Oscillator Price Divergence & Trend Strategy (DPS) // AlgoFyreThe Oscillator Price Divergence & Trend Strategy (DPS) strategy combines price divergence and trend indicators for trend trading. It uses divergence conditions to identify entry points and a trend source for directional bias. The strategy incorporates risk management through dynamic position sizing based on a fixed risk amount. It allows for both long and short positions with customizable stop-loss and take-profit levels. The script includes visualization options for entry, stop-loss, and take-profit levels, enhancing trade analysis.
TABLE OF CONTENTS
🔶 ORIGINALITY
🔸Divergence-Trend Combination
🔸Dynamic Position Sizing
🔸Customizable Risk Management
🔶 FUNCTIONALITY
🔸Indicators
🞘 Trend Indicator
🞘 Oscillator Source
🔸Conditions
🞘 Long Entry
🞘 Short Entry
🞘 Take Profit
🞘 Stop Loss
🔶 INSTRUCTIONS
🔸Adding the Strategy to the Chart
🔸Configuring the Strategy
🔸Backtesting and Practice
🔸Market Awareness
🔸Visual Customization
🔶 CONCLUSION
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🔶 ORIGINALITY The Divergence Trend Trading with Dynamic Position Sizing strategy uniquely combines price divergence indicators with trend analysis to optimize entry and exit points. Unlike static trading strategies, it employs dynamic position sizing based on a fixed risk amount, ensuring consistent risk management. This approach allows traders to adapt to varying market conditions by adjusting position sizes according to predefined risk parameters, enhancing both flexibility and control in trading decisions. The strategy's integration of customizable stop-loss and take-profit levels further refines its risk management capabilities, making it a robust tool for both trending and volatile markets.
🔸Divergence-Trend Combination By combining trend direction with divergence conditions, the strategy enhances the accuracy of entry signals, aligning trades with prevailing market trends.
🔸Dynamic Position Sizing This strategy calculates position sizes dynamically, based on a fixed risk amount, allowing traders to maintain consistent risk exposure across trades.
🔸Customizable Risk Management Traders can set flexible risk-reward ratios and adjust stop-loss and take-profit levels, tailoring the strategy to their risk tolerance and market conditions.
🔶 FUNCTIONALITY The Divergence Trend Trading with Dynamic Position Sizing strategy leverages a combination of trend indicators and price and oscillator divergences to identify optimal trading opportunities. This strategy is designed to capitalize on medium to long-term price movements and works best on h1, h4 or D1 timeframes. It allows traders to manage risk effectively while taking advantage of both long and short positions.
🔸Indicators 🞘 Trend Indicator: A long trend is used to determine market direction, ensuring trades align with prevailing trends.
Recommendation: We recommend using the Adaptive MAs (Hurst, CVaR, Fractal) // AlgoFyre indicator with the following settings for trend detection. However, you can use any trend indicator that suits your trading style, e.g. an EMA 200.
🞘 Oscillator Source: The oscillator source is used for momentum price divergence identification. Any momentum oscillator can be used, e.g. RSI, Stochastic etc. A good oscillator is the Stochastic with the following settings:
🔸Conditions 🞘 Long Entry: A long entry condition is met if price closes above the trend AND selected divergence conditions are met, e.g. regular bullish divergence with a 10 bar lookback period with the divergence being below the 50 point mean. If the info table shows all 3 columns in the same color, the entry conditions are met and a position is opened.
🞘 Short Entry: A short entry condition is met if price closes below the trend AND selected divergence conditions are met, e.g. regular bearish divergence with a 10 bar lookback period with the divergence being above the 50 point mean.
🞘 Take Profit: Take Profit is determined by the Risk to Reward Ratio settings depending on the price distance between the entry price and the stop loss price, e.g. if stop loss is 1% away from entry and Risk Reward Ratio is 3:1 then Take Profit will be set at 3% from entry.
🞘 Stop Loss: Stop loss is a fixed level away from the trend source. For long positions, stop loss is set below the trend, and for short positions, above the trend.
🔶 INSTRUCTIONS The Divergence Trend Trading with Dynamic Position Sizing strategy can be set up by adding it to your TradingView chart and configuring parameters such as the oscillator source, trend source, and risk management settings. This strategy is designed to capitalize on short-term price movements by dynamically adjusting position sizes based on predefined risk parameters. Enhance the accuracy of signals by combining this strategy with additional indicators like trend-following or momentum-based tools. Adjust settings to better manage risk and optimize entry and exit points.
🔸Adding the Strategy to the Chart:
Go to your TradingView chart.
Click on the "Indicators" button at the top.
Search for "Divergence Trend Trading with Dynamic Position Sizing // AlgoFyre" in the indicators list.
Click on the strategy to add it to your chart.
🔸Configuring the Strategy:
Open the strategy settings by clicking on the gear icon next to its name on the chart.
Oscillator Source: Select the source for the oscillator. An oscillator like Stochastic needs to be attached to the chart already in order to be used as an oscillator source to be selectable.
Trend Source: Choose the trend source to determine market direction. A trend indicator like Adaptive MAs (Hurst, CVaR, Fractal) // AlgoFyre needs to be attached to the chart already in order to be used as a trend source to be selectable.
Stop Loss Percentage: Set the stop loss distance from the trend source as a percentage.
Risk/Reward Ratio: Define the desired risk/reward ratio for trades.
🔸Backtesting and Practice:
Backtest the strategy on historical data to understand how it performs in various market environments.
Practice using the strategy on a demo account before implementing it in live trading.
🔸Market Awareness:
Keep an eye on market news and events that might cause extreme price movements. The strategy reacts to price data and might not account for news-driven events that can cause large deviations.
🔸Visual Customization Visualization Settings: Customize the display of entry price, take profit, and stop loss levels.
Color Settings: Switch to the AlgoFyre theme or set custom colors for bullish, bearish, and neutral states.
Table Settings: Enable or disable the information table and adjust its position.
🔶 CONCLUSION
The Divergence Trend Trading with Dynamic Position Sizing strategy provides a robust framework for capitalizing on short-term market trends by combining price divergence with dynamic position sizing. This strategy leverages divergence conditions to identify entry points and utilizes a trend source for directional bias, ensuring trades align with prevailing market conditions. By incorporating dynamic position sizing based on a fixed risk amount, traders can effectively manage risk and adapt to varying market conditions. The strategy's customizable stop-loss and take-profit levels further enhance its risk management capabilities, making it a versatile tool for both trending and volatile markets. With its strategic blend of technical indicators and risk management, the Divergence Trend Trading strategy offers traders a comprehensive approach to optimizing trade execution and maximizing potential returns.
Strategy
NNFX RSI EMA FVMA MACD ALGOThis Pine Script introduces a cutting-edge trading strategy that seamlessly integrates multiple technical indicators—namely, the Flexible Variable Moving Average ( FVMA ), Relative Strength Index ( RSI ), Moving Average Convergence Divergence ( MACD ), and Exponential Moving Average ( EMA )—to deliver a sophisticated trading experience. This script stands out due to its comprehensive approach, robust risk management, and the inclusion of crucial data tables for various timeframes, making it an invaluable tool for traders seeking to enhance their market performance.
Originality of the Strategy:
The originality of this script lies in its unique combination of multiple powerful indicators, enabling traders to benefit from diverse perspectives on market dynamics. This mashup enhances decision-making processes, providing multiple layers of confirmation for trade entries and exits. The strategy is designed to offer an innovative solution for traders looking to improve their performance through well-defined rules and a solid framework.
Flexible Variable Moving Average (FVMA):
The FVMA adapts dynamically to market conditions, offering a more responsive trend line than traditional moving averages. This flexibility allows for quick identification of trends and reversals, crucial for fast-paced trading environments.
Exponential Moving Average (EMA):
By giving greater weight to recent price data, the EMA enhances sensitivity to price changes, allowing for more accurate entries and exits when used alongside the FVMA. This combination maximizes the effectiveness of the strategy in identifying optimal trading opportunities.
Relative Strength Index (RSI):
The RSI helps identify overbought or oversold conditions, integrating seamlessly with other indicators to enhance the strategy's ability to pinpoint potential reversal points. This aspect of the strategy ensures that traders can make informed decisions based on market momentum.
Moving Average Convergence Divergence (MACD):
The MACD serves as an essential confirmation tool, providing insights into trend strength and momentum. This enhances the accuracy of entry and exit signals, allowing traders to make more informed decisions based on robust technical analysis.
Multi-Take Profit (TP) and Stop Loss (SL) Levels:
The strategy supports multiple TPs, allowing traders to lock in profits at various levels while effectively managing risk through a robust SL system. This flexibility caters to diverse trading styles and risk profiles, ensuring that the strategy can adapt to individual trader needs.
Default Properties:
Take Profit Levels: TP1 is set to 2.0, and TP2 is set to 2.9, which is designed to enhance profit potential while maintaining a solid risk-reward ratio.
Stop Loss: A SL is set at 2% of the 5% account balance, which helps to preserve capital and manage risk effectively, adhering to the guideline of not risking more than 5-10% of the account balance per trade.
Labeling System for Exits: Automatic labeling of TP and SL exits on the chart provides clear visualization of trading outcomes. This feature supports informed decision-making and performance tracking, aligning with the guideline of providing transparent results.
Custom Alerts System:
The inclusion of customizable alerts for trade entries, exits, and SL/TP hits keeps traders informed in real-time, enabling prompt actions without constant market monitoring. This is crucial for effective trade management and helps traders respond quickly to market changes.
API Boxes for Automated Trading:
The strategy features API boxes, allowing traders to set up automated trading based on indicator signals. This functionality enables seamless integration with trading platforms, enhancing efficiency and streamlining the trading process, which is particularly valuable for traders looking to optimize their execution.
Data Tables for Enhanced Analysis:
The script includes data tables displaying critical insights across various timeframes: 2-hour, daily, weekly, and monthly. These tables provide a comprehensive overview of market conditions, allowing traders to analyze trends and make informed decisions based on a broad spectrum of data. By leveraging this information, traders can identify high-probability setups and align their strategies with prevailing market trends, significantly increasing their chances of success.
Default Properties:
Initial Capital: £1,000, ensuring a realistic starting point for traders.
Risk per Trade: 5% of the account balance, promoting sustainable trading practices.
Commission: 0.1%, reflecting realistic transaction costs that traders may encounter.
Slippage: 1%, accounting for potential market volatility during trade execution.
Take Profit Levels:
TP1: 2.0
TP2: 2.9
Stop Loss (SL): 2% of the 5% account balance, which is well within acceptable risk parameters.
Compliance with TradingView Guidelines:
This script fully complies with TradingView's guidelines, specifically:
Strategy Results:
The strategy is designed to publish backtesting results that do not mislead traders. The realistic parameters outlined in the default properties ensure that traders have a clear understanding of potential outcomes.
The dataset used for backtesting has sufficient trades to produce a reliable sample size, aligning with the guideline of ideally having more than 100 trades.
Any deviations from recommended practices are justified in the script description, ensuring transparency and adherence to best practices.
The script explains the default properties in detail, providing a thorough understanding of how these settings influence performance.
Why This Script is Worth Paying For:
This Pine Script offers an unparalleled trading experience through its unique combination of technical indicators, comprehensive trade management features, and detailed data tables for multiple timeframes. Here are compelling reasons to invest in this strategy:
Holistic Approach: The integration of multiple indicators ensures a well-rounded perspective on market conditions, increasing the likelihood of successful trades.
Advanced Risk Management: The flexibility of multiple TPs and SLs empowers traders to tailor their risk profiles according to individual strategies, enhancing overall profitability.
Automated Trading Capability: The inclusion of API boxes for automated trading streamlines execution, allowing traders to capitalize on opportunities without the need for manual intervention.
Comprehensive Data Analysis: The detailed data tables provide invaluable insights across different timeframes, enabling traders to make informed decisions based on robust market analysis.
In summary, this innovative Pine Script represents a powerful tool designed to empower traders at all levels. Its originality, synergistic functionality, and comprehensive features create a dynamic and effective trading environment, justifying its value and positioning it as a must-have for anyone serious about achieving consistent trading success.
Support Resistance Pivot EMA Scalp Strategy [Mauserrifle]A strategy that creates signals based on: pivots, EMA 9+20, RSI, ATR, VWAP, wicks and volume.
The strategy is developed as a helper for quick long option scalping. This strategy is primarily designed for intraday trading on the 2m SPY chart with extended hours. However, users can adapt it for use on different symbols and timeframes. These signals are meant as a helper rather than fully automated trading bots.
One of the key elements is its pivot-based calculation, driven by my integrated indicator "Support and Resistance Pivot Points/Lines ". It enables multi-timeframe pivot calculations which are used to generate the signals and offers customizability, allowing you to define rounding methods and cooldown periods to refine pivot levels. The pivots, in combination with EMA crossovers, VWAP trend, and additional filters (RSI, ATR, VWAP, wicks and volume), create an entry and exit strategy for scalping opportunities that is useful for 0/1 DTE options with an average trade time of six minutes with the default setup for SPY. Option trading should be done outside TradingView. At this moment of release there is no option trading support.
All parameters used in the strategy are tweaked based on deep backtests results and real-time behavior. Be mindful that past performance does not guarantee future results.
The strategy is designed for intermediate and advanced users who are familiar intraday option scalping techniques.
How It Works
The strategy identifies entries based on multiple conditions, including: recently above pivot, recent EMA crossovers, RSI range, candle patterns, and VWAP uptrend. It avoids trades below the VWAP lower band due to poor backtesting results in those conditions. It creates a great number of signals when it detects an uptrend, which entails: VWAP and its lower/upper band slopes are going up, and the number of next high pivot points is greater than the number of lower pivot points. This indicates that we hope it will keep going up. In historical testing, this showed favorable results. This uptrend criteria runs on 15m charts max (where up to the VWAP effectiveness is the greatest).
The strategy also checks for candle and volume patterns, identified in backtesting to improve entry levels on historic data. Which include:
A red candle after multiple green ones, hoping to jump on a trend during a small pullback
Zero lower wick
Percentage and volume is up after lower volume candles
Percentage is up and the first and second EMA slopes are going up
Percentage is up, the first EMA is higher than the second, the price low is below the second EMA and price close above it
The VWAP uptrend overrules the candle and volume conditions (thus lots of signals during those moments).
The above is the base for many signals. There is a strict mode that adds extra checks such as:
not trading when there is no next low or high pivot
requiring a VWAP uptrend only
minimum candle percentages
This mode is for analyzing history and seeing performance during these conditions. It is worth it to create a separate alert for strict mode so you are aware of these conditions during trading.
When no stop has been defined, exits will always happen on pivot crossunder confirmations. If a stop is defined (default config), the strategy exits a position when:
the position is negative or no trail has been set
at least 1 bar has past
OR no stop has been defined (overrules previous)
trail has not been activated
The second exit condition happens when the close is below first EMA(9 by default) and when:
the position has been above first EMA
the gap between close and last pivot isn't small
the position is negative or no trail has been set
OR no stop has been defined (overrules above)
trail has not been activated
There are some more variations on this but the above are the most common. These exit conditions are a safety net because the strategy heavily relies on and favors stops. The settings allow changing stops, profit takers and trails. You can configure it to always sell without the conditions above.
The script will paint the pivot lines, trailing activation/stops, EMAs and entry/exits; with extra information in the data panel. For a complete view add VWAP and RSI to your chart, which are available from TradingView official indicator library. The strategy will not rely on those added indicators since VWAP and RSI are programmed in. You can add them to track the behavior of the signals based on these filters you have configured and have a complete view trading this strategy.
As mentioned earlier, the default settings are built for SPY 2m charts, with extended hours and real-time data. Open the strategy on this chart to study how all input parameters are used. If you don't have real-time data you need to adjust the minimum volume settings (set it to 0 at first).
The backtest
The default backtest configuration is set up to simulate SPY option trading.
Start capital is set to 10,000 and we risk around 5% of that per trade (1 contract)
Commission is set to 0.005%. The reason: at the time of this publication the SPY index price is approximately $580. Two ITM 0/1 DTE options contracts, each priced around $280, which is approximately $560. The typical commission for such a trade is around $3. To simulate this commission in the backtest on the SPY index itself, a commission of 0.005% per trade has been applied, approximating the options trading costs.
