The FastSupertrendOpt strategy is a backtesting strategy that utilizes the Supertrend indicator. Here's a breakdown of what the strategy does:
The populate_indicators function doesn't modify the input dataframe and simply returns it as is. The populate_buy_trend function calculates multiple Supertrend indicators based on different parameters provided as inputs.
These indicators are stored in columns named supertrend_1_buy, supertrend_2_buy, and supertrend_3_buy in the dataframe.
The function then checks certain conditions to determine when to place a buy signal.
If the three Supertrend indicators are all in the "up" direction ('up'), and there is some trading volume ('volume' > 0), a 'buy' signal is set to 1 for that particular candle. The populate_sell_trend function is similar to populate_buy_trend but focuses on sell signals. It calculates and stores Supertrend indicators in columns named supertrend_1_sell, supertrend_2_sell, and supertrend_3_sell. If all three indicators are in the "down" direction ('down') and there is some trading volume, a 'sell' signal is set to 1. Lastly, the Supertrend indicator is calculated based on a period and multiplier. The indicator values are stored in columns named ST_
_ and STX__, where and are the specific values used for calculation. The calculated Supertrend values are used to determine the trend direction, either "up" or "down," and are stored in the ST and STX columns, respectively. If the Supertrend value is greater than 0 and the closing price is below the Supertrend value, the trend is considered "down." Otherwise, it is considered "up."
The calculated indicators are added to the dataframe, and any missing values (NaN) are filled with zeros. The function returns a new dataframe with the Supertrend values (ST and STX) for further analysis or visualization. Overall, the strategy uses the Supertrend indicator to generate buy and sell signals based on the trend direction and trading volume.