RSI and ATR Trend Reversal SL/TPQuick History:
I was frustrated with a standard fixed percent TP/SL as they often were not receptive to quick market rallies/reversals. I developed this TP/SL and eventually made it into a full fledge strategy and found it did well enough to publish. This strategy can be used as a standalone or tacked onto another strategy as a TP/SL. It does function as both with a single line. This strategy has been tested with TSLA , AAPL, NVDA, on the 15 minutes timeframe.
HOW IT WORKS:
Inputs:
Length: Simple enough, it determines the length of the RSI and ATR used.
Multiplier: This multiplies the RSI and ATR calculation, more on this later.
Delay to prevent Idealization: TradingView will use the open of the bar the strategy triggers on when calculating the backtest. This can produce unrealistic results depending on the source. If your source is open, set to 0, if anything else, set to 1.
Minimum Difference: This is essentially a traditional SL/TP, it is borderline unnecessary, but if the other parameters are wacky this can be used to ensure the SL/TP. It multiplies the source by the percent, so if it is set to 10, the SL/TP is initialized at src +- 10%.
Source input: Self Explanatory, be sure to update the Delay if you use open.
CALCULATION:
Parameters Initialization:
The strategy uses Heikinashi values for calculations, this is not toggleable in parameters, but can be easily changed by changing hclose to equal src.
FUNCTION INITIALIZATION:
highest_custom and lowest_custom do the same thing as ta.highest and ta.lowest, however the built in ta library does not allow for var int input, so I had to create my own functions to be used here. I actually developed these years ago and have used them in almost every strategy since. Feel especially free to use these in your own scripts.
The rsilev is where the magic happens.
SL/TP min/max are initially calculated to be used later.
Then we begin by establishing variables.
BullGuy is used to determine the length since the last crossup or crossdown, until one happens, it returns na, breaking the function. BearGuy is used in all the calculations, and is the same as BullGuy, unless BullGuy is na, where BearGuy counts up from 1 on each bar from 0.
We create our rsi and have to modify the second one to suit the function. In the case of the upper band, we mirror the lower one. So if the RSI is 80, we want it to be 20 on the upper band.
the upper band and lower band are calculated the exact same way, but mirrored. For the purpose of writing, I'm going to talk about the lower band. Assume everything is mirrored for the upper one. It finds the highest source since the last crossup or crossdown. It then multiplies from 1 / the RSI, this means that a rapid RSI increase will increase the band dramatically, so it is able to capture quick rally/reversals. We add this to the atr to source ratio, as the general volatility is a massive factor to be included. We then multiply this number by our chosen amount, and subtract it from the highest source, creating the band.
We do this same process but mirrored with both bands and compared it to the source. If the source is above the lower band, it suggests an uptrend, so the lower band is outputted, and vice versa for the upper one.
PLOTTING:
We also determine the line color in the same manner as we do the trend direction.
STRATEGY:
We then use the source again, and if it crosses up or down relative to the selected band, we enter a long or short respectively.
This may not be the most superb independent strategy, but it can be very useful as a TP/SL for your chosen entry conditions, especially in volatile markets or tickers.
Thank you for taking the time to read, and please enjoy.
Cerca negli script per "the strat"
Footprint strategyThis strategy uses imbalance volume data obtained by footprint calculation technology.
There are two signals to enter a trade:
trend - the current buy volume on the bar is greater than the current sell volume and there is at least one imbalance line.
reversal - the current bar is falling, but the general market trend is positive (growing) and the imbalance buy volume exceeds the imbalance sell volume.
When any of the conditions is triggered, two orders are placed: Take Profit and Stop loss (according to the percentage value from the inputs).
A little advice on use:
The strategy performs best on a 15 minute timeframe.
It is necessary to choose acceptable values of Take Profit and Stop loss depending on the order of symbol prices.
Inputs related to the strategy:
Stop loss - percentage size of stop loss to exit the trade.
Enable stop loss - stop loss activation.
Take Profit - percentage size of Take Profit.
Calculation timeframe - this is the timeframe from which the volume will be collected for distribution to buy and sell (if you do not have access to the seconds chart, set here 1 minute, the accuracy will be less, but it will work).
Trend timeframe - this is the timeframe from which the trend will be calculated.
Enable trend - activation of trend calculation.
Inputs related to the calculation of footprints (collection of the volume of purchases and sales):
Count show bars - Number of bars from rt bar to history to calculate.
Display all available bars - Strategy calculation on all available bars (based on the available amount of data with reduced resolution (set in Calculation timeframe)).
Ticks Per Row - Sets the price step, calculated by multiplying the entered value by syminfo.mintick.
Auto - The automatic "Ticks Per Row" calculation is based on the first available bar and applied to subsequent bars.
Max row - sets the acceptable number of rows within a bar.
Imbalance Percent - A percentage coefficient to determine the Imbalance of price levels.
Stacked levels - And minimum number of consecutive Imbalance levels required to draw extended lines.
If you have suggestions for improving the strategy and adding new conditions for entering and exiting the trade, please write).
Spot Martingale KuCoin - The Quant ScienceINTRODUCTION
Backtesting software of the Spot Martingale algorithm offered by the KuCoin exchange.
This script replicates the logic used by the KuCoin bot and is useful for analyzing strategy on any cryptocurrency historical series.
It's not intended as an automatic trading algorithm and does not offer the possibility of automatic order execution.
The trader will use this software exclusively to research the best parameters with which to work on KuCoin.
LOGIC OF EXECUTION
The execution of orders is composed as follows:
1) Start Martingale: initial order
2) Martingale-Number: orders following Start Martingale
(A) The software is designed and developed to replicate trading without taking into account technical indicators or particular market conditions. The Initial Order (Start Martingale) will be executed immediately the close of the previous Martingale when the balance of market orders is zero. It will use the capital set in the Properties section for the initial order.
(B) After the first order, the software will open new orders as the price decreases. For orders following Start Martingale, the initial capital, multiplier, and number of orders in the exponential growth context are considered. The multiplier is the factor that determines the proportional increase in capital with each new order. The number of orders, indicates how many times the multiplier is applied to increase the investment.
Example
To find out the capital used in Martingale order number 5, with a Multiple For Position Increase equal to 2 and a starting capital of $100, the formula will be as follows:
Martingale Order = ($100 * (2 * 2 * 2 * 2 * 2)) = $100 * 32 = $3.200
(C) A multiplier is used for each new order that will increase the quantity purchased.
(D) All previously open orders are closed once the take profit is reached.
USER MANUAL
The user interface consists of two main sections:
1. Settings
Percentage Drop for Position Increase (0.1-15%) : percentage distance between Martingale orders. For example, if you set 5% each new order will be opened after a 5% price decrease from the previous one.
Max Position Increases (1-15) : number of Martingale orders to be executed after Start Martingale. For example, if you set 10, up to10 orders will be opened after Start Martingale.
Multiple For Position Increase (1-2x) : capital multiplier. For example, if you set 2 each for each new order, the capital involved will be doubled, order by order.
Take Profit Percentage (0.5-1000%) : percentage take profit, calculated on the average entry price.
2. Date Range Backtesting
The Date Range Backtesting section adjusts the analysis period. The user can easily adjust the UI parameters, and automatically the software will update the data.
LIMITATIONS OF THE MODEL
Although the Martingale model is widely used in position management, even this model has limitations and is subject to real risks during particular market conditions. Knowing these conditions will help you understand which asset is best to use the strategy on.
The main risks in adopting this automatic strategy are 2:
1) The price falls below our last order.
It happens during periods of strong bear-market in which the price collapses abruptly without experiencing any pullback. In this case the algorithm will enter a drawdown phase and the strategy will become a loser. The trader will then have to consider whether to wait for a price recovery or to incur a loss by manually closing the algorithm.
2) The price increases quickly.
It happens during periods of strong bull-market in which the price rises abruptly without experiencing any pullback. In this case the algorithm will not optimize order execution, working only with Start Martingale in the vast majority of trades. Given the exponential nature of the investment, the algorithm will in this case generate a profit that is always less than that of the reference market.
The best market conditions to use this strategy are characterized by high volatility such as correction phases during a bull run and/or markets that exhibit sideways price trends (such as areas of accumulation or congestion where price will generate many false signals).
FEATURES
This script was developed by including features to optimize the user experience.
Includes a dashboard at launch that allows the user to intuitively enter backtesting parameters.
Includes graphical indicator that helps the user analyze the behavior of the strategy.
Includes a date period backtesting feature that allows the user to adjust and choose custom historical periods.
