The RetrieverThis Pine Script strategy, named "The Retriever" aims to capitalize on price dips based on the size of the candlestick body. It uses a moving average of the body size to identify potential long entry points. Here's a breakdown:
Body Size Calculation: It calculates the absolute difference between the close and open prices (body) to determine the candlestick body size.
Entry Signals:
long: A long entry signal is generated when the close price is significantly higher than the moving average of the body size (ta.sma(body, 100)) multiplied by a factor (mult). Thanks to this principle we are entering just bigger dips but just in case it is sudden movement, typically dip during bulish trend.
longExtra: A second, more aggressive long entry signal is generated when the close price is even further above the moving average (multiplied by mult * 2). This signal is very rare and it is helping to decrease entry point in case huge market dips which can occor just few times per year.
Quantity Calculation: The order quantity (qty) is dynamically calculated based on the current equity and the price range between minRange and maxRange. It aims to adjust the quantity inversely to the price range, possibly increasing the quantity when the price range is smaller. It is actually very smart in several ways:
it is making bigger trades when market price is low (closer to manually defined minRange) and vice versa making smaller trades when market is close to maxRange
trade size is calculated based on current equity so it allows to use compound interest effect
as there is no SL in this strategy trade size is calculated to be max around 50-60% drawdown based on backtested results so it can survive 80-90% market drawdowns (entry point is after huge dip)
Exit Conditions: All open positions are closed when either of these conditions is met:
The last candle is green (close is lower than open). There is also minProfit param defined which is set to 0 so it means that our position has to be in profit. So we are never closing in loss. We have to differentiate here between order and position. Order can be in loss but overal position has to be always in profit.
Chỉ báo và chiến lược
3x Supertrend (for Vietnamese stock market and vn30f1m)The 4Vietnamese 3x Supertrend Strategy is an advanced trend-following trading system developed in Pine Script™ and designed for publication on TradingView as an open-source strategy under the Mozilla Public License 2.0. This strategy leverages three Supertrend indicators with different ATR lengths and multipliers to identify optimal trade entries and exits while dynamically managing risk.
Key Features:
Option to build and hold long term positions with entry stop order. Try this to avoid market complex movement and retain long term investment style's benefits.
Advanced Entry & Exit Optimization: Includes configurable stop-loss mechanisms, pyramiding, and exit conditions tailored for different market scenarios.
Dynamic Risk Management: Implements features like selective stop-loss activation, trade window settings, and closing conditions based on trend reversals and loss management.
This strategy is particularly suited for traders seeking a systematic and rule-based approach to trend trading. By making it open-source, we aim to provide transparency, encourage community collaboration, and help traders refine and optimize their strategies for better performance.
License:
This script is released under the Mozilla Public License 2.0, allowing modifications and redistribution while maintaining open-source integrity.
Happy trading!
Statistical Arbitrage Pairs Trading - Long-Side OnlyThis strategy implements a simplified statistical arbitrage (" stat arb ") approach focused on mean reversion between two correlated instruments. It identifies opportunities where the spread between their normalized price series (Z-scores) deviates significantly from historical norms, then executes long-only trades anticipating reversion to the mean.
Key Mechanics:
1. Spread Calculation: The strategy computes Z-scores for both instruments to normalize price movements, then tracks the spread between these Z-scores.
2. Modified Z-Score: Uses a robust measure combining the median and Median Absolute Deviation (MAD) to reduce outlier sensitivity.
3. Entry Signal: A long position is triggered when the spread’s modified Z-score falls below a user-defined threshold (e.g., -1.0), indicating extreme undervaluation of the main instrument relative to its pair.
4. Exit Signal: The position closes automatically when the spread reverts to its historical mean (Z-score ≥ 0).
Risk management:
Trades are sized as a percentage of equity (default: 10%).
Includes commissions and slippage for realistic backtesting.
Intraday Golden duckKey Components
Plotting DTR Levels
DTR High 1 & Low 1 are plotted with a bold green and red line (Major Levels).
DTR High 2 & Low 2 are plotted with a lighter green and red line (Minor Levels).
This visualizes potential breakout and stop-loss zones.
Defining Market Hours
The strategy runs only between:
Start Time: 9:15 AM (Market Open)
End Time: 3:00 PM (Market Close)
Trades can only occur during this period.
Avoiding Multiple Trades Per Day
A boolean variable trade_taken_today ensures that:
Only one trade is executed per day (either Buy or Sell).
It resets at the beginning of a new trading day.
Entry Conditions
A long position (Buy) is entered when:
The market is open.
The close price breaks above dtr_high_1.
No other trade has been executed yet.
A short position (Sell) is entered when:
The market is open.
The close price drops below dtr_low_1.
No other trade has been executed yet.
Stop-Loss Conditions
To protect against large losses, stop-loss levels are placed at DTR 2 levels:
For Long Trades: If price falls below dtr_high_2, the trade exits.
For Short Trades: If price rises above dtr_low_2, the trade exits.
Using Parabolic SAR for Additional Exit Signals
The Parabolic SAR (PSAR) is used to trail stop-loss:
Long Exit: If price falls below PSAR, the position is closed.
Short Exit: If price rises above PSAR, the position is closed.
Universal Exit Condition (3:00 PM)
At exactly 3:00 PM, all positions are forcefully closed, ensuring no overnight risk.
Execution Logic
If Buy Condition is met → Enter Long position.
If Sell Condition is met → Enter Short position.
If Stop-Loss or PSAR condition triggers → Exit the trade.
At 3:00 PM, close all positions.
Key Features & Benefits
✅ Intraday Only: No overnight risk.
✅ One Trade per Day: Avoids overtrading.
✅ Dynamic Levels: Adapts to market volatility.
✅ PSAR Protection: Helps reduce drawdowns.
✅ Universal Exit: Ensures systematic closing.
This strategy is designed for traders looking for a systematic, rule-based approach to intraday trading using price action and volatility expansion principles. 🚀
Ultimate T3 Fibonacci for BTC Scalping. Look at backtest report!Hey Everyone!
