Auf geht's

Educational

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 😊
Pine Script™ Strategie
von RPAlawyer
1
Buy Low over 18 SMA Strategythis is a customizeable strategy to buy on daily chart where you can select after which days you want to buy with a 1or2 day trailing stop on prior low the Nasdaq seams to be most profitable when buying above the wednesdays and fridays high this avoided entries in the bearish move on july 2024 breakeven stops in aug 2023 only small losses in jan/april/sept 2022 all in all a pretty good strategy when exiting below the low of prior two days
Pine Script™ Strategie
von responsibletrad8r
Strategia Bollinger-Fibonacci-SMALa strategia che abbiamo sviluppato combina diversi indicatori tecnici per identificare potenziali opportunità di trading. Essa si basa su: Bande di Bollinger: Misurano la volatilità del mercato e forniscono segnali di sovracomprato e sottovenduto. Ritracciamenti di Fibonacci: Identificano potenziali livelli di supporto e resistenza basati su rapporti matematici. Medie Mobili: Misurano la tendenza del mercato a breve e lungo termine. La strategia entra a mercato quando si verificano determinate condizioni relative a questi indicatori, come ad esempio quando il prezzo attraversa la banda inferiore di Bollinger e si trova vicino a un livello di ritracciamento di Fibonacci. Analisi Dettagliata degli Elementi Bande di Bollinger: Funzione: Misurano la volatilità del mercato e forniscono un'indicazione visiva dei livelli di sovracomprato e sottovenduto. Interpretazione: Quando il prezzo si trova al di fuori delle bande, potrebbe essere un segnale di una condizione di mercato estrema. Nella nostra strategia: Le bande di Bollinger vengono utilizzate per identificare potenziali punti di ingresso e uscita dal mercato. Ritracciamenti di Fibonacci: Funzione: Identificano potenziali livelli di supporto e resistenza basati su rapporti matematici derivati dalla sequenza di Fibonacci. Interpretazione: I livelli di Fibonacci rappresentano i punti in cui il prezzo potrebbe invertire la sua direzione. Nella nostra strategia: I livelli di Fibonacci vengono utilizzati in combinazione con le Bande di Bollinger per confermare i segnali di ingresso. Medie Mobili: Funzione: Misurano la tendenza del mercato a breve e lungo termine. Interpretazione: Un incrocio tra due medie mobili può indicare un cambio di tendenza. Nella nostra strategia: Le medie mobili vengono utilizzate come filtro aggiuntivo per confermare i segnali generati dalle Bande di Bollinger e dai livelli di Fibonacci. Condizioni di Ingresso: La strategia entra a lungo quando: Il prezzo attraversa la banda inferiore di Bollinger in direzione ascendente. Il prezzo si trova al di sotto del livello di ritracciamento di Fibonacci specificato. La media mobile a breve termine è superiore alla media mobile a lungo termine. La strategia entra a corto quando si verificano le condizioni opposte. Stop-Loss e Take Profit: Per gestire il rischio, la strategia utilizza stop-loss e take-profit dinamici, calcolati in base al prezzo di entrata e a una percentuale predefinita. Vantaggi e Svantaggi Vantaggi: Multipla conferma: La strategia si basa su più indicatori, fornendo una conferma più robusta dei segnali di trading. Flessibilità: I parametri della strategia possono essere personalizzati per adattarsi a diversi stili di trading e mercati. Gestione del rischio: L'utilizzo di stop-loss e take-profit aiuta a limitare le perdite e a proteggere i profitti. Svantaggi: Nessuna strategia è infallibile: Anche la migliore strategia può generare perdite. Requisiti di monitoraggio: La strategia richiede un monitoraggio costante per garantire che i parametri siano ancora validi e per reagire ai cambiamenti del mercato. Complessità: La combinazione di più indicatori può rendere la strategia più difficile da comprendere e implementare. Conclusioni Questa strategia rappresenta un tentativo di combinare diversi strumenti tecnici per identificare potenziali opportunità di trading. Tuttavia, è importante sottolineare che il trading comporta sempre un rischio e che nessuna strategia può garantire profitti. È fondamentale testare accuratamente la strategia su dati storici e adattarla al proprio stile di trading personale.
