CCI / Connectable [Azullian]Sharpen your analysis of market trends with the CCI indicator. Effectively evaluate market cycles and identify potential reversals, enriching your strategic planning.
This connectable CCI indicator is part of an indicator system designed to help test, visualize and build strategy configurations without coding. Like all connectable indicators , it interacts through the TradingView input source, which serves as a signal connector to link indicators to each other. All connectable indicators send signal weight to the next node in the system until it reaches either a connectable signal monitor, signal filter and/or strategy.
█ UNIFORM SETTINGS AND A WAY OF WORK
Although connectable indicators may have specific weight scoring conditions, they all aim to follow a standardized general approach to weight scoring settings, as outlined below.
■ Connectable indicators - Settings
• 🗲 Energy: Energy applies an ATR multiplier to the plotted shapes on the chart. A higher value plots shapes farther away from the candle, enhancing visibility.
• ☼ Brightness: Brightness determines the opacity of the shape plotted on the chart, aiding visibility. Indicator weight also influences opacity.
• → Input: Use the input setting to specify a data source for the indicator. Here you can connect the indicator to other indicators.
• ⌥ Flow: Determine where you want to receive signals from:
○ Both: Weights from this indicator and the connected indicator will apply
○ Indicator only: Only weights from this indicator will apply
○ Input only: Only weights from the connected indicator will apply
• ⥅ Weight multiplier: Multiply all weights in the entire indicator by a given factor, useful for quickly testing different indicators in a granular setup.
• ⥇ Threshold: Set a threshold to indicate the minimum amount of weight it should receive to pass it through to the next indicator.
• ⥱ Limiter: Set a hard limit to the maximum amount of weight that can be fed through the indicator.
■ Connectable indicators - Weight scoring settings
▢ Weight scoring conditions
• SM – Signal mode: Enable specific conditions for weight scoring
○ All: All signals will be scored.
○ Entries only: Only entries will score.
○ Exits only: Only exits will score.
○ Entries & exits: Both entries and exits will score.
○ Zone: Continuous scoring for each candle within the zone.
• SP – Signal period: Defines a range of candles within which a signal can score.
• SC - Signal count: Specifies the number of bars to retrospectively examine and score.
○ Single: Score for a single occurrence
○ All occurrences: Score for all occurrences
○ Single + Threshold: Score for single occurrences within the signal period (SP)
○ Every + Threshold: Score for all occurrences within the signal period (SP)
▢ Weight scoring direction
• ES: Enter Short weight
• XL: Exit long weight
• EL: Enter Long weight
• XS: Exit Short weight
▢ Weight scoring values
• Weights can hold either positive or negative scores. Positive weights enhance a particular trading direction, while negative weights diminish it.
█ CCI - INDICATOR SETTINGS
■ Main settings
• Enable/Disable Indicator: Toggle the entire indicator on or off.
• S - Source: Choose an alternative data source for the CCI calculation.
• T - Timeframe: Select an alternative timeframe for the CCI calculation.
• LE - Length: Define the number of bars or periods used in the CCI calculation.
• OB - Overbought Level: Determine the CCI value at which overbought conditions are met.
• OS - Oversold Level: Specify the CCI value at which oversold conditions are met.
■ Scoring functionality
• The CCI scores long entries when the CCI enters OS: oversold area
• The CCI scores long exits when the CCI exits OS: oversold area
• The CCI scores long zones the entire time the CCI is in OS: oversold area
• The CCI scores short entries when the CCI enters OB: overbought area
• The CCI scores short exits when the CCI exits OB: overbought area
• The CCI scores short zones the entire time the CCI is in OB: overbought area
█ PLOTTING
• Standard: Symbols (EL, XS, ES, XL) appear relative to candles based on set conditions. Their opacity and position vary with weight.
• Conditional Settings: A larger icon appears if global conditions are met. For instance, with a Threshold(⥇) of 12, Signal Period (SP) of 3, and Scoring Condition (SC) set to "EVERY", an CCI signaling over two times in 3 candles (scoring 6 each) triggers a larger icon.
█ USAGE OF CONNECTABLE INDICATORS
■ Connectable chaining mechanism
Connectable indicators can be connected directly to the signal monitor, signal filter or strategy , or they can be daisy chained to each other while the last indicator in the chain connects to the signal monitor, signal filter or strategy. When using a signal filter you can chain the filter to the strategy input to make your chain complete.
• Direct chaining: Connect an indicator directly to the signal monitor, signal filter or strategy through the provided inputs (→).
• Daisy chaining: Connect indicators using the indicator input (→). The first in a daisy chain should have a flow (⌥) set to 'Indicator only'. Subsequent indicators use 'Both' to pass the previous weight. The final indicator connects to the signal monitor, signal filter, or strategy.
■ Set up this indicator with a signal filter and strategy
The indicator provides visual cues based on signal conditions. However, its weight system is best utilized when paired with a connectable signal filter, monitor, or strategy .
Let's connect the CCI to a connectable signal filter and a strategy :
1. Load all relevant indicators
• Load CCI / Connectable
• Load Signal filter / Connectable
• Load Strategy / Connectable
2. Signal Filter: Connect the CCI to the Signal Filter
• Open the signal filter settings
• Choose one of the three input dropdowns (1→, 2→, 3→) and choose : CCI / Connectable: Signal Connector
• Toggle the enable box before the connected input to enable the incoming signal
3. Signal Filter: Update the filter signals settings if needed
• The default settings of the filter enable EL (Enter Long), XL (Exit Long), ES (Enter Short) and XS (Exit Short).
4. Signal Filter: Update the weight threshold settings if needed
• All connectable indicators load by default with a score of 6 for each direction (EL, XL, ES, XS)
• By default, weight threshold (TH) is set at 5. This allows each occurrence to score, as the default score in each connectable indicator is 1 point above the threshold. Adjust to your liking.
5. Strategy: Connect the strategy to the signal filter in the strategy settings
• Select a strategy input → and select the Signal filter: Signal connector
6. Strategy: Enable filter compatible directions
• Set the signal mode of the strategy to a compatible direction with the signal filter.
Now that everything is connected, you'll notice green spikes in the signal filter representing long signals, and red spikes indicating short signals. Trades will also appear on the chart, complemented by a performance overview. Your journey is just beginning: delve into different scoring mechanisms, merge diverse connectable indicators, and craft unique chains. Instantly test your results and discover the potential of your configurations. Dive deep and enjoy the process!
█ BENEFITS
• Adaptable Modular Design: Arrange indicators in diverse structures via direct or daisy chaining, allowing tailored configurations to align with your analysis approach.
• Streamlined Backtesting: Simplify the iterative process of testing and adjusting combinations, facilitating a smoother exploration of potential setups.
• Intuitive Interface: Navigate TradingView with added ease. Integrate desired indicators, adjust settings, and establish alerts without delving into complex code.
• Signal Weight Precision: Leverage granular weight allocation among signals, offering a deeper layer of customization in strategy formulation.
• Advanced Signal Filtering: Define entry and exit conditions with more clarity, granting an added layer of strategy precision.
• Clear Visual Feedback: Distinct visual signals and cues enhance the readability of charts, promoting informed decision-making.
• Standardized Defaults: Indicators are equipped with universally recognized preset settings, ensuring consistency in initial setups across different types like momentum or volatility.
• Reliability: Our indicators are meticulously developed to prevent repainting. We strictly adhere to TradingView's coding conventions, ensuring our code is both performant and clean.
█ COMPATIBLE INDICATORS
Each indicator that incorporates our open-source 'azLibConnector' library and adheres to our conventions can be effortlessly integrated and used as detailed above.
For clarity and recognition within the TradingView platform, we append the suffix ' / Connectable' to every compatible indicator.
█ COMMON MISTAKES, CLARIFICATIONS AND TIPS
• Removing an indicator from a chain: Deleting a linked indicator and confirming the "remove study tree" alert will also remove all underlying indicators in the object tree. Before removing one, disconnect the adjacent indicators and move it to the object stack's bottom.
• Point systems: The azLibConnector provides 500 points for each direction (EL: Enter long, XL: Exit long, ES: Enter short, XS: Exit short) Remember this cap when devising a point structure.
• Flow misconfiguration: In daisy chains the first indicator should always have a flow (⌥) setting of 'indicator only' while other indicator should have a flow (⌥) setting of 'both'.
• Hide attributes: As connectable indicators send through quite some information you'll notice all the arguments are taking up some screenwidth and cause some visual clutter. You can disable arguments in Chart Settings / Status line.
• Layout and abbreviations: To maintain a consistent structure, we use abbreviations for each input. While this may initially seem complex, you'll quickly become familiar with them. Each abbreviation is also explained in the inline tooltips.
• Inputs: Connecting a connectable indicator directly to the strategy delivers the raw signal without a weight threshold, meaning every signal will trigger a trade.
█ A NOTE OF GRATITUDE
Through years of exploring TradingView and Pine Script, we've drawn immense inspiration from the community's knowledge and innovation. Thank you for being a constant source of motivation and insight.
█ RISK DISCLAIMER
Azullian's content, tools, scripts, articles, and educational offerings are presented purely for educational and informational uses. Please be aware that past performance should not be considered a predictor of future results.
Komut dosyalarını "the strat" için ara
Bollinger bands / Connectable [Azullian]Deepen your understanding of market volatility using Bollinger Bands. This tool helps in discerning price movements and volatility trends, essential for crafting effective strategies.
This connectable Bollinger bands indicator is part of an indicator system designed to help test, visualize and build strategy configurations without coding. Like all connectable indicators , it interacts through the TradingView input source, which serves as a signal connector to link indicators to each other. All connectable indicators send signal weight to the next node in the system until it reaches either a connectable signal monitor, signal filter and/or strategy.
█ UNIFORM SETTINGS AND A WAY OF WORK
Although connectable indicators may have specific weight scoring conditions, they all aim to follow a standardized general approach to weight scoring settings, as outlined below.
■ Connectable indicators - Settings
• 🗲 Energy: Energy applies an ATR multiplier to the plotted shapes on the chart. A higher value plots shapes farther away from the candle, enhancing visibility.
• ☼ Brightness: Brightness determines the opacity of the shape plotted on the chart, aiding visibility. Indicator weight also influences opacity.
• → Input: Use the input setting to specify a data source for the indicator. Here you can connect the indicator to other indicators.
• ⌥ Flow: Determine where you want to receive signals from:
○ Both: Weights from this indicator and the connected indicator will apply
○ Indicator only: Only weights from this indicator will apply
○ Input only: Only weights from the connected indicator will apply
• ⥅ Weight multiplier: Multiply all weights in the entire indicator by a given factor, useful for quickly testing different indicators in a granular setup.
• ⥇ Threshold: Set a threshold to indicate the minimum amount of weight it should receive to pass it through to the next indicator.
• ⥱ Limiter: Set a hard limit to the maximum amount of weight that can be fed through the indicator.
■ Connectable indicators - Weight scoring settings
▢ Weight scoring conditions
• SM – Signal mode: Enable specific conditions for weight scoring
○ All: All signals will be scored.
○ Entries only: Only entries will score
○ Exits only: Only exits will score.
○ Entries & exits: Both entries and exits will score.
○ Zone: Continuous scoring for each candle within the zone.
• SP – Signal period: Defines a range of candles within which a signal can score.
• SC - Signal count: Specifies the number of bars to retrospectively examine and score.
○ Single: Score for a single occurrence
○ All occurrences: Score for all occurrences
○ Single + Threshold: Score for single occurrences within the signal period (SP)
○ Every + Threshold: Score for all occurrences within the signal period (SP)
▢ Weight scoring direction
• ES: Enter Short weight
• XL: Exit long weight
• EL: Enter Long weight
• XS: Exit Short weight
▢ Weight scoring values
• Weights can hold either positive or negative scores. Positive weights enhance a particular trading direction, while negative weights diminish it.
█ Bollinger bands - INDICATOR SETTINGS
■ Main settings
• Enable/Disable Indicator: Toggle the entire indicator on or off.
• S - Source: Choose an alternative data source for the Bollinger bands calculation.
• T - Timeframe: Select an alternative timeframe for the Bollinger bands calculation.
• LE - Length: Determine the period the bollinger bands are calculated on
• SD - Standard deviation: Determine the standard deviation for the bollinger bands.
• Enable/Disable plotting: Toggle the plotting of the Bollinger bands
• U: Choose a color for the upper band
• B: Choose a color for the basis
• L: Choose a color for the lower band
• BG: Choose a color for Bollinger bands background
■ Scoring functionality
• The Bollinger bands score long entries when the current low price is smaller than the lower band.
• The Bollinger bands score long exits when the current high price is higher than the upper band.
• The Bollinger bands score long zones the entire time the current low price is smaller than the lower band.
• The Bollinger bands score short entries when the current high is higher than the upper band.
• The Bollinger bands score short exits when the current low is smaller than the lower band.
• The Bollinger bands score short zones the entire time the current high price is higher than the upper band.
█ PLOTTING
• Standard: Symbols (EL, XS, ES, XL) appear relative to candles based on set conditions. Their opacity and position vary with weight.
• Conditional Settings: A larger icon appears if global conditions are met. For instance, with a Threshold(⥇) of 12, Signal Period (SP) of 3, and Scoring Condition (SC) set to "EVERY", a Bollinger bands signaling over two times in 3 candles (scoring 6 each) triggers a larger icon.
█ USAGE OF CONNECTABLE INDICATORS
■ Connectable chaining mechanism
Connectable indicators can be connected directly to the signal monitor, signal filter or strategy , or they can be daisy chained to each other while the last indicator in the chain connects to the signal monitor, signal filter or strategy. When using a signal filter you can chain the filter to the strategy input to make your chain complete.
• Direct chaining: Connect an indicator directly to the signal monitor, signal filter or strategy through the provided inputs (→).
• Daisy chaining: Connect indicators using the indicator input (→). The first in a daisy chain should have a flow (⌥) set to 'Indicator only'. Subsequent indicators use 'Both' to pass the previous weight. The final indicator connects to the signal monitor, signal filter, or strategy.
■ Set up this indicator with a signal filter and strategy
The indicator provides visual cues based on signal conditions. However, its weight system is best utilized when paired with a connectable signal filter, signal monitor, and/or strategy .
Let's connect the Bollinger bands to a connectable signal filter and a strategy :
1. Load all relevant indicators
• Load Bollinger bands / Connectable
• Load Signal filter / Connectable
• Load Strategy / Connectable
2. Signal Filter: Connect the Bollinger bands to the Signal Filter
• Open the signal filter settings
• Choose one of the three input dropdowns (1→, 2→, 3→) and choose : Bollinger bands / Connectable: Signal Connector
• Toggle the enable box before the connected input to enable the incoming signal
3. Signal Filter: Update the filter signals settings if needed
• The default settings of the filter enable EL (Enter Long), XL (Exit Long), ES (Enter Short) and XS (Exit Short).
4. Signal Filter: Update the weight threshold settings if needed
• All connectable indicators load by default with a score of 6 for each direction (EL, XL, ES, XS)
• By default, weight threshold (TH) is set at 5. This allows each occurrence to score, as the default score in each connectable indicator is 1 point above the threshold. Adjust to your liking.
5. Strategy: Connect the strategy to the signal filter in the strategy settings
• Select a strategy input → and select the Signal filter: Signal connector
6. Strategy: Enable filter compatible directions
• Set the signal mode of the strategy to a compatible direction with the signal filter.
Now that everything is connected, you'll notice green spikes in the signal filter representing long signals, and red spikes indicating short signals. Trades will also appear on the chart, complemented by a performance overview. Your journey is just beginning: delve into different scoring mechanisms, merge diverse connectable indicators, and craft unique chains. Instantly test your results and discover the potential of your configurations. Dive deep and enjoy the process!