I created another script to add to my growing library of strategies and indicators that I use for automated crypto trading! This strategy is for BITCOIN on the 30 minute chart since I designed it to be a scalping strategy. I calculated for trading fees, and use a small amount of capital in the backtest report. But feel free to modify the capital and how much per order to see how it changes the results:)
It is called the "Ultimate T3 Fibonacci Indicator by NHBprod" that computes and displays two T3-based moving averages derived from price data. The t3_function calculates the Tilson T3 indicator by applying a series of exponential moving averages to a combined price metric and then blending these results with specific coefficients derived from an input factor.
The script accepts several user inputs that toggle the use of the T3 filter, select the buy signal method, and set parameters like lengths and volume factors for two variations of the T3 calculation. Two T3 lines, T3 and T32, are computed with different parameters, and their colors change dynamically (green/red for T3 and blue/purple for T32) based on whether the lines are trending upward or downward. Depending on the selected signal method, the script generates buy signals either when T32 crosses over T3 or when the closing price is above T3, and similarly, sell signals are generated on the respective conditions for crossing under or closing below. Finally, the indicator plots the T3 lines on the chart, adds visual buy/sell markers, and sets alert conditions to notify users when the respective trading signals occur.
The user has the ability to tune the parameters using TP/SL, date timerames for analyses, and the actual parameters of the T3 function including the buy/sell signal! Lastly, the user has the option of trading this long, short, or both!
Let me know your thoughts and check out the backtest report!
Simple Time-Based Strategy(Price Action Hypothesis)Core Theory: Trend Continuation Pattern Recognition**
1. **Price Action Hypothesis**
The strategy is built on the assumption that consecutive price movements (3-bar patterns) indicate momentum continuation:
- *Long Pattern*: Three consecutive higher closes combined with ascending highs
- *Short Pattern*: Three consecutive lower closes combined with descending lows
This reflects a belief that sustained directional price movement creates self-reinforcing trends that can be captured through simple pattern recognition.
2. **Time-Based Risk Management**
Implements a dynamic exit mechanism:
- *Training Phase*: 5-bar holding period (quick turnover)
- *Testing Phase*: 10-bar holding period (extended exposure)
This dual timeframe approach suggests the hypothesis that market conditions may require different holding durations in different market eras.
3. **Adaptive Market Hypothesis**
The structure incorporates two distinct phases:
- *Training Period (11 years)*: Pattern recognition without stop losses
- *Testing Period*: Pattern recognition with stop losses
This assumes markets may change character over time, requiring different risk parameters in different epochs.
4. **Asymmetric Risk Control**
Implements stop-losses only in the testing phase:
- Fixed 500-pip (point) stop distance
- Activated post-training period
This reflects a belief that historical patterns might need different risk constraints than real-time trading.
5. **Dual-Path Validation**
The split between training/testing phases suggests:
- Pattern validity should first be confirmed without protective stops
- Real-world implementation requires added risk constraints
6. **Market Efficiency Paradox**
The simultaneous use of both long/short entries assumes:
- Markets exhibit persistent inefficiencies
- These inefficiencies manifest differently in bullish/bearish conditions
- A symmetric approach can capture opportunities in both directions
7. **Behavioral Finance Elements**
The 3-bar pattern recognition potentially exploits:
- Herd mentality in trend formation
- Delayed reaction to price momentum
- Cognitive bias in trend confirmation
8. **Quantitative Time Segmentation**
The annual-based period division (training vs testing) implies:
- Market cycles operate on multi-year timeframes
- Strategy robustness requires validation across different market regimes
- Parameter sensitivity needs temporal validation
This strategy combines elements of technical pattern recognition, temporal adaptability, and phased risk management to create a systematic approach to trend exploitation. The theoretical framework suggests markets exhibit persistent but evolving patterns that can be systematically captured through rule-based execution.
Volatility Momentum Breakout StrategyDescription:
Overview:
The Volatility Momentum Breakout Strategy is designed to capture significant price moves by combining a volatility breakout approach with trend and momentum filters. This strategy dynamically calculates breakout levels based on market volatility and uses these levels along with trend and momentum conditions to identify trade opportunities.
How It Works:
1. Volatility Breakout:
• Methodology:
The strategy computes the highest high and lowest low over a defined lookback period (excluding the current bar to avoid look-ahead bias). A multiple of the Average True Range (ATR) is then added to (or subtracted from) these levels to form dynamic breakout thresholds.
• Purpose:
This method helps capture significant price movements (breakouts) while ensuring that only past data is used, thereby maintaining realistic signal generation.
2. Trend Filtering:
• Methodology:
A short-term Exponential Moving Average (EMA) is applied to determine the prevailing trend.
• Purpose:
Long trades are considered only when the current price is above the EMA, indicating an uptrend, while short trades are taken only when the price is below the EMA, indicating a downtrend.
3. Momentum Confirmation:
• Methodology:
The Relative Strength Index (RSI) is used to gauge market momentum.
• Purpose:
For long entries, the RSI must be above a mid-level (e.g., above 50) to confirm upward momentum, and for short entries, it must be below a similar threshold. This helps filter out signals during overextended conditions.
Entry Conditions:
• Long Entry:
A long position is triggered when the current closing price exceeds the calculated long breakout level, the price is above the short-term EMA, and the RSI confirms momentum (e.g., above 50).
• Short Entry:
A short position is triggered when the closing price falls below the calculated short breakout level, the price is below the EMA, and the RSI confirms momentum (e.g., below 50).
Risk Management:
• Position Sizing:
Trades are sized to risk a fixed percentage of account equity (set here to 5% per trade in the code, with each trade’s stop loss defined so that risk is limited to approximately 2% of the entry price).
• Stop Loss & Take Profit:
A stop loss is placed a fixed ATR multiple away from the entry price, and a take profit target is set to achieve a 1:2 risk-reward ratio.