DISCLAIMER
This script was released using parameters researched solely for the BTC/USDT pair, 4H timeframe, traded on the KuCoin Exchange (2017-present). Do not consider this combination of parameters as universal and usable on all assets and timeframes.
Universal Algorithm [BackQuant]Universal Algorithm
It is a trading strategy designed CLEAR TREND DETECTION . This script is the culmination of extensive research and development efforts aimed at providing traders with a robust tool capable of adapting to a wide array of market conditions. This description delves into the core components, methodologies, and operational parameters of Universal Algo to offer potential users a clear understanding of its functionalities and the principles underpinning its design.
Core Methodologies and Features:
Integrated Systems: Universal Algo is built around six core systems, each contributing unique analytical perspectives to enhance trade signal reliability. These systems are designed to identify clear trend opportunities for significant gains, while also employing logic to navigate through ranging markets effectively.
Adaptive Market Logic: By incorporating volatility metrics, the algorithm dynamically adjusts to changing market conditions. This ensures that the strategy remains effective across different market regimes, aiming to reduce market noise and improve signal quality.
Selective Shorting Mechanism: While the primary focus is on capturing long positions, it includes an optional shorting feature. This can be activated by users to adapt the strategy during macro downtrends, thus providing a flexible approach to market participation.
Backtesting and Forward-Testing Rigor : The strategy has undergone rigorous testing to validate its performance and reliability. It demonstrates prudent risk management by optimizing conditions under which short positions are considered, aiming to mitigate drawdowns and preserve capital.
Operational Parameters:
Customization Options: The script offers a range of user inputs, allowing for customization of the backtesting starting date, the decision to display the strategy equity curve, among other settings. These inputs cater to diverse trading needs and preferences, offering users control over their strategy implementation.
Transparency and Logic Insight: While specific calculation details and proprietary indicators are integral to maintaining the uniqueness of Universal Algo , the strategy is grounded on well-established financial analysis techniques. These include momentum analysis, volatility assessments, and adaptive thresholding, among others, to formulate its trade signals.
Realistic Trading Conditions : Backtesting, considered realistic trading conditions, including appropriate account size, commission, slippage, and sustainable risk levels per trade. The strategy is designed and tested with a focus on achieving a balance between risk and reward, striving for robustness and reliability rather than unrealistic profitability promises.
Concluding Thoughts:
Universal Algo is offered to the TradingView community as a tool for traders seeking to enhance their market analysis and trading strategies. Its development is driven by a commitment to quality, innovation, and adaptability, aiming to provide valuable insights and decision-support in various market conditions. Potential users are encouraged to evaluate Universal Algo within the context of their overall trading approach and objectives.
Adaptaive MA PSAR Strategy [PivotProphet]This strategy, leverages a dynamic approach to moving averages, an adaptive Parabolic SAR (PSAR), and volume moving averages to create a versatile trading system suitable for various markets. It includes an array of customizable settings that allow traders to adapt the strategy to their preferences and market conditions.
Key Features
Dynamic Moving Averages: Choose between a standard SMA, EMA, or RMA, and explore dynamic versions for adaptive smoothing and trend detection.
Parabolic SAR: Incorporates both standard and adaptive PSAR for trend reversal signals. The adaptive PSAR settings can be fine-tuned for sensitivity and responsiveness.
Volume MA: Enhances trade confirmation with volume moving averages, offering multiple types for a comprehensive market analysis.
Filter Integration: Includes ATR for volatility filtering, ADX for trend strength, RSI for momentum, and MACD for trend confirmation, each with customizable parameters.
Settings Overview
Trend Settings: Choose your preferred MA type and length for trend analysis.
PSAR Settings: Adjust the PSAR start, increment, and maximum values for tailored trend reversal signals.
Adaptive PSAR Settings: Fine-tune the adaptive PSAR with various modes, smoothing periods, and thresholds for enhanced flexibility.
Volume & Volatility Filters: Set up volume MA type and length, and utilize the ATR filter for volatility-based decision-making.
Exit/Entry Conditions: Select from SMA100, PSAR, or Adaptive PSAR for exit conditions, and customize entry conditions with PSAR settings.
Strategy Implementation
The strategy triggers long positions when the price is above the selected MA, accompanied by a favorable PSAR signal and volume exceeding its MA. Short positions are considered under the inverse conditions. Filters such as ADX, RSI, and MACD are applied to refine entry points, while dynamic exit conditions based on the chosen setting ensure disciplined risk management.
Visualization:
SMA and PSAR plots provide a visual representation of the trend and potential reversal points.
Color-coded bars and shapes indicate trading signals and market sentiment.
Designed for versatility, this strategy aims to cater to both novice and experienced traders seeking a robust framework for their trading endeavors. Customize to your heart's content and adapt to the rhythm of the markets with the Adaptive MA PSAR Strategy.
Bitcoin Leverage Sentiment - Strategy [presentTrading]█ Introduction and How it is Different
The "Bitcoin Leverage Sentiment - Strategy " represents a novel approach in the realm of cryptocurrency trading by focusing on sentiment analysis through leveraged positions in Bitcoin. Unlike traditional strategies that primarily rely on price action or technical indicators, this strategy leverages the power of Z-Score analysis to gauge market sentiment by examining the ratio of leveraged long to short positions. By assessing how far the current sentiment deviates from the historical norm, it provides a unique lens to spot potential reversals or continuation in market trends, making it an innovative tool for traders who wish to incorporate market psychology into their trading arsenal.
BTC 4h L/S Performance
local
█ Strategy, How It Works: Detailed Explanation
🔶 Data Collection and Ratio Calculation
Firstly, the strategy acquires data on leveraged long (**`priceLongs`**) and short positions (**`priceShorts`**) for Bitcoin. The primary metric of interest is the ratio of long positions relative to the total of both long and short positions:
BTC Ratio=priceLongs / (priceLongs+priceShorts)
This ratio reflects the prevailing market sentiment, where values closer to 1 indicate a bullish sentiment (dominance of long positions), and values closer to 0 suggest bearish sentiment (prevalence of short positions).
🔶 Z-Score Calculation
The Z-Score is then calculated to standardize the BTC Ratio, allowing for comparison across different time periods. The Z-Score formula is:
Z = (X - μ) / σ
Where:
- X is the current BTC Ratio.
- μ is the mean of the BTC Ratio over a specified period (**`zScoreCalculationPeriod`**).
- σ is the standard deviation of the BTC Ratio over the same period.
The Z-Score helps quantify how far the current sentiment deviates from the historical norm, with high positive values indicating extreme bullish sentiment and high negative values signaling extreme bearish sentiment.
🔶 Signal Generation: Trading signals are derived from the Z-Score as follows:
Long Entry Signal: Occurs when the BTC Ratio Z-Score crosses above the thresholdLongEntry, suggesting bullish sentiment.
- Condition for Long Entry = BTC Ratio Z-Score > thresholdLongEntry
Long Exit/Short Entry Signal: Triggered when the BTC Ratio Z-Score drops below thresholdLongExit for exiting longs or below thresholdShortEntry for entering shorts, indicating a shift to bearish sentiment.
- Condition for Long Exit/Short Entry = BTC Ratio Z-Score < thresholdLongExit or BTC Ratio Z-Score < thresholdShortEntry
Short Exit Signal: Happens when the BTC Ratio Z-Score exceeds the thresholdShortExit, hinting at reducing bearish sentiment and a potential switch to bullish conditions.
- Condition for Short Exit = BTC Ratio Z-Score > thresholdShortExit
🔶Implementation and Visualization: The strategy applies these conditions for trade management, aligning with the selected trade direction. It visualizes the BTC Ratio Z-Score with horizontal lines at entry and exit thresholds, illustrating the current sentiment against historical norms.
█ Trade Direction
The strategy offers flexibility in trade direction, allowing users to choose between long, short, or both, depending on their market outlook and risk tolerance. This adaptability ensures that traders can align the strategy with their individual trading style and market conditions.
█ Usage
To employ this strategy effectively:
1. Customization: Begin by setting the trade direction and adjusting the Z-Score calculation period and entry/exit thresholds to match your trading preferences.
2. Observation: Monitor the Z-Score and its moving average for potential trading signals. Look for crossover events relative to the predefined thresholds to identify entry and exit points.
3. Confirmation: Consider using additional analysis or indicators for signal confirmation, ensuring a comprehensive approach to decision-making.
█ Default Settings
- Trade Direction: Determines if the strategy engages in long, short, or both types of trades, impacting its adaptability to market conditions.