█ BENEFITS
• Adaptable Modular Design: Arrange indicators in diverse structures via direct or daisy chaining, allowing tailored configurations to align with your analysis approach.
• Streamlined Backtesting: Simplify the iterative process of testing and adjusting combinations, facilitating a smoother exploration of potential setups.
• Intuitive Interface: Navigate TradingView with added ease. Integrate desired indicators, adjust settings, and establish alerts without delving into complex code.
• Signal Weight Precision: Leverage granular weight allocation among signals, offering a deeper layer of customization in strategy formulation.
• Advanced Signal Filtering: Define entry and exit conditions with more clarity, granting an added layer of strategy precision.
• Clear Visual Feedback: Distinct visual signals and cues enhance the readability of charts, promoting informed decision-making.
• Standardized Defaults: Indicators are equipped with universally recognized preset settings, ensuring consistency in initial setups across different types like momentum or volatility.
• Reliability: Our indicators are meticulously developed to prevent repainting. We strictly adhere to TradingView's coding conventions, ensuring our code is both performant and clean.
█ COMPATIBLE INDICATORS
Each indicator that incorporates our open-source 'azLibConnector' library and adheres to our conventions can be effortlessly integrated and used as detailed above.
For clarity and recognition within the TradingView platform, we append the suffix ' / Connectable' to every compatible indicator.
█ COMMON MISTAKES, CLARIFICATIONS AND TIPS
• Removing an indicator from a chain: Deleting a linked indicator and confirming the "remove study tree" alert will also remove all underlying indicators in the object tree. Before removing one, disconnect the adjacent indicators and move it to the object stack's bottom.
• Point systems: The azLibConnector provides 500 points for each direction (EL: Enter long, XL: Exit long, ES: Enter short, XS: Exit short) Remember this cap when devising a point structure.
• Flow misconfiguration: In daisy chains the first indicator should always have a flow (⌥) setting of 'indicator only' while other indicator should have a flow (⌥) setting of 'both'.
• Hide attributes: As connectable indicators send through quite some information you'll notice all the arguments are taking up some screenwidth and cause some visual clutter. You can disable arguments in Chart Settings / Status line.
• Layout and abbreviations: To maintain a consistent structure, we use abbreviations for each input. While this may initially seem complex, you'll quickly become familiar with them. Each abbreviation is also explained in the inline tooltips.
• Inputs: Connecting a connectable indicator directly to the strategy delivers the raw signal without a weight threshold, meaning every signal will trigger a trade.
█ A NOTE OF GRATITUDE
Through years of exploring TradingView and Pine Script, we've drawn immense inspiration from the community's knowledge and innovation. Thank you for being a constant source of motivation and insight.
█ RISK DISCLAIMER
Azullian's content, tools, scripts, articles, and educational offerings are presented purely for educational and informational uses. Please be aware that past performance should not be considered a predictor of future results.
azLibConnectorThe AzLibConnector provides a comprehensive suite of functions for facilitating seamless communication and chaining of signal value streams between connectable indicators, signal filters, monitors, and strategies on TradingView. By adeptly integrating both positive and negative weights from Entry Long (EL), Exit Long (XL), Entry Short (ES), and Exit Short (XS) signals into a singular figure, it leverages the source input field of TradingView to efficiently connect indicators in a chain. This results in a streamlined strategy setup without the necessity for Pine Script coding. Emphasizing modularity and uniformity, this library enables users to easily combine indicators into a coherent system, facilitating strategy development and execution with flexibility.
█ LIBRARY USAGE
extract(srcConnector)
Extract signals (EL, XL, ES, XS) from incoming connector signal stream
Parameters:
srcConnector : (series float) Source Connector. The connector stream series to extract the signals from.
Returns: A tuple containing the extracted EL, XL, ES, XS signal values.
compose(signalEL, signalXL, signalES, signalXS)
Compose a connector output signal stream from given EL, XL, ES and XS signals to be used by other Azullian Strategy Builder blocks.
Parameters:
signalEL : (series float) Entry Long signal value.
signalXL : (series float) Exit Long signal value.
signalES : (series float) Entry Short signal value.
signalXS : (series float) Exit Short signal value.
Returns: (series float) A composed connector output signal stream.
█ USAGE OF CONNECTABLE INDICATORS
■ Connectable chaining mechanism
Connectable indicators can be connected directly to the monitor, signal filter or strategy , or they can be daisy chained to each other while the last indicator in the chain connects to the monitor, signal filter or strategy. When using a signal filter or monitor you can chain the filter to the strategy input to make your chain complete.
• Direct chaining: Connect an indicator directly to the monitor, signal filter or strategy through the provided inputs (→).
• Daisy chaining: Connect indicators using the indicator input (→). The first in a daisy chain should have a flow (⌥) set to 'Indicator only'. Subsequent indicators use 'Both' to pass the previous weight. The final indicator connects to the monitor, signal filter, or strategy.
■ Set up the signal filter with a connectable indicator and strategy
Let's connect the MACD to a connectable signal filter and a strategy :
1. Load all relevant indicators
• Load MACD / Connectable
• Load Signal filter / Connectable
• Load Strategy / Connectable
2. Signal Filter: Connect the MACD to the Signal Filter
• Open the signal filter settings
• Choose one of the five input dropdowns (1→, 2→, 3→, 4→, 5→) and choose : MACD / Connectable: Signal Connector
• Toggle the enable box before the connected input to enable the incoming signal
3. Signal Filter: Update the filter settings if needed
• The default filter mode for the trading direction is SWING, and is compatible with the default settings in the strategy and indicators.
4. Signal Filter: Update the weight threshold settings if needed
• All connectable indicators load by default with a score of 6 for each direction (EL, XL, ES, XS)
• By default, weight threshold is 'ABOVE' Threshold 1 (TH1) and Threshold 2 (TH2), both set at 5. This allows each occurrence to score, as the default score is 1 point above the threshold.
5. Strategy: Connect the strategy to the signal filter in the strategy settings
• Select a strategy input → and select the Signal filter: Signal connector
6. Strategy: Enable filter compatible directions
• As the default setting of the filter is SWING, we should also set the SM (Strategy mode) to SWING.
Now that everything is connected, you'll notice green spikes in the signal filter or signal monitor representing long signals, and red spikes indicating short signals. Trades will also appear on the chart, complemented by a performance overview. Your journey is just beginning: delve into different scoring mechanisms, merge diverse connectable indicators, and craft unique chains. Instantly test your results and discover the potential of your configurations. Dive deep and enjoy the process!
█ BENEFITS
• Adaptable Modular Design: Arrange indicators in diverse structures via direct or daisy chaining, allowing tailored configurations to align with your analysis approach.
• Streamlined Backtesting: Simplify the iterative process of testing and adjusting combinations, facilitating a smoother exploration of potential setups.
• Intuitive Interface: Navigate TradingView with added ease. Integrate desired indicators, adjust settings, and establish alerts without delving into complex code.
• Signal Weight Precision: Leverage granular weight allocation among signals, offering a deeper layer of customization in strategy formulation.
• Advanced Signal Filtering: Define entry and exit conditions with more clarity, granting an added layer of strategy precision.
• Clear Visual Feedback: Distinct visual signals and cues enhance the readability of charts, promoting informed decision-making.
• Standardized Defaults: Indicators are equipped with universally recognized preset settings, ensuring consistency in initial setups across different types like momentum or volatility.
• Reliability: Our indicators are meticulously developed to prevent repainting. We strictly adhere to TradingView's coding conventions, ensuring our code is both performant and clean.
█ COMPATIBLE INDICATORS
Each indicator that incorporates our open-source 'azLibConnector' library and adheres to our conventions can be effortlessly integrated and used as detailed above.
For clarity and recognition within the TradingView platform, we append the suffix ' / Connectable' to every compatible indicator.
█ COMMON MISTAKES
• Removing an indicator from a chain: Deleting a linked indicator and confirming the "remove study tree" alert will also remove all underlying indicators in the object tree. Before removing one, disconnect the adjacent indicators and move it to the object stack's bottom.
• Point systems: The azLibConnector provides 500 points for each direction (EL: Enter long, XL: Exit long, ES: Enter short, XS: Exit short) Remember this cap when devising a point structure.
• Flow misconfiguration: In daisy chains the first indicator should always have a flow (⌥) setting of 'indicator only' while other indicator should have a flow (⌥) setting of 'both'.
█ A NOTE OF GRATITUDE
Through years of exploring TradingView and Pine Script, we've drawn immense inspiration from the community's knowledge and innovation. Thank you for being a constant source of motivation and insight.
█ RISK DISCLAIMER
Azullian's content, tools, scripts, articles, and educational offerings are presented purely for educational and informational uses. Please be aware that past performance should not be considered a predictor of future results.
RSI Missmatch(Divergence) OSC. by Neo_ with Missmatch Alert█ Definition
A divergence or missmatch occurs when an asset’s price is moving opposite to a specific technical indicator or is moving in a different direction from other relevant data. The divergence indicator warns traders and technical analysts of changes in a price trend, oftentimes that it is weakening or changing direction.
Divergence or missmatch can be either positive, signifying the possibility of a move that is higher in the asset’s price, or it can be negative, signifying the possibility of a move that is lower in the asset’s price.
█ Takeaways
Divergence or missmatch often works with other indicators and data. It is usually used by technical analysts and traders when the asset’s price is moving counter to the direction of another indicator.
As mentioned above, positive divergence or missmatch indicates that the price could start rising and usually occurs when the price is moving lower, but while another indicator counters this direction by moving higher. In other words, showing bullish signals.
Negative divergence or missmatch indicates that the price could start declining and usually occurs when the price is moving higher, while another indicator moves lower as well. In other words, showing bearish signals.
█ What to look for
Divergence or missmatch is most often used to track and analyze the momentum in an asset’s price and the odds of a price reversal within the current trend. While using divergence, traders and analysts can decide on whether or not they would like to exit the position or set a stop loss in the case the divergence is negative and prices begin to fall.
█ Limitations
It is best to use divergence or missmatch with the aid of other indicators and analysis tools in order to help identify and confirm trend reversals and major market patterns. Divergence should not be relied on by itself to tell you the pertinent information you need to know as an investor. Risk control is key in your analysis and the fact that divergence is not always present in price reversals should definitely be what pushes you to combine it with other tools and indicators.
Additionally, divergence or missmatch can reflect long-term or short-term changes. When making snap decisions, acting on divergence alone could prove detrimental to your trading. Make sure you have other risk factors applied to your charting and general market analysis.
█ What exactly is RSI Missmatches discrepancies using a lookback period in trading?
In trading, lookback period is the number of periods of historical data used for observation and calculation. It is how far into the past the system looks when trying to calculate the variable under consideration. The concept was based on the fact that history can provide information about the future, and my aim was to predict the periods when trend changes would begin within these periods with the RSI oscillator. But this is only true if you're locked back far enough, not locked any further or less!
We already use the idea of looking back in different aspects of our lives, and even in the world of financial trading it can be used in various ways. Of course you will want to learn more about the concept, so in this article we will cover the following topics:
█ What kind of hindsight is this?
The aim here is to check whether trends will change in certain cycles, so we chose the High + Low / 2 formula as the source. Because no matter how much the prices swing up or down, sometimes the rebound can go further. The aim here is to notice the points where the price leaves a needle at the levels where it oscillates and the slowdown in momentum.
█ What does look-back period mean in trade?
To understand what a lookback period means in trading, you need to ask yourself: What is a lookback period in trading? In financial trading, period refers to the duration of a particular trading session. For example, a one-week period means one full week of trading sessions or five trading days. In 5 trading days, the average time is 120 hours in FX markets and 40 hours in stock markets. Regardless of what happens in these cycles, I prefer to choose a time period of 55 periods. Because I noticed that in all the charts I examined, the cycles generally changed during this time period.
█ Let's talk about the meaning of catching Missmatches
As you know, technical indicators are all a mathematical calculation using historical market data (price, volume, or a combination of both). It shows the behavior of the price better and helps in the analysis of price movement. But the indicator can only serve your intended purpose if you get the lookback time right. What we mean here is the setting parameter that determines how much historical data it will use in its calculation. In other words, it is the retrospective review period.
For example, on the RSI indicator you can set this period to 13 periods (default setting) or even 2 periods. The period you choose can determine what the indicator tells you, which in turn determines the strategy you can create with the indicator. The 13- period RSI gives you information about price momentum, so you can effectively use it to create a momentum strategy. On the other hand, the 2-periods RSI can be used to create a mean reversion strategy. To catch any incompatibilities, I set this period to 55 periods. Nothing more, nothing less!
█ Summary
The missmatch indicator helps traders assess changes in the price trend and indicates when price will move with or against the direction of another indicator. It can be either positive or negative, but it is important to note its limitations and that it should be used with other indicators that can also monitor price trends.
We wish you to identify these incompatibilities in the market in the best way possible... Good luck.
█ Tanım
Bir varlığın fiyatı belirli bir teknik göstergenin tersi yönünde hareket ettiğinde veya diğer ilgili verilerden farklı bir yönde hareket ettiğinde bir sapma veya uyumsuzluk meydana gelir. Farklılık göstergesi, tüccarları ve teknik analistleri fiyat eğilimindeki değişiklikler konusunda uyarır; çoğu zaman zayıflıyor veya yön değiştiriyor.
Farklılık veya uyumsuzluk, varlığın fiyatında daha yüksek bir hareket olasılığını işaret ederek pozitif olabilir veya varlığın fiyatında daha düşük bir hareket olasılığını işaret ederek negatif olabilir.
█ Çıkarımlar
Farklılık veya uyumsuzluk çoğu zaman diğer göstergeler ve verilerle de çalışır. Genellikle teknik analistler ve yatırımcılar tarafından varlığın fiyatı başka bir göstergenin yönünün tersine hareket ettiğinde kullanılır.
Yukarıda bahsedildiği gibi pozitif sapma veya uyumsuzluk, fiyatın yükselmeye başlayabileceğini gösterir ve genellikle fiyat düşerken meydana gelir, ancak başka bir gösterge bu yöne yükselerek karşı koyar. Başka bir deyişle yükseliş sinyalleri veriyor.
Negatif sapma veya uyumsuzluk, fiyatın düşmeye başlayabileceğini gösterir ve genellikle fiyat yükselirken başka bir gösterge de düşerken meydana gelir. Başka bir deyişle düşüş sinyalleri veriyor.
█ Nelere bakılmalı
Farklılık veya uyumsuzluk çoğunlukla bir varlığın fiyatındaki momentumu ve mevcut trend içinde fiyatın tersine dönme olasılığını izlemek ve analiz etmek için kullanılır. Farklılaşmayı kullanırken tüccarlar ve analistler, sapmanın negatif olması ve fiyatların düşmeye başlaması durumunda pozisyondan çıkmak isteyip istemeyeceklerine veya zararı durdurma kararı verip veremeyeceklerine karar verebilirler.
█ Sınırlamalar
Trend dönüşlerini ve ana piyasa modellerini tanımlamaya ve doğrulamaya yardımcı olmak için diğer göstergeler ve analiz araçlarının yardımıyla sapmayı veya uyumsuzluğu kullanmak en iyisidir. Bir yatırımcı olarak bilmeniz gereken ilgili bilgileri size söylemesi için farklılığa tek başına güvenilmemelidir. Risk kontrolü analizinizin anahtarıdır ve fiyat dönüşlerinde farklılığın her zaman mevcut olmaması gerçeği kesinlikle sizi onu diğer araç ve göstergelerle birleştirmeye iten şey olmalıdır.
Ek olarak, farklılık veya uyumsuzluk uzun vadeli veya kısa vadeli değişiklikleri yansıtabilir. Ani kararlar verirken yalnızca farklılıklara göre hareket etmek ticaretinize zarar verebilir. Grafiğinize ve genel piyasa analizinize başka risk faktörlerinin uygulandığından emin olun.
