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.
Cari dalam skrip untuk "the strat"
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
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
Zignaly TutorialThis strategy serves as a beginner's guide to connect TradingView signals to Zignaly Crypto Trading Platform.
It was originally tested at BTCUSDT pair and 1D timeframe.
Before using this documentation it's recommended that you:
Use default TradingView strategy script or another script and setup its associated alert manually. Just make the alert pop-up in the screen.
Create a 'Copy-Trader provider' (or Signal Provider) in Zignaly and send signals to it either thanks to your browser or with some basic programming.
SETTINGS
__ SETTINGS - Capital
(CAPITAL) Capital quote invested per order in USDT units {100.0}. This setting is only used when '(ZIG) Provider type' is set to 'Signal Provider'.
(CAPITAL) Capital percentage invested per order (%) {25.0}. This setting is only used when '(ZIG) Provider type' is set to 'Copy Trader Provider'.
__ SETTINGS - Misc
(ZIG) Enable Alert message {True}: Whether to enable alert message or not.
(DEBUG) Enable debug on order comments {True}: Whether to show alerts on order comments or not.
Number of decimal digits for Prices {2}.
(DECIMAL) Maximum number of decimal for contracts {3}.
__ SETTINGS - Zignaly
(ZIG) Integration type {TradingView only}: Hybrid : Both TradingView and Zignaly handle take profit, trailing stops and stop losses. Useful if you are scared about TradingView not firing an alert. It might arise problems if TradingView and Zignaly get out of sync. TradingView only : TradingView sends entry and exit orders to Zignaly so that Zignaly only buys or sells. Zignaly won't handle stop loss or other settings on its own.
(ZIG) Zignaly Alert Type {WebHook}: 'Email' or 'WebHook'.
(ZIG) Provider type {Copy Trader Provider}: 'Copy Trader Provider' or 'Signal Provider'. 'Copy Trader Provider' sends a percentage to manage. 'Signal Provider' sends a quote to manage.
(ZIG) Exchange: 'Binance' or 'Kucoin'.
(ZIG) Exchange Type {Spot}: 'Spot' or 'Futures'.
(ZIG) Leverage {1}. Set it to '1' when '(ZIG) Exchange Type' is set to 'Spot'.
__ SETTINGS - Strategy
(STRAT) Strategy Type: 'Long and Short', 'Long Only' or 'Short Only'.
(STOPTAKE) Take Profit? {false}: Whether to enable Take Profit.
(STOPTAKE) Stop Loss? {True}: Whether to enable Stop Loss.
(TRAILING) Enable Trailing Take Profit (%) {True}: Whether to enable Trailing Take Profit.
(STOPTAKE) Take Profit % {3.0}: Take profit percentage. This setting is only used when '(STOPTAKE) Take Profit?' setting is set to true.
(STOPTAKE) Stop Loss % {2.0}: Stop loss percentage. This setting is only used when '(STOPTAKE) Stop Loss?' setting is set to true.
(TRAILING) Trailing Take Profit Trigger (%) {2.5}: Trailing Stop Trigger Percentage. This setting is only used when '(TRAILING) Enable Trailing Take Profit (%)' setting is set to true.
(TRAILING) Trailing Take Profit as a percentage of Trailing Take Profit Trigger (%) {25.0}: Trailing Stop Distance Percentage. This setting is only used when '(TRAILING) Enable Trailing Take Profit (%)' setting is set to true.
(RECENT) Number of minutes to wait to open a new order after the previous one has been opened {6}.
DEFAULT SETTINGS
By default this strategy has been setup with these beginner settings:
'(ZIG) Integration type' : TradingView only
'(ZIG) Provider type' : 'Copy Trader Provider'
'(ZIG) Exchange' : 'Binance'
'(ZIG) Exchange Type' : 'Spot'
'(STRAT) Strategy Type' : 'Long Only'
'(ZIG) Leverage' : '1' (Or no leverage)
but you can change those settings if needed.
FIRST STEP
For both future of spot markets you should make sure to change '(ZIG) Zignaly Alert Type' to match either WebHook or Email. If you have a non paid account in TradingView as in October 2020 you would have to use Email which it's free to use.