- Timeframe Input: Influences signal frequency and sensitivity, affecting the strategy's responsiveness to market dynamics.
- Z-Score Calculation Period: Affects the strategy’s sensitivity to market changes, with longer periods smoothing data and shorter periods increasing responsiveness.
- Entry and Exit Thresholds: Set the Z-Score levels for initiating or exiting trades, balancing between capturing opportunities and minimizing false signals.
- Impact of Default Settings: Provides a balanced approach to leverage sentiment trading, with adjustments needed to optimize performance across various market conditions.
TTP Intelligent AccumulatorThe intelligent accumulator is a proof of concept strategy. A hybrid between a recurring buy and TA-based entries and exits.
Distribute the amount of equity and add to your position as long as the TA condition is valid.
Use the exit TA condition to define your exit strategy.
Decide between adding only into losing positions to average down or take a riskier approach by allowing to add into a winning position as well.
Take full profit or distribute your exit into multiple take profit exists of the same size.
You can also decide if you allow your exit conditions to close your position in a loss or require a minimum take profit %.
The strategy includes a default built-in TA conditions just for showcasing the idea but the final intent of this script is to delegate the TA entries and exists to external sources.
The internal conditions use RSI length 7 crossing below the BB with std 1 for entries and above for exits.
To control the number of orders use the properties from settings:
- adjust the pyramiding
- adjust the percentage of equity
- make sure that pyramiding * % equity equals 100 to prevent over use of equity (unless using leverage)
The script is designed as an alternative to daily or weekly recurring buys but depending on the accuracy of your TA conditions it might prove profitable also in lower timeframes.
The reason the script is named Intelligent is because recurring buy is most commonly used without any decision making: buy no matter what with certain frequency. This strategy seeks to still perform recurring buys but filtering out some of the potential bad entries that can delay unnecessarily seeing the position in profits. The second reason is also securing an exit strategy from the beginning which no recurring buy option offers out-of-the-box.
Long EMA Strategy with Advanced Exit OptionsThis strategy is designed for traders seeking a trend-following system with a focus on precision and adaptability.
**Core Strategy Concept**
The essence of this strategy lies in use of Exponential Moving Averages (EMAs) to identify potential long (buy) positions based on the relative positions of short-term, medium-term, and long-term EMAs. The use of EMAs is a classic yet powerful approach to trend detection, as these indicators smooth out price data over time, emphasizing the direction of recent price movements and potentially signaling the beginning of new trends.
**Customizable Parameters**
- **EMA Periods**: Users can define the periods for three EMAs - long-term, medium-term, and short-term - allowing for a tailored approach to capture trends based on individual trading styles and market conditions.
- **Volatility Filter**: An optional Average True Range (ATR)-based volatility filter can be toggled on or off. When activated, it ensures that trades are only entered when market volatility exceeds a user-defined threshold, aiming to filter out entries during low-volatility periods which are often characterized by indecisive market movements.
- **Trailing Stop Loss**: A trailing stop loss mechanism, expressed as a percentage of the highest price achieved since entry, provides a dynamic way to manage risk by allowing profits to run while cutting losses.
- **EMA Exit Condition**: This advanced exit option enables closing positions when the short-term EMA crosses below the medium-term EMA, serving as a signal that the immediate trend may be reversing.
- **Close Below EMA Exit**: An additional exit condition, which is disabled by default, allows positions to be closed if the price closes below a user-selected EMA. This provides an extra layer of flexibility and risk management, catering to traders who prefer to exit positions based on specific EMA thresholds.
**Operational Mechanics**
Upon activation, the strategy evaluates the current price in relation to the set EMAs. A long position is considered when the current price is above the long-term EMA, and the short-term EMA is above the medium-term EMA. This setup aims to identify moments where the price momentum is strong and likely to continue.
The strategy's versatility is further enhanced by its optional settings:
- The **Volatility Filter** adjusts the sensitivity of the strategy to market movements, potentially improving the quality of the entries during volatile market conditions.
The Average True Range (ATR) is a key component of this filter, providing a measure of market volatility by calculating the average range between the high and low prices over a specified number of periods. Here's how you can adjust the volatility filter settings for various market conditions, focusing on filtering out low-volatility markets:
Setting Examples for Volatility Filter
1. High Volatility Markets (e.g., Cryptocurrencies, Certain Forex Pairs):
ATR Periods: 14 (default)
ATR Multiplier: Setting the multiplier to a lower value, such as 1.0 or 1.2, can be beneficial in high-volatility markets. This sensitivity allows the strategy to react to volatility changes more quickly, ensuring that you're entering trades during periods of significant movement.
2. Medium Volatility Markets (e.g., Major Equity Indices, Medium-Volatility Forex Pairs):
ATR Periods: 14 (default)
ATR Multiplier: A multiplier of 1.5 (default) is often suitable for medium volatility markets. It provides a balanced approach, ensuring that the strategy filters out low-volatility conditions without being overly restrictive.
3. Low Volatility Markets (e.g., Some Commodities, Low-Volatility Forex Pairs):
ATR Periods: Increasing the ATR period to 20 or 25 can smooth out the volatility measure, making it less sensitive to short-term fluctuations. This adjustment helps in focusing on more significant trends in inherently stable markets.
ATR Multiplier: Raising the multiplier to 2.0 or even 2.5 increases the threshold for volatility, effectively filtering out low-volatility conditions. This setting ensures that the strategy only triggers trades during periods of relatively higher volatility, which are more likely to result in significant price movements.
How to Use the Volatility Filter for Low-Volatility Markets
For traders specifically interested in filtering out low-volatility markets, the key is to adjust the ATR Multiplier to a higher level. This adjustment increases the threshold required for the market to be considered sufficiently volatile for trade entries. Here's a step-by-step guide:
Adjust the ATR Multiplier: Increase the ATR Multiplier to create a higher volatility threshold. A multiplier of 2.0 to 2.5 is a good starting point for very low-volatility markets.
Fine-Tune the ATR Periods: Consider lengthening the ATR calculation period if you find that the strategy is still entering trades in undesirable low-volatility conditions. A longer period provides a more averaged-out measure of volatility, which might better suit your needs.
Monitor and Adjust: Volatility is not static, and market conditions can change. Regularly review the performance of your strategy in the context of current market volatility and adjust the settings as necessary.
Backtest in Different Conditions: Before applying the strategy live, backtest it across different market conditions with your adjusted settings. This process helps ensure that your approach to filtering low-volatility conditions aligns with your trading objectives and risk tolerance.
By fine-tuning the volatility filter settings according to the specific characteristics of the market you're trading in, you can enhance the performance of this strategy
- The **Trailing Stop Loss** and **EMA Exit Conditions** provide two layers of exit strategies, focusing on capital preservation and profit maximization.
**Visualizations**
For clarity and ease of use, the strategy plots the three EMAs and, if enabled, the ATR threshold on the chart. These visual cues not only aid in decision-making but also help in understanding the market's current trend and volatility state.
**How to Use**
Traders can customize the EMA periods to fit their trading horizon, be it short, medium, or long-term trading. The volatility filter and exit options allow for further customization, making the strategy adaptable to different market conditions and personal risk tolerance levels.
By offering a blend of trend-following principles with advanced risk management features, this strategy aims to cater to a wide range of trading styles, from cautious to aggressive. Its strength lies in its flexibility, allowing traders to fine-tune settings to their specific needs, making it a potentially valuable tool in the arsenal of any trader looking for a disciplined approach to navigating the markets.
Octopus Nest Strategy Hello Fellas,
Hereby, I come up with a popular strategy from YouTube called Octopus Nest Strategy. It is a no repaint, lower timeframe scalping strategy utilizing PSAR, EMA and TTM Squeeze.
The strategy considers these market factors:
PSAR -> Trend
EMA -> Trend
TTM Squeeze -> Momentum and Volatility by incorporating Bollinger Bands and Keltner Channels
Note: As you can see there is a potential improvement by incorporating volume.
What's Different Compared To The Original Strategy?
I added an option which allows users to use the Adaptive PSAR of @loxx, which will hopefully improve results sometimes.
Signals
Enter Long -> source above EMA 100, source crosses above PSAR and TTM Squeeze crosses above 0
Enter Short -> source below EMA 100, source crosses below PSAR and TTM Squeeze crosses below 0
Exit Long and Exit Short are triggered from the risk management. Thus, it will just exit on SL or TP.
Risk Management
"High Low Stop Loss" and "Automatic High Low Take Profit" are used here.