Slippage of 3 is set reflecting liquid SPY
The bar magnifier feature is turned on to have more realistic fills
Trading
In backtesting, setting commission and slippage to 0 on the SPY 2m chart shows many trades result around breaking even. Personally, I view them as an opportunity and safety net to help manage emotional decisions for exits. The signals are designed for short option scalps, allowing traders to take small profits and potentially re-enter during the strategy’s position window. It's advisable to take small potential profits, such as 4%, whenever the opportunity arises and consider re-entering if the setup still looks favorable, for example price still above ema9. Exiting a long position below ema9 is a common strategy for 2m scalping.
The average trade duration is approximately 6 minutes (3 bars). The choice between ITM (in-the-money), ATM (at-the-money), or OTM (out-of-the-money) options will depend on your trading style. Personally, I’ve seen better results with ITM options because they tend to move more in sync with the underlying index, thanks to their higher delta.
It’s important to note that the signals are designed to be a helper for manual trading rather than to automate a bot. Users are encouraged to take small profits and re-enter positions if favorable conditions persist. Be mindful that past performance does not guarantee future results.
For the default SPY setup the losses will mostly be 4-10% for ITM options. Be mindful of extreme volatile conditions where losses may reach 30% quickly, especially when trading ATM/OTM options.
The following settings can be changed:
8 pivot timeframes with left/right bars and days rendered
Here you can configure the timeframes for the pivots, which are crucial. The strategy wants that a crossover has happened recently (so it might enter after a crossunder if the crossover was recent) or the price is still above the crossed pivot.
When you decide to use a pivot timeframe higher than your chart, make sure it aligns the same starting point as the chart timeframe. As stated in the 43000478429 docs, there is a dependency between the resolution and the alignment of a starting point:
1–14 minutes — aligns to the beginning of a week
15–29 minutes — aligns to the beginning of a month
from 30 minutes and higher — aligns to the beginning of a year
This alignment also affects the setting of rendered days. I recommend a max value of 5 days for 1-14 minutes timeframes.
Also make sure a higher pivot timeframe can be divided by the lower. For instance I had repaint issues using 3m pivots on a 2m chart. But 4m pivots work fine.
Please look up docs 43000478429 to make sure this information is still up to date.
Pivot rounding
The pivot rounding option is used to add pivots based on a rounded price and limit the number of pivots. While this feature is disabled by default it can be useful with tweaking strategy variations, because many orders are placed at rounded levels and tend to act as strong price barriers.
There are multiple rounding methods: round, ceil/floor, roundn (decimal) and rounding to the minimal tick.
The next feature is a powerful extension called "Cooldown rounding":
Pivot cooldown rounding
This rounds new pivot levels for a cooldown period to keep the previous pivot line instead of adding a new line when they match the rounded value within the cooldown period. The existing line will be extended. This feature is useful because it makes sure the initial line is added to the exact high/low pivot level but any future lines within the rounding will just extend the existing line. This limits the number of pivots while still having precise levels (which normal rounding lacks) and allows more precise pivot trading.
This feature also helps ensure that the number of rendered lines will not exceed 500 too much, which is the render limit on TradingView.
You can set a maximum minutes for the cooldown. The default is 3 years which will enable the cooldown rounding permanently on the intraday (due to the max bar limit).
Pivot always added when new higher/lower pivot
When using cooldown rounding, one may find it useful to override this behavior when a new lower or higher pivot level has been reached. When enabled the new level will be added despite the fact that they may be rounded the same in the cooldown check. This is a good balance between limiting pivots but also allowing preciser trading.
VWAP bands multiplier
This is used to tweak the inner VWAP working for the upper and lower band. The default VWAP multiplier (0.9) is set based on backtesting since it performed better on historic data (the strategy does not trade below the lowerband). When you add the VWAP indicator from the TradingView library to the chart, make sure it uses the same multiplier setting as within this strategy so you have a correct view of the conditions the strategy acts on.
ATR EMA smoothing length
Used to tweak the ATR EMA smoothing. By default it is set up to 4 based on deep backtesting historic data.
EMA lengths
Changing the EMA length allows you to fine tune the EMA crossing behavior. By default the strategy is set up to EMA 9 and 20 which are considered commonly used values on the 2-minute chart.
Trading intraday time restrictions
For intraday charts you can configure when the strategy starts trading after market open and when it stops, including a hard sell. This makes sure there are no open positions left for the day during backtesting and can also aid in your trading style. For example some scalpers will not trade in the first two hours. Having no signals during this time can be beneficial. It is possible to configure these settings based on the number of bars or minutes.
Not trading on days the market closes earlier
By default the strategy does not trade on days the market closes earlier in the US. This makes sure there are no open positions left open during backtesting. Make sure to change it when using it on such a day. The days are: day before independence day, day after thanksgiving, Christmas eve and new years eve.
Not trading below VWAP lowerband
Backtesting has shown poor performance when trading below the VWAP lowerband but you are free to allow it to trade in such conditions. Past performance does not guarantee future results.
Minimum volume
A minimum volume can be set up. The current value is based on better deep backtest results for SPY using real-time data (48000). When you do not have a data plan for SPY, please set it to 0 and tweak based on backtests.
Minimum ATRP
The strategy has shown during my trading that it is sensitive to higher ATRP values and more volatile market conditions. There is more chance the index moves and we can profit from this during option scalping (if it moves in your favor). The default is based on SPY backtesting (0.04%), as a balance to have a lot of trades but also capture minimal movement.
RSI range
A RSI range can be set using a minimum and maximum value so we can limit trading during overbought/oversold conditions. Backtesting for SPY has shown the strategy performs better on historic data within a tighter range, so a default range has been set to 40-65.
Allow orders on every tick (no effect on stop/profit/trail)
This setting is used to allow orders on every tick. The strategy has been developed without trading on every tick but you can change this, for example when you have configured a setup different than the default configuration that you know works well with this. The default setup will not work well with it due to too many constant signals.
Stop percentage + ATRP threshold
One of the most important settings for managing the risk. I recommend setting a stop percentage first and later the ATRP threshold where the stop is calculated based on the current ATRP value. The calculated value will only be in effect when it is greater than the normal stop--the normal stop acts as baseline. The default stop is low (0.03). With a default ATRP threshold stop of 1.12, the calculated value overrules the normal stop when the value is greater. 0.03 acts as a minimum value but in reality the stop will most likely be higher on average for SPY with the default ATRP threshold.
For the default SPY setup the losses will be around 4-10% for ITM options. Be mindful of extreme volatile conditions where losses may reach 30% quickly, especially when trading ATM/OTM options.
Profit taker percentage + ATRP threshold
Same principles as the stop percentage above, but for profit taking. There is a very high ATRP threshold of 4 set by default. Backtests showed that trailing stops perform better on historic data.
Trailing stop
Used to set up a trailing stop. A useful feature to secure profit after a run-up, or get out with a small loss after initial activation. It is important to not use too tight values because they will give unrealistic backtest results and trigger too fast in real-time. Both the trail activation level and trail stop itself can be configured with a percentage value and ATRP value. I recommend setting up the ATRP last. By default the values are 0.05 for activation and 0.03 for the stop based on SPY real-time behavior.
Always sell on pivot crossunder confirmation
The strategy includes pivot crossunder confirmations as sell condition. By default it will not sell on every crossunder confirmation but checks for different conditions (explained in detail earlier in this description). You can change this behavior.
Always sell below first EMA when position has been above
The strategy sells below the first EMA when the position has been above it. By default it will not always sell but checks for different conditions (mentioned earlier in this description). You can change this behavior.
Buy modes pivot
By default the strategy buys between pivots as long as there has been a pivot crossover and EMAs crossover recently or price is still above it. You can change the behavior so it only buys on pivot crossovers or pivot crossover confirmations. Backtesting on the default setup shows decreased performance but for other strategy variations and pivot setups this feature can be useful since many scalpers do not buy between pivots.
Strict mode
There is a strict mode that adds extra checks such as not trading when there is no next low or high pivot, requiring a VWAP uptrend only and minimum candle percentages. This mode is for analyzing history and seeing performance during these conditions. It is worth it to create a separate alert for strict mode so you are aware of these conditions during trading. The deep backtests improved with these setting but past performance does not guarantee future results.
In the strict mode section you can override the stop, minimum ATRP, set up a minimum percentage, only trade VWAP uptrends and to not trade candles without a wick.
A summary and some extra detail
At the time of release only long trades are supported
The strategy is meant for quick scalping but one might find other uses for it
Enable extended hours on intraday charts so it captures more pivots
It does not trade extended hours (pre and post market) since options do not trade during those times
real-time data is recommended and required if a symbol has delayed data by default
You can configure that it trades minutes after market open and hard sells minutes after market open
The entries have a specific label text, example: "833 LE1 / 569.71 / P:569.8". This means: / / . The condition number is only for development/debug purposes for me when you have an issue.
The strategy cannot be tweaked to work on multiple symbols and timeframes with a single config. So you will have to make a config for every timeframe and symbol. I recommend using the Indicator Templates feature of TradingView. This way you can save the settings per timeframe and symbol
The strategy is per default config very dependent on (trailing) stops because it trades between pivots too. It wants that a pivot and EMA crossover has happened more recently than a crossunder. But you can change this behavior to always force crossover buys and crossunder sells.
It’s recommended to set up alerts to notify you of entry and exit signals. Watching the chart alone might cause you to miss trades, especially in fast-moving markets.
Only a max of 500 lines can be rendered on the chart, but the strategy will function with more under the hood. When you exceed 500 you will notice the beginning of the chart has no pivots, but beneath everything functions for backtesting.
Changing settings
Changing the settings for a different symbol and/or timeframe can be a challenging task. Here's a how-to you could use the first time to help you get going:
Set commission and slippage to 0. I prefer to do this so it is more clear whether you are balancing on break-even trades
Enable the pivot timeframe equal or above your chart timeframe. Avoid repainting as discussed earlier by choosing timeframes that align with the same timeframe
Set all volume, ATR, stop, profit takers and trail values to 0
Make sure strict mode is disabled at the bottom of the settings
You now have a clean state and you should see the backtest results purely based on pivot and EMA conditions
Tweak the stop and profit taker, beginning with the simple values and then ATRP threshold
At the last moment tweak the trailing stops. Tight trailing stops create an unrealistic backtest so you will need to tweak them based on real-time behavior of the symbol you're using which you will have to monitor during signals while the market is open. The default values are low (2m intraday SPY). Only with the bar magnifier feature it is somewhat possible to tweak realistic with history data. The tighter they are, the more unrealistic your backtest results. As a starting point, set the trailing stop low and find the highest activation level that doesn't change the results drastically, then increase the stop to the value you think reflects real-time behavior.
Keep refining by testing it during real-time behavior. Does it exit too early according to your own judgment? You need to increase the stop and maybe the activation level.
I hope you will find this useful!
DISCLAIMER
Trading is risky & most day traders lose money. This indicator is purely for informational & educational purposes only. Past performance does not guarantee future results.
Central Pivot Point Cross & Retrace Strategy // AlgoFyreThe Central Pivot Point Cross & Retrace Strategy uses pivot points for trend identification and trade entry. It combines accumulation/distribution indicators with pivot point levels to generate signals. The strategy incorporates dynamic position sizing based on a fixed risk amount and allows for both long and short positions with customizable stop-loss levels.
TABLE OF CONTENTS
🔶 ORIGINALITY
🔸Pivot Point-Based Trading
🔸Accumulation/Distribution
🔸Dynamic Position Sizing
🔸Customizable Risk Management
🔶 FUNCTIONALITY
🔸Indicators
🞘 Pivot Points
🞘 Accumulation/Distribution
🔸Conditions
🞘 Long Entry
🞘 Short Entry
🞘 Take Profit
🞘 Stop Loss
🔶 INSTRUCTIONS
🔸Adding the Strategy to the Chart
🔸Configuring the Strategy
🔸Backtesting and Practice
🔸Market Awareness
🔸Visual Customization
🔶 CONCLUSION
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🔶 ORIGINALITY The Central Pivot Point Cross & Retrace Strategy uniquely combines pivot point analysis with accumulation/distribution indicators to identify optimal entry and exit points. It employs dynamic position sizing based on a fixed risk amount, ensuring consistent risk management across trades. This approach allows traders to adapt to varying market conditions by adjusting position sizes according to predefined risk parameters, enhancing both flexibility and control in trading decisions. The strategy's integration of customizable stop-loss levels further refines its risk management capabilities.
🔸Pivot Point-Based Trading This strategy utilizes daily pivot points to identify key support and resistance levels, providing a framework for trend identification and trade entry. The central pivot point serves as the intraday point of balance between buyers and sellers, with the largest amount of trading volume assumed to take place in this area.
🔸Accumulation/Distribution The strategy incorporates the Accumulation/Distribution (A/D) line, an underrated volume-based indicator, to establish the main trend. The A/D line is used in conjunction with a trend based indicator like the 200-period Exponential Moving Average (EMA) to confirm trend direction and strength.
🔸Dynamic Position Sizing Position sizes are calculated dynamically based on a fixed risk amount, allowing traders to maintain consistent risk exposure across trades.
🔸Customizable Risk Management Traders can set flexible risk-reward ratios and adjust stop-loss and take-profit levels, tailoring the strategy to their risk tolerance and market conditions. The strategy recommends taking partial profits at S1 or R1 levels and moving the stop-loss to break-even for remaining positions.
🔶 FUNCTIONALITY The Central Pivot Point Cross & Retrace Strategy leverages pivot points and accumulation/distribution indicators to identify optimal trading opportunities. This strategy is designed to capitalize on price movements around key pivot levels by dynamically adjusting position sizes based on predefined risk parameters. It allows traders to manage risk effectively while taking advantage of both long and short positions.
🔸Indicators 🞘 Pivot Points: Calculates daily pivot points (PP, R1, R2, S1, S2) to identify key support and resistance levels. The central pivot point is crucial for determining market bias and entry points.
🞘 Accumulation/Distribution: Uses the A/D line and with a trend based indicator like the 200 EMA to determine market direction and trend strength. This combination helps eliminate noise and provides more reliable trend signals. We recommend using the Adaptive MAs (Hurst, CVaR, Fractal) // AlgoFyre , but any moving average could be used.
🔸Conditions 🞘 Long Entry: Initiates a long position when the price crosses above the central pivot point (PP), retraces back to it and the A/D line is above its 200 EMA, indicating an uptrend. A limit entry order is set at the PP for entering the long trade.
🞘 Short Entry: Initiates a short position when the price crosses below the central pivot point (PP), retraces back to it and the A/D line is below its 200 EMA, indicating a downtrend. A limit entry order is set at the PP for entering the short trade.
🞘 Take Profit: 50% of the position is closed as profit when R1 for Longs and S1 for Shorts is reached. The position is fully closed when R2 for Longs and S2 for Shorts is reached.
🞘 Stop Loss: Stop loss is set via strategy settings. When the first 50% take profit for both long and shorts is taken, stop loss for both will be moved to break-even/entry.
🔶 INSTRUCTIONS
The Central Pivot Point Cross & Retrace Strategy can be set up by adding it to your TradingView chart and configuring parameters such as the accumulation/distribution source, stop-loss percentage, and risk management settings. This strategy is designed to capitalize on price movements around key pivot levels by dynamically adjusting position sizes based on predefined risk parameters. Enhance the accuracy of signals by combining this strategy with additional indicators like trend-following or momentum-based tools. Adjust settings to better manage risk and optimize entry and exit points.
🔸Adding the Strategy to the Chart Go to your TradingView chart.
Click on the "Pine Editor" button at the bottom of the chart.
Copy and paste the strategy code into the Pine Editor.
Click "Add to Chart" to apply the strategy.
Add the technical indicator "Accumulation/Distribution" to the chart.
Add the trend indicator " Adaptive MAs (Hurst, CVaR, Fractal) // AlgoFyre " or any other MA to the chart and move it to the "Accumulation/Distribution" pane.
Set the source of your trend indicator to "Accumulation/Distribution".
🔸Configuring the Strategy Open the strategy settings by clicking on the gear icon next to its name on the chart.
Accumulation/Distribution Source: Select the source for the accumulation/distribution indicator.
Accumulation/Distribution EMA Source: Select the source for the trend indicator.
Stop Loss Percentage: Set the stop loss distance from the pivot point as a percentage.
