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The "adx_strategy" is a trading strategy implemented in Python for backtesting purposes. It uses the ADX (Average Directional Index) indicator along with other technical indicators to generate buy and sell signals. Here's a breakdown of what the strategy does:
The strategy operates on 15-minute candlestick data.
It calculates various indicators using the "talib" and "qtpylib" libraries and adds them to the input DataFrame.
ADX (14 periods): Measures the strength of a trend.
PLUS_DI (25 periods): Calculates the positive directional movement. MINUS_DI (25 periods): Calculates the negative directional movement. SAR: Parabolic SAR indicator. MOM (14 periods): Calculates the momentum. The strategy defines the minimal return on investment (ROI) targets for different time periods. A return of 0.26552 is expected within 0 minutes. A return of 0.10255 is expected within 30 minutes. A return of 0.03545 is expected within 210 minutes. No return is expected after 540 minutes. The strategy sets a stop loss level at -0.1255, indicating the maximum acceptable loss before selling. The "populate_buy_trend" function determines the conditions for buying. It checks if the ADX is greater than 16, the MINUS_DI is greater than 4, and if the MINUS_DI crossed above the PLUS_DI. If all conditions are met, it sets the "buy" column to 1 for the corresponding data points. The "populate_sell_trend" function determines the conditions for selling. It checks if the ADX is greater than 43, the PLUS_DI is greater than 24, and if the PLUS_DI crossed above the MINUS_DI. If all conditions are met, it sets the "sell" column to 1 for the corresponding data points. This strategy aims to capture trends in the market by identifying potential buying and selling opportunities based on the ADX and other indicators. It uses a combination of trend strength, directional movement, and momentum to make trading decisions.
It calculates various indicators using the "talib" and "qtpylib" libraries and adds them to the input DataFrame.
ADX (14 periods): Measures the strength of a trend.
PLUS_DI (25 periods): Calculates the positive directional movement. MINUS_DI (25 periods): Calculates the negative directional movement. SAR: Parabolic SAR indicator. MOM (14 periods): Calculates the momentum. The strategy defines the minimal return on investment (ROI) targets for different time periods. A return of 0.26552 is expected within 0 minutes. A return of 0.10255 is expected within 30 minutes. A return of 0.03545 is expected within 210 minutes. No return is expected after 540 minutes. The strategy sets a stop loss level at -0.1255, indicating the maximum acceptable loss before selling. The "populate_buy_trend" function determines the conditions for buying. It checks if the ADX is greater than 16, the MINUS_DI is greater than 4, and if the MINUS_DI crossed above the PLUS_DI. If all conditions are met, it sets the "buy" column to 1 for the corresponding data points. The "populate_sell_trend" function determines the conditions for selling. It checks if the ADX is greater than 43, the PLUS_DI is greater than 24, and if the PLUS_DI crossed above the MINUS_DI. If all conditions are met, it sets the "sell" column to 1 for the corresponding data points. This strategy aims to capture trends in the market by identifying potential buying and selling opportunities based on the ADX and other indicators. It uses a combination of trend strength, directional movement, and momentum to make trading decisions.
stoploss: -0.1255 timeframe: 15m hash(sha256): a5d8ad0f00ef591a8bf87ceb70a05c0c6863c3470cfadb212a0bd6b2484fb4b5
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last change: 2022-08-03 22:47:58