Rangetrading — Indicateurs et Stratégies

AM Range Sniper [jmaxxx]AM Range Sniper Overview AM Range Sniper is a sophisticated morning session trading strategy designed for Micro E-mini Nasdaq-100 Index Futures (MNQ). This strategy capitalizes on the critical 8:30-9:30 AM EST range formation period, implementing precise entry and exit mechanics with advanced risk management. Key Features 🕐 Time-Based Range Analysis Range Definition: Automatically identifies and tracks the 8:30-9:30 AM EST range Trading Window: Active trading from 9:30 AM to 11:00 AM EST (extended for second chance trades) Session Management: Daily reset ensures clean state for each trading session 🎯 Multiple Entry Patterns Breakthrough/Retest: Captures price breakthroughs above range with retest opportunities Long/Short Opportunities: Comprehensive coverage of both directional moves Breakdown: Identifies bearish breakdowns below range support Break Up: Detects bullish breakups above range resistance Range Sweeps: Monitors for range high/low sweeps with reversal entries ⚡ Advanced Risk Management Configurable Stop Losses: Tick-based stop losses for each trade type Take Profit Targets: Automatic target calculations based on range size Hard Close Protection: Automatic position closure at 4 PM EST Second Chance Feature: Optional second trade opportunity if first trade loses 🔧 Professional Features Visual Stop Loss Lines: Real-time stop loss visualization on chart Debug Information Panel: Comprehensive status monitoring Alert Integration: Customizable alert messages for entries/exits Flexible Time Settings: Adjustable for different timezones Strategy Logic Range Formation (8:30-9:30 AM) The strategy monitors the first hour of trading to establish the day's range. This range serves as the foundation for all subsequent trading decisions. Entry Conditions Breakthrough: Price breaks above range high with retest rejection Breakdown: Price breaks below range low with confirmed bearish momentum Break Up: Price breaks above range high with strong bullish confirmation Sweep Entries: Range high/low sweeps followed by reversal signals Risk Management Stop Loss: Configurable tick-based stops for each trade type Take Profit: 1.5x range size targets for breakdown/breakup trades Position Sizing: Percentage-based position sizing Session Limits: Maximum 2 trades per session (with second chance feature) Settings & Customization Core Parameters Enable/disable individual entry patterns Configurable stop loss levels (1-500 ticks) Second chance feature toggle Previous day level integration Visual Customization Customizable stop loss colors and widths Debug panel visibility Range line styling Alert Configuration Custom entry/exit alert messages ***** Automate With ***** APEX NinjaTrader Crosstrade.io ( promo code JMAXXX ) Performance & Reliability Precision Focused: Waits for high-probability setups Risk-Aware: Comprehensive stop loss and position management Session-Based: Clean daily resets prevent carryover issues Professional Grade: Designed for serious traders Ideal For Day Traders: Morning session specialists Futures Traders: MNQ and similar instruments Range Traders: Traders who capitalize on range breakouts Risk-Conscious Traders: Those who prioritize risk management Disclaimer This strategy is for educational and informational purposes. Past performance does not guarantee future results. Always test thoroughly on historical data and paper trading before live implementation. Risk management is crucial - never risk more than you can afford to lose. Created by jmaxxx - Professional trading strategy developer For questions, feedback, or customization requests, please leave a comment below.

Dynamic RSI Mean Reversion Strategy Dynamic RSI Mean Reversion Strategy Overview: This strategy uses an RSI with ATR-Adjusted OB/OS levels in order to enhance the quality of it's mean reversion trades. It also incorporates a form of trend filtering in an effort to minimize downside and maximize upside. The backtest has fewer trades, as it uses substantial filtering to enhance trade quality. As you can see, I didn't cherry pick the results, so the results aren't the most beautiful thing you'll see in your life. I did this to ensure nobody gets misled. If you need a higher frequency of trades, consider removing the trend filter or increasing the length of the EMAs used for trend detection. Features: Dynamic OB/OS Levels: Uses ATR to adjust overbought and oversold thresholds dynamically, making the RSI more responsive in varying volatility conditions. This approach enhances signal strength by expanding the RSI range in high volatility and tightening it in low volatility. Mean Reversion Focus: Designed for mean reversion but incorporates a trend-following filter to reduce countertrend trades. When the RSI is high, it often indicates an uptrend, so a trend filter prevents shorting in these cases and the same goes for downtrends and longing. Trend Filtering: A moving average cross trend filter checks for the trend direction, with the RSI signal line color-coded to reflect trend shifts. Entries occur when the RSI crosses above or below the dynamic thresholds and is not a countertrend trade. Stop Losses: Stop losses are set based on ATR distance from the entry price, providing volatility-adjusted protection. Note: If you're using this strategy on assets with a higher price, remember to increase the initial capital in the strategy settings. Otherwise, the strategy won't generate any (or many) trades and you'll end up with some inaccurate results. Recommended Use: Test it on different assets and timeframes. I’ve found the best results with standard RSI inputs, a relatively slow ATR, and a slower MA cross for trend filtering. Thus, the defaults are set that way. If the trend metrics are too slow, you’ll filter out too many good trades while allowing crummy ones; if too fast, most trades may be filtered out. As always, this has a lot of configurability so experiment to find the balance that works for your trading style.