Risk Amount: Define the fixed risk amount for position sizing.
Base Order Size: Set the base order size for position calculations.
Number of Positions: Specify the maximum number of positions allowed.
Time Frame: Adjust the time frame based on the currency pair or asset being traded (e.g., 15-minute for EUR/USD, 30-minute for GBP/USD).
🔸Backtesting and Practice Backtest the strategy on historical data to understand how it performs in various market environments.
Practice using the strategy on a demo account before implementing it in live trading.
Test different time frames and asset pairs to find the most suitable combinations.
🔸Market Awareness Keep an eye on market news and events that might cause extreme price movements. The strategy reacts to price data and might not account for news-driven events that can cause large deviations.
Remember that this strategy is not recommended for stocks due to the A/D line's inability to account for gaps in its calculation.
🔸Visual Customization Visualization Settings: Customize the display of entry price, take profit, and stop loss levels.
Color Settings: Switch to the AlgoFyre theme or set custom colors for bullish, bearish, and neutral states.
Table Settings: Enable or disable the information table and adjust its position.
🔶 CONCLUSION
The Central Pivot Point Cross & Retrace Strategy provides a robust framework for capitalizing on price movements around key pivot levels by combining pivot point analysis with accumulation/distribution indicators. This strategy leverages pivot point crossovers to identify entry points and utilizes the A/D line crossover with its 200 EMA for trend confirmation, ensuring trades align with prevailing market conditions. By incorporating dynamic position sizing based on a fixed risk amount, traders can effectively manage risk and adapt to varying market conditions. The strategy's focus on trading around the central pivot point and its customizable stop-loss and take-profit levels further enhance its risk management capabilities, making it a versatile tool for both trending and ranging markets. With its strategic blend of technical indicators and risk management, the Central Pivot Point Cross & Retrace Strategy offers traders a comprehensive approach to optimizing trade execution and maximizing potential returns across various currency pairs and commodities.
VIDYA ProTrend Multi-Tier ProfitHello! This time is about a trend-following system.
VIDYA is quite an interesting indicator that adjusts dynamically to market volatility, making it more responsive to price changes compared to traditional moving averages. Balancing adaptability and precision, especially with the more aggressive short trade settings, challenged me to fine-tune the strategy for a variety of market conditions.
█ Introduction and How it is Different
The "VIDYA ProTrend Multi-Tier Profit" strategy is a trend-following system that combines the VIDYA (Variable Index Dynamic Average) indicator with Bollinger Bands and a multi-step take-profit mechanism.
Unlike traditional trend strategies, this system allows for more adaptive profit-taking, adjusting for long and short positions through distinct ATR-based and percentage-based targets. The innovation lies in its dynamic multi-tier approach to profit-taking, especially for short trades, where more aggressive percentages are applied using a multiplier. This flexibility helps adapt to various market conditions by optimizing trade management and profit allocation based on market volatility and trend strength.
BTCUSD 6hr performance
█ Strategy, How it Works: Detailed Explanation
The core of the "VIDYA ProTrend Multi-Tier Profit" strategy lies in the dual VIDYA indicators (fast and slow) that analyze price trends while accounting for market volatility. These indicators work alongside Bollinger Bands to filter trade entries and exits.
🔶 VIDYA Calculation
The VIDYA indicator is calculated using the following formula:
Smoothing factor (𝛼):
alpha = 2 / (Length + 1)
VIDYA formula:
VIDYA(t) = alpha * k * Price(t) + (1 - alpha * k) * VIDYA(t-1)
Where:
k = |Chande Momentum Oscillator (MO)| / 100
🔶 Bollinger Bands as a Volatility Filter
Bollinger Bands are calculated using a rolling mean and standard deviation of price over a specified period:
Upper Band:
BB_upper = MA + (K * stddev)
Lower Band:
BB_lower = MA - (K * stddev)
Where:
MA is the moving average,
K is the multiplier (typically 2), and
stddev is the standard deviation of price over the Bollinger Bands length.
These bands serve as volatility filters to identify potential overbought or oversold conditions, aiding in the entry and exit logic.
🔶 Slope Calculation for VIDYA
The slopes of both fast and slow VIDYAs are computed to assess the momentum and direction of the trend. The slope for a given VIDYA over its length is:
Slope = (VIDYA(t) - VIDYA(t-n)) / n
Where:
n is the length of the lookback period. Positive slope indicates bullish momentum, while negative slope signals bearish momentum.
LOCAL picture
🔶 Entry and Exit Conditions
- Long Entry: Occurs when the price moves above the slow VIDYA and the fast VIDYA is trending upward. Bollinger Bands confirm the signal when the price crosses the upper band, indicating bullish strength.
- Short Entry: Happens when the price drops below the slow VIDYA and the fast VIDYA trends downward. The signal is confirmed when the price crosses the lower Bollinger Band, showing bearish momentum.
- Exit: Based on VIDYA slopes flattening or reversing, or when the price hits specific ATR or percentage-based profit targets.
🔶 Multi-Step Take Profit Mechanism
The strategy incorporates three levels of take profit for both long and short trades:
- ATR-based Take Profit: Each step applies a multiple of the ATR (Average True Range) to the entry price to define the exit point.
The first level of take profit (long):
TP_ATR1_long = Entry Price + (2.618 * ATR)
etc.
█ Trade Direction
The strategy offers flexibility in defining the trading direction:
- Long: Only long trades are considered based on the criteria for upward trends.
- Short: Only short trades are initiated in bearish trends.
- Both: The strategy can take both long and short trades depending on the market conditions.
█ Usage
To use the strategy effectively:
- Adjust the VIDYA lengths (fast and slow) based on your preference for trend sensitivity.
- Use Bollinger Bands as a filter for identifying potential breakout or reversal scenarios.
- Enable the multi-step take profit feature to manage positions dynamically, allowing for partial exits as the price reaches specified ATR or percentage levels.
- Leverage the short trade multiplier for more aggressive take profit levels in bearish markets.
This strategy can be applied to different asset classes, including equities, forex, and cryptocurrencies. Adjust the input parameters to suit the volatility and characteristics of the asset being traded.
█ Default Settings
The default settings for this strategy have been designed for moderate to trending markets:
- Fast VIDYA Length (10): A shorter length for quick responsiveness to price changes. Increasing this length will reduce noise but may delay signals.
- Slow VIDYA Length (30): The slow VIDYA is set longer to capture broader market trends. Shortening this value will make the system more reactive to smaller price swings.
- Minimum Slope Threshold (0.05): This threshold helps filter out weak trends. Lowering the threshold will result in more trades, while raising it will restrict trades to stronger trends.
Multi-Step Take Profit Settings
- ATR Multipliers (2.618, 5.0, 10.0): These values define how far the price should move before taking profit. Larger multipliers widen the profit-taking levels, aiming for larger trend moves. In higher volatility markets, these values might be adjusted downwards.
- Percentage Levels (3%, 8%, 17%): These percentage levels define how much the price must move before taking profit. Increasing the percentages will capture larger moves, while smaller percentages offer quicker exits.
- Short TP Multiplier (1.5): This multiplier applies more aggressive take profit levels for short trades. Adjust this value based on the aggressiveness of your short trade management.
Each of these settings directly impacts the performance and risk profile of the strategy. Shorter VIDYA lengths and lower slope thresholds will generate more trades but may result in more whipsaws. Higher ATR multipliers or percentage levels can delay profit-taking, aiming for larger trends but risking partial gains if the trend reverses too early.
MACD Trend Trading with Dynamic Position Sizing // AlgoFyreThe MACD Trend Trading with Dynamic Position Sizing strategy combines MACD and trend indicators for trend trading. It uses MACD crossovers to identify entry points and a trend source for directional bias. The strategy incorporates risk management through dynamic position sizing based on a fixed risk amount. It allows for both long and short positions with customizable stop-loss and take-profit levels. The script includes visualization options for entry, stop-loss, and take-profit levels, enhancing trade analysis.
TABLE OF CONTENTS
🔶 ORIGINALITY
🔸Dynamic Position Sizing
🔸Trend-MACD Combination
🔸Customizable Risk Management
🔶 FUNCTIONALITY
🔸Indicators
🞘 Trend Indicator
🞘 Moving Average Convergence Divergence (MACD)
🔸Conditions
🞘 Long Entry
🞘 Short Entry
🔶 INSTRUCTIONS
🔸Step-by-Step Guidelines
🞘 Setting Up the Strategy
🞘 Alerts
🔸Customize settings
🔶 CONCLUSION
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🔶 ORIGINALITY The MACD Trend Trading with Dynamic Position Sizing strategy uniquely combines MACD indicators with trend analysis to optimize entry and exit points. Unlike static trading strategies, it employs dynamic position sizing based on a fixed risk amount, ensuring consistent risk management. This approach allows traders to adapt to varying market conditions by adjusting position sizes according to predefined risk parameters, enhancing both flexibility and control in trading decisions. The strategy's integration of customizable stop-loss and take-profit levels further refines its risk management capabilities, making it a robust tool for both trending and volatile markets.
🔸Dynamic Position Sizing This strategy calculates position sizes dynamically, based on a fixed risk amount, allowing traders to maintain consistent risk exposure across trades.
🔸Trend-MACD Combination By combining trend direction with MACD crossovers, the strategy enhances the accuracy of entry signals, aligning trades with prevailing market trends.
🔸Customizable Risk Management Traders can set flexible risk-reward ratios and adjust stop-loss and take-profit levels, tailoring the strategy to their risk tolerance and market conditions.
🔶 FUNCTIONALITY The MACD Trend Trading with Dynamic Position Sizing strategy leverages a combination of trend indicators and the MACD to identify optimal trading opportunities. This strategy is designed to capitalize on short-term price movements by dynamically adjusting position sizes based on predefined risk parameters. It allows traders to manage risk effectively while taking advantage of both long and short positions.
🔸Indicators 🞘 Trend Indicator: Utilizes the trend source to determine market direction, ensuring trades align with prevailing trends.
Recommendation: We recommend using the Adaptive MAs (Hurst, CVaR, Fractal) indicator with the following settings for trend detection. However, you can use any trend indicator that suits your trading style.
🞘 Moving Average Convergence Divergence (MACD): Employs MACD crossovers to generate entry signals, enhancing the accuracy of trade execution. Use the "Moving Average Convergence Divergence" Indicator with the following settings:
🔸Conditions 🞘 Long Entry: Initiates a long position when the price is above the trend source, and a MACD crossover occurs with both MACD and signal lines below zero.
🞘 Short Entry: Initiates a short position when the price is below the trend source, and a MACD crossunder occurs with both MACD and signal lines above zero.
🔶 INSTRUCTIONS
The MACD Trend Trading with Dynamic Position Sizing strategy can be set up by adding it to your TradingView chart and configuring parameters such as the MACD source, trend source, and risk management settings. This strategy is designed to capitalize on short-term price movements by dynamically adjusting position sizes based on predefined risk parameters. Enhance the accuracy of signals by combining this strategy with additional indicators like trend-following or momentum-based tools. Adjust settings to better manage risk and optimize entry and exit points.
🔸Step-by-Step Guidelines
🞘 Setting Up the Strategy
Adding the Strategy to the Chart:
Go to your TradingView chart.
Click on the "Indicators" button at the top.
Search for "MACD Trend Trading with Dynamic Position Sizing" in the indicators list.
Click on the strategy to add it to your chart.
Configuring the Strategy:
Open the strategy settings by clicking on the gear icon next to its name on the chart.
MACD: Select the MACD from the MACD Indicator.
MACD Signal: Select the MACD Signal from the MACD Indicator.
Trend Source: Choose the trend source to determine market direction. If you use the Adaptive MAs (Hurst, CVaR, Fractal) with our settings shown above, choose the MA1 Smoothing Line.
Stop Loss Percentage: Set the stop loss distance from the trend source as a percentage.
Risk/Reward Ratio: Define the desired risk/reward ratio for trades.
Backtesting and Practice:
Backtest the strategy on historical data to understand how it performs in various market environments.
Practice using the strategy on a demo account before implementing it in live trading.
Market Awareness:
Keep an eye on market news and events that might cause extreme price movements. The strategy reacts to price data and might not account for news-driven events that can cause large deviations.
🔶 CONCLUSION
The MACD Trend Trading with Dynamic Position Sizing strategy provides a robust framework for capitalizing on short-term market trends by combining the MACD indicator with dynamic position sizing. This strategy leverages MACD crossovers to identify entry points and utilizes a trend source for directional bias, ensuring trades align with prevailing market conditions. By incorporating dynamic position sizing based on a fixed risk amount, traders can effectively manage risk and adapt to varying market conditions. The strategy's customizable stop-loss and take-profit levels further enhance its risk management capabilities, making it a versatile tool for both trending and volatile markets. With its strategic blend of technical indicators and risk management, the MACD Trend Trading strategy offers traders a comprehensive approach to optimizing trade execution and maximizing potential returns.
Overnight Positioning w EMA - Strategy [presentTrading]I've recently started researching Market Timing strategies, and it’s proving to be quite an interesting area of study. The idea of predicting optimal times to enter and exit the market, based on historical data and various indicators, brings a dynamic edge to trading. Additionally, it is integrated with the 3commas bot for automated trade execution.
I'm still working on it. Welcome to share your point of view.
█ Introduction and How it is Different
The "Overnight Positioning with EMA " is designed to capitalize on market inefficiencies during the overnight trading period. This strategy takes a position shortly before the market closes and exits shortly after it opens the following day. What sets this strategy apart is the integration of an optional Exponential Moving Average (EMA) filter, which ensures that trades are aligned with the underlying trend. The strategy provides flexibility by allowing users to select between different global market sessions, such as the US, Asia, and Europe.
It is integrated with the 3commas bot for automated trade execution and has a built-in mechanism to avoid holding positions over the weekend by force-closing positions on Fridays before the market closes.
BTCUSD 20 mins Performance
█ Strategy, How it Works: Detailed Explanation
The core logic of this strategy is simple: enter trades before market close and exit them after market open, taking advantage of potential price movements during the overnight period. Here’s how it works in more detail:
🔶 Market Timing
The strategy determines the local market open and close times based on the selected market (US, Asia, Europe) and adjusts entry and exit points accordingly. The entry is triggered a specific number of minutes before market close, and the exit is triggered a specific number of minutes after market open.
🔶 EMA Filter
The strategy includes an optional EMA filter to help ensure that trades are taken in the direction of the prevailing trend. The EMA is calculated over a user-defined timeframe and length. The entry is only allowed if the closing price is above the EMA (for long positions), which helps to filter out trades that might go against the trend.
The EMA formula:
```
EMA(t) = +
```
Where:
- EMA(t) is the current EMA value
- Close(t) is the current closing price
- n is the length of the EMA
- EMA(t-1) is the previous period's EMA value
🔶 Entry Logic
The strategy monitors the market time in the selected timezone. Once the current time reaches the defined entry period (e.g., 20 minutes before market close), and the EMA condition is satisfied, a long position is entered.
- Entry time calculation:
```
entryTime = marketCloseTime - entryMinutesBeforeClose * 60 * 1000
```
🔶 Exit Logic
Exits are triggered based on a specified time after the market opens. The strategy checks if the current time is within the defined exit period (e.g., 20 minutes after market open) and closes any open long positions.
- Exit time calculation:
exitTime = marketOpenTime + exitMinutesAfterOpen * 60 * 1000
🔶 Force Close on Fridays
To avoid the risk of holding positions over the weekend, the strategy force-closes any open positions 5 minutes before the market close on Fridays.
- Force close logic:
isFriday = (dayofweek(currentTime, marketTimezone) == dayofweek.friday)
█ Trade Direction
This strategy is designed exclusively for long trades. It enters a long position before market close and exits the position after market open. There is no shorting involved in this strategy, and it focuses on capturing upward momentum during the overnight session.
█ Usage
This strategy is suitable for traders who want to take advantage of price movements that occur during the overnight period without holding positions for extended periods. It automates entry and exit times, ensuring that trades are placed at the appropriate times based on the market session selected by the user. The 3commas bot integration also allows for automated execution, making it ideal for traders who wish to set it and forget it. The strategy is flexible enough to work across various global markets, depending on the trader's preference.
