# source: https://raw.githubusercontent.com/andongniu/okx-btc-trading-system/87abd1bdb603ee44fd52976a4bc639487abdc5b8/user_data/strategies/OptimizedStrategy.py # pragma pylint: disable=missing-docstring, invalid-name, pointless-string-statement # flake8: noqa: F401 # isort: skip_file # --- Do not remove these libs --- import numpy as np import pandas as pd from pandas import DataFrame from datetime import datetime from typing import Optional, Union from freqtrade.strategy import (BooleanParameter, CategoricalParameter, DecimalParameter, IStrategy, IntParameter) # -------------------------------- # Add your lib to import here import ta as ta_lib from freqtrade.persistence import Trade class Github_andongniu_okx_btc_trading_system__OptimizedStrategy__20260227_025219(IStrategy): """ 优化的交易策略 - 结合多个技术指标 1. RSI超买超卖 2. 移动平均线交叉 3. MACD信号 4. 布林带突破 5. 成交量确认 """ INTERFACE_VERSION = 3 timeframe = '5m' can_short: bool = False # 优化的止盈参数 minimal_roi = { "0": 0.15, # 立即止盈15% "15": 0.10, # 15分钟后止盈10% "30": 0.05, # 30分钟后止盈5% "60": 0.02, # 60分钟后止盈2% "120": 0 # 120分钟后平仓 } # 止损设置 stoploss = -0.08 # 8%止损 # 追踪止损 trailing_stop = True trailing_stop_positive = 0.05 trailing_stop_positive_offset = 0.08 trailing_only_offset_is_reached = True process_only_new_candles = True use_exit_signal = True exit_profit_only = False ignore_roi_if_entry_signal = False startup_candle_count: int = 50 # 需要更多数据计算指标 order_types = { 'entry': 'limit', 'exit': 'limit', 'stoploss': 'market', 'stoploss_on_exchange': False } order_time_in_force = { 'entry': 'GTC', 'exit': 'GTC' } # 策略参数 rsi_buy = IntParameter(25, 35, default=30, space="buy") rsi_sell = IntParameter(65, 75, default=70, space="sell") macd_fast = IntParameter(10, 15, default=12, space="buy") macd_slow = IntParameter(20, 26, default=26, space="buy") macd_signal = IntParameter(7, 10, default=9, space="buy") bb_period = IntParameter(18, 22, default=20, space="buy") bb_std = DecimalParameter(1.8, 2.2, default=2.0, space="buy") def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: # RSI指标 dataframe['rsi'] = ta_lib.momentum.rsi(dataframe['close'], window=14) # 移动平均线 dataframe['sma_fast'] = dataframe['close'].rolling(window=20).mean() dataframe['sma_slow'] = dataframe['close'].rolling(window=50).mean() dataframe['ema_fast'] = dataframe['close'].ewm(span=12).mean() dataframe['ema_slow'] = dataframe['close'].ewm(span=26).mean() # MACD指标 macd = ta_lib.trend.macd(dataframe['close'], window_slow=26, window_fast=12, window_sign=9) dataframe['macd'] = macd dataframe['macd_signal'] = ta_lib.trend.macd_signal(dataframe['close'], window_slow=26, window_fast=12, window_sign=9) dataframe['macd_diff'] = ta_lib.trend.macd_diff(dataframe['close'], window_slow=26, window_fast=12, window_sign=9) # 布林带 bb_period = self.bb_period.value bb_std = self.bb_std.value