RECOMMENDED SETTINGS
__ RECOMMENDED SETTINGS - Spot markets
'(ZIG) Exchange Type' setting should be set to 'Spot'
'(STRAT) Strategy Type' setting should be set to 'Long Only'
'(ZIG) Leverage' setting should be set to '1'
__ RECOMMENDED SETTINGS - Future markets
'(ZIG) Exchange Type' setting should be set to 'Futures'
'(STRAT) Strategy Type' setting should be set to 'Long and Short'
'(ZIG) Leverage' setting might be changed if desired.
__ RECOMMENDED SETTINGS - Signal Providers
'(ZIG) Provider type' setting should be set to 'Signal Provider'
'(CAPITAL) Capital quote invested per order in USDT units' setting might be changed if desired.
__ RECOMMENDED SETTINGS - Copy Trader Providers
'(ZIG) Provider type' setting should be set to 'Copy Trader Provider'
'(CAPITAL) Capital percentage invested per order (%)' setting might be changed if desired.
Strategy Properties setting: 'Initial Capital' might be changed if desired.
INTEGRATION TYPE EXPLANATION
'Hybrid': Both TradingView and Zignaly handle take profit, trailing stops and stop losses. Useful if you are scared about TradingView not firing an alert. It might arise problems if TradingView and Zignaly get out of sync.
'TradingView only': TradingView sends entry and exit orders to Zignaly so that Zignaly only buys or sells. Zignaly won't handle stop loss or other settings on its own.
HOW TO USE THIS STRATEGY
Beginner: Copy and paste the strategy and change it to your needs. Turn off '(DEBUG) Enable debug on order comments' setting.
Medium: Reuse functions and inputs from this strategy into your own as if it was a library.
Advanced: Check Strategy Tester. List of trades. Copy and paste the different suggested 'alert_message' variable contents to your script.
Expert: I needed a way to pass data from TradingView script to the alert. Now I know it's the 'alert_message' variable. I can do this own my own.
ALERTS SETUP
This is the important piece of information that allows you to connect TradingView to Zignaly in a semi-automatic manner.
__ ALERTS SETUP - WebHook
Webhook URL: https : // zignaly . com / api / signals.php?key=MYSECRETKEY
Message: { {{strategy.order.alert_message}} , "key" : "MYSECRETKEY" }
__ ALERTS SETUP - Email
Setup a new Hotmail account
Add it as an 'SMS email' in TradingView Profile settings page.
Confirm your own the email address
Create a rule in your Hotmail account that 'Redirects' (not forwards) emails to 'signals @ zignaly . email' when (1): 'Subject' includes 'Alert', (2): 'Email body' contains string 'MYZIGNALYREDIRECTTRIGGER' and (3): 'From' contains 'noreply @ tradingview . com'.
In 'More Actions' check: Send Email-to-SMS
Message: ||{{strategy.order.alert_message}}||key=MYSECRETKEY||
MYZIGNALYREDIRECTTRIGGER
'(DEBUG) Enable debug on order comments' is turned on by default so that you can see in the Strategy Tester. List of Trades. The different orders alert_message that would have been sent to your alert. You might want to turn it off it some many letters in the screen is problem.
STRATEGY ADVICE
If you turn on 'Take Profit' then turn off 'Trailing Take Profit'.
ZIGNALY SIDE ADVICE
If you are a 'Signal Provider' make sure that 'Allow reusing the same signalId if there isn't any open position using it?' setting in the profile tab is set to true.
You can find your 'MYSECRETKEY' in your 'Copy Trader/Signal' provider Edit tab at 'Signal URL'.
ADDITIONAL ZIGNALY DOCUMENTATION
docs . zignaly . com / signals / how-to -- How to send signals to Zignaly
3 Ways to send signals to Zignaly
SIGNALS
FINAL REMARKS
This strategy tries to match the Pine Script Coding Conventions as best as possible.
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.
Everyday 0002 _ MAC 1st Trading Hour WalkoverThis is the second strategy for my Everyday project.
