386 lines
14 KiB
Python
386 lines
14 KiB
Python
"""
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SOL/USDT 优化交易策略
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采用多时间周期分析和缠论技术分析，专注于空头交易
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集成了技术指标确认和风险管理功能
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"""
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# --- Do not remove these libs ---
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from statistics import median
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from freqtrade.strategy import IStrategy
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import sys
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import os
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# 添加父目录到系统路径
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sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
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from ChanLun import ChanLun
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from ChanLun_Classifier import ChanLunClassifier
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from ChanEnum import Chan_FX_TYPE, Chan_KLC_FX, Chan_BI_DIR, Chan_KLC_FX
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# --------------------------------
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from technical.util import resample_to_interval, resampled_merge
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import talib.abstract as ta
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import freqtrade.vendor.qtpylib.indicators as qtpylib
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from pandas import DataFrame
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from datetime import datetime, timedelta
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from freqtrade.persistence import Trade
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from typing import Optional
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import logging
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import numpy as np
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logger = logging.getLogger(__name__)
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# freqtrade trade -c ./user_data/Chan/config/ChanLun_SOL_Optimized.json --strategy ChanLun_SOL_Optimized --strategy-path ./user_data/Chan/strategies
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# freqtrade backtesting -c ./user_data/Chan/config/ChanLun_SOL_Optimized.json --strategy ChanLun_SOL_Optimized --strategy-path ./user_data/Chan/strategies --timerange=20250201-
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class ChanLun_SOL_Optimized(IStrategy):
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"""
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SOL/USDT 优化交易策略 - 专注于空头交易
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"""
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INTERFACE_VERSION: int = 3
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# 优化后的ROI设置，主要针对短期交易
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minimal_roi = {
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"0": 0.012,
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"120": 0.010,
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"240": 0.007,
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"360": 0.005
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}
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# 支持做空
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can_short = True
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only_short = True
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# 杠杆设置（谨慎使用）
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lev = 1.0
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# 止损设置
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stoploss = -0.007 * lev
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# 追踪止损设置
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trailing_stop = True
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trailing_stop_positive = 0.003
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trailing_stop_positive_offset = 0.005
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trailing_only_offset_is_reached = True
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# 仓位管理设置
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position_adjustment_enable = True
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max_entry_position_adjustment = 3
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max_dca_multiplier = 4.0
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# 策略初始化需要的K线数量
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startup_candle_count = 200
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# 时间周期定义
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timeframe = '5m'
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# 时间周期乘数
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time5 = 5
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time15 = 15
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time30 = 30
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time60 = 60
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time4h = 240
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time1d = 1440
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# 缠论模块初始化
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chan = ChanLun()
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def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
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"""
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添加技术指标
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"""
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# 基础技术指标
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for df in [dataframe]:
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# 添加MACD指标
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macd = ta.MACD(df)
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df['macd'] = macd['macd']
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df['macdsignal'] = macd['macdsignal']
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df['macdhist'] = macd['macdhist']
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# 添加移动平均线
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df['ma5'] = ta.MA(df, timeperiod=5)
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df['ma10'] = ta.MA(df, timeperiod=10)
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df['ma20'] = ta.MA(df, timeperiod=20)
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df['ma30'] = ta.EMA(df, timeperiod=30)
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df['ma50'] = ta.MA(df, timeperiod=50)
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df['ma200'] = ta.MA(df, timeperiod=200)
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# 添加RSI指标
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df['rsi'] = ta.RSI(df, timeperiod=14)
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df['rsi_slow'] = ta.RSI(df, timeperiod=21)
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# 添加ATR（波动率）
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df['atr'] = ta.ATR(df, timeperiod=14)
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# 计算布林带
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bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(df), window=20, stds=2)
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df['bb_lowerband'] = bollinger['lower']
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df['bb_middleband'] = bollinger['mid']
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df['bb_upperband'] = bollinger['upper']
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df['bb_width'] = (df['bb_upperband'] - df['bb_lowerband']) / df['bb_middleband']
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# 添加ADX指标（趋势强度）
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df['adx'] = ta.ADX(df, timeperiod=14)
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df['plus_di'] = ta.PLUS_DI(df, timeperiod=14)
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df['minus_di'] = ta.MINUS_DI(df, timeperiod=14)
