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The MarketChyperHyperTestStrategy is a trading strategy implemented as a class that extends the IStrategy interface. It is designed to backtest trading strategies using various technical indicators. Here is a breakdown of what the strategy does:
populate_indicators: This method adds several technical indicators to the given DataFrame.
The indicators include ADX (Average Directional Index), PLUS_DI, MINUS_DI, RSI (Relative Strength Index), MACD (Moving Average Convergence Divergence), and others.
The indicators are calculated based on the provided DataFrame.
populate_buy_trend: This method populates the buy signal for the DataFrame based on the calculated indicators and specified conditions. It determines the buy signal strength based on the trend (downwards, sideways, or upwards) and other factors like Wavetrend and RSI divergence. The buy signal strength is accumulated in the 'total_buy_signal_strength' column of the DataFrame. populate_sell_trend: This method populates the sell signal for the DataFrame based on the calculated indicators and specified conditions. Similar to the buy trend, it considers the trend, Wavetrend, and RSI divergence to determine the sell signal strength. The sell signal strength is accumulated in the 'total_sell_signal_strength' column of the DataFrame. The strategy aims to generate buy and sell signals based on the provided indicators and conditions, taking into account different trends and signal strengths. The specific conditions and weights for each signal can be adjusted by setting corresponding values for the strategy's parameters. Please note that the provided code snippet is incomplete and may require additional context or dependencies to fully understand and execute.
The indicators include ADX (Average Directional Index), PLUS_DI, MINUS_DI, RSI (Relative Strength Index), MACD (Moving Average Convergence Divergence), and others.
The indicators are calculated based on the provided DataFrame.
populate_buy_trend: This method populates the buy signal for the DataFrame based on the calculated indicators and specified conditions. It determines the buy signal strength based on the trend (downwards, sideways, or upwards) and other factors like Wavetrend and RSI divergence. The buy signal strength is accumulated in the 'total_buy_signal_strength' column of the DataFrame. populate_sell_trend: This method populates the sell signal for the DataFrame based on the calculated indicators and specified conditions. Similar to the buy trend, it considers the trend, Wavetrend, and RSI divergence to determine the sell signal strength. The sell signal strength is accumulated in the 'total_sell_signal_strength' column of the DataFrame. The strategy aims to generate buy and sell signals based on the provided indicators and conditions, taking into account different trends and signal strengths. The specific conditions and weights for each signal can be adjusted by setting corresponding values for the strategy's parameters. Please note that the provided code snippet is incomplete and may require additional context or dependencies to fully understand and execute.
stoploss: -0.124 timeframe: 30m hash(sha256): a4100aeae763738afea8aac849c799f816b11d391577954ecabdd8eea1a9be5f indicators: wtOversold wtCrossDown total_buy_signal_strength wtCrossUp wtVwap close ap crossed_above tci wt1 crossed_below trend wt2 esa plus_di bearish_div high macdsignal ci buy_wavetrend_weight apdataframeesa bullish_div macd buy_rsi_divergence_weight adx minus_di rsi total_sell_signal_strength wtOverbought sell_rsi_divergence_weight low sell_wavetrend_weight
Similar Strategies: (based on used indicators)
Strategy: MCHS, Similarity Score: 96.97%
Strategy: MCHSv2, Similarity Score: 96.97%
Strategy: MCHSv3, Similarity Score: 96.97%
Strategy: MCHSv4, Similarity Score: 96.97%
Strategy: MarketChyperHyperStrategy, Similarity Score: 96.97%
last change: 2023-02-04 11:03:27