Pine Script™ Strategie
von Valerioz
Dynamic Volatility Differential Model (DVDM)The Dynamic Volatility Differential Model (DVDM) is a quantitative trading strategy designed to exploit the spread between implied volatility (IV) and historical (realized) volatility (HV). This strategy identifies trading opportunities by dynamically adjusting thresholds based on the standard deviation of the volatility spread. The DVDM is versatile and applicable across various markets, including equity indices, commodities, and derivatives such as the FDAX (DAX Futures). Key Components of the DVDM: 1. Implied Volatility (IV): The IV is derived from options markets and reflects the market’s expectation of future price volatility. For instance, the strategy uses volatility indices such as the VIX (S&P 500), VXN (Nasdaq 100), or RVX (Russell 2000), depending on the target market. These indices serve as proxies for market sentiment and risk perception (Whaley, 2000). 2. Historical Volatility (HV): The HV is computed from the log returns of the underlying asset’s price. It represents the actual volatility observed in the market over a defined lookback period, adjusted to annualized levels using a multiplier of \sqrt{252} for daily data (Hull, 2012). 3. Volatility Spread: The difference between IV and HV forms the volatility spread, which is a measure of divergence between market expectations and actual market behavior. 4. Dynamic Thresholds: Unlike static thresholds, the DVDM employs dynamic thresholds derived from the standard deviation of the volatility spread. The thresholds are scaled by a user-defined multiplier, ensuring adaptability to market conditions and volatility regimes (Christoffersen & Jacobs, 2004). Trading Logic: 1. Long Entry: A long position is initiated when the volatility spread exceeds the upper dynamic threshold, signaling that implied volatility is significantly higher than realized volatility. This condition suggests potential mean reversion, as markets may correct inflated risk premiums. 2. Short Entry: A short position is initiated when the volatility spread falls below the lower dynamic threshold, indicating that implied volatility is significantly undervalued relative to realized volatility. This signals the possibility of increased market uncertainty. 3. Exit Conditions: Positions are closed when the volatility spread crosses the zero line, signifying a normalization of the divergence. Advantages of the DVDM: 1. Adaptability: Dynamic thresholds allow the strategy to adjust to changing market conditions, making it suitable for both low-volatility and high-volatility environments. 2. Quantitative Precision: The use of standard deviation-based thresholds enhances statistical reliability and reduces subjectivity in decision-making. 3. Market Versatility: The strategy’s reliance on volatility metrics makes it universally applicable across asset classes and markets, ensuring robust performance. Scientific Relevance: The strategy builds on empirical research into the predictive power of implied volatility over realized volatility (Poon & Granger, 2003). By leveraging the divergence between these measures, the DVDM aligns with findings that IV often overestimates future volatility, creating opportunities for mean-reversion trades. Furthermore, the inclusion of dynamic thresholds aligns with risk management best practices by adapting to volatility clustering, a well-documented phenomenon in financial markets (Engle, 1982). References: 1. Christoffersen, P., & Jacobs, K. (2004). The importance of the volatility risk premium for volatility forecasting. Journal of Financial and Quantitative Analysis, 39(2), 375-397. 2. Engle, R. F. (1982). Autoregressive conditional heteroskedasticity with estimates of the variance of United Kingdom inflation. Econometrica, 50(4), 987-1007. 3. Hull, J. C. (2012). Options, Futures, and Other Derivatives. Pearson Education. 4. Poon, S. H., & Granger, C. W. J. (2003). Forecasting volatility in financial markets: A review. Journal of Economic Literature, 41(2), 478-539. 5. Whaley, R. E. (2000). The investor fear gauge. Journal of Portfolio Management, 26(3), 12-17. This strategy leverages quantitative techniques and statistical rigor to provide a systematic approach to volatility trading, making it a valuable tool for professional traders and quantitative analysts.
Pine Script™ Strategie
von EdgeTools
Fibonacci Trend - Aynet1. Inputs lookbackPeriod: Defines the number of bars to consider for calculating swing highs and lows. Default is 20. fibLevel1 to fibLevel5: Fibonacci retracement levels to calculate price levels (23.6%, 38.2%, 50%, 61.8%, 78.6%). useTime: Enables or disables time-based Fibonacci projections. riskPercent: Defines the percentage of risk for trading purposes (currently not used in calculations). 2. Functions isSwingHigh(index): Identifies a swing high at the given index, where the high of that candle is higher than both its previous and subsequent candles. isSwingLow(index): Identifies a swing low at the given index, where the low of that candle is lower than both its previous and subsequent candles. 3. Variables swingHigh and swingLow: Store the most recent swing high and swing low prices. swingHighTime and swingLowTime: Store the timestamps of the swing high and swing low. fib1 to fib5: Fibonacci levels based on the difference between swingHigh and swingLow. 4. Swing Point Detection The script checks if the last bar is a swing high or swing low using the isSwingHigh() and isSwingLow() functions. If a swing high is detected: The high price is stored in swingHigh. The timestamp of the swing high is stored in swingHighTime. If a swing low is detected: The low price is stored in swingLow. The timestamp of the swing low is stored in swingLowTime. 5. Fibonacci Levels Calculation If both swingHigh and swingLow are defined, the script calculates the Fibonacci retracement levels (fib1 to fib5) based on the price difference (priceDiff = swingHigh - swingLow). 6. Plotting Fibonacci Levels Fibonacci levels (fib1 to fib5) are plotted as horizontal lines using the line.new() function. Labels (e.g., "23.6%") are added near the lines to indicate the level. Lines and labels are color-coded: 23.6% → Blue 38.2% → Green 50.0% → Yellow 61.8% → Orange 78.6% → Red 7. Filling Between Fibonacci Levels The plot() function creates lines for each Fibonacci level. The fill() function is used to fill the space between two levels with semi-transparent colors: Blue → Between fib1 and fib2 Green → Between fib2 and fib3 Yellow → Between fib3 and fib4 Orange → Between fib4 and fib5 8. Time-Based Fibonacci Projections If useTime is enabled: The time difference (timeDiff) between the swing high and swing low is calculated. Fibonacci time projections are added based on multiples of 23.6%. If the current time reaches a projected time, a label (e.g., "T1", "T2") is displayed near the high price. 9. Trading Logic Two placeholder variables are defined for trading logic: longCondition: Tracks whether a condition for a long trade is met (currently not implemented). shortCondition: Tracks whether a condition for a short trade is met (currently not implemented). These variables can be extended to define entry/exit signals based on Fibonacci levels. How It Works Detect Swing Points: It identifies recent swing high and swing low points on the chart. Calculate Fibonacci Levels: Based on the swing points, it computes retracement levels. Visualize Levels: Plots the levels on the chart with labels and fills between them. Time Projections: Optionally calculates time-based projections for future price movements. Trading Opportunities: The framework provides tools for detecting potential reversal or breakout zones using Fibonacci levels.
