation in favor of a users custom True Range value to be input below.
**Conservative Mode** Custom True Range Value: This is an input box that requires a float value. If Custom True Range is enabled this is where a user will input their desired custom True Range value for Target and Stop Loss calculation.
Stop and Target Description
Because Bottom Feeder is designed only to scalp the various market bottoms that can appear over time in the market and not to identify trends or to trade ranges, it’s imperative that the indicator notify us not just to when to enter our trades, but when to exit! In the service of that, CC Bottom Feeder has a built in Stop and Target system that tracks and displays the stop loss and take profit levels of each individual open trade, whether in Aggressive or Conservative Mode.
Conservative Mode Targeting: In Conservative Mode, Bottom Feeder signals are aggregated into a compound trade. The signal will appear as a green label pointing up below a candle, and will appear upon a candle close. If Bottom Feeder then generates another signal the stop loss and target price will be updated. The process will continue until the aggregated trade completes in either direction. On a trade with multiple signals, a larger position is slowly entered into upon each buy signal.
Aggressive Mode Targeting: In Aggressive Mode, Bottom Feeder signals are individually displayed as they are generated, regardless of how many signals are generated on any single candle. If Bottom Feeder continues to signal, each individual open trade will have their own stop loss and target that will be displayed on the chart until the individual trade completes in either direction. As opposed to a large compound position, aggressive mode represents a higher number of independent signals with their own stop and target levels.
Stop losses and targets are designed to be hard, not soft. That is, they are intended to be stop market orders, not mental stop losses. If price wicks through the target or stop, it will activate.
RAFA's SMC Killer LITEWhat is the SMC Killer?
The Smart Money Concepts (SMC) Killer is a trading indicator that identifies high-probability entry points using three proven strategies:
Break of Structure (BOS) - Trades when price breaks key support/resistance levels
Fair Value Gap (FVG) - Enters when price fills gaps in the market
Order Blocks (OB) - Entry from institutional order clusters (optional display)
This indicator automatically:
✅ Calculates correct entry, take-profit, and stop-loss levels for your asset
✅ Tracks win/loss statistics in real-time
✅ Works on 30+ different futures contracts
✅ Adapts tick size and point value automatically
Asset Selection
Supported Assets
The indicator supports all major futures contracts:
Equity Futures:
ES (E-mini S&P 500)
NQ (E-mini NASDAQ 100)
YM (Mini Dow Jones)
NKD (Nikkei 225)
EMD (E-mini Midcap 400)
RTY (Russell 2000)
Currency Futures:
6A (Australian Dollar)
6B (British Pound)
6C (Canadian Dollar)
6E (Euro FX)
6J (Japanese Yen)
6S (Swiss Franc)
6N (New Zealand Dollar)
Agricultural Futures:
HE (Lean Hogs)
LE (Live Cattle)
GF (Feeder Cattle)
ZC (Corn)
ZW (Wheat)
ZS (Soybeans)
ZM (Soybean Meal)
ZL (Soybean Oil)
Energy Futures:
CL (Crude Oil)
QM (Mini Crude Oil)
NG (Natural Gas)
QG (E-mini Natural Gas)
HO (Heating Oil)
RB (RBOB Gasoline)
Metal Futures:
GC (Gold)
SI (Silver)
HG (Copper)
PL (Platinum)
PA (Palladium)
QI (E-mini Silver)
QO (E-mini Gold)
Micro Futures:
MES (Micro E-mini S&P 500)
MYM (Micro E-mini Dow Jones)
MNQ (Micro E-mini NASDAQ)
M2K (Micro Russell 2000)
MGC (E-Micro Gold)
M6A (E-Micro AUD/USD)
M6E (E-Micro EUR/USD)
MCL (Micro Crude Oil)
How to Select Your Asset
Open the indicator settings (click ⚙️)
Go to ASSET SELECT section
Select Asset Category (e.g., "Metal Futures")
Enter Select Asset Symbol (e.g., "GC" for Gold)
Click OK
The indicator will automatically load the correct:
✅ Tick size
✅ Point value
✅ Risk/reward calculations
Settings Configuration
ASSET SELECT Group
Asset Category: Choose from 6 categories
Select Asset Symbol: Enter symbol (ES, GC, CL, etc.)
STRUCTURE Group
Show Swing Structure: Display swing highs/lows
Swing Length: Bars used for pivot detection (default: 5)
Build Sweep: Show sweep formations (default: ON)
What it does: Identifies the market trend and key turning points
Teal/Green bars = Uptrend
Orange/Red bars = Downtrend
FVG Group
Enable FVG Entry: Use Fair Value Gap strategy
FVG Threshold: Sensitivity filter (default: 0)
What it does: Detects gaps in price action that indicate imbalance
Lower threshold = More signals
Higher threshold = Fewer, high-quality signals
RISK Group
Show Bracket: Display entry/TP/SL lines
Units/Contracts: Number of contracts to trade (default: 6)
Stop Loss ($): Risk amount per trade (default: $250)
Target ($): Profit target per trade (default: $1,000)
Example: If you select ES with $250 stop loss:
The indicator calculates: 250 ÷ (6 contracts × $50 per point) = 0.83 points
Your stop loss line appears 0.83 points below entry
TABLE Group
Show Statistics: Display results table
Position: Table location (default: top_right)
Year: Start tracking from this year
Month: Start tracking from this month
Day: Start tracking from this day
Trading Signals
BUY Signal 🟢
When you see a green "BUY" label below a candle:
Price is breaking higher (Break of Structure)
OR price is filling a gap (Fair Value Gap)
The indicator plots three lines:
Green line = Entry price
Lime/bright green line = Take Profit level
Red line = Stop Loss level
Action: Consider entering a LONG position at market or entry price
SELL Signal 🔴
When you see a red "SELL" label above a candle:
Price is breaking lower (Break of Structure)
OR price is filling a gap (Fair Value Gap)
The indicator plots three lines:
Red line = Entry price
Magenta/pink line = Take Profit level
Orange line = Stop Loss level
Action: Consider entering a SHORT position at market or entry price
Signal Confirmation
✅ Wait for confirmation - Only trade signals on confirmed (closed) bars
✅ Check the trend - Look at candle colors (green uptrend, orange downtrend)
✅ Risk/reward ratio - TP should be at least 2x your SL risk
Risk Management
Position Sizing Example
Trading Gold (GC) with ES Settings:
Units: 6 contracts
Stop Loss: $250
Target: $1,000
Tick Size: 0.1 (automatic for GC)
Point Value: $100 per point (automatic for GC)
Risk per trade: $250
Reward per trade: $1,000
Risk/Reward Ratio: 1:4 (Excellent!)
Stop Loss Strategy
Always place your stop loss below/above the entry lines
The red/orange line shows exactly where to place SL
Never move your stop loss against the trade (unless scaling)
Use hard stops - set them immediately upon entry
Take Profit Strategy
Take profits at the lime/magenta line (TP level)
Consider taking partial profits at 50% of target
Let remaining 50% run to full target
Use trailing stops if price moves in your favor
Risk Per Trade
Formula: (Stop Loss $) ÷ (Units × Point Value)
Example for ES:
Stop Loss: $250
Units: 6
Point Value: $50
Risk per point: 250 ÷ (6 × 50) = 0.83 points
Reading the Chart
Visual Elements
Candle Colors:
🟩 Green/Teal = Uptrend (higher highs and higher lows)
🟥 Orange/Red = Downtrend (lower highs and lower lows)
Signal Labels:
BUY (Green) = Long entry opportunity
SELL (Red) = Short entry opportunity
Bracket Lines:
Entry Line (Solid) = Your entry price
TP Line (Bright color) = Take profit target
SL Line (Red/Orange) = Stop loss level
Success Markers:
✓ (Green checkmark) = Trade hit TP (WIN)
✗ (Red X) = Trade hit SL (LOSS)
Statistics Table
What Each Column Means
📊 ← Current asset being traded
├── Total: Total signals generated (buys + sells)
├── Buy: Number of buy signals
├── Sell: Number of sell signals
├── Win ✓: Trades that hit take profit
├── Loss ✗: Trades that hit stop loss
├── W%: Win rate percentage (wins ÷ total trades)
└── Asset Info: Tick size and point value
Example Reading
📊 ES
Total: 15
Buy: 8
Sell: 7
Win ✓: 10
Loss ✗: 5
W%: 66.7%
Asset Info: Tick: 0.25 | PV: $50
This means:
15 total signals since tracking started
10 wins, 5 losses
66.7% win rate (Professional level!)
Trading ES with 0.25 tick and $50 point value
Trading Examples
Example 1: Gold (GC) Long Trade
Setup:
Asset: Metal Futures → GC
Stop Loss: $150
Target: $600
Units: 2 contracts
What happens:
You see a BUY label on a green candle
Entry line at 2050.0
TP line at 2050.6 (0.6 points higher = $600 profit)
SL line at 2049.85 (0.15 points lower = $150 loss)
Risk/Reward: 1:4 ✅
Trade Result:
Price moves to 2050.6 → Label shows ✓ = WIN
Table updates: Wins increases by 1, Win% increases
Example 2: Crude Oil (CL) Short Trade
Setup:
Asset: Energy Futures → CL
Stop Loss: $500
Target: $2,000
Units: 1 contract
What happens:
You see a SELL label on a red candle
Entry line at 78.50
TP line at 77.50 (1.00 lower = $1,000 profit)
SL line at 79.00 (0.50 higher = $500 loss)
Risk/Reward: 1:2 ✅
Trade Result:
Price drops to 77.50 → Label shows ✓ = WIN
Table updates: Wins increases by 1, Win% increases
Example 3: E-mini S&P (ES) Day Trading
Setup:
Asset: Equity Futures → ES
Stop Loss: $250
Target: $1,000
Units: 6 contracts
Swap Length: 5 (default)
Enable FVG: ON
Morning Session:
See BUY at 5860.25 (swing break)
Hit TP at 5861.08 = WIN ✓
Table shows: Total 1, Buy 1, Win 1, W% 100%
See SELL at 5861.50 (FVG entry)
Hit SL at 5860.67 = LOSS ✗
Table shows: Total 2, Sell 1, Win 1, L% 50%
By end of day: 4 wins, 1 loss, 80% win rate
Troubleshooting
Issue 1: No signals appearing
Solution:
Check if both Show Bracket is ON
Check if Enable FVG Entry is ON
Try changing Swing Length (lower = more signals)
Ensure you're on a 1-hour or higher timeframe
Check chart has enough data (scroll left to see history)
Issue 2: Signals appear but no entry lines
Solution:
Confirm Show Bracket is toggled ON
Check Stop Loss ()andTarget() and Target (
)andTarget() are reasonable amounts
Ensure your Units value is not 0
Try refreshing the chart
Issue 3: Asset not recognized
Solution:
Check spelling of symbol (ES, not E-S)
Verify asset is in the supported list
Check you're in the correct category
Try closing and reopening the chart
Issue 4: Wrong stop loss/target levels
Solution:
Verify correct asset is selected
Check Units setting matches your position size
Verify Stop Loss ($) and Target ($) amounts
Look at Asset Info in table to confirm tick size
Manually calculate: SL $ ÷ (Units × Point Value) = Points
Issue 5: Statistics table not showing
Solution:
Toggle Show Statistics OFF then back ON
Try changing Table Position
Refresh the chart
Check that Show Table is enabled in settings
Issue 6: Indicator acting "heavy" or laggy
Solution:
Turn off Show Swing Structure if not needed
Turn off Show Bracket if reviewing historical trades
Reduce chart's data window (don't load entire years)
Refresh the chart
Pro Tips 🚀
Tip 1: Start with Micro Futures
Micro contracts (MES, MNQ, MCL) have lower cost
Perfect for learning the strategy
Same quality signals, smaller risk
Tip 2: Trade During Peak Hours
Equity Futures: 9:30-16:00 ET (Regular session)
Energy: 18:00-16:00 CT (After hours active)
Metals: 18:00-17:00 CT (Most liquid)
Currencies: 5:00 PM - 4:00 PM ET (24-5 market)
Tip 3: Combine Timeframes
Look for entry on 1-hour chart
Confirm on 15-minute chart
Execute on 5-minute breakout
More confluence = higher probability
Tip 4: Track Your Trades
Keep notes on WIN/LOSS trades
Identify patterns in your losses
Adjust settings based on performance
Use Win% table to monitor improvement
Tip 5: Risk Management First
Never risk more than 2% of account per trade
Respect your stop loss (don't move it)
Take profits when levels are hit
Be patient for high-probability setups
Tip 6: Adjust for Market Conditions
Trending markets: Increase Swing Length (6-8)
Choppy markets: Decrease Swing Length (2-4)
Low volatility: Reduce Stop Loss $
High volatility: Increase Target $
Quick Reference Card
────────────────────────────────────────────────────
SMC KILLER QUICK START ─────────────────────────────────────────────────────
│ 1. Select Asset Category & Symbol
│ 2. Set Units (contracts)
│ 3. Set Stop Loss ($) - your max risk
│ 4. Set Target ($) - your profit goal
│ 5. Wait for BUY (green) or SELL (red) signal
│ 6. Place entry at the entry line
│ 7. Place stop at the red/orange line
│ 8. Place take-profit at the lime/magenta line
│ 9. Close trade when line closes (✓ or ✗)
│ 10. Review statistics and adjust next trade
└─────────────────────────────────────────────────────
BUY Signal = Break Higher OR Fill Gap = LONG
SELL Signal = Break Lower OR Fill Gap = SHORT
Green candles = Uptrend
Orange candles = Downtrend
✓ = Win (took profit)
✗ = Loss (hit stop)
Support & Updates
Check settings are correct for your asset
Ensure adequate chart data is loaded
Test on demo account first
Start with smallest position size
Track performance over 20+ trades
Algorithm Predator - ProAlgorithm Predator - Pro: Advanced Multi-Agent Reinforcement Learning Trading System
Algorithm Predator - Pro combines four specialized market microstructure agents with a state-of-the-art reinforcement learning framework . Unlike traditional indicator mashups, this system implements genuine machine learning to automatically discover which detection strategies work best in current market conditions and adapts continuously without manual intervention.
