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The ADX_15M_USDT strategy is designed for backtesting trading strategies on a 15-minute ticker interval. Here's a brief description of what the strategy does:
It calculates several indicators using the TA-Lib library, such as ADX (Average Directional Index), PLUS_DI (Plus Directional Indicator), MINUS_DI (Minus Directional Indicator), SAR (Stop and Reverse), and MOM (Momentum). The strategy defines a minimal ROI (Return on Investment) table, which specifies the expected returns at different time intervals.
It sets a stop loss level to limit potential losses.
In the populate_indicators method, the strategy populates the DataFrame with the calculated indicators for further analysis.
The populate_buy_trend method determines the conditions for a buy signal. It checks if the ADX is greater than 16, the MINUS_DI is greater than 4, the PLUS_DI is greater than 20, and if the PLUS_DI has crossed above the MINUS_DI. The populate_sell_trend method defines the conditions for a sell signal. It checks if the ADX is greater than 43, the MINUS_DI is greater than 22, the PLUS_DI is greater than 20, and if the MINUS_DI has crossed above the PLUS_DI. Overall, the strategy aims to generate buy and sell signals based on the calculated indicators and their respective thresholds.
It sets a stop loss level to limit potential losses.
In the populate_indicators method, the strategy populates the DataFrame with the calculated indicators for further analysis.
The populate_buy_trend method determines the conditions for a buy signal. It checks if the ADX is greater than 16, the MINUS_DI is greater than 4, the PLUS_DI is greater than 20, and if the PLUS_DI has crossed above the MINUS_DI. The populate_sell_trend method defines the conditions for a sell signal. It checks if the ADX is greater than 43, the MINUS_DI is greater than 22, the PLUS_DI is greater than 20, and if the MINUS_DI has crossed above the PLUS_DI. Overall, the strategy aims to generate buy and sell signals based on the calculated indicators and their respective thresholds.
stoploss: -0.1255 timeframe: 15m hash(sha256): be545fa883891f2a2cb85ac49cd5b7323a85fe16a3321587e2c6c6520716c165
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last change: 2022-07-26 16:30:20