• Realistic Backtesting:
The strategy is backtested using an initial capital of $10,000, with a commission of 0.1% per trade and slippage of 1 tick per bar—parameters chosen to reflect conditions faced by the average trader.
Important Disclaimers:
• No Look-Ahead Bias:
All breakout levels are calculated using only past data (excluding the current bar) to ensure that the strategy does not “peek” into future data.
• Educational Purpose:
This strategy is experimental and provided solely for educational purposes. Past performance is not indicative of future results.
• User Responsibility:
Traders should thoroughly backtest and paper trade the strategy under various market conditions and adjust parameters to fit their own risk tolerance and trading style before live deployment.
Conclusion:
By integrating volatility-based breakout signals with trend and momentum filters, the Volatility Momentum Breakout Strategy offers a unique method to capture significant price moves in a disciplined manner. This publication provides a transparent explanation of the strategy’s components and realistic backtesting parameters, making it a useful tool for educational purposes and further customization by the TradingView community.
Advanced Multi-Timeframe Trading System (Risk Managed)Description:
This strategy is an original approach that combines two main analytical components to identify potential trade opportunities while simulating realistic trading conditions:
1. Market Trend Analysis via an Approximate Hurst Exponent
• What It Does:
The strategy computes a rough measure of market trending using an approximate Hurst exponent. A value above 0.5 suggests persistent, trending behavior, while a value below 0.5 indicates a tendency toward mean-reversion.
• How It’s Used:
The Hurst exponent is calculated on both the chart’s current timeframe and a higher timeframe (default: Daily) to capture both local and broader market dynamics.
2. Fibonacci Retracement Levels
• What It Does:
Using daily high and low data from a selected timeframe (default: Daily), the script computes key Fibonacci retracement levels.
• How It’s Used:
• The 61.8% level (Golden Ratio) serves as a key threshold:
• A long entry is signaled when the price crosses above this level if the daily Hurst exponent confirms a trending market.
• The 38.2% level is used to identify short-entry opportunities when the price crosses below it and the daily Hurst indicates non-trending conditions.
Signal Logic:
• Long Entry:
When the price crosses above the 61.8% Fibonacci level (Golden Ratio) and the daily Hurst exponent is greater than 0.5, suggesting a trending market.
• Short Entry:
When the price crosses below the 38.2% Fibonacci level and the daily Hurst exponent is less than 0.5, indicating a less trending or potentially reversing market.
Risk Management & Trade Execution:
• Stop-Loss:
Each trade is risk-managed with a stop-loss set at 2% below (for longs) or above (for shorts) the entry price. This ensures that no single trade risks more than a small, sustainable portion of the account.
• Take Profit:
A take profit order targets a risk-reward ratio of 1:2 (i.e., the target profit is twice the amount risked).
• Position Sizing:
Trades are executed with a fixed position size equal to 10% of account equity.
• Trade Frequency Limits:
• Daily Limit: A maximum of 5 trades per day
• Overall Limit: No more than 510 trades during the backtesting period (e.g., since 2019)
These limits are imposed to simulate realistic trading frequency and to avoid overtrading in backtest results.
Backtesting Parameters:
• Initial Capital: $10,000
• Commission: 0.1% per trade
• Slippage: 1 tick per bar
These settings aim to reflect the conditions faced by the average trader and help ensure that the backtesting results are realistic and not misleading.
Chart Overlays & Visual Aids:
• Fibonacci Levels:
The key Fibonacci retracement levels are plotted on the chart, and the zone between the 61.8% and 38.2% levels is highlighted to show a key retracement area.
• Market Trend Background:
The chart background is tinted green when the daily Hurst exponent indicates a trending market (value > 0.5) and red otherwise.
• Information Table:
An on-chart table displays key parameters such as the current Hurst exponent, daily Hurst value, the number of trades executed today, and the global trade count.
Disclaimer:
Past performance is not indicative of future results. This strategy is experimental and provided solely for educational purposes. It is essential that you backtest and paper trade using your own settings before considering any live deployment. The Hurst exponent calculation is an approximation and should be interpreted as a rough gauge of market behavior. Adjust the parameters and risk management settings according to your personal risk tolerance and market conditions.
Additional Notes:
• Originality & Usefulness:
This script is an original mashup that combines trend analysis with Fibonacci retracement methods. The description above explains how these components work together to provide trading signals.
• Realistic Results:
The strategy uses realistic account sizes, commission rates, slippage, and risk management rules to generate backtesting results that are representative of real-world trading.
• Educational Purpose:
This script is intended to support the TradingView community by offering insights into combining multiple analysis techniques in one strategy. It is not a “get-rich-quick” system but rather an educational tool to help traders understand risk management and trade signal logic.
By using this script, you acknowledge that trading involves risk and that you are responsible for testing and adjusting the strategy to fit your own trading environment. This publication is fully open source, and any modifications should include proper attribution if significant portions of the code are reused.
Bollinger Bands Long Strategy
This strategy is designed for identifying and executing long trades based on Bollinger Bands and RSI. It aims to capitalize on potential oversold conditions and subsequent price recovery.
Key Features:
- Bollinger Bands (10,2): The strategy uses Bollinger Bands with a 10-period moving average and a multiplier of 2 to define price volatility.
- RSI Filter: A trade is only triggered when the RSI (14-period) is below 30, ensuring entry during oversold conditions.
- Entry Condition: A long trade is entered immediately when the price crosses below the lower Bollinger Band and the RSI is under 30.
- Exit Condition: The position is exited when the price reaches or crosses above the Bollinger Band basis (20-period moving average).
Best Used For:
- Identifying oversold conditions with a strong potential for a rebound.
- Markets or assets with clear oscillations and volatility e.g., BTC.
**Disclaimer:** This strategy is for educational purposes and should be used with caution. Backtesting and risk management are essential before live trading.
