223 lines
8.2 KiB
Python
223 lines
8.2 KiB
Python
import logging
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import numpy as np
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import pandas as pd
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from pandas import DataFrame
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from freqtrade.strategy import IStrategy, DecimalParameter, IntParameter
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import pandas_ta as ta
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logger = logging.getLogger(__name__)
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class FreqaiPrimerML(IStrategy):
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"""
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基于 FreqAI ML 的交易策略 - 单目标回归模型
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预测未来价格上升幅度，用于入场决策
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"""
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# FreqAI 配置 - 直接在类中定义
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freqai_info = {
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"identifier": "freqai_primer_ml",
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"model": "LightGBMRegressor",
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"feature_parameters": {
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"include_timeframes": ["3m", "15m", "1h"],
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"include_shifted_candles": 3,
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"label_period_candles": 6,
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},
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"data_split_parameters": {
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"test_size": 0.2,
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"shuffle": False,
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},
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"model_training_parameters": {
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"n_estimators": 200,
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"learning_rate": 0.05,
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"num_leaves": 31,
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"verbose": -1,
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},
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"fit_live_predictions_candles": 100,
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"live_retrain_candles": 100,
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}
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# 策略参数
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stoploss = -0.15
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trailing_stop = True
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trailing_stop_positive_offset = 0.005
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timeframe = "3m"
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can_short = False
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# 可优化参数
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bb_std = DecimalParameter(2.0, 5.0, decimals=1, default=3.5, optimize=True, space='buy')
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rsi_length = IntParameter(10, 30, default=14, optimize=True, space='buy')
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rsi_oversold = IntParameter(20, 50, default=30, optimize=True, space='buy')
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def informative_pairs(self):
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pairs = self.dp.current_whitelist()
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return [(pair, '15m') for pair in pairs] + [(pair, '1h') for pair in pairs]
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def feature_engineering_expand_all(self, dataframe: DataFrame, period, metadata, **kwargs) -> DataFrame:
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"""
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为 FreqAI 生成扩展特征
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"""
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# RSI 特征
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dataframe[f"%-rsi-{period}"] = ta.rsi(dataframe["close"], length=period)
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# 成交量特征
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dataframe[f"%-volume_ratio-{period}"] = dataframe["volume"] / dataframe["volume"].rolling(period).mean()
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# ADX 趋势强度
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adx_result = ta.adx(dataframe["high"], dataframe["low"], dataframe["close"], length=period)
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dataframe[f"%-adx-{period}"] = adx_result[f"ADX_{period}"]
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# 价格位置特征
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low = dataframe["low"].rolling(period).min()
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high = dataframe["high"].rolling(period).max()
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dataframe[f"%-price_position-{period}"] = (dataframe["close"] - low) / (high - low + 1e-8)
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return dataframe
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def feature_engineering_standard(self, dataframe: DataFrame, metadata, **kwargs) -> DataFrame:
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"""
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标准特征（不需要自动扩展）
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"""
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# 小时时间特征
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dataframe["%-hour_of_day"] = (dataframe["date"].dt.hour + 1) / 25
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# ATR 波动性
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atr_14 = ta.atr(dataframe["high"], dataframe["low"], dataframe["close"], length=14)
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dataframe["%-atr_14"] = atr_14 / dataframe["close"]
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# EMA 比率
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ema_5 = ta.ema(dataframe["close"], length=5)
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ema_10 = ta.ema(dataframe["close"], length=10)
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dataframe["%-ema_ratio"] = ema_5 / (ema_10 + 1e-8)
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return dataframe
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def set_freqai_targets(self, dataframe: DataFrame, metadata, **kwargs) -> DataFrame:
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"""
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为 FreqAI 生成回归目标：预测未来价格上升幅度
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"""
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pair = metadata.get('pair', 'Unknown')
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# 获取标签周期
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label_period = self.freqai_info["feature_parameters"]["label_period_candles"]
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# 计算未来 label_period 根 K 线内的最大上升幅度
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dataframe["&-price_rise"] = 0.0
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for i in range(len(dataframe) - label_period):
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future_high = dataframe["high"].iloc[i:i+label_period].max()
