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The "GodStraNewOpt" strategy is designed to backtest trading strategies. Here is a short description of what the strategy does:
The strategy has two main methods: "populate_indicators" and "populate_buy_trend" and "populate_sell_trend". The "populate_indicators" method is responsible for calculating indicators and optimizing the strategy.
However, in this particular strategy, it only calculates default values of hyperoptable parameters and does not provide significant benefits.
The useful calculations of indicators are performed inside the "populate_buy_trend" and "populate_sell_trend" methods.
The "populate_buy_trend" method populates the buy signals based on certain conditions. It generates conditions using multiple indicators and their corresponding parameters. The method iterates over three sets of indicators and their parameters, generates conditions using a helper function called "condition_generator," and appends them to a list. If any conditions are generated, the strategy sets the 'buy' column of the dataframe to 1 for the rows that satisfy all the conditions. The "populate_sell_trend" method is similar to the "populate_buy_trend" method but populates the sell signals instead. It also iterates over three sets of indicators and their parameters, generates conditions using the "condition_generator" function, and appends them to a list. If any conditions are generated, the strategy sets the 'sell' column of the dataframe to 1 for the rows that satisfy all the conditions. Overall, the strategy calculates indicators, generates buy and sell signals based on specified conditions, and updates the dataframe accordingly for further analysis and evaluation during backtesting.
However, in this particular strategy, it only calculates default values of hyperoptable parameters and does not provide significant benefits.
The useful calculations of indicators are performed inside the "populate_buy_trend" and "populate_sell_trend" methods.
The "populate_buy_trend" method populates the buy signals based on certain conditions. It generates conditions using multiple indicators and their corresponding parameters. The method iterates over three sets of indicators and their parameters, generates conditions using a helper function called "condition_generator," and appends them to a list. If any conditions are generated, the strategy sets the 'buy' column of the dataframe to 1 for the rows that satisfy all the conditions. The "populate_sell_trend" method is similar to the "populate_buy_trend" method but populates the sell signals instead. It also iterates over three sets of indicators and their parameters, generates conditions using the "condition_generator" function, and appends them to a list. If any conditions are generated, the strategy sets the 'sell' column of the dataframe to 1 for the rows that satisfy all the conditions. Overall, the strategy calculates indicators, generates buy and sell signals based on specified conditions, and updates the dataframe accordingly for further analysis and evaluation during backtesting.
stoploss: -0.98 timeframe: 5m hash(sha256): b0c12c06e821646dcc8ef84bfc95a8c1d6b22c09f6f7b31d5d21cdd6a3fcff49 indicators: volume indicator_trend_sma crossed_indicator indicator sharp_indicator
No similar strategies found. (based on used indicators)
last change: 2024-04-28 05:07:23