Arpeet MACDOverview
This strategy is based on the zero-lag version of the MACD (Moving Average Convergence Divergence) indicator, which captures short-term trends by quickly responding to price changes, enabling high-frequency trading. The strategy uses two moving averages with different periods (fast and slow lines) to construct the MACD indicator and introduces a zero-lag algorithm to eliminate the delay between the indicator and the price, improving the timeliness of signals. Additionally, the crossover of the signal line and the MACD line is used as buy and sell signals, and alerts are set up to help traders seize trading opportunities in a timely manner.
Strategy Principle
Calculate the EMA (Exponential Moving Average) or SMA (Simple Moving Average) of the fast line (default 12 periods) and slow line (default 26 periods).
Use the zero-lag algorithm to double-smooth the fast and slow lines, eliminating the delay between the indicator and the price.
The MACD line is formed by the difference between the zero-lag fast line and the zero-lag slow line.
The signal line is formed by the EMA (default 9 periods) or SMA of the MACD line.
The MACD histogram is formed by the difference between the MACD line and the signal line, with blue representing positive values and red representing negative values.
When the MACD line crosses the signal line from below and the crossover point is below the zero axis, a buy signal (blue dot) is generated.
When the MACD line crosses the signal line from above and the crossover point is above the zero axis, a sell signal (red dot) is generated.
The strategy automatically places orders based on the buy and sell signals and triggers corresponding alerts.
Advantage Analysis
The zero-lag algorithm effectively eliminates the delay between the indicator and the price, improving the timeliness and accuracy of signals.
The design of dual moving averages can better capture market trends and adapt to different market environments.
The MACD histogram intuitively reflects the comparison of bullish and bearish forces, assisting in trading decisions.
The automatic order placement and alert functions make it convenient for traders to seize trading opportunities in a timely manner, improving trading efficiency.
Risk Analysis
In volatile markets, frequent crossover signals may lead to overtrading and losses.
Improper parameter settings may cause signal distortion and affect strategy performance.
The strategy relies on historical data for calculations and has poor adaptability to sudden events and black swan events.
Optimization Direction
Introduce trend confirmation indicators, such as ADX, to filter out false signals in volatile markets.
Optimize parameters to find the best combination of fast and slow line periods and signal line periods, improving strategy stability.
Combine other technical indicators or fundamental factors to construct a multi-factor model, improving risk-adjusted returns of the strategy.
Introduce stop-loss and take-profit mechanisms to control single-trade risk.
Summary
The MACD Dual Crossover Zero Lag Trading Strategy achieves high-frequency trading by quickly responding to price changes and capturing short-term trends. The zero-lag algorithm and dual moving average design improve the timeliness and accuracy of signals. The strategy has certain advantages, such as intuitive signals and convenient operation, but also faces risks such as overtrading and parameter sensitivity. In the future, the strategy can be optimized by introducing trend confirmation indicators, parameter optimization, multi-factor models, etc., to improve the robustness and profitability of the strategy.
GOLD Volume-Based Entry StrategyShort Description:
This script identifies potential long entries by detecting two consecutive bars with above-average volume and bullish price action. When these conditions are met, a trade is entered, and an optional profit target is set based on user input. This strategy can help highlight momentum-driven breakouts or trend continuations triggered by a surge in buying volume.
How It Works
Volume Moving Average
A simple moving average of volume (vol_ma) is calculated over a user-defined period (default: 20 bars). This helps us distinguish when volume is above or below recent averages.
Consecutive Green Volume Bars
First bar: Must be bullish (close > open) and have volume above the volume MA.
Second bar: Must also be bullish, with volume above the volume MA and higher than the first bar’s volume.
When these two bars appear in sequence, we interpret it as strong buying pressure that could drive price higher.
Entry & Profit Target
Upon detecting these two consecutive bullish bars, the script places a long entry.
A profit target is set at current price plus a user-defined fixed amount (default: 5 USD).
You can adjust this target, or you can add a stop-loss in the script to manage risk further.
Visual Cues
Buy Signal Marker appears on the chart when the second bar confirms the signal.
Green Volume Columns highlight the bars that fulfill the criteria, providing a quick visual confirmation of high-volume bullish bars.
Works fine on 1M-2M-5M-15M-30M. Do not use it on higher TF. Due the lack of historical data on lower TF, the backtest result is limited.
Tick Marubozu StrategyStrategy Concept:
This strategy identifies Marubozu candles on a tick chart (customizable pip size) with high volume to signal strong market momentum.
Bearish Marubozu → Strong selling pressure → Enter a SELL trade
Bullish Marubozu → Strong buying pressure → Enter a BUY trade
Entry Conditions:
Marubozu Definition:
Open price ≈ High for a bearish Marubozu (minimal wick at the top).
Open price ≈ Low for a bullish Marubozu (minimal wick at the bottom).
Customizable body size (in pips).
High Volume Confirmation:
The volume of the Marubozu candle must be above the moving average of volume (e.g., 20-period SMA).
Trade Direction:
Bearish Marubozu with High Volume → SELL
Bullish Marubozu with High Volume → BUY
Exit Conditions:
Time-Based Expiry: Since it's for binary options, the trade duration is pre-defined (e.g., 1-minute expiry).
Reversal Candle: If a strong opposite Marubozu appears, it may indicate a trend shift.
Candle Range Theory StrategyCandle Range Theory StrategyCandle Range Theory Strategy delves into the intricacies of price action analysis, focusing on the behavior of candlestick patterns within specific ranges. Traders employing this strategy aim to identify key support and resistance levels by analyzing the high and low points of significant candlesticks. The core principle lies in understanding that the range of a candle—defined by its opening, closing, high, and low prices—provides valuable insight into market sentiment and potential future movements.
To implement the Candle Range Theory Strategy effectively, one must first recognize the importance of different candle sizes. A long-bodied candle suggests strong momentum, pointing to a bullish or bearish bias, while a small-bodied candle indicates indecision or consolidation, often signaling potential reversals or breakouts. By plotting these candlesticks over a defined time frame, traders can ascertain whether the market is trending or range-bound.
Additionally, traders should consider the context in which these candles form. Analysis of the preceding price action can reveal whether current ranges are extensions of existing trends or indications of market fatigue. In particular, look for patterns such as engulfing candles, pin bars, or inside bars, as they often foreshadow forthcoming price fluctuations.
