The "DummyStrategy" is a trading strategy implemented in Python for backtesting purposes. Here's a short description of what the strategy does:
The strategy uses Moving Average (MA) indicators to generate buy and sell signals. It sets specific parameters for the number of MA indicators and the gap between them.
In the "populate_indicators" method, the strategy calculates TEMA (Triple Exponential Moving Average) for different combinations of MA counts and gaps.
These calculated values are added as new columns to the input dataframe.
In the "populate_buy_trend" method, the strategy creates a list of conditions based on the MA indicators. It checks if the current MA value is less than the previous MA value for each combination of MA count and gap. If all conditions are met, the strategy marks the corresponding rows in the "buy" column of the dataframe as 1. In the "populate_sell_trend" method, a similar approach is used to generate sell signals. The strategy checks if the current MA value is greater than the previous MA value for each combination of MA count and gap. If any of the conditions are met, the strategy marks the corresponding rows in the "sell" column of the dataframe as 1. The strategy also defines hyperparameters for the buy and sell signals, such as the number of MA indicators and the gaps between them. It sets a minimal ROI (Return on Investment) table and a stoploss value. Overall, the strategy aims to identify buying opportunities when MA values decrease and selling opportunities when MA values increase. It uses a combination of MA counts and gaps to generate signals and make trading decisions.
In the "populate_indicators" method, the strategy calculates TEMA (Triple Exponential Moving Average) for different combinations of MA counts and gaps.
These calculated values are added as new columns to the input dataframe.
In the "populate_buy_trend" method, the strategy creates a list of conditions based on the MA indicators. It checks if the current MA value is less than the previous MA value for each combination of MA count and gap. If all conditions are met, the strategy marks the corresponding rows in the "buy" column of the dataframe as 1. In the "populate_sell_trend" method, a similar approach is used to generate sell signals. The strategy checks if the current MA value is greater than the previous MA value for each combination of MA count and gap. If any of the conditions are met, the strategy marks the corresponding rows in the "sell" column of the dataframe as 1. The strategy also defines hyperparameters for the buy and sell signals, such as the number of MA indicators and the gaps between them. It sets a minimal ROI (Return on Investment) table and a stoploss value. Overall, the strategy aims to identify buying opportunities when MA values decrease and selling opportunities when MA values increase. It uses a combination of MA counts and gaps to generate signals and make trading decisions.
Unable to parse Traceback (Logfile Exceeded Limit)
stoploss: -0.345 timeframe: 5m hash(sha256): 081cbf5e9668ebbf75245a2481b6fac19deba8bf9c11f629a138862bdb089e02 indicators: count*gap key
No similar strategies found. (based on used indicators)
last change: 2024-07-27 04:37:52