# source: https://raw.githubusercontent.com/xielk/freqtrade-test/925e067f080172a4581bc71ae9ee64be247606f4/user_data/strategies/RelaxedFibonacciStrategy.py # pragma 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 from freqtrade.strategy import ( IStrategy, Trade, Order, PairLocks, informative, # @informative decorator # Hyperopt Parameters BooleanParameter, CategoricalParameter, DecimalParameter, IntParameter, RealParameter, # timeframe helpers timeframe_to_minutes, timeframe_to_next_date, timeframe_to_prev_date, # Strategy helper functions merge_informative_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_xielk_freqtrade_test__RelaxedFibonacciStrategy__20250910_084214(IStrategy): """ 宽松版斐波那契策略 - 增加交易机会 策略特点： 1. 大幅放宽交易条件 2. 增加斐波那契容忍度 3. 简化确认条件 4. 提高交易频率 """ # Strategy interface version INTERFACE_VERSION = 3 # 策略时间框架 - 15分钟 timeframe = "15m" # 是否支持做空 can_short: bool = False # 最小ROI设置 minimal_roi = { "120": 0.01, # 2小时后1%收益 "60": 0.02, # 1小时后2%收益 "30": 0.03, # 30分钟后3%收益 "0": 0.05 # 立即5%收益 } # 止损设置 stoploss = -0.06 # 6%止损 # 追踪止损 trailing_stop = True trailing_stop_positive = 0.02 trailing_stop_positive_offset = 0.03 trailing_only_offset_is_reached = True # 只处理新K线 process_only_new_candles = True # 策略参数 use_exit_signal = True exit_profit_only = False ignore_roi_if_entry_signal = False # 策略启动所需的K线数量 startup_candle_count: int = 50 # 宽松的参数设置 fib_period = IntParameter(20, 80, default=30, space="buy") rsi_period = IntParameter(10, 20, default=14, space="buy") rsi_oversold = IntParameter(35, 55, default=45, space="buy") rsi_overbought = IntParameter(45, 65, default=55, space="sell") fib_tolerance = DecimalParameter(0.05, 0.20, default=0.10, space="buy") # 订单类型 order_types = { "entry": "limit", "exit": "limit", "stoploss": "market", "stoploss_on_exchange": False } # 订单时间 order_time_in_force = { "entry": "GTC", "exit": "GTC" } @property def plot_config(self): return { "main_plot": { "fib_0.236": {"color": "red", "type": "line"}, "fib_0.382": {"color": "orange", "type": "line"}, "fib_0.5": {"color": "yellow", "type": "line"}, "fib_0.618": {"color": "green", "type": "line"}, "fib_0.786": {"color": "blue", "type": "line"}, }, "subplots": { "RSI": { "rsi": {"color": "purple"}, } } } def informative_pairs(self): return [] def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: """ 计算技术指标 """ # 计算RSI dataframe["rsi"] = ta.RSI(dataframe, timeperiod=self.rsi_period.value) # 计算斐波那契回归水平 period = self.fib_period.value dataframe["highest"] = dataframe["high"].rolling(window=period).max() dataframe["lowest"] = dataframe["low"].rolling(window=period).min() dataframe["price_range"] = dataframe["highest"] - dataframe["lowest"] # 计算斐波那契回归水平 dataframe["fib_0.236"] = dataframe["highest"] - 0.236 * dataframe["price_range"] dataframe["fib_0.382"] = dataframe["highest"] - 0.382 * dataframe["price_range"] dataframe["fib_0.5"] = dataframe["highest"] - 0.5 * dataframe["price_range"] dataframe["fib_0.618"] = dataframe["highest"] - 0.618 * dataframe["price_range"] dataframe["fib_0.786"] = dataframe["highest"] - 0.786 * dataframe["price_range"] # 计算EMA dataframe["ema_20"] = ta.EMA(dataframe, timeperiod=20) # 计算MACD macd = ta.MACD(dataframe) dataframe["macd"] = macd["macd"] dataframe["macdsignal"] = macd["macdsignal"] return dataframe def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: """ 宽松的买入信号 """ tolerance = self.fib_tolerance.value # 基础条件 base_conditions = ( (dataframe["volume"] > 0) & (dataframe["price_range"] > 0) & (dataframe["highest"].notna()) & (dataframe["lowest"].notna()) ) # 斐波那契条件（大幅放宽） fib_conditions = ( # 价格接近任何斐波那契支撑位 ( ((dataframe["close"] <= dataframe["fib_0.618"] * (1 + tolerance)) & (dataframe["close"] >= dataframe["fib_0.618"] * (1 - tolerance))) | ((dataframe["close"] <= dataframe["fib_0.786"] * (1 + tolerance)) & (dataframe["close"] >= dataframe["fib_0.786"] * (1 - tolerance))) | ((dataframe["close"] <= dataframe["fib_0.5"] * (1 + tolerance)) & (dataframe["close"] >= dataframe["fib_0.5"] * (1 - tolerance))) ) ) # RSI条件（放宽） rsi_conditions = ( (dataframe["rsi"] <= self.rsi_oversold.value) ) # 趋势条件（简化） trend_conditions = ( (dataframe["close"] > dataframe["ema_20"]) ) # 综合买入条件 dataframe.loc[ base_conditions & fib_conditions & rsi_conditions & trend_conditions, "enter_long" ] = 1 return dataframe def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: """ 宽松的卖出信号 """ tolerance = self.fib_tolerance.value # 基础条件 base_conditions = ( (dataframe["volume"] > 0) & (dataframe["price_range"] > 0) & (dataframe["highest"].notna()) & (dataframe["lowest"].notna()) ) # 斐波那契条件（大幅放宽） fib_conditions = ( # 价格接近任何斐波那契阻力位 ( ((dataframe["close"] >= dataframe["fib_0.382"] * (1 - tolerance)) & (dataframe["close"] <= dataframe["fib_0.382"] * (1 + tolerance))) | ((dataframe["close"] >= dataframe["fib_0.236"] * (1 - tolerance)) & (dataframe["close"] <= dataframe["fib_0.236"] * (1 + tolerance))) | ((dataframe["close"] >= dataframe["fib_0.5"] * (1 - tolerance)) & (dataframe["close"] <= dataframe["fib_0.5"] * (1 + tolerance))) ) ) # RSI条件（放宽） rsi_conditions = ( (dataframe["rsi"] >= self.rsi_overbought.value) ) # 综合卖出条件 dataframe.loc[ base_conditions & fib_conditions & rsi_conditions, "exit_long" ] = 1 return dataframe