dataframe['bb_upper'] = ta_lib.volatility.bollinger_hband(dataframe['close'], window=bb_period, window_dev=bb_std) dataframe['bb_middle'] = dataframe['close'].rolling(window=bb_period).mean() dataframe['bb_lower'] = ta_lib.volatility.bollinger_lband(dataframe['close'], window=bb_period, window_dev=bb_std) dataframe['bb_width'] = (dataframe['bb_upper'] - dataframe['bb_lower']) / dataframe['bb_middle'] # 成交量指标 dataframe['volume_sma'] = dataframe['volume'].rolling(window=20).mean() dataframe['volume_ratio'] = dataframe['volume'] / dataframe['volume_sma'] # ATR指标（波动率） dataframe['atr'] = ta_lib.volatility.average_true_range(dataframe['high'], dataframe['low'], dataframe['close'], window=14) # 价格位置指标 dataframe['price_position'] = (dataframe['close'] - dataframe['bb_lower']) / (dataframe['bb_upper'] - dataframe['bb_lower']) # 趋势强度指标 dataframe['trend_strength'] = abs(dataframe['sma_fast'] - dataframe['sma_slow']) / dataframe['atr'] return dataframe def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: """ 多条件买入信号： 1. RSI超卖 2. 价格在布林带下轨附近 3. MACD金叉或即将金叉 4. 成交量放大确认 5. 短期均线上穿长期均线 """ dataframe.loc[ ( # RSI条件 (dataframe['rsi'] < self.rsi_buy.value) & # 价格位置条件（在布林带下轨附近） (dataframe['price_position'] < 0.2) & # MACD条件（金叉或即将金叉） ((dataframe['macd'] > dataframe['macd_signal']) | ((dataframe['macd'] < dataframe['macd_signal']) & (dataframe['macd'].shift(1) > dataframe['macd_signal'].shift(1)))) & # 成交量确认 (dataframe['volume_ratio'] > 1.2) & # 趋势条件（短期均线上穿长期均线） (dataframe['sma_fast'] > dataframe['sma_slow']) & # 波动率过滤（避免高波动期） (dataframe['atr'] / dataframe['close'] < 0.02) & # 趋势强度过滤 (dataframe['trend_strength'] > 0.5) & # 确保有成交量 (dataframe['volume'] > 0) ), 'enter_long'] = 1 return dataframe def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: """ 多条件卖出信号： 1. RSI超买 2. 价格在布林带上轨附近 3. MACD死叉或即将死叉 4. 成交量萎缩 5. 短期均线下穿长期均线 """ dataframe.loc[ ( # RSI条件 (dataframe['rsi'] > self.rsi_sell.value) & # 价格位置条件（在布林带上轨附近） (dataframe['price_position'] > 0.8) & # MACD条件（死叉或即将死叉） ((dataframe['macd'] < dataframe['macd_signal']) | ((dataframe['macd'] > dataframe['macd_signal']) & (dataframe['macd'].shift(1) < dataframe['macd_signal'].shift(1)))) & # 成交量确认 (dataframe['volume_ratio'] < 0.8) & # 趋势条件（短期均线下穿长期均线） (dataframe['sma_fast'] < dataframe['sma_slow']) & # 确保有成交量 (dataframe['volume'] > 0) ), 'exit_long'] = 1 return dataframe def custom_stake_amount(self, pair: str, current_time: datetime, current_rate: float, proposed_stake: float, min_stake: Optional[float], max_stake: float, leverage: float, entry_tag: Optional[str], side: str, **kwargs) -> float: """ 自定义仓位管理：根据ATR调整仓位大小 """ dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe) if len(dataframe) > 0: current_atr = dataframe['atr'].iloc[-1] current_price = dataframe['close'].iloc[-1] # 根据波动率调整仓位：波动率越高，仓位越小 atr_ratio = current_atr / current_price if atr_ratio < 0.01: # 低波动，正常仓位 stake_multiplier = 1.0 elif atr_ratio < 0.02: # 中等波动，减少仓位 stake_multiplier = 0.7 else: # 高波动，大幅减少仓位 stake_multiplier = 0.4 adjusted_stake = proposed_stake * stake_multiplier # 确保在最小和最大限制内 if min_stake is not None: adjusted_stake = max(adjusted_stake, min_stake) adjusted_stake = min(adjusted_stake, max_stake) return adjusted_stake return proposed_stake