█ Default Settings
1. entryMinutesBeforeClose (Default = 20 minutes):
This setting determines how many minutes before the market close the strategy will enter a long position. A shorter duration could mean missing out on potential movements, while a longer duration could expose the position to greater price fluctuations before the market closes.
2. exitMinutesAfterOpen (Default = 20 minutes):
This setting controls how many minutes after the market opens the position will be exited. A shorter exit time minimizes exposure to market volatility at the open, while a longer exit time could capture more of the overnight price movement.
3. emaLength (Default = 100):
The length of the EMA affects how the strategy filters trades. A shorter EMA (e.g., 50) reacts more quickly to price changes, allowing more frequent entries, while a longer EMA (e.g., 200) smooths out price action and only allows entries when there is a stronger underlying trend.
The effect of using a longer EMA (e.g., 200) would be:
```
EMA(t) = +
```
4. emaTimeframe (Default = 240):
This is the timeframe used for calculating the EMA. A higher timeframe (e.g., 360) would base entries on longer-term trends, while a shorter timeframe (e.g., 60) would respond more quickly to price movements, potentially allowing more frequent trades.
5. useEMA (Default = true):
This toggle enables or disables the EMA filter. When enabled, trades are only taken when the price is above the EMA. Disabling the EMA allows the strategy to enter trades without any trend validation, which could increase the number of trades but also increase risk.
6. Market Selection (Default = US):
This setting determines which global market's open and close times the strategy will use. The selection of the market affects the timing of entries and exits and should be chosen based on the user's preference or geographic focus.
Options Series - Index Analysis [MasterPiece]
Powerful Insights 🚀:
This script utilizes multiple technical indicators to provide a comprehensive view of stock trends, which increases the reliability of trading signals.
This script also designed to perform index and stock analysis by comparing price movements to moving averages (MA20) and volume-weighted average price (VWAP).
By analyzing a set of top-weighted stocks within an index, the script offers a macro-level view while also delivering stock-specific insights. This dual focus enhances its utility for traders who need to understand both individual stock movements and broader market dynamics.
⭐ Originality: The script presents a unique fusion of multiple indicators with a data-driven approach to analyzing top-weighted stocks in major indices like Nifty and BankNifty. The integration of widely-used technical analysis tools, such as exponential and simple moving averages (EMA, SMA), volume-weighted average price (VWAP), and volume-body size comparisons, offers a holistic framework for traders. By focusing on the top five stocks in the indices, it leverages weightage-based performance analysis, adding a strategic dimension to index trading. This approach not only evaluates individual stock performance but also synthesizes broader market trends.
⭐ Usefulness: This script serves traders who seek a multi-dimensional method for analyzing both index and stock performance. Its key features include:
Bullish and Bearish Signals: The relationship between price, moving averages (MA20), and VWAP identifies directional trends, generating buy/sell signals for both individual stocks and the overall index.
Volume and Candle Body Analysis: By comparing candle body size with volume, the script provides deeper insights into trend strength and market conviction. This allows traders to gauge whether price movements are supported by sufficient trading volume.
Customization: Users have the flexibility to input specific index and stock symbols, making the script adaptable for different markets and instruments beyond just Nifty and BankNifty.
Signal Overlay: The ability to overlay bar color and volume signals directly on the price chart ensures better trend visualization, offering clear and immediate visual cues for potential trading setups.
⭐ Justification for Mashup: The combination of multiple indicators is logical and complementary. Each component serves a distinct purpose that enhances the overall system:
Trend Identification: Moving averages and VWAP provide insights into short and long-term trends, giving traders a reliable baseline for price direction.
Conviction: The inclusion of volume and candle body size comparisons gives additional weight to price action, allowing traders to confirm whether a trend is backed by meaningful market activity.
⭐ Color Customization for Enhanced Visualization:
The script defines custom colors for various conditions and candles, improving clarity for bullish and bearish trends.
Green for Bullish: Dark green for regular bullish candles, and fluorescent green for stronger bullish signals.
Red for Bearish: Dark red for regular bearish candles, and fluorescent red for stronger bearish signals.
Neutral Conditions: Fluorescent yellow is used for neutral conditions.
⭐ Index and Top Stocks Analysis:
This section analyzes top-weighted stocks for indices ( NSE:NIFTY and NSE:BANKNIFTY ), with NSE:BANKNIFTY being used as the default.
Top Stocks for NSE:NIFTY : HDFCBANK, ICICIBANK, RELIANCE, INFY, ITC.
Top Stocks for NSE:BANKNIFTY : HDFCBANK, ICICIBANK, KOTAKBANK, AXISBANK, SBIN.
Customizable Input: Users can modify the index and stock symbols via input.symbol.
⭐ Signal Generation Based on MA20 and VWAP:
The conditions for bullish or bearish signals are based on the relationship between the stock's close price, MA20, and VWAP.
Bullish Signal: Close price greater than both MA20 and VWAP.
Bearish Signal: Close price less than both MA20 and VWAP.
⭐ Volume Bar Signal for Market Activity:
The script analyzes candle body size and volume to detect significant market movements.
Body Size and Volume Comparison: It checks if the current candle’s body size or volume is greater than the moving average of body size or volume over the past 74 bars.
Green Candle (GC) and Red Candle (RC): Boolean conditions to track whether the close price is higher or lower than the open price.
⭐ Average Signals for Strong Trends:
The script calculates average bullish or bearish signals based on the majority of candles being green or red and significant body size or volume.
Bullish Average Signal: At least 4 out of 6 stocks exhibit bullish conditions (green candles, large bodies, or high volume).
Bearish Average Signal: Similar logic for bearish signals with red candles.
⭐ Overlay of Volume Bar Signals:
The plotshape function overlays the bullish and bearish volume bar signals on the chart, using color and shape to indicate trend changes.
🚀 Conclusion:
This Pine Script code provides a robust framework for index analysis based on top 5 weighted stocks, using two primary indicators—MA20 (20-period Moving Average) and VWAP (Volume Weighted Average Price).
Market Bias Identification: The script identifies bullish and bearish conditions for each stock based on whether the close price is above or below MA20 and VWAP.
Volume and Body Size Comparison: It checks if the current candle’s body size or volume exceeds the average to determine significant market moves.
Visualization with Color & Signals: It overlays color signals for bullish (fluorescent green) and bearish (fluorescent red) markets and provides triangle markers for strong volume-based signals.
Top Stock Analysis: The script provides analysis of top five weighted stocks in the selected index, enhancing precision for broader index analysis.
The Adaptive Pairwise Momentum System [QuantraSystems]The Adaptive Pairwise Momentum System
QuantraSystems guarantees that the information created and published within this document and on the Tradingview platform is fully compliant with applicable regulations, does not constitute investment advice, and is not exclusively intended for qualified investors.
Important Note!
The system equity curve presented here has been generated as part of the process of testing and verifying the methodology behind this script.
Crucially, it was developed after the system was conceptualized, designed, and created, which helps to mitigate the risk of overfitting to historical data. In other words, the system was built for robustness, not for simply optimizing past performance.
This ensures that the system is less likely to degrade in performance over time, compared to hyper-optimized systems that are tailored to past data. No tweaks or optimizations were made to this system post-backtest.
Even More Important Note!!
The nature of markets is that they change quickly and unpredictably. Past performance does not guarantee future results - this is a fundamental rule in trading and investing.
While this system is designed with broad, flexible conditions to adapt quickly to a range of market environments, it is essential to understand that no assumptions should be made about future returns based on historical data. Markets are inherently uncertain, and this system - like all trading systems - cannot predict future outcomes.
Introduction
The Adaptive Pairwise Momentum System is not just an indicator but a comprehensive asset rotation and trend-following system. In short, it aims to find the highest performing asset from the provided range.
The system dynamically optimizes capital allocation across up to four high-performing assets, ensuring that the portfolio adapts swiftly to changing market conditions. The system logic consists of sophisticated quantitative methods, rapid momentum analysis, and robust trend filtering. The overarching goal is to ensure that the portfolio is always invested in the highest-performing asset based on dynamic market conditions, while at the same time managing risk through broader market filters and internal mechanisms like volatility and beta analysis.
Legend
System Equity Curve:
The equity curve displayed in the chart is dynamically colored based on the asset allocation at any given time. This color-coded approach allows traders to immediately identify transitions between assets and the corresponding impact on portfolio performance.
Highlighting of Current Highest Performer:
The current bar in the chart is highlighted based on the confirmed highest performing asset. This is designed to give traders advanced notice of potential shifts in allocation even before a formal position change occurs. The highlighting enables traders to prepare in real time, making it easier to manage positions without lag, particularly in fast-moving markets.
Highlighted Symbols in the Asset Table:
In the table displayed on the right hand side of the screen, the current top-performing symbol is highlighted. This clear signal at a glance provides immediate insight into which asset is currently being favored by the system. This feature enhances clarity and helps traders make informed decisions quickly, without needing to analyze the underlying data manually.
Performance Overview in Tables:
The left table provides insight into both daily and overall system performance from inception, offering traders a detailed view of short-term fluctuations and long-term growth. The right-hand table breaks down essential metrics such as Sharpe ratio, Sortino ratio, Omega ratio, and maximum drawdown for each asset, as well as for the overall system and HODL strategy.
Asset-Specific Signals:
The signals column in the table indicates whether an asset is currently held or being considered for holding based on the system's dynamic rankings. This is a critical visual aid for asset reallocation decisions, signaling when it may be appropriate to either maintain or change the asset of the portfolio.
Core Features and Methodologies
Flexibility in Asset Selection
One of the major advantages of this system is its flexibility. Users can easily modify the number and type of assets included for comparison. You can quickly input different assets and backtest their performance, allowing you to verify how well this system might fit different tokens or market conditions. This flexibility empowers users to adapt the system to a wide range of market environments and tailor it to their unique preferences.
Whole System Risk Mitigation - Macro Trend Filter
One of the features of this script is its integration of a Macro-level Trend Filter for the entire portfolio. The purpose of this filter is to ensure no capital is allocated to any token in the rotation system unless Bitcoin itself is in a positive trend. The logic here is that Bitcoin, as the cryptocurrency market leader, often sets the tone for the entire cryptocurrency market. By using Bitcoins trend direction as a barometer for overall market conditions, we create a system where capital is not allocated during unfavorable or bearish market conditions - significantly reducing exposure to downside risk.
Users have the ability to toggle this filter on and off in the input menu, with five customizable options for the trend filter, including the option to use no filter. These options are:
Nova QSM - a trend aggregate combining the Rolling VWAP, Wave Pendulum Trend, KRO Overlay, and the Pulse Profiler provides the market trend signal confirmation.
Kilonova QSM - a versatile aggregate combining the Rolling VWAP, KRO Overlay, the KRO Base, RSI Volatility Bands, NNTRSI, Regression Smoothed RSI and the RoC Suite.
Quasar QSM - an enhanced version of the original RSI Pulsar. The Quasar QSM refines the trend following approach by utilizing an aggregated methodology.
Pairwise Momentum and Strength Ranking
The backbone of this system is its ability to identify the strongest-performing asset in the selected pool, ensuring that the portfolio is always exposed to the asset showing the highest relative momentum. The system continually ranks these assets against each other and determines the highest performer by measure of past and coincident outperformance. This process occurs rapidly, allowing for swift responses to shifts in market momentum, which ensures capital is always working in the most efficient manner. The speed and precision of this reallocation strategy make the script particularly well-suited for active, momentum-driven portfolios.
Beta-Adjusted Asset Selection as a Tiebreaker
In the circumstance where two (or more) assets exhibit the same relative momentum score, the system introduces another layer of analysis. In the event of a strength ‘tie’ the system will preference maintaining the current position - that is, if the previously strongest asset is now tied, the system will still allocate to the same asset. If this is not the case, the asset with the higher beta is selected. Beta is a measure of an asset’s volatility relative to Bitcoin (BTC).
This ensures that in bullish conditions, the system favors assets with a higher potential for outsized gains due to their inherent volatility. Beta is calculated based on the Average Daily Return of each asset compared to BTC. By doing this, the system ensures that it is dynamically adjusting to risk and reward, allocating to assets with higher risk in favorable conditions and lower risk in less favorable conditions.
Dynamic Asset Reallocation - Opposed to Multi-Asset Fixed Intervals
One of the standout features of this system is its ability to dynamically reallocate capital. Unlike traditional portfolio allocation strategies that may rebalance between a basket of assets monthly or quarterly, this system recalculates and reallocates capital on the next bar close (if required). As soon as a new asset exhibits superior performance relative to others, the system immediately adjusts, closing the previous position and reallocating funds to the top-ranked asset.
This approach is particularly powerful in volatile markets like cryptocurrencies, where trends can shift quickly. By reallocating swiftly, the system maximizes exposure to high-performing assets while minimizing time spent in underperforming ones. Moreover, this process is entirely automated, freeing the trader from manually tracking and measuring individual token strength.
Our research has demonstrated that, from a risk-adjusted return perspective, concentration into the top-performing asset consistently outperforms broad diversification across longer time horizons. By focusing capital on the highest-performing asset, the system captures outsized returns that are not achievable through traditional diversification. However, a more risk-averse investor, or one seeking to reduce drawdowns, may prefer to move the portfolio further left along the theoretical Capital Allocation Line by incorporating a blend of cash, treasury bonds, or other yield-generating assets or even include market neutral strategies alongside the rotation system. This hybrid approach would effectively lower the overall volatility of the portfolio while still maintaining exposure to the system’s outsized returns. In theory, such an investor can reduce risk without sacrificing too much potential upside, creating a more balanced risk-return profile.
Position Changes and Fees/Slippage
Another critical and often overlooked element of this system is its ability to account for fees and slippage. Given the increased speed and frequency of allocation logic compared to the buy-and-hold strategy, it is of vital importance that the system recognises that switching between assets may incur slippage, especially in highly volatile markets. To account for this, the system integrates realistic slippage and fee estimates directly into the equity curve, simulating expected execution costs under typical market conditions and gives users a more realistic view of expected performance.
Number of Position Changes
Understanding the number of position changes in a strategy is critical to assessing its feasibility in real world trading. Frequent position changes can lead to increased costs due to slippage and fees. Monitoring the number of position changes provides insight into the system’s behavior - helping to evaluate how active the strategy is and whether it aligns with the trader's desired time input for position management.
Equity Curve and Performance Calculations
To provide a benchmark, the script also generates a Buy-and-Hold (or "HODL") equity curve that represents an equal split across the four selected assets. This allows users to easily compare the performance of the dynamic rotation system with that of a more traditional investment strategy.
The script tracks key performance metrics for both the dynamic portfolio and the HODL strategy, including:
Sharpe Ratio
The Sharpe Ratio is a key metric that evaluates a portfolio’s risk-adjusted return by comparing its ‘excess’ return to its volatility. Traditionally, the Sharpe Ratio measures returns relative to a risk-free rate. However, in our system’s calculation, we omit the risk-free rate and instead measure returns above a benchmark of 0%. This adjustment provides a more universal comparison, especially in the context of highly volatile assets like cryptocurrencies, where a traditional risk-free benchmark, such as the usual 3-month T-bills, is often irrelevant or too distant from the realities of the crypto market.
By using 0% as the baseline, we focus purely on the strategy's ability to generate raw returns in the face of market risk, which makes it easier to compare performance across different strategies or asset classes. In an environment like cryptocurrency, where volatility can be extreme, the importance of relative return against a highly volatile backdrop outweighs comparisons to a risk-free rate that bears little resemblance to the risk profile of digital assets.
Sortino Ratio
The Sortino Ratio improves upon the Sharpe Ratio by specifically targeting downside risk and leaves the upside potential untouched. In contrast to the Sharpe Ratio (which penalizes both upside and downside volatility), the Sortino Ratio focuses only on negative return deviations. This makes it a more suitable metric for evaluating strategies like the Adaptive Pairwise Momentum Strategy - that aim to minimize drawdowns without restricting upside capture. By measuring returns relative to a 0% baseline, the Sortino ratio provides a clearer assessment of how well the system generates gains while avoiding substantial losses in highly volatile markets like crypto.
Omega Ratio
The Omega Ratio is calculated as the ratio of gains to losses across all return thresholds, providing a more complete view of how the system balances upside and downside risk even compared to the Sortino Ratio. While it achieves a similar outcome to the Sortino Ratio by emphasizing the system's ability to capture gains while limiting losses, it is technically a mathematically superior method. However, we include both the Omega and Sortino ratios in our metric table, as the Sortino Ratio remains more widely recognized and commonly understood by traders and investors of all levels.
