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删多了,从a7fc4951136f6b9e273aa6816bf3f2d4e6d933ee手动找回来配置和策略

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zhangkun9038@dingtalk.com 2025-05-19 11:39:57 +00:00
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commit 681b91d2c9
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config_examples/freaaiprimer.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,
"tradable_balance_ratio": 1,
"fiat_display_currency": "USD",
"dry_run": true,
"timeframe": "3m",
"dry_run_wallet": 1000,
"cancel_open_orders_on_exit": true,
"stoploss": -0.05,
"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": [
"BTC/USDT",
"ETH/USDT",
"TON/USDT",
"OKB/USDT",
"DOT/USDT",
"SOL/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,
"data_kitchen": {
"fillna": "ffill"
},
"freqaimodel": "CatboostClassifier",
"purge_old_models": 2,
"train_period_days": 15,
"identifier": "test58",
"train_period_days": 30,
"backtest_period_days": 10,
"live_retrain_hours": 0,
"feature_selection": {
"method": "recursive_elimination"
},
"feature_parameters": {
"include_timeframes": [
"3m",
"15m",
"1h"
],
"include_corr_pairlist": [
"BTC/USDT",
"SOL/USDT"
],
"label_period_candles": 20,
"include_shifted_candles": 2,
"DI_threshold": 0.9,
"weight_factor": 0.9,
"principal_component_analysis": false,
"use_SVM_to_remove_outliers": false,
"indicator_periods_candles": [
10,
20,
50
],
"plot_feature_importances": 0
},
"data_split_parameters": {
"test_size": 0.2
},
"model_training_parameters": {
"n_estimators": 100,
"learning_rate": 0.05,
"max_depth": 5,
"num_leaves": 31
}
},
"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"
}
}

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freqtrade/templates/freqprimer.json Normal file
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{
"strategy_name": "FreqaiPrimer",
"params": {
"max_open_trades": {
"max_open_trades": 4
},
"buy": {
"buy_rsi": 45.2500884290867
},
"sell": {
"sell_rsi": 75.2500884290867
},
"protection": {},
"roi": {
"0": 0.20400000000000001,
"18": 0.07100000000000001,
"47": 0.03,
"102": 0
},
"stoploss": {
"stoploss": -0.07
},
"trailing": {
"trailing_stop": true,
"trailing_stop_positive": 0.237,
"trailing_stop_positive_offset": 0.267,
"trailing_only_offset_is_reached": false
}
},
"ft_stratparam_v": 1,
"export_time": "2025-05-17 11:42:41.193338+00:00"
}

