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The HyperoptableStrategy is a trading strategy that performs backtesting by applying various technical analysis (TA) indicators to a given DataFrame of exchange data. The strategy consists of three main components: indicator population, buy signal population, and sell signal population. In the populate_indicators method, the strategy adds multiple TA indicators to the DataFrame.
These indicators include ADX (Average Directional Index), MACD (Moving Average Convergence Divergence), MINUS_DI, PLUS_DI, RSI (Relative Strength Index), STOCHF (Stochastic Fast), Bollinger Bands, and EMA (Exponential Moving Average).
The indicators are calculated based on the data in the DataFrame and are stored as additional columns in the DataFrame.
The populate_buy_trend method populates the buy signal for the DataFrame based on the values of the TA indicators. The buy signal conditions include a low RSI value (self.buy_rsi.value), a low fastd value (below 35), a high ADX value (above 30), and a high plus_di value (self.buy_plusdi.value). If these conditions are met, the 'buy' column in the DataFrame is set to 1, indicating a buy signal. Similarly, the populate_sell_trend method populates the sell signal for the DataFrame based on the TA indicators. The sell signal conditions include crossing above a certain RSI value (self.sell_rsi.value), crossing above 70 for the fastd indicator, a high ADX value (above 10), and a high minus_di value (self.sell_minusdi.value). If these conditions are met, the 'sell' column in the DataFrame is set to 1, indicating a sell signal. Overall, the HyperoptableStrategy applies various TA indicators to the input DataFrame and generates buy and sell signals based on the values of these indicators. This strategy can be used for backtesting different trading strategies by evaluating the performance of the generated signals.
These indicators include ADX (Average Directional Index), MACD (Moving Average Convergence Divergence), MINUS_DI, PLUS_DI, RSI (Relative Strength Index), STOCHF (Stochastic Fast), Bollinger Bands, and EMA (Exponential Moving Average).
The indicators are calculated based on the data in the DataFrame and are stored as additional columns in the DataFrame.
The populate_buy_trend method populates the buy signal for the DataFrame based on the values of the TA indicators. The buy signal conditions include a low RSI value (self.buy_rsi.value), a low fastd value (below 35), a high ADX value (above 30), and a high plus_di value (self.buy_plusdi.value). If these conditions are met, the 'buy' column in the DataFrame is set to 1, indicating a buy signal. Similarly, the populate_sell_trend method populates the sell signal for the DataFrame based on the TA indicators. The sell signal conditions include crossing above a certain RSI value (self.sell_rsi.value), crossing above 70 for the fastd indicator, a high ADX value (above 10), and a high minus_di value (self.sell_minusdi.value). If these conditions are met, the 'sell' column in the DataFrame is set to 1, indicating a sell signal. Overall, the HyperoptableStrategy applies various TA indicators to the input DataFrame and generates buy and sell signals based on the values of these indicators. This strategy can be used for backtesting different trading strategies by evaluating the performance of the generated signals.
stoploss: -0.1 timeframe: 5m hash(sha256): 4f8f955be6c69d32c2898825f5a0ca3200ed90f6c41cbe33a5255a724e53bd56
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last change: 2022-07-11 15:59:32