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The "GodStraNew" strategy is a trading strategy implemented as a class. Here's a brief description of what it does:
The strategy has two main functions: "populate_indicators" and "populate_buy_trend" and "populate_sell_trend". In the "populate_indicators" function, all indicators are calculated for all time periods.
However, this strategy only calculates specific indicators in specific time periods inside the buy and sell strategy population methods if they are needed.
This method also calculates the default values of hyper-optimizable parameters, but it doesn't provide significant benefits compared to calculating usable things inside the buy and sell trend populators.
The "populate_buy_trend" function populates the buy trend of the strategy. It generates conditions based on buy indicators, crossed indicators, operators, and real numbers. Multiple sets of indicators and conditions can be specified (buy_indicator0, buy_crossed_indicator0, buy_operator0, buy_real_num0, buy_indicator1, buy_crossed_indicator1, buy_operator1, buy_real_num1, buy_indicator2, buy_crossed_indicator2, buy_operator2, buy_real_num2). If any of the conditions are met, the corresponding rows in the dataframe are marked with a value of 1 in the "buy" column. The "populate_sell_trend" function populates the sell trend of the strategy. It generates conditions based on sell indicators, crossed indicators, operators, and real numbers. Similar to the buy trend, multiple sets of indicators and conditions can be specified (sell_indicator0, sell_crossed_indicator0, sell_operator0, sell_real_num0, sell_indicator1, sell_crossed_indicator1, sell_operator1, sell_real_num1, sell_indicator2, sell_crossed_indicator2, sell_operator2, sell_real_num2). If any of the conditions are met, the corresponding rows in the dataframe are marked with a value of 1 in the "sell" column. Overall, this strategy calculates indicators, generates buy and sell conditions based on specified indicators and parameters, and marks the corresponding rows in the dataframe as "buy" or "sell" based on the fulfilled conditions.
However, this strategy only calculates specific indicators in specific time periods inside the buy and sell strategy population methods if they are needed.
This method also calculates the default values of hyper-optimizable parameters, but it doesn't provide significant benefits compared to calculating usable things inside the buy and sell trend populators.
The "populate_buy_trend" function populates the buy trend of the strategy. It generates conditions based on buy indicators, crossed indicators, operators, and real numbers. Multiple sets of indicators and conditions can be specified (buy_indicator0, buy_crossed_indicator0, buy_operator0, buy_real_num0, buy_indicator1, buy_crossed_indicator1, buy_operator1, buy_real_num1, buy_indicator2, buy_crossed_indicator2, buy_operator2, buy_real_num2). If any of the conditions are met, the corresponding rows in the dataframe are marked with a value of 1 in the "buy" column. The "populate_sell_trend" function populates the sell trend of the strategy. It generates conditions based on sell indicators, crossed indicators, operators, and real numbers. Similar to the buy trend, multiple sets of indicators and conditions can be specified (sell_indicator0, sell_crossed_indicator0, sell_operator0, sell_real_num0, sell_indicator1, sell_crossed_indicator1, sell_operator1, sell_real_num1, sell_indicator2, sell_crossed_indicator2, sell_operator2, sell_real_num2). If any of the conditions are met, the corresponding rows in the dataframe are marked with a value of 1 in the "sell" column. Overall, this strategy calculates indicators, generates buy and sell conditions based on specified indicators and parameters, and marks the corresponding rows in the dataframe as "buy" or "sell" based on the fulfilled conditions.
stoploss: -1 timeframe: 4h hash(sha256): ccd91d678e825cf08bd074c7e1e1a68cd3d63c10de72c6887fd86b471dbc40cd indicators: volume indicator_trend_sma crossed_indicator indicator sharp_indicator
No similar strategies found. (based on used indicators)
last change: 2024-04-28 05:20:54