Core Innovation: Rather than forcing traders to interpret conflicting signals, this system uses 15 different multi-armed bandit algorithms and a full reinforcement learning stack (Q-Learning, TD(λ) with eligibility traces, and Policy Gradient with REINFORCE) to learn optimal agent selection policies. The result is a self-improving system that gets smarter with every trade.
Target Users: Swing traders, day traders, and algorithmic traders seeking systematic signal generation with mathematical rigor. Suitable for stocks, forex, crypto, and futures on liquid instruments (>100k daily volume).
Why These Components Are Combined
The Fundamental Problem
No single indicator works consistently across all market regimes. What works in trending markets fails in ranging conditions. Traditional solutions force traders to manually switch indicators (slow, error-prone) or interpret all signals simultaneously (cognitive overload).
This system solves the problem through automated meta-learning: Deploy multiple specialized agents designed for specific market microstructure conditions, then use reinforcement learning to discover which agent (or combination) performs best in real-time.
Why These Specific Four Agents?
The four agents provide orthogonal failure mode coverage —each agent's weakness is another's strength:
Spoofing Detector - Optimal in consolidation/manipulation; fails in trending markets (hedged by Exhaustion Detector)
Exhaustion Detector - Optimal at trend climax; fails in range-bound markets (hedged by Liquidity Void)
Liquidity Void - Optimal pre-breakout compression; fails in established trends (hedged by Mean Reversion)
Mean Reversion - Optimal in low volatility; fails in strong trends (hedged by Spoofing Detector)
This creates complete market state coverage where at least one agent should perform well in any condition. The bandit system identifies which one without human intervention.
Why Reinforcement Learning vs. Simple Voting?
Traditional consensus systems have fatal flaws: equal weighting assumes all agents are equally reliable (false), static thresholds don't adapt, and no learning means past mistakes repeat indefinitely.
Reinforcement learning solves this through the exploration-exploitation tradeoff: Continuously test underused agents (exploration) while primarily relying on proven winners (exploitation). Over time, the system builds a probability distribution over agent quality reflecting actual market performance.
Mathematical Foundation: Multi-armed bandit problem from probability theory, where each agent is an "arm" with unknown reward distribution. The goal is to maximize cumulative reward while efficiently learning each arm's true quality.
The Four Trading Agents: Technical Explanation
Agent 1: 🎭 Spoofing Detector (Institutional Manipulation Detection)
Theoretical Basis: Market microstructure theory on order flow toxicity and information asymmetry. Based on research by Easley, López de Prado, and O'Hara on high-frequency trading manipulation.
What It Detects:
1. Iceberg Orders (Hidden Liquidity Absorption)
Method: Monitors volume spikes (>2.5× 20-period average) with minimal price movement (<0.3× ATR)
Formula: score += (close > open ? -2.5 : 2.5) when volume > vol_avg × 2.5 AND abs(close - open) / ATR < 0.3
Interpretation: Large volume without price movement indicates institutional absorption (buying) or distribution (selling) using hidden orders
Signal Logic: Contrarian—fade false breakouts caused by institutional manipulation
2. Spoofing Patterns (Fake Liquidity via Layering)
Method: Analyzes candlestick wick-to-body ratios during volume spikes
Formula: if upper_wick > body × 2 AND volume_spike: score += 2.0
Mechanism: Spoofing creates large wicks (orders pulled before execution) with volume evidence
Signal Logic: Wick direction indicates trapped participants; trade against the failed move
3. Post-Manipulation Reversals
Method: Tracks volume decay after manipulation events
Formula: if volume > vol_avg × 3 AND volume / volume < 0.3: score += (close > open ? -1.5 : 1.5)
Interpretation: Sharp volume drop after manipulation indicates exhaustion of manipulative orders
Why It Works: Institutional manipulation creates detectable microstructure anomalies. While retail traders see "mysterious reversals," this agent quantifies the order flow patterns causing them.
Parameter: i_spoof (sensitivity 0.5-2.0) - Controls detection threshold
Best Markets: Consolidations before breakouts, London/NY overlap windows, stocks with institutional ownership >70%
Agent 2: ⚡ Exhaustion Detector (Momentum Failure Analysis)
Theoretical Basis: Technical analysis divergence theory combined with VPIN reversals from market microstructure literature.
What It Detects:
1. Price-RSI Divergence (Momentum Deceleration)
Method: Compares 5-bar price ROC against RSI change
Formula: if price_roc > 5% AND rsi_current < rsi : score += 1.8
Mathematics: Second derivative detecting inflection points
Signal Logic: When price makes higher highs but momentum makes lower highs, expect mean reversion
2. Volume Exhaustion (Buying/Selling Climax)
Method: Identifies strong price moves (>5% ROC) with declining volume (<-20% volume ROC)
Formula: if price_roc > 5 AND vol_roc < -20: score += 2.5
Interpretation: Price extension without volume support indicates retail chasing while institutions exit
3. Momentum Deceleration (Acceleration Analysis)
Method: Compares recent 3-bar momentum to prior 3-bar momentum
Formula: deceleration = abs(mom1) < abs(mom2) × 0.5 where momentum significant (> ATR)
Signal Logic: When rate of price change decelerates significantly, anticipate directional shift
Why It Works: Momentum is lagging, but momentum divergence is leading. By comparing momentum's rate of change to price, this agent detects "weakening conviction" before reversals become obvious.
Parameter: i_momentum (sensitivity 0.5-2.0)
Best Markets: Strong trends reaching climax, parabolic moves, instruments with high retail participation
Agent 3: 💧 Liquidity Void Detector (Breakout Anticipation)
Theoretical Basis: Market liquidity theory and order book dynamics. Based on research into "liquidity holes" and volatility compression preceding expansion.
What It Detects:
1. Bollinger Band Squeeze (Volatility Compression)
Method: Monitors Bollinger Band width relative to 50-period average
Formula: bb_width = (upper_band - lower_band) / middle_band; triggers when < 0.6× average
Mathematical Foundation: Regression to the mean—low volatility precedes high volatility
Signal Logic: When volatility compresses AND cumulative delta shows directional bias, anticipate breakout
2. Volume Profile Gaps (Thin Liquidity Zones)
Method: Identifies sharp volume transitions indicating few limit orders
Formula: if volume < vol_avg × 0.5 AND volume < vol_avg × 0.5 AND volume > vol_avg × 1.5
Interpretation: Sudden volume drop after spike indicates price moved through order book to low-opposition area
Signal Logic: Price accelerates through low-liquidity zones
3. Stop Hunts (Liquidity Grabs Before Reversals)
Method: Detects new 20-bar highs/lows with immediate reversal and rejection wick
Formula: if new_high AND close < high - (high - low) × 0.6: score += 3.0
Mechanism: Market makers push price to trigger stop-loss clusters, then reverse
Signal Logic: Enter reversal after stop-hunt completes
Why It Works: Order book theory shows price moves fastest through zones with minimal liquidity. By identifying these zones before major moves, this agent provides early entry for high-reward breakouts.
Parameter: i_liquidity (sensitivity 0.5-2.0)
Best Markets: Range-bound pre-breakout setups, volatility compression zones, instruments prone to gap moves
Agent 4: 📊 Mean Reversion (Statistical Arbitrage Engine)
Theoretical Basis: Statistical arbitrage theory, Ornstein-Uhlenbeck mean-reverting processes, and pairs trading methodology applied to single instruments.
What It Detects:
1. Z-Score Extremes (Standard Deviation Analysis)
Method: Calculates price distance from 20-period and 50-period SMAs in standard deviation units
Formula: zscore_20 = (close - SMA20) / StdDev(50)
Statistical Interpretation: Z-score >2.0 means price is 2 standard deviations above mean (97.5th percentile)
Trigger Logic: if abs(zscore_20) > 2.0: score += zscore_20 > 0 ? -1.5 : 1.5 (fade extremes)
2. Ornstein-Uhlenbeck Process (Mean-Reverting Stochastic Model)
Method: Models price as mean-reverting stochastic process: dx = θ(μ - x)dt + σdW
Implementation: Calculates spread = close - SMA20, then z-score of spread vs. spread distribution
Formula: ou_signal = (spread - spread_mean) / spread_std
Interpretation: Measures "tension" pulling price back to equilibrium
3. Correlation Breakdown (Regime Change Detection)
Method: Compares 50-period price-volume correlation to 10-period correlation
Formula: corr_breakdown = abs(typical_corr - recent_corr) > 0.5
Enhancement: if corr_breakdown AND abs(zscore_20) > 1.0: score += zscore_20 > 0 ? -1.2 : 1.2
Why It Works: Mean reversion is the oldest quantitative strategy (1970s pairs trading at Morgan Stanley). While simple, it remains effective because markets exhibit periodic equilibrium-seeking behavior. This agent applies rigorous statistical testing to identify when mean reversion probability is highest.
Parameter: i_statarb (sensitivity 0.5-2.0)
Best Markets: Range-bound instruments, low-volatility periods (VIX <15), algo-dominated markets (forex majors, index futures)
Multi-Armed Bandit System: 15 Algorithms Explained
What Is a Multi-Armed Bandit Problem?
Origin: Named after slot machines ("one-armed bandits"). Imagine facing multiple slot machines, each with unknown payout rates. How do you maximize winnings?
Formal Definition: K arms (agents), each with unknown reward distribution with mean μᵢ. Goal: Maximize cumulative reward over T trials. Challenge: Balance exploration (trying uncertain arms to learn quality) vs. exploitation (using known-best arm for immediate reward).
Trading Application: Each agent is an "arm." After each trade, receive reward (P&L). Must decide which agent to trust for next signal.
Algorithm Categories
Bayesian Approaches (probabilistic, optimal for stationary environments):
Thompson Sampling
Bootstrapped Thompson Sampling
Discounted Thompson Sampling
Frequentist Approaches (confidence intervals, deterministic):
UCB1
UCB1-Tuned
KL-UCB
SW-UCB (Sliding Window)
D-UCB (Discounted)
Adversarial Approaches (robust to non-stationary environments):
EXP3-IX
Hedge
FPL-Gumbel
Reinforcement Learning Approaches (leverage learned state-action values):
Q-Values (from Q-Learning)
Policy Network (from Policy Gradient)
Simple Baseline:
Epsilon-Greedy
Softmax
Key Algorithm Details
Thompson Sampling (DEFAULT - RECOMMENDED)
Theoretical Foundation: Bayesian decision theory with conjugate priors. Published by Thompson (1933), rediscovered for bandits by Chapelle & Li (2011).
