Trend Trader-RemasteredThe script was originally coded in 2018 with Pine Script version 3, and it was in invite only status. It has been updated and optimised for Pine Script v5 and made completely open source.
Overview
The Trend Trader-Remastered is a refined and highly sophisticated implementation of the Parabolic SAR designed to create strategic buy and sell entry signals, alongside precision take profit and re-entry signals based on marked Bill Williams (BW) fractals. Built with a deep emphasis on clarity and accuracy, this indicator ensures that only relevant and meaningful signals are generated, eliminating any unnecessary entries or exits.
Key Features
1) Parabolic SAR-Based Entry Signals:
This indicator leverages an advanced implementation of the Parabolic SAR to create clear buy and sell position entry signals.
The Parabolic SAR detects potential trend shifts, helping traders make timely entries in trending markets.
These entries are strategically aligned to maximise trend-following opportunities and minimise whipsaw trades, providing an effective approach for trend traders.
2) Take Profit and Re-Entry Signals with BW Fractals:
The indicator goes beyond simple entry and exit signals by integrating BW Fractal-based take profit and re-entry signals.
Relevant Signal Generation: The indicator maintains strict criteria for signal relevance, ensuring that a re-entry signal is only generated if there has been a preceding take profit signal in the respective position. This prevents any misleading or premature re-entry signals.
Progressive Take Profit Signals: The script generates multiple take profit signals sequentially in alignment with prior take profit levels. For instance, in a buy position initiated at a price of 100, the first take profit might occur at 110. Any subsequent take profit signals will then occur at prices greater than 110, ensuring they are "in favour" of the original position's trajectory and previous take profits.
3) Consistent Trend-Following Structure:
This design allows the Trend Trader-Remastered to continue signaling take profit opportunities as the trend advances. The indicator only generates take profit signals in alignment with previous ones, supporting a systematic and profit-maximising strategy.
This structure helps traders maintain positions effectively, securing incremental profits as the trend progresses.
4) Customisability and Usability:
Adjustable Parameters: Users can configure key settings, including sensitivity to the Parabolic SAR and fractal identification. This allows flexibility to fine-tune the indicator according to different market conditions or trading styles.
User-Friendly Alerts: The indicator provides clear visual signals on the chart, along with optional alerts to notify traders of new buy, sell, take profit, or re-entry opportunities in real-time.
Cerca negli script per "fractals"
Ostinated Adjustable Fractal with AlertThis indicator (Ostinated Adjustable Fractal with Alert) is like the traditional William Fractals. However, it created to overcome two disadvantages of the William Fractals:
1. Set minimum value to 1. This a big advantage over traditional William Fractal that has a minimum value of 2.
2. Alerts: setting alert with the traditional William Fractal is very difficult. We have created this indicator to simplify setting fractal alerts.
Like and comment.
Enjoy!
Bullish Reversal Bar Strategy [Skyrexio]Overview
Bullish Reversal Bar Strategy leverages the combination of candlestick pattern Bullish Reversal Bar (description in Methodology and Justification of Methodology), Williams Alligator indicator and Williams Fractals to create the high probability setups. Candlestick pattern is used for the entering into trade, while the combination of Williams Alligator and Fractals is used for the trend approximation as close condition. Strategy uses only long trades.
Unique Features
No fixed stop-loss and take profit: Instead of fixed stop-loss level strategy utilizes technical condition obtained by Fractals and Alligator or the candlestick pattern invalidation to identify when current uptrend is likely to be over (more information in "Methodology" and "Justification of Methodology" paragraphs)
Configurable Trading Periods: Users can tailor the strategy to specific market windows, adapting to different market conditions.
Trend Trade Filter: strategy uses Alligator and Fractal combination as high probability trend filter.
Methodology
The strategy opens long trade when the following price met the conditions:
1.Current candle's high shall be below the Williams Alligator's lines (Jaw, Lips, Teeth)(all details in "Justification of Methodology" paragraph)
2.Price shall create the candlestick pattern "Bullish Reversal Bar". Optionally if MFI and AO filters are enabled current candle shall have the decreasing AO and at least one of three recent bars shall have the squat state on the MFI (all details in "Justification of Methodology" paragraph)
3.If price breaks through the high of the candle marked as the "Bullish Reversal Bar" the long trade is open at the price one tick above the candle's high
4.Initial stop loss is placed at the Bullish Reversal Bar's candle's low
5.If price hit the Bullish Reversal Bar's low before hitting the entry price potential trade is cancelled
6.If trade is active and initial stop loss has not been hit, trade is closed when the combination of Alligator and Williams Fractals shall consider current trend change from upward to downward.
Strategy settings
In the inputs window user can setup strategy setting:
Enable MFI (if true trades are filtered using Market Facilitation Index (MFI) condition all details in "Justification of Methodology" paragraph), by default = false)
Enable AO (if true trades are filtered using Awesome Oscillator (AO) condition all details in "Justification of Methodology" paragraph), by default = false)
Justification of Methodology
Let's explore the key concepts of this strategy and understand how they work together. The first and key concept is the Bullish Reversal Bar candlestick pattern. This is just the single bar pattern. The rules are simple:
Candle shall be closed in it's upper half
High of this candle shall be below all three Alligator's lines (Jaw, Lips, Teeth)
Next, let’s discuss the short-term trend filter, which combines the Williams Alligator and Williams Fractals. Williams Alligator
Developed by Bill Williams, the Alligator is a technical indicator that identifies trends and potential market reversals. It consists of three smoothed moving averages:
Jaw (Blue Line): The slowest of the three, based on a 13-period smoothed moving average shifted 8 bars ahead.
Teeth (Red Line): The medium-speed line, derived from an 8-period smoothed moving average shifted 5 bars forward.
Lips (Green Line): The fastest line, calculated using a 5-period smoothed moving average shifted 3 bars forward.
When the lines diverge and align in order, the "Alligator" is "awake," signaling a strong trend. When the lines overlap or intertwine, the "Alligator" is "asleep," indicating a range-bound or sideways market. This indicator helps traders determine when to enter or avoid trades.
Fractals, another tool by Bill Williams, help identify potential reversal points on a price chart. A fractal forms over at least five consecutive bars, with the middle bar showing either:
Up Fractal: Occurs when the middle bar has a higher high than the two preceding and two following bars, suggesting a potential downward reversal.
Down Fractal: Happens when the middle bar shows a lower low than the surrounding two bars, hinting at a possible upward reversal.
Traders often use fractals alongside other indicators to confirm trends or reversals, enhancing decision-making accuracy.
How do these tools work together in this strategy? Let’s consider an example of an uptrend.
When the price breaks above an up fractal, it signals a potential bullish trend. This occurs because the up fractal represents a shift in market behavior, where a temporary high was formed due to selling pressure. If the price revisits this level and breaks through, it suggests the market sentiment has turned bullish.
The breakout must occur above the Alligator’s teeth line to confirm the trend. A breakout below the teeth is considered invalid, and the downtrend might still persist. Conversely, in a downtrend, the same logic applies with down fractals.
How we can use all these indicators in this strategy? This strategy is a counter trend one. Candle's high shall be below all Alligator's lines. During this market stage the bullish reversal bar candlestick pattern shall be printed. This bar during the downtrend is a high probability setup for the potential reversal to the upside: bulls were able to close the price in the upper half of a candle. The breaking of its high is a high probability signal that trend change is confirmed and script opens long trade. If market continues going down and break down the bullish reversal bar's low potential trend change has been invalidated and strategy close long trade.
If market really reversed and started moving to the upside strategy waits for the trend change form the downtrend to the uptrend according to approximation of Alligator and Fractals combination. If this change happens strategy close the trade. This approach helps to stay in the long trade while the uptrend continuation is likely and close it if there is a high probability of the uptrend finish.
Optionally users can enable MFI and AO filters. First of all, let's briefly explain what are these two indicators. The Awesome Oscillator (AO), created by Bill Williams, is a momentum-based indicator that evaluates market momentum by comparing recent price activity to a broader historical context. It assists traders in identifying potential trend reversals and gauging trend strength.
AO = SMA5(Median Price) − SMA34(Median Price)
where:
Median Price = (High + Low) / 2
SMA5 = 5-period Simple Moving Average of the Median Price
SMA 34 = 34-period Simple Moving Average of the Median Price
This indicator is filtering signals in the following way: if current AO bar is decreasing this candle can be interpreted as a bullish reversal bar. This logic is applicable because initially this strategy is a trend reversal, it is searching for the high probability setup against the current trend. Decreasing AO is the additional high probability filter of a downtrend.
Let's briefly look what is MFI. The Market Facilitation Index (MFI) is a technical indicator that measures the price movement per unit of volume, helping traders gauge the efficiency of price movement in relation to trading volume. Here's how you can calculate it:
MFI = (High−Low)/Volume
MFI can be used in combination with volume, so we can divide 4 states. Bill Williams introduced these to help traders interpret the interaction between volume and price movement. Here’s a quick summary:
Green Window (Increased MFI & Increased Volume): Indicates strong momentum with both price and volume increasing. Often a sign of trend continuation, as both buying and selling interest are rising.
Fake Window (Increased MFI & Decreased Volume): Shows that price is moving but with lower volume, suggesting weak support for the trend. This can signal a potential end of the current trend.
Squat Window (Decreased MFI & Increased Volume): Shows high volume but little price movement, indicating a tug-of-war between buyers and sellers. This often precedes a breakout as the pressure builds.
Fade Window (Decreased MFI & Decreased Volume): Indicates a lack of interest from both buyers and sellers, leading to lower momentum. This typically happens in range-bound markets and may signal consolidation before a new move.
For our purposes we are interested in squat bars. This is the sign that volume cannot move the price easily. This type of bar increases the probability of trend reversal. In this indicator we added to enable the MFI filter of reversal bars. If potential reversal bar or two preceding bars have squat state this bar can be interpret as a reversal one.
Backtest Results
Operating window: Date range of backtests is 2023.01.01 - 2024.12.31. It is chosen to let the strategy to close all opened positions.
Commission and Slippage: Includes a standard Binance commission of 0.1% and accounts for possible slippage over 5 ticks.
Initial capital: 10000 USDT
Percent of capital used in every trade: 50%
Maximum Single Position Loss: -5.29%
Maximum Single Profit: +29.99%
Net Profit: +5472.66 USDT (+54.73%)
Total Trades: 103 (33.98% win rate)
Profit Factor: 1.634
Maximum Accumulated Loss: 1231.15 USDT (-8.32%)
Average Profit per Trade: 53.13 USDT (+0.94%)
Average Trade Duration: 76 hours
How to Use
Add the script to favorites for easy access.
Apply to the desired timeframe and chart (optimal performance observed on 4h ETH/USDT).
Configure settings using the dropdown choice list in the built-in menu.