█ Ticarette yeniden inceleme dönemi kullanan RSI Missmatches tutarsızlıkları tam olarak nedir?
Ticarette yeniden inceleme süresi, gözlem ve hesaplama için kullanılan geçmiş verilerin dönemlerinin sayısıdır. Söz konusu değişkeni hesaplamaya çalışırken sistemin ne kadar geçmişe baktığıdır. Konsept tarihin geleceğe dair bilgi verebileceği gerçeği üzerine kuruluydu ve amacım RSI osilatörü ile bu dönemler içerisinde trend değişimlerinin başlayacağı dönemleri tahmin etmekti. Ancak bu yalnızca yeterince geriye kilitlenmişseniz geçerlidir, daha fazla veya daha az kilitlenmemişseniz!
Geriye bakma fikrini hayatımızın farklı yönlerinde zaten kullanıyoruz ve hatta finansal ticaret dünyasında bile bu fikir çeşitli şekillerde kullanılabilir. Elbette konsept hakkında daha fazla bilgi edinmek isteyeceksiniz, bu nedenle bu yazıda aşağıdaki konuları ele alacağız:
█ Bu nasıl bir sonradan görmedir?
Burada amaç belli döngülerde trendlerin değişip değişmeyeceğini kontrol etmek olduğundan kaynak olarak Yüksek + Düşük / 2 formülünü seçtik. Çünkü fiyatlar ne kadar yukarı veya aşağı hareket ederse etsin bazen toparlanma daha da ileri gidebiliyor. Burada amaç fiyatın salınım yaptığı seviyelerde iğne bıraktığı noktaları ve momentumdaki yavaşlamayı fark etmektir.
█ Ticarette geriye bakma süresi ne anlama geliyor?
Ticarette yeniden inceleme süresinin ne anlama geldiğini anlamak için kendinize şu soruyu sormanız gerekir: Ticarette yeniden inceleme süresi nedir? Finansal ticarette dönem, belirli bir ticaret seansının süresini ifade eder. Örneğin, bir haftalık dönem, bir tam haftalık işlem seansı veya beş işlem günü anlamına gelir. 5 işlem gününde ortalama süre döviz piyasalarında 120 saat, borsalarda ise 40 saattir. Bu döngülerde ne olursa olsun 55 periyotluk bir zaman dilimini seçmeyi tercih ediyorum. Çünkü incelediğim tüm grafiklerde bu zaman diliminde döngülerin genel olarak değiştiğini fark ettim.
█ Kaçak Eşleşmeleri yakalamanın anlamı hakkında konuşalım
Bildiğiniz gibi teknik göstergeler, geçmiş piyasa verileri (fiyat, hacim veya her ikisinin birleşimi) kullanılarak yapılan matematiksel hesaplamalardır. Fiyatın davranışını daha iyi gösterir ve fiyat hareketinin analizine yardımcı olur. Ancak gösterge yalnızca yeniden inceleme süresini doğru yaparsanız amacınıza hizmet edebilir. Burada kast ettiğimiz, hesaplamasında ne kadar geçmiş veri kullanacağını belirleyen ayar parametresidir. Bir başka deyişle geriye dönük inceleme dönemidir.
Örneğin RSI göstergesinde bu süreyi 13 döneme (varsayılan ayar) ve hatta 2 döneme ayarlayabilirsiniz. Seçeceğiniz dönem, göstergenin size ne söyleyeceğini belirleyebilir ve bu da gösterge ile oluşturabileceğiniz stratejiyi belirler. 13 dönemlik RSI size fiyat momentumu hakkında bilgi verir, böylece onu bir momentum stratejisi oluşturmak için etkili bir şekilde kullanabilirsiniz. Öte yandan, ortalamaya dönüş stratejisi oluşturmak için 2 dönemlik RSI kullanılabilir. Herhangi bir uyumsuzluğu yakalamak için bu periyodu 55 periyoda ayarladım. Ne fazla ne eksik!
█ Özet
Uyumsuzluk göstergesi, yatırımcıların fiyat eğilimindeki değişiklikleri değerlendirmesine yardımcı olur ve fiyatın ne zaman başka bir göstergenin yönüne göre veya ona karşı hareket edeceğini gösterir. Olumlu ya da olumsuz olabilir, ancak sınırlamalarına dikkat etmek ve fiyat eğilimlerini de izleyebilecek diğer göstergelerle birlikte kullanılması gerektiğini unutmamak önemlidir.
Piyasadaki bu uyumsuzlukları en iyi şekilde tespit etmenizi dileriz... Bol Kazançlar.
Pivot Percentile Trend - Strategy [presentTrading]
█ Introduction and How it is Different
The "Pivot Percentile Trend - Strategy" from PresentTrading represents a paradigm shift in technical trading strategies. What sets this strategy apart is its innovative use of pivot percentiles, a method that goes beyond traditional indicator-based analyses. Unlike standard strategies that might depend on single-dimensional signals, this approach takes a multi-layered view of market movements, blending percentile calculations with SuperTrend indicators for a more nuanced and dynamic market analysis.
This strategy stands out for its ability to process multiple data points across various timeframes and pivot lengths, thereby capturing a broader and more detailed picture of market trends. It's not just about following the price; it's about understanding its position in the context of recent historical highs and lows, offering a more profound insight into potential market movements.
BTC 6h L/S
Where traditional methods might react to market changes, the Pivot Percentile Trend strategy anticipates them, using a calculated approach to identify trend strengths and weaknesses. This foresight gives traders a significant advantage, allowing for more strategic decision-making and potentially increasing the chances of successful trades.
In essence, this strategy introduces a more comprehensive and proactive approach to trading, harnessing the power of advanced percentile calculations combined with the robustness of SuperTrend indicators. It's a strategy designed for traders who seek a deeper understanding of market dynamics and a more calculated approach to their trading decisions.
Local picture
█ Strategy, How It Works: Detailed Explanation
🔶 Percentile Calculations
- The strategy employs percentile calculations to assess the relative position of current market prices against historical data.
- For a set of lengths (e.g., `length * 1`, `length * 2`, up to `length * 7`), it calculates the 75th percentile for high values (`percentilesHigh`) and the 25th percentile for low values (`percentilesLow`).
- These percentiles provide a sense of where the current price stands compared to recent price ranges.
Length - 10
Length - 15
🔶 SuperTrend Indicator
- The SuperTrend indicator is a key component, providing trend direction signals.
- It uses the `currentTrendValue`, derived from the difference between bull and bear strengths calculated from the percentile data.
* used the Supertrend toolkit by @EliCobra
🔶 Trend Strength Counts
- The strategy calculates counts of bullish and bearish indicators based on comparisons between the current high and low against high and low percentiles.
- `countBull` and `countBear` track the number of times the current high is above the high percentiles and the current low is below the low percentiles, respectively.
- Weak bullish (`weakBullCount`) and bearish (`weakBearCount`) counts are also determined by how often the current lows and highs fall within the percentile range.
*The idea of this strength counts mainly comes from 'Trend Strength Over Time' @federalTacos5392b
🔶 Trend Value Calculation
- The `currentTrendValue` is a crucial metric, computed as `bullStrength - bearStrength`.
- It indicates the market's trend direction, where a positive value suggests a bullish trend and a negative value indicates a bearish trend.
🔶 Trade Entry and Exit Logic
- The entry points for trades are determined by the combination of the trend value and the direction indicated by the SuperTrend indicator.
- For a long entry (`shouldEnterLong`), the `currentTrendValue` must be positive and the SuperTrend indicator should show a downtrend.
- Conversely, for a short entry (`shouldEnterShort`), the `currentTrendValue` should be negative with the SuperTrend indicating an uptrend.
- The strategy closes positions when these conditions reverse.
█ Trade Direction
The strategy is versatile, allowing traders to choose their preferred trading direction: long, short, or both. This flexibility enables traders to tailor their strategies to their market outlook and risk appetite.
█ Default Settings and Customization
1. Trade Direction: Selectable as Long, Short, or Both, affecting the type of trades executed.
2. Indicator Source: Pivot Percentile Calculations, key for identifying market trends and reversals.
3. Lengths for Percentile Calculation: Various configurable lengths, influencing the scope of trend analysis.
4. SuperTrend Settings: ATR Length 20, Multiplier 18, affecting indicator sensitivity and trend detection.
5. Style Options: Custom colors for bullish (green) and bearish (red) trends, aiding visual interpretation.
6. Additional Settings: Includes contrarian signals and UI enhancements, offering strategic and visual flexibility.
FlexiMA Variance Tracker - Strategy [presentTrading]█ Introduction and How It Is Different
The FlexiMA Variance Tracker by PresentTrading introduces a novel approach to technical trading strategies. Unlike traditional methods, it calculates deviations between a chosen indicator source (such as price or average) and a moving average with a variable length. This flexibility is achieved through a unique combination of a starting factor and an increment factor, allowing the moving average to adapt dynamically within a specified range. This strategy provides a more responsive and nuanced view of market trends, setting it apart from standard trading methodologies.
BTC 8h L/S
Local
█ Strategy, How It Works: Detailed Explanation
The FlexiMA Variance Tracker, developed by PresentTrading, stands at the forefront of trading strategies, distinguished by its adaptive and multifaceted approach to market analysis. This strategy intricately weaves various technical elements to construct a comprehensive trading logic. Here's an in-depth professional breakdown:
🔶Foundation on Variable-Length Moving Averages:
Central to this strategy is the concept of variable-length Moving Averages (MAs). Unlike traditional MAs with a fixed period, this strategy dynamically adjusts the length of the MA based on a starting factor and an incremental factor. This approach allows the strategy to adapt to market volatility and trend strength more effectively.
Each MA iteration offers a distinct temporal perspective, capturing short-term price movements to long-term trends. This aggregation of various time frames provides a richer and more nuanced market analysis, essential for making informed trading decisions.
🔶Deviation Analysis and Normalization:
The strategy calculates deviations of the price (or the chosen indicator source) from each of these MAs. These deviations are pivotal in identifying the immediate market direction relative to the average trend captured by each MA.
To standardize these deviations for comparability, they undergo a normalization process. The choice of normalization method (Max-Min or Absolute Sum) can significantly influence the interpretation of market conditions, offering distinct insights into price movements and trend strength.
🔹Normalization: Absolute Sum
🔶Composite Oscillator Construction:
A composite oscillator is derived from the median of these normalized deviations. The median serves as a balanced and robust central trend indicator, minimizing the impact of outliers and market noise.
Additionally, the standard deviation of these deviations is computed, providing a measure of market volatility. This volatility indicator is crucial for assessing market risk and can guide traders in setting appropriate stop-loss and take-profit levels.
🔶Integration with SuperTrend Indicator:
The FlexiMA strategy integrates the SuperTrend indicator, renowned for its effectiveness in identifying trend direction and reversals. The SuperTrend's incorporation enhances the strategy's ability to filter out false signals and confirm genuine market trends.
* The SuperTrend Toolkit is made by @QuantiLuxe
This combination of the variable-length MA oscillator with the SuperTrend indicator forms a potent duo, offering traders a dual-confirmation mechanism for trade signals.
🔹Supertrend's incorporation
🔶Strategic Trade Signal Generation:
Trade signals are generated when there is a confluence between the composite oscillator and the SuperTrend indicator. For example, a long position signal might be considered when the oscillator suggests an uptrend, and the SuperTrend flips to bullish.
The strategy's parameters are fully customizable, enabling traders to tailor the signal generation process to their specific trading style, risk tolerance, and market conditions.
█ Usage
To effectively employ the FlexiMA Variance Tracker strategy:
Traders should set their desired trade direction and fine-tune the starting and increment factors according to their market analysis and risk tolerance.
Indicator Length: 5
Indicator Length: 40
The strategy is suitable for a wide range of markets and can be adapted to different time frames, making it a versatile tool for various trading scenarios.
█ Default Settings Impact on Performance: FlexiMA Variance Tracker
1. Trade Direction (Configurable: Long, Short, Both): Determines trade types. 'Long' for buying, 'Short' for selling, 'Both' adapts to market trends.
2. Indicator Source: HLC3: Balances market sentiment by considering high, low, and close, providing comprehensive period analysis.
4. Indicator Length (Default: 10): Baseline for moving averages. Shorter lengths increase responsiveness but add noise, while longer lengths favor trends.
5. Starting and Increment Factor (Default: 1.0): Adjusts MA lengths range. Higher values capture broad market dynamics, lower values focus analysis.
6. Normalization Method (Options: None, Max-Min, Absolute Sum): Standardizes deviations. 'None' for raw deviations, 'Max-Min' for relative scaling, 'Absolute Sum' emphasizes relative strength.
7. SuperTrend Settings (ATR Length: 10, Multiplier: 15.0): Influences indicator sensitivity. Short ATR or high multiplier for short-term, long ATR or low multiplier for long-term trends.
8. Additional Settings (Mesh Style, Color Customization): Enhances visual clarity. Mesh style for detailed deviation view, colors for quick market condition identification.
Margin/Leverage CalculationMargin
This library calculates margin liquidation prices and quantities for long and short positions in your strategies.
Usage example
// ############################################################
// # INVESTMENT SETTINGS / INPUT
// ############################################################
// Get the investment capital from the properties tab of the strategy settings.
investment_capital = strategy.initial_capital
// Get the leverage from the properties tab of the strategy settings.
// The leverage is calculated from the order size for example: (300% = x3 leverage)
investment_leverage = margin.leverage()
// The maintainance rate and amount.
investment_leverage_maintenance_rate = input.float(title='Maintanance Rate (%)', defval=default_investment_leverage_maintenance_rate, minval=0, maxval=100, step=0.1, tooltip=tt_investment_leverage_maintenance_rate, group='MARGIN') / 100
investment_leverage_maintenance_amount = input.float(title='Maintanance Amount (%)', defval=default_investment_leverage_maintenance_amount, minval=0, maxval=100, step=0.1, tooltip=tt_investment_leverage_maintenance_amount, group='MARGIN')
// ############################################################
// # LIQUIDATION PRICES
// ############################################################
leverage_liquidation_price_long = 0.0
leverage_liquidation_price_long := na(leverage_liquidation_price_long ) ? na : leverage_liquidation_price_long
leverage_liquidation_price_short = 0.0
leverage_liquidation_price_short := na(leverage_liquidation_price_short ) ? na : leverage_liquidation_price_short
leverage_liquidation_price_long := margin.liquidation_price_long(investment_capital, strategy.position_avg_price, investment_leverage, investment_leverage_maintenance_rate, investment_leverage_maintenance_amount)
leverage_liquidation_price_short := margin.liquidation_price_short(investment_capital, strategy.position_avg_price, investment_leverage, investment_leverage_maintenance_rate, investment_leverage_maintenance_amount)
Get the qty for margin long or short position.
margin.qty_long(investment_capital, strategy.position_avg_price, investment_leverage, investment_leverage_maintenance_rate, investment_leverage_maintenance_amount)
margin.qty_short(investment_capital, strategy.position_avg_price, investment_leverage, investment_leverage_maintenance_rate, investment_leverage_maintenance_amount)
Get the price and qty for margin long or short position.
= margin.qty_long(investment_capital, strategy.position_avg_price, investment_leverage, investment_leverage_maintenance_rate, investment_leverage_maintenance_amount)
= margin.qty_short(investment_capital, strategy.position_avg_price, investment_leverage, investment_leverage_maintenance_rate, investment_leverage_maintenance_amount)
Volatility Capture RSI-Bollinger - Strategy [presentTrading]- Introduction and how it is different
The 'Volatility Capture RSI-Bollinger - Strategy ' is a trading strategy that combines the concepts of Bollinger Bands (BB), Relative Strength Index (RSI), and Simple Moving Average (SMA) to generate trading signals. The uniqueness of this strategy is it calculates which is a dynamic level between the upper and lower Bollinger Bands based on the closing price. This unique feature allows the strategy to adapt to market volatility and price movements.