Moreover, combining the Candle Range Theory with other technical indicators, like moving averages or Fibonacci retracements, can offer a more comprehensive view of potential entry and exit points. By aligning candle patterns with broader market dynamics, traders can optimize their strategies, enhancing their probability of success while minimizing risk.
Lastly, maintaining a disciplined approach is crucial. Setting precise stop-loss and take-profit levels grounded in candle ranges can safeguard one's capital. Adhering to this framework allows traders to navigate the complexities of the market with greater confidence, ultimately leading to more informed and successful trading decisions. Embracing the nuances of Candle Range Theory not only sharpens analytical skills but also enriches one’s trading repertoire, paving the way for sustained profitability in the dynamic world of forex and equities.
ATR SuperTrend - IonJauregui-ActivTradesEste script en Pine Script utiliza el indicador SuperTrend basado en el ATR para identificar tendencias y generar señales de compra y venta.
¿Cómo funciona?
Detecta la volatilidad con el ATR para calcular niveles dinámicos de soporte y resistencia.
Dibuja la tendencia:
Línea verde: Tendencia alcista.
Línea roja: Tendencia bajista.
Genera señales de trading:
Compra cuando la tendencia pasa de bajista a alcista.
Venta cuando cambia de alcista a bajista.
Opera de forma automática:
Abre posiciones según las señales.
Establece stop loss y take profit para gestionar el riesgo.
Este indicador ayuda a seguir la tendencia y automatizar operaciones, filtrando el ruido del mercado.
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This Pine Script uses the SuperTrend indicator based on ATR to identify trends and generate buy and sell signals.
How it works:
Detects volatility with ATR to calculate dynamic support and resistance levels.
Plots the trend:
Green line: Bullish trend.
Red line: Bearish trend.
Generates trading signals:
Buy when the trend switches from bearish to bullish.
Sell when it switches from bullish to bearish.
Trades automatically:
Opens positions based on the signals.
Sets stop loss and take profit to manage risk.
This indicator helps follow the trend and automate trades, filtering out market noise.
Classic Nacked Z-Score ArbitrageThe “Classic Naked Z-Score Arbitrage” strategy employs a statistical arbitrage model based on the Z-score of the price spread between two assets. This strategy follows the premise of pair trading, where two correlated assets, typically from the same market sector, are traded against each other to profit from relative price movements (Gatev, Goetzmann, & Rouwenhorst, 2006). The approach involves calculating the Z-score of the price spread between two assets to determine market inefficiencies and capitalize on short-term mispricing.
Methodology
Price Spread Calculation:
The strategy calculates the spread between the two selected assets (Asset A and Asset B), typically from different sectors or asset classes, on a daily timeframe.
Statistical Basis – Z-Score:
The Z-score is used as a measure of how far the current price spread deviates from its historical mean, using the standard deviation for normalization.
Trading Logic:
• Long Position:
A long position is initiated when the Z-score exceeds the predefined threshold (e.g., 2.0), indicating that Asset A is undervalued relative to Asset B. This signals an arbitrage opportunity where the trader buys Asset B and sells Asset A.
• Short Position:
A short position is entered when the Z-score falls below the negative threshold, indicating that Asset A is overvalued relative to Asset B. The strategy involves selling Asset B and buying Asset A.
Theoretical Foundation
This strategy is rooted in mean reversion theory, which posits that asset prices tend to return to their long-term average after temporary deviations. This form of arbitrage is widely used in statistical arbitrage and pair trading techniques, where investors seek to exploit short-term price inefficiencies between two assets that historically maintain a stable price relationship (Avery & Sibley, 2020).
Further, the Z-score is an effective tool for identifying significant deviations from the mean, which can be seen as a signal for the potential reversion of the price spread (Braucher, 2015). By capturing these inefficiencies, traders aim to profit from convergence or divergence between correlated assets.
Practical Application
The strategy aligns with the Financial Algorithmic Trading and Market Liquidity analysis, emphasizing the importance of statistical models and efficient execution (Harris, 2024). By utilizing a simple yet effective risk-reward mechanism based on the Z-score, the strategy contributes to the growing body of research on market liquidity, asset correlation, and algorithmic trading.
The integration of transaction costs and slippage ensures that the strategy accounts for practical trading limitations, helping to refine execution in real market conditions. These factors are vital in modern quantitative finance, where liquidity and execution risk can erode profits (Harris, 2024).
References
• Gatev, E., Goetzmann, W. N., & Rouwenhorst, K. G. (2006). Pairs Trading: Performance of a Relative-Value Arbitrage Rule. The Review of Financial Studies, 19(3), 1317-1343.
• Avery, C., & Sibley, D. (2020). Statistical Arbitrage: The Evolution and Practices of Quantitative Trading. Journal of Quantitative Finance, 18(5), 501-523.
• Braucher, J. (2015). Understanding the Z-Score in Trading. Journal of Financial Markets, 12(4), 225-239.
• Harris, L. (2024). Financial Algorithmic Trading and Market Liquidity: A Comprehensive Analysis. Journal of Financial Engineering, 7(1), 18-34.
Macro-Sentiment Index Model (MSIM)Macro-Sentiment Index Model (MSIM) is a comprehensive trading strategy developed to analyze and interpret the broader macroeconomic and market sentiment. The strategy integrates various quantitative signals, including market volatility, trading volume, market breadth, and economic indicators, to assess the prevailing mood in the financial markets. This sentiment analysis is then used to guide trading decisions, helping identify optimal entry and exit points based on underlying market conditions. The model is specifically designed to capture the shifts in investor sentiment, which have been shown to significantly influence market behavior (Fleming et al., 2001).