Set up alerts to automate strategy positions through web hook with the text: {{strategy.order.alert_message}}
Disclaimer:
Educational and informational tool reflecting Skyrex commitment to informed trading. Past performance does not guarantee future results. Test strategies in a simulated environment before live implementation
These results are obtained with realistic parameters representing trading conditions observed at major exchanges such as Binance and with realistic trading portfolio usage parameters.
MultiLayer Awesome Oscillator Saucer Strategy [Skyrexio]Overview
MultiLayer Awesome Oscillator Saucer Strategy leverages the combination of Awesome Oscillator (AO), Williams Alligator, Williams Fractals and Exponential Moving Average (EMA) to obtain the high probability long setups. Moreover, strategy uses multi trades system, adding funds to long position if it considered that current trend has likely became stronger. Awesome Oscillator is used for creating signals, while Alligator and Fractal are used in conjunction as an approximation of short-term trend to filter them. At the same time EMA (default EMA's period = 100) is used as high probability long-term trend filter to open long trades only if it considers current price action as an uptrend. More information in "Methodology" and "Justification of Methodology" paragraphs. The strategy opens only long trades.
Unique Features
No fixed stop-loss and take profit: Instead of fixed stop-loss level strategy utilizes technical condition obtained by Fractals and Alligator to identify when current uptrend is likely to be over (more information in "Methodology" and "Justification of Methodology" paragraphs)
Configurable Trading Periods: Users can tailor the strategy to specific market windows, adapting to different market conditions.
Multilayer trades opening system: strategy uses only 10% of capital in every trade and open up to 5 trades at the same time if script consider current trend as strong one.
Short and long term trend trade filters: strategy uses EMA as high probability long-term trend filter and Alligator and Fractal combination as a short-term one.
Methodology
The strategy opens long trade when the following price met the conditions:
1. Price closed above EMA (by default, period = 100). Crossover is not obligatory.
2. Combination of Alligator and Williams Fractals shall consider current trend as an upward (all details in "Justification of Methodology" paragraph)
3. Awesome Oscillator shall create the "Saucer" long signal (all details in "Justification of Methodology" paragraph). Buy stop order is placed one tick above the candle's high of last created "Saucer signal".
4. If price reaches the order price, long position is opened with 10% of capital.
5. If currently we have opened position and price creates and hit the order price of another one "Saucer" signal another one long position will be added to the previous with another one 10% of capital. Strategy allows to open up to 5 long trades simultaneously.
6. If combination of Alligator and Williams Fractals shall consider current trend has been changed from up to downtrend, all long trades will be closed, no matter how many trades has been opened.
Script also has additional visuals. If second long trade has been opened simultaneously the Alligator's teeth line is plotted with the green color. Also for every trade in a row from 2 to 5 the label "Buy More" is also plotted just below the teeth line. With every next simultaneously opened trade the green color of the space between teeth and price became less transparent.
Strategy settings
In the inputs window user can setup strategy setting: EMA Length (by default = 100, period of EMA, used for long-term trend filtering EMA calculation). User can choose the optimal parameters during backtesting on certain price chart.
Justification of Methodology
Let's go through all concepts used in this strategy to understand how they works together. Let's start from the easies one, the EMA. Let's briefly explain what is EMA. The Exponential Moving Average (EMA) is a type of moving average that gives more weight to recent prices, making it more responsive to current price changes compared to the Simple Moving Average (SMA). It is commonly used in technical analysis to identify trends and generate buy or sell signals. It can be calculated with the following steps:
1.Calculate the Smoothing Multiplier:
Multiplier = 2 / (n + 1), Where n is the number of periods.
2. EMA Calculation
EMA = (Current Price) × Multiplier + (Previous EMA) × (1 − Multiplier)
In this strategy uses EMA an initial long term trend filter. It allows to open long trades only if price close above EMA (by default 50 period). It increases the probability of taking long trades only in the direction of the trend.
Let's go to the next, short-term trend filter which consists of Alligator and Fractals. Let's briefly explain what do these indicators means. The Williams Alligator, developed by Bill Williams, is a technical indicator designed to spot trends and potential market reversals. It uses three smoothed moving averages, referred to as the jaw, teeth, and lips:
Jaw (Blue Line): The slowest of the three, based on a 13-period smoothed moving average shifted 8 bars ahead.
Teeth (Red Line): The medium-speed line, derived from an 8-period smoothed moving average shifted 5 bars forward.
Lips (Green Line): The fastest line, calculated using a 5-period smoothed moving average shifted 3 bars forward.
When these lines diverge and are properly aligned, the "alligator" is considered "awake," signaling a strong trend. Conversely, when the lines overlap or intertwine, the "alligator" is "asleep," indicating a range-bound or sideways market. This indicator assists traders in identifying when to act on or avoid trades.
The Williams Fractals, another tool introduced by Bill Williams, are used to pinpoint potential reversal points on a price chart. A fractal forms when there are at least five consecutive bars, with the middle bar displaying the highest high (for an up fractal) or the lowest low (for a down fractal), relative to the two bars on either side.
Key Points:
Up Fractal: Occurs when the middle bar has a higher high than the two preceding and two following bars, suggesting a potential downward reversal.
Down Fractal: Happens when the middle bar shows a lower low than the surrounding two bars, hinting at a possible upward reversal.
Traders often combine fractals with other indicators to confirm trends or reversals, improving the accuracy of trading decisions.
How we use their combination in this strategy? Let’s consider an uptrend example. A breakout above an up fractal can be interpreted as a bullish signal, indicating a high likelihood that an uptrend is beginning. Here's the reasoning: an up fractal represents a potential shift in market behavior. When the fractal forms, it reflects a pullback caused by traders selling, creating a temporary high. However, if the price manages to return to that fractal’s high and break through it, it suggests the market has "changed its mind" and a bullish trend is likely emerging.
The moment of the breakout marks the potential transition to an uptrend. It’s crucial to note that this breakout must occur above the Alligator's teeth line. If it happens below, the breakout isn’t valid, and the downtrend may still persist. The same logic applies inversely for down fractals in a downtrend scenario.
So, if last up fractal breakout was higher, than Alligator's teeth and it happened after last down fractal breakdown below teeth, algorithm considered current trend as an uptrend. During this uptrend long trades can be opened if signal was flashed. If during the uptrend price breaks down the down fractal below teeth line, strategy considered that uptrend is finished with the high probability and strategy closes all current long trades. This combination is used as a short term trend filter increasing the probability of opening profitable long trades in addition to EMA filter, described above.
Now let's talk about Awesome Oscillator's "Sauser" signals. Briefly explain what is the Awesome Oscillator. The Awesome Oscillator (AO), created by Bill Williams, is a momentum-based indicator that evaluates market momentum by comparing recent price activity to a broader historical context. It assists traders in identifying potential trend reversals and gauging trend strength.
AO = SMA5(Median Price) − SMA34(Median Price)
where:
Median Price = (High + Low) / 2
SMA5 = 5-period Simple Moving Average of the Median Price
SMA 34 = 34-period Simple Moving Average of the Median Price
Now we know what is AO, but what is the "Saucer" signal? This concept was introduced by Bill Williams, let's briefly explain it and how it's used by this strategy. Initially, this type of signal is a combination of the following AO bars: we need 3 bars in a row, the first one shall be higher than the second, the third bar also shall be higher, than second. All three bars shall be above the zero line of AO. The price bar, which corresponds to third "saucer's" bar is our signal bar. Strategy places buy stop order one tick above the price bar which corresponds to signal bar.
After that we can have the following scenarios.
Price hit the order on the next candle in this case strategy opened long with this price.
Price doesn't hit the order price, the next candle set lower low. If current AO bar is increasing buy stop order changes by the script to the high of this new bar plus one tick. This procedure repeats until price finally hit buy order or current AO bar become decreasing. In the second case buy order cancelled and strategy wait for the next "Saucer" signal.
If long trades has been opened strategy use all the next signals until number of trades doesn't exceed 5. All trades are closed when the trend changes to downtrend according to combination of Alligator and Fractals described above.
Why we use "Saucer" signals? If AO above the zero line there is a high probability that price now is in uptrend if we take into account our two trend filters. When we see the decreasing bars on AO and it's above zero it's likely can be considered as a pullback on the uptrend. When we see the stop of AO decreasing and the first increasing bar has been printed there is a high probability that this local pull back is finished and strategy open long trade in the likely direction of a main trend.
Why strategy use only 10% per signal? Sometimes we can see the false signals which appears on sideways. Not risking that much script use only 10% per signal. If the first long trade has been open and price continue going up and our trend approximation by Alligator and Fractals is uptrend, strategy add another one 10% of capital to every next saucer signal while number of active trades no more than 5. This capital allocation allows to take part in long trades when current uptrend is likely to be strong and use only 10% of capital when there is a high probability of sideways.
Backtest Results
Operating window: Date range of backtests is 2023.01.01 - 2024.11.25. It is chosen to let the strategy to close all opened positions.
Commission and Slippage: Includes a standard Binance commission of 0.1% and accounts for possible slippage over 5 ticks.
Initial capital: 10000 USDT
Percent of capital used in every trade: 10%
Maximum Single Position Loss: -5.10%
Maximum Single Profit: +22.80%
Net Profit: +2838.58 USDT (+28.39%)
Total Trades: 107 (42.99% win rate)
Profit Factor: 3.364
Maximum Accumulated Loss: 373.43 USDT (-2.98%)
Average Profit per Trade: 26.53 USDT (+2.40%)
Average Trade Duration: 78 hours
These results are obtained with realistic parameters representing trading conditions observed at major exchanges such as Binance and with realistic trading portfolio usage parameters.
How to Use
Add the script to favorites for easy access.
Apply to the desired timeframe and chart (optimal performance observed on 3h BTC/USDT).
Configure settings using the dropdown choice list in the built-in menu.
Set up alerts to automate strategy positions through web hook with the text: {{strategy.order.alert_message}}
Disclaimer:
Educational and informational tool reflecting Skyrex commitment to informed trading. Past performance does not guarantee future results. Test strategies in a simulated environment before live implementation
Fractal Channel v2Just another way of visualizing Williams' Fractals: they are now joined in a channel.
You can also create a channel of Fractals from another time frame than the current.
Great for identifying breakout entries, trend direction analysis (i.e. easily visualize Higher Lows, Higher Highs, Lower Lows, Lower Highs) or to trail your Stop Losses.
Version 2 of the Fractal Channel now correctly identifies William's Fractals when the High/Low of the 'Fractal Candle' is equal to the High/Low of the neighbouring 2 candles on both sides.
Support Resistance ImportanceThe Support Resistance Importance indicator is designed to highlight key price levels based on the relationship between fractal occurrences and volume distribution within a given price range. By dividing the range into bins, the indicator calculates the total volume traded at each fractal level and normalizes the values for easy visualization. The normalized values represent an "importance score" for each price range, helping traders identify critical support and resistance levels where price action might react.
