306 lines
13 KiB
Python
306 lines
13 KiB
Python
# pragma pylint: disable=missing-docstring, invalid-name, pointless-string-statement
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from freqtrade.strategy import IStrategy, merge_informative_pair, IntParameter, DecimalParameter, BooleanParameter
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from pandas import DataFrame
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import pandas as pd
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import talib.abstract as ta
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import numpy as np
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from datetime import datetime
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from typing import Optional
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from freqtrade.persistence import Trade
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import warnings
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# 抑制 pandas FutureWarning 关于 fillna 的隐式降级警告
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warnings.filterwarnings('ignore', category=FutureWarning, message='.*Downcasting object dtype arrays.*')
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pd.set_option('future.no_silent_downcasting', True)
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# freqtrade hyperopt -c ./user_data/Chan/config/Local_Test.json --strategy CryptoFutures1m5mStrategyV2Hyperopt --strategy-path ./user_data/Chan/strategies --timerange=20260101- --epochs 200 -j 4 --space buy
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# freqtrade backtesting -c ./user_data/Chan/config/Local_Test.json --strategy CryptoFutures1m5mStrategyV2Hyperopt --strategy-path ./user_data/Chan/strategies --timerange=20260101-
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class CryptoFutures1m5mStrategyV2Hyperopt(IStrategy):
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"""
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SOL/USDT 合约策略 - 1分钟+5分钟双时间框架 V2 Hyperopt优化版 (Short Only)
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基于V2优化版添加Hyperopt参数：
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1. ATR波动率过滤参数
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2. 时间止损参数
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3. 趋势确认参数（ADX, RSI, EMA200距离）
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"""
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INTERFACE_VERSION = 3
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timeframe = '1m'
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informative_timeframe = '5m'
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can_short = True
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lev = 1.0
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# 硬止损
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stoploss = -0.025
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# 追踪止盈 - 固定值
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trailing_stop = True
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trailing_stop_positive = 0.008
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trailing_stop_positive_offset = 0.032
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trailing_only_offset_is_reached = True
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# ==================== Hyperoptable Parameters ====================
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# ATR波动率过滤 - 可优化
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atr_min = DecimalParameter(low=0.03, high=0.15, default=0.07, decimals=2, space='buy', optimize=True)
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atr_max_mult = DecimalParameter(low=1.5, high=3.5, default=2.2, decimals=1, space='buy', optimize=True)
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# EMA200距离阈值 - 可优化
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ema200_dist = DecimalParameter(low=-3.0, high=-0.5, default=-1.0, decimals=1, space='buy', optimize=True)
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# 5分钟ADX范围 - 可优化
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adx_min = IntParameter(low=15, high=30, default=24, space='buy', optimize=True)
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adx_max = IntParameter(low=35, high=60, default=51, space='buy', optimize=True)
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# 5分钟RSI范围 - 可优化
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rsi_min = IntParameter(low=20, high=40, default=29, space='buy', optimize=True)
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rsi_max = IntParameter(low=40, high=60, default=48, space='buy', optimize=True)
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# 时间止损 - 可优化
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time_stop_1 = IntParameter(low=4, high=12, default=8, space='buy', optimize=True)
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time_stop_2 = IntParameter(low=12, high=20, default=16, space='buy', optimize=True)
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time_stop_3 = IntParameter(low=20, high=36, default=24, space='buy', optimize=True)
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# 1分钟RSI入场阈值 - 可优化
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entry_rsi_min = IntParameter(low=20, high=45, default=30, space='buy', optimize=True)
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# 成交量确认阈值 - 可优化
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volume_threshold = DecimalParameter(low=0.5, high=1.5, default=0.75, decimals=2, space='buy', optimize=True)
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# 完全禁用 exit_signal
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use_exit_signal = False
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process_only_new_candles = True
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startup_candle_count: int = 1100
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def informative_pairs(self):
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return [
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("SOL/USDT:USDT", "5m"),
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]
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def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
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# ==================== 5分钟指标 ====================
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inf_tf = self.informative_timeframe
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informative = self.dp.get_pair_dataframe(pair=metadata['pair'], timeframe=inf_tf)
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# EMA趋势
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informative['ema12'] = ta.EMA(informative['close'], timeperiod=12)
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informative['ema26'] = ta.EMA(informative['close'], timeperiod=26)
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informative['ema50'] = ta.EMA(informative['close'], timeperiod=50)
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# EMA12斜率（3根K线变化率）
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informative['ema12_slope'] = (informative['ema12'] - informative['ema12'].shift(3)) / informative['ema12'].shift(3) * 100
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# MACD
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macd, macd_signal, macd_hist = ta.MACD(informative['close'], fastperiod=12, slowperiod=26, signalperiod=9)
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informative['macd_5m'] = macd
