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The GodStraNew strategy is a backtesting strategy that aims to optimize trading decisions based on indicators. Here's a short description of what the strategy does:
The populate_indicators method calculates default values of hyperoptable parameters and is used for optimization purposes. However, in this strategy, the actual calculation of specific indicators in specific time periods is done inside the populate_buy_trend and populate_sell_trend methods if needed.
The populate_buy_trend method populates the buy signals based on a set of conditions.
It retrieves indicator values, crossed indicators, operators, and numerical values for buying from the strategy's parameters.
Then, it generates conditions using the condition_generator function and appends them to a list. If any conditions are present, it applies them to the DataFrame by setting the 'buy' column to 1. The populate_sell_trend method is similar to populate_buy_trend but populates the sell signals instead. It retrieves sell-related parameters and generates conditions accordingly. If any conditions are present, it applies them to the DataFrame by setting the 'sell' column to 1. Overall, this strategy calculates indicators, generates buy and sell conditions based on indicator values, and applies those conditions to the DataFrame to generate buy and sell signals.
The populate_buy_trend method populates the buy signals based on a set of conditions.
It retrieves indicator values, crossed indicators, operators, and numerical values for buying from the strategy's parameters.
Then, it generates conditions using the condition_generator function and appends them to a list. If any conditions are present, it applies them to the DataFrame by setting the 'buy' column to 1. The populate_sell_trend method is similar to populate_buy_trend but populates the sell signals instead. It retrieves sell-related parameters and generates conditions accordingly. If any conditions are present, it applies them to the DataFrame by setting the 'sell' column to 1. Overall, this strategy calculates indicators, generates buy and sell conditions based on indicator values, and applies those conditions to the DataFrame to generate buy and sell signals.
stoploss: -0.128 timeframe: 4h hash(sha256): a01128993c3f72a9f8e4946938af288cf6d0a1a46b0cd48d3d10f493f62f4dfe 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 21:10:58