Like I wrote the last time - my goal is to create a new strategy everyday
for the rest of 2016 and post it here on TradingView.
I'm a complete beginner so this is my way of learning about coding strategies.
I'll give myself between 15 minutes and 2 hours to complete each creation.
This is basically a repetition of the first strategy I wrote - a Moving Average Crossover,
but I added a tiny thing.
I read that "Statistics have proven that the daily high or low is established within the first hour of trading on more than 70% of the time."
(source: )
My first Moving Average Crossover strategy, tested on VOLVB daily, got stoped out by the volatility
and because of this missed one nice bull run and a very nice bear run.
So I added this single line: if time("60", "1000-1600") regarding when to take exits:
if time("60", "1000-1600")
strategy.exit("Close Long", "Long", profit=2000, loss=500)
strategy.exit("Close Short", "Short", profit=2000, loss=500)
Sweden is UTC+2 so I guess UTC 1000 equals 12.00 in Stockholm. Not sure if this is correct, actually.
Anyway, I hope this means the strategy will only take exits based on price action which occur in the afternoon, when there is a higher probability of a lower volatility.
When I ran the new modified strategy on the same VOLVB daily it didn't get stoped out so easily.
On the other hand I'll have to test this on various stocks .
Reading and learning about how to properly test strategies is on my todo list - all tips on youtube videos or blogs
to read on this topic is very welcome!
Like I said the last time, I'm posting these strategies hoping to learn from the community - so any feedback, advice, or corrections is very much welcome and appreciated!
/pbergden
XAUUSD SMC Strategy (FVG + BoS)This advanced TradingView strategy is built for serious traders who follow Smart Money Concepts (SMC), with a special focus on Fair Value Gaps (FVG) and Break of Structure (BoS). The script automates trade entries and visual signals based on clean price action, while also allowing for performance backtesting through the built-in Strategy Tester.
The purpose of this script is to combine high-probability SMC signals with a clear and flexible backtest engine. Traders can visually confirm institutional moves (like imbalances and structure breaks) while simultaneously analyzing strategy performance, risk-reward, and execution logic — all from one chart.
⚙️ Key Features
FVG Detection: Automatically identifies bullish and bearish fair value gaps, highlighting imbalance zones between institutional buying and selling activity.
Break of Structure (BoS): Clearly shows bullish and bearish structure breaks, helping traders confirm directional bias and spot trend shifts with clarity.
Trade Execution Logic: Designed with actual strategy.entry() and strategy.exit() functions, this script simulates trades using risk-controlled logic with stop loss and take profit targeting.
Spaced Signal Visualization: Avoids chart clutter by spacing FVG and BoS signals, making the visual output clean and readable — even on fast-moving charts like XAUUSD.
Risk-Reward Customization: Easily set your own risk:reward ratio and stop-loss buffer, ensuring each trade is simulated according to your personal strategy or trading plan.
Cool-Down System: Add a delay between trades using the “Bars Between Trades” setting to prevent overtrading during volatile periods.
Live and Historical Insights: Signals appear in real-time, while also being plotted historically — making it easy to scroll back and study how the market behaved under this logic.
Fully Editable Parameters: Customize FVG size, structure lookback, spacing intervals, and more to fit your unique interpretation of SMC methodology.
🧠 How It Works
This strategy combines two key concepts:
FVG (Fair Value Gaps): Price zones where the market moved too quickly and left an imbalance between buyers and sellers. These gaps often act as magnets for price or areas of reversal.
BoS (Break of Structure): Points where the price breaks through a significant high or low, indicating a potential shift in market direction.
When both FVG and BoS align under certain conditions — and the cooldown requirement is satisfied — the strategy places a trade. A take profit and stop loss are automatically applied based on your inputs.
📈 Practical Use Cases
Trend Confirmation: Use BoS signals to validate trend continuation trades, especially when FVG confirms the imbalance.
Reversal Zones: Spot potential turning points when price fills an FVG and breaks previous structure in the opposite direction.