High Low Stop Loss: Utilizes the last high for short and the last low for long to calculate the stop loss level. The last high or low gets multiplied by the user-defined multiplicator and if no recent high or low was found it uses the backup multiplier.
Automatic High Low Take Profit: Utilizes the current stop loss level of "High Low Stop Loss" and gets calculated by the user-defined risk ratio.
Now, follows the bunch of knowledge for the more inexperienced readers.
PSAR: Parabolic Stop And Reverse; Developed by J. Welles Wilders and a classic trend reversal indicator.
The indicator works most effectively in trending markets where large price moves allow traders to capture significant gains. When a security’s price is range-bound, the indicator will constantly be reversing, resulting in multiple low-profit or losing trades.
TTM Squeeze: TTM Squeeze is a volatility and momentum indicator introduced by John Carter of Trade the Markets (now Simpler Trading), which capitalizes on the tendency for price to break out strongly after consolidating in a tight trading range.
The volatility component of the TTM Squeeze indicator measures price compression using Bollinger Bands and Keltner Channels. If the Bollinger Bands are completely enclosed within the Keltner Channels, that indicates a period of very low volatility. This state is known as the squeeze. When the Bollinger Bands expand and move back outside of the Keltner Channel, the squeeze is said to have “fired”: volatility increases and prices are likely to break out of that tight trading range in one direction or the other. The on/off state of the squeeze is shown with small dots on the zero line of the indicator: red dots indicate the squeeze is on, and green dots indicate the squeeze is off.
EMA: Exponential Moving Average; Like a simple moving average, but with exponential weighting of the input data.
Don't forget to check out the settings and keep it up.
Best regards,
simwai
---
Credits to:
@loxx
@Bjorgum
@Greeny
Triple MA HTF strategy - Dynamic SmoothingThe triple MA strategy is a simple but effective method to trade the trend. The advantage of this script over the existing triple MA strategies is that the user can open a lower time frame chart and select higher time frame inputs for different MA types mainting the visibility on the chart. The dynamic smoothing code makes sure the HTF trendlines are not jagged, but a fluid line visiable on the lower time frame chart. The script comes with a MA crossover and crossunder strategy explained below.
Moving Averages (MA) Crossover for Entry:
Long Entry: A long entry signal is triggered when the moving average line 1 crosses above the moving average line 2. This crossover indicates a potential shift in market sentiment towards the upside. However, to validate this signal, the strategy checks if the moving average 3 on a higher time frame (eg. 4 hour) is in an upward trend. This additional filter ensures that the trade aligns with the prevailing trend on a broader time scale, increasing the probability of success.
Short Entry: Conversely, a short entry signal occurs when the moving average line 1 crosses below the moving average line 2. This crossover suggests a possible downturn in market momentum. However, for a short trade to be confirmed, the strategy verifies that the moving average 3 on the higher time frame is in a downward trend. This confirmation ensures that the trade is in harmony with the overarching market direction.
Exit from Long Position: The strategy triggers an exit signal from a long position when the moving average line 1 crosses below the moving average line 2. This crossover indicates a potential reversal in the market trend, prompting the trader to close their long position and take profits or minimize losses.
Exit from Short Position: Similarly, an exit signal from a short position occurs when the moving average line 1 crosses above the moving average line 2. This crossover suggests a potential shift in market sentiment towards the upside, prompting the trader to exit their short position and manage their risk accordingly.
Features of the script
This Triple MA Strategy is basically the HTF Trend Filter displayed 3 times on the chart. For more infomation on how the MA with dynamic smoothing is calculated I recommend reading the following script:
For risk management I included a simple script to opt for % of eauity or # of contracts of in the instrument. For explanation on how the risk management settings work I refer to my ealier published script:
The strategy is a simplified example for setting up an entry and exit logic based on multiple moving avarages. Hence the script is meant for educational purposes only.
MACD + MA HTF Strategy - Dynamic SmoothingMACD + MA HTF Strategy - Dynamic Smoothing
The MACD alone generally gives too many false signals and is therefore often used in combination with different indicators. The basic idea to combine the MACD with a moving average on a higher time frame is a commonly used technique to only enter Longs in a uptrend and only Shorts in a downtrend while trading on lower timeframe charts. However, the main issue in many strategy scripts is that the HTF indicator is not visible on lower timecharts. With this strategy example I used the Dynamic Smoothing code to visualise the HTF MA filter to display on you lower timechart. This way it is easier to optimize the strategy settings to the instrument chart.
Orginality and Usefulness - Dynamic Smoothing
Visualizing a higher time frame on a lower timechart often gives jagged lines on your chart. As the calculation is done on less bars, compared to the bars you have open on your timechart. The dynamic smoothing factor is derived by taking the ratio of minutes of the higher time frame to the current time frame. This ensures the moving average remains fluid and consistent across different time frames, eliminating 'jagged' lines on your chart. This new MA value is then used as HTF filter on top of the MACD entry settings. Always make sure the time chart is equal or lower than the timeframe settings in the configuration settings. The intention of the script is to visualise the higher time frame confirmations while trading on a lower timechart.
Code example how to achieve Dynamic Smoothing:
// Get minutes for current and higher timeframes
// Function to convert a timeframe string to its equivalent in minutes
timeframeToMinutes(tf) =>
multiplier = 1
if (str.endswith(tf, "D"))
multiplier := 1440
else if (str.endswith(tf, "W"))
multiplier := 10080
else if (str.endswith(tf, "M"))
multiplier := 43200
else if (str.endswith(tf, "H"))
multiplier := int(str.tonumber(str.replace(tf, "H", "")))
else
multiplier := int(str.tonumber(str.replace(tf, "m", "")))
multiplier
// Get minutes for current and higher timeframes
currentTFMinutes = timeframeToMinutes(timeframe.period)
higherTFMinutes = timeframeToMinutes(TimeFrame_Trend)
// Calculate the smoothing factor
dynamicSmoothing = math.round(higherTFMinutes / currentTFMinutes)
MA_Value_Smooth = ta.sma(MA_Value_HTF, dynamicSmoothing)
Complete Strategy
The MACD and HTF moving average is used to determine when to enter a new position. However a strategy should consider more factors than only the timing of entering a trade. To complete the strategy I included:
an option to choose from different MA types per indicator
a Risk Management Tool
a Take Profit Logic
a Trailing stop loss
a Hard Stoploss
a visual representation of TP and SL
This is merely an example how to structure a strategy and many different setups are possible.
The features in this script are explained below:
Different MA types
The script supports various MA types like EMA, SMA, DEMA, TEMA, WMA and HMA. You can select the MA type and different timeframe to your liking.
Risk Management Tool
Traders can choose to allocate their position size based on a percentage of equity or a fixed number of contracts. This feature ensures prudent risk management and helps traders align their position sizes with their risk tolerance. In the strategy -0.5 is equal to 50% of equity and 1.5 is 150% of equity used. Make sure to align the % of equity with your maxdrawn results with backtesting. Personally I don't want higher max drawdowns than 15%. For the strategy results the script considers 0.05 commission rate on each trade, to stay conservative. For a more detailed explanation I refer to my earlier published tradingviewblog about risk management:
Take Profit Logic
The take profit logic in this script is designed for optimization, offering three distinct exit levels. Traders can customize these levels based on their risk appetite. The script allows adjustment of both the percentage take profit level and the position size, catering to individual trading strategies and objectives. The default settings closes 33% of the position when TP target is hit. The TP levels are simply calculated by inputting a % of of the entryprice.
Trailing Stop and Hard Stoploss
To mitigate downside risks and protect profits, the script incorporates a well-thought-out trailing stop mechanism based on the Average True Range (ATR). This dynamic trailing stop adapts to market volatility, allowing traders to secure gains while letting profitable positions run. Additionally, to prevent significant losses, a fixed stop loss is implemented, providing an added layer of protection.
Visual Representation of Take Profit and Stoploss Levels
For enhanced visualization, take profit and stoploss levels are displayed on the chart. Take profit levels are depicted with green lines, providing a clear indication of potential exit points. Conversely, the trailing stop loss is presented by the red line, serving as visual cues for risk management. Visualizing indicators makes is easier to optimize settings to your liking.
Ideal Settings and Accessibility
This script is intended to be used on lower timeframe charts like 10 to 30 minutes. You can Align the MACD entry settings equal to your opened timechart or use a slightly higher timeframe. For the MA trend filters, higher timeframe settings such as 30 min, 1 hour, 4 hours, or 1 day are recommended for trading the trend.