How It Works:
Model each agent's reward distribution as Beta(α, β) where α = wins, β = losses
Each step, sample from each agent's beta distribution: θᵢ ~ Beta(αᵢ, βᵢ)
Select agent with highest sample: argmaxᵢ θᵢ
Update winner's distribution after observing outcome
Mathematical Properties:
Optimality: Achieves logarithmic regret O(K log T) (proven optimal)
Bayesian: Maintains probability distribution over true arm means
Automatic Balance: High uncertainty → more exploration; high certainty → exploitation
⚠️ CRITICAL APPROXIMATION: This is a pseudo-random approximation of true Thompson Sampling. True implementation requires random number generation from beta distributions, which Pine Script doesn't provide. This version uses Box-Muller transform with market data (price/volume decimal digits) as entropy source. While not mathematically pure, it maintains core exploration-exploitation balance and learns agent preferences effectively.
When To Use: Best all-around choice. Handles non-stationary markets reasonably well, balances exploration naturally, highly sample-efficient.
UCB1 (Upper Confidence Bound)
Formula: UCB_i = reward_mean_i + sqrt(2 × ln(total_pulls) / pulls_i)
Interpretation: First term (exploitation) + second term (exploration bonus for less-tested arms)
Mathematical Properties:
Deterministic : Always selects same arm given same state
Regret Bound: O(K log T) — same optimality as Thompson Sampling
Interpretable: Can visualize confidence intervals
When To Use: Prefer deterministic behavior, want to visualize uncertainty, stable markets
EXP3-IX (Exponential Weights - Adversarial)
Theoretical Foundation: Adversarial bandit algorithm. Assumes environment may be actively hostile (worst-case analysis).
How It Works:
Maintain exponential weights: w_i = exp(η × cumulative_reward_i)
Select agent with probability proportional to weights: p_i = (1-γ)w_i/Σw_j + γ/K
After outcome, update with importance weighting: estimated_reward = observed_reward / p_i
Mathematical Properties:
Adversarial Regret: O(sqrt(TK log K)) even if environment is adversarial
No Assumptions: Doesn't assume stationary or stochastic reward distributions
Robust: Works even when optimal arm changes continuously
When To Use: Extreme non-stationarity, don't trust reward distribution assumptions, want robustness over efficiency
KL-UCB (Kullback-Leibler Upper Confidence Bound)
Theoretical Foundation: Uses KL-divergence instead of Hoeffding bounds. Tighter confidence intervals.
Formula (conceptual): Find largest q such that: n × KL(p||q) ≤ ln(t) + 3×ln(ln(t))
Mathematical Properties:
Tighter Bounds: KL-divergence adapts to reward distribution shape
Asymptotically Optimal: Better constant factors than UCB1
Computationally Intensive: Requires iterative binary search (15 iterations)
When To Use: Maximum sample efficiency needed, willing to pay computational cost, long-term trading (>500 bars)
Q-Values & Policy Network (RL-Based Selection)
Unique Feature: Instead of treating agents as black boxes with scalar rewards, these algorithms leverage the full RL state representation .
Q-Values Selection:
Uses learned Q-values: Q(state, agent_i) from Q-Learning
Selects agent via softmax over Q-values for current market state
Advantage: Selects based on state-conditional quality (which agent works best in THIS market state)
Policy Network Selection:
Uses neural network policy: π(agent | state, θ) from Policy Gradient
Direct policy over agents given market features
Advantage: Can learn non-linear relationships between market features and agent quality
When To Use: After 200+ RL updates (Q-Values) or 500+ updates (Policy Network) when models converged
Machine Learning & Reinforcement Learning Stack
Why Both Bandits AND Reinforcement Learning?
Critical Distinction:
Bandits treat agents as contextless black boxes: "Agent 2 has 60% win rate"
Reinforcement Learning adds state context: "Agent 2 has 60% win rate WHEN trend_score > 2 and RSI < 40"
Power of Combination: Bandits provide fast initial learning with minimal assumptions. RL provides state-dependent policies for superior long-term performance.
Component 1: Q-Learning (Value-Based RL)
Algorithm: Temporal Difference Learning with Bellman equation.
State Space: 54 discrete states formed from:
trend_state = {0: bearish, 1: neutral, 2: bullish} (3 values)
volatility_state = {0: low, 1: normal, 2: high} (3 values)
RSI_state = {0: oversold, 1: neutral, 2: overbought} (3 values)
volume_state = {0: low, 1: high} (2 values)
Total states: 3 × 3 × 3 × 2 = 54 states
Action Space: 5 actions (No trade, Agent 1, Agent 2, Agent 3, Agent 4)
Total state-action pairs: 54 × 5 = 270 Q-values
Bellman Equation:
Q(s,a) ← Q(s,a) + α ×
Parameters:
α (learning rate): 0.01-0.50, default 0.10 - Controls step size for updates
γ (discount factor): 0.80-0.99, default 0.95 - Values future rewards
ε (exploration): 0.01-0.30, default 0.10 - Probability of random action
Update Mechanism:
Position opens with state s, action a (selected agent)
Every bar position is open: Calculate floating P&L → scale to reward
Perform online TD update
When position closes: Perform terminal update with final reward
Gradient Clipping: TD errors clipped to ; Q-values clipped to for stability.
Why It Works: Q-Learning learns "quality" of each agent in each market state through trial and error. Over time, builds complete state-action value function enabling optimal state-dependent agent selection.
Component 2: TD(λ) Learning (Temporal Difference with Eligibility Traces)
Enhancement Over Basic Q-Learning: Credit assignment across multiple time steps.
The Problem TD(λ) Solves:
Position opens at t=0
Market moves favorably at t=3
Position closes at t=8
Question: Which earlier decisions contributed to success?
Basic Q-Learning: Only updates Q(s₈, a₈) ← reward
TD(λ): Updates ALL visited state-action pairs with decayed credit
Eligibility Trace Formula:
e(s,a) ← γ × λ × e(s,a) for all s,a (decay all traces)
e(s_current, a_current) ← 1 (reset current trace)
Q(s,a) ← Q(s,a) + α × TD_error × e(s,a) (update all with trace weight)
Lambda Parameter (λ): 0.5-0.99, default 0.90
λ=0: Pure 1-step TD (only immediate next state)
λ=1: Full Monte Carlo (entire episode)
λ=0.9: Balance (recommended)
Why Superior: Dramatically faster learning for multi-step tasks. Q-Learning requires many episodes to propagate rewards backwards; TD(λ) does it in one.
Component 3: Policy Gradient (REINFORCE with Baseline)
Paradigm Shift: Instead of learning value function Q(s,a), directly learn policy π(a|s).
Policy Network Architecture:
Input: 12 market features
Hidden: None (linear policy)
Output: 5 actions (softmax distribution)
Total parameters: 12 features × 5 actions + 5 biases = 65 parameters
Feature Set (12 Features):
Price Z-score (close - SMA20) / ATR
Volume ratio (volume / vol_avg - 1)
RSI deviation (RSI - 50) / 50
Bollinger width ratio
Trend score / 4 (normalized)
VWAP deviation
5-bar price ROC
5-bar volume ROC
Range/ATR ratio - 1
Price-volume correlation (20-period)
Volatility ratio (ATR / ATR_avg - 1)
EMA50 deviation
REINFORCE Update Rule:
θ ← θ + α × ∇log π(a|s) × advantage
where advantage = reward - baseline (variance reduction)
Why Baseline? Raw rewards have high variance. Subtracting baseline (running average) centers rewards around zero, reducing gradient variance by 50-70%.
Learning Rate: 0.001-0.100, default 0.010 (much lower than Q-Learning because policy gradients have high variance)
Why Policy Gradient?
Handles 12 continuous features directly (Q-Learning requires discretization)
Naturally maintains exploration through probability distribution
Can converge to stochastic optimal policy
Component 4: Ensemble Meta-Learner (Stacking)
Architecture: Level-1 meta-learner combines Level-0 base learners (Q-Learning, TD(λ), Policy Gradient).
Three Meta-Learning Algorithms:
1. Simple Average (Baseline)
Final_prediction = (Q_prediction + TD_prediction + Policy_prediction) / 3
2. Weighted Vote (Reward-Based)
weight_i ← 0.95 × weight_i + 0.05 × (reward_i + 1)
3. Adaptive Weighting (Gradient-Based) — RECOMMENDED
Loss Function: L = (y_true - ŷ_ensemble)²
Gradient: ∂L/∂weight_i = -2 × (y_true - ŷ_ensemble) × agent_contribution_i
Updates weights via gradient descent with clipping and normalization
Why It Works: Unlike simple averaging, meta-learner discovers which base learner is most reliable in current regime. If Policy Gradient excels in trending markets while Q-Learning excels in ranging, meta-learner learns these patterns and weights accordingly.
Feature Importance Tracking
Purpose: Identify which of 12 features contribute most to successful predictions.
Update Rule: importance_i ← 0.95 × importance_i + 0.05 × |feature_i × reward|
Use Cases:
Feature selection: Drop low-importance features
Market regime detection: Importance shifts reveal regime changes
Agent tuning: If VWAP deviation has high importance, consider boosting agents using VWAP
RL Position Tracking System
Critical Innovation: Proper reinforcement learning requires tracking which decisions led to outcomes.
State Tracking (When Signal Validates):
active_rl_state ← current_market_state (0-53)
active_rl_action ← selected_agent (1-4)
active_rl_entry ← entry_price
active_rl_direction ← 1 (long) or -1 (short)
active_rl_bar ← current_bar_index
Online Updates (Every Bar Position Open):
floating_pnl = (close - entry) / entry × direction
reward = floating_pnl × 10 (scale to meaningful range)
reward = clip(reward, -5.0, 5.0)
Update Q-Learning, TD(λ), and Policy Gradient
Terminal Update (Position Close):
Final Q-Learning update (no next Q-value, terminal state)
Update meta-learner with final result
Update agent memory
Clear position tracking
Exit Conditions:
Time-based: ≥3 bars held (minimum hold period)
Stop-loss: 1.5% adverse move
Take-profit: 2.0% favorable move
Market Microstructure Filters
Why Microstructure Matters
Traditional technical analysis assumes fair, efficient markets. Reality: Markets have friction, manipulation, and information asymmetry. Microstructure filters detect when market structure indicates adverse conditions.
Filter 1: VPIN (Volume-Synchronized Probability of Informed Trading)
Theoretical Foundation: Easley, López de Prado, & O'Hara (2012). "Flow Toxicity and Liquidity in a High-Frequency World."
What It Measures: Probability that current order flow is "toxic" (informed traders with private information).
Calculation:
Classify volume as buy or sell (close > close = buy volume)
Calculate imbalance over 20 bars: VPIN = |Σ buy_volume - Σ sell_volume| / Σ total_volume
Compare to moving average: toxic = VPIN > VPIN_MA(20) × sensitivity
Interpretation:
VPIN < 0.3: Normal flow (uninformed retail)
VPIN 0.3-0.4: Elevated (smart money active)
VPIN > 0.4: Toxic flow (informed institutions dominant)
Filter Logic:
Block LONG when: VPIN toxic AND price rising (don't buy into institutional distribution)
Block SHORT when: VPIN toxic AND price falling (don't sell into institutional accumulation)
Adaptive Threshold: If VPIN toxic frequently, relax threshold; if rarely toxic, tighten threshold. Bounded .
Filter 2: Toxicity (Kyle's Lambda Approximation)
Theoretical Foundation: Kyle (1985). "Continuous Auctions and Insider Trading."
What It Measures: Price impact per unit volume — market depth and informed trading.
Calculation:
price_impact = (close - close ) / sqrt(Σ volume over 10 bars)
impact_zscore = (price_impact - impact_mean) / impact_std
toxicity = abs(impact_zscore)
Interpretation:
Low toxicity (<1.0): Deep liquid market, large orders absorbed easily
High toxicity (>2.0): Thin market or informed trading
Filter Logic: Block ALL SIGNALS when toxicity > threshold. Most dangerous when price breaks from VWAP with high toxicity.
Filter 3: Regime Filter (Counter-Trend Protection)
Purpose: Prevent counter-trend trades during strong trends.
Trend Scoring:
trend_score = 0
trend_score += close > EMA8 ? +1 : -1
trend_score += EMA8 > EMA21 ? +1 : -1
trend_score += EMA21 > EMA50 ? +1 : -1
trend_score += close > EMA200 ? +1 : -1
Range:
Regime Classification:
Strong Bull: trend_score ≥ +3 → Block all SHORT signals
Strong Bear: trend_score ≤ -3 → Block all LONG signals
Neutral: -2 ≤ trend_score ≤ +2 → Allow both directions
Filter 4: Liquidity Boost (Signal Enhancer)
Unique: Unlike other filters (which block), this amplifies signals during low liquidity.
