60 lines
2.0 KiB
Python
60 lines
2.0 KiB
Python
from freqtrade.strategy.interface import IStrategy
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from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
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from pandas import DataFrame
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import numpy as np
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import logging
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logger = logging.getLogger(__name__)
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class AIEnhancedStrategy(IStrategy):
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INTERFACE_VERSION = 3
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can_short = False # 只做多
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timeframe = '5m'
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process_only_new_candles = True
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use_exit_signal = True
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exit_profit_only = False
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ignore_roi_if_entry_signal = False
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def feature_engineering_expand_all(self, dataframe: DataFrame, period: int,
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metadata: dict, **kwargs) -> DataFrame:
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# 自定义特征工程
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for col in ['open', 'high', 'low', 'close', 'volume']:
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dataframe[f'{col}_pct_change'] = dataframe[col].pct_change(periods=period)
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return dataframe
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def set_freqai_targets(self, dataframe: DataFrame, metadata: dict, **kwargs) -> DataFrame:
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# 目标变量：未来n根K线的收盘价变化
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dataframe['&s-close_pct'] = dataframe['close'].pct_change(periods=5).shift(-5)
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return dataframe
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def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
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dk = self.freqai_info.get("dk", None)
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if not dk:
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raise ValueError("FreqaiDataKitchen is not available.")
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dataframe = dk.feature_engineering_standard(dataframe, metadata, self)
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if self.config["runmode"].value in ("live", "dry_run"):
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dataframe = dk.live_models(self, dataframe, metadata=metadata)
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return dataframe
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def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
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# 预测值大于阈值时开多仓
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dataframe.loc[
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(dataframe["&s-close_pct"] > 0.002),
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"enter_long"
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] = 1
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return dataframe
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def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
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# 预测值小于负阈值时平仓
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dataframe.loc[
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(dataframe["&s-close_pct"] < -0.001),
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"exit_long"
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] = 1
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return dataframe
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