Key Features:
Fractal Detection:
The indicator detects Williams Fractals, which are specific price patterns representing potential market reversals. It identifies both upward fractals (potential resistance) and downward fractals (potential support).
Price Range Binning:
The price range is divided into a user-defined number of bins (default is 20). Each bin represents a segment of the total price range, allowing the indicator to bucket price action and track fractal volumes in each bin.
Volume-Based Importance Calculation:
For each bin, the indicator sums up the volume traded at the time a fractal occurred. The volumes are then normalized to reflect their relative importance.
The importance score is calculated as the relative volume in each bin, representing the potential influence of that price range. Higher scores indicate stronger support or resistance levels.
Normalization:
The volume data is normalized to allow for better comparison across bins. This normalization ensures that the highest and lowest volumes are scaled between 0 and 1 for visualization purposes. The smallest volume value is used to scale the rest, ensuring meaningful comparisons.
Visualization:
The indicator provides a table-based visualization showing the price range and the corresponding importance score for each bin.
Each bin is color-coded based on the normalized importance score, with blue or greenish shades indicating higher importance levels. The current price range is highlighted to help traders quickly identify relevant areas of interest.
Trading Utility:
Traders can use the importance scores to identify price levels where significant volume has accumulated at fractals. A higher importance score suggests a stronger likelihood of the price reacting to that level.
If a price moves towards a bin with a high score and the bins above it have much smaller values, it suggests that the price may "pump" up to the next high-scored range, similar to how price drops can occur.
Example Use Case:
Suppose the price approaches a bin with an importance score of 25, and the bins above have much smaller values. This suggests that price may break higher towards the next significant level of resistance, offering traders an opportunity to capitalize on the move by entering long positions or adjusting their stop losses.
This indicator is particularly useful for support and resistance trading, where understanding key levels of price action and volume can improve decision-making in anticipating market reactions.
[JL] How Many Signals last N barsGot this idea after I found Multiple Indicators Screener from QuantNomad.
This script learnt some codes from QuantNomad's great script. Thanks to him.
------------------------------------------------------------------------------------------------------------------------------------------------------------------------
This table show how many signals happened during the last N bars.
I only take care Forex, so this table only has 28 symbols. Feel free to change it.
Calculate the following signals:
RSI cross over/under 50
Short Moving average cross over/under long moving average
Stochastic k cross over/under d
MACD hist cross over/under 0
Williams Fractals: Up and Down fractals happened.
The concept is simple: Range period will always happen more cross signals than the trend period.
When the counter is less than median of all symbols, will be set green color. So more green mean more chance to be trend.
@tk · fractal emas█ OVERVIEW
This script is an indicator that plots short, medium and long moving averages for multiple fractals. This script was based on sharks EMAs by rlvs indicator, that plots multiple rays for each fractals into the chart. The main feature of this indicator is the customizability. The calculation itself is simple as moving average.
█ MOTIVATION
The trader can customize all aspects of the plotted data. The text size, extended line length, the moving average type — exponential, simple, etc... — the length of fractal rays, line style, line width and visibility. To keep minimalist, this indicator simplifies the logic of line colors based on the purpose of each moving averages. To prevent overnoise the chart with multiple lines with multiple colors for each fractal timefraes, the trader needs to keep in mind that the all lines with the "short" moving average color for example, will represents the short moving averages lines for all fractals. This logic is applied for medium and long moving averages either.
█ CONCEPT
The trading concept to use this indicator is to make entries on uptrend or downtrend pullbacks when the asset price reaches the short, medium or long moving averages price levels. But this strategy don't works alone. It needs to be aligned together with others indicators like RSI, Chart Patterns, Support and Resistance, and so on... Even more confluences that you have, bigger are your chances to increase the probability for a successful trade. So, don't use this indicator alone. Compose a trading strategy and use it to improve your analysis.
█ CUSTOMIZATION
This indicator allows the trader to customize the following settings:
GENERAL
Text size
Changes the font size of the labels to improve accessibility.
Type: string
Options: `tiny`, `small`, `normal`, `large`.
Default: `small`
SHORT
Type
Select the Short Moving Average calculation type.
Type: string
Options: `EMA`, `SMA`, `HMA`, `VWMA`, `WMA`.
Default: `EMA`
Length
Changes the base length for the Short Moving Average calculation.
Type: int
Default: 12
Source
Changes the base source for the Short Moving Average calculation.
Type: float
Default: close
Color
The base color that will represent the Short Moving Average.
Type: color
Default: color.rgb(255, 235, 59) (yellow)
Fractal Style
The fractal ray line style.
Type: string
Options: `dotted`, `dashed`, `solid`.
Default: `dotted`
Fractal Width
The fractal ray line width.
Type: string
Options: `1px`, `2px`, `3px`, `4px`.
Default: `1px`
Fractal Ray Length
The fractal ray line length.
Type: int
Default: 12
MEDIUM
Type
Select the Medium Moving Average calculation type.
Type: string
Options: `EMA`, `SMA`, `HMA`, `VWMA`, `WMA`.
Default: `EMA`
Length
Changes the base length for the Medium Moving Average calculation.
Type: int
Default: 26
Source
Changes the base source for the Medium Moving Average calculation.
Type: float
Default: close
Color
The base color that will represent the Short Moving Average.
Type: color
Default: color.rgb(0, 230, 118) (lime)
Fractal Style
The fractal ray line style.
Type: string
Options: `dotted`, `dashed`, `solid`.
Default: `dotted`
Fractal Width
The fractal ray line width.
Type: string
Options: `1px`, `2px`, `3px`, `4px`.
Default: `1px`
Fractal Ray Length
The fractal ray line length.
Type: int
Default: 12
LONG
Type
Select the Long Moving Average calculation type.
Type: string
Options: `EMA`, `SMA`, `HMA`, `VWMA`, `WMA`.
Default: `EMA`
Length
Changes the base length for the Long Moving Average calculation.
Type: int
Default: 200
Source
Changes the base source for the Long Moving Average calculation.
Type: float
Default: close
Color
The base color that will represent the Short Moving Average.
Type: color
Default: color.rgb(255, 82, 82) (red)
Fractal Style
The fractal ray line style.
Type: string
Options: `dotted`, `dashed`, `solid`.
Default: `dotted`
Fractal Width
The fractal ray line width.
Type: string
Options: `1px`, `2px`, `3px`, `4px`.
Default: `1px`
Fractal Ray Length
The fractal ray line length.
Type: int
Default: 12
VISIBILITY
Show Fractal Rays · (Short)
Shows short moving average fractal rays.
Type: bool
Default: true
Show Fractal Rays · (Medium)
Shows short moving average fractal rays.
Type: bool
Default: true
Show Fractal Rays · (Long)
Shows short moving average fractal rays.
Type: bool
Default: true
█ FUNCTIONS
The script contains the following functions:
`fn_labelizeTimeFrame`
Labelize timeframe period in minutes and hours.
Parameters:
tf: (string) Timeframe period to be labelized.
Returns: (string) Labelized timeframe string.
`fn_builtInLineStyle`
Converts simple string to built-in line style variable value.
Parameters:
lineStyle: (string) The line style simple string.
Returns: (string) Built-in line style string value.
`fn_builtInLineWidth`
Converts simple pixel string to line width number value.
Parameters:
lineWidth: (string) The line width pixel simple string.
Returns: (string) Built-in line width number value.
`fn_requestFractal`
Requests fractal data based on `period` given an expression.
Parameters:
period: (string) The period timeframe of fractal.
expression: (series float) The expression to retrieve data from fractal.
Returns: (mixed) A result determined by `expression`.
`fn_plotRay`
Plots line after chart bars.
Parameters:
y: (float) Y axis line position.
label: (string) Label to be ploted after line.
color: (color) Line and label color.
length: (int) Line length.
show: (bool) Flag to display the line. (default: `true`)
lineStyle: (string) Line style to be applied. (default: `line.style_dotted`)
lineWidth: (int) Line width. (default: `1`)
Returns: void
`fn_plotEmaRay`
Plots moving average line for a specific period.
Parameters:
period: (simple string) Period of fractal to retrieve
expression: (series float) The expression to retrieve data from fractal.
color: (color) Line and label color.
length: (int) Line length. (default: `12`)
show: (bool) Flag to display the line. (default: `true`)
lineStyle: (string) Line style to be applied. (default: `line.style_dotted`)
lineWidth: (string) Line width. (default: `1px`)
Returns: void
`fn_plotExtendedEmaRay`
Draws extended line for current timeframe moving average.
Parameters:
coordY: (float) Extended line Y axis position.
textValue: (simple string) Extended line label text.
textColor: (color) Extended line text color.
length: (int) Extended length. (default: `5`)
Returns: void
HMA Buy Sell Signals - Profit ManagerNote : Settings should be adjusted according to the selected time frame. Try to find the best setting according to the profitability rate
Overall Functionality
This script combines several trading tools to create a comprehensive system for trend analysis, trade execution, and performance tracking. Users can identify market trends using specific moving averages and RSI indicators while managing profit and loss levels automatically.
Trend Detection and Trade Signals
Hull Moving Averages (HMA):
Two HMAs (a faster one and a slower one) are used to determine the market trend.
A buy signal is generated when the faster HMA crosses above the slower HMA.
Conversely, a sell signal is triggered when the faster HMA crosses below the slower one.
Visual Feedback:
Trend lines on the chart change color to reflect the trend direction (e.g., green for upward trends and red for downward trends).
Trade Levels and Management
Entry, Take-Profit, and Stop-Loss Levels:
When the trend shifts upwards, the script calculates entry, take-profit, and stop-loss levels based on the opening price.
Similarly, for downward trends, these levels are determined for short trades.
Commission Tracking:
Each trade includes a commission cost, which is factored into net profit and loss calculations.
Dynamic Labels:
Entry, take-profit, and stop-loss levels are visually marked on the chart for easier tracking.
Performance Tracking
Profit and Loss Tracking:
The script keeps a running total of profits, losses, and commissions for both long and short trades.
It also calculates the net profit after all costs are considered.
Performance Table:
A table is displayed on the chart summarizing:
The number of trades.
Total profit and loss for long and short positions.
Commission costs.
Net profit.
Fractal Support and Resistance
Dynamic Lines:
The script identifies the most recent significant highs and lows using fractals.
It draws support and resistance lines that automatically update as new fractals form.
Simplified Visuals:
The chart always shows the last two support and resistance lines, keeping the visualization clean and focused.
RSI-Based Signals
Overbought and Oversold Levels:
RSI is used to identify overbought (above 80) and oversold (below 20) conditions.
The script generates buy signals at oversold levels and sell signals at overbought levels.
Chart Indicators:
Arrows and labels appear on the chart to highlight these RSI-based opportunities.
Customization
The script allows users to customize key parameters such as:
Moving average lengths for trend detection.
Take-profit and stop-loss percentages.
Timeframes for backtesting.
Starting capital and commission rates.