Pine Script™ Strategie
von Aynet-London
Forex Hammer and Hanging Man StrategyThe strategy is based on two key candlestick chart patterns: Hammer and Hanging Man. These chart patterns are widely used in technical analysis to identify potential reversal points in the market. Their relevance in the Forex market, known for its high liquidity and volatile price movements, is particularly pronounced. Both patterns provide insights into market sentiment and trader psychology, which are critical in currency trading, where short-term volatility plays a significant role. 1. Hammer: • Typically occurs after a downtrend. • Signals a potential trend reversal to the upside. • A Hammer has: • A small body (close and open are close to each other). • A long lower shadow, at least twice as long as the body. • No or a very short upper shadow. 2. Hanging Man: • Typically occurs after an uptrend. • Signals a potential reversal to the downside. • A Hanging Man has: • A small body, similar to the Hammer. • A long lower shadow, at least twice as long as the body. • A small or no upper shadow. These patterns are a manifestation of market psychology, specifically the tug-of-war between buyers and sellers. The Hammer reflects a situation where sellers tried to push the price down but were overpowered by buyers, while the Hanging Man shows that buyers failed to maintain the upward movement, and sellers could take control. Relevance of Chart Patterns in Forex In the Forex market, chart patterns are vital tools because they offer insights into price action and market sentiment. Since Forex trading often involves large volumes of trades, chart patterns like the Hammer and Hanging Man are important for recognizing potential shifts in market momentum. These patterns are a part of technical analysis, which aims to forecast future price movements based on historical data, relying on the psychology of market participants. Scientific Literature on the Relevance of Candlestick Patterns 1. Behavioral Finance and Candlestick Patterns: Research on behavioral finance supports the idea that candlestick patterns, such as the Hammer and Hanging Man, are relevant because they reflect shifts in trader psychology and sentiment. According to Lo, Mamaysky, and Wang (2000), patterns like these could be seen as representations of collective investor behavior, influenced by overreaction, optimism, or pessimism, and can often signal reversals in market trends. 2. Statistical Validation of Chart Patterns: Studies by Brock, Lakonishok, and LeBaron (1992) explored the profitability of technical analysis strategies, including candlestick patterns, and found evidence that certain patterns, such as the Hammer, can have predictive value in financial markets. While their study primarily focused on stock markets, their findings are generally applicable to the Forex market as well. 3. Market Efficiency and Candlestick Patterns: The efficient market hypothesis (EMH) posits that all available information is reflected in asset prices, but some studies suggest that markets may not always be perfectly efficient, allowing for profitable exploitation of certain chart patterns. For instance, Jegadeesh and Titman (1993) found that momentum strategies, which often rely on price patterns and trends, could generate significant returns, suggesting that patterns like the Hammer or Hanging Man may provide a slight edge, particularly in short-term Forex trading. Testing the Strategy in Forex Using the Provided Script The provided script allows traders to test and evaluate the Hammer and Hanging Man patterns in Forex trading by entering positions when these patterns appear and holding the position for a specified number of periods. This strategy can be tested to assess its performance across different currency pairs and timeframes. 1. Testing on Different Timeframes: • The effectiveness of candlestick patterns can vary across different timeframes, as market dynamics change with the level of detail in each timeframe. Shorter timeframes may provide more frequent signals, but with higher noise, while longer timeframes may produce more reliable signals, but with fewer opportunities. This multi-timeframe analysis could be an area to explore to enhance the strategy’s robustness. 2. Exit Strategies: • The script incorporates an exit strategy where positions are closed after holding them for a specified number of periods. This is useful for testing how long the reversal patterns typically take to play out and when the optimal exit occurs for maximum profitability. It can also help to adjust the exit logic based on real-time market behavior. Conclusion The Hammer and Hanging Man patterns are widely recognized in technical analysis as potential reversal signals, and their application in Forex trading is valuable due to the market’s high volatility and liquidity. This strategy leverages these candlestick patterns to enter and exit trades based on shifts in market sentiment and psychology. Testing and optimization, as offered by the script, can help refine the strategy and improve its effectiveness. For further refinement, it could be valuable to consider combining candlestick patterns with other technical indicators or using multi-timeframe analysis to confirm patterns and increase the probability of successful trades. References: • 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), 1705-1770. • 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. • 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. This provides a theoretical basis for the use of candlestick patterns in trading, supported by academic literature and research on market psychology and efficiency.
