The "GodStra" strategy is a backtesting strategy implemented as a class that inherits from the "IStrategy" interface. Here's a breakdown of what the strategy does:
populate_indicators function:
This function takes a DataFrame and a metadata dictionary as inputs. It removes any rows with missing values (NaN) from the DataFrame.
It adds technical analysis indicators to the DataFrame using the add_all_ta_features function.
The modified DataFrame is then returned.
populate_buy_trend function: This function takes a DataFrame and a metadata dictionary as inputs. It iterates over a range determined by the length of buy_params divided by 5. For each iteration, it retrieves specific buy parameters based on the iteration index. It compares the values of certain indicators and crosses based on the specified operators. Depending on the operator, different conditions are appended to the conditions list. The DataFrame is updated by assigning a value of 1 to the 'buy' column where all the conditions are satisfied. The modified DataFrame is then returned. populate_sell_trend function: This function takes a DataFrame and a metadata dictionary as inputs. It follows a similar structure to populate_buy_trend, but it focuses on sell parameters. For each iteration, it retrieves specific sell parameters based on the iteration index. It compares the values of certain indicators and crosses based on the specified operators. Depending on the operator, different conditions are appended to the conditions list. The DataFrame is updated by assigning a value of 1 to the 'sell' column where all the conditions are satisfied. The modified DataFrame is then returned. Overall, the strategy involves populating indicators, evaluating buy conditions based on specified parameters, and evaluating sell conditions based on different parameters. The strategy aims to generate buy and sell signals based on the defined conditions in order to backtest trading strategies.
It adds technical analysis indicators to the DataFrame using the add_all_ta_features function.
The modified DataFrame is then returned.
populate_buy_trend function: This function takes a DataFrame and a metadata dictionary as inputs. It iterates over a range determined by the length of buy_params divided by 5. For each iteration, it retrieves specific buy parameters based on the iteration index. It compares the values of certain indicators and crosses based on the specified operators. Depending on the operator, different conditions are appended to the conditions list. The DataFrame is updated by assigning a value of 1 to the 'buy' column where all the conditions are satisfied. The modified DataFrame is then returned. populate_sell_trend function: This function takes a DataFrame and a metadata dictionary as inputs. It follows a similar structure to populate_buy_trend, but it focuses on sell parameters. For each iteration, it retrieves specific sell parameters based on the iteration index. It compares the values of certain indicators and crosses based on the specified operators. Depending on the operator, different conditions are appended to the conditions list. The DataFrame is updated by assigning a value of 1 to the 'sell' column where all the conditions are satisfied. The modified DataFrame is then returned. Overall, the strategy involves populating indicators, evaluating buy conditions based on specified parameters, and evaluating sell conditions based on different parameters. The strategy aims to generate buy and sell signals based on the defined conditions in order to backtest trading strategies.
stoploss: -0.345 timeframe: 12h hash(sha256): a409ef2cd8c7ca376bdadb11d41735eca250f977cdb4b2ba46b37ba4fe843098
Was not able to fetch indicators from Strategyfile.
last change: 2024-04-29 01:10:42