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# 添加量比指标
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df['volume_ma20'] = df['volume'].rolling(window=20).mean()
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df['volume_ratio'] = df['volume'] / df['volume_ma20']
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# 计算下降趋势确认指标
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dataframe['downtrend'] = (
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(dataframe['ma5'] < dataframe['ma10']) &
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(dataframe['ma10'] < dataframe['ma30']) &
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(dataframe['close'] < dataframe['ma10']) &
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(dataframe['close'].shift(1) > dataframe['close'])
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)
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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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入场信号逻辑 - 放宽条件以产生更多交易信号
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"""
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# 关闭多头交易
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dataframe['enter_long'] = 0
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# 空头入场条件 - 条件1：价格下跌趋势
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dataframe.loc[
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(
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# 价格下跌趋势 - 放宽为仅需一根K线下跌
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(dataframe['close'] < dataframe['close'].shift(1)) &
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# 价格在均线下方 - 使用更短期均线
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(dataframe['close'] < dataframe['ma20']) &
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# RSI条件放宽 - 只要不是极度超卖
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(dataframe['rsi'] > 30) &
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# 成交量条件放宽
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(dataframe['volume'] > dataframe['volume'].rolling(window=10).mean()) &
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# MACD空头
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(dataframe['macd'] < dataframe['macdsignal'])
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),
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['enter_short', 'enter_tag']] = (1, 'short_trend_simple')
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# 空头入场条件 - 条件2：突破下降
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dataframe.loc[
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(
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# 价格突破支撑位
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(dataframe['close'] < dataframe['low'].shift(1).rolling(window=5).min()) &
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# 下降动量增强
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(dataframe['close'].pct_change() < -0.005) &
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# 非超卖区
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(dataframe['rsi'] > 35) &
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# 确保不与第一个条件重复
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(~dataframe['enter_short'].astype(bool))
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),
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['enter_short', 'enter_tag']] = (1, 'short_breakdown')
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# 空头入场条件 - 条件3：均线死叉
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dataframe.loc[
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(
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# 短期均线下穿长期均线
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(qtpylib.crossed_below(dataframe['ma5'], dataframe['ma10'])) &
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# 价格已经在中期均线下方
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(dataframe['close'] < dataframe['ma20']) &
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# 确保不与其他条件重复
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(~dataframe['enter_short'].astype(bool))
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),
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['enter_short', 'enter_tag']] = (1, 'short_ma_cross')
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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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出场信号逻辑 - 优化盈利能力和降低风险
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"""
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# 清除之前的出场条件
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dataframe['exit_short'] = 0
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dataframe['exit_long'] = 0
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# 空头出场条件 - 价格反转
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price_reversal = (
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# 价格反转
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(dataframe['close'] > dataframe['close'].shift(1)) &
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(dataframe['close'] > dataframe['open']) & # 收阳
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(dataframe['volume'] > dataframe['volume'].rolling(window=10).mean()) # 放量上涨
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)
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# 空头出场条件 - 超卖反弹
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oversold_bounce = (
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# RSI超卖
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(dataframe['rsi'] < 30) &
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(dataframe['rsi'] > dataframe['rsi'].shift(1)) # RSI回升
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)
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# 空头出场条件 - 盈利保护
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profit_protection = (
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# 突破下轨后快速回升
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(dataframe['close'] < dataframe['bb_lowerband']) &
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(dataframe['close'] > dataframe['close'].shift(1)) &
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(dataframe['close'].shift(1) > dataframe['close'].shift(2)) # 连续上涨
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)
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# 空头出场条件 - 趋势转变
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trend_change = (
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# 价格突破短期均线
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(qtpylib.crossed_above(dataframe['close'], dataframe['ma10'])) |
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# MACD柱状图由负转正
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(dataframe['macdhist'] > 0) &
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(dataframe['macdhist'].shift(1) < 0)
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)
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# 组合所有出场条件
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dataframe.loc[price_reversal, ['exit_short', 'exit_tag']] = (1, 'price_reversal')
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dataframe.loc[oversold_bounce, ['exit_short', 'exit_tag']] = (1, 'oversold_bounce')
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dataframe.loc[profit_protection, ['exit_short', 'exit_tag']] = (1, 'profit_protection')
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dataframe.loc[trend_change, ['exit_short', 'exit_tag']] = (1, 'trend_change')
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return dataframe
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def custom_stoploss(self, pair: str, trade: Trade, current_time: datetime,
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current_rate: float, current_profit: float, **kwargs) -> float:
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"""
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自定义止损逻辑 - 更精细的动态止损
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"""
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# 获取当前的dataframe