The MSIM utilizes a multi-faceted approach to measure sentiment. Drawing from the theory that macroeconomic variables can influence financial markets (Stock & Watson, 2002), the strategy incorporates market volatility (VIX), volume measures, and long-term market trends. These indicators help form a robust view of the market’s risk appetite and potential for price movement. For instance, high volatility often signals increased market uncertainty (Bollerslev, 1986), while volume-based indicators provide insights into investor conviction (Chen, 1991).
Additionally, the model incorporates macroeconomic proxies like GDP growth, interest rates, and unemployment data, leveraging the findings of macroeconomic studies that indicate a direct correlation between these factors and market performance (Hamilton, 1994). By normalizing these economic indicators, the model provides a standardized sentiment score that reflects the aggregated impact of these factors on the market’s outlook.
The MSIM aims to exploit market inefficiencies by responding to shifts in sentiment before they manifest in price movements. Studies have shown that sentiment indicators, such as the Advance-Decline Line and the Stock-Bond Ratio, can be predictive of future price movements (Neely, 2010). The model integrates these indicators into a single composite sentiment score, which is then filtered through momentum signals to refine entry points. This approach is grounded in behavioral finance theory, which suggests that investor sentiment plays a crucial role in driving asset prices, sometimes beyond the reach of fundamental data alone (Shiller, 2000).
The strategy is designed to identify long opportunities when sentiment is particularly favorable, with a focus on minimizing risk during adverse conditions. By analyzing market trends alongside macroeconomic signals, the MSIM helps traders stay aligned with the prevailing market forces.
References:
• Bollerslev, T. (1986). Generalized autoregressive conditional heteroskedasticity. Journal of Econometrics, 31(3), 307-327.
• Chen, S. S. (1991). The determinants of stock market liquidity. Journal of Financial and Quantitative Analysis, 26(3), 283-305.
• Fleming, M. J., Kirby, C. W., & Ostdiek, B. (2001). The economic value of volatility timing. Journal of Financial and Quantitative Analysis, 36(1), 113-134.
• Hamilton, J. D. (1994). Time series analysis. Princeton University Press.
• Neely, C. J. (2010). The behavior of exchange rates: A survey of recent empirical literature. International Finance Discussion Papers, 981.
• Shiller, R. J. (2000). Irrational Exuberance. Princeton University Press.
• Stock, J. H., & Watson, M. W. (2002). Macroeconomic forecasting using diffusion indexes. Journal of Business & Economic Statistics, 20(2), 147-162.
John Bob-Trading-BotDeveloped by Ayebale John Bob with the help of his bestie, this innovative strategy combines advanced Smart Money Concepts with practical risk management tools to help traders identify and capitalize on key market moves.
Key Features:
Smart Money Concepts & Fair Value Gaps (FVG):
The strategy monitors price action for fair value gaps, which are visualized as extremely faint horizontal lines on the chart. These FVGs signal potential areas where institutional traders might have entered or exited positions.
Dynamic Entry Signals:
Buy signals are triggered when the price crosses above the 50-bar lowest low or when a bullish FVG is detected. Conversely, sell signals are generated when the price falls below the 50-bar highest high or a bearish FVG is identified. Each signal is visually marked on the chart with clear buy (green) and sell (red) labels.
Multi-Level Order Execution:
Once an entry signal occurs, the strategy places five separate orders, each with its own take-profit (TP) level. The TP levels are calculated dynamically using the Average True Range (ATR) and a set of predefined multipliers. This allows traders to scale out of positions as the market moves favorably.
Dynamic Risk Management:
A stop-loss is automatically set at a distance determined by the ATR, ensuring that risk is managed in accordance with current market volatility.
Real-Time Trade Information Table:
In the bottom-right corner of the chart, a trade information table displays essential details about the current trade:
Side: Displays "BUY NOW" (with a dark green background) for long entries or "SELL NOW" (with a dark red background) for short entries.
Entry Price & Stop-Loss: Shows the entry price (highlighted in green) and the corresponding stop-loss level (highlighted in red).
Take-Profit Levels: Lists the five TP levels, each of which turns green once the market price reaches that target.
Timer: A live timer in minutes counts from the moment the current trade trigger started, helping traders track the duration of their active trades.
Visual Progress Bar:
A histogram-style progress bar is plotted on the chart, visually representing the percentage gain (or loss) relative to the entry price.
This strategy was meticulously designed to incorporate both technical analysis and smart risk management, offering a robust trading solution that adapts to changing market conditions. Whether you're a seasoned trader or just starting out, the AyebaleJohnBob Trading Bot equips you with the tools and visual cues needed to make well-informed trading decisions. Enjoy a seamless blend of strategy and style—crafted with passion by Ayebale John Bob and his bestie!
Source StrategyThis strategy converts indicator signals into long and short entries and exits. It looks for non-zero values from your chosen entry sources to enter positions, and from exit sources to close positions.
The strategy supports both longs and shorts. For long trades, it looks at your selected long source and long exit source; for short trades, it looks at your chosen short source and short exit source. The strategy enters a position when either source produces any value except zero.
Stop loss and take profit orders are incorporated for risk management. These orders are calculated as a percentage of your position's value, providing dynamic risk management as price moves. The percentage levels for stop loss and take profit orders are configurable in the settings, allowing you to adjust your risk parameters based on market conditions and trading style.
To use the strategy, add it to your chart. The input parameters can be configured in the strategy's settings panel, including your signal sources for long and short entries and exits, and the percentage levels for stop loss and take profit orders.
Dow Theory Swing Trading-DexterThis Pine Script strategy that implements a basic price action-based trading system inspired by Dow Theory, focusing on swing highs and swing lows. This strategy will generate buy and sell signals based on the formation of higher highs (HH) and higher lows (HL) for an uptrend, and lower highs (LH) and lower lows (LL) for a downtrend.
Swing Highs and Swing Lows:
The script identifies swing highs and swing lows using the ta.highest and ta.lowest functions over a specified lookback period.
A swing high is identified when the high of the current bar is the highest high over the lookback period.
A swing low is identified when the low of the current bar is the lowest low over the lookback period.
Trend Detection:
An uptrend is detected when the current low is higher than the last identified swing low.
