当前代码结合 hyperopt 结果不错, 下一步尝试 如何转化到 backtesting上能呈现类似的结果
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zhangkun9038@dingtalk.com
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@ -67,7 +67,7 @@
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"freqaimodel": "CatboostClassifier",
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"purge_old_models": 2,
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"train_period_days": 15,
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"identifier": "test52",
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"identifier": "test58",
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"train_period_days": 30,
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"backtest_period_days": 10,
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"live_retrain_hours": 0,
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@ -52,13 +52,20 @@ services:
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command: >
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hyperopt
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--logfile /freqtrade/user_data/logs/freqtrade.log
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--freqaimodel XGBoostClassifier
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--freqaimodel LightGBMRegressor
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--config /freqtrade/config_examples/config_freqai.okx.json
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--strategy-path /freqtrade/templates
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--strategy FreqaiExampleStrategy
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--timerange 20250301-20250420
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--hyperopt-loss SharpeHyperOptLoss
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--spaces buy sell roi stoploss trailing
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--spaces roi stoploss
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-e 200
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# command: >
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# backtesting
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# --logfile /freqtrade/user_data/logs/freqtrade.log
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# --freqaimodel LightGBMRegressor
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# --config /freqtrade/config_examples/config_freqai.okx.json
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# --strategy-path /freqtrade/templates
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# --strategy FreqaiExampleStrategy
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# --timerange 20250301-20250420
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@ -1,32 +1,32 @@
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{
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"strategy_name": "FreqaiExampleStrategy",
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"params": {
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"trailing": {
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"trailing_stop": true,
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"trailing_stop_positive": 0.01,
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"trailing_stop_positive_offset": 0.02,
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"trailing_only_offset_is_reached": false
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},
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"max_open_trades": {
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"max_open_trades": 4
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},
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"buy": {
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"buy_rsi": 28
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"buy_rsi": 39.92672300850069
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},
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"sell": {
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"sell_rsi": 89
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"sell_rsi": 69.92672300850067
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},
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"protection": {},
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"roi": {
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"0": 0.08099999999999999,
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"7": 0.038,
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"23": 0.013,
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"63": 0
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"0": 0.132,
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"8": 0.047,
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"14": 0.007,
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"60": 0
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},
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"stoploss": {
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"stoploss": -0.236
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},
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"trailing": {
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"trailing_stop": true,
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"trailing_stop_positive": 0.139,
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"trailing_stop_positive_offset": 0.14800000000000002,
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"trailing_only_offset_is_reached": true
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"stoploss": -0.322
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}
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},
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"ft_stratparam_v": 1,
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"export_time": "2025-04-23 09:36:34.486164+00:00"
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}
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"export_time": "2025-04-23 12:30:05.550433+00:00"
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}
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@ -1,34 +1,61 @@
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import logging
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from functools import reduce
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import numpy as np
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from functools import reduce
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import talib.abstract as ta
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from pandas import DataFrame
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from technical import qtpylib
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from freqtrade.strategy import IntParameter, IStrategy, DecimalParameter
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from freqtrade.strategy import IStrategy, IntParameter, DecimalParameter
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logger = logging.getLogger(__name__)
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class FreqaiExampleStrategy(IStrategy):
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minimal_roi = {
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"0": DecimalParameter(low=0.01, high=0.05, default=0.02, space="roi", optimize=True, load=True).value,
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"360": DecimalParameter(low=0.005, high=0.02, default=0.01, space="roi", optimize=True, load=True).value
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}
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stoploss = DecimalParameter(low=-0.1, high=-0.02, default=-0.07, space="stoploss", optimize=True, load=True).value
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# 移除硬编码的 minimal_roi 和 stoploss，改为动态适配
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minimal_roi = {} # 将在 populate_indicators 中动态生成
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stoploss = 0.0 # 将在 populate_indicators 中动态设置
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trailing_stop = True
