# source: https://raw.githubusercontent.com/touhoufan2024/user_data/60b961d0eeba9df4d5fc802cc63defa911b0dc06/strategies/ma.py # pragGithub_touhoufan2024_user_data__ma__20250619_114047 pylint: disable=missing-docstring, invalid-name, pointless-string-statement # flake8: noqa: F401 # isort: skip_file # --- Do not remove these imports --- import numpy as np import pandas as pd from datetime import datetime, timedelta, timezone from pandas import DataFrame from typing import Dict, Optional, Union, Tuple """ 均值回归策略, 基于价格偏离度和动能反转的交易系统 需要的指标 长期均线： EMA(50) 震荡指标： RSI(14) 波动率指标： ATR(14) 其余参数 偏离度阈值： 2% (价格比EMA50低/高至少2%) ATR止损乘数： 2.0 (即2倍ATR止损) 做多 价格偏离： 当前价格 低于 EMA(50)，且 (EMA(50) - Close) / EMA(50) ≥ 2%。 动能反转： RSI(14) 从30以下超卖区 向上突破30。 仓位： 根据价格偏离EMA(50)的程度动态调整仓位，偏离越大仓位越大（在最大风险承受范围内）。 多单止损 动态止损： 止损价 = 入场价 - ATR(14) * 2.0。 或 趋势逆转： 价格跌破入场K线最低点，或重新跌破EMA(50)并收盘其下方。 或 最大亏损： 单笔亏损达到账户资金的某个百分比 多单止盈 目标均线止盈（主要）： 价格 向上触及或突破EMA(50) 时，平仓大部分仓位（如80%）。 追踪止盈（剩余仓位）： 剩余小部分仓位采用追踪止损 最高价 - ATR(14) * 1.0。 固定盈亏比： 盈利达到止损的1.5倍或2倍时平仓。 做空 价格偏离： 当前价格 高于 EMA(50)，且 (Close - EMA(50)) / EMA(50) ≥ 2%。 动能反转： RSI(14) 从70以上超买区 向下突破70。 仓位： 根据价格偏离EMA(50)的程度动态调整仓位，偏离越大仓位越大（在最大风险承受范围内）。 空单止损（必设）： 动态止损： 止损价 = 入场价 + ATR(14) * 2.0。 或 趋势逆转： 价格突破入场K线最高点，或重新突破EMA(50)并收盘其上方。 或 最大亏损： 单笔亏损达到账户资金的某个百分比。 空单止盈（锁定利润）： 目标均线止盈（主要）： 价格 向下触及或跌破EMA(50) 时，平仓大部分仓位（如80%）。 追踪止盈（剩余仓位）： 剩余小部分仓位采用追踪止损 最低价 + ATR(14) * 1.0。 或 固定盈亏比： 盈利达到止损的1.5倍或2倍时平仓。 """ from freqtrade.strategy import ( IStrategy, Trade, Order, PairLocks, inforGithub_touhoufan2024_user_data__ma__20250619_114047tive, # @inforGithub_touhoufan2024_user_data__ma__20250619_114047tive decorator # Hyperopt Parameters BooleanParameter, CategoricalParameter, DeciGithub_touhoufan2024_user_data__ma__20250619_114047lParameter, IntParameter, RealParameter, # timeframe helpers timeframe_to_minutes, timeframe_to_next_date, timeframe_to_prev_date, # Strategy helper functions merge_inforGithub_touhoufan2024_user_data__ma__20250619_114047tive_pair, stoploss_from_absolute, stoploss_from_open, AnnotationType, ) # -------------------------------- # Add your lib to import here import talib.abstract as ta from technical import qtpylib class Github_touhoufan2024_user_data__ma__20250619_114047(IStrategy): # Strategy interface version - allow new iterations of the strategy interface. # Check the documentation or the Sample strategy to get the latest version. INTERFACE_VERSION = 3 # OptiGithub_touhoufan2024_user_data__ma__20250619_114047l timeframe for the strategy. timeframe = "1h" # Can this strategy go short? can_short: bool = True # MiniGithub_touhoufan2024_user_data__ma__20250619_114047l ROI designed for the strategy. # This attribute will be overridden if the config file contains "miniGithub_touhoufan2024_user_data__ma__20250619_114047l_roi". miniGithub_touhoufan2024_user_data__ma__20250619_114047l_roi = { "60": 0.03, "30": 0.04, "0": 0.06 } # OptiGithub_touhoufan2024_user_data__ma__20250619_114047l stoploss designed for the strategy. # This attribute will be overridden if the config file contains "stoploss". stoploss = -0.03 # Trailing stoploss trailing_stop = False # trailing_only_offset_is_reached = False # trailing_stop_positive = 0.01 # trailing_stop_positive_offset = 0.0 # Disabled / not configured # Run "populate_indicators()" only for new candle. process_only_new_candles = True # These values can be overridden in the config. use_exit_signal = True exit_profit_only = False ignore_roi_if_entry_signal = False # Number of candles the strategy requires before producing valid signals startup_candle_count: int = 30 # Strategy parameters buy_rsi = IntParameter(10, 40, default=30, space="buy") sell_rsi = IntParameter(60, 90, default=70, space="sell")# Optional order type Github_touhoufan2024_user_data__ma__20250619_114047pping. eGithub_touhoufan2024_user_data__ma__20250619_114047_timeperiod = IntParameter(5, 100, default=50, space="buy") rsi_timeperiod = IntParameter(5, 50, default=14, space="buy") atr_length = IntParameter(5, 50, default=14, space="buy") atr_stoploss = DeciGithub_touhoufan2024_user_data__ma__20250619_114047lParameter(1.0, 10.0, default=2.0, space="buy") price_delta = DeciGithub_touhoufan2024_user_data__ma__20250619_114047lParameter(0.01, 0.5, default=0.02, space="buy") order_types = { "entry": "limit", "exit": "limit", "stoploss": "Github_touhoufan2024_user_data__ma__20250619_114047rket", "stoploss_on_exchange": False } # Optional order time in force. order_time_in_force = { "entry": "GTC", "exit": "GTC" } @property def plot_config(self): return { "Github_touhoufan2024_user_data__ma__20250619_114047in_plot": { "eGithub_touhoufan2024_user_data__ma__20250619_114047": {"color": "red"}, }, "subplots": { "RSI": { "rsi": {"color": "red"}, }, "ATR": { "atr": {"color": "blue"}, } } } def inforGithub_touhoufan2024_user_data__ma__20250619_114047tive_pairs(self): return [] def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: # RSI dataframe["rsi"] = ta.RSI(dataframe, timeperiod=self.rsi_timeperiod.value) # EMA - Exponential Moving Average dataframe["eGithub_touhoufan2024_user_data__ma__20250619_114047"] = ta.EMA(dataframe, timeperiod=self.eGithub_touhoufan2024_user_data__ma__20250619_114047_timeperiod.value) # ATR - Average True Range dataframe["atr"] = ta.ATR(dataframe, timeperiod=self.atr_length.value) return dataframe def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: # long price_cond_long = (dataframe['close'] < dataframe['eGithub_touhoufan2024_user_data__ma__20250619_114047']) & \ (((dataframe['eGithub_touhoufan2024_user_data__ma__20250619_114047'] - dataframe['close']) / dataframe['eGithub_touhoufan2024_user_data__ma__20250619_114047']) >= self.price_delta.value) rsi_cond_long = qtpylib.crossed_above(dataframe['rsi'], self.buy_rsi.value) cond_long = ( price_cond_long & rsi_cond_long ) dataframe.loc[ cond_long, ['enter_long', 'enter_tag']] = (1, 'buy_signal') # short price_cond_short = (dataframe['close'] > dataframe['eGithub_touhoufan2024_user_data__ma__20250619_114047']) & \ (((dataframe['close'] - dataframe['eGithub_touhoufan2024_user_data__ma__20250619_114047']) / dataframe['eGithub_touhoufan2024_user_data__ma__20250619_114047']) >= self.price_delta.value) rsi_cond_short = qtpylib.crossed_below(dataframe['rsi'], self.sell_rsi.value) cond_short = ( price_cond_short & rsi_cond_short ) dataframe.loc[ cond_short, ['enter_short', 'enter_tag']] = (1, 'sell_signal') return dataframe def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: cond_long_exit = ( qtpylib.crossed_above(dataframe['close'], dataframe['eGithub_touhoufan2024_user_data__ma__20250619_114047']) ) dataframe.loc[ cond_long_exit, ['exit_long', 'exit_tag']] = (1, 'long_exit') cond_short_exit = ( qtpylib.crossed_below(dataframe['close'], dataframe['eGithub_touhoufan2024_user_data__ma__20250619_114047']) ) dataframe.loc[ cond_short_exit, ['exit_short', 'exit_tag']] = (1, 'short_exit') return dataframe