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The Strategy041_2 strategy is designed to backtest trading strategies by adding various technical analysis indicators to a given DataFrame. Here's a brief description of what the strategy does:
populate_indicators: This method adds multiple technical analysis indicators to the DataFrame, including WaveTrend, MACD, Bollinger Bands, ADX, MFI, Momentum, RSI, Fisher RSI, Stochastic Fast, Awesome Oscillator, SAR, and moving averages (SMA). These indicators provide information about the price trend, volatility, momentum, and other factors relevant to trading.
populate_buy_trend: Based on the populated indicators, this method populates the buy signal for the DataFrame.
It applies a set of conditions that must be met for a buy signal to be generated.
These conditions involve indicators such as ADX, Stochastic Fast, closing price, Bollinger Bands, and MFI. If the conditions are met, the 'buy' column in the DataFrame is set to 1. populate_sell_trend: Similar to the buy signal, this method populates the sell signal for the DataFrame based on the populated indicators. It applies conditions involving indicators such as MACD, Awesome Oscillator, SAR, and Fisher RSI. If the conditions are met, the 'sell' column in the DataFrame is set to 1. Other calculations: There are additional calculations performed in the code. One calculates the Chaikin Money Flow (CMF) indicator using close price, low price, high price, and volume. Another part resamples the DataFrame to a different time interval and calculates moving averages for the resampled data. The resampled data is then merged back into the original DataFrame. Overall, this strategy combines a variety of technical analysis indicators to generate buy and sell signals based on predefined conditions. It aims to test the effectiveness of these signals in backtesting trading strategies.
populate_buy_trend: Based on the populated indicators, this method populates the buy signal for the DataFrame.
It applies a set of conditions that must be met for a buy signal to be generated.
These conditions involve indicators such as ADX, Stochastic Fast, closing price, Bollinger Bands, and MFI. If the conditions are met, the 'buy' column in the DataFrame is set to 1. populate_sell_trend: Similar to the buy signal, this method populates the sell signal for the DataFrame based on the populated indicators. It applies conditions involving indicators such as MACD, Awesome Oscillator, SAR, and Fisher RSI. If the conditions are met, the 'sell' column in the DataFrame is set to 1. Other calculations: There are additional calculations performed in the code. One calculates the Chaikin Money Flow (CMF) indicator using close price, low price, high price, and volume. Another part resamples the DataFrame to a different time interval and calculates moving averages for the resampled data. The resampled data is then merged back into the original DataFrame. Overall, this strategy combines a variety of technical analysis indicators to generate buy and sell signals based on predefined conditions. It aims to test the effectiveness of these signals in backtesting trading strategies.
stoploss: -0.1 timeframe: 5m hash(sha256): 9b4a4e8e2fec6f19aaa138da0102cd513e5e2f786f4f7f8c7e6688a43bf34571 indicators: resample_medium wave1 resample_long close wave2 bb_lowerband mfi fastk tci wt1 volume hlc3 esa wt2 resample_short wt1wt2 mom date fastd sar high ci macdsignal sma_40 fisher_rsi fisher_rsi_norma macd open high low close adx lower minus_di rsi ao mom_trend low resample_sma sma_100
No similar strategies found. (based on used indicators)
last change: 2023-07-05 16:46:24