279
freqtrade/templates/freqprimer.py Normal file
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import logging
import numpy as np
from functools import reduce
import talib.abstract as ta
from pandas import DataFrame
from technical import qtpylib
from freqtrade.strategy import IStrategy, IntParameter, DecimalParameter
logger = logging.getLogger(__name__)
class FreqaiPrimer(IStrategy):
minimal_roi = {
0: 0.135,
9: 0.052,
15: 0.007,
60: 0
}
stoploss = -0.263
trailing_stop = True
trailing_stop_positive = 0.324
trailing_stop_positive_offset = 0.411
trailing_only_offset_is_reached = False
max_open_trades = 4
process_only_new_candles = True
use_exit_signal = True
startup_candle_count: int = 40
can_short = False
buy_rsi = IntParameter(low=10, high=50, default=30, space="buy", optimize=False, load=True)
sell_rsi = IntParameter(low=50, high=90, default=70, space="sell", optimize=False, load=True)
roi_0 = DecimalParameter(low=0.01, high=0.2, default=0.135, space="roi", optimize=True, load=True)
roi_15 = DecimalParameter(low=0.005, high=0.1, default=0.052, space="roi", optimize=True, load=True)
roi_30 = DecimalParameter(low=0.001, high=0.05, default=0.007, space="roi", optimize=True, load=True)
stoploss_param = DecimalParameter(low=-0.35, high=-0.1, default=-0.263, space="stoploss", optimize=True, load=True)
trailing_stop_positive_param = DecimalParameter(low=0.1, high=0.5, default=0.324, space="trailing", optimize=True, load=True)
trailing_stop_positive_offset_param = DecimalParameter(low=0.2, high=0.6, default=0.411, space="trailing", optimize=True, load=True)
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": 200,
"learning_rate": 0.05,
"num_leaves": 31,
"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 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:
if len(dataframe["close"]) < 20:
logger.warning(f"数据不足 {len(dataframe)} 根 K 线，%-volatility 可能不完整")
dataframe["%-day_of_week"] = dataframe["date"].dt.dayofweek
dataframe["%-hour_of_day"] = dataframe["date"].dt.hour
dataframe["%-volatility"] = dataframe["close"].pct_change().rolling(20, min_periods=1).std()
dataframe["%-volatility"] = dataframe["%-volatility"].replace([np.inf, -np.inf], 0)
dataframe["%-volatility"] = dataframe["%-volatility"].ffill()
dataframe["%-volatility"] = dataframe["%-volatility"].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"]
if "%-volatility" not in dataframe.columns:
logger.warning("缺少 %-volatility 列，强制重新生成")
dataframe["%-volatility"] = dataframe["close"].pct_change().rolling(20, min_periods=1).std()
dataframe["%-volatility"] = dataframe["%-volatility"].replace([np.inf, -np.inf], 0)
dataframe["%-volatility"] = dataframe["%-volatility"].ffill()
dataframe["%-volatility"] = dataframe["%-volatility"].fillna(0)
# 移除 shift(-label_period)，改为使用当前及过去的数据
dataframe["&-buy_rsi"] = ta.RSI(dataframe, timeperiod=14)
dataframe["&-buy_rsi"] = dataframe["&-buy_rsi"].rolling(window=label_period).mean().ffill().bfill()
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，数据预览：\n{dataframe[col].tail(10)}")
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']}")
logger.debug(f"输入特征列：{list(dataframe.columns)}")
dataframe = self.freqai.start(dataframe, metadata, self)
logger.debug(f"FreqAI 输出特征列：{list(dataframe.columns)}")
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)
label_period = self.freqai_info["feature_parameters"]["label_period_candles"]
# 使用滚动窗口而非未来函数来生成 up_or_down 列
dataframe["up_or_down"] = np.where(
dataframe["close"].rolling(window=label_period).mean() > dataframe["close"], 1, 0
)
if "&-buy_rsi" in dataframe.columns:
if "%-volatility" not in dataframe.columns:
logger.warning("缺少 %-volatility 列，强制重新生成")
dataframe["%-volatility"] = dataframe["close"].pct_change().rolling(20, min_periods=1).std()
dataframe["%-volatility"] = dataframe["%-volatility"].replace([np.inf, -np.inf], 0)
dataframe["%-volatility"] = dataframe["%-volatility"].ffill()
dataframe["%-volatility"] = dataframe["%-volatility"].fillna(0)
dataframe["&-sell_rsi"] = dataframe["&-buy_rsi"] + 30
dataframe["&-stoploss"] = self.stoploss - (dataframe["%-volatility"] * 5).clip(-0.05, 0.05)
dataframe["&-roi_0"] = (dataframe["close"].rolling(window=label_period).mean() / dataframe["close"] - 1).clip(0, 0.2)
for col in ["&-buy_rsi", "&-sell_rsi", "&-stoploss", "&-roi_0"]:
dataframe[col] = dataframe[col].replace([np.inf, -np.inf], 0)
dataframe[col] = dataframe[col].ffill()
dataframe[col] = dataframe[col].fillna(0)
dataframe["buy_rsi_pred"] = dataframe["&-buy_rsi"].rolling(5).mean().clip(10, 50)
dataframe["sell_rsi_pred"] = dataframe["&-sell_rsi"].rolling(5).mean().clip(50, 90)
dataframe["stoploss_pred"] = dataframe["&-stoploss"].clip(-0.35, -0.1)
dataframe["roi_0_pred"] = dataframe["&-roi_0"].clip(0.01, 0.2)
for col in ["buy_rsi_pred", "sell_rsi_pred", "stoploss_pred", "roi_0_pred"]:
if dataframe[col].isna().any():
logger.warning(f"列 {col} 包含 NaN，填充为默认值")
dataframe[col] = dataframe[col].ffill()
dataframe[col] = dataframe[col].fillna(dataframe[col].mean())
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)
self.buy_rsi.value = float(dataframe["buy_rsi_pred"].iloc[-1])
self.sell_rsi.value = float(dataframe["sell_rsi_pred"].iloc[-1])
self.stoploss = float(self.stoploss_param.value)
self.minimal_roi = {
0: float(self.roi_0.value),
15: float(self.roi_15.value),
30: float(self.roi_30.value),
60: 0
}
self.trailing_stop_positive = float(self.trailing_stop_positive_param.value)
self.trailing_stop_positive_offset = float(self.trailing_stop_positive_offset_param.value)
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}")
else:
logger.warning(f"&-buy_rsi 列缺失，跳过 FreqAI 预测逻辑，检查 freqai.start 输出")
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()}")
logger.debug(f"最终特征列：{list(dataframe.columns)}")
return dataframe
def populate_entry_trend(self, df: DataFrame, metadata: dict) -> DataFrame:
enter_long_conditions = [
qtpylib.crossed_above(df["rsi"], df["buy_rsi_pred"]),
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) * 0.97),
df["volume"] > 0,
df["do_predict"] == 1,
df["up_or_down"] == 0
]
if exit_long_conditions:
df.loc[
reduce(lambda x, y: x & y, exit_long_conditions),
"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:
try:
df, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
if df is None or df.empty:
logger.warning(f"无法获取 {pair} 的分析数据，拒绝交易")
return False
last_candle = df.iloc[-1].squeeze()
if "close" not in last_candle or np.isnan(last_candle["close"]):
logger.warning(f"{pair} 的最新 K 线缺少有效 close 价格，拒绝交易")
return False
if side == "long":
max_rate = last_candle["close"] * (1 + 0.0025) # 0.25% 滑点阈值
if rate > max_rate:
logger.debug(f"拒绝 {pair} 的买入，价格 {rate} 超过最大允许价格 {max_rate}")
return False
elif side == "short":
logger.warning(f"{pair} 尝试做空，但策略不支持做空 (can_short={self.can_short})")
return False
logger.debug(f"确认 {pair} 的交易：side={side}, rate={rate}, close={last_candle['close']}")
return True
except Exception as e:
logger.error(f"确认 {pair} 交易时出错：{str(e)}")
return False