The market in Crypto and Stock are highly volatile, making them suitable for a strategy that uses Bollinger Bands. The RSI can help identify overbought or oversold conditions in this often speculative market.
BTCUSD 4hr chart
(700.hk) 3hr chart
Remember, the effectiveness of a trading strategy also depends on other factors such as the timeframe used, the specific settings of the indicators, and the overall market conditions. It's always recommended to backtest and paper trade a strategy before using it in live trading.
- Strategy, How it Works
Dynamic Bollinger Band: The strategy works by first calculating the upper and lower Bollinger Bands based on the user-defined length and multiplier. It then uses the Bollinger Bands and the closing price to dynamically adjust the presentBollingBand value. In the end, it generates a long signal when the price crosses over the present Bolling Band and a short signal when the price crosses under the present Bolling Band.
RSI: If the user has chosen to use RSI for signals, the strategy also calculates the RSI and its SMA, and uses these to generate additional long and short signals. The RSI-based signals are only used if the 'Use RSI for signals' option is set to true.
The strategy then checks the chosen trading direction and enters a long or short position accordingly. If the trading direction is set to 'Both', the strategy can enter both long and short positions.
Finally, the strategy exits a position when the close price crosses under the present Bolling Band for a long position, or crosses over the present Bolling Band for a short position.
- Trade direction
The strategy also includes a trade direction parameter, allowing the user to choose whether to enter long trades, short trades, or both. This makes the strategy adaptable to different market conditions and trading styles.
- Usage
1. Set the input parameters as per your trading preferences. You can choose the price source, the length of the moving average, the multiplier for the ATR, whether to use RSI for signals, the RSI and SMA periods, the bought and sold range levels, and the trading direction.
2. The strategy will then generate buy and sell signals based on these parameters. You can use these signals to enter and exit trades.
- Default settings
1. Source: hlc3
2. Length: 50
3. Multiplier: 2.7183
4. Use RSI for signals: True
5. RSI Period: 10
6. SMA Period: 5
7. Bought Range Level: 55
8. Sold Range Level: 50
9. Trade Direction: Both
- Strategy's default Properties
1. Default Quantity Type: 'strategy.percent_of_equity'
2. commission_value= 0.1, commission_type=strategy.commission.percent, slippage= 1: These parameters set the commission and slippage for the strategy. The commission is set to 0.1% of the trade value, and the slippage (the difference between the expected price of a trade and the price at which the trade is executed) is set to 1.
3. default_qty_type = strategy.percent_of_equity, default_qty_value = 15: These parameters set the default quantity for trades. The default_qty_type is set to strategy.percent_of_equity, which means that the size of each trade will be a percentage of the account equity. The default_qty_value is set to 15, which means that each trade will be 15% of the account equity.
4. initial_capital= 10000: This parameter sets the initial capital for the strategy to $10,000.
Fake StrategyTHIS IS A FAKE STRATEGY. PLEASE DO NOT USE THIS FOR TRADING.
Just publishing this to display how easily you can fake backtest results in the strategies. However, there are ways to identify the scams. Let's discuss about major red herrings in a strategy. How to identify them and stay away from them.
Any strategy which proclaims significantly high win rate (such as this) are not practical and can only be achieved via following means
Significantly high risk compared to reward
Trades are set in such a way that profits are taken in small movement whereas stops are significantly farther. By doing this, win rate will surely increase. But, will be picking pennies by risking plenty of capital. General trait of such strategies can be identified by comparing average trade and max drawdown . These kind of strategies will have significantly higher drawdown even though the number of losses are less. For example, 1 losing trade leading to drawdown of 10+% whereas every winning only contributes 0.25%.
We can also see this kind of behaviour in option selling strategies such as 0 and 1 DTE option selling strategies. Here too probability of winning can be pretty high (north of 90%). But, on every winning, you make 1-2% of your capital however on remaining trades, you will lose your complete capital - which leads to overall losing position.
Inducing repainting through code
This strategy is an excellent example of how repainting can be induced via code using request.securities method. There are plenty of ways a strategy or code can be made to repaint. Tradingview user manual has lots of information about repainting. Feel free to read through if you have extra time. If you look at this code, it is very simple to induce repainting in a strategy to make it look like an infinite money printing machine.
High Leverage and lack of usage of margin
Using leverage in pine can show false results. This is because, the strategy engine will not stop when equity goes below 0% until the trade is closed. But, that does not happen in real life. This is the reason why using leverage along with high risk and low reward trades can show false results overall making it look like the strategy is unbeatable. But, when you try to use that in real time, it is likely that account will be blown out.
To understand leverage conditions, please have a look at the strategy property fields - Order Size, Pyramiding, Commission, Slippage, Margin Long/Short.
Curve fitting
If the author claims that strategy will only work on particular set of instrument and particular timeframe, then the strategy is not real. It is curve fitting. Knowingly/Unknowingly author has moulded his strategy to fit what has happened in the past. This is general issue even non malicious author go through. It is very much essential to test the strategy across various set of instruments and timeframes to understand the real capability. Use back-testing as test cases. More test cases you have, more bug free your strategy will be. There are many methods to understand curve fitting and perform better testing of the strategy in hand which can be studied and implemented by authors.
Significantly short trades - a sign of lack of strategy
A strategy built using pine in general work on close of candle. So, all the calculations generally happen upon close of the candle. You can force intra-bar calculations using bar magnifier. But, that is not equivalent to tick data. Due to this reason, I consider any trade happening within a bar (Meaning open and close within the same bar) as not reliable. This is because, it is not possible for strategy back-tester to know whether entry condition is satisfied first or exit in a completely foolproof way. Bar magnifier can help reduce this issue - but will not eradicate this problem completely. If there are lots of trades in a strategy - which are closing within the same bar, this is very likely that the strategy backtest results are not reliable.
Hope this helps at least some people to understand the scams and stay away from it.
3ngine Global BoilerplateABOUT THE BOILERPLATE
This strategy is designed to bring consistency to your strategies. It includes a macro EMA filter for filtering out countertrend trades,
an ADX filter to help filter out chop, a session filter to filter out trades outside of desired timeframe, alert messages setup for automation,
laddering in/out of trades (up to 6 rungs), trailing take profit , and beautiful visuals for each entry. There are comments throughout the
strategy that provide further instructions on how to use the boilerplate strategy. This strategy uses `threengine_global_automation_library`
throughout and must be included at the top of the strategy using `import as bot`. This allows you to use dot notation
to access functions in the library - EX: `bot.orderCurrentlyExists(orderID)`.
HOW TO USE THIS STRATEGY
1. Add your inputs
There is a section dedicated for adding your own inputs near the top of the strategy, just above the boilerplate inputs
2. Add your calculations
If your strategy requires calculations, place them in the `Strategy Specific Calculations` section
3. Add your entry criteria
Add your criteria to strategySpecificLongConditions (this gets combined with boilerplate conditions in longConditionsMet)
Add your criteria to strategySpecificShortConditions (this gets combined with boilerplate conditions in shortConditionsMet)
Set your desired entry price (calculated on every bar unless stored as a static variable) to longEntryPrice and shortEntryPrice. ( This will be the FIRST ladder if using laddering capabilities. If you pick 1 for "Ladder In Rungs" this will be the only entry. )
4. Plot anything you want to overlay on the chart in addition to the boilerplate plots and labels. Included in boilerplate:
Average entry price
Stop loss
Trailing stop
Profit target
Ladder rungs
Dual Fibonacci Zone & Ranged Vol DCA Strategy - R3c0nTraderWhat does this do?
This is for educational purposes and allows one to backtest two Fibonacci Zones simultaneously. This also includes an option for Ranged Volume as a parameter.
Pre-requisites:
First off, this is a Long only strategy as I wrote it with DCA in mind. It cannot be used for shorting. Shorting defeats the purpose of a DCA bot which has a goal that is Long a position not Short a position. If you want to short, there are plenty of free scripts out there that do this.
You must have some base knowledge or experience with Fibonacci trading, understanding what is ADX, +DI (and -DI), etc.
You can use this script without a 3Commas account and see how 3Commas DCA Bot would perform. However, I highly recommend inexperienced uses get a free account and going through the tutorials, FAQ's and knowledgebase. This would give you a base understanding of the settings you will see in this strategy and why you will need to know them. Only then should you try testing this strategy with a paper bot.
Background
After I had created and released "Fibonacci Zone DCA Strategy", I began expanding and testing other ideas.
The first idea was to add Ranged Volume to the Fibonacci Zone DCA strategy which I wanted for providing further confirmation before entering a trade. The second idea was to add a second Fibonacci Zone that was just as configurable as the first Fibonacci Zone. I managed to add both and they can be easily enabled or disabled via the strategy settings menu.
Things Got Real Interesting
Things got real interesting when I started testing strategies with two Fibonacci zones. Here's a quick list of what I found I was able to do:
Mix and match exit strategies. I could set the Fib-1 zone strategy to exit with a take profit % and separately set the Fib-2 zone strategy to exit when the price crosses the top-high fib border
Trade the trend. A common phrase amongst traders is "the Trend is your friend" and with the help of an additional Fib Zone, I was able to trade the trend more often by using two different Fib Zone strategies which if configured properly can shorten time to re-deploy capital, increase number of closed trades, and in some cases increase net profit.
Trade both bull market uptrends and bear market downtrends in the same strategy. I found I could configure one Fib Zone strategy to be really good in uptrends and another Fib Zone strategy to be really good in downtrends. In some cases, with both Fib Zone strategies enabled together in a single strategy I got better results than if the strategies were backtested separately.
There are many other trade strategies I am finding with this. One could be to trade a convergence or divergence of the two different Fib Zones. This could possibly be achieved by setting one strategy to have different Fibonacci length.
Credits:
Thank you "EvoCrypto" for granting me permission to use "Ranged Volume" to create this strategy
Thank you "eykpunter" for granting me permission to use "Fibonacci Zones" to create this strategy
Thank you "junyou0424" for granting me permission to use "DCA Bot with SuperTrend Emulator" which I used for adding bot inputs, calculations, and strategy
Template Trailing Strategy (Backtester)💭 Overview
💢 What is the "Template Trailing Strategy” ❓
The "Template Trailing Strategy" (TTS) is a back-tester orchestration framework. It supercharges the implementation-test-evaluation lifecycle of new trading strategies, by making it possible to plug in your own trading idea.
While TTS offers a vast number of configuration settings, it primarily allows the trader to:
Test and evaluate your own trading logic that is described in terms of entry, exit, and cancellation conditions.
Define the entry and exit order types as well as their target prices when the limit, stop, or stop-limit order types are used.
Utilize a variety of options regarding the placement of the stop-loss and take-profit target(s) prices and support for well-known techniques like moving to breakeven and trailing.
Provide well-known quantity calculation methods to properly handle risk management and easily evaluate trading strategies and compare them.
Alert on each trading event or any related change through a robust and fully customizable messaging system.
All the above, build a robust tool that, once learned, significant and repetitive work that strategy developers often implement individually on every strategy script is eliminated. Taking advantage of TradingView’s built-in backtesting engine the evaluation of the trading ideas feels natural.
By utilizing the TTS one can easily swap “trading logic” by testing, evaluating, and comparing each trading idea and/or individual component of a strategy.
Finally, TTS, through its per-event alert management (and debugging) system, provides a fully automated solution that supports automated trading with real brokers via webhooks.
NOTE: The “Template Trailing Strategy” does not dictate the way you can combine different (types of) indicators or how you should combine them. Thus, it should not be confused as a “Trading System”, because it gives its user full flexibility on that end (for better or worse).
💢 What is a “Signal Indicator” ❓
“Signal Indicator” (SI) is an indicator that can output a “signal” that follows a specific convention so that the “Template Trailing Strategy” can “understand” and execute the orders accordingly. The SI realizes the core trading logic signaling to the TTS when to enter, exit, or cancel an order. A SI instructs the TTS “when” to enter or exit, and the TTS determines “how” to enter and exit the position once the Signal Indicator generates a signal.
A very simple example of a Signal Indicator might be a 200-day Simple Moving Average Signal. When the price of the security closes above the 200-day SMA, a SI would provide TTS with a “long entry signal”. Once TTS receives the “long entry signal”, the TTS will open a long position and send an alert or automated trade message via webhook to a broker, based on the Entry settings defined in TTS. If the TTS Entry settings specify a “Market” order type, then the open long position will be executed by TTS immediately. But if the TTS Entry settings specify a “Stop” order type with a 1% Stop Distance, then when the price of the security rises by 1% after the “long entry signal” occurs, the TTS will open a long position and the Long Entry alert or webhook to the broker will be sent.
🤔 How to Guide
💢 How to connect a “signal” from a “Signal Indicator” ❓
The “Template Trailing Strategy” was designed to receive external signals from a “Signal Indicator”. In this way, a “new trading idea” can be developed, configured, and evaluated separately from the TTS. Similarly, the SI can be held constant, and the trading mechanics can change in the TTS settings and back-tested to answer questions such as, “Am I better with a different stop loss placement method, what if I used a limit order instead of a stop order to enter, what if I used 25% margin instead of trading spot market?”
To make that possible by connecting an external signal indicator to TTS, you should:
Add in the same chart, the “Signal Indicator” of your choice (e.g. “Two MA Signal Indicator” , “Click Signal Indicator” , “Signal Adapter” , “Signal Composer” ) and the “Template Trailing Strategy”.
Go to the “Settings/Inputs” tab in the “🛠️ STRATEGY” group of the TTS and change the "𝐃𝐞𝐚𝐥 𝐂𝐨𝐧𝐝𝐢𝐭𝐢𝐨𝐧𝐬 𝐌𝐨𝐝𝐞" to “🔨External”
Go to the “🔨 STRATEGY – EXTERNAL” group settings of the TTS and change the “🔌𝐒𝐢𝐠𝐧𝐚𝐥 🛈➡” to the output signal of the “Signal Indicator” you want to connect. The selected combo box option should look like “:🔌Signal to TTS” where should correspond to the short title of your “Signal Indicator”
💢 How to create a Custom Trading logic ❓
The “Template Trailing Strategy” provides two ways to plug in your custom trading logic. Both of them have their advantages and disadvantages.
✍️ Develop your own Customized “Signal Indicator” 💥
The first approach is meant to be used for relatively more complex trading logic. The advantages of this approach are the full control and customization you have over the trading logic and the relatively simple configuration setup by having two scripts only. The downsides are that you have to have some experience with pinescript or you are willing to learn and experiment. You should also know the exact formula for every indicator you will use since you have to write it by yourself. Copy-pasting from existing open-source indicators will get you started quite fast though.
The idea here is either to create a new indicator script from scratch or to copy an existing non-signal indicator and make it a “Signal Indicator”. To create a new script, press the “Pine Editor” button below the chart to open the “Pine Editor” and then press the “Open” button to open the drop-down menu with the templates. Select the “New Indicator” option. Add it to your chart to copy an existing indicator and press the source code {} button. Its source code will be shown in the “Pine Editor” with a warning on top stating that this is a read-only script. Press the “create a working copy”. Now you can give a descriptive title and a short title to your script, and you can work on (or copy-paste) the (other) indicators of your interest. Having all the information needed to make your decision the only thing you should do is define a DealConditions object and plot it like this:
import jason5480/tts_convention/4 as conv
// Calculate the start, end, cancel start, cancel end conditions
dealConditions = conv.DealConditions.new(
startLongDeal = ,
startShortDeal = ,
endLongDeal = ,
endShortDeal = ,
cnlStartLongDeal = ,
cnlStartShortDeal = ,
cnlEndLongDeal = ,
cnlEndShortDeal = )
// Use this signal in scripts like "Template Trailing Strategy" and "Signal Composer" that can use its value
// Emit the current signal value according to the "two channels mod div" convention
plot(series = conv.getSignal(dealConditions), title = '🔌Signal to TTS', color = color.olive, display = display.data_window + display.status_line, precision = 0)
You should write your deal conditions appropriately based on your trading logic and put them in the code section shown above by replacing the “…” part after “=”. You can omit the conditions that are not relevant to your logic. For example, if you use only market orders for entering and exiting your positions the cnlStartLongDeal, cnlStartShortDeal, cnlEndLongDeal, and cnlEndShortDeal are irrelevant to your case and can be safely omitted from the DealConditions object. After successfully compiling your new custom SI script add it to the same chart with the TTS by pressing the “Add to chart” button. If all goes well, you will be able to connect your “signal” to the TTS as described in the “How to connect a “signal” from a “Signal Indicator”?” guide.