Z-Score RSI Strategy Overview The Z-Score RSI Indicator is an experimental take on momentum analysis. By applying the Relative Strength Index (RSI) to a Z-score of price data, it measures how far prices deviate from their mean, scaled by standard deviation. This isn’t your traditional use of RSI, which is typically based on price data alone. Nevertheless, this unconventional approach can yield unique insights into market trends and potential reversals. Theory and Interpretation The RSI calculates the balance between average gains and losses over a set period, outputting values from 0 to 100. Typically, people look at the overbought or oversold levels to identify momentum extremes that might be likely to lead to a reversal. However, I’ve often found that RSI can be effective for trend-following when observing the crossover of its moving average with the midline or the crossover of the RSI with its own moving average. These crossovers can provide useful trend signals in various market conditions. By combining RSI with a Z-score of price, this indicator estimates the relative strength of the price’s distance from its mean. Positive Z-score trends may signal a potential for higher-than-average prices in the near future (scaled by the standard deviation), while negative trends suggest the opposite. Essentially, when the Z-Score RSI indicates a trend, it reflects that the Z-score (the distance between the average and current price) is likely to continue moving in the trend’s direction. Generally, this signals a potential price movement, though it’s important to note that this could also occur if there’s a shift in the mean or standard deviation, rather than a meaningful change in price itself. While the Z-Score RSI could be an insightful addition to a comprehensive trading system, it should be interpreted carefully. Mean shifts may validate the indicator’s predictions without necessarily indicating any notable price change, meaning it’s best used in tandem with other indicators or strategies. Recommendations Before putting this indicator to use, conduct thorough backtesting and avoid overfitting. The added parameters allow fine-tuning to fit various assets, but be careful not to optimize purely for the highest historical returns. Doing so may create an overly tailored strategy that performs well in backtests but fails in live markets. Keep it balanced and look for robust performance across multiple scenarios, as overfitting is likely to lead to disappointing real-world results.

Megabar Breakout (Range & Volume & RSI)Hey there, This strategy is based on the idea that certain events lead to what are called Megabars. Megabars are bars that have a very large range and volume. I wanted to verify whether these bars indicate the start of a trend and whether one should follow the trend. Summary of the Code: The code is based on three indicators: the range of the bar, the volume of the bar, and the RSI. When certain values of these indicators are met, a Megabar is identified. The direction of the Megabar indicates the direction in which we should trade. Why do I combine these indicators? I want to identify special bars that have the potential to mark the beginning of a breakout. Therefore, a bar needs to exhibit high volume, have a large range (huge price movement), and we also use the Relative Strength Index (RSI) to assess potential momentum. Only if all three criteria are met within one candle, do we use this as an identifier for a megabar. Explanation of Drawings on the Chart: As you can see, there is a green background on my chart. The green background symbolizes the time when I'm entering a trade. Only if a Megabar happens during that time, I'm ready to enter a trade. The time is between 6 AM and 4 PM CET. It's just because I prefer that time. Also, the strategy draws an error every time a Megabar happens based on VOL and Range only (not on the RSI). That makes it pretty easy to go through your chart and check the biggest bars manually. You can activate or deactivate these settings via the input data of the strategy. When Do We Enter a Trade? We wait for a Megabar to happen during our trading session. If the Megabar is bullish, we open a LONG trade at the opening price of the next candle. If the Megabar is bearish, we open a SHORT trade at the opening price of the next candle. Where Do We Put Our Take Profit & Stop Loss? The default setting is TP = 40 Pips and SL = 30 Pips. In that case, we are always trading with a risk-reward ratio of 1.33 by default. You can easily change these settings via the input data of the strategy. Strategy Results The criteria for Megabars were chosen by me in a way that makes Megabars something special. They are not intended to occur too frequently, as the fundamental idea of this strategy would otherwise not hold. This results in only 37 closed trades within the last 12 months. If you change the criterias for a megabar to a milder one, you will create more Megabars and therefore more trades. It's up to you. I have adapted this strategy to the 30-minute chart of the EURUSD. In the evaluation, we consider a period of 12 months, which I believe is sufficient. My default settings for the indicators look like this: Avg Length Vol 20 Avg Multiplier Vol 3 Avg Length Range 20 Avg Multiplier Range 4 Value SMA RSI for Long Trades 50 Value SMA RSI for Short Trades 70 IMPORTANT: The current performance overview does not display the results of these settings. Please change the settings to my default ones so that you can see how I use this strategy. I do not recommend trading this strategy without further testing. The script is meant to reflect a basic idea and be used as a tool to identify Megabars. I have made this strategy completely public so that it can be further developed. One can take this framework and test it on different timeframes and different markets.