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informative['macd_signal_5m'] = macd_signal
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informative['macd_hist_5m'] = macd_hist
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# ADX趋势强度
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informative['adx_5m'] = ta.ADX(informative['high'], informative['low'], informative['close'], timeperiod=14)
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# RSI（5分钟）
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informative['rsi_5m'] = ta.RSI(informative['close'], timeperiod=14)
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# ATR（5分钟）
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informative['atr_5m'] = ta.ATR(informative['high'], informative['low'], informative['close'], timeperiod=14)
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informative['atr_pct_5m'] = informative['atr_5m'] / informative['close'] * 100
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# ATR 长期均值
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informative['atr_pct_ma_5m'] = informative['atr_pct_5m'].rolling(window=100).mean()
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# EMA200 大趋势过滤
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informative['ema200'] = ta.EMA(informative['close'], timeperiod=200)
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informative['ema200_dist_pct'] = (informative['close'] - informative['ema200']) / informative['ema200'] * 100
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# EMA200斜率
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informative['ema200_slope'] = (informative['ema200'] - informative['ema200'].shift(20)) / informative['ema200'].shift(20) * 100
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# 大趋势过滤（Short Only）- 使用hyperopt参数
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informative['below_ema200'] = informative['ema200_dist_pct'] < self.ema200_dist.value
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# 牛市暂停
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informative['bull_pause'] = (
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(informative['ema200_slope'] > 0) &
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(informative['ema200_dist_pct'] > 0)
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)
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# 5分钟趋势判断（仅Short）- 使用hyperopt参数
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informative['trend_bear_5m'] = (
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(informative['ema12'] < informative['ema26']) &
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(informative['ema26'] < informative['ema50']) &
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(informative['ema12_slope'] < -0.05) &
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(informative['adx_5m'] > self.adx_min.value) &
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(informative['adx_5m'] < self.adx_max.value) &
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(informative['close'] < informative['ema12']) &
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(informative['rsi_5m'] < self.rsi_max.value) &
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(informative['rsi_5m'] > self.rsi_min.value)
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)
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# 做空条件
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informative['can_long_5m'] = False
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informative['can_short_5m'] = (
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informative['trend_bear_5m'] &
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informative['below_ema200'] &
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(~informative['bull_pause'])
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)
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# ATR波动率过滤 - 使用hyperopt参数
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informative['atr_ok_5m'] = (
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(informative['atr_pct_5m'] > self.atr_min.value) &
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(informative['atr_pct_5m'] < informative['atr_pct_ma_5m'] * self.atr_max_mult.value)
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)
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# 成交量确认 - 使用hyperopt参数
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informative['volume_ma_5m'] = ta.SMA(informative['volume'], timeperiod=20)
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informative['volume_ok_5m'] = informative['volume'] > informative['volume_ma_5m'] * self.volume_threshold.value
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# 合并5分钟数据到1分钟
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dataframe = merge_informative_pair(dataframe, informative, self.timeframe, inf_tf, ffill=True)
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# ==================== 1分钟指标 ====================
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macd_1m, signal_1m, hist_1m = ta.MACD(dataframe['close'], fastperiod=12, slowperiod=26, signalperiod=9)
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dataframe['macd'] = macd_1m
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dataframe['macd_signal'] = signal_1m
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dataframe['macd_hist'] = hist_1m
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dataframe['ema9'] = ta.EMA(dataframe['close'], timeperiod=9)
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dataframe['ema21'] = ta.EMA(dataframe['close'], timeperiod=21)
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dataframe['rsi'] = ta.RSI(dataframe['close'], timeperiod=14)
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dataframe['vol_ma20'] = ta.SMA(dataframe['volume'], timeperiod=20)
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# 1分钟MACD斜率
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dataframe['macd_slope'] = (dataframe['macd'] - dataframe['macd'].shift(3)) / 3
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# 1分钟做空入场信号
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dataframe['price_high_5'] = dataframe['high'].rolling(window=5).max()
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dataframe['macd_high_5'] = dataframe['macd'].rolling(window=5).max()
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# 顶背离
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dataframe['top_divergence'] = (
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(dataframe['high'] >= dataframe['price_high_5'] * 0.999) &
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(dataframe['macd'] < dataframe['macd_high_5']) &
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(dataframe['macd_slope'] < 0) &
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(dataframe['macd'] < dataframe['macd_signal']) &
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(dataframe['volume'] > dataframe['vol_ma20'] * 0.6)
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)
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# EMA死叉
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dataframe['ema_cross_down'] = (
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(dataframe['ema9'] < dataframe['ema21']) &
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(dataframe['ema9'].shift(1) >= dataframe['ema21'].shift(1)) &
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(dataframe['rsi'] < 55) &
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(dataframe['rsi'] > 35) &