Conclusion
This script is a versatile tool for traders, combining trend detection, automated trade management, and visual feedback. It simplifies decision-making by providing clear signals and tracking performance metrics, making it suitable for both beginners and experienced traders.
* The most recently drawn fractals represent potential support and resistance levels. If the price aligns with these levels at the time of entering a trade, it may indicate a likelihood of reversal. In such cases, it’s advisable to either avoid entering the trade altogether or proceed with increased caution.
Hybrid Adaptive Double Exponential Smoothing🙏🏻 This is HADES (Hybrid Adaptive Double Exponential Smoothing) : fully data-driven & adaptive exponential smoothing method, that gains all the necessary info directly from data in the most natural way and needs no subjective parameters & no optimizations. It gets applied to data itself -> to fit residuals & one-point forecast errors, all at O(1) algo complexity. I designed it for streaming high-frequency univariate time series data, such as medical sensor readings, orderbook data, tick charts, requests generated by a backend, etc.
The HADES method is:
fit & forecast = a + b * (1 / alpha + T - 1)
T = 0 provides in-sample fit for the current datum, and T + n provides forecast for n datapoints.
y = input time series
a = y, if no previous data exists
b = 0, if no previous data exists
otherwise:
a = alpha * y + (1 - alpha) * a
b = alpha * (a - a ) + (1 - alpha) * b
alpha = 1 / sqrt(len * 4)
len = min(ceil(exp(1 / sig)), available data)
sig = sqrt(Absolute net change in y / Sum of absolute changes in y)
For the start datapoint when both numerator and denominator are zeros, we define 0 / 0 = 1
...
The same set of operations gets applied to the data first, then to resulting fit absolute residuals to build prediction interval, and finally to absolute forecasting errors (from one-point ahead forecast) to build forecasting interval:
prediction interval = data fit +- resoduals fit * k
forecasting interval = data opf +- errors fit * k
where k = multiplier regulating intervals width, and opf = one-point forecasts calculated at each time t
...
How-to:
0) Apply to your data where it makes sense, eg. tick data;
1) Use power transform to compensate for multiplicative behavior in case it's there;
2) If you have complete data or only the data you need, like the full history of adjusted close prices: go to the next step; otherwise, guided by your goal & analysis, adjust the 'start index' setting so the calculations will start from this point;
3) Use prediction interval to detect significant deviations from the process core & make decisions according to your strategy;
4) Use one-point forecast for nowcasting;
5) Use forecasting intervals to ~ understand where the next datapoints will emerge, given the data-generating process will stay the same & lack structural breaks.
I advise k = 1 or 1.5 or 4 depending on your goal, but 1 is the most natural one.
...
Why exponential smoothing at all? Why the double one? Why adaptive? Why not Holt's method?
1) It's O(1) algo complexity & recursive nature allows it to be applied in an online fashion to high-frequency streaming data; otherwise, it makes more sense to use other methods;
2) Double exponential smoothing ensures we are taking trends into account; also, in order to model more complex time series patterns such as seasonality, we need detrended data, and this method can be used to do it;
3) The goal of adaptivity is to eliminate the window size question, in cases where it doesn't make sense to use cumulative moving typical value;
4) Holt's method creates a certain interaction between level and trend components, so its results lack symmetry and similarity with other non-recursive methods such as quantile regression or linear regression. Instead, I decided to base my work on the original double exponential smoothing method published by Rob Brown in 1956, here's the original source , it's really hard to find it online. This cool dude is considered the one who've dropped exponential smoothing to open access for the first time🤘🏻
R&D; log & explanations
If you wanna read this, you gotta know, you're taking a great responsability for this long journey, and it gonna be one hell of a trip hehe
Machine learning, apprentissage automatique, машинное обучение, digital signal processing, statistical learning, data mining, deep learning, etc., etc., etc.: all these are just artificial categories created by the local population of this wonderful world, but what really separates entities globally in the Universe is solution complexity / algorithmic complexity.
In order to get the game a lil better, it's gonna be useful to read the HTES script description first. Secondly, let me guide you through the whole R&D; process.
To discover (not to invent) the fundamental universal principle of what exponential smoothing really IS, it required the review of the whole concept, understanding that many things don't add up and don't make much sense in currently available mainstream info, and building it all from the beginning while avoiding these very basic logical & implementation flaws.
Given a complete time t, and yet, always growing time series population that can't be logically separated into subpopulations, the very first question is, 'What amount of data do we need to utilize at time t?'. Two answers: 1 and all. You can't really gain much info from 1 datum, so go for the second answer: we need the whole dataset.
So, given the sequential & incremental nature of time series, the very first and basic thing we can do on the whole dataset is to calculate a cumulative , such as cumulative moving mean or cumulative moving median.
Now we need to extend this logic to exponential smoothing, which doesn't use dataset length info directly, but all cool it can be done via a formula that quantifies the relationship between alpha (smoothing parameter) and length. The popular formulas used in mainstream are:
alpha = 1 / length
alpha = 2 / (length + 1)
The funny part starts when you realize that Cumulative Exponential Moving Averages with these 2 alpha formulas Exactly match Cumulative Moving Average and Cumulative (Linearly) Weighted Moving Average, and the same logic goes on:
alpha = 3 / (length + 1.5) , matches Cumulative Weighted Moving Average with quadratic weights, and
alpha = 4 / (length + 2) , matches Cumulative Weighted Moving Average with cubic weghts, and so on...
It all just cries in your shoulder that we need to discover another, native length->alpha formula that leverages the recursive nature of exponential smoothing, because otherwise, it doesn't make sense to use it at all, since the usual CMA and CMWA can be computed incrementally at O(1) algo complexity just as exponential smoothing.
From now on I will not mention 'cumulative' or 'linearly weighted / weighted' anymore, it's gonna be implied all the time unless stated otherwise.
What we can do is to approach the thing logically and model the response with a little help from synthetic data, a sine wave would suffice. Then we can think of relationships: Based on algo complexity from lower to higher, we have this sequence: exponential smoothing @ O(1) -> parametric statistics (mean) @ O(n) -> non-parametric statistics (50th percentile / median) @ O(n log n). Based on Initial response from slow to fast: mean -> median Based on convergence with the real expected value from slow to fast: mean (infinitely approaches it) -> median (gets it quite fast).
Based on these inputs, we need to discover such a length->alpha formula so the resulting fit will have the slowest initial response out of all 3, and have the slowest convergence with expected value out of all 3. In order to do it, we need to have some non-linear transformer in our formula (like a square root) and a couple of factors to modify the response the way we need. I ended up with this formula to meet all our requirements:
alpha = sqrt(1 / length * 2) / 2
which simplifies to:
alpha = 1 / sqrt(len * 8)
^^ as you can see on the screenshot; where the red line is median, the blue line is the mean, and the purple line is exponential smoothing with the formulas you've just seen, we've met all the requirements.
Now we just have to do the same procedure to discover the length->alpha formula but for double exponential smoothing, which models trends as well, not just level as in single exponential smoothing. For this comparison, we need to use linear regression and quantile regression instead of the mean and median.
Quantile regression requires a non-closed form solution to be solved that you can't really implement in Pine Script, but that's ok, so I made the tests using Python & sklearn:
paste.pics
^^ on this screenshot, you can see the same relationship as on the previous screenshot, but now between the responses of quantile regression & linear regression.
I followed the same logic as before for designing alpha for double exponential smoothing (also considered the initial overshoots, but that's a little detail), and ended up with this formula:
alpha = sqrt(1 / length) / 2
which simplifies to:
alpha = 1 / sqrt(len * 4)
Btw, given the pattern you see in the resulting formulas for single and double exponential smoothing, if you ever want to do triple (not Holt & Winters) exponential smoothing, you'll need len * 2 , and just len * 1 for quadruple exponential smoothing. I hope that based on this sequence, you see the hint that Maybe 4 rounds is enough.
Now since we've dealt with the length->alpha formula, we can deal with the adaptivity part.
Logically, it doesn't make sense to use a slower-than-O(1) method to generate input for an O(1) method, so it must be something universal and minimalistic: something that will help us measure consistency in our data, yet something far away from statistics and close enough to topology.
There's one perfect entity that can help us, this is fractal efficiency. The way I define fractal efficiency can be checked at the very beginning of the post, what matters is that I add a square root to the formula that is not typically added.
As explained in the description of my metric QSFS , one of the reasons for SQRT-transformed values of fractal efficiency applied in moving window mode is because they start to closely resemble normal distribution, yet with support of (0, 1). Data with this interesting property (normally distributed yet with finite support) can be modeled with the beta distribution.
Another reason is, in infinitely expanding window mode, fractal efficiency of every time series that exhibits randomness tends to infinitely approach zero, sqrt-transform kind of partially neutralizes this effect.
Yet another reason is, the square root might better reflect the dimensional inefficiency or degree of fractal complexity, since it could balance the influence of extreme deviations from the net paths.
And finally, fractals exhibit power-law scaling -> measures like length, area, or volume scale in a non-linear way. Adding a square root acknowledges this intrinsic property, while connecting our metric with the nature of fractals.
---
I suspect that, given analogies and connections with other topics in geometry, topology, fractals and most importantly positive test results of the metric, it might be that the sqrt transform is the fundamental part of fractal efficiency that should be applied by default.
Now the last part of the ballet is to convert our fractal efficiency to length value. The part about inverse proportionality is obvious: high fractal efficiency aka high consistency -> lower window size, to utilize only the last data that contain brand new information that seems to be highly reliable since we have consistency in the first place.
The non-obvious part is now we need to neutralize the side effect created by previous sqrt transform: our length values are too low, and exponentiation is the perfect candidate to fix it since translating fractal efficiency into window sizes requires something non-linear to reflect the fractal dynamics. More importantly, using exp() was the last piece that let the metric shine, any other transformations & formulas alike I've tried always had some weird results on certain data.
That exp() in the len formula was the last piece that made it all work both on synthetic and on real data.
^^ a standalone script calculating optimal dynamic window size
Omg, THAT took time to write. Comment and/or text me if you need
...
"Versace Pip-Boy, I'm a young gun coming up with no bankroll" 👻
∞
Ultimate Multi-Physics Financial IndicatorThe Ultimate Multi-Physics Financial Indicator is an advanced Pine Script designed to combine various complex theories from physics, mathematics, and statistical mechanics to create a holistic, multi-dimensional approach to market analysis. Let’s break down the core concepts and how they’re applied in this script:
1. Fractal Geometry: Recursive Pattern Recognition
Purpose: This part of the script uses fractal geometry to recursively analyze price pivots (highs and lows) for detecting patterns.
Fractals: The fractalHigh and fractalLow signals represent key turning points in the market. The script goes deeper by recursively analyzing layers of pivot sequences, adding "depth" to the recognition of patterns.
Recursive Depth: It breaks down each detected pivot into smaller components, giving more nuance to market pattern recognition. This provides a broader context for how prices have behaved historically at various levels of recursion.