Case Study
Notes
For the sake of brevity, the Important Notes section found in the header of this text will not be rewritten. Instead, it will be highlighted that now is the perfect time to reread these notes. Reading this case study in the context of what has been mentioned above is of key importance.
As a second note, it is worth mentioning that certain market periods are referred to as either “Bull” or “Bear” markets - terms I personally find to be vague and undefinable - and therefore unfavorable. They will be used nevertheless, due to their familiarity and ease of understanding in this context. Substitute phrases could be “Macro Uptrend” or “Macro Downtrend.”
Overview
This case study provides an in-depth performance analysis of the Adaptive Pairwise Momentum System , a long-only system that dynamically allocates to outperforming assets and moves into cash during unfavorable conditions.
This backtest includes realistic assumptions for slippage and fees, applying a 0.5% cost for every position change, which includes both asset reallocation and moving to a cash position. Additionally, the system was tested using the top four cryptocurrencies by market capitalization as of the test start date of 01/01/2022 in order to minimize selection bias.
The top tokens on this date (excluding Stablecoins) were:
Bitcoin
Ethereum
Solana
BNB
This decision was made in order to avoid cherry picking assets that might have exhibited exceptional historical performance - minimizing skew in the back test. Furthermore, although this backtest focuses on these specific assets, the system is built to be flexible and adaptable, capable of being applied to a wide range of assets beyond those initially tested.
Any potential lookahead bias or repainting in the calculations has been addressed by implementing the lookback modifier for all repainting sensitive data, including asset ratios, asset scoring, and beta values. This ensures that no future information is inadvertently used in the asset allocation process.
Additionally, a fixed lookback period of one bar is used for the trend filter during allocations - meaning that the trend filter from the prior bar must be positive for an allocation to occur on the current bar. It is also important to note that all the data displayed by the indicator is based on the last confirmed (closed) bar, ensuring that the entire system is repaint-proof.
The study spans the 2022 cryptocurrency bear market through the subsequent bull market of 2023 and 2024. The stress test highlights how the system reacted to one of the most challenging market downturns in crypto history - which includes events such as:
Luna and TerraUSD crash
Three Arrows Capital liquidation
Celsius bankruptcy
Voyager Digital bankruptcy
FTX collapse
Silicon Valley + Signature + Silvergate banking collapses
Subsequent USDC deppegging
And arguably more important, 2022 was characterized by a tightening of monetary policy after the unprecedented monetary easing in response to the Covid pandemic of 2020/2021. This shift undeniably puts downward pressure on asset prices, most probably to the extent that this had a causal role to many of the above events.
By incorporating these real-world challenges, the backtest provides a more accurate and robust performance evaluation that avoids overfitting or excessive optimization for one specific market condition.
The Bear Market of 2022: Stress Test and System Resilience
During the 2022 bear market, where the overall crypto market experienced deep and consistent corrections, the Adaptive Pairwise Momentum System demonstrated its ability to mitigate downside risk effectively.
Dynamic Allocation and Cash Exposure:
The system rotated in and out of cash, as indicated by the gray period on the system equity curve. This allocation to cash during downtrending periods, specifically in late 2022, acted as the systems ‘risk-off’ exposure - the purest form of such an exposure. This prevented the system from experiencing the magnitude of drawdown suffered by the ‘Buy-and-Hold (HODL) investors.
In contrast, a passive HODL strategy would have suffered a staggering 75.32% drawdown, as it remained fully allocated to chosen assets during the market's decline. The active Pairwise Momentum system’s smaller drawdown of 54.35% demonstrates its more effective capital preservation mechanisms.
The Bull Market of 2023 and 2024: Capturing Market Upside
Following the crypto bear market, the system effectively capitalized on the recovery and subsequent bull market of 2023 and 2024.
Maximizing Market Gains:
As trends began turning bullish in early 2023, the system caught the momentum and promptly allocated capital to only the quantified highest performing asset of the time - resulting in a parabolic rise in the system's equity curve. Notably, the curve transitions from gray to purple during this period, indicating that Solana (SOL) was the top-performing asset selected by the system.
This allocation to Solana is particularly striking because, at the time, it was an asset many in the market shunned due to its association with the FTX collapse just months prior. However, this highlights a key advantage of quantitative systems like the one presented here: decisions are driven purely from objective data - free from emotional or subjective biases. Unlike human traders, who are inclined (whether consciously or subconsciously) to avoid assets that are ‘out of favor,’ this system focuses purely on price performance, often uncovering opportunities that are overlooked by discretionary based investors. This ability to make data-driven decisions ensures that the strategy is always positioned to capture the best risk-adjusted returns, even in scenarios where judgment might fail.
Minimizing Volatility and Drawdown in Uptrends
While the system captured substantial returns during the bull market it also did so with lower volatility compared to HODL. The sharpe ratio of 4.05 (versus HODL’s 3.31) reflects the system's superior risk-adjusted performance. The allocation shifts, combined with tactical periods of cash holding during minor corrections, ensured a smoother equity curve growth compared to the buy-and-hold approach.
Final Summary
The percentage returns are mentioned last for a reason - it is important to emphasize that risk-adjusted performance is paramount. In this backtest, the Pairwise Momentum system consistently outperforms due to its ability to dynamically manage risk (as seen in the superior Sharpe, Sortino and Omega ratios). With a smaller drawdown of 54.35% compared to HODL’s 75.32%, the system demonstrates its resilience during market downturns, while also capturing the highest beta on the upside during bullish phases.
The system delivered 266.26% return since the backtest start date of January 1st 2022, compared to HODL’s 10.24%, resulting in a performance delta of 256.02%
While this backtest goes some of the way to verifying the system’s feasibility, it’s important to note that past performance is not indicative of future results - especially in volatile and evolving markets like cryptocurrencies. Market behavior can shift, and in particular, if the market experiences prolonged sideways action, trend following systems such as the Adaptive Pairwise Momentum Strategy WILL face significant challenges.
Neural Momentum StrategyThis strategy combines Exponential Moving Average (EMA) analysis with a multi-timeframe approach. It uses a neural scoring system to evaluate market momentum and generate precise trading signals. The strategy is implemented in Pine Script v5 and is designed for use on TradingView.
Key Components
The strategy utilizes short-term (10-period) and long-term (25-period) EMAs. It calculates the difference between these EMAs to assess trend direction and strength. A neural scoring system evaluates EMA crossovers (weight: 12 points), trend strength (weight: 10 points), and price acceleration (weight: 4 points). The system implements a score smoothing algorithm using a 10-period EMA.
Multi-timeframe Analysis
The strategy automatically selects a higher timeframe based on the current chart timeframe. It calculates scores for both the current and higher timeframes, then combines these scores using a weighted average. The higher timeframe factor ranges from 3 to 6, depending on the current timeframe.
Trading Logic
Entry occurs when the final combined score turns positive after a change. Exit happens when the final combined score turns negative after a change. The strategy recalculates scores on each bar, ensuring responsive trading decisions.
Risk Management
An optional adaptive stop-loss system based on Average True Range (ATR) is available. The default ATR period is 10, and the stop factor is 1.2. Stop levels are dynamically adjusted on the higher timeframe.
Customization Options
Users can adjust EMA periods, signal line period, scoring weights, and enable/disable multi-timeframe analysis. The strategy allows setting specific date ranges for backtesting and deployment.
Position Sizing
The strategy uses a percentage-of-equity position sizing method, with a default of 30% of account equity per trade.
Code Structure
The strategy is built using TradingView's strategy framework. It employs efficient use of the request.security() function for multi-timeframe analysis. The main calculation function, calculate_score(), computes the neural score based on EMA differences and acceleration.
Performance Considerations
The strategy adapts to various market conditions through its multi-faceted scoring system. Multi-timeframe analysis helps filter out noise and identify stronger trends. The neural scoring approach aims to capture subtle market dynamics often missed by traditional indicators.
Limitations
Performance may vary across different markets and timeframes. The strategy's effectiveness relies on proper calibration of its numerous parameters. Users should thoroughly backtest and forward test before live implementation.
To summarize, the Neural Momentum Strategy represents a sophisticated approach to market analysis. It combines traditional technical indicators with advanced scoring techniques and multi-timeframe analysis. This strategy is designed for traders seeking a data-driven and adaptive method. It aims to identify high-probability trading opportunities across various market conditions.
This Neural Momentum Strategy is for informational and educational purposes only. It should not be considered financial advice. The strategy may exhibit slight repainting behavior due to the nature of multi-timeframe analysis and the use of the request.security() function. Historical values might change as new data becomes available.
Trading carries a high level of risk, and may not be suitable for all investors. Before deciding to trade, you should carefully consider your investment objectives, level of experience, and risk appetite. The possibility exists that you could sustain a loss of some or all of your initial investment. Therefore, you should not invest money that you cannot afford to lose.
Past performance is not indicative of future results. The author and TradingView are not responsible for any losses incurred as a result of using this strategy. Always exercise caution when using this or any trading strategy, and thoroughly test it before implementing in live trading scenarios.
Users are solely responsible for any trading decisions they make based on this strategy. It is strongly recommended that you seek advice from an independent financial advisor if you have any doubts.
Bitcoin CME-Spot Z-Spread - Strategy [presentTrading]This time is a swing trading strategy! It measures the sentiment of the Bitcoin market through the spread of CME Bitcoin Futures and Bitfinex BTCUSD Spot prices. By applying Bollinger Bands to the spread, the strategy seeks to capture mean-reversion opportunities when prices deviate significantly from their historical norms
█ Introduction and How it is Different
The Bitcoin CME-Spot Bollinger Bands Strategy is designed to capture mean-reversion opportunities by exploiting the spread between CME Bitcoin Futures and Bitfinex BTCUSD Spot prices. The strategy uses Bollinger Bands to detect when the spread between these two correlated assets has deviated significantly from its historical norm, signaling potential overbought or oversold conditions.
What sets this strategy apart is its focus on spread trading between futures and spot markets rather than price-based indicators. By applying Bollinger Bands to the spread rather than individual prices, the strategy identifies price inefficiencies across markets, allowing traders to take advantage of the natural reversion to the mean that often occurs in these correlated assets.
BTCUSD 8hr Performance
█ Strategy, How It Works: Detailed Explanation
The strategy relies on Bollinger Bands to assess the volatility and relative deviation of the spread between CME Bitcoin Futures and Bitfinex BTCUSD Spot prices. Bollinger Bands consist of a moving average and two standard deviation bands, which help measure how much the spread deviates from its historical mean.
🔶 Spread Calculation:
The spread is calculated by subtracting the Bitfinex spot price from the CME Bitcoin futures price:
Spread = CME Price - Bitfinex Price
This spread represents the difference between the futures and spot markets, which may widen or narrow based on supply and demand dynamics in each market. By analyzing the spread, the strategy can detect when prices are too far apart (potentially overbought or oversold), indicating a trading opportunity.
🔶 Bollinger Bands Calculation:
The Bollinger Bands for the spread are calculated using a simple moving average (SMA) and the standard deviation of the spread over a defined period.
1. Moving Average (SMA):
The simple moving average of the spread (mu_S) over a specified period P is calculated as:
mu_S = (1/P) * sum(S_i from i=1 to P)
Where S_i represents the spread at time i, and P is the lookback period (default is 200 bars). The moving average provides a baseline for the normal spread behavior.
2. Standard Deviation:
The standard deviation (sigma_S) of the spread is calculated to measure the volatility of the spread:
sigma_S = sqrt((1/P) * sum((S_i - mu_S)^2 from i=1 to P))
3. Upper and Lower Bollinger Bands:
The upper and lower Bollinger Bands are derived by adding and subtracting a multiple of the standard deviation from the moving average. The number of standard deviations is determined by a user-defined parameter k (default is 2.618).
- Upper Band:
Upper Band = mu_S + (k * sigma_S)
- Lower Band:
Lower Band = mu_S - (k * sigma_S)
These bands provide a dynamic range within which the spread typically fluctuates. When the spread moves outside of these bands, it is considered overbought or oversold, potentially offering trading opportunities.
Local view
🔶 Entry Conditions:
- Long Entry: A long position is triggered when the spread crosses below the lower Bollinger Band, indicating that the spread has become oversold and is likely to revert upward.
Spread < Lower Band
- Short Entry: A short position is triggered when the spread crosses above the upper Bollinger Band, indicating that the spread has become overbought and is likely to revert downward.
Spread > Upper Band
🔶 Risk Management and Profit-Taking:
The strategy incorporates multi-step take profits to lock in gains as the trade moves in favor. The position is gradually reduced at predefined profit levels, reducing risk while allowing part of the trade to continue running if the price keeps moving favorably.
Additionally, the strategy uses a hold period exit mechanism. If the trade does not hit any of the take-profit levels within a certain number of bars, the position is closed automatically to avoid excessive exposure to market risks.
█ Trade Direction
The trade direction is based on deviations of the spread from its historical norm:
- Long Trade: The strategy enters a long position when the spread crosses below the lower Bollinger Band, signaling an oversold condition where the spread is expected to narrow.
- Short Trade: The strategy enters a short position when the spread crosses above the upper Bollinger Band, signaling an overbought condition where the spread is expected to widen.
These entries rely on the assumption of mean reversion, where extreme deviations from the average spread are likely to revert over time.
█ Usage
The Bitcoin CME-Spot Bollinger Bands Strategy is ideal for traders looking to capitalize on price inefficiencies between Bitcoin futures and spot markets. It’s especially useful in volatile markets where large deviations between futures and spot prices occur.
- Market Conditions: This strategy is most effective in correlated markets, like CME futures and spot Bitcoin. Traders can adjust the Bollinger Bands period and standard deviation multiplier to suit different volatility regimes.
- Backtesting: Before deployment, backtesting the strategy across different market conditions and timeframes is recommended to ensure robustness. Adjust the take-profit steps and hold periods to reflect the trader’s risk tolerance and market behavior.
█ Default Settings
The default settings provide a balanced approach to spread trading using Bollinger Bands but can be adjusted depending on market conditions or personal trading preferences.
🔶 Bollinger Bands Period (200 bars):
This defines the number of bars used to calculate the moving average and standard deviation for the Bollinger Bands. A longer period smooths out short-term fluctuations and focuses on larger, more significant trends. Adjusting the period affects the responsiveness of the strategy:
- Shorter periods (e.g., 100 bars): Makes the strategy more reactive to short-term market fluctuations, potentially generating more signals but increasing the risk of false positives.
- Longer periods (e.g., 300 bars): Focuses on longer-term trends, reducing the frequency of trades and focusing only on significant deviations.
🔶 Standard Deviation Multiplier (2.618):
The multiplier controls how wide the Bollinger Bands are around the moving average. By default, the bands are set at 2.618 standard deviations away from the average, ensuring that only significant deviations trigger trades.
- Higher multipliers (e.g., 3.0): Require a more extreme deviation to trigger trades, reducing trade frequency but potentially increasing the accuracy of signals.
- Lower multipliers (e.g., 2.0): Make the bands narrower, increasing the number of trade signals but potentially decreasing their reliability.
🔶 Take-Profit Levels:
The strategy has four take-profit levels to gradually lock in profits:
- Level 1 (3%): 25% of the position is closed at a 3% profit.
- Level 2 (8%): 20% of the position is closed at an 8% profit.
- Level 3 (14%): 15% of the position is closed at a 14% profit.
- Level 4 (21%): 10% of the position is closed at a 21% profit.
Adjusting these take-profit levels affects how quickly profits are realized:
- Lower take-profit levels: Capture gains more quickly, reducing risk but potentially cutting off larger profits.
- Higher take-profit levels: Let trades run longer, aiming for bigger gains but increasing the risk of price reversals before profits are locked in.
🔶 Hold Days (20 bars):
The strategy automatically closes the position after 20 bars if none of the take-profit levels are hit. This feature prevents trades from being held indefinitely, especially if market conditions are stagnant. Adjusting this:
- Shorter hold periods: Reduce the duration of exposure, minimizing risks from market changes but potentially closing trades too early.
- Longer hold periods: Allow trades to stay open longer, increasing the chance for mean reversion but also increasing exposure to unfavorable market conditions.