Pine Script™ Strategie
von EdgeTools
Adaptive Momentum Reversion StrategyThe Adaptive Momentum Reversion Strategy: An Empirical Approach to Market Behavior The Adaptive Momentum Reversion Strategy seeks to capitalize on market price dynamics by combining concepts from momentum and mean reversion theories. This hybrid approach leverages a Rate of Change (ROC) indicator along with Bollinger Bands to identify overbought and oversold conditions, triggering trades based on the crossing of specific thresholds. The strategy aims to detect momentum shifts and exploit price reversions to their mean. Theoretical Framework Momentum and Mean Reversion: Momentum trading assumes that assets with a recent history of strong performance will continue in that direction, while mean reversion suggests that assets tend to return to their historical average over time (Fama & French, 1988; Poterba & Summers, 1988). This strategy incorporates elements of both, looking for periods when momentum is either overextended (and likely to revert) or when the asset’s price is temporarily underpriced relative to its historical trend. Rate of Change (ROC): The ROC is a straightforward momentum indicator that measures the percentage change in price over a specified period (Wilder, 1978). The strategy calculates the ROC over a 2-period window, making it responsive to short-term price changes. By using ROC, the strategy aims to detect price acceleration and deceleration. Bollinger Bands: Bollinger Bands are used to identify volatility and potential price extremes, often signaling overbought or oversold conditions. The bands consist of a moving average and two standard deviation bounds that adjust dynamically with price volatility (Bollinger, 2002). The strategy employs two sets of Bollinger Bands: one for short-term volatility (lower band) and another for longer-term trends (upper band), with different lengths and standard deviation multipliers. Strategy Construction Indicator Inputs: ROC Period: The rate of change is computed over a 2-period window, which provides sensitivity to short-term price fluctuations. Bollinger Bands: Lower Band: Calculated with a 18-period length and a standard deviation of 1.7. Upper Band: Calculated with a 21-period length and a standard deviation of 2.1. Calculations: ROC Calculation: The ROC is computed by comparing the current close price to the close price from rocPeriod days ago, expressing it as a percentage. Bollinger Bands: The strategy calculates both upper and lower Bollinger Bands around the ROC, using a simple moving average as the central basis. The lower Bollinger Band is used as a reference for identifying potential long entry points when the ROC crosses above it, while the upper Bollinger Band serves as a reference for exits, when the ROC crosses below it. Trading Conditions: Long Entry: A long position is initiated when the ROC crosses above the lower Bollinger Band, signaling a potential shift from a period of low momentum to an increase in price movement. Exit Condition: A position is closed when the ROC crosses under the upper Bollinger Band, or when the ROC drops below the lower band again, indicating a reversal or weakening of momentum. Visual Indicators: ROC Plot: The ROC is plotted as a line to visualize the momentum direction. Bollinger Bands: The upper and lower bands, along with their basis (simple moving averages), are plotted to delineate the expected range for the ROC. Background Color: To enhance decision-making, the strategy colors the background when extreme conditions are detected—green for oversold (ROC below the lower band) and red for overbought (ROC above the upper band), indicating potential reversal zones. Strategy Performance Considerations The use of Bollinger Bands in this strategy provides an adaptive framework that adjusts to changing market volatility. When volatility increases, the bands widen, allowing for larger price movements, while during quieter periods, the bands contract, reducing trade signals. This adaptiveness is critical in maintaining strategy effectiveness across different market conditions. The strategy’s pyramiding setting is disabled (pyramiding=0), ensuring that only one position is taken at a time, which is a conservative risk management approach. Additionally, the strategy includes transaction costs and slippage parameters to account for real-world trading conditions. Empirical Evidence and Relevance The combination of momentum and mean reversion has been widely studied and shown to provide profitable opportunities under certain market conditions. Studies such as Jegadeesh and Titman (1993) confirm that momentum strategies tend to work well in trending markets, while mean reversion strategies have been effective during periods of high volatility or after sharp price movements (De Bondt & Thaler, 1985). By integrating both strategies into one system, the Adaptive Momentum Reversion Strategy may be able to capitalize on both trending and reverting market behavior. Furthermore, research by Chan (1996) on momentum-based trading systems demonstrates that adaptive strategies, which adjust to changes in market volatility, often outperform static strategies, providing a compelling rationale for the use of Bollinger Bands in this context. Conclusion The Adaptive Momentum Reversion Strategy provides a robust framework for trading based on the dual concepts of momentum and mean reversion. By using ROC in combination with Bollinger Bands, the strategy is capable of identifying overbought and oversold conditions while adapting to changing market conditions. The use of adaptive indicators ensures that the strategy remains flexible and can perform across different market environments, potentially offering a competitive edge for traders who seek to balance risk and reward in their trading approaches. References Bollinger, J. (2002). Bollinger on Bollinger Bands. McGraw-Hill Professional. Chan, L. K. C. (1996). Momentum, Mean Reversion, and the Cross-Section of Stock Returns. Journal of Finance, 51(5), 1681-1713. De Bondt, W. F., & Thaler, R. H. (1985). Does the Stock Market Overreact? Journal of Finance, 40(3), 793-805. Fama, E. F., & French, K. R. (1988). Permanent and Temporary Components of Stock Prices. Journal of Political Economy, 96(2), 246-273. Jegadeesh, N., & Titman, S. (1993). Returns to Buying Winners and Selling Losers: Implications for Stock Market Efficiency. Journal of Finance, 48(1), 65-91. Poterba, J. M., & Summers, L. H. (1988). Mean Reversion in Stock Prices: Evidence and Implications. Journal of Financial Economics, 22(1), 27-59. Wilder, J. W. (1978). New Concepts in Technical Trading Systems. Trend Research.
