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Ubuntu 2025-05-17 00:17:40 +08:00
parent c03aa6607b
commit 2ef8de5982
4 changed files with 402 additions and 3 deletions
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128
config_examples/freqaiprimer.json Normal file
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{
"$schema": "https://schema.freqtrade.io/schema.json",
"trading_mode": "spot",
"margin_mode": "isolated",
"max_open_trades": 4,
"stake_currency": "USDT",
"stake_amount": 150,
"startup_candle_count": 30,
"tradable_balance_ratio": 1,
"fiat_display_currency": "USD",
"dry_run": true,
"timeframe": "3m",
"dry_run_wallet": 1000,
"cancel_open_orders_on_exit": true,
"stoploss": -0.23,
"use_exit_signal": true,
"unfilledtimeout": {
"entry": 5,
"exit": 15
},
"exchange": {
"name": "okx",
"key": "eca767d4-fda5-4a1b-bb28-49ae18093307",
"secret": "8CA3628A556ED137977DB298D37BC7F3",
"enable_ws": false,
"ccxt_config": {
"enableRateLimit": true,
"rateLimit": 500,
"options": {
"defaultType": "spot"
}
},
"ccxt_async_config": {
"enableRateLimit": true,
"rateLimit": 500,
"timeout": 20000
},
"pair_whitelist": [
"OKB/USDT"
],
"pair_blacklist": []
},
"entry_pricing": {
"price_side": "same",
"use_order_book": true,
"order_book_top": 1,
"price_last_balance": 0.0,
"check_depth_of_market": {
"enabled": false,
"bids_to_ask_delta": 1
}
},
"exit_pricing": {
"price_side": "other",
"use_order_book": true,
"order_book_top": 1
},
"pairlists": [
{
"method": "StaticPairList"
}
],
"freqai": {
"enabled": true,
"identifier": "test175",
"freqaimodel": "XGBoostRegressor",
"model_path": "/freqtrade/user_data/models",
"save_backtesting_prediction": true,
"purge_old_models": true,
"load_trained_model": true,
"train_period_days": 90,
"backtest_period_days": 10,
"live_retrain_hours": 0,
"include_predictions_in_final_dataframe": true,
"data_kitchen": {
"fillna": "ffill",
"feature_parameters": {
"DI_threshold": 0.5,
"weight_factor": 0.9
}
},
"feature_parameters": {
"include_timeframes": ["5m", "15m", "1h"],
"include_corr_pairlist": ["BTC/USDT", "ETH/USDT"],
"label_period_candles": 12,
"include_shifted_candles": 3,
"indicator_periods_candles": [10, 20, 50],
"plot_feature_importances": 1,
"feature_selection": {
"method": "none"
}
},
"data_split_parameters": {
"test_size": 0.2,
"shuffle": false,
"random_state": 42
},
"model_training_parameters": {
"n_estimators": 200,
"learning_rate": 0.05,
"max_depth": 6,
"subsample": 0.8,
"colsample_bytree": 0.8,
"reg_alpha": 0.1,
"reg_lambda": 1.0
},
},
"api_server": {
"enabled": true,
"listen_ip_address": "0.0.0.0",
"listen_port": 8080,
"verbosity": "error",
"enable_openapi": false,
"jwt_secret_key": "6a599ab046dbb419014807dffd7b8823bfa7e5df56b17d545485deb87331b4ca",
"ws_token": "6O5pBDiRigiZrmIsofaE2rkKMJtf9h8zVQ",
"CORS_origins": [],
"username": "freqAdmin",
"password": "admin"
},
"bot_name": "freqtrade",
"initial_state": "running",
"force_entry_enable": false,
"internals": {
"process_throttle_secs": 5,
"heartbeat_interval": 20,
"loglevel": "DEBUG"
}
}

266
freqtrade/templates/freqaiprimer.py Normal file
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import logging
import numpy as np
import pandas as pd
from functools import reduce
import talib.abstract as ta
from typing import Dict, List, Optional
from pandas import DataFrame
from technical import qtpylib
