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The Strategy_4Hyperopt class is a trading strategy implementation that performs backtesting. It uses various technical analysis (TA) indicators to make buy and sell decisions. Here's a breakdown of what the strategy does:
populate_indicators method:
Calculates and adds the following TA indicators to the DataFrame:
Average Directional Index (ADX)
Slow Average Directional Index (slowADX)
Commodity Channel Index (CCI)
Stochastic Fast %D and %K (fastd and fastk)
Previous values of fast %D and %K (fastd-previous and fastk-previous)
Slow Stochastic Fast %D and %K (slowfastd and slowfastk)
Previous values of slow Stochastic %D and %K (slowfastd-previous and slowfastk-previous)
Exponential Moving Average (EMA) with a time period of 5
Mean volume (average volume)
Returns the updated DataFrame with added indicators.
populate_buy_trend method: Takes the DataFrame with indicators and metadata as input.
Constructs a list of conditions based on the strategy's configuration parameters.
Each condition represents a rule for initiating a buy signal. The conditions include checks on ADX, CCI, Stochastic %D and %K values, previous values, and mean volume. If any of the conditions are met, the 'buy' column of the DataFrame is set to 1 for the corresponding row. Returns the updated DataFrame with the 'buy' column. populate_sell_trend method: Takes the DataFrame with indicators and metadata as input. Constructs a list of conditions based on the strategy's configuration parameters. Each condition represents a rule for initiating a sell signal. The conditions include checks on slowADX, Stochastic %D and %K values, previous values, and the closing price compared to the EMA. If any of the conditions are met, the 'sell' column of the DataFrame is set to 1 for the corresponding row. Returns the updated DataFrame with the 'sell' column. Overall, this strategy calculates multiple TA indicators and uses them to determine buy and sell signals based on configurable conditions.
populate_buy_trend method: Takes the DataFrame with indicators and metadata as input.
Constructs a list of conditions based on the strategy's configuration parameters.
Each condition represents a rule for initiating a buy signal. The conditions include checks on ADX, CCI, Stochastic %D and %K values, previous values, and mean volume. If any of the conditions are met, the 'buy' column of the DataFrame is set to 1 for the corresponding row. Returns the updated DataFrame with the 'buy' column. populate_sell_trend method: Takes the DataFrame with indicators and metadata as input. Constructs a list of conditions based on the strategy's configuration parameters. Each condition represents a rule for initiating a sell signal. The conditions include checks on slowADX, Stochastic %D and %K values, previous values, and the closing price compared to the EMA. If any of the conditions are met, the 'sell' column of the DataFrame is set to 1 for the corresponding row. Returns the updated DataFrame with the 'sell' column. Overall, this strategy calculates multiple TA indicators and uses them to determine buy and sell signals based on configurable conditions.
stoploss: -0.1 timeframe: 5m hash(sha256): 1e92695c2ed8ea473b4879f651fd73489854c94f66e139f4ba0661f7270e2019
Was not able to fetch indicators from Strategyfile.
last change: 2022-07-25 18:18:56