Pine Script™ Strategie
von EdgeTools
EMA Crossover Strategy with Take Profit and Candle HighlightingStrategy Overview: This strategy is based on the Exponential Moving Averages (EMA), specifically the EMA 20 and EMA 50. It takes advantage of EMA crossovers to identify potential trend reversals and uses multiple take-profit levels and a stop-loss for risk management. Key Components: EMA Crossover Signals: Buy Signal (Uptrend): A buy signal is generated when the EMA 20 crosses above the EMA 50, signaling the start of a potential uptrend. Sell Signal (Downtrend): A sell signal is generated when the EMA 20 crosses below the EMA 50, signaling the start of a potential downtrend. Take Profit Levels: Once a buy or sell signal is triggered, the strategy calculates multiple take-profit levels based on the range of the previous candle. The user can define multipliers for each take-profit level. Take Profit 1 (TP1): 50% of the previous candle's range above or below the entry price. Take Profit 2 (TP2): 100% of the previous candle's range above or below the entry price. Take Profit 3 (TP3): 150% of the previous candle's range above or below the entry price. Take Profit 4 (TP4): 200% of the previous candle's range above or below the entry price. These levels are adjusted dynamically based on the previous candle's high and low, so they adapt to changing market conditions. Stop Loss: A stop-loss is set to manage risk. The default stop-loss is 3% from the entry price, but this can be adjusted in the settings. The stop-loss is triggered if the price moves against the position by this amount. Trend Direction Highlighting: The strategy highlights the bars (candles) with colors: Green bars indicate an uptrend (when EMA 20 crosses above EMA 50). Red bars indicate a downtrend (when EMA 20 crosses below EMA 50). These visual cues help users easily identify the market direction. Strategy Entries and Exits: Entries: The strategy enters a long (buy) position when the EMA 20 crosses above the EMA 50 and a short (sell) position when the EMA 20 crosses below the EMA 50. Exits: The strategy exits the positions at any of the defined take-profit levels or the stop-loss. Multiple exit levels provide opportunities to take profit progressively as the price moves in the favorable direction. Entry and Exit Conditions in Detail: Buy Entry Condition (Uptrend): A buy position is opened when EMA 20 crosses above EMA 50, signaling the start of an uptrend. The strategy calculates take-profit levels above the entry price based on the previous bar's range (high-low) and the multipliers for TP1, TP2, TP3, and TP4. Sell Entry Condition (Downtrend): A sell position is opened when EMA 20 crosses below EMA 50, signaling the start of a downtrend. The strategy calculates take-profit levels below the entry price, similarly based on the previous bar's range. Exit Conditions: Take Profit: The strategy attempts to exit the position at one of the take-profit levels (TP1, TP2, TP3, or TP4). If the price reaches any of these levels, the position is closed. Stop Loss: The strategy also has a stop-loss set at a default value (3% below the entry for long trades, and 3% above for short trades). The stop-loss helps to protect the position from significant losses. Backtesting and Performance Metrics: The strategy can be backtested using TradingView's Strategy Tester. The results will show how the strategy would have performed historically, including key metrics like: Net Profit Max Drawdown Win Rate Profit Factor Average Trade Duration These performance metrics can help users assess the strategy's effectiveness over historical periods and optimize the input parameters (e.g., multipliers, stop-loss level). Customization: The strategy allows for the adjustment of several key input values via the settings panel: Take Profit Multipliers: Users can customize the multipliers for each take-profit level (TP1, TP2, TP3, TP4). Stop Loss Percentage: The user can also adjust the stop-loss percentage to a custom value. EMA Periods: The default periods for the EMA 50 and EMA 20 are fixed, but they can be adjusted for different market conditions. Pros of the Strategy: EMA Crossover Strategy: A classic and well-known strategy used by traders to identify the start of new trends. Multiple Take Profit Levels: By taking profits progressively at different levels, the strategy locks in gains as the price moves in favor of the position. Clear Trend Identification: The use of green and red bars makes it visually easier to follow the market's direction. Risk Management: The stop-loss and take-profit features help to manage risk and optimize profit-taking. Cons of the Strategy: Lagging Indicators: The strategy relies on EMAs, which are lagging indicators. This means that the strategy might enter trades after the trend has already started, leading to missed opportunities or less-than-ideal entry prices. No Confirmation Indicators: The strategy purely depends on the crossover of two EMAs and does not use other confirming indicators (e.g., RSI, MACD), which might lead to false signals in volatile markets. How to Use in Real-Time Trading: Use for Backtesting: Initially, use this strategy in backtest mode to understand how it would have performed historically with your preferred settings. Paper Trading: Once comfortable, you can use paper trading to test the strategy in real-time market conditions without risking real money. Live Trading: After testing and optimizing the strategy, you can consider using it for live trading with proper risk management in place (e.g., starting with a small position size and adjusting parameters as needed). Summary: This strategy is designed to identify trend reversals using EMA crossovers, with customizable take-profit levels and a stop-loss to manage risk. It's well-suited for traders looking for a systematic way to enter and exit trades based on clear market signals, while also providing flexibility to adjust for different risk profiles and trading styles.
