The TaSearch5m strategy is designed for backtesting trading strategies using historical price data on a 5-minute timeframe. It utilizes various technical analysis indicators to generate buy and sell signals. Here's a breakdown of the important parts of the strategy:
populate_indicators function: This function is responsible for populating the DataFrame with indicators.
It renames the columns of the DataFrame and applies the find_extremes method from the TaSearch class.
buy_past_rsi function: This function identifies potential buy opportunities based on the relative strength index (RSI) indicator.
It iterates over the DataFrame in reverse order and checks if the RSI is below 25 within a specific range. It assigns a value to the buy_past_rsi column of the DataFrame to indicate the strength of the buy signal. buy_stride function: This function identifies potential buy opportunities based on the RSI indicator and the percentage change in price. It iterates over the DataFrame in reverse order and checks if the RSI is between 20 and 35 within a specific range. It also checks if the percentage change is below a certain threshold. It assigns values to the buy_stride and buy_past_rsi columns of the DataFrame to indicate the strength of the buy signal. populate_buy_trend function: This function determines the buy signals based on the values in the buy_stride, buy_past_rsi, and market columns of the DataFrame. It sets the buy column to 1 for the rows that meet certain conditions. populate_sell_trend function: This function determines the sell signals based on the RSI indicator. It sets the sell column to 1 for the rows where the RSI is above 80. Overall, the strategy combines multiple indicators and conditions to identify potential buy and sell opportunities in the market.
It renames the columns of the DataFrame and applies the find_extremes method from the TaSearch class.
buy_past_rsi function: This function identifies potential buy opportunities based on the relative strength index (RSI) indicator.
It iterates over the DataFrame in reverse order and checks if the RSI is below 25 within a specific range. It assigns a value to the buy_past_rsi column of the DataFrame to indicate the strength of the buy signal. buy_stride function: This function identifies potential buy opportunities based on the RSI indicator and the percentage change in price. It iterates over the DataFrame in reverse order and checks if the RSI is between 20 and 35 within a specific range. It also checks if the percentage change is below a certain threshold. It assigns values to the buy_stride and buy_past_rsi columns of the DataFrame to indicate the strength of the buy signal. populate_buy_trend function: This function determines the buy signals based on the values in the buy_stride, buy_past_rsi, and market columns of the DataFrame. It sets the buy column to 1 for the rows that meet certain conditions. populate_sell_trend function: This function determines the sell signals based on the RSI indicator. It sets the sell column to 1 for the rows where the RSI is above 80. Overall, the strategy combines multiple indicators and conditions to identify potential buy and sell opportunities in the market.
stoploss: -0.05 timeframe: 5m hash(sha256): 5a59eef83ac4fe9a510cc786c768d2b49e4c352efb26ca2cf181c68a347f1bba
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last change: 2024-04-29 02:14:38