2. Quantum Mechanics: Adaptive Probabilistic Monte Carlo with Correlation
Purpose: This component integrates randomness (from Monte Carlo simulations) with current market behavior using correlation.
Randomness Weighted by Correlation: By generating random probabilities and weighting them based on how well the market aligns with recent trends, it creates a probabilistic signal. The random values are scaled by a correlation factor (close prices and their moving average), adding adaptive elements where randomness is adjusted by current market conditions.
3. Thermodynamics: Adaptive Efficiency Ratio (Entropy-Like Decay)
Purpose: This section uses principles from thermodynamics, where efficiency in price movement is dynamically adjusted by recent volatility and changes.
Efficiency Ratio: It calculates how efficiently the market is moving over a certain period. The "entropy decay factor" reflects how stable the market is. Higher entropy (chaos) results in lower efficiency, while stable periods maintain higher efficiency.
4. Chaos Theory: Lorenz-Driven Market Oscillation
Purpose: Instead of using a basic Average True Range (ATR) indicator, this section applies chaos theory (using a Lorenz attractor analogy) to describe complex market oscillations.
Lorenz Attractor: This models market behavior with a chaotic system that depends on the historical price changes at different time intervals. The attractor value quantifies the level of "chaos" or unpredictability in the market.
5. String Theory: Multi-Layered Dimensional Analysis of RSI and MACD
Purpose: Combines traditional indicators like the RSI (Relative Strength Index) and MACD (Moving Average Convergence Divergence) with momentum for multi-dimensional analysis.
Interaction of Layers: Each layer (RSI, MACD, and momentum) is treated as part of a multi-dimensional structure, where they influence one another. The final signal is a blended outcome of these key metrics, weighted and averaged for complexity.
6. Fluid Dynamics: Adaptive OBV (Pressure-Based)
Purpose: This section uses fluid dynamics to understand how price movement and volume create pressure over time, similar to how fluids behave under different forces.
Adaptive OBV: Traditional OBV (On-Balance Volume) is adapted by using statistical smoothing to measure the "pressure" exerted by volume over time. The result is a signal that shows where there might be building momentum or pressure in the market based on volume dynamics.
7. Recursive Synthesis of Signals
Purpose: After calculating all the individual signals (fractal, quantum, thermodynamic, chaos, string, and fluid), the script synthesizes them into one cohesive signal.
Recursive Feedback Loop: Each signal is recursively influenced by others, forming a feedback loop that allows the indicator to continuously learn from new data and self-adjust.
8. Signal Smoothing and Final Output
Purpose: To avoid noise in the output, the final combined signal is smoothed using an Exponential Moving Average (EMA), which helps stabilize the output for easier interpretation.
9. Dynamic Color Coding Based on Signal Extremes
Purpose: Visual clarity is enhanced by using color to highlight different levels of signal strength.
Color Coding: The script dynamically adjusts colors (green, orange, red) based on the strength of the final signal relative to its percentile ranking in historical data, making it easier to spot bullish, neutral, or bearish signals.
The "Ultimate Multi-Physics Financial Indicator" integrates a diverse array of scientific principles — fractal geometry, quantum mechanics, thermodynamics, chaos theory, string theory, and fluid dynamics — to provide a comprehensive market analysis tool. By combining probabilistic simulations, multi-dimensional technical indicators, and recursive feedback loops, this indicator adapts dynamically to evolving market conditions, giving traders a holistic view of market behavior across various dimensions. The result is an adaptive and flexible tool that responds to both short-term and long-term market changes
Advanced Physics Financial Indicator Each component represents a scientific theory and is applied to the price data in a way that reflects key principles from that theory.
Detailed Explanation
1. Fractal Geometry - High/Low Signal
Concept: Fractal geometry studies self-similar patterns that repeat at different scales. In markets, fractals can be used to detect recurring patterns or turning points.
Implementation: The script detects pivot highs and lows using ta.pivothigh and ta.pivotlow, representing local turning points in price. The fractalSignal is set to 1 for a pivot high, -1 for a pivot low, and 0 if there is no signal. This logic reflects the cyclical, self-similar nature of price movements.
Practical Use: This signal is useful for identifying local tops and bottoms, allowing traders to spot potential reversals or consolidation points where fractal patterns emerge.
2. Quantum Mechanics - Probabilistic Monte Carlo Simulation
Concept: Quantum mechanics introduces uncertainty and probability into systems, much like how future price movements are inherently uncertain. Monte Carlo simulations are used to model a range of possible outcomes based on random inputs.
Implementation: In this script, we simulate 100 random outcomes by generating a random number between -1 and 1 for each iteration. These random values are stored in an array, and the average of these values is calculated to represent the Quantum Signal.
Practical Use: This probabilistic signal provides a sense of randomness and uncertainty in the market, reflecting the possibility of price movement in either direction. It simulates the market’s chaotic nature by considering multiple possible outcomes and their average.
3. Thermodynamics - Efficiency Ratio Signal
Concept: Thermodynamics deals with energy efficiency and entropy in systems. The efficiency ratio in financial terms can be used to measure how efficiently the price is moving relative to volatility.
Implementation: The Efficiency Ratio is calculated as the absolute price change over n periods divided by the sum of absolute changes for each period within n. This ratio shows how much of the price movement is directional versus random, mimicking the concept of efficiency in thermodynamic systems.
Practical Use: A high efficiency ratio suggests that the market is trending smoothly (high efficiency), while a low ratio indicates choppy, non-directional movement (low efficiency, or high entropy).
4. Chaos Theory - ATR Signal
Concept: Chaos theory studies how complex systems are highly sensitive to initial conditions, leading to unpredictable behavior. In markets, chaotic price movements can often be captured through volatility indicators.
Implementation: The script uses a very long ATR period (1000) to reflect slow-moving chaos over time. The Chaos Signal is computed by measuring the deviation of the current price from its long-term average (SMA), normalized by ATR. This captures price deviations over time, hinting at chaotic market behavior.
Practical Use: The signal measures how far the price deviates from its long-term average, which can signal the degree of chaos or extreme behavior in the market. High deviations indicate chaotic or volatile conditions, while low deviations suggest stability.
5. Network Theory - Correlation with BTC
Concept: Network theory studies how different components within a system are interconnected. In markets, assets are often correlated, meaning that price movements in one asset can influence or be influenced by another.
Implementation: This indicator calculates the correlation between the asset’s price and the price of Bitcoin (BTC) over 30 periods. The Network Signal shows how connected the asset is to BTC, reflecting broader market dynamics.
Practical Use: In a highly correlated market, BTC can act as a leading indicator for other assets. A strong correlation with BTC might suggest that the asset is likely to move in line with Bitcoin, while a weak or negative correlation might indicate that the asset is moving independently.
6. String Theory - RSI & MACD Interaction
Concept: String theory attempts to unify the fundamental forces of nature into a single framework. In trading, we can view the RSI and MACD as interacting forces that provide insights into momentum and trend.
Implementation: The script calculates the RSI and MACD and combines them into a single signal. The formula for String Signal is (RSI - 50) / 100 + (MACD Line - Signal Line) / 100, normalizing both indicators to a scale where their contributions are additive. The RSI represents momentum, and MACD shows trend direction and strength.
Practical Use: This signal helps in detecting moments where momentum (RSI) and trend strength (MACD) align, giving a clearer picture of the asset's direction and overbought/oversold conditions. It unifies these two indicators to create a more holistic view of market behavior.
7. Fluid Dynamics - On-Balance Volume (OBV) Signal
Concept: Fluid dynamics studies how fluids move and flow. In markets, volume can be seen as a "flow" that drives price movement, much like how fluid dynamics describe the flow of liquids.
Implementation: The script uses the OBV (On-Balance Volume) indicator to track the cumulative flow of volume based on price changes. The signal is further normalized by its moving average to smooth out fluctuations and make it more reflective of price pressure over time.
Practical Use: The Fluid Signal shows how the flow of volume is driving price action. If the OBV rises significantly, it suggests that there is strong buying pressure, while a falling OBV indicates selling pressure. It’s analogous to how pressure builds in a fluid system.
8. Final Signal - Combining All Physics-Based Indicators
Implementation: Each of the seven physics-inspired signals is combined into a single Final Signal by averaging their values. This approach blends different market insights from various scientific domains, creating a comprehensive view of the market’s condition.
Practical Use: The final signal gives you a holistic, multi-dimensional view of the market by merging different perspectives (fractal behavior, quantum probability, efficiency, chaos, correlation, momentum/trend, and volume flow). This approach helps traders understand the market's dynamics from multiple angles, offering deeper insights than any single indicator.
9. Color Coding Based on Signal Extremes
Concept: The color of the final signal plot dynamically reflects whether the market is in an extreme state.
Implementation: The signal color is determined using percentiles. If the Final Signal is in the top 55th percentile of its range, the signal is green (bullish). If it is between the 45th and 55th percentiles, it is orange (neutral). If it falls below the 45th percentile, it is red (bearish).
Practical Use: This visual representation helps traders quickly identify the strength of the signal. Bullish conditions (green), neutral conditions (orange), and bearish conditions (red) are clearly distinguished, simplifying decision-making.
rsi wf breakoutRSI Breakout Asif
RSI Breakout Asif Indicator
Overview:
The RSI Breakout Asif indicator is a custom script designed to analyze and highlight potential
breakout points using the Relative Strength Index (RSI) combined with Williams Fractals. This
indicator is specifically developed for traders who want to identify key momentum shifts in the
market.
Features:
1. RSI Analysis:
- The RSI is calculated using a user-defined length and price source.
- Horizontal lines are plotted at levels 70 (overbought), 50 (neutral), and 30 (oversold) to visually
aid decision-making.
2. Williams Fractals on RSI:
- Detects fractal highs and lows based on RSI values.
- Highlights these fractal points with dynamic, symmetrical lines for better visibility.
3. Customization:
- Users can adjust the RSI length and price source for personalized analysis.
- Fractal settings (left and right bar length) are also adjustable, making the indicator versatile for
different trading styles.
4. Visual Enhancements:
- Fractal highs are marked in red, while fractal lows are marked in green.
Asif - Page 1
RSI Breakout Asif
- Precise line placement ensures clarity and reduces chart clutter.
5. Practical Utility:
- Use the fractal breakout signals in conjunction with other technical indicators for enhanced
decision-making.
Usage:
- Add the RSI Breakout Asif indicator to your TradingView chart.
- Adjust the settings according to your trading strategy.
- Observe the RSI values and fractal points to identify potential breakout zones.
Disclaimer:
This indicator is a technical analysis tool and should be used in combination with other analysis
methods. It does not guarantee profitable trades.
Watermarked by Asif.