Pine Script™ Strategie
von Quince2010
1
Forex Pair Yield Momentum This Pine Script strategy leverages yield differentials between the 2-year government bond yields of two countries to trade Forex pairs. Yield spreads are widely regarded as a fundamental driver of currency movements, as highlighted by international finance theories like the Interest Rate Parity (IRP), which suggests that currencies with higher yields tend to appreciate due to increased capital flows: 1. Dynamic Yield Spread Calculation: • The strategy dynamically calculates the yield spread (yield_a - yield_b) for the chosen Forex pair. • Example: For GBP/USD, the spread equals US 2Y Yield - UK 2Y Yield. 2. Momentum Analysis via Bollinger Bands: • Yield momentum is computed as the difference between the current spread and its moving Bollinger Bands are applied to identify extreme deviations: • Long Entry: When momentum crosses below the lower band. • Short Entry: When momentum crosses above the upper band. 3. Reversal Logic: • An optional checkbox reverses the trading logic, allowing long trades at the upper band and short trades at the lower band, accommodating different market conditions. 4. Trade Management: • Positions are held for a predefined number of bars (hold_periods), and each trade uses a fixed contract size of 100 with a starting capital of $20,000. Theoretical Basis: 1. Yield Differentials and Currency Movements: • Empirical studies, such as Clarida et al. (2009), confirm that interest rate differentials significantly impact exchange rate dynamics, especially in carry trade strategies . • Higher-yields tend to appreciate against lower-yielding currencies due to speculative flows and demand for higher returns. 2. Bollinger Bands for Momentum: • Bollinger Bands effectively capture deviations in yield momentum, identifying opportunities where price returns to equilibrium (mean reversion) or extends in trend-following scenarios (momentum breakout). • As Bollinger (2001) emphasized, this tool adapts to market volatility by dynamically adjusting thresholds . References: 1. Dornbusch, R. (1976). Expectations and Exchange Rate Dynamics. Journal of Political Economy. 2. Obstfeld, M., & Rogoff, K. (1996). Foundations of International Macroeconomics. 3. Clarida, R., Davis, J., & Pedersen, N. (2009). Currency Carry Trade Regimes. NBER. 4. Bollinger, J. (2001). Bollinger on Bollinger Bands. 5. Mendelsohn, L. B. (2006). Forex Trading Using Intermarket Analysis.
Pine Script™ Strategie
von EdgeTools
Buy & Hold aka. HODL StrategyThis is a simply HODL or Buy & Hold strategy, which is super useful to see the risk and reward of such a strategy. The benefit of using this strategy is that you also get to see the Max Drawdown (Risk). This way you can compare it to the Net Profit (Reward) and decide if it's worth it for you. This strategy buys on the Start Date and sells either on the End Date or on the last candle if the End Date is in the future. Remember that the strategy must close the trade (sell) otherwise you don't see any results in the Strategy Tester (this is how it works).
Pine Script™ Strategie
von michaelionita
Engulfing Candlestick StrategyEver wondered whether the Bullish or Bearish Engulfing pattern works or has statistical significance? This script is for you. It works across all markets and timeframes. The Engulfing Candlestick Pattern is a widely used technical analysis pattern that traders use to predict potential price reversals. It consists of two candles: a small candle followed by a larger one that "engulfs" the previous candle. This pattern is considered bullish when it occurs in a downtrend (bullish engulfing) and bearish when it occurs in an uptrend (bearish engulfing). Statistical Significance of the Engulfing Pattern: While many traders rely on candlestick patterns for making decisions, research on the statistical significance of these patterns has produced mixed results. A study by Dimitrios K. Koutoupis and K. M. Koutoupis (2014), titled "Testing the Effectiveness of Candlestick Chart Patterns in Forex Markets," indicates that candlestick patterns, including the engulfing pattern, can provide some predictive power, but their success largely depends on the market conditions and timeframe used. The researchers concluded that while some candlestick patterns can be useful, traders must combine them with other indicators or market knowledge to improve their predictive accuracy. Another study by Brock, Lakonishok, and LeBaron (1992), "Simple Technical Trading Rules and the Stochastic Properties of Stock Returns," explores the profitability of technical indicators, including candlestick patterns, and finds that simple trading rules, such as those based on moving averages or candlestick patterns, can occasionally outperform a random walk in certain market conditions. However, Jorion (1997), in his work "The Risk of Speculation: The Case of Technical Analysis," warns that the reliability of candlestick patterns, including the engulfing patterns, can vary significantly across different markets and periods. Therefore, it's important to use these patterns as part of a broader trading strategy that includes other risk management techniques and technical indicators. Application Across Markets: This script applies to all markets (e.g., stocks, commodities, forex) and timeframes, making it a versatile tool for traders seeking to explore the statistical effectiveness of the bullish and bearish engulfing patterns in their own trading. Conclusion: This script allows you to backtest and visualize the effectiveness of the Bullish and Bearish Engulfing patterns across any market and timeframe. While the statistical significance of these patterns may vary, the script provides a clear framework for evaluating their performance in real-time trading conditions. Always remember to combine such patterns with other risk management strategies and indicators to enhance their predictive power.