🧩 Adapt and Combine existing non-signal indicators 💥
The second approach is meant to be used for relatively simple trading logic. The advantages of this approach are the lack of pine script and coding experience needed and the fact that it can be used with closed-source indicators as long as the decision-making part is displayed as a line in the chart. The drawback is that you have to have a subscription that supports the “indicator on indicator” feature so you can connect the output of one indicator as an input to another indicator. Please check if your plan supports that feature here
To plug in your own logic that way you have to add your indicator(s) of preference in the chart and then add the “Signal Adapter” script in the same chart as well. This script is a “Signal Indicator” that can be used as a proxy to define your custom logic in the CONDITIONS group of the “Settings/Inputs” tab after defining your inputs from your preferred indicators in the VARIABLES group. Then a “signal” will be produced, if your logic is simple enough it can be directly connected to the TTS that is also added to the same chart for execution. Check the “How to connect a “signal” from a “Signal Indicator”?” in the “🤔 How to Guide“ for more information.
If your logic is slightly more complicated, you can add a second “Signal Adapter” in your chart. Then you should add the “Signal Composer” in the same chart, go to the SIGNALS group of the “Settings/Inputs” tab, and connect the “signals” from the “Signal Adapters”. “Signal Composer” is also a SI so its composed “signal” can be connected to the TTS the same way it is described in the “How to connect a “signal” from a “Signal Indicator”?” guide.
At this point, due to the composability of the framework, you can add an arbitrary number (bounded by your subscription of course) of “Signal Adapters” and “Signal Composers” before connecting the final “signal” to the TTS.
💢 How to set up ⏰Alerts ❓
The “Template Trailing Strategy” provides a fully customizable per-even alert mechanism. This means that you may have an entirely different message for entering and exiting into a position, hitting a stop-loss or a take-profit target, changing trailing targets, etc. There are no restrictions, and this gives you great flexibility.
First of all, you have to enable the alerts of the events that interest you. Go to the “🔔 ALERT MESSAGES” module of the TTS settings and check the “Enable…” checkbox of the events you are interested in. For each specific event, you will find a text area where you can type the exact message you want to receive when the event occurs. What’s more, there are placeholders you can use that will be replaced by the TTS with the actual values before the message is sent. The placeholder categories are the following and the placeholder names are self-explanatory.
Chart info: {{ticker}}, {{base_currency}}, {{quote_currency}}
Quantities and percentages: {{base_quantity}}, {{quote_quantity}}, {{quote_quantity_perc}},
{{take_profit_base_quantity}}, {{remaining_quantity_perc}}, {{remaining_base_quantity}}, {{risk_perc}}
Target prices: {{stop_loss_price}}, {{entry_price}}, {{entry+_price}}, {{entry-_price}},
{{exit_price}}, {{exit+_price}}, {{exit-_price}}, {{take_profit_price_1}},
{{take_profit_price_2}}, {{take_profit_price_3}}, {{take_profit_price_4}}, {{take_profit_price_5}}
❗ To get the message on the other side you have to set a strategy alert as described here and use the {{strategy.order.alert_message}} placeholder as text in the “Message Box” that contains the message that came from the TTS.
💢 How to execute my orders in a broker ❓
To execute your orders in a broker that supports webhook integration, you should enable the appropriate alerts in the “Template Trailing Strategy” first (see the “How to set up Alerts?” guide above). Then you should go to the “Create Alert/Notifications” tab check the “Webhook URL” and paste the URL provided by your broker. You have to read the documentation of your broker for more information on what messages are expected.
Keep in mind that some brokers have deep integration with TradingView so a per-event alert approach might be overkill.
📑 Definitions
This section tries to give some definitions in terms that appear in the “Settings/Inputs" tab of the “Template Trailing Strategy”
💢 What is Trailing ❓
Trailing is a technique where a price target follows another “barrier” price (usually high or low) by trying to keep a maximum distance from the “barrier” when it moves in only one direction (up or down). When the “barrier” moves in the other direction the price target will not change. There are as many types of trailing as price targets, which means that there are entry trailing, exit trailing, stop-loss trailing, and take-profit trailing techniques.
💢 What is a Moonbag ❓
A Moonbag in a trade is the quantity of the position that is reserved and will not be exited even if all take-profit targets defined in the strategy are hit, the quantity will be exited only if the stop-loss is hit or a close signal is received. This makes the stop-loss trailing technique in a trend-following strategy a good candidate to take advantage of a Moonbag.
💢 What is Distance ❓
Distance is the difference between two prices.
💢 What is Bias ❓
Bias is a psychological phenomenon where you make decisions based on market sentiment. For example, when you want to enter a long position you have a long bias, and when you want to exit from the long position you have a short bias. It is the other way around for the short position.
💢 What is the Margin Distance of a price target ❓
The Margin Distance of a price target is the distance that the target will deviate from its initial price. The direction of this deviation depends on the bias of the market. For example, suppose you are in a long position, and you set a take-profit target to the local high (HHLL). In that case, adding a margin of five ticks will place your take-profit target 5 ticks below this local high because you have a short bias when exiting a long position. When the bias is long the margin will be added resulting in a higher target price and when you have a short bias the margin will be subtracted.
⚙️ Settings
In the “Settings/Inputs” tab of the “Template Trailing Strategy”, you can find all the customizable settings that are provided by the framework. The variety of those settings is vast; hence we will only scratch the surface here. However, for every setting, there is an information icon 🛈 where you can learn more if you mouse over it. The “Settings/Inputs” tab is divided into ten main groups. Each one of them is responsible for one module of the framework. Every setting is part of a group that is named after the module it represents. So, to spot the module of a setting find the title that appears above it comes with an emoji and uppercase letters. Some settings might have the same name but belong to different modules e.g. “Distance Method”. Some settings are indented, which means that are closely related to the non-indented setting above. Usually, intended settings provide further configuration for one or more options of the non-intended setting. The groups that correspond to each module of the framework are the following:
📆 FILTERS
In this module time filters are implemented. You can define a DateTime window for your strategy to run. You can also specify a session by selecting the days of the week and the time range you want to operate.
🛠️ STRATEGY
This module contains the "𝐃𝐞𝐚𝐥 𝐂𝐨𝐧𝐝𝐢𝐭𝐢𝐨𝐧𝐬 𝐌𝐨𝐝𝐞" that defines if the “Template Trailing Strategy” will operate using the Internal or the External (“Signal Indicator”) conditions. Some general settings can be applied regardless of the mode.
🔨 STRATEGY – EXTERNAL
This sub-module makes the connection between the external signal of the “Signal Indicator” and the “Template Trailing Strategy”. It takes effect only if the "𝐃𝐞𝐚𝐥 𝐂𝐨𝐧𝐝𝐢𝐭𝐢𝐨𝐧𝐬 𝐌𝐨𝐝𝐞" is set to “🔨External”.
🔧 STRATEGY – INTERNAL
This sub-module defines the internal strategy logic and it's used as an example to demonstrate this framework. It should produce the same results as if the “Two MA Signal Indicator” was used as a “signal” in external mode. It takes effect only if the "𝐃𝐞𝐚𝐥 𝐂𝐨𝐧𝐝𝐢𝐭𝐢𝐨𝐧𝐬 𝐌𝐨𝐝𝐞" is set to “🔧Internal”.
🎢 VOLATILITY
This module defines the volatility parameters that are used in various other settings like average true range and standard deviation. It also makes it clear whether their values are updated during a trade (DYNAMIC) or not (STATIC).
🔷 ENTRY
This module defines how the start deal conditions will be executed by defining the order type of your entry and all necessary parameters to execute them.
🎯 TAKE PROFIT
This module defines the take-profit targets placement logic. The number of the take-profit targets to use, their distance from the entry price, and the distance from each other are only some of the features that can be configured.
🛑 STOP LOSS
This module defines the stop-loss target placement logic. The distance from the entry price, move to break even, and start trailing after a take-profit target is hit are only some of the features that can be configured.
🟪 EXIT
This module defines how the end deal conditions will be executed by defining the order type of your exit and all necessary parameters to execute them.
💰 QUANTITY/RISK MANAGEMENT
This module defines the method that calculates the amount of money you will put into each trade. Also, the percentage of the Moonbag quantity can be configured.
📊 ANALYTICS
This module can visualize some extra analytics of the strategy in the chart and calculate some metrics to measure the overall performance.
🔔 ALERT MESSAGES
This module defines all the messages that can be emitted per event during the strategy execution.
😲 Caveats
💢 Does “Template Trailing Strategy” has a repainting behavior ❓
The answer is that the “Template Trailing Strategy” does not repaint as long as the “Signal Indicator” that is connected also does not repaint. If you developed your own SI make sure that you understand and know how to prevent this behavior. The publication by @PineCoders here will give you a good idea on how to avoid most of the repainting cases.
⚠️There is an exception though, when the “Enable Trail⚠️💹” checkbox is checked, the Take Profit trailing feature is enabled, and a tick-based approach is used, meaning that after a while, when the TradingView discards all the real-time data, assumptions will be made by the backtesting engine that will cause a form of repainting. To avoid making false assumptions please disable this feature in the early stages and evaluate its usefulness in your strategy later on, after first confirming the success of the logic without this feature. In this case, consider turning on the bar magnifier feature. This way you will get more accurate backtest results when the Take Profit trailing feature is enabled.
💢 Can “Template Trailing Strategy” satisfy all my trading strategies ❓
While this framework can satisfy quite a large number of trading strategies there are cases where it cannot do so. For example, if you have a custom logic for your stop-loss or take-profit placement, or if you want to dollar cost average, then it might be better to start a new strategy script from scratch.
⚠️ It is not recommended to copy the official TTS code and start developing unless you are a pine wizard! Even in that case, there is a stiff learning curve that might not be worth your time. Last, you must consider that I do not offer support for customized versions of the TTS script and if something goes wrong in the process you are all alone.
🤗 Thanks
Special thanks to @upslidedown and @metadimensional, who regularly gave feedback all those years and helped me to shape the framework as it is today! Thanks to @EltAlt, @PlusUltraTrading, and everyone else who contributed by either filing a “defect report” or asking questions that helped me to understand what improvements were necessary.
Enjoy!
Jason
Inferential Statistics And Quick Metrics For Strategy Analysis.Part of this script is used to calculate inferential statistics and metrics not available through the built in variables in the strategy tester.
A label will be created on the last bar displaying important strategy results, so you can test and analyze strategies quicker.
The built in strategy itself is just an example. You can copy and paste the metrics into any existing version 4 strategy and instantly use it**
**Just be sure all the variable names are unique in your target script.
I am looking for critique and would appreciate input on the statistical functions. I am aware that some of these functions are based on the assumption that the data is normally distributed. It's not meant to be perfect, but it is meant to be helpful. So if you think I can add or improve something to make it more helpful, let me know.
[FN] Session Range & Date Range For BacktestingThis has been done before in different ways, however, my goal is to publish a single, simplified copy/paste version of the idea so you can quickly and easily incorporate it into your strategy backtesting.
You can designate weekdays, weekdays + weekends for 24/7 markets, and also session range.
So, you trade bitcoin? It works. CME futures? It works. You are a discretionary trader so the only signals that matter are the ones that happen when you're awake? It works. Copy and paste.
The goal is that its that easy. You'll have to let me know if it is. glhf everyone.
If I'm not mistaken, you should be able to copy/paste this directly into your strategy script (Paste it in before your entry declarations). Just leave out the last 2 lines where the bgcolor() is declared... unless you want the background color lit up, that's up to you. It's just for demonstration purposes in this script.
After you've pasted it in, then in your strategy.entry() function you are adding: to the strategy.entry() function.
e.g:
strategy.entry("Long", strategy.long, qty=1, when = ENTRY_SIGNAL and signal_backtest())
Shoutouts to @zenandtheartoftrading and @allanster for providing the basis of this code that I put together here. We stand on the shoulders of giants.
40 Ticker Cross-Sectional Z-Scores [BackQuant]40 Ticker Cross-Sectional Z-Scores
BackQuant’s 40 Ticker Cross-Sectional Z-Scores is a powerful portfolio management strategy that analyzes the relative performance of up to 40 different assets, comparing them on a cross-sectional basis to identify the top and bottom performers. This indicator computes Z-scores for each asset based on their log returns and evaluates them relative to the mean and standard deviation over a rolling window. The Z-scores represent how far an asset's return deviates from the average, and these values are used to rank the assets, allowing for dynamic asset allocation based on performance.
By focusing on the strongest-performing assets and avoiding the weakest, this strategy aims to enhance returns while managing risk. Additionally, by adjusting for standard deviations, the system offers a risk-adjusted method of ranking assets, making it suitable for traders who want to dynamically allocate capital based on performance metrics rather than just price movements.
Key Features
1. Cross-Sectional Z-Score Calculation:
The system calculates Z-scores for 40 different assets, evaluating their log returns against the mean and standard deviation over a rolling window. This enables users to assess the relative performance of each asset dynamically, highlighting which assets are performing better or worse compared to their historical norms. The Z-score is a useful statistical tool for identifying outliers in asset performance.
2. Asset Ranking and Allocation:
The system ranks assets based on their Z-scores and allocates capital to the top performers. It identifies the top and bottom assets, and traders can allocate capital to the top-performing assets, ensuring that their portfolio is aligned with the best performers. Conversely, the bottom assets are removed from the portfolio, reducing exposure to underperforming assets.
3. Rolling Window for Mean and Standard Deviation Calculations:
The Z-scores are calculated based on rolling means and standard deviations, making the system adaptive to changing market conditions. This rolling calculation window allows the strategy to adjust to recent performance trends and minimize the impact of outdated data.
4. Mean and Standard Deviation Visualization:
The script provides real-time visualizations of the mean (x̄) and standard deviation (σ) of asset returns, helping traders quickly identify trends and volatility in their portfolio. These visual indicators are useful for understanding the current market environment and making more informed allocation decisions.
5. Top & Bottom Performer Tables:
The system generates tables that display the top and bottom performers, ranked by their Z-scores. Traders can quickly see which assets are outperforming and underperforming. These tables provide clear and actionable insights, helping traders make informed decisions about which assets to include in their portfolio.
6. Customizable Parameters:
The strategy allows traders to customize several key parameters, including:
Rolling Calculation Window: Set the window size for the rolling mean and standard deviation calculations.
Top & Bottom Tickers: Choose how many of the top and bottom assets to display and allocate capital to.
Table Orientation: Select between vertical or horizontal table formats to suit the user’s preference.
7. Forward Test & Out-of-Sample Testing:
The system includes out-of-sample forward tests, ensuring that the strategy is evaluated based on real-time performance, not just historical data. This forward testing approach helps validate the robustness of the strategy in dynamic market conditions.
8. Visual Feedback and Alerts:
The system provides visual feedback on the current asset rankings and allocations, with dynamic labels and plots on the chart. Additionally, users receive alerts when allocations change, keeping them informed of important adjustments.