Logic: if volume < vol_avg × 0.7: agent_scores × 1.2
Why It Works: Low liquidity often precedes explosive moves (breakouts). By increasing agent sensitivity during compression, system catches pre-breakout signals earlier.
Technical Implementation & Approximations
⚠️ Critical Approximations Required by Pine Script
1. Thompson Sampling: Pseudo-Random Beta Distribution
Academic Standard: True random sampling from beta distributions using cryptographic RNG
This Implementation: Box-Muller transform for normal distribution using market data (price/volume decimal digits) as entropy source, then scale to beta distribution mean/variance
Impact: Not cryptographically random, may have subtle biases in specific price ranges, but maintains correct mean and approximate variance. Sufficient for bandit agent selection.
2. VPIN: Simplified Volume Classification
Academic Standard: Lee-Ready algorithm or exchange-provided aggressor flags with tick-by-tick data
This Implementation: Bar-based classification: if close > close : buy_volume += volume
Impact: 10-15% precision loss. Works well in directional markets, misclassifies in choppy conditions. Still captures order flow imbalance signal.
3. Policy Gradient: Simplified Per-Action Updates
Academic Standard: Full softmax gradient updating all actions (selected action UP, others DOWN proportionally)
This Implementation: Only updates selected action's weights
Impact: Valid approximation for small action spaces (5 actions). Slower convergence than full softmax but still learns optimal policy.
4. Kyle's Lambda: Simplified Price Impact
Academic Standard: Regression over multiple time scales with signed order flow
This Implementation: price_impact = Δprice_10 / sqrt(Σvolume_10); z_score calculation
Impact: 15-20% precision loss. No proper signed order flow. Still detects informed trading signals at extremes (>2σ).
5. Other Simplifications:
Hawkes Process: Fixed exponential decay (0.9) not MLE-optimized
Entropy: Ratio approximation not true Shannon entropy H(X) = -Σ p(x)·log₂(p(x))
Feature Engineering: 12 features vs. potential 100+ with polynomial interactions
RL Hybrid Updates: Both online and terminal (non-standard but empirically effective)
Overall Precision Loss Estimate: 10-15% compared to academic implementations with institutional data feeds.
Practical Trade-off: For retail trading with OHLCV data, these approximations provide 90%+ of the edge while maintaining full transparency, zero latency, no external dependencies, and runs on any TradingView plan.
How to Use: Practical Guide
Initial Setup (5 Minutes)
Select Trading Mode: Start with "Balanced" for most users
Enable ML/RL System: Toggle to TRUE, select "Full Stack" ML Mode
Bandit Configuration: Algorithm: "Thompson Sampling", Mode: "Switch" or "Blend"
Microstructure Filters: Enable all four filters, enable "Adaptive Microstructure Thresholds"
Visual Settings: Enable dashboard (Top Right), enable all chart visuals
Learning Phase (First 50-100 Signals)
What To Monitor:
Agent Performance Table: Watch win rates develop (target >55%)
Bandit Weights: Should diverge from uniform (0.25 each) after 20-30 signals
RL Core Metrics: "RL Updates" should increase when position open
Filter Status: "Blocked" count indicates filter activity
Optimization Tips:
Too few signals: Lower min_confidence to 0.25, increase agent sensitivities to 1.1-1.2
Too many signals: Raise min_confidence to 0.35-0.40, decrease agent sensitivities to 0.8-0.9
One agent dominates (>70%): Consider "Lock Agent" feature
Signal Interpretation
Dashboard Signal Status:
⚪ WAITING FOR SIGNAL: No agent signaling
⏳ ANALYZING...: Agent signaling but not confirmed
🟡 CONFIRMING 2/3: Building confirmation (2 of 3 bars)
🟢 LONG ACTIVE : Validated long entry
🔴 SHORT ACTIVE : Validated short entry
Kill Zone Boxes: Entry price (triangle marker), Take Profit (Entry + 2.5× ATR), Stop Loss (Entry - 1.5× ATR). Risk:Reward = 1:1.67
Risk Management
Position Sizing:
Risk per trade = 1-2% of capital
Position size = (Capital × Risk%) / (Entry - StopLoss)
Stop-Loss Placement:
Initial: Entry ± 1.5× ATR (shown in kill zone)
Trailing: After 1:1 R:R achieved, move stop to breakeven
Take-Profit Strategy:
TP1 (2.5× ATR): Take 50% off
TP2 (Runner): Trail stop at 1× ATR or use opposite signal as exit
Memory Persistence
Why Save Memory: Every chart reload resets the system. Saving learned parameters preserves weeks of learning.
When To Save: After 200+ signals when agent weights stabilize
What To Save: From Memory Export panel, copy all alpha/beta/weight values and adaptive thresholds
How To Restore: Enable "Restore From Saved State", input all values into corresponding fields
What Makes This Original
Innovation 1: Genuine Multi-Armed Bandit Framework
This implements 15 mathematically rigorous bandit algorithms from academic literature (Thompson Sampling from Chapelle & Li 2011, UCB family from Auer et al. 2002, EXP3 from Auer et al. 2002, KL-UCB from Garivier & Cappé 2011). Each algorithm maintains proper state, updates according to proven theory, and converges to optimal behavior. This is real learning, not superficial parameter changes.
Innovation 2: Full Reinforcement Learning Stack
Beyond bandits learning which agent works best globally, RL learns which agent works best in each market state. After 500+ positions, system builds 54-state × 5-action value function (270 learned parameters) capturing context-dependent agent quality.
Innovation 3: Market Microstructure Integration
Combines retail technical analysis with institutional-grade microstructure metrics: VPIN from Easley, López de Prado, O'Hara (2012), Kyle's Lambda from Kyle (1985), Hawkes Processes from Hawkes (1971). These detect informed trading, manipulation, and liquidity dynamics invisible to technical analysis.
Innovation 4: Adaptive Threshold System
Dynamic quantile-based thresholds: Maintains histogram of each agent's score distribution (24 bins, exponentially decayed), calculates 80th percentile threshold from histogram. Agent triggers only when score exceeds its own learned quantile. Proper non-parametric density estimation automatically adapts to instrument volatility, agent behavior shifts, and market regime changes.
Innovation 5: Episodic Memory with Transfer Learning
Dual-layer architecture: Short-term memory (last 20 trades, fast adaptation) + Long-term memory (condensed episodes, historical patterns). Transfer mechanism consolidates knowledge when STM reaches threshold. Mimics hippocampus → neocortex consolidation in human memory.
Limitations & Disclaimers
General Limitations
No Predictive Guarantee: Pattern recognition ≠ prediction. Past performance ≠ future results.
Learning Period Required: Minimum 50-100 bars for reliable statistics. Initial performance may be suboptimal.
Overfitting Risk: System learns patterns in historical data. May not generalize to unprecedented conditions.
Approximation Limitations: See technical implementation section (10-15% precision loss vs. academic standards)
Single-Instrument Limitation: No multi-asset correlation, sector context, or VIX integration.
Forward-Looking Bias Disclaimer
CRITICAL TRANSPARENCY: The RL system uses an 8-bar forward-looking window for reward calculation.
What This Means: System learns from rewards incorporating future price information (bars 101-108 relative to entry at bar 100).
Why Acceptable:
✅ Signals do NOT look ahead: Entry decisions use only data ≤ entry bar
✅ Learning only: Forward data used for optimization, not signal generation
✅ Real-time mirrors backtest: In live trading, system learns identically
⚠️ Implication: Dashboard "Agent Win%" reflects this 8-bar evaluation. Real-time performance may differ slightly if positions held longer, slippage/fees not captured, or market microstructure changes.
Risk Warnings
No Guarantee of Profit: All trading involves risk of loss
System Failures: Bugs possible despite extensive testing
Market Conditions: Optimized for liquid markets (>100k daily volume). Performance degrades in illiquid instruments, major news events, flash crashes
Broker-Specific Issues: Execution slippage, commission/fees, overnight financing costs
Appropriate Use
This Indicator Is:
✅ Entry trigger system
✅ Risk management framework (stop/target)
✅ Adaptive agent selection engine
✅ Learning system that improves over time
This Indicator Is NOT:
❌ Complete trading strategy (requires position sizing, portfolio management)
❌ Replacement for fundamental analysis
❌ Guaranteed profit generator
❌ Suitable for complete beginners without training
Recommended Complementary Analysis: Market context (support/resistance), volume profile, fundamental catalysts, correlation with related instruments, broader market regime
Recommended Settings by Instrument
Stocks (Large Cap, >$1B):
Mode: Balanced | ML/RL: Enabled, Full Stack | Bandit: Thompson Sampling, Switch
Agent Sensitivity: Spoofing 1.0-1.2, Exhaustion 0.9-1.1, Liquidity 0.8-1.0, StatArb 1.1-1.3
Microstructure: All enabled, VPIN 1.2, Toxicity 1.5 | Timeframe: 15min-1H
Forex Majors (EURUSD, GBPUSD):
Mode: Balanced to Conservative | ML/RL: Enabled, Full Stack | Bandit: Thompson Sampling, Blend
Agent Sensitivity: Spoofing 0.8-1.0, Exhaustion 0.9-1.1, Liquidity 0.7-0.9, StatArb 1.2-1.5
Microstructure: All enabled, VPIN 1.0-1.1, Toxicity 1.3-1.5 | Timeframe: 5min-30min
Crypto (BTC, ETH):
Mode: Aggressive to Balanced | ML/RL: Enabled, Full Stack | Bandit: Thompson Sampling OR EXP3-IX
Agent Sensitivity: Spoofing 1.2-1.5, Exhaustion 1.1-1.3, Liquidity 1.2-1.5, StatArb 0.7-0.9
Microstructure: All enabled, VPIN 1.4-1.6, Toxicity 1.8-2.2 | Timeframe: 15min-4H
Futures (ES, NQ, CL):
Mode: Balanced | ML/RL: Enabled, Full Stack | Bandit: UCB1 or Thompson Sampling
Agent Sensitivity: All 1.0-1.2 (balanced)
Microstructure: All enabled, VPIN 1.1-1.3, Toxicity 1.4-1.6 | Timeframe: 5min-30min
Conclusion
Algorithm Predator - Pro synthesizes academic research from market microstructure theory, reinforcement learning, and multi-armed bandit algorithms. Unlike typical indicator mashups, this system implements 15 mathematically rigorous bandit algorithms, deploys a complete RL stack (Q-Learning, TD(λ), Policy Gradient), integrates institutional microstructure metrics (VPIN, Kyle's Lambda), adapts continuously through dual-layer memory and meta-learning, and provides full transparency on approximations and limitations.
The system is designed for serious algorithmic traders who understand that no indicator is perfect, but through proper machine learning, we can build systems that improve over time and adapt to changing markets without manual intervention.
Use responsibly. Risk disclosure applies. Past performance ≠ future results.
Taking you to school. — Dskyz, Trade with insight. Trade with anticipation.
Manifold Singularity EngineManifold Singularity Engine: Catastrophe Theory Detection Through Multi-Dimensional Topology Analysis
The Manifold Singularity Engine applies catastrophe theory from mathematical topology to multi-dimensional price space analysis, identifying potential reversal conditions by measuring manifold curvature, topological complexity, and fractal regime states. Unlike traditional reversal indicators that rely on price pattern recognition or momentum oscillators, this system reconstructs the underlying geometric surface (manifold) that price evolves upon and detects points where this topology undergoes catastrophic folding—mathematical singularities that correspond to forced directional changes in price dynamics.
The indicator combines three analytical frameworks: phase space reconstruction that embeds price data into a multi-dimensional coordinate system, catastrophe detection that measures when this embedded manifold reaches critical curvature thresholds indicating topology breaks, and Hurst exponent calculation that classifies the current fractal regime to adaptively weight detection sensitivity. This creates a geometry-based reversal detection system with visual feedback showing topology state, manifold distortion fields, and directional probability projections.
What Makes This Approach Different
Phase Space Embedding Construction
The core analytical method reconstructs price evolution as movement through a three-dimensional coordinate system rather than analyzing price as a one-dimensional time series. The system calculates normalized embedding coordinates: X = normalize(price_velocity, window) , Y = normalize(momentum_acceleration, window) , and Z = normalize(volume_weighted_returns, window) . These coordinates create a trajectory through phase space where price movement traces a path across a geometric surface—the market manifold.