Pine Script™ Strategie
von EdgeTools
Up Gap Strategy with DelayThis strategy, titled “Up Gap Strategy with Delay,” is based on identifying up gaps in the price action of an asset. A gap is defined as the percentage difference between the current bar’s open price and the previous bar’s close price. The strategy triggers a long position if the gap exceeds a user-defined threshold and includes a delay period before entering the position. After entering, the position is held for a set number of periods before being closed. Key Features: 1. Gap Threshold: The strategy defines an up gap when the gap size exceeds a specified threshold (in percentage terms). The gap threshold is an input parameter that allows customization based on the user’s preference. 2. Delay Period: After the gap occurs, the strategy waits for a delay period before initiating a long position. This delay can help mitigate any short-term volatility that might occur immediately after the gap. 3. Holding Period: Once the position is entered, it is held for a user-defined number of periods (holdingPeriods). This is to capture the potential post-gap trend continuation, as gaps often indicate strong directional momentum. 4. Gap Plotting: The strategy visually plots up gaps on the chart by placing a green label beneath the bar where the gap condition is met. Additionally, the background color turns green to highlight up-gap occurrences. 5. Exit Condition: The position is exited after the defined holding period. The strategy ensures that the position is closed after this time, regardless of whether the price is in profit or loss. Scientific Background: The gap theory has been widely studied in financial literature and is based on the premise that gaps in price often represent areas of significant support or resistance. According to research by Kaufman (2002), gaps in price action can be indicators of future price direction, particularly when they occur after a period of consolidation or a trend reversal. Moreover, Gaps and their Implications in Technical Analysis (Murphy, 1999) highlights that gaps can reflect imbalances between supply and demand, leading to high momentum and potential price continuation or reversal. In trading strategies, utilizing gaps with specific conditions, such as delay and holding periods, can enhance the ability to capture significant price moves. The strategy’s delay period helps avoid potential market noise immediately after the gap, while the holding period seeks to capitalize on the price continuation that often follows gap formation. This methodology aligns with momentum-based strategies, which rely on the persistence of trends in financial markets. Several studies, including Jegadeesh & Titman (1993), have documented the existence of momentum effects in stock prices, where past price movements can be predictive of future returns. Conclusion: This strategy incorporates gap detection and momentum principles, supported by empirical research in technical analysis, to attempt to capitalize on price movements following significant gaps. By waiting for a delay period and holding the position for a specified time, it aims to mitigate the risk associated with early volatility while maximizing the potential for sustained price moves.
Pine Script™ Strategie
von EdgeTools
IU Higher Timeframe MA Cross StrategyIU Higher Timeframe MA Cross Strategy The IU Higher Timeframe MA Cross Strategy is a versatile trading tool designed to identify trend by utilizing two customizable moving averages (MAs) across different timeframes and types. This strategy includes detailed entry and exit rules with fully configurable inputs, offering flexibility to suit various trading styles. Key Features: - Two moving averages (MA1 and MA2) with customizable types, lengths, sources, and timeframes. - Both long and short trade setups based on MA crossovers. - Integrated risk management with adjustable stop-loss and take-profit levels based on a user-defined risk-to-reward (RTR) ratio. - Clear visualization of MAs, entry points, stop-loss, and take-profit zones. Inputs: 1. Risk-to-Reward Ratio (RTR): - Defines the take-profit level in relation to the stop-loss distance. Default is 2. 2. MA1 Settings: - Source: Select the data source for calculating MA1 (e.g., close, open, high, low). Default is close. - Timeframe: Specify the timeframe for MA1 calculation. Default is 60 (60-minute chart). - Length: Set the lookback period for MA1 calculation. Default is 20. - Type: Choose the type of moving average (options: SMA, EMA, SMMA, WMA, VWMA). Default is EMA. - Smooth: Option to enable or disable smoothing of MA1 to merge gaps. Default is true. 3. MA2 Settings: - Source: Select the data source for calculating MA2 (e.g., close, open, high, low). Default is close. - Timeframe: Specify the timeframe for MA2 calculation. Default is 60 (60-minute chart). - Length: Set the lookback period for MA2 calculation. Default is 50. - Type: Choose the type of moving average (options: SMA, EMA, SMMA, WMA, VWMA). Default is EMA. - Smooth: Option to enable or disable smoothing of MA2 to merge gaps. Default is true. Entry Rules: - Long Entry: - Triggered when MA1 crosses above MA2 (crossover). - Entry is confirmed only when the bar is closed and no existing position is active. - Short Entry: - Triggered when MA1 crosses below MA2 (crossunder). - Entry is confirmed only when the bar is closed and no existing position is active. Exit Rules: - Stop-Loss: - For long positions: Set at the low of the bar preceding the entry. - For short positions: Set at the high of the bar preceding the entry. - Take-Profit: - For long positions: Calculated as (Entry Price - Stop-Loss) * RTR + Entry Price. - For short positions: Calculated as Entry Price - (Stop-Loss - Entry Price) * RTR. Visualization: - Plots MA1 and MA2 on the chart with distinct colors for easy identification. - Highlights stop-loss and take-profit levels using shaded zones for clear visual representation. - Displays the entry level for active positions. This strategy provides a robust framework for traders to identify and act on trend reversals while maintaining strict risk management. The flexibility of its inputs allows for seamless customization to adapt to various market conditions and trading preferences.