9. Risk Management via Z-Scores and Std Dev:
The system’s approach to asset selection is based on Z-scores, which normalize performance relative to the historical mean. By incorporating standard deviation, it accounts for the volatility and risk associated with each asset. This allows for more precise risk management and portfolio construction.
10. Note on Mean Reversion Strategy:
If you take the inverse of the signals provided by this indicator, the strategy can be used for mean-reversion rather than trend-following. This would involve buying the underperforming assets and selling the outperforming ones. However, it's important to note that this approach does not work well with highly correlated assets, as the relationship between the assets could result in the same directional movement, undermining the effectiveness of the mean-reversion strategy.
References
www.uts.edu.au
onlinelibrary.wiley.com
www.cmegroup.com
Final Thoughts
The 40 Ticker Cross-Sectional Z-Scores strategy offers a data-driven approach to portfolio management, dynamically allocating capital based on the relative performance of assets. By using Z-scores and standard deviations, this strategy ensures that capital is directed to the strongest performers while avoiding weaker assets, ultimately improving the risk-adjusted returns of the portfolio. Whether you’re focused on trend-following or looking to explore mean-reversion strategies, this flexible system can be tailored to suit your investment goals.
Momentum + Keltner Stochastic Combo)The Momentum-Keltner-Stochastic Combination Strategy: A Technical Analysis and Empirical Validation
This study presents an advanced algorithmic trading strategy that implements a hybrid approach between momentum-based price dynamics and relative positioning within a volatility-adjusted Keltner Channel framework. The strategy utilizes an innovative "Keltner Stochastic" concept as its primary decision-making factor for market entries and exits, while implementing a dynamic capital allocation model with risk-based stop-loss mechanisms. Empirical testing demonstrates the strategy's potential for generating alpha in various market conditions through the combination of trend-following momentum principles and mean-reversion elements within defined volatility thresholds.
1. Introduction
Financial market trading increasingly relies on the integration of various technical indicators for identifying optimal trading opportunities (Lo et al., 2000). While individual indicators are often compromised by market noise, combinations of complementary approaches have shown superior performance in detecting significant market movements (Murphy, 1999; Kaufman, 2013). This research introduces a novel algorithmic strategy that synthesizes momentum principles with volatility-adjusted envelope analysis through Keltner Channels.
2. Theoretical Foundation
2.1 Momentum Component
The momentum component of the strategy builds upon the seminal work of Jegadeesh and Titman (1993), who demonstrated that stocks which performed well (poorly) over a 3 to 12-month period continue to perform well (poorly) over subsequent months. As Moskowitz et al. (2012) further established, this time-series momentum effect persists across various asset classes and time frames. The present strategy implements a short-term momentum lookback period (7 bars) to identify the prevailing price direction, consistent with findings by Chan et al. (2000) that shorter-term momentum signals can be effective in algorithmic trading systems.
2.2 Keltner Channels
Keltner Channels, as formalized by Chester Keltner (1960) and later modified by Linda Bradford Raschke, represent a volatility-based envelope system that plots bands at a specified distance from a central exponential moving average (Keltner, 1960; Raschke & Connors, 1996). Unlike traditional Bollinger Bands that use standard deviation, Keltner Channels typically employ Average True Range (ATR) to establish the bands' distance from the central line, providing a smoother volatility measure as established by Wilder (1978).
2.3 Stochastic Oscillator Principles
The strategy incorporates a modified stochastic oscillator approach, conceptually similar to Lane's Stochastic (Lane, 1984), but applied to a price's position within Keltner Channels rather than standard price ranges. This creates what we term "Keltner Stochastic," measuring the relative position of price within the volatility-adjusted channel as a percentage value.
3. Strategy Methodology
3.1 Entry and Exit Conditions
The strategy employs a contrarian approach within the channel framework:
Long Entry Condition:
Close price > Close price periods ago (momentum filter)
KeltnerStochastic < threshold (oversold within channel)
Short Entry Condition:
Close price < Close price periods ago (momentum filter)
KeltnerStochastic > threshold (overbought within channel)
Exit Conditions:
Exit long positions when KeltnerStochastic > threshold
Exit short positions when KeltnerStochastic < threshold
This methodology aligns with research by Brock et al. (1992) on the effectiveness of trading range breakouts with confirmation filters.
3.2 Risk Management
Stop-loss mechanisms are implemented using fixed price movements (1185 index points), providing definitive risk boundaries per trade. This approach is consistent with findings by Sweeney (1988) that fixed stop-loss systems can enhance risk-adjusted returns when properly calibrated.
3.3 Dynamic Position Sizing
The strategy implements an equity-based position sizing algorithm that increases or decreases contract size based on cumulative performance:
$ContractSize = \min(baseContracts + \lfloor\frac{\max(profitLoss, 0)}{equityStep}\rfloor - \lfloor\frac{|\min(profitLoss, 0)|}{equityStep}\rfloor, maxContracts)$
This adaptive approach follows modern portfolio theory principles (Markowitz, 1952) and Kelly criterion concepts (Kelly, 1956), scaling exposure proportionally to account equity.
4. Empirical Performance Analysis
Using historical data across multiple market regimes, the strategy demonstrates several key performance characteristics:
Enhanced performance during trending markets with moderate volatility
Reduced drawdowns during choppy market conditions through the dual-filter approach
Optimal performance when the threshold parameter is calibrated to market-specific characteristics (Pardo, 2008)
5. Strategy Limitations and Future Research
While effective in many market conditions, this strategy faces challenges during:
Rapid volatility expansion events where stop-loss mechanisms may be inadequate
Prolonged sideways markets with insufficient momentum
Markets with structural changes in volatility profiles
Future research should explore:
Adaptive threshold parameters based on regime detection
Integration with additional confirmatory indicators
Machine learning approaches to optimize parameter selection across different market environments (Cavalcante et al., 2016)
References
Brock, W., Lakonishok, J., & LeBaron, B. (1992). Simple technical trading rules and the stochastic properties of stock returns. The Journal of Finance, 47(5), 1731-1764.
Cavalcante, R. C., Brasileiro, R. C., Souza, V. L., Nobrega, J. P., & Oliveira, A. L. (2016). Computational intelligence and financial markets: A survey and future directions. Expert Systems with Applications, 55, 194-211.
Chan, L. K. C., Jegadeesh, N., & Lakonishok, J. (2000). Momentum strategies. The Journal of Finance, 51(5), 1681-1713.
Jegadeesh, N., & Titman, S. (1993). Returns to buying winners and selling losers: Implications for stock market efficiency. The Journal of Finance, 48(1), 65-91.
Kaufman, P. J. (2013). Trading systems and methods (5th ed.). John Wiley & Sons.
Kelly, J. L. (1956). A new interpretation of information rate. The Bell System Technical Journal, 35(4), 917-926.
Keltner, C. W. (1960). How to make money in commodities. The Keltner Statistical Service.
Lane, G. C. (1984). Lane's stochastics. Technical Analysis of Stocks & Commodities, 2(3), 87-90.
Lo, A. W., Mamaysky, H., & Wang, J. (2000). Foundations of technical analysis: Computational algorithms, statistical inference, and empirical implementation. The Journal of Finance, 55(4), 1705-1765.
Markowitz, H. (1952). Portfolio selection. The Journal of Finance, 7(1), 77-91.
Moskowitz, T. J., Ooi, Y. H., & Pedersen, L. H. (2012). Time series momentum. Journal of Financial Economics, 104(2), 228-250.
Murphy, J. J. (1999). Technical analysis of the financial markets: A comprehensive guide to trading methods and applications. New York Institute of Finance.
Pardo, R. (2008). The evaluation and optimization of trading strategies (2nd ed.). John Wiley & Sons.
Raschke, L. B., & Connors, L. A. (1996). Street smarts: High probability short-term trading strategies. M. Gordon Publishing Group.
Sweeney, R. J. (1988). Some new filter rule tests: Methods and results. Journal of Financial and Quantitative Analysis, 23(3), 285-300.
Wilder, J. W. (1978). New concepts in technical trading systems. Trend Research.
Daily Bollinger Band StrategyOverview of the Daily Bollinger Band Strategy
1. Strategy Overview and Features
This strategy is a tool for backtesting a trading method that uses Bollinger Bands. It is *not* a tool for automated trading.
1-1. Main Display Items
The main chart displays the Bollinger Bands and the 200-day moving average.
It also shows the entry and exit points along with the position size (in units of 100 shares).
1-2. Summary of Trading Rules
For long (buy) strategies, the trade enters when the price crosses above the +1σ line of the Bollinger Bands, aiming to ride an upward trend. The position is exited when the price crosses below the middle band.
For short (sell) strategies, the trade enters when the price crosses below the -1σ line of the Bollinger Bands, aiming to ride a downward trend. The position is exited when the price crosses above the middle band.
1-3. Strategic Enhancements
The strategy uses the slope of the 200-day moving average to determine the trend direction and enter trades accordingly. This improves the win rate and payoff ratio.
Additionally, to reduce the probability of ruin, the risk per trade is limited to 1.0% of capital, and position sizing is adjusted using ATR (a volatility indicator).
2. Trading Rules
2-1. Chart Type
Only daily charts are used.
2-2. Indicators Used
(1) Bollinger Bands** (used for entry and exit signals)
- Period: Fixed at 80 days
- Upper and lower bands: Fixed at ±1σ
(2) Moving Average** (used to determine trend direction)
- Period: Fixed at 200 days
- Trend direction is judged based on whether the difference from the previous day is positive (upward) or negative (downward)
2-3. Buy Rules
Setup:
- Price crosses above the +1σ line from below
- Both the middle band and 200-day moving average are upward sloping
Entry:
- Buy at the next day’s market open using a market order
Exit:
- If the price crosses below the middle band, sell at the next day’s open using a market order
2-4. Sell Rules
Setup:
- Price crosses below the -1σ line from above
- Both the middle band and 200-day moving average are downward sloping
Entry:
- Sell at the next day’s market open using a market order
Exit:
- If the price crosses above the middle band, buy back at the next day’s open using a market order
2-5. Risk Management Rules
- Risk per trade: 1.0% of total capital (acceptable loss = capital × 1.0%)
- Position size: Acceptable loss ÷ 2ATR (rounded down to the nearest unit of 100 shares)
2-6. Other Notes
- No brokerage fees
- No pyramiding
- No partial exits
- No reverse positions (no “stop-and-reverse” trades)
3. Strategy Parameters
The following settings can be specified:
3-1. Period Settings
- Start date: Set the start date for the backtest period
- Stop date: Set the end date for the backtest period
3-2. Display of Trend and Signals
- Show trend: When checked, the background color of the bars is light red for an uptrend and light blue for a downtrend
- Show signal: When checked, entry and exit signals are displayed (note: signals are executed at the next day’s open, so there is a one-day lag in the display)
3-3. Capital Management Settings
- Funds: Capital available for trading (in JPY)
- Risk rate: Specify what percentage of the capital to risk per trade
Settings in the “Properties” tab are not used in this strategy.
4. Backtest Results (Example)
Here are the backtest results conducted by the author:
- Target Stocks: All components of the Nikkei 225
- Test Period: January 4, 2000 – December 30, 2024
- Data Points: 12,886
- Win Rate: 33.45%
- Net Profit: ¥82,132,380
- Payoff Ratio: 2.450
- Expected Value: ¥6,373.8
- Risk Rate: 1.0%
- Probability of Ruin: 0.00%
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デイリー・ボリンジャーバンド・ストラテジーの概要
1. ストラテジーの概要と特徴
このストラテジーは、ボリンジャーバンドを使ったトレード手法のバックテストを行うツールです。自動売買を行うツールではありません。
1-1. 主な表示項目
メインチャートにボリンジャーバンドと 200日移動平均線を表示します。
また、エントリーと手仕舞いのタイミングと数量（100株単位）も表示されます。
1-2. トレードルールの概要
買い戦略の場合、ボリンジャーバンドの +1σ 超えでエントリーして上昇トレンドに乗り、ミドルバンドを割ったら決済します。
売り戦略の場合、ボリンジャーバンドの -1σ 割りでエントリーして下降トレンドに乗り、ミドルバンドを上抜けたら決済します。
1-3. ストラテジーの工夫点
200日移動平均線の傾きを見てトレンド方向にエントリーをしています。こうして勝率とペイオフレシオの成績を向上しています。
また、破産確率を抑えるために、リスク資金比率を 1.0% にして、ATR（ボラティリティ指標） を使って注文数を調整しています。
2. 売買ルール
2-1. 使用するチャート
日足チャートに限定します
2-2. 使用する指標
(1) ボリンジャーバンド（仕掛けと手仕舞いのシグナルに使用）
期間は80日に固定
上下バンドは ±1σ に固定
(2) 移動平均線（トレンドの方向を見るために使用）
期間は200日に固定
移動平均の値の前日との差がプラスのとき上向き、マイナスのとき下向きと判断
2-3. 買いのルール
セットアップ：ボリンジャーバンドの +1σ を価格が下から上に交差 かつ ミドルバンドと 200日移動平均線が上向き
仕掛け：翌日の寄り付きに成行で買う
手仕舞い：ボリンジャーバンドのミドルバンドを価格が上から下に交差したら、翌日の寄り付きに成行で売る
2-4. 売りのルール
セットアップ：ボリンジャーバンドの -1σ を価格が上から下に交差 かつ ミドルバンドと 200日移動平均線が下向き
仕掛け：翌日の寄り付きに成行で売る
手仕舞い：ボリンジャーバンドのミドルバンドを価格が下から上に交差したら、翌日の寄り付きに成行で買い戻す
2-5. 資金管理のルール
リスク資金比率：資産の 1.0%（許容損失 = 資産 × 1.0%）
注文数：許容損失 ÷ 2ATR（単元株数未満は切り捨て）
2-6. その他
仲介手数料：なし
ピラミッディング：なし
分割決済：なし
ドテン：しない
3. ストラテジーのパラメーター
次の項目が指定できます。
3-1. 期間の設定
Staer date : バックテストの検証期間の開始日を指定します
Stop date : バックテストの検証期間の終了日を指定します
3-2. トレンドとシグナルの表示
Show trend : チェックを入れると、バーの背景色が、トレンドが上昇のときは薄い赤で、下落のときは薄い青で表示されます
Show signal : チェックを入れると、エントリーと手仕舞いのシグナルを表示します（シグナルの出た翌日の寄り付きに売買をするので表示に1日のずれがあります）
3-3. 資金管理用の設定
Funds : トレード用の資金(円)
Risk rate : 許容損失を資金の何%にするかで指定します
「プロパティタブ」で設定する値は、このストラテジーでは有効ではありません。
4. バックテストの結果（例）
作者がバックテストを実施した結果をお知らせします。
対象銘柄：日経225構成銘柄すべて
対象期間：2000年1月4日～2024年12月30日
データ件数：12,886
勝率：33.45%
純利益：82,132,380
ペイオフレシオ：2.450
期待値：6,373.8
リスク資金比率：1.0%
破産確率：0.00%
[SM-042] EMA 5-8-13 with ADX FilterWhat is the strategy?
The strategy combines three exponential moving averages (EMAs) — 5, 8, and 13 periods — with an optional ADX (Average Directional Index) filter. It is designed to enter long or short positions based on EMA crossovers and to exit positions when the price crosses a specific EMA. The ADX filter, if enabled, adds a condition that only allows trades when the ADX value is above a certain threshold, indicating trend strength.
Who is it for?
This strategy is for traders leveraging EMAs and trend strength indicators to make trade decisions. It can be used by anyone looking for a simple trend-following strategy, with the flexibility to adjust for trend strength using the ADX filter.
When is it used?
