414 lines
15 KiB
Python
414 lines
15 KiB
Python
# strategies/SimpleRSIStrategy_Fixed.py
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import talib.abstract as ta
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import pandas as pd
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import numpy as np
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from freqtrade.strategy import IStrategy
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from freqtrade.persistence import Trade
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from typing import Dict, List, Optional
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from functools import reduce
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import logging
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from dingtalk import DingTalkBot
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import numpy as np
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import pandas as pd
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from datetime import datetime, timedelta, timezone
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from pandas import DataFrame
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from typing import Optional, Union
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from freqtrade.strategy import (
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IStrategy,
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Trade,
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Order,
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PairLocks,
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informative, # @informative decorator
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# Hyperopt Parameters
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BooleanParameter,
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CategoricalParameter,
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DecimalParameter,
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IntParameter,
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RealParameter,
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# timeframe helpers
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timeframe_to_minutes,
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timeframe_to_next_date,
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timeframe_to_prev_date,
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# Strategy helper functions
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merge_informative_pair,
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stoploss_from_absolute,
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stoploss_from_open,
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)
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webhook = "https://oapi.dingtalk.com/robot/send?access_token=87c7abfcdd69b699c32da4e4f5981cd2ca6b0445474fc6ffb36f2ed0f6262fbb" # 填写你的webhook
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secret = "SECf3d6b43f2f8a87ab91feffd052e71ec314fbf57a1842e483fe07af3c0a0e5aa6" # 填写你的加签token（如果有），否则留空
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# CTA 群机器人
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# webhook = "https://oapi.dingtalk.com/robot/send?access_token=87c7abfcdd69b699c32da4e4f5981cd2ca6b0445474fc6ffb36f2ed0f6262fbb"
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# secret = "SECf3d6b43f2f8a87ab91feffd052e71ec314fbf57a1842e483fe07af3c0a0e5aa6"
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dingtalk = DingTalkBot(webhook, secret)
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logger = logging.getLogger(__name__)
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class SimpleRSIStrategyFixed(IStrategy):
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"""
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修复版简化 RSI 策略：结合 EMA 交叉 + RSI 超卖 + 上升趋势
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修复内容：
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1. 添加缺失的导入语句
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2. 修复除零错误
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3. 优化性能
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4. 改进代码可读性
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5. 添加数据验证
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"""
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INTERFACE_VERSION = 3
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# Can this strategy go short?
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can_short: bool = False
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# Minimal ROI designed for the strategy.
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# 基于x.py策略，使用更保守的ROI设置
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# minimal_roi = {
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# "30": 0.3, # 20% 目标收益
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# # ""
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# }
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# Optimal stoploss designed for the strategy.
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# 基于x.py策略的卖出条件：跌破MA60且跌幅超过10%
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stoploss = -0.05
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# 超参数定义
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minimal_roi = {
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"0": 100
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}
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timeframe = '1h'
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# 策略参数
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rsi_period_short = 6
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rsi_period_long = 12
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ema_period_short = 10
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ema_period_long = 20
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rsi_oversold_threshold = 20
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rsi_oversold_tolerance = 1.05
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ema_trend_threshold = 0.01
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ema_breakout_threshold = 0.02
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ema_separation_threshold = 0.02
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def order_filled(self, pair: str, trade: Trade, order: Order, current_time: datetime, **kwargs) -> None:
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# 交易对
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trading_pair = pair
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# 时间
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fill_time = order.order_filled_date or current_time
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# 价格
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fill_price = order.average or order.price
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# 买入还是卖出
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side = order.ft_order_side # 'buy' 或 'sell'
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# 仓位(当前持仓数量)
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position = trade.amount
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# 或者使用日志
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logger.info(
