添加新的策略
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jackyu66git
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# pragma pylint: disable=missing-docstring, invalid-name, pointless-string-statement
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import logging
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from functools import reduce
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from datetime import datetime
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from typing import Optional
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import numpy as np
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import talib.abstract as ta
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from pandas import DataFrame
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from freqtrade.strategy import IStrategy, merge_informative_pair
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from freqtrade.persistence import Trade
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# freqtrade backtesting -c ./user_data/Chan/config/FreqAI_Test.json --strategy CryptoFuturesAIStrategy --strategy-path ./user_data/Chan/strategies --freqaimodel LightGBMRegressor --timerange=20260201-
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# freqtrade trade -c ./user_data/Chan/config/FreqAI_Test.json --strategy CryptoFuturesAIStrategy --strategy-path ./user_data/Chan/strategies --freqaimodel LightGBMRegressor
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logger = logging.getLogger(__name__)
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class CryptoFuturesAIStrategy(IStrategy):
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"""
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SOL/USDT 合约 AI 策略 - FreqAI + LightGBM
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核心思路：
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1. 用 FreqAI 的 LightGBM 回归模型预测未来价格变化方向和幅度
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2. 模型自动在滚动窗口上重新训练，适应市场变化
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3. 结合传统技术指标作为特征输入，让 AI 学习最优组合
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4. 用 z-score 动态阈值代替固定参数，自适应不同市场环境
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5. 保留 trailing_stop 作为风控（这是原策略的盈利核心）
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相比固定参数策略的优势：
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- 参数自适应：模型每隔一段时间重新训练，适应市场状态变化
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- 特征自动选择：LightGBM 自动学习哪些指标在当前市场最有用
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- 动态阈值：用预测值的统计分布来决定入场，而非固定数值
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- 多维度输入：同时考虑价格、成交量、波动率、时间等多维信息
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"""
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INTERFACE_VERSION = 3
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timeframe = '5m' # FreqAI 用5分钟作为基础时间框架，更稳定
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can_short = True
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# === 风控参数（保留原策略的盈利核心） ===
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stoploss = -0.025
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trailing_stop = True
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trailing_stop_positive = 0.008
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trailing_stop_positive_offset = 0.015
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trailing_only_offset_is_reached = True
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use_custom_stoploss = False
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use_exit_signal = False # 禁用exit_signal，让trailing_stop管理退出
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process_only_new_candles = True
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startup_candle_count: int = 100 # 需要足够的历史数据计算指标
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# =====================================================
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# FreqAI 特征工程函数
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# =====================================================
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def feature_engineering_expand_all(
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self, dataframe: DataFrame, period: int, metadata: dict, **kwargs
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) -> DataFrame:
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"""
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自动扩展特征 - 精简版，减少特征数量防止内存溢出
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"""
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# 核心动量指标
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dataframe["%-rsi-period"] = ta.RSI(dataframe, timeperiod=period)
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dataframe["%-adx-period"] = ta.ADX(dataframe, timeperiod=period)
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dataframe["%-ema-period"] = ta.EMA(dataframe, timeperiod=period)
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dataframe["%-roc-period"] = ta.ROC(dataframe, timeperiod=period)
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# 相对成交量
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dataframe["%-relative_volume-period"] = (
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dataframe["volume"] / dataframe["volume"].rolling(period).mean()
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)
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return dataframe
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def feature_engineering_expand_basic(
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self, dataframe: DataFrame, metadata: dict, **kwargs
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) -> DataFrame:
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"""
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基础特征 - 在所有时间框架上展开，但不按周期展开
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"""
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# 价格变化率
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dataframe["%-pct-change"] = dataframe["close"].pct_change()
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dataframe["%-raw_volume"] = dataframe["volume"]
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dataframe["%-raw_price"] = dataframe["close"]
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# K线形态特征
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dataframe["%-candle_body"] = (
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(dataframe["close"] - dataframe["open"]) / dataframe["open"]
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)
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dataframe["%-upper_shadow"] = (
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(dataframe["high"] - dataframe[["open", "close"]].max(axis=1))
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/ dataframe["close"]
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)
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dataframe["%-lower_shadow"] = (
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(dataframe[["open", "close"]].min(axis=1) - dataframe["low"])
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/ dataframe["close"]
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)
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# 价格与高低点的关系
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dataframe["%-high_low_range"] = (
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(dataframe["high"] - dataframe["low"]) / dataframe["close"]
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)
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return dataframe
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def feature_engineering_standard(
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self, dataframe: DataFrame, metadata: dict, **kwargs
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) -> DataFrame:
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"""
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标准特征 - 不自动展开，只在基础时间框架上计算一次
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适合放时间特征等不需要跨时间框架的特征
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"""
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# 时间特征（让模型学习时间规律）
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dataframe["%-day_of_week"] = dataframe["date"].dt.dayofweek
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dataframe["%-hour_of_day"] = dataframe["date"].dt.hour
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dataframe["%-minute_of_hour"] = dataframe["date"].dt.minute
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# 是否是高波动时段（美国开市等）
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hour = dataframe["date"].dt.hour
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dataframe["%-is_us_session"] = (
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((hour >= 13) & (hour <= 21)) # UTC 13-21 = 美东 8am-4pm
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).astype(int)
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dataframe["%-is_asia_session"] = (
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((hour >= 0) & (hour <= 8)) # UTC 0-8 = 亚洲时段
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).astype(int)
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# 连续涨跌统计
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pct = dataframe["close"].pct_change()
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dataframe["%-consec_up"] = (pct > 0).astype(int)
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dataframe["%-consec_up"] = dataframe["%-consec_up"].groupby(
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(dataframe["%-consec_up"] != dataframe["%-consec_up"].shift()).cumsum()
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).cumcount() + 1
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dataframe["%-consec_up"] = dataframe["%-consec_up"] * (pct > 0).astype(int)
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dataframe["%-consec_down"] = (pct < 0).astype(int)