This embedding approach differs fundamentally from traditional technical analysis by treating price not as a sequential data stream but as a dynamical system evolving on a curved surface in multi-dimensional space. The trajectory's geometric properties (curvature, complexity, folding) contain information about impending directional changes that single-dimension analysis cannot capture. When this manifold undergoes rapid topological deformation, price must respond with directional change—this is the mathematical basis for catastrophe detection.
Statistical normalization using z-score transformation (subtracting mean, dividing by standard deviation over a rolling window) ensures the coordinate system remains scale-invariant across different instruments and volatility regimes, allowing identical detection logic to function on forex, crypto, stocks, or indices without recalibration.
Catastrophe Score Calculation
The catastrophe detection formula implements a composite anomaly measurement combining multiple topology metrics: Catastrophe_Score = 0.45×Curvature_Percentile + 0.25×Complexity_Ratio + 0.20×Condition_Percentile + 0.10×Gradient_Percentile . Each component measures a distinct aspect of manifold distortion:
Curvature (κ) is computed using the discrete Laplacian operator: κ = √ , which measures how sharply the manifold surface bends at the current point. High curvature values indicate the surface is folding or developing a sharp corner—geometric precursors to catastrophic topology breaks. The Laplacian measures second derivatives (rate of change of rate of change), capturing acceleration in the trajectory's path through phase space.
Topological Complexity counts sign changes in the curvature field over the embedding window, measuring how chaotically the manifold twists and oscillates. A smooth, stable surface produces low complexity; a highly contorted, unstable surface produces high complexity. This metric detects when the geometric structure becomes informationally dense with multiple local extrema, suggesting an imminent topology simplification event (catastrophe).
Condition Number measures the Jacobian matrix's sensitivity: Condition = |Trace| / |Determinant|, where the Jacobian describes how small changes in price produce changes in the embedding coordinates. High condition numbers indicate numerical instability—points where the coordinate transformation becomes ill-conditioned, suggesting the manifold mapping is approaching a singularity.
Each metric is converted to percentile rank within a rolling window, then combined using weighted sum. The percentile transformation creates adaptive thresholds that automatically adjust to each instrument's characteristic topology without manual recalibration. The resulting 0-100% catastrophe score represents the current bar's position in the distribution of historical manifold distortion—values above the threshold (default 65%) indicate statistically extreme topology states where reversals become geometrically probable.
This multi-metric ensemble approach prevents false signals from isolated anomalies: all four geometric features must simultaneously indicate distortion for a high catastrophe score, ensuring only true manifold breaks trigger detection.
Hurst Exponent Regime Classification
The Hurst exponent calculation implements rescaled range (R/S) analysis to measure the fractal dimension of price returns: H = log(R/S) / log(n) , where R is the range of cumulative deviations from mean and S is the standard deviation. The resulting value classifies market behavior into three fractal regimes:
Trending Regime (H > 0.55) : Persistent price movement where future changes are positively correlated with past changes. The manifold exhibits directional momentum with smooth topology evolution. In this regime, catastrophe signals receive 1.2× confidence multiplier because manifold breaks in trending conditions produce high-magnitude directional changes.
Mean-Reverting Regime (H < 0.45) : Anti-persistent price movement where future changes tend to oppose past changes. The manifold exhibits oscillatory topology with frequent small-scale distortions. Catastrophe signals receive 0.8× confidence multiplier because reversal significance is diminished in choppy conditions where the manifold constantly folds at minor scales.
Random Walk Regime (H ≈ 0.50) : No statistical correlation in returns. The manifold evolution is geometrically neutral with moderate topology stability. Standard 1.0× confidence multiplier applies.
This adaptive weighting system solves a critical problem in reversal detection: the same geometric catastrophe has different trading implications depending on the fractal regime. A manifold fold in a strong trend suggests a significant reversal opportunity; the same fold in mean-reversion suggests a minor oscillation. The Hurst-based regime filter ensures detection sensitivity automatically adjusts to market character without requiring trader intervention.
The implementation uses logarithmic price returns rather than raw prices to ensure
stationarity, and applies the calculation over a configurable window (default 5 bars) to balance responsiveness with statistical validity. The Hurst value is then smoothed using exponential moving average to reduce noise while maintaining regime transition detection.
Multi-Layer Confirmation Architecture
The system implements five independent confirmation filters that must simultaneously validate
before any singularity signal generates:
1. Catastrophe Threshold : The composite anomaly score must exceed the configured threshold (default 0.65 on 0-1 scale), ensuring the manifold distortion is statistically extreme relative to recent history.
2. Pivot Structure Confirmation : Traditional swing high/low patterns (using ta.pivothigh and ta.pivotlow with configurable lookback) must form at the catastrophe bar. This ensures the geometric singularity coincides with observable price structure rather than occurring mid-swing where interpretation is ambiguous.
3. Swing Size Validation : The pivot magnitude must exceed a minimum threshold measured in ATR units (default 1.5× Average True Range). This filter prevents signals on insignificant price jiggles that lack meaningful reversal potential, ensuring only substantial swings with adequate risk/reward ratios generate signals.
4. Volume Confirmation : Current volume must exceed 1.3× the 20-period moving average, confirming genuine market participation rather than low-liquidity price noise. Manifold catastrophes without volume support often represent false topology breaks that don't translate to sustained directional change.
5. Regime Validity : The market must be classified as either trending (ADX > configured threshold, default 30) or volatile (ATR expansion > configured threshold, default 40% above 30-bar average), and must NOT be in choppy/ranging state. This critical filter prevents trading during geometrically unfavorable conditions where edge deteriorates.
All five conditions must evaluate true simultaneously for a signal to generate. This conjunction-based logic (AND not OR) dramatically reduces false positives while preserving true reversal detection. The architecture recognizes that geometric catastrophes occur frequently in noisy data, but only those catastrophes that align with confirming evidence across price structure, participation, and regime characteristics represent tradable opportunities.
A cooldown mechanism (default 8 bars between signals) prevents signal clustering at extended pivot zones where the manifold may undergo multiple small catastrophes during a single reversal process.
Direction Classification System
Unlike binary bull/bear systems, the indicator implements a voting mechanism combining four
directional indicators to classify each catastrophe:
Pivot Vote : +1 if pivot low, -1 if pivot high, 0 otherwise
Trend Vote : Based on slow frequency (55-period EMA) slope—+1 if rising, -1 if falling, 0 if flat
Flow Vote : Based on Y-gradient (momentum acceleration)—+1 if positive, -1 if negative, 0 if neutral
Mid-Band Vote : Based on price position relative to medium frequency (21-period EMA)—+1 if above, -1 if below, 0 if at
The total vote sum classifies the singularity: ≥2 votes = Bullish , ≤-2 votes = Bearish , -1 to +1 votes = Neutral (skip) . This majority-consensus approach ensures directional classification requires alignment across multiple timeframes and analysis dimensions rather than relying on a single indicator. Neutral signals (mixed voting) are displayed but should not be traded, as they represent geometric catastrophes without clear directional resolution.
Core Calculation Methodology
Embedding Coordinate Generation
Three normalized phase space coordinates are constructed from price data:
X-Dimension (Velocity Space):
price_velocity = close - close
X = (price_velocity - mean) / stdev over hurstWindow
Y-Dimension (Acceleration Space):
momentum = close - close
momentum_accel = momentum - momentum
Y = (momentum_accel - mean) / stdev over hurstWindow
Z-Dimension (Volume-Weighted Space):
vol_normalized = (volume - mean) / stdev over embedLength
roc = (close - close ) / close
Z = (roc × vol_normalized - mean) / stdev over hurstWindow
These coordinates define a point in 3D phase space for each bar. The trajectory connecting these points is the reconstructed manifold.
Gradient Field Calculation
First derivatives measure local manifold slope:
dX/dt = X - X
dY/dt = Y - Y
Gradient_Magnitude = √
The gradient direction indicates where the manifold is "pushing" price. Positive Y-gradient suggests upward topological pressure; negative Y-gradient suggests downward pressure.
Curvature Tensor Components
Second derivatives measure manifold bending using discrete Laplacian:
Laplacian_X = X - 2×X + X
Laplacian_Y = Y - 2×Y + Y
Laplacian_Magnitude = √
This is then normalized:
Curvature_Normalized = (Laplacian_Magnitude - mean) / stdev over embedLength
High normalized curvature (>1.5) indicates sharp manifold folding.
Complexity Accumulation
Sign changes in curvature field are counted:
Sign_Flip = 1 if sign(Curvature ) ≠ sign(Curvature ), else 0
Topological_Complexity = sum(Sign_Flip) over embedLength window
This measures oscillation frequency in the geometry. Complexity >5 indicates chaotic topology.
Condition Number Stability Analysis
Jacobian matrix sensitivity is approximated:
dX/dp = dX/dt / (price_change + epsilon)
dY/dp = dY/dt / (price_change + epsilon)
Jacobian_Determinant = (dX/dt × dY/dp) - (dX/dp × dY/dt)
Jacobian_Trace = dX/dt + dY/dp
Condition_Number = |Trace| / (|Determinant| + epsilon)
High condition numbers indicate numerical instability near singularities.
Catastrophe Score Assembly
Each metric is converted to percentile rank over embedLength window, then combined:
Curvature_Percentile = percentrank(abs(Curvature_Normalized), embedLength)
Gradient_Percentile = percentrank(Gradient_Magnitude, embedLength)
Condition_Percentile = percentrank(abs(Condition_Z_Score), embedLength)
Complexity_Ratio = clamp(Topological_Complexity / embedLength, 0, 1)
Final score:
Raw_Anomaly = 0.45×Curvature_P + 0.25×Complexity_R + 0.20×Condition_P + 0.10×Gradient_P
Catastrophe_Score = Raw_Anomaly × Hurst_Multiplier
Values are clamped to range.
Hurst Exponent Calculation
Rescaled range analysis on log returns:
Calculate log returns: r = log(close) - log(close )
Compute cumulative deviations from mean
Find range: R = max(cumulative_dev) - min(cumulative_dev)
Calculate standard deviation: S = stdev(r, hurstWindow)
Compute R/S ratio
Hurst = log(R/S) / log(hurstWindow)
Clamp to and smooth with 5-period EMA
Regime Classification Logic
Volatility Regime:
ATR_MA = SMA(ATR(14), 30)
Vol_Expansion = ATR / ATR_MA
Is_Volatile = Vol_Expansion > (1.0 + minVolExpansion)
Trend Regime (Corrected ADX):
Calculate directional movement (DM+, DM-)
Smooth with Wilder's RMA(14)
Compute DI+ and DI- as percentages
Calculate DX = |DI+ - DI-| / (DI+ + DI-) × 100
ADX = RMA(DX, 14)
Is_Trending = ADX > (trendStrength × 100)
Chop Detection:
Is_Chopping = NOT Is_Trending AND NOT Is_Volatile
Regime Validity:
Regime_Valid = (Is_Trending OR Is_Volatile) AND NOT Is_Chopping
Signal Generation Logic
For each bar:
Check if catastrophe score > topologyStrength threshold
Verify regime is valid
Confirm Hurst alignment (trending or mean-reverting with pivot)
Validate pivot quality (price extended outside spectral bands then re-entered)
Confirm volume/volatility participation
Check cooldown period has elapsed
If all true: compute directional vote
If vote ≥2: Bullish Singularity
If vote ≤-2: Bearish Singularity
If -1 to +1: Neutral (display but skip)
All conditions must be true for signal generation.
Visual System Architecture
Spectral Decomposition Layers
Three harmonic frequency bands visualize entropy state:
Layer 1 (Surface Frequency):
Center: EMA(8)
Width: ±0.3 × 0.5 × ATR
Transparency: 75% (most visible)
Represents fast oscillations
Layer 2 (Mid Frequency):
Center: EMA(21)
Width: ±0.5 × 0.5 × ATR
Transparency: 85%
Represents medium cycles
Layer 3 (Deep Frequency):
Center: EMA(55)
Width: ±0.7 × 0.5 × ATR
Transparency: 92% (most transparent)
Represents slow baseline
Convergence of layers indicates low entropy (stable topology). Divergence indicates high entropy (catastrophe building). This decomposition reveals how different frequency components of price movement interact—when all three align, the manifold is in equilibrium; when they separate, topology is unstable.
