The "TechnicalExampleStrategy" is a trading strategy implemented in Python for backtesting purposes. It utilizes the pandas library for data manipulation and the technical.indicators module, specifically the "cmf" function. The strategy follows these key steps:
The timeframe for the strategy is set to 5 minutes.
The "populate_indicators" function is used to calculate the Chaikin Money Flow (CMF) indicator for the given dataframe using a period of 21.
The CMF indicator values are added as a new column named 'cmf' in the dataframe.
The "populate_buy_trend" function identifies potential buy signals based on the following condition: if the CMF value is less than 0. For the identified buy signals, a new column named 'buy' is set to 1 in the dataframe. The "populate_sell_trend" function identifies potential sell signals based on the following condition: if the CMF value is greater than 0. For the identified sell signals, a new column named 'sell' is set to 1 in the dataframe. Additionally, the strategy sets the "minimal_roi" parameter to achieve a 0.01 return on investment (ROI) and the "stoploss" parameter to -0.05, indicating a 5% stop loss. Overall, the strategy aims to generate buy signals when the CMF indicator is negative and sell signals when it is positive, potentially capturing price reversals or shifts in market sentiment.
The "populate_indicators" function is used to calculate the Chaikin Money Flow (CMF) indicator for the given dataframe using a period of 21.
The CMF indicator values are added as a new column named 'cmf' in the dataframe.
The "populate_buy_trend" function identifies potential buy signals based on the following condition: if the CMF value is less than 0. For the identified buy signals, a new column named 'buy' is set to 1 in the dataframe. The "populate_sell_trend" function identifies potential sell signals based on the following condition: if the CMF value is greater than 0. For the identified sell signals, a new column named 'sell' is set to 1 in the dataframe. Additionally, the strategy sets the "minimal_roi" parameter to achieve a 0.01 return on investment (ROI) and the "stoploss" parameter to -0.05, indicating a 5% stop loss. Overall, the strategy aims to generate buy signals when the CMF indicator is negative and sell signals when it is positive, potentially capturing price reversals or shifts in market sentiment.
startup_candle_count : 50 Recursive Analysis found no issues while using 50 startup_candle_count
stoploss: -0.05 timeframe: 5m hash(sha256): 51b068b3c458ef460d3c234a637ec6a40c98ad01dd7a4944bf2d8b7b4c60c3cc indicators: cmf cm
No similar strategies found. (based on used indicators)
last change: 2024-05-04 05:57:15