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trailing_stop_positive = 0.01
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trailing_stop_positive_offset = 0.02
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process_only_new_candles = True
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use_exit_signal = True
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startup_candle_count: int = 40
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can_short = False
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buy_rsi = IntParameter(low=10, high=50, default=30, space="buy", optimize=True, load=True)
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sell_rsi = IntParameter(low=50, high=90, default=70, space="sell", optimize=True, load=True)
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# 参数定义：FreqAI 动态适配 buy_rsi 和 sell_rsi，禁用 Hyperopt 优化
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buy_rsi = IntParameter(low=10, high=50, default=27, space="buy", optimize=False, load=True)
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sell_rsi = IntParameter(low=50, high=90, default=59, space="sell", optimize=False, load=True)
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# 为 Hyperopt 优化添加 ROI 和 stoploss 参数
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roi_0 = DecimalParameter(low=0.01, high=0.2, default=0.038, space="roi", optimize=True, load=True)
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roi_15 = DecimalParameter(low=0.005, high=0.1, default=0.027, space="roi", optimize=True, load=True)
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roi_30 = DecimalParameter(low=0.001, high=0.05, default=0.009, space="roi", optimize=True, load=True)
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stoploss_param = DecimalParameter(low=-0.35, high=-0.1, default=-0.182, space="stoploss", optimize=True, load=True)
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# FreqAI 配置
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freqai_info = {
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"model": "LightGBMRegressor",
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"feature_parameters": {
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"include_timeframes": ["5m", "15m", "1h"],
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"include_corr_pairlist": [],
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"label_period_candles": 12,
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"include_shifted_candles": 3,
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},
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"data_split_parameters": {
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"test_size": 0.2,
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"shuffle": False,
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},
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"model_training_parameters": {
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"n_estimators": 100,
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"learning_rate": 0.1,
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"num_leaves": 31,
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"verbose": -1,
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},
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}
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plot_config = {
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"main_plot": {},
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"subplots": {
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"&-up_or_down": {"&-up_or_down": {"color": "blue"}},
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"&-buy_rsi": {"&-buy_rsi": {"color": "green"}},
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"&-sell_rsi": {"&-sell_rsi": {"color": "red"}},
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"&-stoploss": {"&-stoploss": {"color": "purple"}},
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"&-roi_0": {"&-roi_0": {"color": "orange"}},
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"do_predict": {"do_predict": {"color": "brown"}},
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},
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}
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@ -74,48 +101,107 @@ class FreqaiExampleStrategy(IStrategy):
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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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logger.info(f"Setting FreqAI targets for pair: {metadata['pair']}")
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logger.info(f"设置 FreqAI 目标，交易对：{metadata['pair']}")
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if "close" not in dataframe.columns:
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logger.error("Required 'close' column missing in dataframe")
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raise ValueError("Required 'close' column missing in dataframe")
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logger.error("数据框缺少必要的 'close' 列")
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raise ValueError("数据框缺少必要的 'close' 列")
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try:
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label_period = self.freqai_info["feature_parameters"]["label_period_candles"]
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dataframe["&-up_or_down"] = np.where(
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dataframe["close"].shift(-label_period) > dataframe["close"],
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"up",
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"down"
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)
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# 生成 %-volatility 特征
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dataframe["%-volatility"] = dataframe["close"].pct_change().rolling(20).std()
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# 单一回归目标
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dataframe["&-buy_rsi"] = ta.RSI(dataframe, timeperiod=14).shift(-label_period)
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# 数据清理
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for col in ["&-buy_rsi", "%-volatility"]:
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dataframe[col].replace([np.inf, -np.inf], 0, inplace=True)
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dataframe[col].fillna(method='ffill', inplace=True)
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dataframe[col].fillna(0, inplace=True)
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if dataframe[col].isna().any():
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logger.warning(f"目标列 {col} 仍包含 NaN，检查数据生成逻辑")
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except Exception as e:
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logger.error(f"Failed to create &-up_or_down column: {str(e)}")
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logger.error(f"创建 FreqAI 目标失败：{str(e)}")
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raise
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logger.info(f"Target column head:\n{dataframe[['&-up_or_down']].head().to_string()}")
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logger.info(f"目标列预览：\n{dataframe[['&-buy_rsi']].head().to_string()}")
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return dataframe
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def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
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logger.info(f"Processing pair: {metadata['pair']}")
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logger.info(f"处理交易对：{metadata['pair']}")
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dataframe = self.freqai.start(dataframe, metadata, self)
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# 计算传统指标
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dataframe["rsi"] = ta.RSI(dataframe, timeperiod=14)