Disclaimer
Trading involves significant risk and may not be suitable for all investors. The information provided in this script is for educational purposes only and should not be considered as financial advice. Past performance is not indicative of future results. By using this script, you acknowledge that you understand and accept these risks.
Self Optimizing PSAR [Starbots]Self Optimizing Parabolic SAR Strategy (non-repainting)
Strategy constantly backtest 169 different combinations of Parabolic SAR indicator for maximum profitability and trades based on the best performing combination at that time.
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# Parabolic SAR (PSAR)
Parabolic SAR is a time and price technical analysis tool created by J. Welles Wilder and it's primarily used to identify points of potential stops and reverses. In fact, the SAR in Parabolic SAR stands for "Stop and Reverse". The indicator's calculations create a parabola which is located below price during a Bullish Trend and above Price during a Bearish Trend.
You can read more about this indicator here:
www.tradingview.com
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The logic of self - optimizing:
This script is always backtesting 169 different combinations of Parabolic SAR settings in the background and saves the net. profit gained for every single one of them, then strategy selects and use the best performing combination of settings currently available for you to trade.
It's recalculating on every bar close - if one of the parameters starts performing better than others - have a higher net profit gain (it's literally like running 169 backtests with different settings) strategy switches to that parameter and continues trading like that until one of the other indicator parameters starts performing better again and switches to that settings.
We are optimizing our strategy based on 13 different 'Increment' factors of PSAR. We keep the 'Start' factor (default 0.02) and 'Max Value' factor (default 0.2) at default for all of them.
According to creator of this indicator J. Welles Wilder, we usually want to change only 'Increment' factors of PSAR in the calculation and leave the rest at default and that's what we do, we are changing only 'Increment' input.
Inputs : (you don't need to change them at all, it's a good balance for fast and slow detection of trends on PSAR)
Start = 0.02
Max value = 0.2
Increment1 = 0.005, Increment2 = 0.01, Increment3 = 0.015
Increment4 = 0.02, Increment5 = 0.025, Increment6 = 0.03
Increment7 = 0.035, Increment8 = 0.04, Increment9 = 0.045
Increment10 = 0.05, Increment11 = 0.055, Increment12 = 0.06
Increment13 = 0.065
PSAR buy / sell conditions looks like this:
PSAR1 = start 0.02, max value 0.2, increment1 0.005
PSAR2 = start 0.02, max value 0.2, increment2 0.01
PSAR3 = start 0.02, max value 0.2, increment3 0.015
PSAR4 = start 0.02, max value 0.2, increment3 0.02
...
PSAR13 = start 0.02, max value 0.2, increment13 0.065
Backtester in the background works like this:
backtest buying PSAR1 settings with selling PSAR1 settings => save net. profit
backtest buy PSAR1 with sell PSAR2 ;
backtest buy PSAR1 with sell PSAR3 ;
backtest buy PSAR1 with sell PSAR4 ;
..........
backtest buy PSAR1 with sell PSAR13 ;
..........
backtest buy PSAR13 with sell PSAR1 ;
backtest buy PSAR13 with sell PSAR2 ;
......
backtest buy PSAR13 with sell PSAR13 ;
=>
It will backtest 16x16=169 different PSAR settings and save their profits.
Your strategy then trades based on the best performing (highest net.profit) PSAR Setting currently available. It will check the calculations and backtest them on every new bar close - it's like running 169 strategies at time, and manually selecting the best performing one.
________________________________________________________________________
If you wish to use it as INDICATOR - turn on 'Recalculate after every tick' in Properties tab to have this script updating constantly and use it as a normal Indicator tool for manual trading.
Strategy example is backtested on Daily chart of SHIBUSDT Binance
All settings at default. (1000 capital, 100 order size, 0.1% fee, 1 tick slippage)
Settings:
-Start = default Parabolic SAR setting is 0.02
-Max Value = default Parabolic SAR setting is 0.2
--Recommended PSAR Increment settings:
0.02 is default, higher timeframes usually performs good on the faster Increment factors 0.03-0.05+, smaller timeframes on slow Increment factors 0.005-0.02. I recommend you the most common and logical 13 different Increment factors for optimizing in the strategy as default already (from 0.005 to 0.065 - strategy will then optimize and trade based on the most profitable combination).
- Noise-Intensity Filter 🐎0.00-0.20%🐢
This will punish the tiny trades made by certain combinations and give more advantage to big average trades. It's basically like fee calculation, it will deduct 0.xx% fee from every trade when optimizing on their backtests.
You will usually want to have it around 0.05-0.10% like your fees on exchange.
-> 🐎Less than <0.10% allows strategy to be VERY SENSITIVE to market. (a lot of trades - quick buy-sell changes)
-> 🐢More than >0.10% will slow down the strategy, it will be LESS SENSITIVE to market volatility. (less trades - slowly switches the trend direction from buy to sell)
Close Trades on Neutral
After a lot of Trades, Algo starts developing self-intelligence. It can also have a neutral score. (Grey Plots). Sell when the strategy is neutral.
Other settings:
-Take Profit, Multiple Take Profit, Trailing Take Profit, Stop Loss, Trailing Stop Loss with functional alerts.
-Backtesting Range - backtest within your desired time window. Example: 'from 01 / 01 /2020 to 01 / 01 /2023'.
- Strategy is trading on the bar close without repaint. You can trade Long-Sell/Short Sell or Long-Short both directions. Alerts available, insert webhook messages in the inputs.
- Turn on Profit Calendar for better overview of how your strategy performs monthly/annualy
- Notes window : add your custom comments in here or save your webhook message text inside here for later use. I find this helpful to save texts inside.
Recommended TF : 4h, 8h, 1d (Trend Indicators are good at detecting directions of the market, but we can have a lot of noise and false movements on charts, you want to avoid that and ride the long term movements)
This script is fairly simple to use. It's self-optimizing and adjusting to the markets on the go.
AI SuperTrend x Pivot Percentile - Strategy [PresentTrading]█ Introduction and How it is Different
The AI SuperTrend x Pivot Percentile strategy is a sophisticated trading approach that integrates AI-driven analysis with traditional technical indicators. Combining the AI SuperTrend with the Pivot Percentile strategy highlights several key advantages:
1. Enhanced Accuracy in Trend Prediction: The AI SuperTrend utilizes K-Nearest Neighbors (KNN) algorithm for trend prediction, improving accuracy by considering historical data patterns. This is complemented by the Pivot Percentile analysis which provides additional context on trend strength.
2. Comprehensive Market Analysis: The integration offers a multi-faceted approach to market analysis, combining AI insights with traditional technical indicators. This dual approach captures a broader range of market dynamics.
BTC 6H L/S Performance
Local
█ Strategy: How it Works - Detailed Explanation
🔶 AI-Enhanced SuperTrend Indicators
1. SuperTrend Calculation:
- The SuperTrend indicator is calculated using a moving average and the Average True Range (ATR). The basic formula is:
- Upper Band = Moving Average + (Multiplier × ATR)
- Lower Band = Moving Average - (Multiplier × ATR)
- The moving average type (SMA, EMA, WMA, RMA, VWMA) and the length of the moving average and ATR are adjustable parameters.
- The direction of the trend is determined based on the position of the closing price in relation to these bands.
2. AI Integration with K-Nearest Neighbors (KNN):
- The KNN algorithm is applied to predict trend direction. It uses historical price data and SuperTrend values to classify the current trend as bullish or bearish.
- The algorithm calculates the 'distance' between the current data point and historical points. The 'k' nearest data points (neighbors) are identified based on this distance.
- A weighted average of these neighbors' trends (bullish or bearish) is calculated to predict the current trend.
For more please check: Multi-TF AI SuperTrend with ADX - Strategy
🔶 Pivot Percentile Analysis
1. Percentile Calculation:
- This involves calculating the percentile ranks for high and low prices over a set of predefined lengths.
- The percentile function is typically defined as:
- Percentile = Value at (P/100) × (N + 1)th position
- Where P is the desired percentile, and N is the number of data points.
2. Trend Strength Evaluation:
- The calculated percentiles for highs and lows are used to determine the strength of bullish and bearish trends.
- For instance, a high percentile rank in the high prices may indicate a strong bullish trend, and vice versa for bearish trends.
For more please check: Pivot Percentile Trend - Strategy
🔶 Strategy Integration
1. Combining SuperTrend and Pivot Percentile:
- The strategy synthesizes the insights from both AI-enhanced SuperTrend and Pivot Percentile analysis.