Energy Radiance Fields
Concentric boxes emanate from each singularity bar:
For each singularity, 5 layers are generated:
Layer n: bar_index ± (n × 1.5 bars), close ± (n × 0.4 × ATR)
Transparency gradient: inner 75% → outer 95%
Color matches signal direction
These fields visualize the "energy well" of the catastrophe—wider fields indicate stronger topology distortion. The exponential expansion creates a natural radiance effect.
Singularity Node Geometry
N-sided polygon (default hexagon) at each signal bar:
Vertices calculated using polar coordinates
Rotation angle: bar_index × 0.1 (creates animation)
Radius: ATR × singularity_strength × 2
Connects vertices with colored lines
The rotating geometric primitive marks the exact catastrophe bar with visual prominence.
Gradient Flow Field
Directional arrows display manifold slope:
Spawns every 3 bars when gradient_magnitude > 0.1
Symbol: "↗" if dY/dt > 0.1, "↘" if dY/dt < -0.1, "→" if neutral
Color: Bull/bear/neutral based on direction
Density limited to flowDensity parameter
Arrows cluster when gradient is strong, creating intuitive topology visualization.
Probability Projection Cones
Forward trajectory from each singularity:
Projects 10 bars forward
Direction based on vote classification
Center line: close + (direction × ATR × 3)
Uncertainty width: ATR × singularity_strength × 2
Dashed boundaries, solid center
These are mathematical projections based on current gradient, not price targets. They visualize expected manifold evolution if topology continues current trajectory.
Dashboard Metrics Explanation
The real-time control panel displays six core metrics plus regime status:
H (Hurst Exponent):
Value: Current Hurst (0-1 scale)
Label: TREND (>0.55), REVERT (<0.45), or RANDOM (0.45-0.55)
Icon: Direction arrow based on regime
Purpose: Shows fractal character—only trade when favorable
Σ (Catastrophe Score):
Value: Current composite anomaly (0-100%)
Bar gauge shows relative strength
Icon: ◆ if above threshold, ○ if below
Purpose: Primary signal strength indicator
κ (Curvature):
Value: Normalized Laplacian magnitude
Direction arrow shows sign
Color codes severity (green<0.8, yellow<1.5, red≥1.5)
Purpose: Shows manifold bending intensity
⟳ (Topology Complexity):
Value: Count of sign flips in curvature
Icon: ◆ if >3, ○ otherwise
Color codes chaos level
Purpose: Indicates geometric instability
V (Volatility Expansion):
Value: ATR expansion percentage above 30-bar average
Icon: ● if volatile, ○ otherwise
Purpose: Confirms energy present for reversal
T (Trend Strength):
Value: ADX reading (0-100)
Icon: ● if trending, ○ otherwise
Purpose: Shows directional bias strength
R (Regime):
Label: EXPLOSIVE / TREND / VOLATILE / CHOP / NEUTRAL
Icon: ✓ if valid, ✗ if invalid
Purpose: Go/no-go filter for trading
STATE (Bottom Display):
Shows: "◆ BULL SINGULARITY" (green), "◆ BEAR SINGULARITY" (red), "◆ WEAK/NEUTRAL" (orange), or "— Monitoring —" (gray)
Purpose: Current signal status at a glance
How to Use This Indicator
Initial Setup and Configuration
Apply the indicator to your chart with default settings as a starting point. The default parameters (21-bar embedding, 5-bar Hurst window, 2.5σ singularity threshold, 0.65 topology confirmation) are optimized for balanced detection across most instruments and timeframes. For very fast markets (scalping crypto, 1-5min charts), consider reducing embedding depth to 13-15 bars and Hurst window to 3 bars for more responsive detection. For slower markets (swing trading stocks, 4H-Daily charts), increase embedding depth to 34-55 bars and Hurst window to 8-10 bars for more stable topology measurement.
Enable the dashboard (top right recommended) to monitor real-time metrics. The control panel is your primary decision interface—glancing at the dashboard should instantly communicate whether conditions favor trading and what the current topology state is. Position and size the dashboard to remain visible but not obscure price action.
Enable regime filtering (strongly recommended) to prevent trading during choppy/ranging conditions where geometric edge deteriorates. This single setting can dramatically improve overall performance by eliminating low-probability environments.
Reading Dashboard Metrics for Trade Readiness
Before considering any trade, verify the dashboard shows favorable conditions:
Hurst (H) Check:
The Hurst Exponent reading is your first filter. Only consider trades when H > 0.50 . Ideal conditions show H > 0.60 with "TREND" label—this indicates persistent directional price movement where manifold catastrophes produce significant reversals. When H < 0.45 (REVERT label), the market is mean-reverting and catastrophes represent minor oscillations rather than substantial pivots. Do not trade in mean-reverting regimes unless you're explicitly using range-bound strategies (which this indicator is not optimized for). When H ≈ 0.50 (RANDOM label), edge is neutral—acceptable but not ideal.
Catastrophe (Σ) Monitoring:
Watch the Σ percentage build over time. Readings consistently below 50% indicate stable topology with no imminent reversals. When Σ rises above 60-65%, manifold distortion is approaching critical levels. Signals only fire when Σ exceeds the configured threshold (default 65%), so this metric pre-warns you of potential upcoming catastrophes. High-conviction setups show Σ > 75%.
Regime (R) Validation:
The regime classification must read TREND, VOLATILE, or EXPLOSIVE—never trade when it reads CHOP or NEUTRAL. The checkmark (✓) must be present in the regime cell for trading conditions to be valid. If you see an X (✗), skip all signals until regime improves. This filter alone eliminates most losing trades by avoiding geometrically unfavorable environments.
Combined High-Conviction Profile:
The strongest trading opportunities show simultaneously:
H > 0.60 (strong trending regime)
Σ > 75% (extreme topology distortion)
R = EXPLOSIVE or TREND with ✓
κ (Curvature) > 1.5 (sharp manifold fold)
⟳ (Complexity) > 4 (chaotic geometry)
V (Volatility) showing elevated ATR expansion
When all metrics align in this configuration, the manifold is undergoing severe distortion in a favorable fractal regime—these represent maximum-conviction reversal opportunities.
Signal Interpretation and Entry Logic
Bullish Singularity (▲ Green Triangle Below Bar):
This marker appears when the system detects a manifold catastrophe at a price low with bullish directional consensus. All five confirmation filters have aligned: topology score exceeded threshold, pivot low structure formed, swing size was significant, volume/volatility confirmed participation, and regime was valid. The green color indicates the directional vote totaled +2 or higher (majority bullish).
Trading Approach: Consider long entry on the bar immediately following the signal (bar after the triangle). The singularity bar itself is where the geometric catastrophe occurred—entering after allows you to see if price confirms the reversal. Place stop loss below the singularity bar's low (with buffer of 0.5-1.0 ATR for volatility). Initial target can be the previous swing high, or use the probability cone projection as a guide (though not a guarantee). Monitor the dashboard STATE—if it flips to "◆ BEAR SINGULARITY" or Hurst drops significantly, consider exiting even if target not reached.
Bearish Singularity (▼ Red Triangle Above Bar):
This marker appears when the system detects a manifold catastrophe at a price high with bearish directional consensus. Same five-filter confirmation process as bullish signals. The red color indicates directional vote totaled -2 or lower (majority bearish).
Trading Approach: Consider short entry on the bar following the signal. Place stop loss above the singularity bar's high (with buffer). Target previous swing low or use cone projection as reference. Exit if opposite signal fires or Hurst deteriorates.
Neutral Signal (● Orange Circle at Price Level):
This marker indicates the catastrophe detection system identified a topology break that passed catastrophe threshold and regime filters, but the directional voting system produced a mixed result (vote between -1 and +1). This means the four directional components (pivot, trend, flow, mid-band) are not in agreement about which way the reversal should resolve.
Trading Approach: Skip these signals. Neutral markers are displayed for analytical completeness but should not be traded. They represent geometric catastrophes without clear directional resolution—essentially, the manifold is breaking but the direction of the break is ambiguous. Trading neutral signals dramatically increases false signal rate. Only trade green (bullish) or red (bearish) singularities.
Visual Confirmation Using Spectral Layers
The three colored ribbons (spectral decomposition layers) provide entropy visualization that helps confirm signal quality:
Divergent Layers (High Entropy State):
When the three frequency bands (fast 8-period, medium 21-period, slow 55-period) are separated with significant gaps between them, the manifold is in high entropy state—different frequency components of price movement are pulling in different directions. This geometric tension precedes catastrophes. Strong signals often occur when layers are divergent before the signal, then begin reconverging immediately after.
Convergent Layers (Low Entropy State):
When all three ribbons are tightly clustered or overlapping, the manifold is in equilibrium—all frequency components agree. This stable geometry makes catastrophe detection more reliable because topology breaks clearly stand out against the baseline stability. If you see layers converge, then a singularity fires, then layers diverge, this pattern suggests a genuine regime transition.
Signal Quality Assessment:
High-quality singularity signals should show:
Divergent layers (high entropy) in the 5-10 bars before signal
Singularity bar occurs when price has extended outside at least one of the spectral bands (shows pivot extended beyond equilibrium)
Close of singularity bar re-enters the spectral band zone (shows mean reversion starting)
Layers begin reconverging in 3-5 bars after signal (shows new equilibrium forming)
This pattern visually confirms the geometric narrative: manifold became unstable (divergence), reached critical distortion (extended outside equilibrium), broke catastrophically (singularity), and is now stabilizing in new direction (reconvergence).
Using Energy Fields for Trade Management
The concentric glowing boxes around each singularity visualize the topology distortion
magnitude:
Wide Energy Fields (5+ Layers Visible):
Large radiance indicates strong catastrophe with high manifold curvature. These represent significant topology breaks and typically precede larger price moves. Wide fields justify wider profit targets and longer hold times. The outer edge of the largest box can serve as a dynamic support/resistance zone—price often respects these geometric boundaries.
Narrow Energy Fields (2-3 Layers):
Smaller radiance indicates moderate catastrophe. While still valid signals (all filters passed), expect smaller follow-through. Use tighter profit targets and be prepared for quicker exit if momentum doesn't develop. These are valid but lower-conviction trades.
Field Interaction Zones:
When energy fields from consecutive signals overlap or touch, this indicates a prolonged topology distortion region—often corresponds to consolidation zones or complex reversal patterns (head-and-shoulders, double tops/bottoms). Be more cautious in these areas as the manifold is undergoing extended restructuring rather than a clean catastrophe.
Probability Cone Projections
The dashed cone extending forward from each singularity is a mathematical projection, not a
price target:
Cone Direction:
The center line direction (upward for bullish, downward for bearish, flat for neutral) shows the expected trajectory based on current manifold gradient and singularity direction. This is where the topology suggests price "should" go if the catastrophe completes normally.
Cone Width:
The uncertainty band (upper and lower dashed boundaries) represents the range of outcomes given current volatility (ATR-based). Wider cones indicate higher uncertainty—expect more price volatility even if direction is correct. Narrower cones suggest more constrained movement.
Price-Cone Interaction:
Price following near the center line = catastrophe resolving as expected, geometric projection accurate
Price breaking above upper cone = stronger-than-expected reversal, consider holding for larger targets
Price breaking below lower cone (for bullish signal) = catastrophe failing, manifold may be re-folding in opposite direction, consider exit
Price oscillating within cone = normal reversal process, hold position
The 10-bar projection length means cones show expected behavior over the next ~10 bars. Don't confuse this with longer-term price targets.
Gradient Flow Field Interpretation
The directional arrows (↗, ↘, →) scattered across the chart show the manifold's Y-gradient (vertical acceleration dimension):
Upward Arrows (↗):
Positive Y-gradient indicates the momentum acceleration dimension is pushing upward—the manifold topology has upward "slope" at this location. Clusters of upward arrows suggest bullish topological pressure building. These often appear before bullish singularities fire.
Downward Arrows (↘):
Negative Y-gradient indicates downward topological pressure. Clusters precede bearish singularities.
Horizontal Arrows (→):
Neutral gradient indicates balanced topology with no strong directional pressure.
Using Flow Field:
The arrows provide real-time topology state information even between singularity signals. If you're in a long position from a bullish singularity and begin seeing increasing downward arrows appearing, this suggests manifold gradient is shifting—consider tightening stops. Conversely, if arrows remain upward or neutral, topology supports continuation.
