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The provided code represents an ensemble trading strategy implemented in Python using the freqtrade library. Here's a brief description of what the strategy does:
The strategy is called EnsembleStrategyV2 and is a subclass of IStrategy from the freqtrade.strategy module. It combines multiple trading strategies to make buy and sell decisions.
The strategy uses a set of predefined strategies listed in the STRATEGIES variable.
It generates combinations of these strategies using the combinations function from the itertools module and stores them in the STRAT_COMBINATIONS variable.
The maximum number of combinations is determined by the length of STRATEGIES minus one. The strategy has parameters for setting buy and sell thresholds, as well as the number of strategies to use for buying and selling. It also has predefined parameters for buy and sell hyperspace optimization. The strategy defines a minimal return-on-investment (ROI) table and a stop-loss value. It supports trailing stops with positive offset values. The __init__ method logs the selected buy and sell strategies. The strategy provides methods for obtaining informative pairs, getting individual strategies, and populating indicators, buy signals, and sell signals. The populate_indicators method is responsible for adding indicators to the input dataframe. The populate_buy_trend method populates the buy signal for each strategy and calculates an overall buy signal based on the mean threshold. The populate_sell_trend method initializes the sell signal as zero for each data point. The custom_sell method is a custom selling logic that generates sell signals based on the selected sell strategies and their corresponding thresholds. Overall, this strategy combines multiple strategies to make buy and sell decisions and provides flexibility for optimizing and customizing the trading parameters.
The strategy uses a set of predefined strategies listed in the STRATEGIES variable.
It generates combinations of these strategies using the combinations function from the itertools module and stores them in the STRAT_COMBINATIONS variable.
The maximum number of combinations is determined by the length of STRATEGIES minus one. The strategy has parameters for setting buy and sell thresholds, as well as the number of strategies to use for buying and selling. It also has predefined parameters for buy and sell hyperspace optimization. The strategy defines a minimal return-on-investment (ROI) table and a stop-loss value. It supports trailing stops with positive offset values. The __init__ method logs the selected buy and sell strategies. The strategy provides methods for obtaining informative pairs, getting individual strategies, and populating indicators, buy signals, and sell signals. The populate_indicators method is responsible for adding indicators to the input dataframe. The populate_buy_trend method populates the buy signal for each strategy and calculates an overall buy signal based on the mean threshold. The populate_sell_trend method initializes the sell signal as zero for each data point. The custom_sell method is a custom selling logic that generates sell signals based on the selected sell strategies and their corresponding thresholds. Overall, this strategy combines multiple strategies to make buy and sell decisions and provides flexibility for optimizing and customizing the trading parameters.
stoploss: -0.148 timeframe: 1h hash(sha256): 80f309fb73bdb02abae8d96bc28968092c89ae584d4735dc3b6cfabc7b4684e8 indicators: f"strat_buy_signal_strategy_name sell f"strat_sell_signal_strategy_name buy
No similar strategies found. (based on used indicators)
last change: 2024-04-29 02:34:03