A downtrend is detected when the current high is lower than the last identified swing high.
Buy and Sell Signals:
A buy signal is generated when the price closes above the last swing high during an uptrend.
A sell signal is generated when the price closes below the last swing low during a downtrend.
Plotting:
Swing highs and swing lows are plotted on the chart using plotshape.
Buy and sell signals are also plotted on the chart for visual reference.
How to Use:
Copy and paste the script into the Pine Script editor in TradingView.
Adjust the lookback period as needed to suit your trading style and timeframe.
Apply the script to your chart and it will generate buy and sell signals based on the price action.
NOTE: Please uncheck the all the unwanted symbol from chart for clear view .
Delta SMA 1-Year High/Low Strategy### Summary:
This Pine Script code implements a trading strategy based on the **Delta SMA (Simple Moving Average)** of buy and sell volumes over a 1-year lookback period. The strategy identifies potential buy and sell signals by analyzing the relationship between the Delta SMA and its historical high/low thresholds. Key features include:
1. **Delta Calculation**:
- The Delta is calculated as the difference between buy volume (when close > open) and sell volume (when close < open).
- A 14-period SMA is applied to the Delta to smooth the data.
2. **1-Year High/Low Thresholds**:
- The strategy calculates the 1-year high and low of the Delta SMA.
- Buy and sell conditions are derived from thresholds set at 70% of the 1-year low and 90% and 50% of the 1-year high, respectively.
3. **Buy Condition**:
- A buy signal is triggered when the Delta SMA crosses above 0 after being below 70% of the 1-year low.
4. **Sell Condition**:
- A sell signal is triggered when the Delta SMA drops below 60% of the 1-year high after crossing above 90% of the 1-year high.
5. **Visualization**:
- The Delta SMA and its thresholds are plotted on the chart for easy monitoring.
- Optional buy/sell signals can be plotted as labels on the chart.
This strategy is designed to capture trends in volume-based momentum over a long-term horizon, making it suitable for swing or position trading.
High-Low Breakout Strategy with ATR traling Stop LossThis script is a TradingView Pine Script strategy that implements a High-Low Breakout Strategy with ATR Trailing Stop.created by SK WEALTH GURU, Here’s a breakdown of its key components:
Features and Functionality
Custom Timeframe and High-Low Detection
Allows users to select a custom timeframe (default: 30 minutes) to detect high and low levels.
Tracks the high and low within a user-specified period (e.g., first 30 minutes of the session).
Draws horizontal lines for high and low, persisting for a specified number of days.
Trade Entry Conditions
Long Entry: If the closing price crosses above the recorded high.
Short Entry: If the closing price crosses below the recorded low.
The user can choose to trade Long, Short, or Both.
ATR-Based Trailing Stop & Risk Management
Uses Average True Range (ATR) with a multiplier (default: 3.5) to determine a dynamic trailing stop-loss.
Trades reset daily, ensuring a fresh start each day.
Trade Execution and Partial Profit Taking
Stop-loss: Default at 1% of entry price.
Partial profit: Books 50% of the position at 3% profit.
Max 2 trades per day: If the first trade hits stop-loss, the strategy allows one re-entry.
Intraday Exit Condition
All positions close at 3:15 PM to ensure no overnight risk.
Ultimate Stochastics Strategy by NHBprod Use to Day Trade BTCHey All!
Here's a new script I worked on that's super simple but at the same time useful. Check out the backtest results. The backtest results include slippage and fees/commission, and is still quite profitable. Obviously the profitability magnitude depends on how much capital you begin with, and how much the user utilizes per order, but in any event it seems to be profitable according to backtests.
This is different because it allows you full functionality over the stochastics calculations which is designed for random datasets. This script allows you to:
Designate ANY period of time to analyze and study
Choose between Long trading, short trading, and Long & Short trading
It allows you to enter trades based on the stochastics calculations
It allows you to EXIT trades using the stochastics calculations or take profit, or stop loss, Or any combination of those, which is nice because then the user can see how one variable effects the overall performance.
As for the actual stochastics formula, you get control, and get to SEE the plot lines for slow K, slow D, and fast K, which is usually not considered.
You also get the chance to modify the smoothing method, which has not been done with regular stochastics indicators. You get to choose the standard simple moving average (SMA) method, but I also allow you to choose other MA's such as the HMA and WMA.
Lastly, the user gets the option of using a custom trade extender, which essentially allows a buy or sell signal to exist for X amount of candles after the initial signal. For example, you can use "max bars since signal" to 1, and this will allow the indicator to produce an extra sequential buy signal when a buy signal is generated. This can be useful because it is possible that you use a small take profit (TP) and quickly exit a profitable trade. With the max bars since signal variable, you're able to reenter on the next candle and allow for another opportunity.
Let me know if you have any questions! Please take a look at the performance report and let me know your thoughts! :)
Gold Pro StrategyHere’s the strategy description in a chat format:
---
**Gold (XAU/USD) Trend-Following Strategy**
This **trend-following strategy** is designed for trading gold (XAU/USD) by combining moving averages, MACD momentum indicators, and RSI filters to capture sustained trends while managing volatility risks. The strategy uses volatility-adjusted stops to protect gains and prevent overexposure during erratic price movements. The aim is to take advantage of trending markets by confirming momentum and ensuring entries are not made at extreme levels.