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dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
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if len(dataframe) == 0:
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return self.stoploss
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# 获取最新的K线数据
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last_candle = dataframe.iloc[-1].squeeze()
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# 计算ATR止损
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atr_value = last_candle['atr']
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# 根据盈利情况动态调整止损策略
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if current_profit >= 0.03:
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# 盈利较高，保护大部分利润，使用较紧的止损
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return current_profit * 0.6
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elif current_profit >= 0.015:
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# 中等盈利，保护部分利润
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return current_profit * 0.4
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elif current_profit >= 0.008:
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# 小额盈利，保本为主
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return current_profit * 0.15
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elif current_profit > 0:
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# 微小盈利，保本为主
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return 0
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else:
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# 亏损情况下，判断是否需要立即止损
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# 趋势强烈反转，尽快止损
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if (last_candle['close'] > last_candle['ma5']) and (last_candle['macd'] > last_candle['macdsignal']):
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# 趋势向上反转，立即减小止损
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return current_profit * 0.5
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# 下跌动量减弱，略微放宽止损
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if last_candle['rsi'] < 20 and last_candle['rsi'] > last_candle['rsi_slow']:
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# RSI超卖且反弹迹象，提供更多空间
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return self.stoploss * 1.3
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# 默认返回原始止损设置
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return self.stoploss
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def custom_stake_amount(self, pair: str, current_time: datetime, current_rate: float,
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proposed_stake: float, min_stake: float | None, max_stake: float,
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leverage: float, entry_tag: str | None, side: str,
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**kwargs) -> float:
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"""
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自定义仓位大小计算
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"""
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# 为DCA预留资金空间
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return proposed_stake / self.max_dca_multiplier
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def adjust_trade_position(self, trade: Trade, current_time: datetime,
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current_rate: float, current_profit: float,
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min_stake: float | None, max_stake: float,
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current_entry_rate: float, current_exit_rate: float,
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current_entry_profit: float, current_exit_profit: float,
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**kwargs) -> float | None | tuple[float | None, str | None]:
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"""
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动态调整仓位 - 优化加仓策略
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"""
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# 获取交易数据
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dataframe, _ = self.dp.get_analyzed_dataframe(trade.pair, self.timeframe)
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if len(dataframe) == 0:
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return None
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filled_entries = trade.select_filled_orders(trade.entry_side)
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if not filled_entries:
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return None
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last_entry = filled_entries[-1]
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count_of_entries = trade.nr_of_successful_entries
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# 空头加仓逻辑
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if last_entry.side == "sell":
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# 获取最新K线
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last_candle = dataframe.iloc[-1]
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prev_candle = dataframe.iloc[-2] if len(dataframe) > 1 else last_candle
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# 计算加仓金额 - 基于亏损程度动态调整
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stake_amount = filled_entries[0].stake_amount
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# 条件1：价格突破新低 + 高阶空头趋势
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if (current_profit < -0.005 and
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last_candle['close'] < prev_candle['low'] and
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last_candle['macd'] < last_candle['macdsignal'] and
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count_of_entries < 2):
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# 根据亏损程度调整加仓量 - 亏损越多加仓越少
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adjustment_factor = max(0.5, 1.0 + current_profit) # 限制最低为0.5
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new_stake = stake_amount * adjustment_factor
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return new_stake, "short_dca_new_low"
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# 条件2：小幅反弹后继续下跌
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if (current_profit < -0.003 and
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last_candle['close'] < last_candle['open'] and # 阴线
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last_candle['close'] < last_candle['ma20'] and # 价格在中期均线下方
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prev_candle['close'] > prev_candle['open'] and # 前一根是阳线
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count_of_entries < 3):
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# 使用标准金额加仓
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return stake_amount * 0.8, "short_dca_dip_continuation"
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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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"""
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在进入交易前进行额外的确认
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"""
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# 始终允许空头交易，不做额外检查
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if side == "sell":
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return True
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return False
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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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"""
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返回杠杆率
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"""
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return self.lev
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def get_ticker_indicator(self):
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"""
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获取当前时间框架的分钟数
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"""
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return int(self.timeframe[:-1]) |