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The ReinforcedAverageStrategy is a trading strategy implemented in Python for backtesting purposes. It is designed to buy and sell assets based on crossover signals. However, it is not a highly performing strategy and is primarily intended as a proof of concept.
Here are some key features of the strategy: Minimal ROI: The minimal return on investment (ROI) for this strategy is set to 0.5.
It means that the strategy aims to achieve a minimum ROI of 0.5 before considering a sell signal.
Stoploss: The strategy has a predefined stoploss of -0.2, which means that if the asset's price drops by 20% from the entry point, the strategy will trigger a sell signal to limit potential losses. Timeframe: The strategy operates on a 4-hour timeframe, which means that it analyzes price data over 4-hour intervals. Trailing stoploss: The strategy does not utilize a trailing stoploss feature. Indicators: The strategy uses various technical indicators provided by the TA-Lib library and Freqtrade's qtpylib module. It calculates the exponential moving averages (EMA) with different time periods (8 and 21) to identify crossover signals. Additionally, it calculates Bollinger Bands and simple moving averages (SMA) for graphing purposes. Buy and sell signals: The populate_buy_trend() function populates the buy signal based on the crossover of the short-term EMA above the medium-term EMA, along with additional conditions such as the asset's price being above a resampled SMA and positive volume. The populate_sell_trend() function populates the sell signal based on the crossover of the medium-term EMA above the short-term EMA and positive volume. Please note that this strategy is provided as a starting point and may require further optimization or customization to suit specific trading requirements.
Here are some key features of the strategy: Minimal ROI: The minimal return on investment (ROI) for this strategy is set to 0.5.
It means that the strategy aims to achieve a minimum ROI of 0.5 before considering a sell signal.
Stoploss: The strategy has a predefined stoploss of -0.2, which means that if the asset's price drops by 20% from the entry point, the strategy will trigger a sell signal to limit potential losses. Timeframe: The strategy operates on a 4-hour timeframe, which means that it analyzes price data over 4-hour intervals. Trailing stoploss: The strategy does not utilize a trailing stoploss feature. Indicators: The strategy uses various technical indicators provided by the TA-Lib library and Freqtrade's qtpylib module. It calculates the exponential moving averages (EMA) with different time periods (8 and 21) to identify crossover signals. Additionally, it calculates Bollinger Bands and simple moving averages (SMA) for graphing purposes. Buy and sell signals: The populate_buy_trend() function populates the buy signal based on the crossover of the short-term EMA above the medium-term EMA, along with additional conditions such as the asset's price being above a resampled SMA and positive volume. The populate_sell_trend() function populates the sell signal based on the crossover of the medium-term EMA above the short-term EMA and positive volume. Please note that this strategy is provided as a starting point and may require further optimization or customization to suit specific trading requirements.
stoploss: -0.2 timeframe: 4h hash(sha256): 9fb9bec08a64b96822d3219baa7f6efc3a000cad999afc9d18f27ef532a4c773
Was not able to fetch indicators from Strategyfile.
last change: 2023-06-30 12:22:21