By understanding how these default settings affect the strategy’s performance, traders can optimize the Bitcoin CME-Spot Bollinger Bands Strategy to their preferences, adapting it to different market environments and risk tolerances.
Rolling Straddle PremiumScript is Basically intended to provide insight's on the Rolling Straddle premium for the selected index based on the input settings.
Important thing to consider for the script to work seamlessly:
Specify the LTP in the input field (need not be very accurate)
Specify the Expiry Date for the Option Strike.
Ensure Profile matches to the chart script (Index Script)
Note: Zones marked in Blue, is the max level that indicator can track the option prices. beyond which it may fail to track, during such time consider reloading the indicator with Latest LTP .
Labels on the chart indicate that If i had shorted the Straddle, what would be my current position of that Straddle. however the rational behind shorting is only the pivot high points (not sure if this is right or wrong! )
Note On Labels: Labels are delayed basis the pivot point candles specified in the indicator settings.
EN: Entry Price (Straddle Premium) of the Strike Specified.
Cur: Current Price ( Current Straddle Premium ) of the Strike Specified.
SH: Max Straddle Premium ( Increase in Premium ) since position is active.
SL: Min Straddle Premium ( Premium Erosion ) since position is active.
Multi-Step FlexiMA - Strategy [presentTrading]It's time to come back! hope I can not to be busy for a while.
█ Introduction and How It Is Different
The FlexiMA Variance Tracker is a unique trading strategy that calculates a series of deviations between the price (or another indicator source) and a variable-length moving average (MA). Unlike traditional strategies that use fixed-length moving averages, the length of the MA in this system varies within a defined range. The length changes dynamically based on a starting factor and an increment factor, creating a more adaptive approach to market conditions.
This strategy integrates Multi-Step Take Profit (TP) levels, allowing for partial exits at predefined price increments. It enables traders to secure profits at different stages of a trend, making it ideal for volatile markets where taking full profits at once might lead to missed opportunities if the trend continues.
BTCUSD 6hr Performance
█ Strategy, How It Works: Detailed Explanation
🔶 FlexiMA Concept
The FlexiMA (Flexible Moving Average) is at the heart of this strategy. Unlike traditional MA-based strategies where the MA length is fixed (e.g., a 50-period SMA), the FlexiMA varies its length with each iteration. This is done using a **starting factor** and an **increment factor**.
The formula for the moving average length at each iteration \(i\) is:
`MA_length_i = indicator_length * (starting_factor + i * increment_factor)`
Where:
- `indicator_length` is the user-defined base length.
- `starting_factor` is the initial multiplier of the base length.
- `increment_factor` increases the multiplier in each iteration.
Each iteration applies a **simple moving average** (SMA) to the chosen **indicator source** (e.g., HLC3) with a different length based on the above formula. The deviation between the current price and the moving average is then calculated as follows:
`deviation_i = price_current - MA_i`
These deviations are normalized using one of the following methods:
- **Max-Min normalization**:
`normalized_i = (deviation_i - min(deviations)) / range(deviations)`
- **Absolute Sum normalization**:
`normalized_i = deviation_i / sum(|deviation_i|)`
The **median** and **standard deviation (stdev)** of the normalized deviations are then calculated as follows:
`median = median(normalized deviations)`
For the standard deviation:
`stdev = sqrt((1/(N-1)) * sum((normalized_i - mean)^2))`
These values are plotted to provide a clear indication of how the price is deviating from its variable-length moving averages.
For more detail:
🔶 Multi-Step Take Profit
This strategy uses a multi-step take profit system, allowing for exits at different stages of a trade based on the percentage of price movement. Three take-profit levels are defined:
- Take Profit Level 1 (TP1): A small, quick profit level (e.g., 2%).
- Take Profit Level 2 (TP2): A medium-level profit target (e.g., 8%).
- Take Profit Level 3 (TP3): A larger, more ambitious target (e.g., 18%).
At each level, a corresponding percentage of the trade is exited:
- TP Percent 1: E.g., 30% of the position.
- TP Percent 2: E.g., 20% of the position.
- TP Percent 3: E.g., 15% of the position.
This approach ensures that profits are locked in progressively, reducing the risk of market reversals wiping out potential gains.
Local
🔶 Trade Entry and Exit Conditions
The entry and exit signals are determined by the interaction between the **SuperTrend Polyfactor Oscillator** and the **median** value of the normalized deviations:
- Long entry: The SuperTrend turns bearish, and the median value of the deviations is positive.
- Short entry: The SuperTrend turns bullish, and the median value is negative.
Similarly, trades are exited when the SuperTrend flips direction.
* The SuperTrend Toolkit is made by @EliCobra
█ Trade Direction
The strategy allows users to specify the desired trade direction:
- Long: Only long positions will be taken.
- Short: Only short positions will be taken.
- Both: Both long and short positions are allowed based on the conditions.
This flexibility allows the strategy to adapt to different market conditions and trading styles, whether you're looking to buy low and sell high, or sell high and buy low.
█ Usage
This strategy can be applied across various asset classes, including stocks, cryptocurrencies, and forex. The primary use case is to take advantage of market volatility by using a flexible moving average and multiple take-profit levels to capture profits incrementally as the market moves in your favor.
How to Use:
1. Configure the Inputs: Start by adjusting the **Indicator Length**, **Starting Factor**, and **Increment Factor** to suit your chosen asset. The defaults work well for most markets, but fine-tuning them can improve performance.
2. Set the Take Profit Levels: Adjust the three **TP levels** and their corresponding **percentages** based on your risk tolerance and the expected volatility of the market.
3. Monitor the Strategy: The SuperTrend and the FlexiMA variance tracker will provide entry and exit signals, automatically managing the positions and taking profits at the pre-set levels.
█ Default Settings
The default settings for the strategy are configured to provide a balanced approach that works across different market conditions:
Indicator Length (10):
This controls the base length for the moving average. A lower length makes the moving average more responsive to price changes, while a higher length smooths out fluctuations, making the strategy less sensitive to short-term price movements.
Starting Factor (1.0):
This determines the initial multiplier applied to the moving average length. A higher starting factor will increase the average length, making it slower to react to price changes.
Increment Factor (1.0):
This increases the moving average length in each iteration. A larger increment factor creates a wider range of moving average lengths, allowing the strategy to track both short-term and long-term trends simultaneously.
Normalization Method ('None'):
Three methods of normalization can be applied to the deviations:
- None: No normalization applied, using raw deviations.
- Max-Min: Normalizes based on the range between the maximum and minimum deviations.
- Absolute Sum: Normalizes based on the total sum of absolute deviations.
Take Profit Levels:
- TP1 (2%): A quick exit to capture small price movements.
- TP2 (8%): A medium-term profit target for stronger trends.
- TP3 (18%): A long-term target for strong price moves.
Take Profit Percentages:
- TP Percent 1 (30%): Exits 30% of the position at TP1.
- TP Percent 2 (20%): Exits 20% of the position at TP2.
- TP Percent 3 (15%): Exits 15% of the position at TP3.
Effect of Variables on Performance:
- Short Indicator Lengths: More responsive to price changes but prone to false signals.
- Higher Starting Factor: Slows down the response, useful for longer-term trend following.
- Higher Increment Factor: Widens the variability in moving average lengths, making the strategy adapt to both short-term and long-term price trends.
- Aggressive Take Profit Levels: Allows for quick profit-taking in volatile markets but may exit positions prematurely in strong trends.
The default configuration offers a moderate balance between short-term responsiveness and long-term trend capturing, suitable for most traders. However, users can adjust these variables to optimize performance based on market conditions and personal preferences.
HFT V.2 EnhancedTitle: HFT V.2 Enhanced - ATR Dynamic Stop-Loss & Take-Profit
Description:
The HFT V.2 Enhanced strategy is designed for high-frequency trading with dynamic trade management and robust entry/exit logic. This strategy uses simple moving averages (SMA) for trend identification and the relative strength index (RSI) for momentum confirmation. In this enhanced version, the strategy also incorporates dynamic stop-loss and take-profit levels based on the Average True Range (ATR), offering better adaptability to market volatility.
Features:
Moving Average Crossover: Uses a fast and slow SMA to capture trend reversals and generate trade entries.
RSI Confirmation: Ensures momentum is in the direction of the trade by incorporating the RSI threshold for both long and short entries.
Dynamic Stop-Loss and Take-Profit: Stop-loss and take-profit levels are calculated based on the ATR, allowing the strategy to adjust its exit points according to market volatility. This helps manage risk more effectively and capture larger trends.
Auto-Close Opposing Positions: Automatically closes any open long positions when a short entry is triggered, and vice versa.
Once-Per-Bar Execution: Ensures that a position is entered only once per bar, avoiding multiple trades within the same bar.
Parameters:
Fast MA Length: Defines the length of the fast-moving average.
Slow MA Length: Defines the length of the slow-moving average.
RSI Length: Sets the period for the RSI indicator.
RSI Threshold: Controls the RSI level for confirming momentum (50 by default).
ATR Length: Determines the period for the ATR calculation.
ATR Multiplier for Stop-Loss/Take-Profit: Adjusts the sensitivity of the stop-loss and take-profit levels based on ATR.
How it Works:
Long Entry: The strategy opens a long trade when the fast SMA crosses above the slow SMA, and the RSI is above the user-defined threshold. A dynamic stop-loss is placed below the entry price, and a take-profit target is set based on ATR.
Short Entry: The strategy opens a short trade when the fast SMA crosses below the slow SMA, and the RSI is below the inverse threshold. A stop-loss is placed above the entry price, and a take-profit target is set using ATR.
Risk Management: The strategy adapts to changing market conditions by dynamically adjusting its stop-loss and take-profit levels, ensuring it remains responsive to market volatility.
This script is ideal for traders looking for a high-frequency strategy with advanced trade management, including dynamic exits and volatility-based risk management.
Disclaimer: Always backtest and optimize the parameters to fit your trading style and risk tolerance before using the strategy in live trading.
Strategy Framework: 37 Strategies Unified with RM & PS BTCEURStrategy Framework: 37 Strategies Unified with Risk Management and Position Sizing
This comprehensive framework integrates over 37 independent strategies into a single, powerful trading system. Each strategy contributes its unique market perspective, culminating in a holistic decision-making process. The framework is further enhanced with sophisticated risk management and position sizing techniques.
Key Strategies Include:
• Moving average analysis
• Market structure evaluation
• Percentage rank calculations
• Sine wave correlation
• Fourier Frequency Transform (FFT) for signal composition analysis
• Bayesian statistical methods
• Seasonality patterns
• Signal-to-noise ratio assessment
• Horizontal & Indecision levels identification
• Trendlines and channels recognition
• Various oscillator-based strategies
• Open interest analysis
• Volume and volatility measurements
This diverse array of strategies provides a multi-faceted view of the asset, offering a clear and comprehensive understanding of market dynamics.
Optimization and Implementation:
• Each strategy is designed for easy optimization, with a maximum of 4 parameters.
• All strategies produce consistent signal types, which are aggregated for final market direction decisions.
• Individual optimization of each strategy is performed using the Zorro Platform, a professional C++ based tool.
• All strategies are tested to work by themselves with Walk-Forward back testing
• Strategies that don't enhance market regime definition are excluded, ensuring efficiency.
Two-Tiered Approach:
1. Market Regime Identification: The combined output of all strategies determines the market regime, visually represented by a color-coded cloud.
2. Trade Execution: Based on the identified regime, the system applies different entry and exit rules, employing trend-following in bull markets and mean reversion in bear markets.
This framework is optimized for cryptocurrencies, including BTC and ETH and others, offering a robust solution for trading in these volatile markets.
The color of the cloud encodes the market regime as determined by the 37 strategies, guiding the application of distinct trading rules for bull and bear markets.
This invitation-only TradingView script represents a culmination of extensive research and optimization, designed to provide serious traders with a powerful tool for navigating the complex cryptocurrency markets.
The strategy comes pre-configured with optimized parameters by default, so there's no need to make any adjustments. However, it’s important to use the timeframes and exchanges selected on screen . Also, a Premium account with 20.000 bars is needed since since starting points are important for the parameter optimizations. If you have any questions or concerns about the strategy, feel free to reach out.
For automation, I recommend using a tool like Autoview . The strategy is fully compatible with automated trading; you just need to select your exchange and set the maximum order size you're comfortable trading.
Free Month for Testing:
You are eligible for a free one-month trial to test the strategy before committing. This allows you to fully explore its capabilities without any immediate cost.
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Important Information:
This is a premium script with access granted on an invite-only basis.
To request access or if you have further questions, please send me a direct message. There is a free month allowance for testing purposes.
Please note that this script involves complex calculations, and on rare occasions, you may encounter an error message from TradingView stating, "Calculation Takes Too Long." This is usually due to a temporary issue with server resources. If this happens, simply modify any parameter of the indicator and revert it back—this should resolve the issue.
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General Disclaimer:
Trading stocks, futures, Forex, options, ETFs, cryptocurrencies, or any other financial instrument involves significant risks and rewards. You must be fully aware of the risks involved and be willing to accept them before participating in these markets.
Do not trade with money you cannot afford to lose. This communication is not a solicitation or an offer to buy or sell any financial instrument.
No guarantees are made regarding potential profits or losses from any account. Past performance of any trading strategy or methodology is not necessarily indicative of future results.
Optimized Heikin Ashi Strategy with Buy/Sell OptionsStrategy Name:
Optimized Heikin Ashi Strategy with Buy/Sell Options
Description:
The Optimized Heikin Ashi Strategy is a trend-following strategy designed to capitalize on market trends by utilizing the smoothness of Heikin Ashi candles. This strategy provides flexible options for trading, allowing users to choose between Buy Only (long-only), Sell Only (short-only), or using both in alternating conditions based on the Heikin Ashi candle signals. The strategy works on any market, but it performs especially well in markets where trends are prevalent, such as cryptocurrency or Forex.
This script offers customizable parameters for the backtest period, Heikin Ashi timeframe, stop loss, and take profit levels, allowing traders to optimize the strategy for their preferred markets or assets.
Key Features:
Trade Type Options:
Buy Only: Enter a long position when a green Heikin Ashi candle appears and exit when a red candle appears.
Sell Only: Enter a short position when a red Heikin Ashi candle appears and exit when a green candle appears.
Stop Loss and Take Profit:
Customizable stop loss and take profit percentages allow for flexible risk management.
The default stop loss is set to 2%, and the default take profit is set to 4%, maintaining a favorable risk/reward ratio.
Heikin Ashi Timeframe:
Traders can select the desired timeframe for Heikin Ashi candle calculation (e.g., 4-hour Heikin Ashi candles for a 1-hour chart).
The strategy smooths out price action and reduces noise, providing clearer signals for entry and exit.
Inputs:
Backtest Start Date / End Date: Specify the period for testing the strategy’s performance.
Heikin Ashi Timeframe: Select the timeframe for Heikin Ashi candle generation. A higher timeframe helps smooth the trend, which is beneficial for trading lower timeframes.
Stop Loss (in %) and Take Profit (in %): Enable or disable stop loss and take profit, and adjust the levels based on market conditions.
Trade Type: Choose between Buy Only or Sell Only based on your market outlook and strategy preference.
Strategy Performance:
In testing with BTC/USD, this strategy performed well in a 4-hour Heikin Ashi timeframe applied on a 1-hour chart over a period from January 1, 2024, to September 12, 2024. The results were as follows:
Initial Capital: 1 USD
Order Size: 100% of equity
Net Profit: +30.74 USD (3,073.52% return)
Percent Profitable: 78.28% of trades were winners.
Profit Factor: 15.825, indicating that the strategy's profitable trades far outweighed its losses.
Max Drawdown: 4.21%, showing low risk exposure relative to the large profit potential.
This strategy is ideal for both beginner and advanced traders who are looking to follow trends and avoid market noise by using Heikin Ashi candles. It is also well-suited for traders who prefer automated risk management through the use of stop loss and take profit levels.
Recommended Use:
Best Markets: This strategy works well on trending markets like cryptocurrency, Forex, or indices.
Timeframes: Works best when applied to lower timeframes (e.g., 1-hour chart) with a higher Heikin Ashi timeframe (e.g., 4-hour candles) to smooth out price action.