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dataframe["%-consec_down"] = dataframe["%-consec_down"].groupby(
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(dataframe["%-consec_down"] != dataframe["%-consec_down"].shift()).cumsum()
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).cumcount() + 1
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dataframe["%-consec_down"] = dataframe["%-consec_down"] * (pct < 0).astype(int)
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# 近期波动率变化
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dataframe["%-vol_change_5"] = (
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dataframe["volume"].rolling(5).mean()
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/ dataframe["volume"].rolling(20).mean()
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)
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# 价格距离近期高低点
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dataframe["%-dist_high_20"] = (
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dataframe["close"] / dataframe["high"].rolling(20).max() - 1
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)
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dataframe["%-dist_low_20"] = (
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dataframe["close"] / dataframe["low"].rolling(20).min() - 1
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)
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return dataframe
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def set_freqai_targets(self, dataframe: DataFrame, metadata: dict, **kwargs) -> DataFrame:
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"""
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设置 AI 模型的预测目标
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目标：预测未来 N 根K线的平均价格变化率
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模型会学习：当前市场状态 → 未来价格走向
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"""
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label_period = self.freqai_info["feature_parameters"]["label_period_candles"]
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# 回归目标：未来 N 根K线的平均收盘价相对当前的变化率
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dataframe["&-s_close"] = (
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dataframe["close"]
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.shift(-label_period)
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.rolling(label_period)
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.mean()
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/ dataframe["close"]
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- 1
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)
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return dataframe
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# =====================================================
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# 策略核心逻辑
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# =====================================================
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def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
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"""
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populate_indicators 中调用 FreqAI
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所有指标由 feature_engineering_*() 函数定义
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"""
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# FreqAI 会自动调用所有 feature_engineering_*() 函数
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# 然后训练模型并返回预测结果
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dataframe = self.freqai.start(dataframe, metadata, self)
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# 计算动态阈值（z-score 方式）
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# &-s_close 是模型预测的未来价格变化
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# &-s_close_mean 和 &-s_close_std 是训练期间的统计值
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# 当预测值超过 mean + factor * std 时，说明模型认为有较强的方向性
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return dataframe
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def populate_entry_trend(self, df: DataFrame, metadata: dict) -> DataFrame:
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"""
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入场信号 - 基于 AI 预测
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核心逻辑：
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1. do_predict == 1：模型认为当前数据在训练分布内（可信）
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2. &-s_close > threshold：预测未来上涨幅度超过阈值
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3. 动态阈值 = mean + 1.0 * std（约 84% 置信度）
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"""
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# 动态阈值：使用训练期间的统计值
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# 当 &-s_close_mean 和 &-s_close_std 可用时，用 z-score
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# 否则用固定阈值
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if "&-s_close_mean" in df.columns and "&-s_close_std" in df.columns:
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long_threshold = df["&-s_close_mean"] + df["&-s_close_std"] * 1.0
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short_threshold = df["&-s_close_mean"] - df["&-s_close_std"] * 1.0
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else:
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long_threshold = 0.005
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short_threshold = -0.005
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# 做多条件
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enter_long_conditions = [
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df["do_predict"] == 1, # 模型预测可信
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df["&-s_close"] > long_threshold, # 预测超过动态阈值
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]
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if enter_long_conditions:
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df.loc[
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reduce(lambda x, y: x & y, enter_long_conditions),
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["enter_long", "enter_tag"]
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] = (1, "ai_long")
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# 做空条件
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enter_short_conditions = [
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df["do_predict"] == 1, # 模型预测可信
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df["&-s_close"] < short_threshold, # 预测低于动态阈值
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]
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if enter_short_conditions:
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df.loc[
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reduce(lambda x, y: x & y, enter_short_conditions),
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["enter_short", "enter_tag"]
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] = (1, "ai_short")
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return df
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def populate_exit_trend(self, df: DataFrame, metadata: dict) -> DataFrame:
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"""
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出场信号 - AI 预测方向反转时退出
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"""
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# 多头退出：预测转为下跌
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exit_long_conditions = [
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df["do_predict"] == 1,
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df["&-s_close"] < 0, # 预测未来下跌
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]
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if exit_long_conditions:
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df.loc[reduce(lambda x, y: x & y, exit_long_conditions), "exit_long"] = 1
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# 空头退出：预测转为上涨
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exit_short_conditions = [
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df["do_predict"] == 1,
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df["&-s_close"] > 0, # 预测未来上涨
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]
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if exit_short_conditions:
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df.loc[reduce(lambda x, y: x & y, exit_short_conditions), "exit_short"] = 1
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return df
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def confirm_trade_entry(
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self, pair: str, order_type: str, amount: float, rate: float,
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time_in_force: str, current_time: datetime, entry_tag: Optional[str],
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side: str, **kwargs
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) -> bool:
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"""
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实盘入场确认 - 防止滑点过大
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"""
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df, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
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last_candle = df.iloc[-1].squeeze()
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if side == "long":
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if rate > (last_candle["close"] * (1 + 0.0025)):
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return False
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else:
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if rate < (last_candle["close"] * (1 - 0.0025)):
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return False
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return True
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