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(dataframe['volume'] > dataframe['vol_ma20'] * 1.0)
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)
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# 熊市回调
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dataframe['is_bear_candle'] = (
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(dataframe['close'] < dataframe['open']) &
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((dataframe['open'] - dataframe['close']) / dataframe['open'] > 0.008)
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)
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dataframe['bear_pullback'] = (
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dataframe['is_bear_candle'].shift(2) &
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(dataframe['close'].shift(1) > dataframe['open'].shift(1)) &
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(dataframe['high'] < dataframe['high'].shift(2)) &
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(dataframe['close'] < dataframe['open']) &
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(dataframe['close'] < dataframe['ema9'])
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)
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# 时间过滤
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dataframe['hour_utc'] = dataframe['date'].dt.hour
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dataframe['is_bad_hour'] = dataframe['hour_utc'].isin([4, 5, 6, 7])
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# 安全转换5分钟布尔列
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bool_cols = [
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'can_long_5m_5m', 'can_short_5m_5m',
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'trend_bear_5m_5m',
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'atr_ok_5m_5m',
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'below_ema200_5m', 'bull_pause_5m',
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'volume_ok_5m_5m',
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]
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for col in bool_cols:
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if col in dataframe.columns:
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dataframe[col] = dataframe[col].astype(bool).fillna(False)
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num_cols = ['atr_pct_5m_5m', 'rsi_5m_5m', 'macd_hist_5m_5m', 'atr_pct_ma_5m_5m',
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'ema200_dist_pct_5m', 'ema200_slope_5m']
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for col in num_cols:
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if col in dataframe.columns:
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dataframe[col] = dataframe[col].astype(float).fillna(0.0)
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return dataframe
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def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
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time_ok = ~dataframe['is_bad_hour']
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atr_ok = dataframe['atr_ok_5m_5m']
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# 5分钟MACD方向确认
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macd_bear_5m = dataframe['macd_hist_5m_5m'] < 0
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# 1分钟MACD方向确认
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macd_bear_1m = dataframe['macd_hist'] < 0
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# 成交量确认
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volume_ok = dataframe['volume_ok_5m_5m']
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# 做空入场 - 使用hyperopt参数
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dataframe.loc[
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(time_ok) &
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(atr_ok) &
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(dataframe['can_short_5m_5m']) &
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(macd_bear_5m) &
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(macd_bear_1m) &
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(volume_ok) &
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(dataframe['rsi'] > self.entry_rsi_min.value) &
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(
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dataframe['top_divergence'] |
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dataframe['ema_cross_down'] |
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dataframe['bear_pullback']
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) &
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(dataframe['volume'] > 0),
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'enter_short'
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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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dataframe.loc[:, 'exit_long'] = 0
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dataframe.loc[:, 'exit_short'] = 0
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return dataframe
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def custom_exit(self, pair: str, trade: Trade, current_time: datetime,
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current_rate: float, current_profit: float, **kwargs) -> str | bool | None:
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"""自定义出场逻辑：时间止损 - 使用hyperopt参数"""
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trade_duration = (current_time - trade.open_date_utc).total_seconds() / 3600
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# 时间止损：持仓过久且亏损
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if trade_duration > self.time_stop_1.value and current_profit < -0.005:
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return 'time_stop_1'
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if trade_duration > self.time_stop_2.value and current_profit < 0:
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return 'time_stop_2'
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# 持仓超过24小时强制平仓
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if trade_duration > self.time_stop_3.value:
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return 'time_stop_3'
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return None
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def confirm_trade_entry(self, pair: str, order_type: str, amount: float, rate: float,
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time_in_force: str, current_time: datetime, entry_tag: Optional[str],
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side: str, **kwargs) -> bool:
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"""入场确认 - 时间过滤安全网"""
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hour_utc = current_time.utcnow().hour if current_time.tzinfo is None else current_time.hour
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if hour_utc in {4, 5, 6, 7}:
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return False
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return True
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def leverage(self, pair: str, current_time: datetime, current_rate: float,
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proposed_leverage: float, max_leverage: float, entry_tag: Optional[str], side: str,
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**kwargs) -> float:
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return self.lev
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