Backtest SMC Logic: Validate your theory by testing this strategy over historical XAUUSD data using real stop loss / take profit logic.
Refine Entry Timing: Study how structure breaks and FVGs align in different timeframes to improve entry precision.
💡 Best For
Traders learning or mastering Smart Money Concepts
Price action purists who avoid indicators
XAUUSD scalpers, day traders, or swing traders
Strategy developers who want visual + backtested confirmation
📌 Notes
The default strategy places trades every 10 candles to ensure the engine runs — replace this with your refined FVG + BoS logic as needed.
Works best on Gold (XAUUSD) 4H, 1H, or 15m timeframes depending on volatility and structure spacing.
🔒 Disclaimer
This strategy is for educational purposes only. It does not constitute financial advice. Always test and validate any trading logic before applying it to a live market. Trading carries risk — use proper risk management.
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.
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! 🚀
00 Averaging Down Backtest Strategy by RPAlawyer v21FOR EDUCATIONAL PURPOSES ONLY! THE CODE IS NOT YET FULLY DEVELOPED, BUT IT CAN PROVIDE INTERESTING DATA AND INSIGHTS IN ITS CURRENT STATE.
This strategy is an 'averaging down' backtester strategy. The goal of averaging/doubling down is to buy more of an asset at a lower price to reduce your average entry price.
This backtester code proves why you shouldn't do averaging down, but the code can be developed (and will be developed) further, and there might be settings even in its current form that prove that averaging down can be done effectively.
Different averaging down strategies exist:
- Linear/Fixed Amount: buy $1000 every time price drops 5%
- Grid Trading: Placing orders at price levels, often with increasing size, like $1000 at -5%, $2000 at -10%
- Martingale: doubling the position size with each new entry
- Reverse Martingale: decreasing position size as price falls: $4000, then $2000, then $1000
- Percentage-Based: position size based on % of remaining capital, like 10% of available funds at each level
- Dynamic/Adaptive: larger entries during high volatility, smaller during low
- Logarithmic: position sizes increase logarithmically as price drops
Unlike the above average costing strategies, it applies averaging down (I use DCA as a synonym) at a very strong trend reversal. So not at a certain predetermined percentage negative PNL % but at a trend reversal signaled by an indicator - hence it most closely resembles a dynamically moving grid DCA strategy.
Both entering the trade and averaging down assume a strong trend. The signals for trend detection are provided by an indicator that I published under the name '00 Parabolic SAR Trend Following Signals by RPAlawyer', but any indicator that generates numeric signals of 1 and -1 for buy and sell signals can be used.
The indicator must be connected to the strategy: in the strategy settings under 'External Source' you need to select '00 Parabolic SAR Trend Following Signals by RPAlawyer: Connector'. From this point, the strategy detects when the indicator generates buy and sell signals.
The strategy considers a strong trend when a buy signal appears above a very conservative ATR band, or a sell signal below the ATR band. The conservative ATR is chosen to filter ranging markets. This very conservative ATR setting has a default multiplier of 8 and length of 40. The multiplier can be increased up to 10, but there will be very few buy and sell signals at that level and DCA requirements will be very high. Trade entry and DCA occur at these strong trends. In the settings, the 'ATR Filter' setting determines the entry condition (e.g., ATR Filter multiplier of 9), and the 'DCA ATR' determines when DCA will happen (e.g., DCA ATR multiplier of 6).
The DCA levels and DCA amounts are determined as follows:
The first DCA occurs below the DCA Base Deviation% level (see settings, default 3%) which acts as a threshold. The thick green line indicates the long position avg price, and the thin red line below the green line indicates the 3% DCA threshold for long positions. The thick red line indicates the short position avg price, and the thin red line above the thick red line indicates the short position 3% DCA threshold. DCA size multiplier defines the DCA amount invested.