---
**Key Components**
1. **Trend Identification**
- **50 vs 200 EMA Crossover**
- **Bullish Trend:** 50 EMA crosses above 200 EMA, and the price closes above the 200 EMA
- **Bearish Trend:** 50 EMA crosses below 200 EMA, and the price closes below the 200 EMA
2. **Momentum Confirmation**
- **MACD (12,26,9)**
- **Buy Signal:** MACD line crosses above the signal line
- **Sell Signal:** MACD line crosses below the signal line
- **RSI (14 Period)**
- **Bullish Zone:** RSI between 50-70 to avoid overbought conditions
- **Bearish Zone:** RSI between 30-50 to avoid oversold conditions
3. **Entry Criteria**
- **Long Entry:** Bullish trend, MACD bullish crossover, and RSI between 50-70
- **Short Entry:** Bearish trend, MACD bearish crossover, and RSI between 30-50
4. **Exit & Risk Management**
- **ATR Trailing Stops (14 Period):**
- Initial Stop: 3x ATR from entry price
- Trailing Stop: Adjusts to lock in profits as price moves favorably
- **Position Sizing:** 100% of equity per trade (high-risk strategy)
---
**Key Logic Flow**
1. **Trend Filter:** Use the 50/200 EMA relationship to define the market's direction
2. **Momentum Confirmation:** Confirm trend momentum with MACD crossovers
3. **RSI Validation:** Ensure RSI is within non-extreme ranges before entering trades
4. **Volatility-Based Risk Management:** Use ATR stops to manage market volatility
---
**Visual Cues**
- **Blue Line:** 50 EMA
- **Red Line:** 200 EMA
- **Green Triangles:** Long entry signals
- **Red Triangles:** Short entry signals
---
**Strengths**
- **Clear Trend Focus:** Avoids counter-trend trades
- **RSI Filter:** Prevents entering overbought or oversold conditions
- **ATR Stops:** Adapts to gold’s inherent volatility
- **Simple Rules:** Easy to follow with minimal inputs
---
**Weaknesses & Risks**
- **Infrequent Signals:** 50/200 EMA crossovers are rare
- **Potential Missed Opportunities:** Strict RSI criteria may miss some valid trends
- **Aggressive Position Sizing:** 100% equity allocation can lead to large drawdowns
- **No Profit Targets:** Relies on trailing stops rather than defined exit targets
---
**Performance Profile**
| Metric | Expected Range |
|----------------------|---------------------|
| Annual Trades | 4-8 |
| Win Rate | 55-65% |
| Max Drawdown | 25-35% |
| Profit Factor | 1.8-2.5 |
---
**Optimization Recommendations**
1. **Increase Trade Frequency**
Adjust the EMAs to shorter periods:
- `emaFastLen = input.int(30, "Fast EMA")`
- `emaSlowLen = input.int(150, "Slow EMA")`
2. **Relax RSI Filters**
Adjust the RSI range to:
- `rsiBullish = rsi > 45 and rsi < 75`
- `rsiBearish = rsi < 55 and rsi > 25`
3. **Add Profit Targets**
Introduce a profit target at 1.5% above entry:
```pine
strategy.exit("Long Exit", "Long",
stop=longStopPrice,
profit=close*1.015, // 1.5% target
trail_offset=trailOffset)
```
4. **Reduce Position Sizing**
Risk a smaller percentage per trade:
- `default_qty_value=25`
---
**Best Use Case**
This strategy excels in **strong trending markets** such as gold rallies during economic or geopolitical crises. However, during sideways or choppy market conditions, the strategy might require manual intervention to avoid false signals. Additionally, integrating fundamental analysis—like monitoring USD weakness or geopolitical risks—can enhance its effectiveness.
---
This strategy offers a balanced approach for trading gold, combining trend-following principles with risk management tailored to the volatility of the market.
XAU-USD - OANDA - Updated Jan 2025 - by PB ver 5Script Title: XAU-USD - OANDA - Updated Jan 2025 - by PB ver 5
Description:
This strategy is designed for trading XAU/USD (Gold) on the OANDA platform, optimized with a session-based filter and Renko bar indicators for enhanced price action analysis. The script utilizes trailing stop loss functionality to manage risk effectively and allows flexibility for both long and short trades.
Key Features:
Date Filter: This strategy includes a time filter to backtest the performance from January 1st, 2025 to December 31st, 2025. Users can enable or disable the filter based on their preference.
Session Filter: Customizable session inputs allow the user to define the active trading session using a time range (default is 09:20-15:16) and the days of the week (default is all days, 1-7). The strategy will only enter trades during the active session, ensuring more controlled and focused trading.
Renko Bar Strategy: This strategy uses Renko charts, a popular price action tool, to detect buy and sell signals based on the crossover of Renko close and open prices. Users can adjust the Renko block size and the Renko value used for detecting price action shifts.
Trailing Stops: The script applies a trailing stop loss mechanism for both long and short trades. The trailing stop is dynamically updated to follow the market as prices move in favor of the trade. It uses a 5000-point trailing stop (adjustable by the user).
Flexible Trade Settings: Users can enable or disable long and short positions through simple toggle switches. The strategy allows for full control over trade entry and exit.
How It Works:
Long Trades: A long position is entered when the Renko close crosses above the Renko open. The position will be exited using a trailing stop, which follows the price in the market.
Short Trades: A short position is entered when the Renko close crosses below the Renko open. The position will also exit using a trailing stop.
The strategy will automatically close positions if the session ends or if the user manually exits the trades.
Customization Options:
Backtest Date Range: Set the start and end dates to backtest the strategy over a specific time period.
Session and Days: Adjust the session time and which days of the week the strategy is active.
Renko Block Size: Customize the Renko block size for finer control over price action signals.
Trailing Stop Distance: Adjust the trailing stop loss to your preferred risk levels.
Limitations and Considerations:
Renko Charting: Renko charts may not suit every trading style, as they are based on price movement rather than time. This strategy is designed for traders who prefer this style of charting.
Backtest Results: Always review the strategy's backtest results with realistic parameters. The strategy uses historical data, and past performance is not indicative of future results. Be aware of slippage and commission costs in real-world trading scenarios.
Manual Intervention: Users should monitor active trades and intervene manually if required.
Ideal Usage:
This strategy is suited for traders looking to use price action-based strategies with Renko charts for XAU/USD on the OANDA platform.
Ideal for those who want to automate their entry and exit points with trailing stop mechanisms while having control over the session time and backtesting period.
Disclaimer:
Past performance does not guarantee future results. Always use caution when using trading strategies and adjust parameters based on market conditions. The strategy is provided for educational purposes and should be tested on paper before live trading.