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f"订单成交 - 交易对: {trading_pair}, "
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f"时间: {fill_time}, "
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f"价格: {fill_price}, "
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f"方向: {side}, "
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f"仓位: {position}"
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)
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# dingtalk.send_text(
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# content=f"订单成交 - 交易对: {trading_pair}, 时间: {fill_time}, 价格: {fill_price}, 方向: {side}, 仓位: {position}")
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return None
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def populate_indicators(self, dataframe: pd.DataFrame, metadata: Dict) -> pd.DataFrame:
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"""
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添加技术指标到数据框
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"""
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df = dataframe.copy()
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# 将date设置为索引用于resample
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df['date'] = df['date'].dt.tz_convert('Asia/Shanghai')
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df = df.set_index('date')
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df_resample = df.resample('24h', label='right').agg({
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'open': 'first',
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'high': 'max',
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'low': 'min',
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'close': 'last',
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'volume': 'sum'
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})
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# RSI 指标 (6, 12) 及位移
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df_resample['rsi_6'] = ta.RSI(df_resample['close'], timeperiod=6)
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df_resample['rsi_12'] = ta.RSI(df_resample['close'], timeperiod=12)
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for i in range(1, 6):
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df_resample[f'rsi_6_shift{i}'] = df_resample['rsi_6'].shift(i)
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df_resample[f'rsi_12_shift{i}'] = df_resample['rsi_12'].shift(i)
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# EMA 指标 (10, 20, 30) 及位移
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df_resample['ema_10'] = ta.EMA(df_resample['close'], timeperiod=10)
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df_resample['ema_20'] = ta.EMA(df_resample['close'], timeperiod=20)
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df_resample['ema_30'] = ta.EMA(df_resample['close'], timeperiod=30)
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for i in range(1, 6):
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df_resample[f'ema_10_shift{i}'] = df_resample['ema_10'].shift(i)
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df_resample[f'ema_20_shift{i}'] = df_resample['ema_20'].shift(i)
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df_resample[f'ema_30_shift{i}'] = df_resample['ema_30'].shift(i)
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# 方便条件判断的差值与比率（带除零保护）
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df_resample['ema_10_20_diff'] = df_resample['ema_10'] - df_resample['ema_20']
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df_resample['ema_10_20_ratio'] = np.where(
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df_resample['ema_20'] != 0,
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df_resample['ema_10_20_diff'] / df_resample['ema_20'],
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0
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)
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for i in range(1, 6):
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df_resample[f'ema_10_20_diff_shift{i}'] = df_resample['ema_10_20_diff'].shift(i)
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df_resample[f'ema_10_20_ratio_shift{i}'] = np.where(
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df_resample[f'ema_20_shift{i}'] != 0,
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df_resample[f'ema_10_20_diff_shift{i}'] / df_resample[f'ema_20_shift{i}'],
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0
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)
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df_resample = df_resample.drop(columns=['open', 'high', 'low', 'close', 'volume'])
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df_resample.index = df_resample.index.tz_convert('UTC')
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dataframe = dataframe.join(df_resample,
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on='date',
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how='left').ffill()
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dataframe.to_csv("rsi_eth2.csv", index=False, encoding='utf-8-sig')
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logger.debug(f"Indicators populated successfully for {metadata.get('pair', 'unknown')}")
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return dataframe
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def populate_entry_trend(self, dataframe: pd.DataFrame, metadata: Dict) -> pd.DataFrame:
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"""
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生成买入信号
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"""
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try:
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# 初始化信号列
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dataframe.loc[:, 'enter_long'] = 0
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# 数据验证
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required_columns = [
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'ema_10', 'ema_20', 'ema_30',
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'rsi_6', 'rsi_12',
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'ema_10_shift1', 'ema_10_shift2', 'ema_10_shift3', 'ema_10_shift4', 'ema_10_shift5',
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'ema_20_shift1', 'ema_20_shift2', 'ema_20_shift3', 'ema_20_shift4', 'ema_20_shift5',
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'rsi_6_shift1', 'rsi_6_shift2', 'rsi_6_shift3',
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'rsi_12_shift1', 'rsi_12_shift2', 'rsi_12_shift3',
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'ema_10_20_ratio', 'ema_10_20_ratio_shift5',
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'ema_10_20_diff', 'ema_10_20_diff_shift1', 'ema_10_20_diff_shift2', 'ema_10_20_diff_shift3', 'ema_10_20_diff_shift4', 'ema_10_20_diff_shift5',
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'close'
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]
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if not all(col in dataframe.columns for col in required_columns):
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logger.warning(f"Missing required columns: {required_columns}")