- **Long trades**: When the 5-period EMA crosses above the 8-period EMA, with an optional ADX condition (if enabled) that requires the ADX value to be above a specified threshold.
- **Short trades**: When the 5-period EMA crosses below the 8-period EMA, with the ADX filter again optional.
- **Exits**: The strategy exits a long position when the price falls below the 13-period EMA and exits a short position when the price rises above the 13-period EMA.
Where is it applied?
This strategy is applied on a chart with any asset on TradingView, with the EMAs and ADX plotted for visual reference. The strategy uses `strategy.entry` to open positions and `strategy.close` to close them based on the set conditions.
Why is it useful?
This strategy helps traders identify trending conditions and filter out potential false signals by using both EMAs (to capture short-term price movements) and the ADX (to confirm the strength of the trend). The ADX filter can be turned off if not desired, making the strategy flexible for both trending and range-bound markets.
How does it work?
- **EMA Crossover**: The strategy enters a long position when the 5-period EMA crosses above the 8-period EMA, and enters a short position when the 5-period EMA crosses below the 8-period EMA.
- **ADX Filter**: If enabled, the strategy checks whether the ADX value is above a set threshold (default is 20) before allowing a trade.
- **Exit Conditions**: Long positions are closed when the price falls below the 13-period EMA, and short positions are closed when the price rises above the 13-period EMA.
- **Plotting**: The strategy plots the three EMAs and the ADX value on the chart for visualization. It also displays a horizontal line at the ADX threshold.
This setup allows for clear decision-making based on the interaction between different time-frame EMAs and trend strength as indicated by ADX.
Multi-Timeframe Parabolic SAR Strategy ver 1.0Multi-Timeframe Parabolic SAR Strategy (MTF PSAR) - Enhanced Trend Trading
This strategy leverages the power of the Parabolic SAR (Stop and Reverse) indicator across multiple timeframes to provide robust trend identification, precise entry/exit signals, and dynamic trailing stop management. By combining the insights of both the current chart's timeframe and a user-defined higher timeframe, this strategy aims to improve trading accuracy, reduce risk, and capture more significant market moves.
Key Features:
Dual Timeframe Analysis: Simultaneously analyzes the Parabolic SAR on the current chart and a higher timeframe (e.g., Daily PSAR on a 1-hour chart). This allows you to align your trades with the dominant trend and filter out noise from lower timeframes.
Configurable PSAR: Fine-tune the PSAR calculation with adjustable Start, Increment, and Maximum values to optimize sensitivity for your trading style and the asset's volatility.
Independent Timeframe Control: Choose to display and trade based on either or both the current timeframe PSAR and the higher timeframe PSAR. Focus on the most relevant information for your analysis.
Clear Visual Signals: Distinct colors for the current and higher timeframe PSAR dots provide a clear visual representation of potential entry and exit points.
Multiple Entry Strategies: The strategy offers flexible entry conditions, allowing you to trade based on:
Confirmation: Both current and higher timeframe PSAR signals agree and the current timeframe PSAR has just flipped direction. (Most conservative)
Current Timeframe Only: Trades based solely on the current timeframe PSAR, ideal for when the higher timeframe is less relevant or disabled.
Higher Timeframe Only: Trades based solely on the higher timeframe PSAR.
Dynamic Trailing Stop (PSAR-Based): Implements a trailing stop-loss based on the current timeframe's Parabolic SAR. This helps protect profits by automatically adjusting the stop-loss as the price moves in your favor. Exits are triggered when either the current or HTF PSAR flips.
No Repainting: Uses lookahead=barmerge.lookahead_off in the security() function to ensure that the higher timeframe data is accessed without any data leakage, preventing repainting issues.
Fully Configurable: All parameters (PSAR settings, higher timeframe, visibility, colors) are adjustable through the strategy's settings panel, allowing for extensive customization and optimization.
Suitable for Various Trading Styles: Applicable to swing trading, day trading, and trend-following strategies across various markets (stocks, forex, cryptocurrencies, etc.).
How it Works:
PSAR Calculation: The strategy calculates the standard Parabolic SAR for both the current chart's timeframe and the selected higher timeframe.
Trend Identification: The direction of the PSAR (dots below price = uptrend, dots above price = downtrend) determines the current trend for each timeframe.
Entry Signals: The strategy generates buy/sell signals based on the chosen entry strategy (Confirmation, Current Timeframe Only, or Higher Timeframe Only). The Confirmation strategy offers the highest probability signals by requiring agreement between both timeframes.
Trailing Stop Exit: Once a position is entered, the strategy uses the current timeframe PSAR as a dynamic trailing stop. The stop-loss is automatically adjusted as the PSAR dots move, helping to lock in profits and limit losses. The strategy exits when either the Current or HTF PSAR changes direction.
Backtesting and Optimization: The strategy automatically backtests on the chart's historical data, allowing you to evaluate its performance and optimize the settings for different assets and timeframes.
Example Use Cases:
Trend Confirmation: A trader on a 1-hour chart observes a bullish PSAR flip on the current timeframe. They check the MTF PSAR strategy and see that the Daily PSAR is also bullish, confirming the strength of the uptrend and providing a high-probability long entry signal.
Filtering Noise: A trader on a 5-minute chart wants to avoid whipsaws caused by short-term price fluctuations. They use the strategy with a 1-hour higher timeframe to filter out noise and only trade in the direction of the dominant trend.
Dynamic Risk Management: A trader enters a long position and uses the current timeframe PSAR as a trailing stop. As the price rises, the PSAR dots move upwards, automatically raising the stop-loss and protecting profits. The trade is exited when the current (or HTF) PSAR flips to bearish.
Disclaimer:
The Parabolic SAR is a lagging indicator and can produce false signals, particularly in ranging or choppy markets. This strategy is intended for educational and informational purposes only and should not be considered financial advice. It is essential to backtest and optimize the strategy thoroughly, use it in conjunction with other technical analysis tools, and implement sound risk management practices before using it with real capital. Past performance is not indicative of future results. Always conduct your own due diligence and consider your risk tolerance before making any trading decisions.
Strategy SuperTrend SDI WebhookThis Pine Script™ strategy is designed for automated trading in TradingView. It combines the SuperTrend indicator and Smoothed Directional Indicator (SDI) to generate buy and sell signals, with additional risk management features like stop loss, take profit, and trailing stop. The script also includes settings for leverage trading, equity-based position sizing, and webhook integration.
Key Features
1. Date-based Trade Execution
The strategy is active only between the start and end dates set by the user.
times ensures that trades occur only within this predefined time range.
2. Position Sizing and Leverage
Uses leverage trading to adjust position size dynamically based on initial equity.
The user can set leverage (leverage) and percentage of equity (usdprcnt).
The position size is calculated dynamically (initial_capital) based on account performance.
3. Take Profit, Stop Loss, and Trailing Stop
Take Profit (tp): Defines the target profit percentage.
Stop Loss (sl): Defines the maximum allowable loss per trade.
Trailing Stop (tr): Adjusts dynamically based on trade performance to lock in profits.
4. SuperTrend Indicator
SuperTrend (ta.supertrend) is used to determine the market trend.
If the price is above the SuperTrend line, it indicates an uptrend (bullish).
If the price is below the SuperTrend line, it signals a downtrend (bearish).
Plots visual indicators (green/red lines and circles) to show trend changes.
5. Smoothed Directional Indicator (SDI)
SDI helps to identify trend strength and momentum.
It calculates +DI (bullish strength) and -DI (bearish strength).
If +DI is higher than -DI, the market is considered bullish.
If -DI is higher than +DI, the market is considered bearish.
The background color changes based on the SDI signal.
6. Buy & Sell Conditions
Long Entry (Buy) Conditions:
SDI confirms an uptrend (+DI > -DI).
SuperTrend confirms an uptrend (price crosses above the SuperTrend line).
Short Entry (Sell) Conditions:
SDI confirms a downtrend (+DI < -DI).
SuperTrend confirms a downtrend (price crosses below the SuperTrend line).
Optionally, trades can be filtered using crossovers (occrs option).
7. Trade Execution and Exits
Market entries:
Long (strategy.entry("Long")) when conditions match.
Short (strategy.entry("Short")) when bearish conditions are met.
Trade exits:
Uses predefined take profit, stop loss, and trailing stop levels.
Positions are closed if the strategy is out of the valid time range.
Usage
Automated Trading Strategy:
Can be integrated with webhooks for automated execution on supported trading platforms.
Trend-Following Strategy:
Uses SuperTrend & SDI to identify trend direction and strength.
Risk-Managed Leverage Trading:
Supports position sizing, stop losses, and trailing stops.
Backtesting & Optimization:
Can be used for historical performance analysis before deploying live.
Conclusion
This strategy is suitable for traders who want to automate their trading using SuperTrend and SDI indicators. It incorporates risk management tools like stop loss, take profit, and trailing stop, making it adaptable for leverage trading. Traders can customize settings, conduct backtests, and integrate it with webhooks for real-time trade execution. 🚀
Important Note:
This script is provided for educational and template purposes and does not constitute financial advice. Traders and investors should conduct their research and analysis before making any trading decisions.
[3Commas] HA & MAHA & MA
🔷What it does: This tool is designed to test a trend-following strategy using Heikin Ashi candles and moving averages. It enters trades after pullbacks, aiming to let profits run once the risk-to-reward ratio reaches 1:1 while securing the position.
🔷Who is it for: It is ideal for traders looking to compare final results using fixed versus dynamic take profits by adjusting parameters and trade direction—a concept applicable to most trading strategies.
🔷How does it work: We use moving averages to define the market trend, then wait for opposite Heikin Ashi candles to form against it. Once these candles reverse in favor of the trend, we enter the trade, using the last swing created by the pullback as the stop loss. By applying the breakeven ratio, we protect the trade and let it run, using the slower moving average as a trailing stop.
A buy signal is generated when:
The previous candle is bearish (ha_bear ), indicating a pullback.
The fast moving average (ma1) is above the slow moving average (ma2), confirming an uptrend.
The current candle is bullish (ha_bull), showing trend continuation.
The Heikin Ashi close is above the fast moving average (ma1), reinforcing the bullish bias.
The real price close is above the open (close > open), ensuring bullish momentum in actual price data.
The signal is confirmed on the closed candle (barstate.isconfirmed) to avoid premature signals.
dir is undefined (na(dir)), preventing repeated signals in the same direction.
A sell signal is generated when:
The previous candle is bullish (ha_bull ), indicating a temporary upward move before a potential reversal.
The fast moving average (ma1) is below the slow moving average (ma2), confirming a downtrend.
The current candle is bearish (ha_bear), showing trend continuation to the downside.
The Heikin Ashi close is below the fast moving average (ma1), reinforcing bearish pressure.
The real price close is below the open (close < open), confirming bearish momentum in actual price data.
The signal is confirmed after the candle closes (barstate.isconfirmed), avoiding premature entries.
dir is undefined (na(dir)), preventing consecutive signals in the same direction.
In simple terms, this setup looks for trend continuation after a pullback, confirming entries with both Heikin Ashi and real price action, supported by moving average alignment to avoid false signals.
If the price reaches a 1:1 risk-to-reward ratio, the stop will be moved to the entry point. However, if the slow moving average surpasses this level, it will become the new exit point, acting as a trailing stop
🔷Why It’s Unique
Easily visualizes the benefits of using risk-to-reward ratios when trading instead of fixed percentages.
Provides a simple and straightforward approach to trading, embracing the "keep it simple" concept.
Offers clear visualization of DCA Bot entry and exit points based on user preferences.
Includes an option to review the message format before sending signals to bots, with compatibility for multi-pair and futures contract pairs.
🔷 Considerations Before Using the Indicator
⚠️Very important: The indicator must be used on charts with real price data, such as Japanese candlesticks, line charts, etc. Do not use it on Heikin Ashi charts, as this may lead to unrealistic results.
🔸Since this is a trend-following strategy, use it on timeframes above 4 hours, where market noise is reduced and trends are clearer. Also, carefully review the statistics before using it, focusing on pairs that tend to have long periods of well-defined trends.
🔸Disadvantages:
False Signals in Ranges: Consolidating markets can generate unreliable signals.
Lagging Indicator: Being based on moving averages, it may react late to sudden price movements.
🔸Advantages:
Trend Focused: Simplifies the identification of trending markets.
Noise Reduction: Uses Heikin Ashi candles to identify trend continuation after pullbacks.
Broad Applicability: Suitable for forex, crypto, stocks, and commodities.
🔸The strategy provides a systematic way to analyze markets but does not guarantee successful outcomes. Use it as an additional tool rather than relying solely on an automated system.
Trading results depend on various factors, including market conditions, trader discipline, and risk management. Past performance does not ensure future success, so always approach the market cautiously.
🔸Risk Management: Define stop-loss levels, position sizes, and profit targets before entering any trade. Be prepared for potential losses and ensure your approach aligns with your overall trading plan.
🔷 STRATEGY PROPERTIES
Symbol: BINANCE:BTCUSDT (Spot).
Timeframe: 4h.
Test Period: All historical data available.
Initial Capital: 10000 USDT.
Order Size per Trade: 1% of Capital, you can use a higher value e.g. 5%, be cautious that the Max Drawdown does not exceed 10%, as it would indicate a very risky trading approach.
Commission: Binance commission 0.1%, adjust according to the exchange being used, lower numbers will generate unrealistic results. By using low values e.g. 5%, it allows us to adapt over time and check the functioning of the strategy.
Slippage: 5 ticks, for pairs with low liquidity or very large orders, this number should be increased as the order may not be filled at the desired level.
Margin for Long and Short Positions: 100%.
Indicator Settings: Default Configuration.
MA1 Length: 9.
MA2 Length: 18.
MA Calculations: EMA.
Take Profit Ratio: Disable. Ratio 1:4.
Breakeven Ratio: Enable, Ratio 1:1.
Strategy: Long & Short.
🔷 STRATEGY RESULTS
⚠️Remember, past results do not guarantee future performance.
Net Profit: +324.88 USDT (+3.25%).
Max Drawdown: -81.18 USDT (-0.78%).
Total Closed Trades: 672.
Percent Profitable: 35.57%.
Profit Factor: 1.347.
Average Trade: +0.48 USDT (+0.48%).
Average # Bars in Trades: 13.
🔷 HOW TO USE
🔸 Adjust Settings:
The default values—MA1 (9) and MA2 (18) with EMA calculation—generally work well. However, you can increase these values, such as 20 and 40, to better identify stronger trends.
🔸 Choose a Symbol that Typically Trends:
Select an asset that tends to form clear trends. Keep in mind that the Strategy Tester results may show poor performance for certain assets, making them less suitable for sending signals to bots.
🔸 Experiment with Ratios:
Test different take profit and breakeven ratios to compare various scenarios—especially to observe how the strategy performs when only the trade is protected.
🔸This is an example of how protecting the trade works: once the price moves in favor of the position with a 1:1 risk-to-reward ratio, the stop loss is moved to the entry price. If the Slow MA surpasses this level, it will act as a trailing stop, aiming to follow the trend and maximize potential gains.
🔸In contrast, in this example, for the same trade, if we set a take profit at a 1:3 risk-to-reward ratio—which is generally considered a good risk-reward relationship—we can see how a significant portion of the upward move is left on the table.
🔸Results Review:
It is important to check the Max Drawdown. This value should ideally not exceed 10% of your capital. Consider adjusting the trade size to ensure this threshold is not surpassed.
Remember to include the correct values for commission and slippage according to the symbol and exchange where you are conducting the tests. Otherwise, the results will not be realistic.
If you are satisfied with the results, you may consider automating your trades. However, it is strongly recommended to use a small amount of capital or a demo account to test proper execution before committing real funds.
🔸Create alerts to trigger the DCA Bot:
Verify Messages: Ensure the message matches the one specified by the DCA Bot.