If the loss exceeds 3% AND a buy signal arrives below the lower ATR band for longs, or a sell signal arrives above the upper ATR band for shorts, then the first DCA will be executed. So the first DCA won't happen at 3%, rather 3% is a threshold where the additional condition is that the price must close above or below the ATR band (let's say the first DCA occured at 8%) – this is why the code resembles a dynamic grid strategy, where the grid moves such that alongside the first 3% threshold, a strong trend must also appear for DCA. At this point, the thick green/red line moves because the avg price is modified as a result of the DCA, and the thin red line indicating the next DCA level also moves. The next DCA level is determined by the first DCA level, meaning modified avg price plus an additional +8% + (3% * the Step Scale Multiplier in the settings). This next DCA level will be indicated by the modified thin red line, and the price must break through this level and again break through the ATR band for the second DCA to occur.
Since all this wasn't complicated enough, and I was always obsessed by the idea that when we're sitting in an underwater position for days, doing DCA and waiting for the price to correct, we can actually enter a short position on the other side, on which we can realize profit (if the broker allows taking hedge positions, Binance allows this in Europe).
This opposite position in this strategy can open from the point AFTER THE FIRST DCA OF THE BASE POSITION OCCURS. This base position first DCA actually indicates that the price has already moved against us significantly so time to earn some money on the other side. Breaking through the ATR band is also a condition for entry here, so the hedge position entry is not automatic, and the condition for further DCA is breaking through the DCA Base Deviation (default 3%) and breaking through the ATR band. So for the 'hedge' or rather opposite position, the entry and further DCA conditions are the same as for the base position. The hedge position avg price is indicated by a thick black line and the Next Hedge DCA Level is indicated by a thin black line.
The TPs are indicated by green labels for base positions and red labels for hedge positions.
No SL built into the strategy at this point but you are free to do your coding.
Summary data can be found in the upper right corner.
The fantastic trend reversal indicator Machine learning: Lorentzian Classification by jdehorty can be used as an external indicator, choose 'backtest stream' for the external source. The ATR Band multiplicators need to be reduced to 5-6 when using Lorentz.
The code can be further developed in several aspects, and as I write this, I already have a few ideas 😊
three Supertrend EMA Strategy by Prasanna +DhanuThe indicator described in your Pine Script is a Supertrend EMA Strategy that combines the Supertrend and EMA (Exponential Moving Average) to create a trend-following strategy. Here’s a detailed breakdown of how this indicator works:
1. EMA (Exponential Moving Average):
The EMA is a moving average that places more weight on recent prices, making it more responsive to price changes compared to a simple moving average (SMA). In this strategy, the EMA is used to determine the overall trend direction.
Input Parameter:
ema_length: This is the period for the EMA, set to 50 periods by default. A shorter EMA will respond more quickly to price movements, while a longer EMA is smoother and less sensitive to short-term fluctuations.
How it's used:
If the price is above the EMA, it indicates an uptrend.
If the price is below the EMA, it indicates a downtrend.
2. Supertrend Indicator:
The Supertrend indicator is a trend-following tool based on the Average True Range (ATR), which is a volatility measure. It helps to identify the direction of the trend by setting a dynamic support or resistance level.
Input Parameters:
supertrend_atr_period: The period used for calculating the ATR, set to 10 periods by default.
supertrend_multiplier1: Multiplier for the first Supertrend, set to 3.0.
supertrend_multiplier2: Multiplier for the second Supertrend, set to 2.0.
supertrend_multiplier3: Multiplier for the third Supertrend, set to 1.0.
Each Supertrend line has a different multiplier, which affects its sensitivity to price changes. The ATR period defines how many periods of price data are used to calculate the ATR.
How the Supertrend works:
If the Supertrend value is below the price, the trend is considered bullish (uptrend).
If the Supertrend value is above the price, the trend is considered bearish (downtrend).
The Supertrend will switch between up and down based on price movement and ATR, providing a dynamic trend-following signal.
3. Three Supertrend Lines:
In this strategy, three Supertrend lines are calculated with different multipliers and the same ATR period (10 periods). Each line is more or less sensitive to price changes, and they are plotted on the chart in different colors based on whether the trend is bullish (green) or bearish (red).
Supertrend 1: The most sensitive Supertrend with a multiplier of 3.0.
Supertrend 2: A moderately sensitive Supertrend with a multiplier of 2.0.