Range Breaker Strategy Description: Range Breaker The Range Breaker strategy is a breakout trading strategy that aims to capture profits when the price of a financial instrument moves out of a defined range. The strategy identifies swing highs and swing lows over a specified lookback period and enters long or short positions when the price breaks above the swing high or below the swing low, respectively. It also employs stop targets based on a percentage to manage risk and protect profits. Beginner's Guide: Understand the concepts: a. Swing High: A swing high is a local peak in price where the price is higher than the surrounding prices. b. Swing Low: A swing low is a local trough in price where the price is lower than the surrounding prices. c. Lookback Period: The number of bars or periods the strategy analyzes to determine swing highs and swing lows. d. Stop Target: A predetermined price level at which the strategy will exit the position to manage risk and protect profits. Configure the strategy: a. Set the initial capital, order size, commission, and pyramiding as needed for your specific trading account. b. Choose the desired lookback period to identify the swing highs and lows. c. Set the stop target multiplier and stop target percentage as desired to manage risk and protect profits. Backtest the strategy: a. Set the backtest start date to analyze the strategy's historical performance. b. Observe the backtesting results to evaluate the strategy's effectiveness and adjust the parameters if necessary. Implement the strategy: a. Apply the strategy to your preferred financial instrument on the TradingView platform. b. Monitor the strategy's performance and adjust the parameters as needed to optimize its effectiveness. Risk management: a. Always use a stop target to protect your trading capital and manage risk. b. Don't risk more than a small percentage of your trading capital on a single trade. c. Be prepared to adjust the strategy or stop trading it if the market conditions change significantly. Adjusting the Lookback Period and Timeframes for Optimal Strategy Performance The Range Breaker strategy uses a lookback period to identify swing highs and lows, which serve as the basis for determining entry and exit points for long and short positions. By adjusting the lookback period and analyzing different timeframes, you can potentially find the best strategy configuration for each specific asset. Adjusting the lookback period: The lookback period is a critical parameter that affects the sensitivity of the strategy to price movements. A shorter lookback period will make the strategy more sensitive to smaller price fluctuations, resulting in more frequent trading signals. On the other hand, a longer lookback period will make the strategy less sensitive, generating fewer signals but potentially capturing larger price movements. To optimize the lookback period for a specific asset, you can test different lookback values and compare their performance in terms of risk-adjusted returns, win rate, and other relevant metrics. Keep in mind that using an overly short lookback period may lead to overtrading and increased transaction costs, while an overly long lookback period may cause the strategy to miss profitable trading opportunities. Analyzing different timeframes: Timeframes refer to the duration of each bar or candlestick on the chart. Shorter timeframes (e.g., 5-minute, 15-minute, or 30-minute) focus on intraday price movements, while longer timeframes (e.g., daily, weekly, or monthly) capture longer-term trends. The choice of timeframe affects the number of trading signals generated by the strategy and the length of time each position is held. To find the best strategy for each asset, you can test the Range Breaker strategy on different timeframes and analyze its performance. Keep in mind that shorter timeframes may require more active monitoring and management due to the increased frequency of trading signals. Longer timeframes, on the other hand, may require more patience as positions are held for extended periods. Finding the best strategy for each asset: Every asset has unique price characteristics that may affect the performance of a trading strategy. To find the best strategy for each asset, you should: a. Test various lookback periods and timeframes, observing the strategy's performance in terms of profitability, risk-adjusted returns, and win rate. b. Consider the asset's historical price behavior, such as its volatility, liquidity, and trend-following or mean-reverting tendencies. c. Evaluate the strategy's performance during different market conditions, such as bullish, bearish, or sideways markets, to ensure its robustness. d. Keep in mind that each asset may require a unique set of strategy parameters for optimal performance, and there may be no one-size-fits-all solution. By experimenting with different lookback periods and timeframes, you can fine-tune the Range Breaker strategy for each specific asset, potentially improving its overall performance and adaptability to changing market conditions. Always practice proper risk management and be prepared to make adjustments as needed. Remember that trading strategies carry inherent risk, and past performance is not indicative of future results. Always practice proper risk management and consider your own risk tolerance before trading with real money.

