Not Enough Data to display!
Average Overall
| Not Enough Data! / Avg statistics not populated yet. |
|---|
The "GodStraNew7" strategy is a backtesting strategy implemented as a class that inherits from the "IStrategy" interface. The strategy consists of three main components: indicator population, buy trend population, and sell trend population. In the "populate_indicators" method, the strategy calculates default values of hyperoptable parameters and optimizes the strategy.
However, this particular strategy focuses on calculating specific indicators in specific time periods within the buy and sell trend population methods, rather than in the indicator population method.
Therefore, the "populate_indicators" method doesn't provide significant benefits apart from calculating default values.
The "populate_buy_trend" method is responsible for populating the buy trend. It initializes an empty list of conditions and then iterates through three sets of buy indicators, buy crossed indicators, buy operators, and buy real numbers. For each set, a condition is generated using the "condition_generator" function, which takes the corresponding indicators, operator, and real number as input. The generated condition is appended to the list of conditions. If there are any conditions in the list, the strategy sets the 'buy' column of the dataframe to 1 for the rows that satisfy all the conditions. Similarly, the "populate_sell_trend" method populates the sell trend. It initializes an empty list of conditions and then iterates through three sets of sell indicators, sell crossed indicators, sell operators, and sell real numbers. For each set, a condition is generated using the "condition_generator" function. The generated condition is appended to the list of conditions. If there are any conditions in the list, the strategy sets the 'sell' column of the dataframe to 1 for the rows that satisfy all the conditions. Overall, the strategy calculates specific indicators and conditions for buy and sell signals, populates the corresponding trends based on these conditions, and modifies the 'buy' and 'sell' columns of the dataframe accordingly.
However, this particular strategy focuses on calculating specific indicators in specific time periods within the buy and sell trend population methods, rather than in the indicator population method.
Therefore, the "populate_indicators" method doesn't provide significant benefits apart from calculating default values.
The "populate_buy_trend" method is responsible for populating the buy trend. It initializes an empty list of conditions and then iterates through three sets of buy indicators, buy crossed indicators, buy operators, and buy real numbers. For each set, a condition is generated using the "condition_generator" function, which takes the corresponding indicators, operator, and real number as input. The generated condition is appended to the list of conditions. If there are any conditions in the list, the strategy sets the 'buy' column of the dataframe to 1 for the rows that satisfy all the conditions. Similarly, the "populate_sell_trend" method populates the sell trend. It initializes an empty list of conditions and then iterates through three sets of sell indicators, sell crossed indicators, sell operators, and sell real numbers. For each set, a condition is generated using the "condition_generator" function. The generated condition is appended to the list of conditions. If there are any conditions in the list, the strategy sets the 'sell' column of the dataframe to 1 for the rows that satisfy all the conditions. Overall, the strategy calculates specific indicators and conditions for buy and sell signals, populates the corresponding trends based on these conditions, and modifies the 'buy' and 'sell' columns of the dataframe accordingly.
stoploss: -1 timeframe: 5m hash(sha256): 4cef8a6c7382c6d909f353dc75e9d84d664f5a0f4e547f0e6211bf9da0c7da2a indicators: volume indicator_trend_sma crossed_indicator indicator sharp_indicator
No similar strategies found. (based on used indicators)
last change: 2024-04-28 15:38:51