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The NormalizerStrategy is a backtesting strategy that calculates and uses various indicators to generate buy and sell signals for trading. Here's a brief explanation of how it works:
populate_indicators():
The strategy takes a DataFrame and a metadata dictionary as inputs. It defines a list of lookback periods, representing different time intervals.
For each lookback period, it calculates a normalized value using the Fischer transformation on the closing prices of the asset.
These normalized values are added as new columns to the DataFrame with the prefix "norm_" followed by the respective lookback period.
The strategy also creates a list of column names that start with "norm" to be used later. Finally, it calculates the sum of the normalized values for each row and adds the result as a new column named "pct_sum". The modified DataFrame is returned. populate_buy_trend(): This method populates the "buy" column in the DataFrame with a value of 1 under specific conditions. It checks if the "pct_sum" value is less than 0.2 (indicating a low sum of normalized values) and if the volume is greater than 0 (ensuring non-zero volume). If both conditions are met, the "buy" column is set to 1 for that row. The modified DataFrame is returned. populate_sell_trend(): Similar to populate_buy_trend(), this method populates the "sell" column in the DataFrame with a value of 1 under specific conditions. It checks if the "pct_sum" value is greater than 8 (indicating a high sum of normalized values) and if the volume is greater than 0 (ensuring non-zero volume). If both conditions are met, the "sell" column is set to 1 for that row. The modified DataFrame is returned. In summary, the NormalizerStrategy calculates normalized values based on different lookback periods and sums them up to identify potential trading opportunities. It generates "buy" signals when the sum of normalized values is low and "sell" signals when the sum is high, while also considering non-zero volume.
For each lookback period, it calculates a normalized value using the Fischer transformation on the closing prices of the asset.
These normalized values are added as new columns to the DataFrame with the prefix "norm_" followed by the respective lookback period.
The strategy also creates a list of column names that start with "norm" to be used later. Finally, it calculates the sum of the normalized values for each row and adds the result as a new column named "pct_sum". The modified DataFrame is returned. populate_buy_trend(): This method populates the "buy" column in the DataFrame with a value of 1 under specific conditions. It checks if the "pct_sum" value is less than 0.2 (indicating a low sum of normalized values) and if the volume is greater than 0 (ensuring non-zero volume). If both conditions are met, the "buy" column is set to 1 for that row. The modified DataFrame is returned. populate_sell_trend(): Similar to populate_buy_trend(), this method populates the "sell" column in the DataFrame with a value of 1 under specific conditions. It checks if the "pct_sum" value is greater than 8 (indicating a high sum of normalized values) and if the volume is greater than 0 (ensuring non-zero volume). If both conditions are met, the "sell" column is set to 1 for that row. The modified DataFrame is returned. In summary, the NormalizerStrategy calculates normalized values based on different lookback periods and sums them up to identify potential trading opportunities. It generates "buy" signals when the sum of normalized values is low and "sell" signals when the sum is high, while also considering non-zero volume.
stoploss: -0.99 timeframe: 1h hash(sha256): 26caa899d1d395bd3cfa60c032e3ea77360ecbfad2f23e7c948d4d45f22d32f4
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last change: 2022-07-02 19:54:08