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The "GodStraNew" strategy is designed for backtesting trading strategies on a website. It implements the IStrategy interface. Here's a brief description of what the strategy does:
populate_indicators: This method calculates default values of hyperoptable parameters and can calculate indicators for all time periods.
However, in this strategy, specific indicators are calculated inside the buy and sell strategy populator methods when needed.
Therefore, using this method doesn't provide significant benefits apart from calculating default values.
populate_buy_trend: This method populates the "buy" trend in the dataframe based on specified conditions. It retrieves indicator values and settings for three sets of indicators (buy_indicator0, buy_indicator1, buy_indicator2) along with their corresponding crossed indicators, operators, and real numbers. It uses a condition generator function to generate conditions based on the provided parameters and appends them to a list. If any conditions are present, it sets the "buy" column to 1 for the rows where all conditions are satisfied. populate_sell_trend: This method populates the "sell" trend in the dataframe using a similar approach to the populate_buy_trend method. It retrieves indicator values and settings for three sets of indicators (sell_indicator0, sell_indicator1, sell_indicator2) along with their corresponding crossed indicators, operators, and real numbers. It generates conditions based on the parameters and appends them to a list. If any conditions are present, it sets the "sell" column to 1 for the rows where all conditions are satisfied. Overall, the strategy calculates indicators and generates buy and sell signals based on specified conditions, which can be used for backtesting trading strategies on the website.
However, in this strategy, specific indicators are calculated inside the buy and sell strategy populator methods when needed.
Therefore, using this method doesn't provide significant benefits apart from calculating default values.
populate_buy_trend: This method populates the "buy" trend in the dataframe based on specified conditions. It retrieves indicator values and settings for three sets of indicators (buy_indicator0, buy_indicator1, buy_indicator2) along with their corresponding crossed indicators, operators, and real numbers. It uses a condition generator function to generate conditions based on the provided parameters and appends them to a list. If any conditions are present, it sets the "buy" column to 1 for the rows where all conditions are satisfied. populate_sell_trend: This method populates the "sell" trend in the dataframe using a similar approach to the populate_buy_trend method. It retrieves indicator values and settings for three sets of indicators (sell_indicator0, sell_indicator1, sell_indicator2) along with their corresponding crossed indicators, operators, and real numbers. It generates conditions based on the parameters and appends them to a list. If any conditions are present, it sets the "sell" column to 1 for the rows where all conditions are satisfied. Overall, the strategy calculates indicators and generates buy and sell signals based on specified conditions, which can be used for backtesting trading strategies on the website.
stoploss: -0.128 timeframe: 4h hash(sha256): 2d0a016998658a6accabd46e241f254a72a5ec1a0af7fbdcd94852788486191b 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
Similar Strategies: (based on used indicators)
Strategy: GodStraNew, Similarity Score: 95.45%
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last change: 2024-04-28 16:29:33