206 lines
7.7 KiB
Python
206 lines
7.7 KiB
Python
# pragma pylint: disable=missing-docstring, invalid-name, pointless-string-statement
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# flake8: noqa: F401
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# isort: skip_file
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# --- Do not remove these libs ---
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import numpy as np
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import pandas as pd
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from pandas import DataFrame
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from datetime import datetime
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from typing import Optional, Union
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from freqtrade.strategy import (BooleanParameter, CategoricalParameter, DecimalParameter,
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IntParameter, merge_informative_pair, stoploss_from_absolute,
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stoploss_from_open)
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from freqtrade.strategy.interface import IStrategy
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import talib.abstract as ta
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import freqtrade.vendor.qtpylib.indicators as qtpylib
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class TrendStrategy(IStrategy):
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"""
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趋势策略 - 基于EMA和RSI的趋势跟踪策略
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策略逻辑：
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买入条件：
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1. EMA多头排列 + 短期趋势加速：EMA10 > EMA20 且 EMA10继续上行
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2. 双周期RSI深度超卖反弹：6周期RSI和12周期RSI都进入超卖区域
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卖出条件：
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1. EMA10连续下跌（动量走弱）
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2. 空头趋势加速（EMA价差达到近期最大）
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"""
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# Strategy interface version - allow new iterations of the strategy
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INTERFACE_VERSION = 3
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# Optimal timeframe for the strategy
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timeframe = '1d'
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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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# minimal_roi = {
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# "0": 0.1 # 10% ROI，让止损和卖出条件来控制退出
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# }
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# Optimal stoploss designed for the strategy
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stoploss = -0.05 # 5% 止损
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# Trailing stoploss
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trailing_stop = False
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trailing_stop_positive = None
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trailing_stop_positive_offset = 0.0
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trailing_only_offset_is_reached = False
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# Run "populate_indicators" only for new candle
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process_only_new_candles = False
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# These values can be overridden in the config
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use_exit_signal = True
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exit_profit_only = False
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ignore_roi_if_entry_signal = False
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# Number of candles the strategy requires before producing valid signals
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startup_candle_count: int = 30
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# Optional order type mapping
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order_types = {
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'entry': 'limit',
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'exit': 'limit',
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'stoploss': 'market',
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'stoploss_on_exchange': False
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}
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# Optional order time in force
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order_time_in_force = {
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'entry': 'gtc',
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'exit': 'gtc'
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}
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# 策略参数
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ema_short_window = IntParameter(8, 15, default=10, space="buy")
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ema_long_window = IntParameter(15, 25, default=20, space="buy")
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rsi_short_window = IntParameter(4, 8, default=6, space="buy")
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rsi_long_window = IntParameter(10, 15, default=12, space="buy")
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# RSI超卖阈值
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rsi_short_oversold = DecimalParameter(15.0, 25.0, default=21.0, space="buy")
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rsi_long_oversold = DecimalParameter(20.0, 30.0, default=26.25, space="buy")
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# 趋势检查窗口
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trend_check_window = IntParameter(3, 7, default=3, space="buy")
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momentum_check_window = IntParameter(3, 7, default=5, space="sell")
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diff_check_window = IntParameter(4, 8, default=6, space="sell")
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def informative_pairs(self):
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"""
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Define additional, informative pair/interval combinations to be cached from the exchange.
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These pairs will automatically be available for use in the `populate_indicators` method.
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"""
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return []
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def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
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"""
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Adds several different TA indicators to the given DataFrame
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Performance Note: For the best performance be frugal on the number of indicators
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you are using. Let uncomment only the indicator you are using in your strategies
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or your hyperopt configuration, otherwise you will waste your memory and CPU usage.
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:param dataframe: Dataframe with data from the exchange
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:param metadata: Additional information, like the currently traded pair
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:return: a Dataframe with all mandatory indicators for the strategy
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"""
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# EMA指标
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dataframe['ema_short'] = ta.EMA(dataframe, timeperiod=self.ema_short_window.value)
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dataframe['ema_long'] = ta.EMA(dataframe, timeperiod=self.ema_long_window.value)
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# RSI指标
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dataframe['rsi_short'] = ta.RSI(dataframe, timeperiod=self.rsi_short_window.value)
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dataframe['rsi_long'] = ta.RSI(dataframe, timeperiod=self.rsi_long_window.value)
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# 计算EMA价差（用于卖出条件）
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dataframe['ema_diff'] = dataframe['ema_long'] - dataframe['ema_short']
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# 计算EMA短期趋势（用于买入条件）
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dataframe['ema_short_rising'] = dataframe['ema_short'] > dataframe['ema_short'].shift(1)
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dataframe['ema_short_rising_2'] = dataframe['ema_short'].shift(1) > dataframe['ema_short'].shift(2)
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# 计算RSI超卖条件
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dataframe['rsi_short_oversold'] = (
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dataframe['rsi_short'].shift(1).rolling(window=self.trend_check_window.value).max() < self.rsi_short_oversold.value
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)
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dataframe['rsi_long_oversold'] = (
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dataframe['rsi_long'].shift(1).rolling(window=self.trend_check_window.value).max() < self.rsi_long_oversold.value
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)
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# 计算EMA10连续下跌条件（用于卖出）
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dataframe['ema_short_falling'] = (
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dataframe['ema_short'] < dataframe['ema_short'].shift(1).rolling(window=self.momentum_check_window.value).min()
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)
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# 计算价差扩大条件（用于卖出）
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dataframe['ema_diff_expanding'] = (
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dataframe['ema_diff'] == dataframe['ema_diff'].rolling(window=self.diff_check_window.value).max()
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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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Based on TA indicators, populates the entry signal for the given dataframe
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:param dataframe: DataFrame populated with indicators
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:param metadata: Additional information, like the currently traded pair
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:return: DataFrame with entry columns populated
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"""
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# 条件1：EMA多头排列 + 短期趋势加速
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ema_bullish_cross = (
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(dataframe['ema_short'] > dataframe['ema_long']) &
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(dataframe['ema_short_rising'] & dataframe['ema_short_rising_2'])
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)
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# 条件2：双周期RSI深度超卖反弹
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rsi_oversold_bounce = (
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dataframe['rsi_short_oversold'] &
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dataframe['rsi_long_oversold']
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)
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# 合并买入条件：任一条件满足即产生买入信号
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dataframe.loc[
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ema_bullish_cross | rsi_oversold_bounce,
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'enter_long'
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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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"""
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Based on TA indicators, populates the exit signal for the given dataframe
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:param dataframe: DataFrame populated with indicators
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:param metadata: Additional information, like the currently traded pair
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:return: DataFrame with exit columns populated
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"""
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# 卖出条件：EMA10连续下跌 + 空头趋势加速
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sell_condition = (
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dataframe['ema_short_falling'] &
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dataframe['ema_diff_expanding']
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)
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dataframe.loc[
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sell_condition,
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'exit_long'
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] = 1
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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
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这里保持原有的5%止损，但可以根据需要添加更复杂的止损逻辑
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"""
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return self.stoploss
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