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The ADXMomentumOtimizeHyperOpt strategy is designed to backtest trading signals based on several technical indicators. Here's a short description of what the strategy does:
The strategy uses the following indicators:
ADX (Average Directional Index): Measures the strength of a market trend. PLUS_DI and MINUS_DI: Positive and negative directional indicators that help determine the market direction.
SAR (Stop and Reverse): Identifies potential trend reversals and provides stop-loss levels.
MOM (Momentum): Measures the rate of price change over a specified period.
The strategy has predefined parameters for ROI (Return on Investment) targets and a stop-loss level. The ideal timeframe for this strategy is 5 minutes. The strategy requires at least 20 candles (time periods) before generating valid signals. The populate_indicators function calculates the values of the indicators mentioned above and adds them to the input dataframe. The populate_buy_trend function generates a "buy" signal when specific conditions are met, including high ADX, positive momentum, and the positive directional indicator (DI+) being higher than the negative directional indicator (DI-). The populate_sell_trend function generates a "sell" signal when specific conditions are met, including high ADX, negative momentum, and the negative directional indicator (DI-) being higher than the positive directional indicator (DI+). By backtesting this strategy using historical price data, you can evaluate its performance and determine its effectiveness in generating profitable trading signals.
SAR (Stop and Reverse): Identifies potential trend reversals and provides stop-loss levels.
MOM (Momentum): Measures the rate of price change over a specified period.
The strategy has predefined parameters for ROI (Return on Investment) targets and a stop-loss level. The ideal timeframe for this strategy is 5 minutes. The strategy requires at least 20 candles (time periods) before generating valid signals. The populate_indicators function calculates the values of the indicators mentioned above and adds them to the input dataframe. The populate_buy_trend function generates a "buy" signal when specific conditions are met, including high ADX, positive momentum, and the positive directional indicator (DI+) being higher than the negative directional indicator (DI-). The populate_sell_trend function generates a "sell" signal when specific conditions are met, including high ADX, negative momentum, and the negative directional indicator (DI-) being higher than the positive directional indicator (DI+). By backtesting this strategy using historical price data, you can evaluate its performance and determine its effectiveness in generating profitable trading signals.
startup_candle_count : 20 sar: -0.277%
stoploss: -0.219 timeframe: 5m hash(sha256): c0a29e7986667e073dae9312a05dfb3bfe2a8d52b434b86ee305d8d0d538f19e indicators: adx di mom di #Stop sar
No similar strategies found. (based on used indicators)
last change: 2023-07-02 21:21:55