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return dataframe
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# 按给定逻辑实现买入条件
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ema_10 = dataframe['ema_10']
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ema_20 = dataframe['ema_20']
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ema_10_s1 = dataframe['ema_10_shift1']
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ema_10_s2 = dataframe['ema_10_shift2']
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ema_10_s3 = dataframe['ema_10_shift3']
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ema_10_s4 = dataframe['ema_10_shift4']
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ema_10_s5 = dataframe['ema_10_shift5']
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ema_20_s1 = dataframe['ema_20_shift1']
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ema_20_s2 = dataframe['ema_20_shift2']
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ema_20_s3 = dataframe['ema_20_shift3']
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ema_20_s4 = dataframe['ema_20_shift4']
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ema_20_s5 = dataframe['ema_20_shift5']
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rsi_6 = dataframe['rsi_6']
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rsi_12 = dataframe['rsi_12']
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rsi_6_s1 = dataframe['rsi_6_shift1']
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rsi_6_s2 = dataframe['rsi_6_shift2']
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rsi_6_s3 = dataframe['rsi_6_shift3']
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rsi_12_s1 = dataframe['rsi_12_shift1']
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rsi_12_s2 = dataframe['rsi_12_shift2']
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rsi_12_s3 = dataframe['rsi_12_shift3']
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close = dataframe['close']
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# 比率与安全除法
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ratio_now = np.where(ema_20 != 0, (ema_10 - ema_20) / ema_20, 0)
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ratio_s5 = np.where(ema_20_s5 != 0, (ema_10_s5 - ema_20_s5) / ema_20_s5, 0)
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ratio_pos_and_small = (ratio_s5 != 0) & ((ratio_now / ratio_s5) > 0) & ((ratio_now / ratio_s5) < 0.2)
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cond_1 = (
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(
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(ema_10 > ema_20)
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& (ema_10_s1 > ema_20_s1)
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& (ema_10_s1 > ema_10_s2)
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& (ema_10 > ema_10_s1)
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& (ratio_now > 0.01)
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)
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(
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(ema_10 > 1.02 * ema_10_s1)
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& (ema_10_s1 > ema_10_s2)
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& (ema_10 > ema_20)
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)
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(
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ratio_pos_and_small
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& ((ema_20 - ema_10) < (ema_20_s1 - ema_10_s1))
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& ((ema_20 - ema_10) < (ema_20_s2 - ema_10_s2))
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& ((ema_20 - ema_10) < (ema_20_s3 - ema_10_s3))
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& ((ema_20 - ema_10) < (ema_20_s4 - ema_10_s4))
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& ((ema_20 - ema_10) < (ema_20_s5 - ema_10_s5))
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& (ema_20_s5 > ema_10_s5)
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& (ema_10 < close)
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)
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)
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cond_rsi = (
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(rsi_6 > 1.5 * rsi_6_s1)
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& (rsi_6 > 0.95 * rsi_12)
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& (rsi_6_s1 < 20 * 1.05)
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& (rsi_6_s2 < 20 * 1.05)
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& (rsi_6_s3 < 20 * 1.05)
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& (rsi_12_s1 < 25 * 1.05)
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& (rsi_12_s2 < 25 * 1.05)
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& (rsi_12_s3 < 25 * 1.05)
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)
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buy_condition = cond_1 | cond_rsi
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dataframe.loc[buy_condition, 'enter_long'] = 1
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# 记录信号统计
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signal_count = buy_condition.sum()
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if signal_count > 0:
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logger.info(f"Generated {signal_count} buy signals for {metadata.get('pair', 'unknown')}")
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return dataframe
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except Exception as e:
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logger.error(f"Error in populate_entry_trend: {e}")
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return dataframe
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def populate_exit_trend(self, dataframe: pd.DataFrame, metadata: Dict) -> pd.DataFrame:
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"""
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生成卖出信号
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"""
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try:
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dataframe.loc[:, 'exit_long'] = 0
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# 数据验证
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required_columns = [
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'ema_10', 'ema_20',
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'ema_10_shift1', 'ema_10_shift2', 'ema_10_shift3', 'ema_10_shift4', 'ema_10_shift5',
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'ema_20_shift1', 'ema_20_shift2', 'ema_20_shift3', 'ema_20_shift4', 'ema_20_shift5',
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'ema_10_20_ratio', 'ema_10_20_ratio_shift4',
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]
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if not all(col in dataframe.columns for col in required_columns):
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logger.warning(f"Missing required columns: {required_columns}")
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return dataframe
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ema_10 = dataframe['ema_10']
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ema_20 = dataframe['ema_20']