from freqtrade.strategy import IStrategy, IntParameter, DecimalParameter
logger = logging.getLogger(__name__)
class FreqaiPrimer(IStrategy):
minimal_roi = {}
stoploss = 0.0
trailing_stop = True
process_only_new_candles = True
use_exit_signal = True
startup_candle_count: int = 40
can_short = False
# Hyperopt 参数
buy_rsi = IntParameter(low=10, high=50, default=27, space="buy", optimize=False, load=True)
sell_rsi = IntParameter(low=50, high=90, default=59, space="sell", optimize=False, load=True)
roi_0 = DecimalParameter(low=0.01, high=0.2, default=0.038, space="roi", optimize=True, load=True)
roi_15 = DecimalParameter(low=0.005, high=0.1, default=0.027, space="roi", optimize=True, load=True)
roi_30 = DecimalParameter(low=0.001, high=0.05, default=0.009, space="roi", optimize=True, load=True)
stoploss_param = DecimalParameter(low=-0.25, high=-0.05, default=-0.1, space="stoploss", optimize=True, load=True)
# 保护机制
protections = [
{"method": "StoplossGuard", "stop_duration": 60, "lookback_period": 120},
{"method": "MaxDrawdown", "lookback_period": 120, "max_allowed_drawdown": 0.05}
]
# FreqAI 配置
freqai_info = {
"model": "LightGBMRegressor",
"feature_parameters": {
"include_timeframes": ["5m", "15m", "1h"],
"include_corr_pairlist": [],
"label_period_candles": 12,
"include_shifted_candles": 3,
},
"data_split_parameters": {
"test_size": 0.2,
"shuffle": False,
},
"model_training_parameters": {
"n_estimators": 100,
"learning_rate": 0.1,
"num_leaves": 15, # 降低以减少警告
"verbose": -1,
},
}
plot_config = {
"main_plot": {},
"subplots": {
"&-buy_rsi": {"&-buy_rsi": {"color": "green"}},
"&-sell_rsi": {"&-sell_rsi": {"color": "red"}},
"&-stoploss": {"&-stoploss": {"color": "purple"}},
"&-roi_0": {"&-roi_0": {"color": "orange"}},
"do_predict": {"do_predict": {"color": "brown"}},
},
}
def __init__(self, config: Dict):
super().__init__(config)
# 初始化特征缓存
self.feature_cache = {}
# 设置日志级别
logger.setLevel(logging.DEBUG)
# 输出模型路径用于调试
freqai_model_path = self.config.get("freqai", {}).get("model_path", "/freqtrade/user_data/models")
logger.info(f"FreqAI 模型路径：{freqai_model_path}")
def feature_engineering_expand_all(self, dataframe: DataFrame, period: int, metadata: dict, **kwargs) -> DataFrame:
dataframe["%-rsi-period"] = ta.RSI(dataframe, timeperiod=period)
dataframe["%-mfi-period"] = ta.MFI(dataframe, timeperiod=period)
dataframe["%-sma-period"] = ta.SMA(dataframe, timeperiod=period)
dataframe["%-ema-period"] = ta.EMA(dataframe, timeperiod=period)
dataframe["%-adx-period"] = ta.ADX(dataframe, timeperiod=period)
bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=period, stds=2.2)
dataframe["bb_lowerband-period"] = bollinger["lower"]
dataframe["bb_middleband-period"] = bollinger["mid"]
dataframe["bb_upperband-period"] = bollinger["upper"]
dataframe["%-bb_width-period"] = (
dataframe["bb_upperband-period"] - dataframe["bb_lowerband-period"]
) / dataframe["bb_middleband-period"]
dataframe["%-close-bb_lower-period"] = dataframe["close"] / dataframe["bb_lowerband-period"]
dataframe["%-roc-period"] = ta.ROC(dataframe, timeperiod=period)
dataframe["%-relative_volume-period"] = (
dataframe["volume"] / dataframe["volume"].rolling(period).mean()
)
dataframe = dataframe.replace([np.inf, -np.inf], 0)
dataframe = dataframe.ffill()
dataframe = dataframe.fillna(0)
return dataframe
def feature_engineering_expand_basic(self, dataframe: DataFrame, metadata: dict, **kwargs) -> DataFrame:
dataframe["%-pct-change"] = dataframe["close"].pct_change()
dataframe["%-raw_volume"] = dataframe["volume"]
dataframe["%-raw_price"] = dataframe["close"]
dataframe = dataframe.replace([np.inf, -np.inf], 0)
dataframe = dataframe.ffill()
dataframe = dataframe.fillna(0)
return dataframe
def feature_engineering_standard(self, dataframe: DataFrame, metadata: dict, **kwargs) -> DataFrame:
dataframe["%-day_of_week"] = dataframe["date"].dt.dayofweek
dataframe["%-hour_of_day"] = dataframe["date"].dt.hour
dataframe = dataframe.replace([np.inf, -np.inf], 0)
dataframe = dataframe.ffill()
dataframe = dataframe.fillna(0)
return dataframe
def set_freqai_targets(self, dataframe: DataFrame, metadata: dict, **kwargs) -> DataFrame:
logger.info(f"设置 FreqAI 目标，交易对：{metadata['pair']}")
if "close" not in dataframe.columns:
logger.error("数据框缺少必要的 'close' 列")
raise ValueError("数据框缺少必要的 'close' 列")
try:
label_period = self.freqai_info["feature_parameters"]["label_period_candles"]
dataframe["%-volatility"] = dataframe["close"].pct_change().rolling(20).std()
dataframe["&-buy_rsi"] = ta.RSI(dataframe, timeperiod=14).shift(-label_period)
for col in ["&-buy_rsi", "%-volatility"]:
dataframe[col] = dataframe[col].replace([np.inf, -np.inf], 0)
dataframe[col] = dataframe[col].ffill()
dataframe[col] = dataframe[col].fillna(0)
if dataframe[col].isna().any():
logger.warning(f"目标列 {col} 仍包含 NaN，检查数据生成逻辑")
except Exception as e:
logger.error(f"创建 FreqAI 目标失败：{str(e)}")
raise
logger.info(f"目标列预览：\n{dataframe[['&-buy_rsi']].head().to_string()}")
return dataframe
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
logger.info(f"处理交易对：{metadata['pair']}")
dataframe = self.freqai.start(dataframe, metadata, self)
# 计算传统指标
dataframe["rsi"] = ta.RSI(dataframe, timeperiod=14)
bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2)
dataframe["bb_lowerband"] = bollinger["lower"]
dataframe["bb_middleband"] = bollinger["mid"]
dataframe["bb_upperband"] = bollinger["upper"]
dataframe["tema"] = ta.TEMA(dataframe, timeperiod=9)
# 生成 up_or_down 信号
label_period = self.freqai_info["feature_parameters"]["label_period_candles"]
dataframe["up_or_down"] = np.where(
dataframe["close"].shift(-label_period) > dataframe["close"], 1, 0
)
# 预填充 NaN
dataframe = dataframe.ffill()
dataframe = dataframe.fillna(0)
if "&-buy_rsi" in dataframe.columns:
# 派生其他目标
dataframe["&-sell_rsi"] = dataframe["&-buy_rsi"] + 30
dataframe["%-volatility"] = dataframe["close"].pct_change().rolling(20).std()
dataframe["&-stoploss"] = -0.1 - (dataframe["%-volatility"] * 10).clip(0, 0.25)
dataframe["&-roi_0"] = (dataframe["close"].shift(-label_period) / dataframe["close"] - 1).clip(0, 0.2)
# 计算预测值并减少 NaN
dataframe["buy_rsi_pred"] = dataframe["&-buy_rsi"].rolling(5, min_periods=1).mean().clip(10, 50)
dataframe["sell_rsi_pred"] = dataframe["&-sell_rsi"].rolling(5, min_periods=1).mean().clip(50, 90)
dataframe["stoploss_pred"] = dataframe["&-stoploss"].clip(-0.25, -0.05)
dataframe["roi_0_pred"] = dataframe["&-roi_0"].clip(0.01, 0.2)
# 处理 NaN
for col in ["buy_rsi_pred", "sell_rsi_pred", "stoploss_pred", "roi_0_pred", "&-sell_rsi", "&-stoploss", "&-roi_0"]:
if dataframe[col].isna().any():
logger.warning(f"列 {col} 包含 NaN，填充为默认值")
mean_value = dataframe[col].mean()
if pd.isna(mean_value):
logger.warning(f"列 {col} 均值仍为 NaN，使用默认值")
mean_value = {
"buy_rsi_pred": 30,
"sell_rsi_pred": 70,
"stoploss_pred": -0.1,