Don't confuse arrow direction with immediate price direction—arrows show geometric slope, not price prediction. They're confirmatory context, not entry signals themselves.
Parameter Optimization for Your Trading Style
For Scalping / Fast Trading (1m-15m charts):
Embedding Depth: 13-15 bars (faster topology reconstruction)
Hurst Window: 3 bars (responsive fractal detection)
Singularity Threshold: 2.0-2.3σ (more sensitive)
Topology Confirmation: 0.55-0.60 (lower barrier)
Min Swing Size: 0.8-1.2 ATR (accepts smaller moves)
Pivot Lookback: 3-4 bars (quick pivot detection)
This configuration increases signal frequency for active trading but requires diligent monitoring as false signal rate increases. Use tighter stops.
For Day Trading / Standard Approach (15m-4H charts):
Keep default settings (21 embed, 5 Hurst, 2.5σ, 0.65 confirmation, 1.5 ATR, 5 pivot)
These are balanced for quality over quantity
Best win rate and risk/reward ratio
Recommended for most traders
For Swing Trading / Position Trading (4H-Daily charts):
Embedding Depth: 34-55 bars (stable long-term topology)
Hurst Window: 8-10 bars (smooth fractal measurement)
Singularity Threshold: 3.0-3.5σ (only extreme catastrophes)
Topology Confirmation: 0.75-0.85 (high conviction only)
Min Swing Size: 2.5-4.0 ATR (major moves only)
Pivot Lookback: 8-13 bars (confirmed swings)
This configuration produces infrequent but highly reliable signals suitable for position sizing and longer hold times.
Volatility Adaptation:
In extremely volatile instruments (crypto, penny stocks), increase Min Volatility Expansion to 0.6-0.8 to avoid over-signaling during "always volatile" conditions. In stable instruments (major forex pairs, blue-chip stocks), decrease to 0.3 to allow signals during moderate volatility spikes.
Trend vs Range Preference:
If you prefer trading only strong trends, increase Min Trend Strength to 0.5-0.6 (ADX > 50-60). If you're comfortable with volatility-based trading in weaker trends, decrease to 0.2 (ADX > 20). The default 0.3 balances both approaches.
Complete Trading Workflow Example
Step 1 - Pre-Session Setup:
Load chart with MSE indicator. Check dashboard position is visible. Verify regime filter is enabled. Review recent signals to gauge current instrument behavior.
Step 2 - Market Assessment:
Observe dashboard Hurst reading. If H < 0.45 (mean-reverting), consider skipping this session or using other strategies. If H > 0.50, proceed. Check regime shows TREND, VOLATILE, or EXPLOSIVE with checkmark—if CHOP, wait for regime shift alert.
Step 3 - Signal Wait:
Monitor catastrophe score (Σ). Watch for it climbing above 60%. Observe spectral layers—look for divergence building. If you see curvature (κ) rising above 1.0 and complexity (⟳) increasing, catastrophe is building. Do not anticipate—wait for the actual signal marker.
Step 4 - Signal Recognition:
▲ Bullish or ▼ Bearish triangle appears at a bar. Dashboard STATE changes to "◆ BULL/BEAR SINGULARITY". Energy field appears around the signal bar. Check signal quality:
Was Σ > 70% at signal? (Higher quality)
Are energy fields wide? (Stronger catastrophe)
Did layers diverge before and reconverge after? (Clean break)
Is Hurst still > 0.55? (Good regime)
Step 5 - Entry Decision:
If signal is green/red (not orange neutral), all confirmations look strong, and no immediate contradicting factors appear, prepare entry on next bar open. Wait for confirmation bar to form—ideally it should close in the signal direction (bullish signal → bar closes higher, bearish signal → bar closes lower).
Step 6 - Position Entry:
Enter at open or shortly after open of bar following signal bar. Set stop loss: for bullish signals, place stop at singularity_bar_low - (0.75 × ATR); for bearish signals, place stop at singularity_bar_high + (0.75 × ATR). The buffer accommodates volatility while protecting against catastrophe failure.
Step 7 - Trade Management:
Monitor dashboard continuously:
If Hurst drops below 0.45, consider reducing position
If opposite singularity fires, exit immediately (manifold has re-folded)
If catastrophe score drops below 40% and stays there, topology has stabilized—consider partial profit taking
Watch gradient flow arrows—if they shift to opposite direction persistently, tighten stops
Step 8 - Profit Taking:
Use probability cone as a guide—if price reaches outer cone boundary, consider taking partial profits. If price follows center line cleanly, hold for larger target. Traditional technical targets work well: previous swing high/low, round numbers, Fibonacci extensions. Don't expect precision—manifold projections give direction and magnitude estimates, not exact prices.
Step 9 - Exit:
Exit on: (a) opposite signal appears, (b) dashboard shows regime became invalid (checkmark changes to X), (c) technical target reached, (d) Hurst deteriorates significantly, (e) stop loss hit, or (f) time-based exit if using session limits. Never hold through opposite singularity signals—the manifold has broken in the other direction and your trade thesis is invalidated.
Step 10 - Post-Trade Review:
After exit, review: Did the probability cone projection align with actual price movement? Were the energy fields proportional to move size? Did spectral layers show expected reconvergence? Use these observations to calibrate your interpretation of signal quality over time.
Best Performance Conditions
This topology-based approach performs optimally in specific market environments:
Favorable Conditions:
Well-Developed Swing Structure: Markets with clear rhythm of advances and declines where pivots form at regular intervals. The manifold reconstruction depends on swing formation, so instruments that trend in clear waves work best. Stocks, major forex pairs during active sessions, and established crypto assets typically exhibit this characteristic.
Sufficient Volatility for Topology Development: The embedding process requires meaningful price movement to construct multi-dimensional coordinates. Extremely quiet markets (tight consolidations, holiday trading, after-hours) lack the volatility needed for manifold differentiation. Look for ATR expansion above average—when volatility is present, geometry becomes meaningful.
Trending with Periodic Reversals: The ideal environment is not pure trend (which rarely reverses) nor pure range (which reverses constantly at small scale), but rather trending behavior punctuated by occasional significant counter-trend reversals. This creates the catastrophe conditions the system is designed to detect: manifold building directional momentum, then undergoing sharp topology break at extremes.
Liquid Instruments Where EMAs Reflect True Flow: The spectral layers and frequency decomposition require that moving averages genuinely represent market consensus. Thinly traded instruments with sporadic orders don't create smooth manifold topology. Prefer instruments with consistent volume where EMA calculations reflect actual capital flow rather than random tick sequences.
Challenging Conditions:
Extremely Choppy / Whipsaw Markets: When price oscillates rapidly with no directional persistence (Hurst < 0.40), the manifold undergoes constant micro-catastrophes that don't translate to tradable reversals. The regime filter helps avoid these, but awareness is important. If you see multiple neutral signals clustering with no follow-through, market is too chaotic for this approach.
Very Low Volatility Consolidation: Tight ranges with ATR below average cause the embedding coordinates to compress into a small region of phase space, reducing geometric differentiation. The manifold becomes nearly flat, and catastrophe detection loses sensitivity. The regime filter's volatility component addresses this, but manually avoiding dead markets improves results.
Gap-Heavy Instruments: Stocks that gap frequently (opening outside previous close) create discontinuities in the manifold trajectory. The embedding process assumes continuous evolution, so gaps introduce artifacts. Most gaps don't invalidate the approach, but instruments with daily gaps >2% regularly may show degraded performance. Consider using higher timeframes (4H, Daily) where gaps are less proportionally significant.
Parabolic Moves / Blowoff Tops: When price enters an exponential acceleration phase (vertical rally or crash), the manifold evolves too rapidly for the standard embedding window to track. Catastrophe detection may lag or produce false signals mid-move. These conditions are rare but identifiable by Hurst > 0.75 combined with ATR expansion >2.0× average. If detected, consider sitting out or using very tight stops as geometry is in extreme distortion.
The system adapts by reducing signal frequency in poor conditions—if you notice long periods with no signals, the topology likely lacks the geometric structure needed for reliable catastrophe detection. This is a feature, not a bug: it prevents forced trading during unfavorable environments.
Theoretical Justification for Approach
Why Manifold Embedding?
Traditional technical analysis treats price as a one-dimensional time series: current price is predicted from past prices in sequential order. This approach ignores the structure of price dynamics—the relationships between velocity, acceleration, and participation that govern how price actually evolves.
Dynamical systems theory (from physics and mathematics) provides an alternative framework: treat price as a state variable in a multi-dimensional phase space. In this view, each market condition corresponds to a point in N-dimensional space, and market evolution is a trajectory through this space. The geometry of this space (its topology) constrains what trajectories are possible.
Manifold embedding reconstructs this hidden geometric structure from observable price data. By creating coordinates from velocity, momentum acceleration, and volume-weighted returns, we map price evolution onto a 3D surface. This surface—the manifold—reveals geometric relationships that aren't visible in price charts alone.
The mathematical theorem underlying this approach (Takens' Embedding Theorem from dynamical systems theory) proves that for deterministic or weakly stochastic systems, a state space reconstruction from time-delayed observations of a single variable captures the essential dynamics of the full system. We apply this principle: even though we only observe price, the embedded coordinates (derivatives of price) reconstruct the underlying dynamical structure.
Why Catastrophe Theory?
Catastrophe theory, developed by mathematician René Thom (Fields Medal 1958), describes how continuous systems can undergo sudden discontinuous changes when control parameters reach critical values. A classic example: gradually increasing force on a beam causes smooth bending, then sudden catastrophic buckling. The beam's geometry reaches a critical curvature where topology must break.
Markets exhibit analogous behavior: gradual price changes build tension in the manifold topology until critical distortion is reached, then abrupt directional change occurs (reversal). Catastrophes aren't random—they're mathematically necessary when geometric constraints are violated.
The indicator detects these geometric precursors: high curvature (manifold bending sharply), high complexity (topology oscillating chaotically), high condition number (coordinate mapping becoming singular). These metrics quantify how close the manifold is to a catastrophic fold. When all simultaneously reach extreme values, topology break is imminent.
This provides a logical foundation for reversal detection that doesn't rely on pattern recognition or historical correlation. We're measuring geometric properties that mathematically must change when systems reach critical states. This is why the approach works across different instruments and timeframes—the underlying geometry is universal.
Why Hurst Exponent?
Markets exhibit fractal behavior: patterns at different time scales show statistical self-similarity. The Hurst exponent quantifies this fractal structure by measuring long-range dependence in returns.
Critically for trading, Hurst determines whether recent price movement predicts future direction (H > 0.5) or predicts the opposite (H < 0.5). This is regime detection: trending vs mean-reverting behavior.
The same manifold catastrophe has different trading implications depending on regime. In trending regime (high Hurst), catastrophes represent significant reversal opportunities because the manifold has been building directional momentum that suddenly breaks. In mean-reverting regime (low Hurst), catastrophes represent minor oscillations because the manifold constantly folds at small scales.
By weighting catastrophe signals based on Hurst, the system adapts detection sensitivity to the current fractal regime. This is a form of meta-analysis: not just detecting geometric breaks, but evaluating whether those breaks are meaningful in the current fractal context.
Why Multi-Layer Confirmation?
Geometric anomalies occur frequently in noisy market data. Not every high-curvature point represents a tradable reversal—many are artifacts of microstructure noise, order flow imbalances, or low-liquidity ticks.
The five-filter confirmation system (catastrophe threshold, pivot structure, swing size, volume, regime) addresses this by requiring geometric anomalies to align with observable market evidence. This conjunction-based logic implements the principle: extraordinary claims require extraordinary evidence .
A manifold catastrophe (extraordinary geometric event) alone is not sufficient. We additionally require: price formed a pivot (visible structure), swing was significant (adequate magnitude), volume confirmed participation (capital backed the move), and regime was favorable (trending or volatile, not chopping). Only when all five dimensions agree do we have sufficient evidence that the geometric anomaly represents a genuine reversal opportunity rather than noise.
This multi-dimensional approach is analogous to medical diagnosis: no single test is conclusive, but when multiple independent tests all suggest the same condition, confidence increases dramatically. Each filter removes a different category of false signals, and their combination creates a robust detection system.
The result is a signal set with dramatically improved reliability compared to any single metric alone. This is the power of ensemble methods applied to geometric analysis.
