The "GodStraNew" strategy is designed for backtesting trading strategies. Here's a breakdown of its functionality:
The strategy defines two main methods: "populate_indicators" and "populate_buy_trend" and "populate_sell_trend". The "populate_indicators" method calculates default values for hyperoptable parameters and returns the input dataframe without making any modifications.
The "populate_buy_trend" method generates buy signals based on specified conditions.
It initializes an empty list called "conditions" and then proceeds to generate conditions using various indicators and their corresponding values.
The "condition_generator" function is called to generate the conditions. The generated conditions are added to the "conditions" list. If any conditions exist, the strategy marks the corresponding rows in the "buy" column of the dataframe as 1. The "populate_sell_trend" method is similar to the "populate_buy_trend" method but generates sell signals instead. It also initializes an empty list called "conditions" and generates conditions using sell indicators and their values. The conditions are added to the "conditions" list, and if any conditions exist, the strategy marks the corresponding rows in the "sell" column of the dataframe as 1. The strategy returns the modified dataframe with buy and sell signals marked. Overall, the strategy calculates indicators, generates buy and sell signals based on specified conditions, and marks the corresponding rows in the dataframe to indicate the signals.
The "populate_buy_trend" method generates buy signals based on specified conditions.
It initializes an empty list called "conditions" and then proceeds to generate conditions using various indicators and their corresponding values.
The "condition_generator" function is called to generate the conditions. The generated conditions are added to the "conditions" list. If any conditions exist, the strategy marks the corresponding rows in the "buy" column of the dataframe as 1. The "populate_sell_trend" method is similar to the "populate_buy_trend" method but generates sell signals instead. It also initializes an empty list called "conditions" and generates conditions using sell indicators and their values. The conditions are added to the "conditions" list, and if any conditions exist, the strategy marks the corresponding rows in the "sell" column of the dataframe as 1. The strategy returns the modified dataframe with buy and sell signals marked. Overall, the strategy calculates indicators, generates buy and sell signals based on specified conditions, and marks the corresponding rows in the dataframe to indicate the signals.
stoploss: -0.128 timeframe: 4h hash(sha256): 9805a00995fee0e307dc4adb4e12327249fc622f48d60c4c49b8efe7e878e2a5 indicators: Momentum Indicators volume Statistic Functions Overlap Studies Pattern Recognition indicator_trend_sma crossed_indicator Price Transform indicator Volume Indicators sharp_indicator Cycle Indicators Volatility Indicators
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last change: 2024-04-29 12:53:04