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The ReinforcedQuickieHyperStrategy is a trading strategy implemented as a class in Python. It is designed to generate buy and sell signals based on technical indicators. Here is a breakdown of what the strategy does:
populate_indicators: This method calculates various technical indicators such as exponential moving averages (EMAs), Bollinger Bands, minimum and maximum values, Commodity Channel Index (CCI), Money Flow Index (MFI), and Relative Strength Index (RSI).
These indicators provide insights into market trends, volatility, and overbought/oversold conditions.
populate_buy_trend: This method populates the buy signal for the given dataframe based on the calculated indicators.
The conditions for a buy signal include: Price being below short-term and medium-term EMAs. Price being at the minimum value. Price being below the lower Bollinger Band. A specific pattern in the moving average and price levels, combined with indicators such as CCI, RSI, and MFI. Volume being lower than a rolling average and other conditions related to a resampled simple moving average. populate_sell_trend: This method populates the sell signal for the given dataframe based on the calculated indicators. The conditions for a sell signal include: Price being above short-term and medium-term EMAs. Price being at or above the maximum value. Price being above the upper Bollinger Band. The MFI indicator being above 80. A specific pattern in the opening and closing prices, combined with the RSI indicator. Resampling and merging: The code resamples the original dataframe to a specified interval, aggregates the OHLC (open, high, low, close) values, calculates a simple moving average, and interpolates missing values. The resampled data is then merged with the original dataframe. The HyperOpt class is not provided, so its functionality is unknown based on the given code.
These indicators provide insights into market trends, volatility, and overbought/oversold conditions.
populate_buy_trend: This method populates the buy signal for the given dataframe based on the calculated indicators.
The conditions for a buy signal include: Price being below short-term and medium-term EMAs. Price being at the minimum value. Price being below the lower Bollinger Band. A specific pattern in the moving average and price levels, combined with indicators such as CCI, RSI, and MFI. Volume being lower than a rolling average and other conditions related to a resampled simple moving average. populate_sell_trend: This method populates the sell signal for the given dataframe based on the calculated indicators. The conditions for a sell signal include: Price being above short-term and medium-term EMAs. Price being at or above the maximum value. Price being above the upper Bollinger Band. The MFI indicator being above 80. A specific pattern in the opening and closing prices, combined with the RSI indicator. Resampling and merging: The code resamples the original dataframe to a specified interval, aggregates the OHLC (open, high, low, close) values, calculates a simple moving average, and interpolates missing values. The resampled data is then merged with the original dataframe. The HyperOpt class is not provided, so its functionality is unknown based on the given code.
stoploss: -0.05 timeframe: 5m hash(sha256): b2694ca58a3f5e8f6f48751976814c6bf4e7b65b9686af1f09b0206e458057a7 indicators: upper close bb_lowerband mfi macdhist volume average date max open high macdsignal mid cci macd open high low close min lower bb_middleband rsi bb_upperband low resample_sma
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last change: 2022-07-02 19:54:08