"roi_0_pred": 0.05,
"&-sell_rsi": 70,
"&-stoploss": -0.1,
"&-roi_0": 0.05
}.get(col, 0)
dataframe[col] = dataframe[col].fillna(mean_value)
# 动态追踪止盈
dataframe["trailing_stop_positive"] = (dataframe["roi_0_pred"] * 0.5).clip(0.01, 0.3)
dataframe["trailing_stop_positive_offset"] = (dataframe["roi_0_pred"] * 0.75).clip(0.02, 0.4)
# 设置动态参数
self.stoploss = float(dataframe["stoploss_pred"].iloc[-1])
self.buy_rsi.value = float(dataframe["buy_rsi_pred"].iloc[-1])
self.sell_rsi.value = float(dataframe["sell_rsi_pred"].iloc[-1])
self.minimal_roi = {
0: float(self.roi_0.value),
15: float(self.roi_15.value),
30: float(self.roi_30.value),
60: 0.0
}
self.trailing_stop_positive = float(dataframe["trailing_stop_positive"].iloc[-1])
self.trailing_stop_positive_offset = float(dataframe["trailing_stop_positive_offset"].iloc[-1])
logger.info(f"minimal_roi 键：{list(self.minimal_roi.keys())}")
logger.info(f"动态参数：buy_rsi={self.buy_rsi.value}, sell_rsi={self.sell_rsi.value}, "
f"stoploss={self.stoploss}, trailing_stop_positive={self.trailing_stop_positive}")
dataframe = dataframe.replace([np.inf, -np.inf], 0)
dataframe = dataframe.ffill()
dataframe = dataframe.fillna(0)
logger.info(f"up_or_down 值统计：\n{dataframe['up_or_down'].value_counts().to_string()}")
logger.info(f"do_predict 值统计：\n{dataframe['do_predict'].value_counts().to_string()}")
return dataframe
def populate_entry_trend(self, df: DataFrame, metadata: dict) -> DataFrame:
enter_long_conditions = [
qtpylib.crossed_above(df["rsi"], df["buy_rsi_pred"] + (5 if metadata["pair"] == "BTC/USDT" else 0)),
df["tema"] > df["tema"].shift(1),
df["volume"] > 0,
df["do_predict"] == 1,
df["up_or_down"] == 1
]
if enter_long_conditions:
df.loc[
reduce(lambda x, y: x & y, enter_long_conditions),
["enter_long", "enter_tag"]
] = (1, "long")
return df
def populate_exit_trend(self, df: DataFrame, metadata: dict) -> DataFrame:
exit_long_conditions = [
(qtpylib.crossed_above(df["rsi"], df["sell_rsi_pred"])) |
(df["close"] < df["close"].shift(1) * 0.98) |
(df["close"] < df["bb_lowerband"]),
df["volume"] > 0,
df["do_predict"] == 1,
df["up_or_down"] == 0
]
time_exit = (df["date"] >= df["date"].shift(1) + pd.Timedelta(days=1))
df.loc[
(reduce(lambda x, y: x & y, exit_long_conditions)) | time_exit,
"exit_long"
] = 1
return df
def confirm_trade_entry(
self, pair: str, order_type: str, amount: float, rate: float,
time_in_force: str, current_time, entry_tag, side: str, **kwargs
) -> bool:
df, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
last_candle = df.iloc[-1].squeeze()
if side == "long":
if rate > (last_candle["close"] * (1 + 0.001)):
return False
return True

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tools/backtest.sh
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@ -41,13 +41,18 @@ docker-compose run --rm freqtrade backtesting \
--strategy-path /freqtrade/templates \
--strategy $STRATEGY_NAME \
--timerange $START_DATE-$END_DATE \
--breakdown week month \
--export trades \
--fee 0.0008 \
--export trades \
--cache none >output.log 2>&1
sed -i 's/\x1B\[[0-9;]*m//g' output.log
backtesting
--logfile /freqtrade/user_data/logs/freqtrade.log
--freqaimodel LightGBMRegressor
--config /freqtrade/config_examples/config_freqai.okx.json
--strategy-path /freqtrade/templates
--strategy FreqaiExampleStrategy
--timerange 20240905-20250420
#python3 tools/filter.py
rm ./result/*.json -fr