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ema_10_s1 = dataframe['ema_10_shift1']
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ema_10_s2 = dataframe['ema_10_shift2']
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ema_10_s3 = dataframe['ema_10_shift3']
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ema_10_s4 = dataframe['ema_10_shift4']
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ema_10_s5 = dataframe['ema_10_shift5']
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ema_20_s1 = dataframe['ema_20_shift1']
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ema_20_s2 = dataframe['ema_20_shift2']
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ema_20_s3 = dataframe['ema_20_shift3']
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ema_20_s4 = dataframe['ema_20_shift4']
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ema_20_s5 = dataframe['ema_20_shift5']
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ratio_now = np.where(ema_20 != 0, (ema_10 - ema_20) / ema_20, 0)
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ratio_s4 = np.where(ema_20_s4 != 0, (ema_10_s4 - ema_20_s4) / ema_20_s4, 0)
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ratio_pos_and_lt03 = (ratio_s4 != 0) & ((ratio_now / ratio_s4) > 0) & ((ratio_now / ratio_s4) < 0.3)
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cond_block_1 = (
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(
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(ema_10 < ema_20)
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& (ema_10_s1 < ema_20_s1)
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& (ema_10_s2 < ema_20_s2)
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& (ema_10_s5 > ema_20_s5)
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)
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(
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(ema_10 < 0.995 * ema_10_s1)
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& (ema_10_s1 < ema_10_s2)
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& (ema_10 < ema_20)
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)
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)
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cond_block_2 = (
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(ratio_now < 0.02)
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& ratio_pos_and_lt03
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& (ema_10_s4 > ema_20_s4)
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& (ema_10 < ema_10_s1)
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& (ema_10 < ema_10_s2)
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& (ema_10 < ema_10_s3)
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& (ema_10 < ema_10_s4)
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)
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widening_now_vs_history = (
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((ema_20 - ema_10) > (ema_20_s1 - ema_10_s1))
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& ((ema_20 - ema_10) > (ema_20_s2 - ema_10_s2))
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& ((ema_20 - ema_10) > (ema_20_s3 - ema_10_s3))
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& ((ema_20 - ema_10) > (ema_20_s4 - ema_10_s4))
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& ((ema_20 - ema_10) > (ema_20_s5 - ema_10_s5))
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)
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sell_condition = (
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(
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cond_block_1
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& (ema_10 < ema_10_s1)
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& (ema_10 < ema_10_s2)
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& (ema_10 < ema_10_s3)
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& (ema_10 < ema_10_s4)
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& (ema_10 < ema_10_s5)
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& widening_now_vs_history
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)
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(
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cond_block_2
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& ((ema_20 - ema_10) > (ema_20_s1 - ema_10_s1))
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& ((ema_20 - ema_10) > (ema_20_s2 - ema_10_s2))
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& ((ema_20 - ema_10) > (ema_20_s3 - ema_10_s3))
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& ((ema_20 - ema_10) > (ema_20_s4 - ema_10_s4))
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)
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)
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dataframe.loc[sell_condition, 'exit_long'] = 1
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# 记录信号统计
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signal_count = sell_condition.sum()
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if signal_count > 0:
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logger.info(f"Generated {signal_count} sell signals for {metadata.get('pair', 'unknown')}")
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return dataframe
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except Exception as e:
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logger.error(f"Error in populate_exit_trend: {e}")
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return dataframe
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# def custom_stoploss(self, pair: str, trade: Trade, current_time, current_rate, current_profit, **kwargs) -> float:
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# """
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# 自定义止损逻辑
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# """
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# try:
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# # 动态止损：根据RSI调整止损点
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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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# last_candle = dataframe.iloc[-1]
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# rsi_short = last_candle.get('rsi_6', 50)
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# # RSI越高，止损越宽松
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# if rsi_short > 70:
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# return -0.15 # 更宽松的止损
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# elif rsi_short < 30:
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# return -0.05 # 更严格的止损
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# else:
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# return self.stoploss # 默认止损
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# else:
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# return self.stoploss
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# except Exception as e:
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# logger.error(f"Error in custom_stoploss: {e}")
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# return self.stoploss
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