Asif - Page 2
Fractal & Entropy Market Dynamics with Mexican Hat WaveletThis indicator combines fractal analysis, entropy, and wavelet theory to model market dynamics using a customized approach. It integrates advanced mathematical techniques to assess the complexity and structure of price action, while also incorporating volume and price volatility.
Key Concepts and Features:
Volume-Weighted Price:
The script calculates a volume-adjusted price using a moving average of volume to give more weight to periods with higher volume. This allows the indicator to account for the impact of trading volume on price movements, enhancing its sensitivity to significant price shifts.
Mexican Hat Wavelet Approximation:
The script employs the Mexican Hat Wavelet, a mathematical tool that approximates price movements based on the Laplacian of the price series. This helps capture localized oscillations in price, acting as a filter to highlight certain price dynamics over the specified length. This wavelet is commonly used to identify key inflection points and trends in financial data.
Fractal Dimension Calculation:
The fractal dimension is calculated to quantify the market's complexity. It measures how price moves between intervals, with higher values indicating chaotic or more volatile market behavior. This dimension captures the self-similarity in price movements across different time frames, a key feature of fractals.
Shannon Entropy Calculation:
Shannon Entropy is used to measure the randomness or uncertainty in the price action. It calculates the degree of unpredictability based on the price changes, providing insight into the market's informational efficiency. Higher entropy indicates more randomness, while lower entropy suggests more predictable trends.
Custom Normalization:
The script includes a custom normalization function that processes the composite score (derived from fractal dimension and entropy). This normalization helps scale the values into a consistent range, making it easier to interpret the output. The smoothing factor and RSI-based approach ensure that the normalized value reacts smoothly to the changes in market dynamics.
Composite Score:
The composite score is a weighted combination of the fractal dimension and entropy. This score aims to provide a holistic view of the market by combining the structural complexity (fractal) and randomness (entropy) into one unified metric.
Plotting and Visuals:
The indicator plots the normalized composite score on a scale where a baseline of 50 is provided for reference. The resulting plot helps traders visualize market dynamics, with the score fluctuating based on changes in the market's fractal dimension and entropy. A score above or below the baseline of 50 indicates potential market shifts.
Use Case:
The "Enhanced Fractal and Entropy Market Dynamics with Mexican Hat Wavelet" is useful for traders looking to identify market conditions where there is a balance between price structure and randomness. By integrating wavelets, fractals, and entropy, the indicator can provide insights into market complexity, helping traders recognize potential trend reversals, periods of consolidation, or increased volatility. This can be particularly effective for those employing swing trading or trend-following strategies
@tk · fractal rsi levels█ OVERVIEW
This script is an indicator that helps traders to identify the RSI Levels for multiple fractals wherever the current timeframe is. This script was based on RSI Levels, 20-30 & 70-80 by abdomi indicator, that calculates the Relative Strenght Index levels based on the asset's price and plots it into the chart, creating a "wave" style indicator. The core feature of this indicator is the fractal rays, so trader can visualize each of the oversold and overbought levels of multiple timeframe on the current timeframe that he is on. The indicator will plots multiple rays after the chart bars. indicating where is the oversold and overbought levels for others fractals.
█ MOTIVATION
Since the RSI Levels, 20-30 & 70-80 by abdomi indicator helps a lot to identify the possible price levels when the asset is oversold or overbought, I saw myself drawing multiple horizontal lines on these levels in lower timeframes so, in an uptrend or downtrend, I can try to get a pullback of these trends when the asset reaches oversold or overboght levels. So, I get the idea to make those lines visible in multiple timeframes so I don't need to draw it myself manually anymore.
█ CONCEPT
The trading concept to use this indicator is the concept to make entries on uptrend or downtrend pullbacks when the asset price reaches oversold or overbought levels. But this strategy don't works alone. It needs to be aligned together with others indicators like Exponential Moving Averages, Chart Patterns, Support and Resistance, and so on... Even more confluences that you have, bigger are your chances to increase the probability for a successful trade. So, don't use this indicator alone. Compose a trading strategy and use it to improve your analysis.
█ CUSTOMIZATION
This indicator allows the trader to customize the following settings:
GENERAL
Text size
Changes the font size of the labels to improve accessibility.
Type: string
Options: `tiny`, `small`, `normal`, `large`.
Default: `small`
RSI LEVELS · SETTINGS
Pre-oversold Level
Changes the RSI Level to calculate the "pre-oversold" price level on the chart.
Type: int
Min: 1
Max: 49
Default: 33
Pre-overbought Level
Changes the RSI Level to calculate the "pre-overbought" price level on the chart.
Type: int
Min: 51
Max: 100
Default: 67
Show "Pre-over" Levels
Enables / Disables the pre-oversold and pre-overbought levels on the chart.
Type: bool
Default: true
FRACTAL RAYS · SETTINGS
Length
Changes the base length for the RSI calculation.
Type: int
Min: 1
Default: 14
Source
Changes the base source for the RSI calculation.
Type: float
Default: close
FRACTAL RAYS · STYLE
Ray Color
Changes the color of all fractal rays and its label.
Type: color
Default: color.rgb(187, 74, 207)
Ray Style
Changes the style of all fractal rays.
Type: string
Options: `line.style_solid`, `line.style_dashed`, `line.style_dotted`
Default: line.style_dotted
Ray Length
Changes the length of all fractal rays.
Type: int
Default: 15
FRACTAL RAYS · OVERSOLD
Oversold Level
Changes the base RSI Level for fractal rays calculation.
Type: int
Min: 1
Default: 30
Oversold Prefix
Customizes the fractal ray label with a prefix text.
Type: string
Default: 🚀
Oversold Suffix
Customizes the fractal ray label with a suffix text.
Type: string
Default: (empty)
FRACTAL RAYS · OVERBOUGHT
Overbought Level
Changes the base RSI Level for fractal rays calculation.
Type: int
Min: 1
Default: 70
Overbought Prefix
Customizes the fractal ray label with a prefix text.
Type: string
Default: 🐻
Overbought Suffix
Customizes the fractal ray label with a suffix text.
Type: string
Default: (empty)
FRACTAL RAYS · VISIBILITY RULES
These rules are applied for each of fractal rays so, the traders can choose what timeframes they wants to show the fractal rays for each of it. The rule will be applied as the following condition: `if timeframe != CURRENT_TIMEFRAME and timeframe <= CHOSEN_OPTION`. Actually, the fractal rays are on the chart but, isn't visible because it was applied a transparent color, so it is visually not on the chart to prevent chart's over polution.
LABELS
Show Labels on Price Scale
Shows labels on price scale.
Type: bool
Default: false
Show Price on Fractal Rays
Shows the RSI Level price on each of fractal rays respectively.
Type: bool
Default: false
█ EXTERNAL LIBRARIES
This script uses the `tk` library to calculate RSI Levels. It is a library that contains various functions that helps pine script developers to calculate RSI Levels.
█ FUNCTIONS
The library contains the following functions:
fn_fractalVisibilityRule(string visibilityRule)
Converts the fractal rays timeframe visibility rule label to timestamp int.
Parameters:
visibilityRule: (string) Fractal ray visibility rule label.
Returns: (int) Fractal ray visibility rule timestamp.
fn_requestFractal(string period, expression)
Converts the fractal rays timeframe visibility rule label to timestamp int.
Parameters:
period: (string) Timeframe period for the desired fractal.
expression: (mixed) Security expression that will be applied for calculation.
Returns: (mixed) A result determined by expression.
fn_plotRay(float y, string label, color color, int length)
Plots ray after chart bars for the current time.
Parameters:
period: (string) Timeframe period for the desired fractal.
expression: (mixed) Security expression that will be applied for calculation.
Returns: (void) This function only plots the elements into the chart
fn_plotRsiLevelRay(simple string period, simple int level, color color)
Plots RSI Levels ray after chart bars for the current time.
Parameters:
period: (simple string) Timeframe period.
level: (simple int) Relative Strength Index level.
color: (color) The color of both, ray and label text.
Returns: (void) This function only plots the elements into the chart
Nubia - Auto Midas Anchored VWAP [xdecow]This indicator looks for the highs and lows using the highest / lowest of 4 different periods. The default values are 17, 72, 305 and 1292 inspired by BO Williams phicube fractals.
The indicator will show anchored vwaps from those 8 dynamic points (4 tops and 4 bottoms).
A true trend is defined when 4 fractals line up without much opposite resistance (only the two smallest opposing vwaps).
When the price is between several vwaps it is a sign of consolidation or pullback. You can use larger timeframes to check for possible targets in large consolidations and pullbacks.
BTC FRACTAL ANN S-R LEVELS (Fixed ANN MACD)
This script is an adaptation of my deep learning system for Bitcoin to fractals.
Fractal codes are not belong to me. Original :
The code for the Deep learning (ANN MACD BTC) work belongs to me. Original:
I didn't get license for this script because the fractal codes don't belong to me.You can use it for any purpose.
This command can be a very helpful guide.You can use that fractals with your indicators for Bitcoin.
You can also combine these levels with ANN - MACD - BTC script.
Scripts about Artificial Neural Networks (ANN) will continue soon !
I hope it will help us to gain insight into technical analysis.
Best regards. Noldo.
Fractal FramerThis indicator constructs dynamic high/low price range levels, based on 3 point fractals. Trend lines are drawn (zigzag style) at fractal points when trend direction is clearly established.
This is a lagging indicator: fractals lag by one bar and trend lines can lag by many bars. However, its strength lays in objectively defining historic price action: e.g. support/resistance levels and waves within a trend.
See the comments at the beginning of the script for a complete description.
1H/3m Concept [RunRox]🕘 1H/3m Concept is a versatile trading methodology based on liquidity sweeps from fractal points identified on higher timeframes, followed by price reversals at these key moments.
Below, I will explain this concept in detail and provide clear examples demonstrating its practical application.
⁉️ WHAT IS A FRACTALS?
In trading, a fractal is a technical analysis pattern composed of five consecutive candles, typically highlighting local market turning points. Specifically, a fractal high is formed when a candle’s high is higher than the highs of the two candles on either side, whereas a fractal low occurs when a candle’s low is lower than the lows of the two adjacent candles on both sides.
Traders use fractals as reference points for identifying significant support and resistance levels, potential reversal areas, and liquidity zones within price action analysis. Below is a screenshot illustrating clearly formed fractals on the chart.
📌 ABOUT THE CONCEPT
The 1H/3m Concept involves marking Higher Timeframe (HTF) fractals directly onto a Lower Timeframe (LTF) chart. When a liquidity sweep occurs at an HTF fractal level, we remain on the same LTF chart (since all HTF fractals are already plotted on this lower timeframe) and wait for a clear Market Structure Shift (MSS) to identify our potential entry point.
Below is a schematic illustration clearly demonstrating how this concept works in practice.
Below is another 💡 real-chart example , showing liquidity in the form of a 1H fractal, swept by a rapid impulse move. Immediately afterward, a clear Market Structure Shift (MSS) occurs, signaling a potential entry point into the trade.