- It compares the trend direction indicated by the SuperTrend with the strength of the trend as suggested by the Pivot Percentile analysis.
2. Signal Generation:
- A trading signal is generated when both the AI-enhanced SuperTrend and the Pivot Percentile analysis agree on the trend direction.
- For instance, a bullish signal is generated when both the SuperTrend is bullish, and the Pivot Percentile analysis shows strength in bullish trends.
🔶 Risk Management and Filters
- ADX and DMI Filter: The strategy uses the Average Directional Index (ADX) and the Directional Movement Index (DMI) as filters to assess the trend's strength and direction.
- Dynamic Trailing Stop Loss: Based on the SuperTrend indicator, the strategy dynamically adjusts stop-loss levels to manage risk effectively.
This strategy stands out for its ability to combine real-time AI analysis with established technical indicators, offering traders a nuanced and responsive tool for navigating complex market conditions. The equations and algorithms involved are pivotal in accurately identifying market trends and potential trade opportunities.
█ Usage
To effectively use this strategy, traders should:
1. Understand the AI and Pivot Percentile Indicators: A clear grasp of how these indicators work will enable traders to make informed decisions.
2. Interpret the Signals Accurately: The strategy provides bullish, bearish, and neutral signals. Traders should align these signals with their market analysis and trading goals.
3. Monitor Market Conditions: Given that this strategy is sensitive to market dynamics, continuous monitoring is crucial for timely decision-making.
4. Adjust Settings as Needed: Traders should feel free to tweak the input parameters to suit their trading preferences and to respond to changing market conditions.
█Default Settings and Their Impact on Performance
1. Trading Direction (Default: "Both")
Effect: Determines whether the strategy will take long positions, short positions, or both. Adjusting this setting can align the strategy with the trader's market outlook or risk preference.
2. AI Settings (Neighbors: 3, Data Points: 24)
Neighbors: The number of nearest neighbors in the KNN algorithm. A higher number might smooth out noise but could miss subtle, recent changes. A lower number makes the model more sensitive to recent data but may increase noise.
Data Points: Defines the amount of historical data considered. More data points provide a broader context but may dilute recent trends' impact.
3. SuperTrend Settings (Length: 10, Factor: 3.0, MA Source: "WMA")
Length: Affects the sensitivity of the SuperTrend indicator. A longer length results in a smoother, less sensitive indicator, ideal for long-term trends.
Factor: Determines the bandwidth of the SuperTrend. A higher factor creates wider bands, capturing larger price movements but potentially missing short-term signals.
MA Source: The type of moving average used (e.g., WMA - Weighted Moving Average). Different MA types can affect the trend indicator's responsiveness and smoothness.
4. AI Trend Prediction Settings (Price Trend: 10, Prediction Trend: 80)
Price Trend and Prediction Trend Lengths: These settings define the lengths of weighted moving averages for price and SuperTrend, impacting the responsiveness and smoothness of the AI's trend predictions.
5. Pivot Percentile Settings (Length: 10)
Length: Influences the calculation of pivot percentiles. A shorter length makes the percentile more responsive to recent price changes, while a longer length offers a broader view of price trends.
6. ADX and DMI Settings (ADX Length: 14, Time Frame: 'D')
ADX Length: Defines the period for the Average Directional Index calculation. A longer period results in a smoother ADX line.
Time Frame: Sets the time frame for the ADX and DMI calculations, affecting the sensitivity to market changes.
7. Commission, Slippage, and Initial Capital
These settings relate to transaction costs and initial investment, directly impacting net profitability and strategy feasibility.
Four WMA Strategy with TP and SLBasically I read a research paper on how they used different moving averages for long entries and short entries, and it kind of dawned on me that I always used the same one for long entry or exit, or even swing trading. So I smashed this together to see what would happen.
The strategy combines the use of four different WMAs for identifying trade entry points, along with a predefined take profit (TP) and stop loss (SL) for risk management. Here's a detailed description of its features and how it operates:
Main Features
1. **WMAs as the Core Indicator**:
- The strategy uses four WMAs with different lengths. Two WMAs (`longM1` and `longM2`) are used for long entry signals, and the other two (`shortM1` and `shortM2`) for short entry signals.
- The lengths of these WMAs are adjustable through input parameters.
2. **Trade Entry Conditions**:
- A long entry is signaled when the shorter WMA crosses under the longer WMA .
- Conversely, a short entry is signaled when the shorter WMA crosses under the longer WMA.
3. **Take Profit and Stop Loss**:
- The strategy includes a take profit and stop loss mechanism.
- The TP and SL levels are set as a percentage of the entry price, with the percentage values being adjustable through input parameters.
4. **Visual Representation**:
- The WMAs are plotted on the chart for visual aid, each with a distinct color for easy identification.
How It Works
- The strategy continuously monitors the crossing of WMAs to detect potential entry points for long and short positions.
- Upon detecting a long or short condition, it automatically enters a trade and sets the corresponding TP and SL levels based on the current price and the specified percentages.
- The strategy then actively manages the trade, exiting the position when either the TP or SL level is reached.
Drawbacks
- **Overreliance on WMAs**: The strategy heavily relies on WMAs for trade signals. While WMAs are useful for identifying trends, they might not always provide timely entry and exit signals.
- **Market Conditions**: It may not perform well in highly volatile or sideways markets where WMA crossovers could lead to false signals.
- **Risk Management**: The fixed percentage for TP and SL might not be suitable for all market conditions. Traders might need to adjust these values frequently based on market volatility and their risk tolerance.
Apparently I need to emphasize to use brains when using indicators and setting them up to achieve the results you can or want. Also risk of 12% is considered very high so I lowered the numbers to 5%, which tanked the profits, try adjusting them on your own. Check the properties settings for more info on comission and slippage.
Conclusion
The "Four WMA Strategy with TP and SL" is suitable for traders who prefer a moving average-based approach to trading, combined with a straightforward mechanism for risk management through take profit and stop loss. However, like all strategies, it should be used with an understanding of its limitations and ideally tested thoroughly in various market conditions before applying it to live trading.
Pairs strategyHello, Tradingview community,
I am been playing with this idea that nowadays trading instruments are interconnected and when one goes too far "out of order" it should return to the mean.
So, here's a relatively simple idea.
This is a LONG-ONLY strategy.
Buy when your traded instrument's last bar closes down, and the comparing instrument closes up.
Sell when close is higher than the previous bar's high.
Best results I found with medium timeframes: 45min, 120min, 180min.
Also, feel free to test non-typical timeframes such as 59min, 119min, 179min, etc.
My reasoning for medium timeframes would be, that they are big enough to avoid "market noise"
of smaller timeframes + commissions & slippage is less negligible, and small enough to avoid exposure of higher timeframes, although, I haven't tested D timeframe and above.
The best results, I found were with instruments that aren't directly correlated. I mostly tested equities and equity futures, so for equity indexes, equity index futures, or large-cap stocks, NASDAQ:SMH , NASDAQ:NVDA , EURUSD, and Crude Oil would be a good candidate for comparing symbols.
When testing either futures or stocks, please adjust the commission for each asset, for stocks I use % equity, so it compounds over time, whereas, for futures, I use 1 contract all the time.
Here's NASDAQ:MSFT on 119min chart
Here's AMEX:SPY on 59min chart using NASDAQ:NVDA as comparison
Here's CME_MINI:ES1! on 179min chart using NYMEX:CL1! as comparison
To change comparison symbol just insert your symbol between the brackets on both fields down here.
SymbolClose = request.security("YOUR SYMBOL HERE", timeframe.period, close)
SymbolOpen = request.security("YOUR SYMBOL HERE", timeframe.period, open)
Since I am still relatively new to testing, hence, I am publishing this idea, so you can point out some crucial things I may have missed.
Thanks,
Enjoy the strategy!
Scale In : Scale OutScale In : Scale Out strategy is an adaptation and extension of dollar-cost-averaging.
As the name implies it not only scales in - allocates a given percentage of available capital to buy at each bar - it also scales out - sells a given percentage of holdings at each bar when a target profit level is reached.
The strategy can potentially mitigate risks associated with market timing.
Although dollar-cost-averaging is often recommended as a strategy for building a position, the management of taking and retaining profits is not often addressed. This strategy demonstrates the potential benefits of managing both the building and (full or partial) liquidation of an investment.
We do not provide any mechanism for managing stop losses. We assume a scale in/out strategy will typically be applied to investing in assets with a high conviction thesis based on criteria external to the strategy. If the strategy does not perform, then the thesis may need to be re-evaluated, and the position liquidated. Even in this case, scaling out should still be considered.
