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The adx_opt_live strategy is based on the ADX (Average Directional Index) Momentum strategy. It aims to identify trends in the market and generate buy and sell signals accordingly. Here's a breakdown of how the strategy works:
The strategy uses various technical indicators, calculated using the talib library, to make trading decisions.
These indicators include: ADX (Average Directional Index): Measures the strength of a trend.
PLUS_DI and MINUS_DI: Calculate the positive and negative directional indicators.
SAR (Stop and Reverse): Provides potential reversal points. MOM (Momentum): Measures the rate of price change. The populate_indicators function is responsible for calculating these indicators and adding them to the dataframe. The populate_buy_trend function determines the conditions for a buy signal. In this case, it looks for the following criteria: Negative momentum (mom < 0). MINUS_DI value greater than 48, indicating a strong downtrend. PLUS_DI value lower than MINUS_DI, suggesting a potential reversal. The populate_sell_trend function determines the conditions for a sell signal. It checks for the opposite conditions compared to the buy signal: Positive momentum (mom > 0). MINUS_DI value greater than 48. PLUS_DI value higher than MINUS_DI. When the conditions for a buy or sell signal are met, the respective columns ('buy' or 'sell') in the dataframe are set to 1. The strategy also includes additional parameters such as minimal ROI (Return on Investment), stop-loss level, and trailing stop. These parameters can be adjusted based on market conditions and risk preferences. Overall, the adx_opt_live strategy uses the ADX indicator and other technical indicators to identify trends and generate buy and sell signals, aiming to profit from market movements.
These indicators include: ADX (Average Directional Index): Measures the strength of a trend.
PLUS_DI and MINUS_DI: Calculate the positive and negative directional indicators.
SAR (Stop and Reverse): Provides potential reversal points. MOM (Momentum): Measures the rate of price change. The populate_indicators function is responsible for calculating these indicators and adding them to the dataframe. The populate_buy_trend function determines the conditions for a buy signal. In this case, it looks for the following criteria: Negative momentum (mom < 0). MINUS_DI value greater than 48, indicating a strong downtrend. PLUS_DI value lower than MINUS_DI, suggesting a potential reversal. The populate_sell_trend function determines the conditions for a sell signal. It checks for the opposite conditions compared to the buy signal: Positive momentum (mom > 0). MINUS_DI value greater than 48. PLUS_DI value higher than MINUS_DI. When the conditions for a buy or sell signal are met, the respective columns ('buy' or 'sell') in the dataframe are set to 1. The strategy also includes additional parameters such as minimal ROI (Return on Investment), stop-loss level, and trailing stop. These parameters can be adjusted based on market conditions and risk preferences. Overall, the adx_opt_live strategy uses the ADX indicator and other technical indicators to identify trends and generate buy and sell signals, aiming to profit from market movements.
stoploss: -0.327 timeframe: 1m hash(sha256): dd418b329c4534cc92d974455186dfcdefbef2a751486775dbd0345aa69b4c19
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last change: 2022-07-11 10:54:15