Supertrend 3: The least sensitive Supertrend with a multiplier of 1.0.
Each Supertrend line signals a bullish trend when its value is below the price and a bearish trend when its value is above the price.
4. Strategy Rules:
This strategy uses the three Supertrend lines combined with the EMA to generate trade signals.
Entry Conditions:
A long entry is triggered when all three Supertrend lines are in an uptrend (i.e., all three Supertrend lines are below the price), and the price is above the EMA. This suggests a strong bullish market condition.
A short entry is triggered when all three Supertrend lines are in a downtrend (i.e., all three Supertrend lines are above the price), and the price is below the EMA. This suggests a strong bearish market condition.
Exit Conditions:
A long exit occurs when the third Supertrend (the least sensitive one) switches to a downtrend (i.e., the price falls below it).
A short exit occurs when the third Supertrend switches to an uptrend (i.e., the price rises above it).
5. Visualization:
The strategy also plots the following on the chart:
The EMA is plotted as a blue line, which helps identify the overall trend.
The three Supertrend lines are plotted with different colors:
Supertrend 1: Green (for uptrend) and Red (for downtrend).
Supertrend 2: Green (for uptrend) and Red (for downtrend).
Supertrend 3: Green (for uptrend) and Red (for downtrend).
Summary of the Strategy:
The strategy combines three Supertrend indicators (with different multipliers) and an EMA to capture both short-term and long-term trends.
Long positions are entered when all three Supertrend lines are bullish and the price is above the EMA.
Short positions are entered when all three Supertrend lines are bearish and the price is below the EMA.
Exits occur when the third Supertrend line (the least sensitive) signals a change in trend direction.
This combination of indicators allows for a robust trend-following strategy that adapts to both short-term volatility and long-term trend direction. The Supertrend lines provide quick reaction to price changes, while the EMA offers a smoother, more stable trend direction for confirmation.
The indicator described in your Pine Script is a Supertrend EMA Strategy that combines the Supertrend and EMA (Exponential Moving Average) to create a trend-following strategy. Here’s a detailed breakdown of how this indicator works:
1. EMA (Exponential Moving Average):
The EMA is a moving average that places more weight on recent prices, making it more responsive to price changes compared to a simple moving average (SMA). In this strategy, the EMA is used to determine the overall trend direction.
Input Parameter:
ema_length: This is the period for the EMA, set to 50 periods by default. A shorter EMA will respond more quickly to price movements, while a longer EMA is smoother and less sensitive to short-term fluctuations.
How it's used:
If the price is above the EMA, it indicates an uptrend.
If the price is below the EMA, it indicates a downtrend.
2. Supertrend Indicator:
The Supertrend indicator is a trend-following tool based on the Average True Range (ATR), which is a volatility measure. It helps to identify the direction of the trend by setting a dynamic support or resistance level.
Input Parameters:
supertrend_atr_period: The period used for calculating the ATR, set to 10 periods by default.
supertrend_multiplier1: Multiplier for the first Supertrend, set to 3.0.
supertrend_multiplier2: Multiplier for the second Supertrend, set to 2.0.
supertrend_multiplier3: Multiplier for the third Supertrend, set to 1.0.
Each Supertrend line has a different multiplier, which affects its sensitivity to price changes. The ATR period defines how many periods of price data are used to calculate the ATR.
How the Supertrend works:
If the Supertrend value is below the price, the trend is considered bullish (uptrend).
If the Supertrend value is above the price, the trend is considered bearish (downtrend).
The Supertrend will switch between up and down based on price movement and ATR, providing a dynamic trend-following signal.
3. Three Supertrend Lines:
In this strategy, three Supertrend lines are calculated with different multipliers and the same ATR period (10 periods). Each line is more or less sensitive to price changes, and they are plotted on the chart in different colors based on whether the trend is bullish (green) or bearish (red).
Supertrend 1: The most sensitive Supertrend with a multiplier of 3.0.
Supertrend 2: A moderately sensitive Supertrend with a multiplier of 2.0.
Supertrend 3: The least sensitive Supertrend with a multiplier of 1.0.