Mean Reversion with Incremental Entry by HedgerLabsThe "Mean Reversion with Incremental Entry" strategy, designed by HedgerLabs, is an advanced TradingView strategy script focusing on the mean reversion technique in financial markets. This strategy is engineered for traders who prefer a systematic approach with an emphasis on incremental entries based on price movements relative to a moving average.
Key Features:
Moving Average Based Strategy: Central to this strategy is the simple moving average (SMA), around which all trade entries and exits revolve. Traders can customize the MA length, making it flexible for various trading styles and timeframes.
Incremental Entry Mechanism: Unique to this strategy is the incremental entry system. The strategy initiates an initial trade when the price deviates from the MA by a specified percentage. Subsequent entries are made at incremental steps, defined by the trader, as the price moves further away from the MA. This method can potentially capitalize on increasing market volatility.
Dynamic Position Management: The strategy intelligently manages positions by entering long when the price is below the MA and short when above, allowing for adaptive positioning in different market conditions.
Automated Exit Logic: Exit points are determined when the price touches the MA, aiming to close positions at potential reversal points for optimized trade outcomes.
Continuous Market Analysis: With 'calc_on_every_tick' enabled, the strategy constantly evaluates market conditions, ensuring prompt reaction to price movements.
Usage Scenario:
This strategy is particularly beneficial in markets exhibiting mean-reverting behavior. It is suitable for traders focusing on swing trading or those who prefer to scale into positions during periods of high volatility.
Disclaimer:
Please remember that this strategy is for informational and educational purposes only and is not intended as financial or investment advice. Trading in financial markets carries risks, including the potential loss of capital. We advise doing your own research and consulting with a financial expert before making any investment decisions.
COSTAR Strategy [SS]A little late posting this but here it is, as promised!
This is the companion to the COSTAR indicator.
What it does:
It creates a co-integration paired relationship with a separate, cointegrated ticker. It then plots out the expected range based on the value of the cointegrated pair. When the current ticker is below the value of its co-integrated partner, it becomes a "Buy" and should be longed. When it becomes overvalued in comparison, it becomes a "Sell" and should be shorted.
The example above is with BA and USO, which have a strong inverse relationship.
How it works:
I made the strategy version a bit more intuitive. Instead of you selecting the parameters for your model, it will autoselect the ideal parameters based on your desired co-integrated pair. You simply enter the ticker you want to compare against, and it will sort through the values at various lags to find significance and stationarity. It will then create a model and plot the model out for you on your chart, as you can see above.
The premise of the strategy:
The premise of the strategy is as stated before. You long when the ticker is undervalued in comparison to its co-integrated pair, and short when it is overvalued. The conditions for entry are simply a co-integrated pair being over the expected range (short) or below the expected range (long).
The condition to exit is a "re-integration", or a crossover of the expected value of the ticker (the centreline).
What if it can't find a relationship?
In some instances, the indicator will not be able to determine a co-integrated relationship, owning to a lack of stationarity between the data. When this happens, you will get the following error:
The indicator provides you with prompts, such as switching the timeframe or trying an alternative ticker. In the case displayed above, if we simply switch to the 1 hour timeframe, we have a viable model with great backtest results:
You can toggle in the settings menu the various parameters, such as timeframe, fills and displays.
And that is the strategy in a nutshell, be sure to check out its partner indicator, COSTAR, for more information on the premise of using co-integrated models for trading. And let me know your questions below!
Safe trades everyone!
RMI Trend Sync - Strategy [presentTrading]█ Introduction and How It Is Different
The "RMI Trend Sync - Strategy " combines the strength of the Relative Momentum Index (RMI) with the dynamic nature of the Supertrend indicator. This strategy diverges from traditional methodologies by incorporating a dual analytical framework, leveraging both momentum and trend indicators to offer a more holistic market perspective. The integration of the RMI provides an enhanced understanding of market momentum, while the Super Trend indicator offers clear insights into the end of market trends, making this strategy particularly effective in diverse market conditions.
BTC 4h long/short performance
█ Strategy: How It Works - Detailed Explanation
- Understanding the Relative Momentum Index (RMI)
The Relative Momentum Index (RMI) is an adaptation of the traditional Relative Strength Index (RSI), designed to measure the momentum of price movements over a specified period. While RSI focuses on the speed and change of price movements, RMI incorporates the direction and magnitude of those movements, offering a more nuanced view of market momentum.
- Principle of RMI
Calculation Method: RMI is calculated by first determining the average gain and average loss over a given period (Length). It differs from RSI in that it uses the price change (close-to-close) rather than absolute gains or losses. The average gain is divided by the average loss, and this ratio is then normalized to fit within a 0-100 scale.
- Momentum Analysis in the Strategy
Thresholds for Decision Making: The strategy uses predetermined thresholds (pmom for positive momentum and nmom for negative momentum) to trigger trading decisions. When RMI crosses above the positive threshold and other conditions align (e.g., a bullish trend), it signals a potential long entry. Similarly, crossing below the negative threshold in a bearish trend may trigger a short entry.
- Super Trend and Trend Analysis
The Super Trend indicator is calculated based on a higher time frame, providing a broader view of the market trend. This indicator uses the Average True Range (ATR) to adapt to market volatility, making it an effective tool for identifying trend reversals.
The strategy employs a Volume Weighted Moving Average (VWMA) alongside the Super Trend, enhancing its capability to identify significant trend shifts.
ETH 4hr long/short performance
█ Trade Direction
The strategy offers flexibility in selecting the trading direction: long, short, or both. This versatility allows traders to adapt to their market outlook and risk tolerance, whether looking to capitalize on bullish trends, bearish trends, or a combination of both.
█ Usage
To effectively use the "RMI Trend Sync" strategy, traders should first set their preferred trading direction and adjust the RMI and Super Trend parameters according to their risk appetite and trading goals.
The strategy is designed to adapt to various market conditions, making it suitable for different asset classes and time frames.
█ Default Settings
RMI Settings: Length: 21, Positive Momentum Threshold: 70, Negative Momentum Threshold: 30
Super Trend Settings: Length: 10, Higher Time Frame: 480 minutes, Super Trend Factor: 3.5, MA Source: WMA
Visual Settings: Display Range MA: True, Bullish Color: #00bcd4, Bearish Color: #ff5252
Additional Settings: Band Length: 30, RWMA Length: 20
Ironman [Decentrader]Ironman
What is it? how it does it? And how to use it:
i) Ironman is a multifaceted strategy builder, which uses coloured candles which represent certain customisable inputs being in confluence with one another and the set scenario.
ii) There are 7 customised technical indicators which can be input as a basis for the analytical review.
iii) Determine a primary indicator which dictates a bullish or bearish trend (and colour) and then optionally add up to 6 other indicators to be required to be in confluence which adds another colour to be represented.
An example might be two moving averages crossing as the main trend determination. The primary determinant is dictated as the trend being “bullish or bearish” and the added confluence adds an additional layer being “very bullish or very bearish”
iv) Users select which conditions they wish to enter and exit trades on using the Bullish / Very Bullish and Bearish / Very Bearish settings. This can be combined with other timeframes.
v) The selected inputs for each indicator will show in a table contained in the bottom right-hand corner. Active indicators within the system will be highlighted.
vi) Ironman is built to include various take profit and stop loss options such as trailing stops, and fixed percentage targets which can be included in the strategy. Different timeframes can be used to determine the stop if users wish to do so.
vii) Users can require that there is also confluence with a differing time period or choose long and short-only options which can be dictated independently or based upon filtering criteria using moving averages.
viii) Using the strategy settings, users are also able to choose backtesting periods.
ix) Position label settings allow users to show various backtesting options such as profit by position, total backtesting results and results for the active position.
x) Ironman enables users to automate trading easily using the input boxes under Alert messages which also allows connection to a third party which can conduct execution. Always make sure to thoroughly test the strategy if it is being automated.
xi) To get the best out of Ironman, build up a strategy for the timeframe and asset you are looking at and back-test outcomes as variables are layered in. Ensure to backtest over a suitable length of time.
xii) When optimising input variables, it can sometimes visually assist in having the underlying inputs on the screen via the standard indicators.
xiii) There are many boxes of information in the input variables, which explain how to use each part. Users can also add features such as a marker showing on the chart where all indicators are bullish/bearish, or where RSI is overbought / over sold.
xiv) Users can further customise the style of the tool under the style tab in the indicator settings.
