mirror of
https://github.com/xavierchuan/FX-ML-Trading-Engine.git
synced 2026-08-13 00:58:05 +00:00
150 lines
4.9 KiB
Python
150 lines
4.9 KiB
Python
from typing import List, Optional
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import pandas as pd
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import numpy as np
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from datetime import datetime
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from ..data.base import MarketDataEvent
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from .base import Strategy, SignalEvent
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class RSIMeanReversionStrategy(Strategy):
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"""
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RSI均值回归策略
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当RSI超买时做空，超卖时做多
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"""
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def __init__(
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self,
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instrument: str,
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position_size: float = 1.0,
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max_positions: int = 1,
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rsi_period: int = 14,
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overbought: float = 70.0,
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oversold: float = 30.0,
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stop_loss_atr: float = 2.0,
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atr_period: int = 14
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):
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super().__init__(instrument, position_size, max_positions)
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self.rsi_period = rsi_period
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self.overbought = overbought
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self.oversold = oversold
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self.stop_loss_atr = stop_loss_atr
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self.atr_period = atr_period
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self.last_rsi = None
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self.last_atr = None
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@staticmethod
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def calculate_rsi(data: pd.Series, period: int = 14) -> pd.Series:
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"""计算RSI指标"""
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delta = data.diff()
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gain = (delta.where(delta > 0, 0)).rolling(window=period).mean()
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loss = (-delta.where(delta < 0, 0)).rolling(window=period).mean()
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rs = gain / loss
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return 100 - (100 / (1 + rs))
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@staticmethod
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def calculate_atr(data: pd.DataFrame, period: int = 14) -> pd.Series:
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"""计算ATR指标"""
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high = data['high']
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low = data['low']
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close = data['close']
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tr1 = high - low
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tr2 = abs(high - close.shift())
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tr3 = abs(low - close.shift())
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tr = pd.concat([tr1, tr2, tr3], axis=1).max(axis=1)
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return tr.rolling(window=period).mean()
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async def on_data(self, event: MarketDataEvent) -> Optional[SignalEvent]:
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"""
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处理实时市场数据
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Args:
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event: 市场数据事件
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Returns:
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如果触发信号则返回SignalEvent，否则返回None
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"""
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if self.historical_data is None:
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return None
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# 更新数据
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current_price = event.data['mid']
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self.historical_data.loc[event.timestamp] = current_price
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# 计算指标
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close_prices = self.historical_data['close']
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rsi = self.calculate_rsi(close_prices, self.rsi_period).iloc[-1]
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atr = self.calculate_atr(self.historical_data, self.atr_period).iloc[-1]
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self.last_rsi = rsi
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self.last_atr = atr
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# 生成信号
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if self.can_open_position():
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if rsi > self.overbought:
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return SignalEvent(
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instrument=self.instrument,
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timestamp=event.timestamp,
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signal_type="SHORT",
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direction="SELL",
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strength=self.position_size,
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stop_loss=current_price + self.stop_loss_atr * atr
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)
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elif rsi < self.oversold:
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return SignalEvent(
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instrument=self.instrument,
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timestamp=event.timestamp,
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signal_type="LONG",
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direction="BUY",
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strength=self.position_size,
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stop_loss=current_price - self.stop_loss_atr * atr
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)
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return None
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async def calculate_signals(self, data: pd.DataFrame) -> List[SignalEvent]:
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"""
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基于历史数据计算交易信号
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Args:
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data: 历史市场数据
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Returns:
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交易信号列表
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"""
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signals = []
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self.historical_data = data.copy()
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# 计算指标
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close_prices = data['close']
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rsi = self.calculate_rsi(close_prices, self.rsi_period)
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atr = self.calculate_atr(data, self.atr_period)
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# 生成信号
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for i in range(self.rsi_period, len(data)):
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timestamp = data.index[i]
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current_price = close_prices[i]
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current_rsi = rsi[i]
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current_atr = atr[i]
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if current_rsi > self.overbought:
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signals.append(SignalEvent(
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instrument=self.instrument,
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timestamp=timestamp,
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signal_type="SHORT",
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direction="SELL",
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strength=self.position_size,
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stop_loss=current_price + self.stop_loss_atr * current_atr
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))
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elif current_rsi < self.oversold:
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signals.append(SignalEvent(
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instrument=self.instrument,
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timestamp=timestamp,
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signal_type="LONG",
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direction="BUY",
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strength=self.position_size,
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stop_loss=current_price - self.stop_loss_atr * current_atr
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))
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return signals |