The strategy, called "GodStraNewOpt," is designed for backtesting trading strategies on a website. Here's a brief description of what it does:
The populate_indicators method is responsible for calculating various indicators for the strategy. However, this strategy optimizes the calculation process by only computing specific indicators in specific time periods within the populate_buy_trend and populate_sell_trend methods when needed.
The populate_indicators method primarily calculates default values for hyperoptable parameters, providing limited benefits compared to calculating usable things inside the buy and sell trend populators.
The populate_buy_trend method populates the buy signals for the strategy.
It creates a list of conditions based on the specified indicators, crossed indicators, operators, and real numbers for buying. Multiple sets of indicators can be defined (buy_indicator0, buy_indicator1, buy_indicator2) along with their respective crossed indicators, operators, and real numbers. The condition_generator function generates a condition based on these inputs, and all conditions are appended to the conditions list. If any conditions exist, the strategy sets the 'buy' column to 1 for the corresponding rows in the dataframe. The populate_sell_trend method works similarly to the populate_buy_trend method but populates the sell signals instead. It creates a list of conditions based on the specified sell indicators, crossed indicators, operators, and real numbers. Multiple sets of sell indicators can be defined (sell_indicator0, sell_indicator1, sell_indicator2) along with their respective crossed indicators, operators, and real numbers. The condition_generator function generates a condition for each set, and all conditions are appended to the conditions list. If any conditions exist, the strategy sets the 'sell' column to 1 for the corresponding rows in the dataframe. In summary, this strategy calculates indicators, generates buy and sell conditions based on specified parameters, and sets the corresponding signals in the dataframe for backtesting purposes.
The populate_indicators method primarily calculates default values for hyperoptable parameters, providing limited benefits compared to calculating usable things inside the buy and sell trend populators.
The populate_buy_trend method populates the buy signals for the strategy.
It creates a list of conditions based on the specified indicators, crossed indicators, operators, and real numbers for buying. Multiple sets of indicators can be defined (buy_indicator0, buy_indicator1, buy_indicator2) along with their respective crossed indicators, operators, and real numbers. The condition_generator function generates a condition based on these inputs, and all conditions are appended to the conditions list. If any conditions exist, the strategy sets the 'buy' column to 1 for the corresponding rows in the dataframe. The populate_sell_trend method works similarly to the populate_buy_trend method but populates the sell signals instead. It creates a list of conditions based on the specified sell indicators, crossed indicators, operators, and real numbers. Multiple sets of sell indicators can be defined (sell_indicator0, sell_indicator1, sell_indicator2) along with their respective crossed indicators, operators, and real numbers. The condition_generator function generates a condition for each set, and all conditions are appended to the conditions list. If any conditions exist, the strategy sets the 'sell' column to 1 for the corresponding rows in the dataframe. In summary, this strategy calculates indicators, generates buy and sell conditions based on specified parameters, and sets the corresponding signals in the dataframe for backtesting purposes.
stoploss: -1 timeframe: 4h hash(sha256): 83df331fd3b18e060f733c1c6483068be88d9a6f65ae0bc5cd91e29da6522b53 indicators: volume indicator_trend_sma crossed_indicator indicator sharp_indicator
No similar strategies found. (based on used indicators)
last change: 2024-04-29 21:34:44