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The "GodStraNewOptQuick" strategy is a backtesting strategy that aims to optimize trading decisions based on specific indicators. It has separate methods for populating indicators, buying trends, and selling trends. In the "populate_indicators" method, the strategy calculates default values for hyperoptable parameters and can calculate indicators for all time periods.
However, it mentions that it may take a long time to generate certain indicators that are not used in the optimization.
Instead, the strategy prefers to calculate specific indicators within the buy and sell trend populator methods when needed.
The "populate_buy_trend" method populates the conditions for buying by using a series of indicators. It takes multiple sets of indicators (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) and generates conditions based on these indicators using the "condition_generator" function. The generated conditions are appended to a list of conditions. If any conditions exist, the strategy marks the corresponding rows in the dataframe as a buy signal. Similarly, the "populate_sell_trend" method populates the conditions for selling. It takes multiple sets of indicators (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), generates conditions based on these indicators using the "condition_generator" function, and appends them to the list of conditions. If any conditions exist, the strategy marks the corresponding rows in the dataframe as a sell signal. Overall, the strategy aims to optimize trading decisions by evaluating multiple indicators and generating buy and sell signals based on predetermined conditions.
However, it mentions that it may take a long time to generate certain indicators that are not used in the optimization.
Instead, the strategy prefers to calculate specific indicators within the buy and sell trend populator methods when needed.
The "populate_buy_trend" method populates the conditions for buying by using a series of indicators. It takes multiple sets of indicators (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) and generates conditions based on these indicators using the "condition_generator" function. The generated conditions are appended to a list of conditions. If any conditions exist, the strategy marks the corresponding rows in the dataframe as a buy signal. Similarly, the "populate_sell_trend" method populates the conditions for selling. It takes multiple sets of indicators (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), generates conditions based on these indicators using the "condition_generator" function, and appends them to the list of conditions. If any conditions exist, the strategy marks the corresponding rows in the dataframe as a sell signal. Overall, the strategy aims to optimize trading decisions by evaluating multiple indicators and generating buy and sell signals based on predetermined conditions.
stoploss: -0.15 timeframe: 5m hash(sha256): 006d7d6af558f89b1fde473229f5dacb22d0fa468fbc29cf07e3a760683cb17f indicators: volume indicator_trend_sma crossed_indicator indicator sharp_indicator
No similar strategies found. (based on used indicators)
last change: 2024-04-29 18:53:02