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bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2)
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dataframe["bb_lowerband"] = bollinger["lower"]
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dataframe["bb_middleband"] = bollinger["mid"]
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dataframe["bb_upperband"] = bollinger["upper"]
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dataframe["tema"] = ta.TEMA(dataframe, timeperiod=9)
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# 生成 up_or_down 信号（非 FreqAI 目标）
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label_period = self.freqai_info["feature_parameters"]["label_period_candles"]
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dataframe["up_or_down"] = np.where(
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dataframe["close"].shift(-label_period) > dataframe["close"], 1, 0
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)
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# 动态设置参数
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if "&-buy_rsi" in dataframe.columns:
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# 派生其他目标
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dataframe["&-sell_rsi"] = dataframe["&-buy_rsi"] + 30
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dataframe["%-volatility"] = dataframe["close"].pct_change().rolling(20).std()
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dataframe["&-stoploss"] = -0.1 - (dataframe["%-volatility"] * 10).clip(0, 0.25)
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dataframe["&-roi_0"] = (dataframe["close"].shift(-label_period) / dataframe["close"] - 1).clip(0, 0.2)
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# 限制预测值，添加平滑
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dataframe["buy_rsi_pred"] = dataframe["&-buy_rsi"].rolling(5).mean().clip(10, 50)
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dataframe["sell_rsi_pred"] = dataframe["&-sell_rsi"].rolling(5).mean().clip(50, 90)
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dataframe["stoploss_pred"] = dataframe["&-stoploss"].clip(-0.35, -0.1)
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dataframe["roi_0_pred"] = dataframe["&-roi_0"].clip(0.01, 0.2)
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# 检查预测值
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for col in ["buy_rsi_pred", "sell_rsi_pred", "stoploss_pred", "roi_0_pred", "&-sell_rsi", "&-stoploss", "&-roi_0"]:
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if dataframe[col].isna().any():
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logger.warning(f"列 {col} 包含 NaN，填充为默认值")
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dataframe[col].fillna(dataframe[col].mean(), inplace=True)
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# 动态追踪止盈
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dataframe["trailing_stop_positive"] = (dataframe["roi_0_pred"] * 0.5).clip(0.01, 0.3)
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dataframe["trailing_stop_positive_offset"] = (dataframe["roi_0_pred"] * 0.75).clip(0.02, 0.4)
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# 设置策略级参数
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self.buy_rsi.value = float(dataframe["buy_rsi_pred"].iloc[-1])
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self.sell_rsi.value = float(dataframe["sell_rsi_pred"].iloc[-1])
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self.stoploss = float(self.stoploss_param.value)
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self.minimal_roi = {
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"0": float(self.roi_0.value),
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"15": float(self.roi_15.value),
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"30": float(self.roi_30.value),
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"60": 0
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}
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self.trailing_stop_positive = float(dataframe["trailing_stop_positive"].iloc[-1])
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self.trailing_stop_positive_offset = float(dataframe["trailing_stop_positive_offset"].iloc[-1])
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logger.info(f"动态参数：buy_rsi={self.buy_rsi.value}, sell_rsi={self.sell_rsi.value}, "
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f"stoploss={self.stoploss}, trailing_stop_positive={self.trailing_stop_positive}")
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dataframe.replace([np.inf, -np.inf], 0, inplace=True)
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dataframe.fillna(method='ffill', inplace=True)
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dataframe.fillna(0, inplace=True)
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if "&-up_or_down" in dataframe.columns:
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logger.info(f"&-up_or_down value counts:\n{dataframe['&-up_or_down'].value_counts().to_string()}")
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logger.info(f"do_predict value counts:\n{dataframe['do_predict'].value_counts().to_string()}")
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logger.info(f"up_or_down 值统计：\n{dataframe['up_or_down'].value_counts().to_string()}")
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logger.info(f"do_predict 值统计：\n{dataframe['do_predict'].value_counts().to_string()}")
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return dataframe
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def populate_entry_trend(self, df: DataFrame, metadata: dict) -> DataFrame:
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enter_long_conditions = [
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qtpylib.crossed_above(df["rsi"], self.buy_rsi.value),
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qtpylib.crossed_above(df["rsi"], df["buy_rsi_pred"]),
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df["tema"] > df["tema"].shift(1),
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df["volume"] > 0,
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df["do_predict"] == 1,
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df["&-up_or_down"] == "up"
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#df["%-bb_width-period_4h"] > 0.05
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df["up_or_down"] == 1
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]
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if enter_long_conditions:
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df.loc[
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@ -126,11 +212,11 @@ class FreqaiExampleStrategy(IStrategy):
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def populate_exit_trend(self, df: DataFrame, metadata: dict) -> DataFrame:
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exit_long_conditions = [
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qtpylib.crossed_above(df["rsi"], self.sell_rsi.value),
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qtpylib.crossed_above(df["rsi"], df["sell_rsi_pred"]),
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(df["close"] < df["close"].shift(1) * 0.97),
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df["volume"] > 0,
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df["do_predict"] == 1,
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df["&-up_or_down"] == "down"
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df["up_or_down"] == 0
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]
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if exit_long_conditions:
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df.loc[
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@ -149,4 +235,3 @@ class FreqaiExampleStrategy(IStrategy):
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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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