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current_price = dataframe["close"].iloc[i]
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# 计算上升幅度（百分比）
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max_rise_pct = (future_high - current_price) / current_price if current_price != 0 else 0
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dataframe.loc[i, "&-price_rise"] = max(0, max_rise_pct) # 只记录上升，下跌记为0
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# 平滑处理
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dataframe["&-price_rise"] = dataframe["&-price_rise"].rolling(3).mean().fillna(0)
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logger.debug(f"[{pair}] 目标标签统计: 均值={dataframe['&-price_rise'].mean():.6f}, "
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f"最小={dataframe['&-price_rise'].min():.6f}, "
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f"最大={dataframe['&-price_rise'].max():.6f}")
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return dataframe
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def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
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"""
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主指标计算函数 - FreqAI 在最开始调用
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"""
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pair = metadata.get('pair', 'Unknown')
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# 🔧 第一步：调用 FreqAI 进行预测和训练
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if hasattr(self, 'freqai') and self.freqai is not None:
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dataframe = self.freqai.start(dataframe, metadata, self)
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logger.debug(f"[{pair}] FreqAI 预测已生成，列: {list(dataframe.columns)}")
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else:
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logger.warning(f"[{pair}] FreqAI 未初始化")
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# 计算基础指标
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rsi_length = self.rsi_length.value
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bb_std = self.bb_std.value
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bb_length = 40
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# 布林带
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bb = ta.bbands(dataframe['close'], length=bb_length, std=bb_std)
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dataframe['bb_lower'] = bb[f'BBL_{bb_length}_{bb_std}']
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dataframe['bb_upper'] = bb[f'BBU_{bb_length}_{bb_std}']
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dataframe['bb_middle'] = bb[f'BBM_{bb_length}_{bb_std}']
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# RSI
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dataframe['rsi'] = ta.rsi(dataframe['close'], length=rsi_length)
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# EMA
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dataframe['ema_50'] = ta.ema(dataframe['close'], length=50)
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dataframe['ema_200'] = ta.ema(dataframe['close'], length=200)
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# 成交量均线
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dataframe['volume_ma'] = dataframe['volume'].rolling(20).mean()
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# MACD
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macd = ta.macd(dataframe['close'])
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dataframe['macd'] = macd['MACD_12_26_9']
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dataframe['macd_signal'] = macd['MACDs_12_26_9']
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# 填充缺失值
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dataframe = dataframe.fillna(0).bfill()
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return dataframe
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def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
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"""
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入场逻辑 - 使用 ML 预测
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"""
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conditions = []
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# 条件1: 价格接近下轨
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conditions.append(dataframe['close'] <= dataframe['bb_lower'] * 1.02)
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# 条件2: RSI 超卖
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conditions.append(dataframe['rsi'] < self.rsi_oversold.value)
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# 条件3: MACD 底部
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conditions.append(dataframe['macd'] < dataframe['macd_signal'])
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# 条件4: EMA 多头排列
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conditions.append(dataframe['ema_50'] > dataframe['ema_200'])
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# 传统条件
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traditional_signal = (conditions[0] & conditions[1] & conditions[2] & conditions[3])
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# ML 增强：使用 FreqAI 预测的价格上升幅度
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if '&-price_rise' in dataframe.columns:
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ml_signal = dataframe['&-price_rise'] > 0.01 # 预测上升 > 1%
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final_signal = traditional_signal & ml_signal
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logger.debug(f"[{metadata['pair']}] 使用 ML 增强的入场信号")
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else:
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final_signal = traditional_signal
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logger.debug(f"[{metadata['pair']}] 仅使用传统入场信号（ML 未初始化）")
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dataframe.loc[final_signal, 'enter_long'] = 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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"""
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出场逻辑
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"""
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conditions = []
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# 条件1: 价格突破上轨
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conditions.append(dataframe['close'] >= dataframe['bb_upper'] * 0.99)
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# 条件2: RSI 超买
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conditions.append(dataframe['rsi'] > 70)
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# 条件3: MACD 下降
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conditions.append(dataframe['macd'] > dataframe['macd_signal'])
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# 至少满足 2 个条件
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condition_count = conditions[0].astype(int) + conditions[1].astype(int) + conditions[2].astype(int)
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final_signal = condition_count >= 2
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dataframe.loc[final_signal, 'exit_long'] = 1
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return dataframe
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