Important Disclaimers
This indicator applies mathematical topology and catastrophe theory to multi-dimensional price space reconstruction. It identifies geometric conditions where manifold curvature, topological complexity, and coordinate singularities suggest potential reversal zones based on phase space analysis. It should not be used as a standalone trading system.
The embedding coordinates, catastrophe scores, and Hurst calculations are deterministic mathematical formulas applied to historical price data. These measurements describe current and recent geometric relationships in the reconstructed manifold but do not predict future price movements. Past geometric patterns and singularity markers do not guarantee future market behavior will follow similar topology evolution.
The manifold reconstruction assumes certain mathematical properties (sufficient embedding dimension, quasi-stationarity, continuous dynamics) that may not hold in all market conditions. Gaps, flash crashes, circuit breakers, news events, and other discontinuities can violate these assumptions. The system attempts to filter problematic conditions through regime classification, but cannot eliminate all edge cases.
The spectral decomposition, energy fields, and probability cones are visualization aids that represent mathematical constructs, not price predictions. The probability cone projects current gradient forward assuming topology continues current trajectory—this is a mathematical "if-then" statement, not a forecast. Market topology can and does change unexpectedly.
All trading involves substantial risk. The singularity markers represent analytical conditions where geometric mathematics align with threshold criteria, not certainty of directional change. Use appropriate risk management for every trade: position sizing based on account risk tolerance (typically 1-2% maximum risk per trade), stop losses placed beyond recent structure plus volatility buffer, and never risk capital needed for living expenses.
The confirmation filters (pivot, swing size, volume, regime) are designed to reduce false signals but cannot eliminate them entirely. Markets can produce geometric anomalies that pass all filters yet fail to develop into sustained reversals. This is inherent to probabilistic systems operating on noisy real-world data.
No indicator can guarantee profitable trades or eliminate losses. The catastrophe detection provides an analytical framework for identifying potential reversal conditions, but actual trading outcomes depend on numerous factors including execution, slippage, spreads, position sizing, risk management, psychological discipline, and market conditions that may change after signal generation.
Use this tool as one component of a comprehensive trading plan that includes multiple forms of analysis, proper risk management, emotional discipline, and realistic expectations about win rates and drawdowns. Combine catastrophe signals with additional confirmation methods such as support/resistance analysis, volume patterns, multi-timeframe alignment, and broader market context.
The spacing filter, cooldown mechanism, and regime validation are designed to reduce noise and over-signaling, but market conditions can change rapidly and render any analytical signal invalid. Always use stop losses and never risk capital you cannot afford to lose. Past performance of detection accuracy does not guarantee future results.
Technical Implementation Notes
All calculations execute on closed bars only—signals and metric values do not repaint after bar close. The indicator does not use any lookahead bias in its calculations. However, the pivot detection mechanism (ta.pivothigh and ta.pivotlow) inherently identifies pivots with a lag equal to the lookback parameter, meaning the actual pivot occurred at bar but is recognized at bar . This is standard behavior for pivot functions and is not repainting—once recognized, the pivot bar never changes.
The normalization system (z-score transformation over rolling windows) requires approximately 30-50 bars of historical data to establish stable statistics. Values in the first 30-50 bars after adding the indicator may show instability as the rolling means and standard deviations converge. Allow adequate warmup period before relying on signals.
The spectral layer arrays, energy field boxes, gradient flow labels, and node geometry lines are subject to TradingView drawing object limits (500 lines, 500 boxes, 500 labels per indicator as specified in settings). The system implements automatic cleanup by deleting oldest objects when limits approach, but on very long charts with many signals, some historical visual elements may be removed to stay within limits. This does not affect signal generation or dashboard metrics—only historical visual artifacts.
Dashboard and visual rendering update only on the last bar to minimize computational overhead. The catastrophe detection logic executes on every bar, but table cells and drawing objects refresh conditionally to optimize performance. If experiencing chart lag, reduce visual complexity: disable spectral layers, energy fields, or flow field to improve rendering speed. Core signal detection continues to function with all visual elements disabled.
The Hurst calculation uses logarithmic returns rather than raw price to ensure stationarity, and implements clipping to range to handle edge cases where R/S analysis produces invalid values (which can occur during extended periods of identical prices or numerical overflow). The 5-period EMA smoothing reduces noise while maintaining responsiveness to regime transitions.
The condition number calculation adds epsilon (1e-10) to denominators to prevent division by zero when Jacobian determinant approaches zero—which is precisely the singularity condition we're detecting. This numerical stability measure ensures the indicator doesn't crash when detecting the very phenomena it's designed to identify.
The indicator has been tested across multiple timeframes (5-minute through daily) and multiple asset classes (forex majors, stock indices, individual equities, cryptocurrencies, commodities, futures). It functions identically across all instruments due to the adaptive normalization approach and percentage-based metrics. No instrument-specific code or parameter sets are required.
The color scheme system implements seven preset themes plus custom mode. Color assignments are applied globally and affect all visual elements simultaneously. The opacity calculation system multiplies component-specific transparency with master opacity to create hierarchical control—adjusting master opacity affects all visuals proportionally while maintaining their relative transparency relationships.
All alert conditions trigger only on bar close to prevent false alerts from intrabar fluctuations. The regime transition alerts (VALID/INVALID) are particularly useful for knowing when trading edge appears or disappears, allowing traders to adjust activity levels accordingly.
— Dskyz, Trade with insight. Trade with anticipation.
Martingale with MACD+KDJ opening conditionsStrategy Overview:
This strategy is based on a Martingale trading approach, incorporating MACD and KDJ indicators. It features pyramiding, trailing stops, and dynamic profit-taking mechanisms, suitable for both long and short trades. The strategy increases position size progressively using a Multiplier, a key feature of Martingale systems.
Key Concepts:
Martingale Strategy: A trading system where positions are doubled or increased after a loss to recover previous losses with a single successful trade. In this script, the position size is incremented using a Multiplier for each addition.
Pyramiding: Allows adding to existing trades when market conditions are favorable, enhancing profitability during trends.
Settings:
Basic Inputs:
Initial Order: Defines the starting size of the position.
Default: 150.0
MACD Settings: Customize the fast, slow, and signal smoothing lengths.
Default: Fast Length: 9, Slow Length: 26, Signal Smoothing: 9
KDJ Settings: Customize the length and smoothing parameters for KDJ.
Default: Length: 14, Smooth K: 3, Smooth D: 3
Max Additions: Sets the number of additional positions (pyramiding).
Default: 5 (Min: 1, Max: 10)
Position Sizing: Percent to add to positions on favorable conditions.
Default: 1.0%
Martingale Multiplier:
Add Multiplier: This value controls the scaling of additional positions according to the Martingale principle. After each loss, a new position is added, and its size is increased by the Multiplier factor. For example, with a multiplier of 2, each new addition will be twice as large as the previous one, accelerating recovery if the price moves favorably.
Default: 1.0 (no multiplication)
Can be adjusted up to 10x to aggressively increase position size after losses.
Trade Execution:
Long Trades:
Entry Condition: A long position is opened when the MACD line crosses over the signal line, and the KDJ’s %K crosses above %D.
Additions (Martingale): After the initial long position, new positions are added if the price drops by the defined percentage, and each new addition is increased using the Multiplier. This continues up to the set Max Additions.
Short Trades:
Entry Condition: A short position is opened when the MACD line crosses under the signal line, and the KDJ’s %K crosses below %D.
Additions (Martingale): After the initial short position, new positions are added if the price rises by the defined percentage, and each new addition is increased using the Multiplier.
Exit Conditions:
Take Profit: Exits are triggered when the price reaches the take-profit threshold.
Stop Loss: If the price moves unfavorably, the position will be closed at the set stop-loss level.
Trailing Stop: Adjusts dynamically as the price moves in favor of the trade to lock in profits.
On-Chart Visuals:
Long Signals: Blue triangles below the bars indicate long entries, and green triangles mark additional long positions.
Short Signals: Red triangles above the bars indicate short entries, and orange triangles mark additional short positions.
Information Table:
The strategy displays a table with key metrics:
Open Price: The entry price of the trade.
Average Price: The average price of the current position.
Additions: The number of additional positions taken.
Next Add Price: The price level for the next position.
Take Profit: The price at which profits will be taken.
Stop Loss: The stop-loss level to minimize risk.
Usage Instructions:
Adjust the parameters to your trading style using the input settings.
The Multiplier amplifies your position size after each addition, so use it cautiously, especially in volatile markets.
Monitor the signals and table on the chart for entry/exit decisions and trade management.
Larry Connors %b Strategy (Bollinger Band)Larry Connors’ %b Strategy is a mean-reversion trading approach that uses Bollinger Bands to identify buy and sell signals based on the %b indicator. This strategy was developed by Larry Connors, a renowned trader and author known for his systematic, data-driven trading methods, particularly those focusing on short-term mean reversion.
The %b indicator measures the position of the current price relative to the Bollinger Bands, which are volatility bands placed above and below a moving average. The strategy specifically targets times when prices are oversold within a long-term uptrend and aims to capture rebounds by buying at relatively low points and selling at relatively high points.
Strategy Rules
The basic rules of the %b Strategy are:
1. Trend Confirmation: The closing price must be above the 200-day moving average. This filter ensures that trades are made in alignment with a longer-term uptrend, thereby avoiding trades against the primary market trend.
2. Oversold Conditions: The %b indicator must be below 0.2 for three consecutive days. The %b value below 0.2 indicates that the price is near the lower Bollinger Band, suggesting an oversold condition.
3. Entry Signal: Enter a long position at the close when conditions 1 and 2 are met.
4. Exit Signal: Exit the position when the %b value closes above 0.8, signaling an overbought condition where the price is near the upper Bollinger Band.
How the Strategy Works
This strategy operates on the premise of mean reversion, which suggests that extreme price movements will revert to the mean over time. By entering positions when the %b value indicates an oversold condition (below 0.2) in a confirmed uptrend, the strategy attempts to capture short-term price rebounds. The exit rule (when %b is above 0.8) aims to lock in profits once the price reaches an overbought condition, often near the upper Bollinger Band.
Who Was Larry Connors?
Larry Connors is a well-known figure in the world of financial markets and trading. He co-authored several influential trading books, including “Short-Term Trading Strategies That Work” and “High Probability ETF Trading.” Connors is recognized for his quantitative approach, focusing on systematic, rules-based strategies that leverage historical data to validate trading edges.
His work primarily revolves around short-term trading strategies, often using technical indicators like RSI (Relative Strength Index), Bollinger Bands, and moving averages. Connors’ methodologies have been widely adopted by traders seeking structured approaches to exploit short-term inefficiencies in the market.
Risks of the Strategy
While the %b Strategy can be effective, particularly in mean-reverting markets, it is not without risks:
1. Mean Reversion Assumption: The strategy is based on the assumption that prices will revert to the mean. In trending or sharply falling markets, this reversion may not occur, leading to sustained losses.
2. False Signals in Choppy Markets: In volatile or sideways markets, the strategy may generate multiple false signals, resulting in whipsaw trades that can erode capital through frequent small losses.
3. No Stop Loss: The basic implementation of the strategy does not include a stop loss, which increases the risk of holding losing trades longer than intended, especially if the market continues to move against the position.
4. Performance During Market Crashes: During major market downturns, the strategy’s buy signals could be triggered frequently as prices decline, compounding losses without the presence of a risk management mechanism.
Scientific References and Theoretical Basis
The %b Strategy relies on the concept of mean reversion, which has been extensively studied in finance literature. Studies by Avellaneda and Lee (2010) and Bouchaud et al. (2018) have demonstrated that mean-reverting strategies can be profitable in specific market environments, particularly when combined with volatility filters like Bollinger Bands. However, the same studies caution that such strategies are highly sensitive to market conditions and often perform poorly during periods of prolonged trends.
Bollinger Bands themselves were popularized by John Bollinger and are widely used to assess price volatility and detect potential overbought and oversold conditions. The %b value is a critical part of this analysis, as it standardizes the position of price relative to the bands, making it easier to compare conditions across different securities and time frames.
Conclusion
Larry Connors’ %b Strategy is a well-known mean-reversion technique that leverages Bollinger Bands to identify buying opportunities in uptrending markets when prices are temporarily oversold. While the strategy can be effective under the right conditions, traders should be aware of its limitations and risks, particularly in trending or highly volatile markets. Incorporating risk management techniques, such as stop losses, could help mitigate some of these risks, making the strategy more robust against adverse market conditions.