Leverage: The strategy performs well with leverage, but users should consider using 2x to 3x leverage to avoid excessive risk and potential liquidation. The strategy's low drawdown allows for moderate leverage use while maintaining risk control.
Customization: Traders can adjust the stop loss and take profit percentages based on their risk appetite and market conditions. A default setting of a 2% stop loss and 4% take profit provides a balanced risk/reward ratio.
Notes:
Risk Management: Traders should enable stop loss and take profit settings to maintain effective risk management and prevent large drawdowns during volatile market conditions.
Optimization: This strategy can be further optimized by adjusting the Heikin Ashi timeframe and risk parameters based on specific market conditions and assets.
Backtesting: The built-in backtesting functionality allows traders to test the strategy across different market conditions and historical data to ensure robustness before applying it to live trading.
How to Apply:
Select your preferred market and chart.
Choose the appropriate Heikin Ashi timeframe based on the chart's timeframe. (e.g., use 4-hour Heikin Ashi candles for 1-hour chart trends).
Adjust stop loss and take profit based on your risk management preference.
Run backtesting to evaluate its performance before applying it in live trading.
This strategy can be further modified and optimized based on personal trading style and market conditions. It’s important to monitor performance regularly and adjust settings as needed to align with market behavior.
LRS-Strategy: 200-EMA Buffer & Long/Short Signals LRS-Strategy: 200-EMA Buffer & Long/Short Signals
This indicator is designed to help traders implement the Leveraged Return Strategy (LRS) using the 200-day Exponential Moving Average (EMA) as a key trend-following signal. The indicator offers clear long and short signals by analyzing the price movements relative to the 200-day EMA, enhanced by customizable buffer zones for increased precision.
Key Features:
200-Day EMA: The main trend indicator. When the price is above the 200-day EMA, the market is considered in an uptrend, and when it is below, it indicates a downtrend.
Customizable Buffer Zones: Users can define a percentage buffer around the 200-day EMA (default is 3%). The upper and lower buffer zones help filter out noise and prevent premature signals.
Precise Long/Short Signals:
Long Signal: Triggered when the price moves from below the lower buffer zone, crosses the 200-day EMA, and then breaks above the upper buffer zone.
Short Signal: Triggered when the price moves from above the upper buffer zone, crosses the 200-day EMA, and then breaks below the lower buffer zone.
Alternating Signals: Ensures that a new signal (long or short) is only generated after the opposite signal has been triggered, preventing multiple signals of the same type without a reversal.
Clear Visual Aids: The indicator displays the 200-day EMA and buffer zones on the chart, along with buy (long) and sell (short) signals. This makes it easy to track trends and time entries/exits.
How to Use:
Long Entry: Look for the price to move below the lower buffer, cross the 200-day EMA from below, and then break out of the upper buffer to confirm a long signal.
Short Entry: Look for the price to move above the upper buffer, cross below the 200-day EMA, and then break below the lower buffer to confirm a short signal.
This indicator is perfect for traders who prefer a structured, trend-following approach, using clear rules to minimize noise and identify meaningful long or short opportunities.
Price-Volume w Trendline - Strategy [presentTrading]█ Introduction and How it is Different
The Price-Volume with Trendline Strategy is an innovative strategy that combines volume profile analysis, price-based Z-scores, and dynamic trendline filtering to identify optimal entry and exit points in the market. What sets this strategy apart is the integration of volume concentration (Point of Control or PoC) with dynamic volatility thresholds. Additionally, this strategy introduces a multi-step take profit (TP) mechanism that adjusts based on predefined levels, allowing traders to exit trades progressively while capitalizing on market momentum.
BTCUSD 6hr LS Performance
█ Strategy, How it Works: Detailed Explanation
The combination of multiple indicators and methodologies serves to create a more robust and reliable trading system. Each element is carefully chosen for its complementary role in providing accurate signals while minimizing false entries and exits. Here’s why the different components were chosen and how they work together:
- PoC and Z-Scores: The volume profile identifies key price areas, while the Z-score measures deviations from the mean. Together, they highlight points where the market is likely to react. For example, when the Z-score indicates an oversold condition near a PoC support level, it increases the probability of a reversal, providing a clear entry signal.
- Trendlines and Z-Scores: Trendlines serve as a secondary filter to ensure that price deviations identified by Z-scores align with broader market trends. This ensures that trades are only entered when the price has both deviated from its average and broken through a significant trendline level, reducing the likelihood of false signals.
- Multi-Step TP and Risk Management: Finally, the multi-step take profit logic works in tandem with the entry signals generated by the PoC, Z-scores, and trendlines. As the price moves in favor of the trade, profits are gradually locked in, ensuring the trader captures gains while still leaving room for further upside.
🔶 Point of Control (PoC) and Volume Profile Analysis
The PoC identifies the price level with the highest volume concentration within a specified lookback period. This price level represents where the most trading activity has occurred, often acting as a strong support or resistance. By breaking down the range into several rows (bins), the strategy identifies how much volume was traded at each price level.
🔶 Z-Score Calculation
The Z-score is a statistical metric that measures how far the current price is from its mean, expressed in terms of standard deviations. This is calculated both for price deviation and PoC-based deviation.
🔶 Trendline Breakout Filtering
The trendline filtering is a crucial aspect that refines entry signals by confirming trend continuation or reversals. It calculates trendlines based on pivot highs and lows using the selected method (e.g., ATR or standard deviation).
🔶 Multi-Step Take Profit
The multi-step take profit mechanism allows the strategy to take partial profits at several predefined levels. For example, when the price reaches 3%, 8%, 14%, or 21% above (or below) the entry price, it exits portions of the position. This is a useful technique for locking in profits as the market moves favorably.
Local
█ Usage
The Price-Volume with Trendline Strategy can be applied to various asset classes, including stocks, cryptocurrencies, and commodities. It is particularly effective in volatile markets where price deviations and volume concentrations signal potential reversals or trend continuations. By adjusting the settings for volatility and the lookback period, this strategy can be tailored to both short-term intraday trades and longer-term swing trades.
█ Default Settings
The default settings in the strategy play a vital role in shaping its performance.
- POC_lookbackLength (144): This defines the number of bars used to calculate the PoC. A longer lookback captures more data, leading to a more stable PoC, but may result in delayed signals. A shorter lookback increases responsiveness but may introduce noise.
- priceDeviationLength (200): This determines the period for calculating the standard deviation of price. A higher length smooths out the volatility, reducing the likelihood of false signals. Shorter lengths make the strategy more sensitive to sudden price movements.
- TL_length (14): Controls the swing detection period for trendline calculation. A shorter length will generate more frequent trendline breakouts, while a longer length captures only significant moves.
- Stop Loss and Take Profit: The strategy offers both fixed and SuperTrend-based stop losses. SuperTrend is adaptive to volatility, while fixed stop losses provide simpler risk control. The multi-step take profit ensures that profits are secured progressively, which can improve performance in trending markets by reducing the risk of full reversals.
Each of these settings can significantly affect the strategy’s risk-reward balance. For instance, increasing the stop loss level or the take profit percentages allows the strategy to stay in trades longer, potentially increasing profit per trade but at the cost of larger drawdowns. Conversely, tighter stops and smaller profit targets result in more frequent trades with lower average profit per trade.
Intramarket Difference Index StrategyHi Traders !!
The IDI Strategy:
In layman’s terms this strategy compares two indicators across markets and exploits their differences.
note: it is best the two markets are correlated as then we know we are trading a short to long term deviation from both markets' general trend with the assumption both markets will trend again sometime in the future thereby exhausting our trading opportunity.
📍 Import Notes:
This Strategy calculates trade position size independently (i.e. risk per trade is controlled in the user inputs tab), this means that the ‘Order size’ input in the ‘Properties’ tab will have no effect on the strategy. Why ? because this allows us to define custom position size algorithms which we can use to improve our risk management and equity growth over time. Here we have the option to have fixed quantity or fixed percentage of equity ATR (Average True Range) based stops in addition to the turtle trading position size algorithm.
‘Pyramiding’ does not work for this strategy’, similar to the order size input togeling this input will have no effect on the strategy as the strategy explicitly defines the maximum order size to be 1.
This strategy is not perfect, and as of writing of this post I have not traded this algo.
Always take your time to backtests and debug the strategy.
🔷 The IDI Strategy:
By default this strategy pulls data from your current TV chart and then compares it to the base market, be default BINANCE:BTCUSD . The strategy pulls SMA and RSI data from either market (we call this the difference data), standardizes the data (solving the different unit problem across markets) such that it is comparable and then differentiates the data, calling the result of this transformation and difference the Intramarket Difference (ID). The formula for the the ID is
ID = market1_diff_data - market2_diff_data (1)
Where
market(i)_diff_data = diff_data / ATR(j)_market(i)^0.5,
where i = {1, 2} and j = the natural numbers excluding 0
Formula (1) interpretation is the following
When ID > 0: this means the current market outperforms the base market
When ID = 0: Markets are at long run equilibrium
When ID < 0: this means the current market underperforms the base market
To form the strategy we define one of two strategy type’s which are Trend and Mean Revesion respectively.
🔸 Trend Case:
Given the ‘‘Strategy Type’’ is equal to TREND we define a threshold for which if the ID crosses over we go long and if the ID crosses under the negative of the threshold we go short.
The motivating idea is that the ID is an indicator of the two symbols being out of sync, and given we know volatility clustering, momentum and mean reversion of anomalies to be a stylised fact of financial data we can construct a trading premise. Let's first talk more about this premise.
For some markets (cryptocurrency markets - synthetic symbols in TV) the stylised fact of momentum is true, this means that higher momentum is followed by higher momentum, and given we know momentum to be a vector quantity (with magnitude and direction) this momentum can be both positive and negative i.e. when the ID crosses above some threshold we make an assumption it will continue in that direction for some time before executing back to its long run equilibrium of 0 which is a reasonable assumption to make if the market are correlated. For example for the BTCUSD - ETHUSD pair, if the ID > +threshold (inputs for MA and RSI based ID thresholds are found under the ‘‘INTRAMARKET DIFFERENCE INDEX’’ group’), ETHUSD outperforms BTCUSD, we assume the momentum to continue so we go long ETHUSD.
In the standard case we would exit the market when the IDI returns to its long run equilibrium of 0 (for the positive case the ID may return to 0 because ETH’s difference data may have decreased or BTC’s difference data may have increased). However in this strategy we will not define this as our exit condition, why ?
This is because we want to ‘‘let our winners run’’, to achieve this we define a trailing Donchian Channel stop loss (along with a fixed ATR based stop as our volatility proxy). If we were too use the 0 exit the strategy may print a buy signal (ID > +threshold in the simple case, market regimes may be used), return to 0 and then print another buy signal, and this process can loop may times, this high trade frequency means we fail capture the entire market move lowering our profit, furthermore on lower time frames this high trade frequencies mean we pay more transaction costs (due to price slippage, commission and big-ask spread) which means less profit.
By capturing the sum of many momentum moves we are essentially following the trend hence the trend following strategy type.
Here we also print the IDI (with default strategy settings with the MA difference type), we can see that by letting our winners run we may catch many valid momentum moves, that results in a larger final pnl that if we would otherwise exit based on the equilibrium condition(Valid trades are denoted by solid green and red arrows respectively and all other valid trades which occur within the original signal are light green and red small arrows).
another example...
Note: if you would like to plot the IDI separately copy and paste the following code in a new Pine Script indicator template.
indicator("IDI")
// INTRAMARKET INDEX
var string g_idi = "intramarket diffirence index"
ui_index_1 = input.symbol("BINANCE:BTCUSD", title = "Base market", group = g_idi)
// ui_index_2 = input.symbol("BINANCE:ETHUSD", title = "Quote Market", group = g_idi)
type = input.string("MA", title = "Differrencing Series", options = , group = g_idi)
ui_ma_lkb = input.int(24, title = "lookback of ma and volatility scaling constant", group = g_idi)
ui_rsi_lkb = input.int(14, title = "Lookback of RSI", group = g_idi)
ui_atr_lkb = input.int(300, title = "ATR lookback - Normalising value", group = g_idi)
ui_ma_threshold = input.float(5, title = "Threshold of Upward/Downward Trend (MA)", group = g_idi)
ui_rsi_threshold = input.float(20, title = "Threshold of Upward/Downward Trend (RSI)", group = g_idi)
//>>+----------------------------------------------------------------+}
// CUSTOM FUNCTIONS |
//<<+----------------------------------------------------------------+{
// construct UDT (User defined type) containing the IDI (Intramarket Difference Index) source values
// UDT will hold many variables / functions grouped under the UDT
type functions
float Close // close price
float ma // ma of symbol
float rsi // rsi of the asset
float atr // atr of the asset
// the security data
getUDTdata(symbol, malookback, rsilookback, atrlookback) =>
indexHighTF = barstate.isrealtime ? 1 : 0
= request.security(symbol, timeframe = timeframe.period,
expression = [close , // Instentiate UDT variables
ta.sma(close, malookback) ,
ta.rsi(close, rsilookback) ,
ta.atr(atrlookback) ])
data = functions.new(close_, ma_, rsi_, atr_)
data
// Intramerket Difference Index
idi(type, symbol1, malookback, rsilookback, atrlookback, mathreshold, rsithreshold) =>
threshold = float(na)
index1 = getUDTdata(symbol1, malookback, rsilookback, atrlookback)
index2 = getUDTdata(syminfo.tickerid, malookback, rsilookback, atrlookback)
// declare difference variables for both base and quote symbols, conditional on which difference type is selected
var diffindex1 = 0.0, var diffindex2 = 0.0,
// declare Intramarket Difference Index based on series type, note
// if > 0, index 2 outpreforms index 1, buy index 2 (momentum based) until equalibrium
// if < 0, index 2 underpreforms index 1, sell index 1 (momentum based) until equalibrium
// for idi to be valid both series must be stationary and normalised so both series hae he same scale
intramarket_difference = 0.0
if type == "MA"
threshold := mathreshold
diffindex1 := (index1.Close - index1.ma) / math.pow(index1.atr*malookback, 0.5)
diffindex2 := (index2.Close - index2.ma) / math.pow(index2.atr*malookback, 0.5)
intramarket_difference := diffindex2 - diffindex1
else if type == "RSI"
threshold := rsilookback
diffindex1 := index1.rsi
diffindex2 := index2.rsi
intramarket_difference := diffindex2 - diffindex1
//>>+----------------------------------------------------------------+}
// STRATEGY FUNCTIONS CALLS |
//<<+----------------------------------------------------------------+{
// plot the intramarket difference
= idi(type,
ui_index_1,
ui_ma_lkb,
ui_rsi_lkb,
ui_atr_lkb,
ui_ma_threshold,
ui_rsi_threshold)
//>>+----------------------------------------------------------------+}
plot(intramarket_difference, color = color.orange)
hline(type == "MA" ? ui_ma_threshold : ui_rsi_threshold, color = color.green)
hline(type == "MA" ? -ui_ma_threshold : -ui_rsi_threshold, color = color.red)
hline(0)
Note it is possible that after printing a buy the strategy then prints many sell signals before returning to a buy, which again has the same implication (less profit. Potentially because we exit early only for price to continue upwards hence missing the larger "trend"). The image below showcases this cenario and again, by allowing our winner to run we may capture more profit (theoretically).
This should be clear...
🔸 Mean Reversion Case:
We stated prior that mean reversion of anomalies is an standerdies fact of financial data, how can we exploit this ?
We exploit this by normalizing the ID by applying the Ehlers fisher transformation. The transformed data is then assumed to be approximately normally distributed. To form the strategy we employ the same logic as for the z score, if the FT normalized ID > 2.5 (< -2.5) we buy (short). Our exit conditions remain unchanged (fixed ATR stop and trailing Donchian Trailing stop)
🔷 Position Sizing:
If ‘‘Fixed Risk From Initial Balance’’ is toggled true this means we risk a fixed percentage of our initial balance, if false we risk a fixed percentage of our equity (current balance).
Note we also employ a volatility adjusted position sizing formula, the turtle training method which is defined as follows.
Turtle position size = (1/ r * ATR * DV) * C
Where,
r = risk factor coefficient (default is 20)
ATR(j) = risk proxy, over j times steps
DV = Dollar Volatility, where DV = (1/Asset Price) * Capital at Risk
🔷 Risk Management:
Correct money management means we can limit risk and increase reward (theoretically). Here we employ
Max loss and gain per day
Max loss per trade
Max number of consecutive losing trades until trade skip
To read more see the tooltips (info circle).