EUR/USD 45 MIN Strategy - FinexBOTThis strategy uses three indicators:
RSI (Relative Strength Index) - It indicates if a stock is potentially overbought or oversold.
CCI (Commodity Channel Index) - It measures the current price level relative to an average price level over a certain period of time.
Williams %R - It is a momentum indicator that shows whether a stock is at the high or low end of its trading range.
Long (Buy) Trades Open:
When all three indicators suggest that the stock is oversold (RSI is below 25, CCI is below -130, and Williams %R is below -85), the strategy will open a buy position, assuming there is no current open trade.
Short (Sell) Trades Open:
When all three indicators suggest the stock is overbought (RSI is above 75, CCI is above 130, and Williams %R is above -15), the strategy will open a sell position, assuming there is no current open trade.
SL (Stop Loss) and TP (Take Profit):
SL (Stop Loss) is 0.45%.
TP (Take Profit) is 1.2%.
The strategy automatically sets these exit points as a percentage of the entry price for both long and short positions to manage risks and secure profits. You can easily adopt these inputs according to your strategy. However, default settings are recommended.
Indian NIFTY Correlation Daytrade/Swing StrategyINTRODUCTION :
This is a daytrading/swing strategy designed mainly for indian market where internally has been adapted to NIFTY market and as well using for internal calculations the values of the candles from NIFTY asset.
With it we search to use with the most correlated asset from the indian market.
For this example I choosed BANKNIFTY
STRATEGY:
The strategy initially uses as candle values the data from the NIFTY asset.
With them I am dividing the work into two calculation parts such as :
-For first part logic, I am doing calculations regarding the volatility of NIFTY, where I initially take into consideration INDIAVIX to have an idea of the expected implied volatility of NIFTY asset and then I compare it with different tools such as ATR, BB and Percentile location of the volatility.
Based on all these factors I take into account the location of the volatility which is atm and if there is a possibility of a strong movement(trend) or sidemarket situation.
-Once I am done with the values of the volatility, the next process in the script logic is to start looking into the trend.
For it I am using different tools such as volume checker, support and resistence key points, pivot points, price actions patterns and different moving averages.
-Risk management part : once we are done with calculation for the entry, the next part is to have an idea where to exit. In this case I am making use of a dynamic risk management which is compressed from multiple ideas such as : we can exit if there were a big gap on the next day in our initial direction, we can also exit based of an internal daily ATR calculation value(we use initially 15min timeframe chart) and lastly if we are around some key points like support/resistence or other different chart patterns like double top, double bot and so on.
CASE EXAMPLE:
As I said before we are initially using for calculation the NIFTY chart with 15min timeframe. With it we can apply to any indian etf,stocks,future. All the assets are going to have the same time of entry and the same time of exit(we get this from NIFTY) and we plot it on the chart we are using, so its key point to look for assets which have a min 75-80% correlation with NIFTY. For this example I used BANKNIFTY chart.
So a type of entry would be this way
Lets assume NIFTY50 is on 19.000 level
INDIAVIX level is currently at 11 which can be translated : 11 / sqrt(250)
So 11 means that on a yearly base we expect the asset to move 11% upwards or downwards
and in a year we have aprox 250 days. So we divide the 11 by sqrt of 250 to get an idea of a daily expected move from the implied volatility of india VIX
11/15.87 = +-0.69%
So INDIAVIX tells us that the values for today nifty is 19000+0,69% and 19000-0.69%
After that I am looking into the daily ATR, and I see that the expected is around 0.8% and is ascending over the last 2 weeks.
Lastly I am looking at the percentile which is currently the volatility on both ATR and INDIAVIX, and I get a value of 90th percentile.
With this my biased is that we are going to expect a short trend, but i cant confirm on the volatility alone so next step is start looking into technical analysis.
I look at volume and is increasing, I look at different price actions paterns and pivots and I see a lower low and a lower high (a descending pattern).
I also see the price is below the key MA like SMA50/100/200, VWAP and so on.
With all of this I get more confirmation that the asset is in a short trend.
Internally once a certain specific % of confirmation from all the logics is achieved, it will trigger a long/short entry, so in this case lets assume we have 80% of our indicators pointing to the short, is going to enter a short.
Now for a long scenario the scene would be , indiavix is around 9,5, ATR is descending. We are around 40th percentile of the volatility.
Our asset is above multiple moving averages, vwap , etc
We have an increasing volume towards bullish side.
And so on( overall 75% of our indicators are pointing towards the long side)
Now for the exit, since we are dealing with a daytrade/swing mentality, short on average we keep the trade open for a less period of time than long ( 19 bars of 15min candles, compared to 57 bars of 15min for long) , so most of the times for short we are going to exit next day and if the trend is still in our favour we re enter the trade.
For long we can stay much more time, sometimes even weeks and we exit mainly when the % of confirmation of indicators point out a reversal/short confirmation fo a big pice action pattern.
STRATEGY RESULTS
For strategy analysis I have used BANKNIFTY NSE with deep history to get access to data from 2011 until present( giving more than 2500 trades) .
For inputs I am using 0.02% comission total ( the comission applied from ZERODHA indian exchange is close to 0.0175% total) so I used it a bit higher in order to take into account some slippages.
For capital THE REASON I USED 100% of the capital allocation is to make a proper comparison with the buy an hold from the same period
Lets assume we had an account of 1M ruppes initially in 2011, we start using 100% of it and then the new values automatically compounded with the new profits and losses so directly compare with 1M of rupees in shares on BANKNIFTY ETFs bought in 2011(buy n hold) until present day.
STRATEGY ACCESS
Strategy is free to be tested for everyone, just let me know in private that you wish to get access to it.
Multi-TF AI SuperTrend with ADX - Strategy [PresentTrading]
## █ Introduction and How it is Different
The trading strategy in question is an enhanced version of the SuperTrend indicator, combined with AI elements and an ADX filter. It's a multi-timeframe strategy that incorporates two SuperTrends from different timeframes and utilizes a k-nearest neighbors (KNN) algorithm for trend prediction. It's different from traditional SuperTrend indicators because of its AI-based predictive capabilities and the addition of the ADX filter for trend strength.
BTC 8hr Performance
ETH 8hr Performance
## █ Strategy, How it Works: Detailed Explanation (Revised)
### Multi-Timeframe Approach
The strategy leverages the power of multiple timeframes by incorporating two SuperTrend indicators, each calculated on a different timeframe. This multi-timeframe approach provides a holistic view of the market's trend. For example, a 8-hour timeframe might capture the medium-term trend, while a daily timeframe could capture the longer-term trend. When both SuperTrends align, the strategy confirms a more robust trend.
### K-Nearest Neighbors (KNN)
The KNN algorithm is used to classify the direction of the trend based on historical SuperTrend values. It uses weighted voting of the 'k' nearest data points. For each point, it looks at its 'k' closest neighbors and takes a weighted average of their labels to predict the current label. The KNN algorithm is applied separately to each timeframe's SuperTrend data.
### SuperTrend Indicators
Two SuperTrend indicators are used, each from a different timeframe. They are calculated using different moving averages and ATR lengths as per user settings. The SuperTrend values are then smoothed to make them suitable for KNN-based prediction.
### ADX and DMI Filters
The ADX filter is used to eliminate weak trends. Only when the ADX is above 20 and the directional movement index (DMI) confirms the trend direction, does the strategy signal a buy or sell.
### Combining Elements
A trade signal is generated only when both SuperTrends and the ADX filter confirm the trend direction. This multi-timeframe, multi-indicator approach reduces false positives and increases the robustness of the strategy.
By considering multiple timeframes and using machine learning for trend classification, the strategy aims to provide more accurate and reliable trade signals.
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## █ Trade Direction
The strategy allows users to specify the trade direction as 'Long', 'Short', or 'Both'. This is useful for traders who have a specific market bias. For instance, in a bullish market, one might choose to only take 'Long' trades.
## █ Usage
Parameters: Adjust the number of neighbors, data points, and moving averages according to the asset and market conditions.
Trade Direction: Choose your preferred trading direction based on your market outlook.
ADX Filter: Optionally, enable the ADX filter to avoid trading in a sideways market.
Risk Management: Use the trailing stop-loss feature to manage risks.
## █ Default Settings
Neighbors (K): 3
Data points for KNN: 12
SuperTrend Length: 10 and 5 for the two different SuperTrends
ATR Multiplier: 3.0 for both
ADX Length: 21
ADX Time Frame: 240
Default trading direction: Both
By customizing these settings, traders can tailor the strategy to fit various trading styles and assets.