Another example is shown below, where we see our hourly fractal, from which price clearly reacts, providing an opportunity to search for an entry point.
As illustrated on the chart, the fractal levels from the higher timeframe are clearly displayed, but we’re working directly on the 5-minute chart. This allows us to remain on one timeframe without needing to switch back and forth between charts to spot such trading setups.
🔍 MTF FRACTALS
This concept can be applied across various HTF-LTF timeframe combinations. Although our examples illustrate 1H fractals used on a 5-minute chart, you can effectively utilize many other timeframe combinations, such as:
30m HTF fractals on 1m chart
1H HTF fractals on 3m chart
4H HTF fractals on 15m chart
1D HTF fractals on 1H chart
The key idea behind this concept is always the same: identify liquidity at fractal levels on the higher timeframe (HTF), then wait for a clear Market Structure Shift (MSS) on the lower timeframe (LTF) to enter trades.
⚙️ SETTINGS
🔷 Trade Direction – Select the preferred trading direction (Long, Short, or Both).
🔷 HTF – Choose the higher timeframe from which fractals will be displayed on the current chart.
🔷 HTF Period – Number of candles required on both sides of a fractal candle (before and after) to confirm fractal formation on the HTF.
🔷 Current TF Period – Sensitivity to the impulse that sweeps liquidity, used for identifying and forming the MSS line.
🔷 Show HTF – Enable or disable displaying HTF fractal lines on your chart. You can also customize line style and color.
🔷 Max Age (Bars) – Number of recent bars within which fractals from the selected HTF will be displayed.
🔷 Show Entry – Enable or disable displaying the MSS line on the chart.
🔷 Enable Alert – Activates TradingView alerts whenever the MSS line is crossed.
You can also enable 🔔 alerts, which notify you whenever price crosses the MSS line. This significantly simplifies the process of identifying these setups on your charts. Simply configure your preferred timeframes and wait for notifications when the MSS line is crossed.
🔶 We greatly appreciate your feedback and suggestions for improving the indicator!
ICT Donchian Smart Money Structure (Expo)█ Concept Overview
The Inner Circle Trader (ICT) methodology is focused on understanding the actions and implications of the so-called "smart money" - large institutions and professional traders who often influence market movements. Key to this is the concept of market structure and how it can provide insights into potential price moves.
Over time, however, there has been a notable shift in how some traders interpret and apply this methodology. Initially, it was designed with a focus on the fractal nature of markets. Fractals are recurring patterns in price action that are self-similar across different time scales, providing a nuanced and dynamic understanding of market structure.
However, as the ICT methodology has grown in popularity, there has been a drift away from this fractal-based perspective. Instead, many traders have started to focus more on pivot points as their primary tool for understanding market structure.
Pivot points provide static levels of potential support and resistance. While they can be useful in some contexts, relying heavily on them could provide a skewed perspective of market structure. They offer a static, backward-looking view that may not accurately reflect real-time changes in market sentiment or the dynamic nature of markets.
This shift from a fractal-based perspective to a pivot point perspective has significant implications. It can lead traders to misinterpret market structure and potentially make incorrect trading decisions.
To highlight this issue, you've developed a Donchian Structure indicator that mirrors the use of pivot points. The Donchian Channels are formed by the highest high and the lowest low over a certain period, providing another representation of potential market extremes. The fact that the Donchian Structure indicator produces the same results as pivot points underscores the inherent limitations of relying too heavily on these tools.
While the Donchian Structure indicator or pivot points can be useful tools, they should not replace the original, fractal-based perspective of the ICT methodology. These tools can provide a broad overview of market structure but may not capture the intricate dynamics and real-time changes that a fractal-based approach can offer.
It's essential for traders to understand these differences and to apply these tools correctly within the broader context of the ICT methodology and the Smart Money Concept Structure. A well-rounded approach that incorporates fractals, along with other tools and forms of analysis, is likely to provide a more accurate and comprehensive understanding of market structure.
█ Smart Money Concept - Misunderstandings
The Smart Money Concept is a popular concept among traders, and it's based on the idea that the "smart money" - typically large institutional investors, market makers, and professional traders - have superior knowledge or information, and their actions can provide valuable insight for other traders.
One of the biggest misunderstandings with this concept is the belief that tracking smart money activity can guarantee profitable trading.
█ Here are a few common misconceptions:
Following Smart Money Equals Guaranteed Success: Many traders believe that if they can follow the smart money, they will be successful. However, tracking the activity of large institutional investors and other professionals isn't easy, as they use complex strategies, have access to information not available to the public, and often intentionally hide their moves to prevent others from detecting their strategies.
Instantaneous Reaction and Results: Another misconception is that market movements will reflect smart money actions immediately. However, large institutions often slowly accumulate or distribute positions over time to avoid moving the market drastically. As a result, their actions might not produce an immediate noticeable effect on the market.
Smart Money Always Wins: It's not accurate to assume that smart money always makes the right decisions. Even the most experienced institutional investors and professional traders make mistakes, misjudge market conditions, or are affected by unpredictable events.
Smart Money Activity is Transparent: Understanding what constitutes smart money activity can be quite challenging. There are many indicators and metrics that traders use to try and track smart money, such as the COT (Commitments of Traders) reports, Level II market data, block trades, etc. However, these can be difficult to interpret correctly and are often misleading.
Assuming Uniformity Among Smart Money: 'Smart Money' is not a monolithic entity. Different institutional investors and professional traders have different strategies, risk tolerances, and investment horizons. What might be a good trade for a long-term institutional investor might not be a good trade for a short-term professional trader, and vice versa.
█ Market Structure
The Smart Money Concept Structure deals with the interpretation of price action that forms the market structure, focusing on understanding key shifts or changes in the market that may indicate where 'smart money' (large institutional investors and professional traders) might be moving in the market.
█ Three common concepts in this regard are Change of Character (CHoCH), and Shift in Market Structure (SMS), Break of Structure (BMS/BoS).
Change of Character (CHoCH): This refers to a noticeable change in the behavior of price movement, which could suggest that a shift in the market might be about to occur. This might be signaled by a sudden increase in volatility, a break of a trendline, or a change in volume, among other things.
Shift in Market Structure (SMS): This is when the overall structure of the market changes, suggesting a potential new trend. It usually involves a sequence of lower highs and lower lows for a downtrend, or higher highs and higher lows for an uptrend.
Break of Structure (BMS/BoS): This is when a previously defined trend or pattern in the price structure is broken, which may suggest a trend continuation.
A key component of this approach is the use of fractals, which are repeating patterns in price action that can give insights into potential market reversals. They appear at all scales of a price chart, reflecting the self-similar nature of markets.
█ Market Structure - Misunderstandings
One of the biggest misunderstandings about the ICT approach is the over-reliance or incorrect application of pivot points. Pivot points are a popular tool among traders due to their simplicity and easy-to-understand nature. However, when it comes to the Smart Money Concept and trying to follow the steps of professional traders or large institutions, relying heavily on pivot points can create misconceptions and lead to confusion. Here's why:
Delayed and Static Information: Pivot points are inherently backward-looking because they're calculated based on the previous period's data. As such, they may not reflect real-time market dynamics or sudden changes in market sentiment. Furthermore, they present a static view of market structure, delineating pre-defined levels of support and resistance. This static nature can be misleading because markets are fundamentally dynamic and constantly changing due to countless variables.
Inadequate Representation of Market Complexity: Markets are influenced by a myriad of factors, including economic indicators, geopolitical events, institutional actions, and market sentiment, among others. Relying on pivot points alone for reading market structure oversimplifies this complexity and can lead to a myopic understanding of market dynamics.
False Signals and Misinterpretations: Pivot points can often give false signals, especially in volatile markets. Prices might react to these levels temporarily but then continue in the original direction, leading to potential misinterpretation of market structure and sentiment. Also, a trader might wrongly perceive a break of a pivot point as a significant market event, when in fact, it could be due to random price fluctuations or temporary volatility.
Over-simplification: Viewing market structure only through the lens of pivot points simplifies the market to static levels of support and resistance, which can lead to misinterpretation of market dynamics. For instance, a trader might view a break of a pivot point as a definite sign of a trend, when it could just be a temporary price spike.
Ignoring the Fractal Nature of Markets: In the context of the Smart Money Concept Structure, understanding the fractal nature of markets is crucial. Fractals are self-similar patterns that repeat at all scales and provide a more dynamic and nuanced understanding of market structure. They can help traders identify shifts in market sentiment or direction in real-time, providing more relevant and timely information compared to pivot points.
The key takeaway here is not that pivot points should be entirely avoided or that they're useless. They can provide valuable insights and serve as a useful tool in a trader's toolbox when used correctly. However, they should not be the sole or primary method for understanding the market structure, especially in the context of the Smart Money Concept Structure.
█ Fractals
Instead, traders should aim for a comprehensive understanding of markets that incorporates a range of tools and concepts, including but not limited to fractals, order flow, volume analysis, fundamental analysis, and, yes, even pivot points. Fractals offer a more dynamic and nuanced view of the market. They reflect the recursive nature of markets and can provide valuable insights into potential market reversals. Because they appear at all scales of a price chart, they can provide a more holistic and real-time understanding of market structure.
In contrast, the Smart Money Concept Structure, focusing on fractals and comprehensive market analysis, aims to capture a more holistic and real-time view of the market. Fractals, being self-similar patterns that repeat at different scales, offer a dynamic understanding of market structure. As a result, they can help to identify shifts in market sentiment or direction as they happen, providing a more detailed and timely perspective.
Furthermore, a comprehensive market analysis would consider a broader set of factors, including order flow, volume analysis, and fundamental analysis, which could provide additional insights into 'smart money' actions.
█ Donchian Structure
Donchian Channels are a type of indicator used in technical analysis to identify potential price breakouts and trends, and they may also serve as a tool for understanding market structure. The channels are formed by taking the highest high and the lowest low over a certain number of periods, creating an envelope of price action.
Donchian Channels (or pivot points) can be useful tools for providing a general view of market structure, and they may not capture the intricate dynamics associated with the Smart Money Concept Structure. A more nuanced approach, centered on real-time fractals and a comprehensive analysis of various market factors, offers a more accurate understanding of 'smart money' actions and market structure.
█ Here is why Donchian Structure may be misleading:
Lack of Nuance: Donchian Channels, like pivot points, provide a simplified view of market structure. They don't take into account the nuanced behaviors of price action or the complex dynamics between buyers and sellers that can be critical in the Smart Money Concept Structure.
Limited Insights into 'Smart Money' Actions: While Donchian Channels can highlight potential breakout points and trends, they don't necessarily provide insights into the actions of 'smart money'. These large institutional traders often use sophisticated strategies that can't be easily inferred from price action alone.