Multi-Pair Configuration: For multi-pair setups, enable the option to add the symbol in the correct format.
Signal Settings: Enable whether you want to receive long or short signals (Entry | TP | SL), copy and paste the the messages for the DCA Bots configured.
Alert Setup:
When creating an alert, set the condition to the indicator and choose "alert() function call only.
Enter any desired Alert Name.
Open the Notifications tab, enable Webhook URL, and paste the Webhook URL.
For more details, refer to the section: "How to use TradingView Custom Signals".
Finalize Alerts: Click Create, you're done! Alerts will now be sent automatically in the correct format.
🔷 INDICATOR SETTINGS
MA 1: Fast MA Length
MA 2: Slow MA Length
MA Calc: MA's Calculations (SMA,EMA, RMA,WMA)
TP Ratio: This is the take profit ratio relative to the stop loss, where the trade will be closed in profit.
BE Ratio: This is the breakeven ratio relative to the stop loss, where the stop loss will be updated to breakeven or if the MA2 is greater than this level.
Strategy: Order Type direction in which trades are executed.
Use Custom Test Period: When enabled signals only works in the selected time window. If disabled it will use all historical data available on the chart.
Test Start and End: Once the Custom Test Period is enabled, here you select the start and end date that you want to analyze.
Check Messages: Enable the table to review the messages to be sent to the bot.
Entry | TP | SL: Enable this options to send Buy Entry, Take Profit (TP), and Stop Loss (SL) signals.
Deal Entry and Deal Exit : Copy and paste the message for the deal start signal and close order at Market Price of the DCA Bot. This is the message that will be sent with the alert to the Bot, you must verify that it is the same as the bot so that it can process properly so that it executes and starts the trade.
DCA Bot Multi-Pair: You must activate it if you want to use the signals in a DCA Bot Multi-pair in the text box you must enter (using the correct format) the symbol in which you are creating the alert, you can check the format of each symbol when you create the bot.
👨🏻💻💭 We hope this tool helps enhance your trading. Your feedback is invaluable, so feel free to share any suggestions for improvements or new features you'd like to see implemented.
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The information and publications within the 3Commas TradingView account are not meant to be and do not constitute financial, investment, trading, or other types of advice or recommendations supplied or endorsed by 3Commas and any of the parties acting on behalf of 3Commas, including its employees, contractors, ambassadors, etc.
AO/AC Trading Zones Strategy [Skyrexio] Overview
AO/AC Trading Zones Strategy leverages the combination of Awesome Oscillator (AO), Acceleration/Deceleration Indicator (AC), Williams Fractals, Williams Alligator and Exponential Moving Average (EMA) to obtain the high probability long setups. Moreover, strategy uses multi trades system, adding funds to long position if it considered that current trend has likely became stronger. Combination of AO and AC is used for creating so-called trading zones to create the signals, while Alligator and Fractal are used in conjunction as an approximation of short-term trend to filter them. At the same time EMA (default EMA's period = 100) is used as high probability long-term trend filter to open long trades only if it considers current price action as an uptrend. More information in "Methodology" and "Justification of Methodology" paragraphs. The strategy opens only long trades.
Unique Features
No fixed stop-loss and take profit: Instead of fixed stop-loss level strategy utilizes technical condition obtained by Fractals and Alligator to identify when current uptrend is likely to be over. In some special cases strategy uses AO and AC combination to trail profit (more information in "Methodology" and "Justification of Methodology" paragraphs)
Configurable Trading Periods: Users can tailor the strategy to specific market windows, adapting to different market conditions.
Multilayer trades opening system: strategy uses only 10% of capital in every trade and open up to 5 trades at the same time if script consider current trend as strong one.
Short and long term trend trade filters: strategy uses EMA as high probability long-term trend filter and Alligator and Fractal combination as a short-term one.
Methodology
The strategy opens long trade when the following price met the conditions:
1. Price closed above EMA (by default, period = 100). Crossover is not obligatory.
2. Combination of Alligator and Williams Fractals shall consider current trend as an upward (all details in "Justification of Methodology" paragraph)
3. Both AC and AO shall print two consecutive increasing values. At the price candle close which corresponds to this condition algorithm opens the first long trade with 10% of capital.
4. If combination of Alligator and Williams Fractals shall consider current trend has been changed from up to downtrend, all long trades will be closed, no matter how many trades has been opened.
5. If AO and AC both continue printing the rising values strategy opens the long trade on each candle close with 10% of capital while number of opened trades reaches 5.
6. If AO and AC both has printed 5 rising values in a row algorithm close all trades if candle's low below the low of the 5-th candle with rising AO and AC values in a row.
Script also has additional visuals. If second long trade has been opened simultaneously the Alligator's teeth line is plotted with the green color. Also for every trade in a row from 2 to 5 the label "Buy More" is also plotted just below the teeth line. With every next simultaneously opened trade the green color of the space between teeth and price became less transparent.
Strategy settings
In the inputs window user can setup strategy setting:
EMA Length (by default = 100, period of EMA, used for long-term trend filtering EMA calculation).
User can choose the optimal parameters during backtesting on certain price chart.
Justification of Methodology
Let's explore the key concepts of this strategy and understand how they work together. We'll begin with the simplest: the EMA.
The Exponential Moving Average (EMA) is a type of moving average that assigns greater weight to recent price data, making it more responsive to current market changes compared to the Simple Moving Average (SMA). This tool is widely used in technical analysis to identify trends and generate buy or sell signals. The EMA is calculated as follows:
1.Calculate the Smoothing Multiplier:
Multiplier = 2 / (n + 1), Where n is the number of periods.
2. EMA Calculation
EMA = (Current Price) × Multiplier + (Previous EMA) × (1 − Multiplier)
In this strategy, the EMA acts as a long-term trend filter. For instance, long trades are considered only when the price closes above the EMA (default: 100-period). This increases the likelihood of entering trades aligned with the prevailing trend.
Next, let’s discuss the short-term trend filter, which combines the Williams Alligator and Williams Fractals. Williams Alligator
Developed by Bill Williams, the Alligator is a technical indicator that identifies trends and potential market reversals. It consists of three smoothed moving averages:
Jaw (Blue Line): The slowest of the three, based on a 13-period smoothed moving average shifted 8 bars ahead.
Teeth (Red Line): The medium-speed line, derived from an 8-period smoothed moving average shifted 5 bars forward.
Lips (Green Line): The fastest line, calculated using a 5-period smoothed moving average shifted 3 bars forward.
When the lines diverge and align in order, the "Alligator" is "awake," signaling a strong trend. When the lines overlap or intertwine, the "Alligator" is "asleep," indicating a range-bound or sideways market. This indicator helps traders determine when to enter or avoid trades.
Fractals, another tool by Bill Williams, help identify potential reversal points on a price chart. A fractal forms over at least five consecutive bars, with the middle bar showing either:
Up Fractal: Occurs when the middle bar has a higher high than the two preceding and two following bars, suggesting a potential downward reversal.
Down Fractal: Happens when the middle bar shows a lower low than the surrounding two bars, hinting at a possible upward reversal.
Traders often use fractals alongside other indicators to confirm trends or reversals, enhancing decision-making accuracy.
How do these tools work together in this strategy? Let’s consider an example of an uptrend.
When the price breaks above an up fractal, it signals a potential bullish trend. This occurs because the up fractal represents a shift in market behavior, where a temporary high was formed due to selling pressure. If the price revisits this level and breaks through, it suggests the market sentiment has turned bullish.
The breakout must occur above the Alligator’s teeth line to confirm the trend. A breakout below the teeth is considered invalid, and the downtrend might still persist. Conversely, in a downtrend, the same logic applies with down fractals.
In this strategy if the most recent up fractal breakout occurs above the Alligator's teeth and follows the last down fractal breakout below the teeth, the algorithm identifies an uptrend. Long trades can be opened during this phase if a signal aligns. If the price breaks a down fractal below the teeth line during an uptrend, the strategy assumes the uptrend has ended and closes all open long trades.
By combining the EMA as a long-term trend filter with the Alligator and fractals as short-term filters, this approach increases the likelihood of opening profitable trades while staying aligned with market dynamics.
Now let's talk about the trading zones concept and its signals. To understand this we need to briefly introduce what is AO and AC. The Awesome Oscillator (AO), developed by Bill Williams, is a momentum indicator designed to measure market momentum by contrasting recent price movements with a longer-term historical perspective. It helps traders detect potential trend reversals and assess the strength of ongoing trends.
The formula for AO is as follows:
AO = SMA5(Median Price) − SMA34(Median Price)
where:
Median Price = (High + Low) / 2
SMA5 = 5-period Simple Moving Average of the Median Price
SMA 34 = 34-period Simple Moving Average of the Median Price
The Acceleration/Deceleration (AC) Indicator, introduced by Bill Williams, measures the rate of change in market momentum. It highlights shifts in the driving force of price movements and helps traders spot early signs of trend changes. The AC Indicator is particularly useful for identifying whether the current momentum is accelerating or decelerating, which can indicate potential reversals or continuations. For AC calculation we shall use the AO calculated above is the following formula:
AC = AO − SMA5(AO) , where SMA5(AO)is the 5-period Simple Moving Average of the Awesome Oscillator
When the AC is above the zero line and rising, it suggests accelerating upward momentum.
When the AC is below the zero line and falling, it indicates accelerating downward momentum.
When the AC is below zero line and rising it suggests the decelerating the downtrend momentum. When AC is above the zero line and falling, it suggests the decelerating the uptrend momentum.
Now let's discuss the trading zones concept and how it can create the signal. Zones are created by the combination of AO and AC. We can divide three zone types:
Greed zone: when the AO and AC both are rising
Red zone: when the AO and AC both are decreasing
Gray zone: when one of AO or AC is rising, the other is falling
Gray zone is considered as uncertainty. AC and AO are moving in the opposite direction. Strategy skip such price action to decrease the chance to stuck in the losing trade during potential sideways. Red zone is also not interesting for the algorithm because both indicators consider the trend as bearish, but strategy opens only long trades. It is waiting for the green zone to increase the chance to open trade in the direction of the potential uptrend. When we have 2 candles in a row in the green zone script executes a long trade with 10% of capital.
Two green zone candles in a row is considered by algorithm as a bullish trend, but now so strong, that's the reason why trade is going to be closed when the combination of Alligator and Fractals will consider the the trend change from bullish to bearish. If id did not happens, algorithm starts to count the green zone candles in a row. When we have 5 in a row script change the trade closing condition. Such situation is considered is a high probability strong bull market and all trades will be closed if candle's low will be lower than fifth green zone candle's low. This is used to increase probability to secure the profit. If long trades are initiated, the strategy continues utilizing subsequent signals until the total number of trades reaches a maximum of 5. Each trade uses 10% of capital.
Why we use trading zones signals? If currently strategy algorithm considers the high probability of the short-term uptrend with the Alligator and Fractals combination pointed out above and the long-term trend is also suggested by the EMA filter as bullish. Rising AC and AO values in the direction of the most likely main trend signaling that we have the high probability of the fastest bullish phase on the market. The main idea is to take part in such rapid moves and add trades if this move continues its acceleration according to indicators.
Backtest Results
Operating window: Date range of backtests is 2023.01.01 - 2024.12.31. It is chosen to let the strategy to close all opened positions.
Commission and Slippage: Includes a standard Binance commission of 0.1% and accounts for possible slippage over 5 ticks.
Initial capital: 10000 USDT
Percent of capital used in every trade: 10%
Maximum Single Position Loss: -9.49%
Maximum Single Profit: +24.33%
Net Profit: +4374.70 USDT (+43.75%)
Total Trades: 278 (39.57% win rate)
Profit Factor: 2.203
Maximum Accumulated Loss: 668.16 USDT (-5.43%)
Average Profit per Trade: 15.74 USDT (+1.37%)
Average Trade Duration: 60 hours
How to Use
Add the script to favorites for easy access.
Apply to the desired timeframe and chart (optimal performance observed on 4h BTC/USDT).
Configure settings using the dropdown choice list in the built-in menu.
Set up alerts to automate strategy positions through web hook with the text: {{strategy.order.alert_message}}
Disclaimer:
Educational and informational tool reflecting Skyrex commitment to informed trading. Past performance does not guarantee future results. Test strategies in a simulated environment before live implementation
These results are obtained with realistic parameters representing trading conditions observed at major exchanges such as Binance and with realistic trading portfolio usage parameters.
TSI Long/Short for BTC 2HThe TSI Long/Short for BTC 2H strategy is an advanced trend-following system designed specifically for trading Bitcoin (BTC) on a 2-hour timeframe. It leverages the True Strength Index (TSI) to identify momentum shifts and executes both long and short trades in response to dynamic market conditions.
Unlike traditional moving average-based strategies, this script uses a double-smoothed momentum calculation, enhancing signal accuracy and reducing noise. It incorporates automated position sizing, customizable leverage, and real-time performance tracking, ensuring a structured and adaptable trading approach.
🔹 What Makes This Strategy Unique?
Unlike simple crossover strategies or generic trend-following approaches, this system utilizes a customized True Strength Index (TSI) methodology that dynamically adjusts to market conditions.
🔸 True Strength Index (TSI) Filtering – The script refines the TSI by applying double exponential smoothing, filtering out weak signals and capturing high-confidence momentum shifts.
🔸 Adaptive Entry & Exit Logic – Instead of fixed thresholds, it compares the TSI value against a dynamically determined high/low range from the past 100 bars to confirm trade signals.
🔸 Leverage & Risk Optimization – Position sizing is dynamically adjusted based on account equity and leverage settings, ensuring controlled risk exposure.
🔸 Performance Monitoring System – A built-in performance tracking table allows traders to evaluate monthly and yearly results directly on the chart.
📊 Core Strategy Components
1️⃣ Momentum-Based Trade Execution
The strategy generates long and short trade signals based on the following conditions:
✅ Long Entry Condition – A buy signal is triggered when the TSI crosses above its 100-bar highest value (previously set), confirming bullish momentum.
✅ Short Entry Condition – A sell signal is generated when the TSI crosses below its 100-bar lowest value (previously set), indicating bearish pressure.
Each trade execution is fully automated, reducing emotional decision-making and improving trading discipline.
2️⃣ Position Sizing & Leverage Control
Risk management is a key focus of this strategy:
🔹 Dynamic Position Sizing – The script calculates position size based on:
Account Equity – Ensuring trade sizes adjust dynamically with capital fluctuations.
Leverage Multiplier – Allows traders to customize risk exposure via an adjustable leverage setting.
🔹 No Fixed Stop-Loss – The strategy relies on reversals to exit trades, meaning each position is closed when the opposite signal appears.
This design ensures maximum capital efficiency while adapting to market conditions in real time.
3️⃣ Performance Visualization & Tracking
Understanding historical performance is crucial for refining strategies. The script includes:
📌 Real-Time Trade Markers – Buy and sell signals are visually displayed on the chart for easy reference.
📌 Performance Metrics Table – Tracks monthly and yearly returns in percentage form, helping traders assess profitability over time.
📌 Trade History Visualization – Completed trades are displayed with color-coded boxes (green for long trades, red for short trades), visually representing profit/loss dynamics.
📢 Why Use This Strategy?
✔ Advanced Momentum Detection – Uses a double-smoothed TSI for more accurate trend signals.
✔ Fully Automated Trading – Removes emotional bias and enforces discipline.
✔ Customizable Risk Management – Adjust leverage and position sizing to suit your risk profile.
✔ Comprehensive Performance Tracking – Integrated reporting system provides clear insights into past trades.
This strategy is ideal for Bitcoin traders looking for a structured, high-probability system that adapts to both bullish and bearish trends on the 2-hour timeframe.
📌 How to Use: Simply add the script to your 2H BTC chart, configure your leverage settings, and let the system handle trade execution and tracking! 🚀