🔷 Take Profit:
By defualt the script uses a Donchain Channel as a trailing stop and take profit, In addition to this the script defines a fixed ATR stop losses (by defualt, this covers cases where the DC range may be to wide making a fixed ATR stop usefull), ATR take profits however are defined but optional.
ATR SL and TP defined for all trades
🔷 Hurst Regime (Regime Filter):
The Hurst Exponent (H) aims to segment the market into three different states, Trending (H > 0.5), Random Geometric Brownian Motion (H = 0.5) and Mean Reverting / Contrarian (H < 0.5). In my interpretation this can be used as a trend filter that eliminates market noise.
We utilize the trending and mean reverting based states, as extra conditions required for valid trades for both strategy types respectively, in the process increasing our trade entry quality.
🔷 Example model Architecture:
Here is an example of one configuration of this strategy, combining all aspects discussed in this post.
Future Updates
- Automation integration (next update)
Trend Signals with TP & SL [UAlgo] StrategyThe "Trend Signals with TP & SL Strategy" is a trading strategy designed to capture trend continuation signals while incorporating sophisticated risk management techniques. This strategy is tailored for traders who wish to capitalize on trending market conditions with precise entry and exit points, automatically calculating Take Profit (TP) and Stop Loss (SL) levels based on either Average True Range (ATR) or percentage values. The strategy aims to enhance trade management by preventing multiple simultaneous positions and dynamically adapting to changing market conditions.
This strategy is highly configurable, allowing traders to adjust sensitivity, the ATR calculation method, and the cloud moving average length. Additionally, the strategy can display buy and sell signals directly on the chart, along with visual representation of entry points, stop losses, and take profits. It also features a cloud-based trend analysis using a MACD-driven color fill that indicates the strength and direction of the trend.
🔶 Key Features
Configurable Trend Continuation Signals:
Source Selection: The strategy uses the midpoint of the high-low range as the default source, but it is adjustable.
Sensitivity: The sensitivity of the trend signals can be adjusted using a multiplier, ranging from 0.5 to 5.
ATR Calculation: The strategy allows users to choose between two ATR calculation methods for better adaptability to different market conditions.
Cloud Moving Average: Traders can adjust the cloud moving average length, which is used in conjunction with MACD to provide a visual trend indication.
Take Profit & Stop Loss Management:
ATR-Based or Percent-Based: The strategy offers flexibility in setting TP and SL levels, allowing traders to choose between ATR-based multipliers or fixed percentage values.
Dynamic Adjustment: TP and SL levels are dynamically adjusted according to the selected method, ensuring trades are managed based on real-time market conditions.
Prevention of Multiple Positions:
Single Position Control: To reduce risk and enhance strategy reliability, the strategy includes an option to prevent multiple positions from being opened simultaneously.
Visual Trade Indicators:
Buy/Sell Signals: Clearly displays buy and sell signals on the chart for easy interpretation.
Entry, SL, and TP Lines: Draws lines for entry price, stop loss, and take profit directly on the chart, helping traders to monitor trades visually.
Trend Cloud: A color-filled cloud based on MACD and the cloud moving average provides a visual cue of the trend’s direction and strength.
Performance Summary Table:
In-Chart Statistics: A table in the top right of the chart displays key performance metrics, including total trades, wins, losses, and win rate percentage, offering a quick overview of the strategy’s effectiveness.
🔶 Interpreting the Indicator
Trend Signals: The strategy identifies trend continuation signals based on price action relative to an ATR-based threshold. A buy signal is generated when the price crosses above a key level, indicating an uptrend. Conversely, a sell signal occurs when the price crosses below a level, signaling a downtrend.
Cloud Visualization: The cloud, derived from MACD and moving averages, changes color to reflect the current trend. A positive cloud in aqua suggests an uptrend, while a red cloud indicates a downtrend. The transparency of the cloud offers further nuance, with more solid colors denoting stronger trends.
Entry and Exit Management: Once a trend signal is generated, the strategy automatically sets TP and SL levels based on your chosen method (ATR or percentage). The stop loss and take profit lines will appear on the chart, showing where the strategy will exit the trade. If the price reaches either the SL or TP, the trade is closed, and the respective line is deleted from the chart.
Performance Metrics: The strategy’s performance is tracked in real-time with an in-chart table. This table provides essential information about the number of trades executed, the win/loss ratio, and the overall win rate. This information helps traders assess the strategy's effectiveness and make necessary adjustments.
This strategy is designed for those who seek to engage with trending markets, offering robust tools for entry, exit, and overall trade management. By understanding and leveraging these features, traders can potentially improve their trading outcomes and risk management.
🔷 Related Script
🔶 Disclaimer
Use with Caution: This indicator is provided for educational and informational purposes only and should not be considered as financial advice. Users should exercise caution and perform their own analysis before making trading decisions based on the indicator's signals.
Not Financial Advice: The information provided by this indicator does not constitute financial advice, and the creator (UAlgo) shall not be held responsible for any trading losses incurred as a result of using this indicator.
Backtesting Recommended: Traders are encouraged to backtest the indicator thoroughly on historical data before using it in live trading to assess its performance and suitability for their trading strategies.
Risk Management: Trading involves inherent risks, and users should implement proper risk management strategies, including but not limited to stop-loss orders and position sizing, to mitigate potential losses.
No Guarantees: The accuracy and reliability of the indicator's signals cannot be guaranteed, as they are based on historical price data and past performance may not be indicative of future results.
Innocent Heikin Ashi Ethereum StrategyHello there, im back!
If you are familiar with my previous scripts, this one will seem like the future's nostalgia!
Functionality:
As you can see, all candles are randomly colored. This has no deeper meaning, it should remind you to switch to Heikin Ashi. The Strategy works on standard candle stick charts, but should be used with Heikin Ashi to see the actual results. (Regular OHLC calculations are included.)
Same as in my previous scripts we import our PVSRA Data from @TradersReality open source Indicator.
With this data and the help of moving averages, we have got an edge in the market.
Signal Logic:
When a "violently green" candle appears (high buy volume + tick speed) above the 50 EMA indicates a change in trend and sudden higher prices. Depending on OHLC of the candle itself and volume, Take Profit and Stop Loss is calculated. (The price margin is the only adjustable setting). Additionally, to make this script as simple and easily useable as possible, all other adjustable variables have been already set to the best suitable value and the chart was kept plain, except for the actual entries and exits.
Basic Settings and Adjustables:
Main Input 1: TP and SL combined price range. (Double, Triple R:R equally.)
Trade Inputs: All standard trade size and contract settings for testing available.
Special Settings:
Checkbox 1: Calculate Signal in Heikin Ashi chart, including regular candle OHLC („Open, High, Low, Close“)
Checkbox 2/3: Calculate by order fill or every tick.
Checkbox 4: Possible to fill orders on bar close.
Timeframe and practical usage:
Made for the 5 Minute to 1 hour timeframe.
Literally ONLY works on Ethereum and more or less on Bitcoin.
EVERY other asset has absolute 0% profitability.
Have fun and share with your friends!
Thanks for using!
Example Chart:
Dual Timeframe Williams Percent RangeThis is a dual timeframe Williams Percent Range indicator.
Function:
The idea behind this indicator is for trader to see what the Williams %r is doing on higher timeframes without the need to change the chart. I added the "Smoothing" function to take the jagged lines out of the higher timeframe. It has a better flow this way.
If we choose the 4H and the Daily timeframes for example. In this bullish situation I wait for the Daily WPR to cross above the -50 mid line. Then the faster 4H WPR will eventually hit the bottom and begin to rise again back into the trend.
This is the "Reset" of the 4H WPR and when the 4H WPR crosses up above the -50 mid line again it means price should begin to rise on the chart. I added the option to change the colour when the signal lines cross the -50. It is good to use a fast time frame so you can see the WPR hitting the bottom in an uptrend, but not too fast.
The Heiken Ashi candle sticks are a very good addition to this system. You can also use a colour changing 200 EMA if you run the "1H/Daily" in the WPR. Or the 50 EMA if you run the Daily 4H.
This system could be used on lower timeframes too but I have not tested it there.
The Dual WPR indicator, the colour changing 50 EMA and Heiken Ashi have been optimised for the 4H/Daily.
If you want to set alerts the the faster WPR line crossing the -50 is good, on candle close.
This way you will only need one alert per chart.
If you get an alert on the EURUSD 4H that the 4H WPR has crossed up then look to see what what the Daily WPR is doing. If it is also above the -50 mid line then EURUSD is probably trending up.
Thank you to TradingView for supplying the Williams %r template.
I hope this helps some other traders out there.
I combined the Supertrend and the Coloured EMA in the main screen into one indicator.
This is my first indicator published :-)
Have fun out there and good luck.
Eddie T.
Multi-Step FlexiSuperTrend - Strategy [presentTrading]At the heart of this endeavor is a passion for continuous improvement in the art of trading
█ Introduction and How it is Different
The "Multi-Step FlexiSuperTrend - Strategy " is an advanced trading strategy that integrates the well-known SuperTrend indicator with a nuanced and dynamic approach to market trend analysis. Unlike conventional SuperTrend strategies that rely on static thresholds and fixed parameters, this strategy introduces multi-step take profit mechanisms that allow traders to capitalize on varying market conditions in a more controlled and systematic manner.
What sets this strategy apart is its ability to dynamically adjust to market volatility through the use of an incremental factor applied to the SuperTrend calculation. This adjustment ensures that the strategy remains responsive to both minor and major market shifts, providing a more accurate signal for entries and exits. Additionally, the integration of multi-step take profit levels offers traders the flexibility to scale out of positions, locking in profits progressively as the market moves in their favor.
BTC 6hr Long/Short Performance
█ Strategy, How it Works: Detailed Explanation
The Multi-Step FlexiSuperTrend strategy operates on the foundation of the SuperTrend indicator, but with several enhancements that make it more adaptable to varying market conditions. The key components of this strategy include the SuperTrend Polyfactor Oscillator, a dynamic normalization process, and multi-step take profit levels.
🔶 SuperTrend Polyfactor Oscillator
The SuperTrend Polyfactor Oscillator is the heart of this strategy. It is calculated by applying a series of SuperTrend calculations with varying factors, starting from a defined "Starting Factor" and incrementing by a specified "Increment Factor." The indicator length and the chosen price source (e.g., HLC3, HL2) are inputs to the oscillator.
The SuperTrend formula typically calculates an upper and lower band based on the average true range (ATR) and a multiplier (the factor). These bands determine the trend direction. In the FlexiSuperTrend strategy, the oscillator is enhanced by iteratively applying the SuperTrend calculation across different factors. The iterative process allows the strategy to capture both minor and significant trend changes.
For each iteration (indexed by `i`), the following calculations are performed:
1. ATR Calculation: The Average True Range (ATR) is calculated over the specified `indicatorLength`:
ATR_i = ATR(indicatorLength)
2. Upper and Lower Bands Calculation: The upper and lower bands are calculated using the ATR and the current factor:
Upper Band_i = hl2 + (ATR_i * Factor_i)
Lower Band_i = hl2 - (ATR_i * Factor_i)
Here, `Factor_i` starts from `startingFactor` and is incremented by `incrementFactor` in each iteration.
3. Trend Determination: The trend is determined by comparing the indicator source with the upper and lower bands:
Trend_i = 1 (uptrend) if IndicatorSource > Upper Band_i
Trend_i = 0 (downtrend) if IndicatorSource < Lower Band_i
Otherwise, the trend remains unchanged from the previous value.
4. Output Calculation: The output of each iteration is determined based on the trend:
Output_i = Lower Band_i if Trend_i = 1
Output_i = Upper Band_i if Trend_i = 0
This process is repeated for each iteration (from 0 to 19), creating a series of outputs that reflect different levels of trend sensitivity.
Local
🔶 Normalization Process
To make the oscillator values comparable across different market conditions, the deviations between the indicator source and the SuperTrend outputs are normalized. The normalization method can be one of the following:
1. Max-Min Normalization: The deviations are normalized based on the range of the deviations:
Normalized Value_i = (Deviation_i - Min Deviation) / (Max Deviation - Min Deviation)
2. Absolute Sum Normalization: The deviations are normalized based on the sum of absolute deviations:
Normalized Value_i = Deviation_i / Sum of Absolute Deviations
This normalization ensures that the oscillator values are within a consistent range, facilitating more reliable trend analysis.
For more details:
🔶 Multi-Step Take Profit Mechanism
One of the unique features of this strategy is the multi-step take profit mechanism. This allows traders to lock in profits at multiple levels as the market moves in their favor. The strategy uses three take profit levels, each defined as a percentage increase (for long trades) or decrease (for short trades) from the entry price.
1. First Take Profit Level: Calculated as a percentage increase/decrease from the entry price:
TP_Level1 = Entry Price * (1 + tp_level1 / 100) for long trades
TP_Level1 = Entry Price * (1 - tp_level1 / 100) for short trades
The strategy exits a portion of the position (defined by `tp_percent1`) when this level is reached.
2. Second Take Profit Level: Similar to the first level, but with a higher percentage:
TP_Level2 = Entry Price * (1 + tp_level2 / 100) for long trades
TP_Level2 = Entry Price * (1 - tp_level2 / 100) for short trades
The strategy exits another portion of the position (`tp_percent2`) at this level.
3. Third Take Profit Level: The final take profit level:
TP_Level3 = Entry Price * (1 + tp_level3 / 100) for long trades
TP_Level3 = Entry Price * (1 - tp_level3 / 100) for short trades
The remaining portion of the position (`tp_percent3`) is exited at this level.
This multi-step approach provides a balance between securing profits and allowing the remaining position to benefit from continued favorable market movement.
█ Trade Direction
The strategy allows traders to specify the trade direction through the `tradeDirection` input. The options are:
1. Both: The strategy will take both long and short positions based on the entry signals.
2. Long: The strategy will only take long positions.
3. Short: The strategy will only take short positions.
This flexibility enables traders to tailor the strategy to their market outlook or current trend analysis.
█ Usage
To use the Multi-Step FlexiSuperTrend strategy, traders need to set the input parameters according to their trading style and market conditions. The strategy is designed for versatility, allowing for various market environments, including trending and ranging markets.
Traders can also adjust the multi-step take profit levels and percentages to match their risk management and profit-taking preferences. For example, in highly volatile markets, traders might set wider take profit levels with smaller percentages at each level to capture larger price movements.
The normalization method and the incremental factor can be fine-tuned to adjust the sensitivity of the SuperTrend Polyfactor Oscillator, making the strategy more responsive to minor market shifts or more focused on significant trends.
█ Default Settings
The default settings of the strategy are carefully chosen to provide a balanced approach between risk management and profit potential. Here is a breakdown of the default settings and their effects on performance:
1. Indicator Length (10): This parameter controls the lookback period for the ATR calculation. A shorter length makes the strategy more sensitive to recent price movements, potentially generating more signals. A longer length smooths out the ATR, reducing sensitivity but filtering out noise.
2. Starting Factor (0.618): This is the initial multiplier used in the SuperTrend calculation. A lower starting factor makes the SuperTrend bands closer to the price, generating more frequent trend changes. A higher starting factor places the bands further away, filtering out minor fluctuations.
3. Increment Factor (0.382): This parameter controls how much the factor increases with each iteration of the SuperTrend calculation. A smaller increment factor results in more gradual changes in sensitivity, while a larger increment factor creates a wider range of sensitivity across the iterations.
4. Normalization Method (None): The default is no normalization, meaning the raw deviations are used. Normalization methods like Max-Min or Absolute Sum can make the deviations more consistent across different market conditions, improving the reliability of the oscillator.
5. Take Profit Levels (2%, 8%, 18%): These levels define the thresholds for exiting portions of the position. Lower levels (e.g., 2%) capture smaller profits quickly, while higher levels (e.g., 18%) allow positions to run longer for more significant gains.
6. Take Profit Percentages (30%, 20%, 15%): These percentages determine how much of the position is exited at each take profit level. A higher percentage at the first level locks in more profit early, reducing exposure to market reversals. Lower percentages at higher levels allow for a portion of the position to benefit from extended trends.