Each Supertrend line signals a bullish trend when its value is below the price and a bearish trend when its value is above the price.
4. Strategy Rules:
This strategy uses the three Supertrend lines combined with the EMA to generate trade signals.
Entry Conditions:
A long entry is triggered when all three Supertrend lines are in an uptrend (i.e., all three Supertrend lines are below the price), and the price is above the EMA. This suggests a strong bullish market condition.
A short entry is triggered when all three Supertrend lines are in a downtrend (i.e., all three Supertrend lines are above the price), and the price is below the EMA. This suggests a strong bearish market condition.
Exit Conditions:
A long exit occurs when the third Supertrend (the least sensitive one) switches to a downtrend (i.e., the price falls below it).
A short exit occurs when the third Supertrend switches to an uptrend (i.e., the price rises above it).
5. Visualization:
The strategy also plots the following on the chart:
The EMA is plotted as a blue line, which helps identify the overall trend.
The three Supertrend lines are plotted with different colors:
Supertrend 1: Green (for uptrend) and Red (for downtrend).
Supertrend 2: Green (for uptrend) and Red (for downtrend).
Supertrend 3: Green (for uptrend) and Red (for downtrend).
Summary of the Strategy:
The strategy combines three Supertrend indicators (with different multipliers) and an EMA to capture both short-term and long-term trends.
Long positions are entered when all three Supertrend lines are bullish and the price is above the EMA.
Short positions are entered when all three Supertrend lines are bearish and the price is below the EMA.
Exits occur when the third Supertrend line (the least sensitive) signals a change in trend direction.
This combination of indicators allows for a robust trend-following strategy that adapts to both short-term volatility and long-term trend direction. The Supertrend lines provide quick reaction to price changes, while the EMA offers a smoother, more stable trend direction for confirmation.
The indicator described in your Pine Script is a Supertrend EMA Strategy that combines the Supertrend and EMA (Exponential Moving Average) to create a trend-following strategy. Here’s a detailed breakdown of how this indicator works:
1. EMA (Exponential Moving Average):
The EMA is a moving average that places more weight on recent prices, making it more responsive to price changes compared to a simple moving average (SMA). In this strategy, the EMA is used to determine the overall trend direction.
Input Parameter:
ema_length: This is the period for the EMA, set to 50 periods by default. A shorter EMA will respond more quickly to price movements, while a longer EMA is smoother and less sensitive to short-term fluctuations.
How it's used:
If the price is above the EMA, it indicates an uptrend.
If the price is below the EMA, it indicates a downtrend.
2. Supertrend Indicator:
The Supertrend indicator is a trend-following tool based on the Average True Range (ATR), which is a volatility measure. It helps to identify the direction of the trend by setting a dynamic support or resistance level.
Input Parameters:
supertrend_atr_period: The period used for calculating the ATR, set to 10 periods by default.
supertrend_multiplier1: Multiplier for the first Supertrend, set to 3.0.
supertrend_multiplier2: Multiplier for the second Supertrend, set to 2.0.
supertrend_multiplier3: Multiplier for the third Supertrend, set to 1.0.
Each Supertrend line has a different multiplier, which affects its sensitivity to price changes. The ATR period defines how many periods of price data are used to calculate the ATR.
How the Supertrend works:
If the Supertrend value is below the price, the trend is considered bullish (uptrend).
If the Supertrend value is above the price, the trend is considered bearish (downtrend).
The Supertrend will switch between up and down based on price movement and ATR, providing a dynamic trend-following signal.
3. Three Supertrend Lines:
In this strategy, three Supertrend lines are calculated with different multipliers and the same ATR period (10 periods). Each line is more or less sensitive to price changes, and they are plotted on the chart in different colors based on whether the trend is bullish (green) or bearish (red).
Supertrend 1: The most sensitive Supertrend with a multiplier of 3.0.
Supertrend 2: A moderately sensitive Supertrend with a multiplier of 2.0.
Supertrend 3: The least sensitive Supertrend with a multiplier of 1.0.