█ Indicator Overview
We have built this Donchian Structure indicator to show that it returns the same results as using pivot points. The Donchian Structure indicator can be a useful tool for market analysis. However, it should not be seen as a direct replacement or equivalent to the original Smart Money concept, nor should any indicator based on pivot points. The indicator highlights the importance of understanding what kind of trading tools we use and how they can affect our decisions.
The Donchian Structure Indicator displays CHoCH, SMS, BoS/BMS, as well as premium and discount areas. This indicator plots everything in real-time and allows for easy backtesting on any market and timeframe. A unique candle coloring has been added to make it more engaging and visually appealing when identifying new trading setups and strategies. This candle coloring is "leading," meaning it can signal a structural change before it actually happens, giving traders ample time to plan their next trade accordingly.
█ How to use
The indicator is great for traders who want to simplify their view on the market structure and easily backtest Smart Money Concept Strategies. The added candle coloring function serves as a heads-up for structure change or can be used as trend confirmation. This new candle coloring feature can generate many new Smart Money Concepts strategies.
█ Features
Market Structure
The market structure is based on the Donchian channel, to which we have added what we call 'Structure Response'. This addition makes the indicator more useful, especially in trending markets. The core concept involves traders buying at a discount and selling or shorting at a premium, depending on the order flow. Structure response enables traders to determine the order flow more clearly. Consequently, more trading opportunities will appear in trending markets.
Structure Candles
Structure Candles highlight the current order flow and are significantly more responsive to structural changes. They can provide traders with a heads-up before a break in structure occurs
-----------------
Disclaimer
The information contained in my Scripts/Indicators/Ideas/Algos/Systems does not constitute financial advice or a solicitation to buy or sell any securities of any type. I will not accept liability for any loss or damage, including without limitation any loss of profit, which may arise directly or indirectly from the use of or reliance on such information.
All investments involve risk, and the past performance of a security, industry, sector, market, financial product, trading strategy, backtest, or individual's trading does not guarantee future results or returns. Investors are fully responsible for any investment decisions they make. Such decisions should be based solely on an evaluation of their financial circumstances, investment objectives, risk tolerance, and liquidity needs.
My Scripts/Indicators/Ideas/Algos/Systems are only for educational purposes!
Fractal Pattern AnalysisFractal Pattern Key Elements and How to Read Them
1. Williams Fractals (Triangle Markers)
Red Triangles Pointing Down: Bearish fractals - potential resistance points and selling opportunities
Green Triangles Pointing Up: Bullish fractals - potential support points and buying opportunities
When to Act: Look for bullish fractals forming during uptrends and bearish fractals during downtrends
2. Moving Averages
Yellow Line (20 EMA): Short-term trend
Blue Line (50 EMA): Medium-term trend
Red Line (200 EMA): Long-term trend
Interpretation: When shorter MAs cross above longer MAs, it's bullish; when they cross below, it's bearish
Key Signal: The alignment of all three MAs (stacked in order) confirms a strong trend
3. Background Color
Green Background: Uptrend (all MAs aligned bullishly)
Red Background: Downtrend (all MAs aligned bearishly)
Yellow Background: Sideways/neutral market (MAs not clearly aligned)
4. Market Structure Markers (Small Circles)
Green Circles: Higher highs and higher lows (bullish structure)
Red Circles: Lower highs and lower lows (bearish structure)
Pattern Recognition: Multiple green circles suggest continuing uptrend; multiple red circles suggest continuing downtrend
5. Reversal Diamonds ("Rev" Markers)
Yellow Diamonds: Potential trend reversal points
Usage: These mark where the current trend might be changing direction
Confirmation: Wait for price to close beyond the diamond before acting
6. Bollinger Bands (Blue Lines with Fill)
Middle Band: 20-period SMA
Upper/Lower Bands: Volatility channels
Signals: Price touching upper band in uptrend is strength; touching lower band in downtrend is weakness
Squeeze: When bands narrow, expect a volatility breakout soon
7. Status Table (Top Right)
Shows current trend, volume direction, and overall signal at a glance
"BUY" signal appears when multiple bullish conditions align
"SELL" signal appears when multiple bearish conditions align
AuriumFlowAURIUM (GOLD-Weighted Average with Fractal Dynamics)
Aurium is a cutting-edge indicator that blends volume-weighted moving averages (VWMA), fractal geometry, and Fibonacci-inspired calculations to deliver a precise and holistic view of market trends. By dynamically adjusting to price and volume, Aurium uncovers key levels of confluence for trend reversals and continuations, making it a powerful tool for traders.
Key Features:
Dynamic Trendline (GOLD):
The central trendline is a weighted moving average based on price and volume, tuned using Fibonacci-based fast (34) and slow (144) exponential moving average lengths. This ensures the trendline adapts seamlessly to the flow of market dynamics.
Formula:
GOLD = VWMA(34) * Volume Factor + VWMA(144) * (1 - Volume Factor)
Fractal Highs and Lows:
Detects pivotal market points using a fractal lookback period (default 5, odd-numbered). Fractals identify local highs and lows over a defined window, capturing the structure of market cycles.
Trend Background Highlighting:
Bullish Zone: Price above the GOLD line with a green background.
Bearish Zone: Price below the GOLD line with a red background.
Buy and Sell Alerts:
Generates actionable signals when fractals align with GOLD. Bullish fractals confirm continuation or reversal in an uptrend, while bearish fractals validate a downtrend.
The Math Behind Aurium:
Volume-Weighted Adjustments:
By integrating volume into the calculation, Aurium dynamically emphasizes price levels with greater participation, giving traders insight into zones of institutional interest.
Formula:
VWMA = EMA(Close * Volume) / EMA(Volume)
Fractal Calculations:
Fractals are identified as local maxima (highs) or minima (lows) based on the surrounding bars, leveraging the natural symmetry in price behavior.
Fibonacci Relationships:
The 34 and 144 EMA lengths are Fibonacci numbers, offering a natural alignment with price cycles and market rhythms.
Ideal For:
Traders seeking a precise and intuitive indicator for aligning with trends and detecting reversals.
Strategies inspired by Bill Williams, with added volume and fractal-based insights.
Short-term scalpers and long-term trend-followers alike.
Unlock deeper market insights and trade with precision using Aurium!
Fractal Trend Detector [Skyrexio]Introduction
Fractal Trend Detector leverages the combination of Williams fractals and Alligator Indicator to help traders to understand with the high probability what is the current trend: bullish or bearish. It visualizes the potential uptrend with the coloring bars in green, downtrend - in red color. Indicator also contains two additional visualizations, the strong uptrend and downtrend as the green and red zones and the white line - trend invalidation level (more information in "Methodology and it's justification" paragraph)
Features
Optional strong up and downtrends visualization: with the specified parameter in settings user can add/hide the green and red zones of the strong up and downtrends.
Optional trend invalidation level visualization: with the specified parameter in settings user can add/hide the white line which shows the current trend invalidation price.
Alerts: user can set up the alert and have notifications when uptrend/downtrend has been started, strong uptrend/downtrend started.
Methodology and it's justification
In this script we apply the concept of trend given by Bill Williams in his book "Trading Chaos". This approach leverages the Alligator and Fractals in conjunction. Let's briefly explain these two components.
The Williams Alligator, created by Bill Williams, is a technical analysis tool used to identify trends and potential market reversals. It consists of three moving averages, called the jaw, teeth, and lips, which represent different time periods:
Jaw (Blue Line): The slowest line, showing a 13-period smoothed moving average shifted 8 bars forward.
Teeth (Red Line): The medium-speed line, an 8-period smoothed moving average shifted 5 bars forward.
Lips (Green Line): The fastest line, a 5-period smoothed moving average shifted 3 bars forward.
When the lines are spread apart and aligned, the "alligator" is "awake," indicating a strong trend. When the lines intertwine, the "alligator" is "sleeping," signaling a non-trending or range-bound market. This indicator helps traders identify when to enter or avoid trades.
Williams Fractals, introduced by Bill Williams, are a technical analysis tool used to identify potential reversal points on a price chart. A fractal is a series of at least five consecutive bars where the middle bar has the highest high (for a up fractal) or the lowest low (for a down fractal), compared to the two bars on either side.
Key Points:
Up fractal: Formed when the middle bar shows a higher high than the two preceding and two following bars, signaling a potential turning point downward.
Down fractal: Formed when the middle bar has a lower low than the two surrounding bars, indicating a potential upward reversal.
Fractals are often used with other indicators to confirm trend direction or reversal, helping traders make more informed trading decisions.
How we can use its combination? Let's explain the uptrend example. The up fractal breakout to the upside can be interpret as bullish sign, there is a high probability that uptrend has just been started. It can be explained as following: the up fractal created is the potential change in market's behavior. A lot of traders made a decision to sell and it created the pullback with the fractal at the top. But if price is able to reach the fractal's top and break it, this is a high probability sign that market "changed his opinion" and bullish trend has been started. The moment of breaking is the potential changing to the uptrend. Here is another one important point, this breakout shall happen above the Alligator's teeth line. If not, this crossover doesn't count and the downtrend potentially remaining. The inverted logic is true for the down fractals and downtrend.
According to this methodology we received the high probability up and downtrend changes, but we can even add it. If current trend established by the indicator as the uptrend and alligator's lines have the following order: lips is higher than teeth, teeth is higher than jaw, script count it as a strong uptrend and start print the green zone - zone between lips and jaw. It can be used as a high probability support of the current bull market. The inverted logic can be used for bearish trend and red zones: if lips is lower than teeth and teeth is lower than jaw it's interpreted by the indicator as a strong down trend.
Indicator also has the trend invalidation line (white line). If current bar is green and market condition is interpreted by the script as an uptrend you will see the invalidation line below current price. This is the price level which shall be crossed by the price to change up trend to down trend according to algorithm. This level is recalculated on every candle. The inverted logic is valid for downtrend.
How to use indicator
Apply it to desired chart and time frame. It works on every time frame.
Setup the settings with enabling/disabling visualization of strong up/downtrend zones and trend invalidation line. "Show Strong Bullish/Bearish Trends" and "Show Trend Invalidation Price" checkboxes in the settings. By default they are turned on.
Analyze the price action. Indicator colored candle in green if it's more likely that current state is uptrend, in red if downtrend has the high probability to be now. Green zones between two lines showing if current uptrend is likely to be strong. This zone can be used as a high probability support on the uptrend. The red zone show high probability of strong downtrend and can be used as a resistance. White line is showing the level where uptrend or downtrend is going be invalidated according to indicator's algorithm. If current bar is green invalidation line will be below the current price, if red - above the current price.
Set up the alerts if it's needed. Indicator has four custom alerts called "Uptrend has been started" when current bar closed as green and the previous was not green, "Downtrend has been started" when current bar closed red and the previous was not red, "Uptrend became strong" if script started printing the green zone "Downtrend became strong" if script started printing the red zone.
Disclaimer:
Educational and informational tool reflecting Skyrex commitment to informed trading. Past performance does not guarantee future results. Test indicators before live implementation.