Strategy - Bobo PAPATR Hi I've revamped this bot mentioned in the linked idea to make it work with v4 of pine. In doing so there are some very significant changes to how it works. The main one is that it no longer uses traditional daily pivot calculations to calculate the bands. It creates a more dynamic intraday set of pivot points based on recent price action rather than yesterday's ohlc. As published, the bot is tuned for a 15 min time frame. But it actually works well on lower time frames you just need to adjust the lookback periods in settings a bit to re tune it. It's also tuned to ES really but will need tweaking for a different instrument at the very least. The basic concept is recent price action is used to calculate a 'middle' around which red and green bands are located. Their position or width is largely determined by recent volatility. The middle line is again calculated from recent price action. The three lines from that form a tradeable range with green at the top and red at the bottom. The strategy is simple enough, it shorts as it sinks from outside red, and longs when rising above green. The basic principle being that once you enter that range you have a high probability of hitting the middle before you hit your stop loss. So the basic principle is you are trying to capture the inherent ranginess of liquid indices like S&P 500. That back and forth movement that happens. The bot is capturing this by fading extremes of a recent range but the problem with that is you'dd get murdered in a strong trend. To mitigate that there is a trend calculation running in the background the will prevent trading against firm trends mostly. So the bot should trade mostly in rangy conditions because that is what it is trying to do. Bot will close issue close signals automatically upon crossing the middle, it also will close automatically at predefined stops or limits. These values are denominated in market mintick values. For example the CFD SPX500 has a mintick of 0.1. Therefore a stop value of 100 will equate to 10 points on the index. If trading the same market via ES1! the mintick value is different - 0.25. So in this case a value of 40 is required to set the stop at 10 points. Anyway shout if you have questions. Hope it's useful. TVC:SPX OANDA:SPX500USD

Forex Master v4.0 (EUR/USD Mean-Reversion Algorithm)DESCRIPTION Forex Master v4.0 is a mean-reversion algorithm currently optimized for trading the EUR/USD pair on the 5M chart interval. All indicator inputs use the period's closing price and all trades are executed at the open of the period following the period where the trade signal was generated. There are 3 main components that make up Forex Master v4.0: I. Trend Filter The algorithm uses a version of the ADX indicator as a trend filter to trade only in certain time periods where price is more likely to be range-bound (i.e., mean-reverting). This indicator is composed of a Fast ADX and a Slow ADX, both using the same look-back period of 50. However, the Fast ADX is smoothed with a 6-period EMA and the Slow ADX is smoothed with a 12-period EMA. When the Fast ADX is above the Slow ADX, the algorithm does not trade because this indicates that price is likelier to trend, which is bad for a mean-reversion system. Conversely, when the Fast ADX is below the Slow ADX, price is likelier to be ranging so this is the only time when the algorithm is allowed to trade. II. Bollinger Bands When allowed to trade by the Trend Filter, the algorithm uses the Bollinger Bands indicator to enter long and short positions. The Bolliger Bands indicator has a look-back period of 20 and a standard deviation of 1.5 for both upper and lower bands. When price crosses over the lower band, a Long Signal is generated and a long position is entered. When price crosses under the upper band, a Short Signal is generated and a short position is entered. III. Money Management Rule 1 - Each trade will use a limit order for a fixed quantity of 50,000 contracts (0.50 lot). The only exception is Rule Rule 2 - Order pyramiding is enabled and up to 10 consecutive orders of the same signal can be executed (for example: 14 consecutive Long Signals are generated over 8 hours and the algorithm sends in 10 different buy orders at various prices for a total of 350,000 contracts). Rule 3 - Every order will include a bracket with both TP and SL set at 50 pips (note: the algorithm only closes the current open position and does not enter the opposite trade once a TP or SL has been hit). Rule 4 - When a new opposite trade signal is generated, the algorithm sends in a larger order to close the current open position as well as open a new one (for example: 14 consecutive Long Signals are generated over 8 hours and the algorithm sends in 10 different buy orders at various prices for a total of 350,000 contracts. A Short Signal is generated shortly after the 14th Long Signal. The algorithm then sends in a sell order for 400,000 contracts to close the 350,000 contracts long position and open a new short position of 50,000 contracts).