Each Supertrend line signals a bullish trend when its value is below the price and a bearish trend when its value is above the price.
4. Strategy Rules:
This strategy uses the three Supertrend lines combined with the EMA to generate trade signals.
Entry Conditions:
A long entry is triggered when all three Supertrend lines are in an uptrend (i.e., all three Supertrend lines are below the price), and the price is above the EMA. This suggests a strong bullish market condition.
A short entry is triggered when all three Supertrend lines are in a downtrend (i.e., all three Supertrend lines are above the price), and the price is below the EMA. This suggests a strong bearish market condition.
Exit Conditions:
A long exit occurs when the third Supertrend (the least sensitive one) switches to a downtrend (i.e., the price falls below it).
A short exit occurs when the third Supertrend switches to an uptrend (i.e., the price rises above it).
5. Visualization:
The strategy also plots the following on the chart:
The EMA is plotted as a blue line, which helps identify the overall trend.
The three Supertrend lines are plotted with different colors:
Supertrend 1: Green (for uptrend) and Red (for downtrend).
Supertrend 2: Green (for uptrend) and Red (for downtrend).
Supertrend 3: Green (for uptrend) and Red (for downtrend).
Summary of the Strategy:
The strategy combines three Supertrend indicators (with different multipliers) and an EMA to capture both short-term and long-term trends.
Long positions are entered when all three Supertrend lines are bullish and the price is above the EMA.
Short positions are entered when all three Supertrend lines are bearish and the price is below the EMA.
Exits occur when the third Supertrend line (the least sensitive) signals a change in trend direction.
This combination of indicators allows for a robust trend-following strategy that adapts to both short-term volatility and long-term trend direction. The Supertrend lines provide quick reaction to price changes, while the EMA offers a smoother, more stable trend direction for confirmation.
Custom Strategy: ETH Martingale 2.0Strategic characteristics
ETH Little Martin 2.0 is a self-developed trading strategy based on the Martingale strategy, mainly used for trading ETH (Ethereum). The core idea of this strategy is to place orders in the same direction at a fixed price interval, and then use Martin's multiple investment principle to reduce losses, but this is also the main source of losses.
Parameter description:
1 Interval： The minimum spacing for taking profit, stop loss, and opening/closing of orders. Different targets have different spacing. Taking ETH as an example, it is generally recommended to have a spacing of 2% for fluctuations in the target.
2 Base Price： This is the price at which you triggered the first order. Similarly, I am using ETH as an example. If you have other targets, I suggest using the initial value of a price that can be backtesting. The Base Price is only an initial order price and has no impact on subsequent orders.
3 Initial Order Amount： Users can set an initial order amount to control the risk of each transaction. If the stop loss is reached, we will double the amount based on this value. This refers to the value of the position held, not the number of positions held.
4 Loss Multiplier： The strategy will increase the next order amount based on the set multiple after the stop loss, in order to make up for the previous losses through a larger position. Note that after taking profit, it will be reset to 1 times the Initial Order Amount.
5. Long Short Operation: The first order of the strategy is a multiple entry, and in subsequent orders, if the stop loss is reached, a reverse order will be opened. The position value of a one-way order is based on the Loss Multiplier multiple investment, so it is generally recommended that the Loss Multiplier default to 2.
Improvement direction
Although this strategy already has a certain trading logic, there are still some improvement directions that can be considered:
1. Dynamic adjustment of spacing: Currently, the spacing is fixed, and it can be considered to dynamically adjust the spacing based on market volatility to improve the adaptability of the strategy. Try using dynamic spacing, which may be more suitable for the actual market situation.
2. Filtering criteria: Orders and no orders can be optimized separately. The biggest problem with this strategy is that it will result in continuous losses during fluctuations, and eventually increase the investment amount. You can consider filtering out some fluctuations or only focusing on trend trends.
3. Risk management: Add more risk management measures, such as setting a maximum loss limit to avoid huge losses caused by continuous stop loss.
4. Optimize the stop loss multiple: Currently, the stop loss multiple is fixed, and it can be considered to dynamically adjust the multiple according to market conditions to reduce risk.
