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The NormalizerStrategyHO2 is a backtesting strategy that calculates and utilizes normalized indicators for trading decisions. Here's a breakdown of what the strategy does:
populate_indicators method:
It takes a DataFrame and metadata as input. The strategy defines a list of lookback periods, which are specific time intervals used for calculating indicators.
For each lookback period in the list, it calculates a normalized indicator called "norm" based on the closing prices of the asset.
The calculated indicators are added as new columns to the DataFrame.
Additionally, a column named "pct_sum" is created by summing up the values of all the "norm" columns. populate_buy_trend method: It takes the populated DataFrame and metadata as input. The strategy identifies buy signals based on two conditions: The value of the "pct_sum" column is less than 0.2. The volume of trading activity is greater than 0 (ensuring non-zero volume). When both conditions are met, the corresponding "buy" column is set to 1. populate_sell_trend method: It takes the populated DataFrame and metadata as input. The strategy identifies sell signals based on two conditions: The value of the "pct_sum" column is greater than 8. The volume of trading activity is greater than 0 (ensuring non-zero volume). When both conditions are met, the corresponding "sell" column is set to 1. Overall, the strategy calculates normalized indicators, evaluates buy and sell signals based on the values of these indicators and trading volume, and sets the corresponding columns in the DataFrame to indicate the presence of buy or sell signals.
For each lookback period in the list, it calculates a normalized indicator called "norm" based on the closing prices of the asset.
The calculated indicators are added as new columns to the DataFrame.
Additionally, a column named "pct_sum" is created by summing up the values of all the "norm" columns. populate_buy_trend method: It takes the populated DataFrame and metadata as input. The strategy identifies buy signals based on two conditions: The value of the "pct_sum" column is less than 0.2. The volume of trading activity is greater than 0 (ensuring non-zero volume). When both conditions are met, the corresponding "buy" column is set to 1. populate_sell_trend method: It takes the populated DataFrame and metadata as input. The strategy identifies sell signals based on two conditions: The value of the "pct_sum" column is greater than 8. The volume of trading activity is greater than 0 (ensuring non-zero volume). When both conditions are met, the corresponding "sell" column is set to 1. Overall, the strategy calculates normalized indicators, evaluates buy and sell signals based on the values of these indicators and trading volume, and sets the corresponding columns in the DataFrame to indicate the presence of buy or sell signals.
startup_candle_count : 610 norm_610: -100.000% pct_sum: -26.112%
stoploss: -0.99 timeframe: 1h hash(sha256): 267d4725d39c78df94d349508197450ca29f2090eca7b945543b616120a64ece indicators: pct_sum volume f"norm_look
No similar strategies found. (based on used indicators)
last change: 2023-06-28 06:22:53