Pine Script™ Strategie
von Shivam_Mandrai
Gold Trade Setup Strategy Title: Profitable Gold Setup Strategy with Adaptive Moving Average & Supertrend Introduction: This trading strategy for Gold (XAU/USD) combines the Adaptive Moving Average (AMA) and Supertrend, tailored for high-probability setups during specific trading hours. The AMA identifies the trend, while the Supertrend confirms entry and exit points. The strategy is optimized for swing and intraday traders looking to capitalize on Gold’s price movements with precise trade timing. Strategy Components: 1. Adaptive Moving Average (AMA): • Reacts dynamically to market conditions, filtering noise in choppy markets. • Serves as the primary trend indicator. 2. Supertrend: • Confirms entry signals with clear buy and sell levels. • Acts as a trailing stop-loss to protect profits. Trading Rules: Trading Hours: • Only take trades between 8:30 AM and 10:30 PM IST. • Avoid trading outside these hours to reduce noise and low-volume setups. Buy Setup: 1. Trend Confirmation: The Adaptive Moving Average (AMA) must be green. 2. Signal Confirmation: The Supertrend should turn green after the AMA is green. 3. Trigger: Take the trade when the high of the trigger candle (the candle that turned Supertrend green) is broken. Sell Setup (Optional if included): • Reverse the rules for a short trade: AMA and Supertrend should both indicate bearish conditions (red), and take the trade when the low of the trigger candle is broken. Stop-Loss and Targets: • Place the stop-loss at the low of the trigger candle for long trades. • Set a 1:2 risk-reward ratio or use the Supertrend line as a trailing stop-loss. Timeframes: • Recommended timeframes: 1H, 4H, or Daily for swing trading. • For intraday trading, use 15-minute or 30-minute charts. Why This Strategy Works: • Combines trend-following (AMA) with momentum-based entries (Supertrend). • Focused trading hours filter out low-probability setups. • Provides precise entry, stop-loss, and target levels for disciplined trading. Conclusion: This Gold Setup Strategy is designed for traders seeking a structured approach to trading Gold. Follow the rules strictly, backtest the strategy extensively, and share your results. Let’s master the Gold market together! Tags: #Gold #XAUUSD #SwingTrading #Intraday #Supertrend #AMA #TechnicalAnalysis #GoldStrategy
Pine Script™ Strategie
von Arthavidhi
IU 4 Bar UP StrategyIU 4 Bar UP Strategy The IU 4 Bar UP Strategy is a trend-following strategy designed to identify and execute long trades during strong bullish momentum, combined with confirmation from the SuperTrend indicator. This strategy is suitable for traders aiming to capitalize on sustained upward market movements. Features : 1. SuperTrend Confirmation: Incorporates the SuperTrend indicator as a dynamic support/resistance line to filter trades in the direction of the trend. 2. 4 Consecutive Bullish Bars: Detects a series of 4 bullish candles as a signal for strong upward momentum, ensuring robust trade setups. 3. Dynamic Alerts: Sends alerts for trade entries and exits to keep traders informed. 4. Visual Enhancements: - Plots the SuperTrend indicator on the chart. - Changes the background color while a trade is active for easy visualization. Inputs : - SuperTrend ATR Period: The period used to calculate the Average True Range (ATR) for the SuperTrend indicator. - SuperTrend ATR Factor: The multiplier for the ATR in the SuperTrend calculation. Entry Conditions : A long entry is triggered when: 1. The last 4 consecutive candles are bullish (closing prices are higher than opening prices). 2. The current price is above the SuperTrend line. 3. The strategy is not already in a position. 4. The bar is confirmed (not a partially formed bar). When all these conditions are met, the strategy enters a long position and provides an alert: "Long Entry triggered" Exit Conditions : The strategy exits the long position when: 1. The closing price drops below the SuperTrend line. 2. An alert is generated: "Close the long Trade" Visualization : - The SuperTrend line is plotted, dynamically colored: - Green when the trend is bullish. - Red when the trend is bearish. - The background color turns semi-transparent green while a trade is active, indicating a long position. Do use proper risk management while using this strategy.
Pine Script™ Strategie
von Shivam_Mandrai
Temporary Help Services Jobs - Trend Allocation StrategyThis strategy is designed to capitalize on the economic trends represented by the Temporary Help Services (TEMPHELPS) index, which is published by the Federal Reserve Economic Data (FRED). Temporary Help Services Jobs are often regarded as a leading indicator of labor market conditions, as changes in temporary employment levels frequently precede broader employment trends. Methodology: Data Source: The strategy uses the FRED dataset TEMPHELPS for monthly data on temporary help services. Trend Definition: Uptrend: When the current month's value is greater than the previous month's value. Downtrend: When the current month's value is less than the previous month's value. Entry Condition: A long position is opened when an uptrend is detected, provided no position is currently held. Exit Condition: The long position is closed when a downtrend is detected. Scientific Basis: The TEMPHELPS index serves as a leading economic indicator, as noted in studies analyzing labor market cyclicality (e.g., Katz & Krueger, 1999). Temporary employment is often considered a proxy for broader economic conditions, particularly in predicting recessions or recoveries. Incorporating this index into trading strategies allows for aligning trades with potential macroeconomic shifts, as suggested by research on employment trends and market performance (Autor, 2001; Valetta & Bengali, 2013). Usage: This strategy is best suited for long-term investors or macroeconomic trend followers who wish to leverage labor market signals for equity or futures trading. It operates exclusively on end-of-month data, ensuring minimal transaction costs and noise.
Pine Script™ Strategie
von EdgeTools