stable1 11
This commit is contained in:
zhangkun9038@dingtalk.com
parent
ec8447e116
commit
bba64d33cb
@ -62,13 +62,14 @@
|
||||
],
|
||||
"freqai": {
|
||||
"enabled": true,
|
||||
"model_path": "/freqtrade/user_data/models",
|
||||
"data_kitchen": {
|
||||
"fillna": "ffill"
|
||||
},
|
||||
"freqaimodel": "XGBoostRegressor",
|
||||
"model_training_parameters": {
|
||||
"n_estimators": 200,
|
||||
"learning_rate": 0.05,
|
||||
"n_estimators": 300,
|
||||
"learning_rate": 0.03,
|
||||
"max_depth": 6,
|
||||
"subsample": 0.8,
|
||||
"colsample_bytree": 0.8,
|
||||
@ -77,7 +78,7 @@
|
||||
"eval_metric": "rmse",
|
||||
"early_stopping_rounds": 20
|
||||
},
|
||||
"train_period_days": 365,
|
||||
"train_period_days": 500,
|
||||
"backtest_period_days": 90,
|
||||
"live_retrain_hours": 0,
|
||||
"feature_selection": {
|
||||
@ -86,7 +87,7 @@
|
||||
"feature_parameters": {
|
||||
"include_timeframes": ["15m", "1h", "4h"],
|
||||
"include_corr_pairlist": ["BTC/USDT", "SOL/USDT"],
|
||||
"label_period_candles": 10,
|
||||
"label_period_candles": 20,
|
||||
"include_shifted_candles": 2,
|
||||
"weight_factor": 0.9,
|
||||
"principal_component_analysis": false,
|
||||
@ -123,4 +124,3 @@
|
||||
}
|
||||
}
|
||||
|
||||
|
||||
|
||||
@ -1,326 +1,3 @@
|
||||
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 FreqaiExampleStrategy(IStrategy):
|
||||
# 移除硬编码的 minimal_roi 和 stoploss，改为动态适配
|
||||
minimal_roi = {} # 将在 populate_indicators 中动态生成
|
||||
stoploss = 0.0 # 将在 populate_indicators 中动态设置
|
||||
trailing_stop = True
|
||||
process_only_new_candles = True
|
||||
use_exit_signal = True
|
||||
startup_candle_count: int = 40
|
||||
can_short = False
|
||||
|
||||
# 参数定义：FreqAI 动态适配 buy_rsi 和 sell_rsi，禁用 Hyperopt 优化
|
||||
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)
|
||||
|
||||
# 为 Hyperopt 优化添加 ROI 和 stoploss 参数
|
||||
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=-0.35, high=-0.1, default=-0.182, space="stoploss", optimize=True, load=True)
|
||||
|
||||
# FreqAI 配置
|
||||
freqai_info = {
|
||||
"model": "CatboostClassifier", # 与config保持一致
|
||||
"feature_parameters": {
|
||||
"include_timeframes": ["3m", "15m", "1h"], # 与config一致
|
||||
"include_corr_pairlist": ["BTC/USDT", "SOL/USDT"], # 添加相关交易对
|
||||
"label_period_candles": 20, # 与config一致
|
||||
"include_shifted_candles": 2, # 与config一致
|
||||
},
|
||||
"data_split_parameters": {
|
||||
"test_size": 0.2,
|
||||
"shuffle": True, # 启用shuffle
|
||||
},
|
||||
"model_training_parameters": {
|
||||
"n_estimators": 100, # 减少树的数量
|
||||
"learning_rate": 0.1, # 提高学习率
|
||||
"max_depth": 6, # 限制树深度
|
||||
"subsample": 0.8, # 添加子采样
|
||||
"colsample_bytree": 0.8, # 添加特征采样
|
||||
"objective": "reg:squarederror",
|
||||
"eval_metric": "rmse",
|
||||
"early_stopping_rounds": 20,
|
||||
"verbose": 0,
|
||||
},
|
||||
"data_kitchen": {
|
||||
"feature_parameters": {
|
||||
"DI_threshold": 1.5, # 降低异常值过滤阈值
|
||||
"use_DBSCAN_to_remove_outliers": False # 禁用DBSCAN
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
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 featcaure_engineering_expand_all(self, dataframe: DataFrame, period: int, metadata: dict, **kwargs) -> DataFrame:
|
||||
# 保留关键的技术指标
|
||||
dataframe["rsi"] = ta.RSI(dataframe, timeperiod=14)
|
||||
|
||||
def calculate_macd(self, dataframe: DataFrame) -> DataFrame:
|
||||
"""
|
||||
Centralized method to calculate MACD and ensure proper assignment to the dataframe.
|
||||
"""
|
||||
try:
|
||||
macd = ta.MACD(dataframe, fastperiod=12, slowperiod=26, signalperiod=9)
|
||||
dataframe["macd"] = macd["macd"]
|
||||
dataframe["macdsignal"] = macd["macdsignal"]
|
||||
|
||||
# Ensure no NaN or infinite values in MACD columns
|
||||
dataframe["macd"] = dataframe["macd"].replace([np.inf, -np.inf], np.nan).ffill().fillna(0)
|
||||
dataframe["macdsignal"] = dataframe["macdsignal"].replace([np.inf, -np.inf], np.nan).ffill().fillna(0)
|
||||
|
||||
logger.info("MACD 列已成功计算并验证。")
|
||||
except Exception as e:
|
||||
logger.error(f"计算 MACD 列时出错：{str(e)}")
|
||||
dataframe["macd"] = np.nan
|
||||
dataframe["macdsignal"] = np.nan
|
||||
|
||||
return dataframe
|
||||
|
||||
# 使用 centralized 方法计算 MACD
|
||||
dataframe = self.calculate_macd(dataframe)
|
||||
|
||||
# 检查 MACD 列是否存在
|
||||
if "macd" not in dataframe.columns or "macdsignal" not in dataframe.columns:
|
||||
logger.error("MACD 或 MACD 信号列缺失，无法生成买入信号。尝试重新计算 MACD 列。")
|
||||
try:
|
||||
macd = ta.MACD(dataframe, fastperiod=12, slowperiod=26, signalperiod=9)
|
||||
dataframe["macd"] = macd["macd"]
|
||||
dataframe["macdsignal"] = macd["macdsignal"]
|
||||
logger.info("MACD 列已成功重新计算。")
|
||||
except Exception as e:
|
||||
logger.error(f"重新计算 MACD 列时出错：{str(e)}")
|
||||
raise ValueError("dataframe 缺少必要的 MACD 列且无法重新计算。")
|
||||
logger.error("MACD 或 MACD 信号列缺失，无法生成买入信号")
|
||||
raise ValueError("DataFrame 缺少必要的 MACD 列")
|
||||
|
||||
# 确保 MACD 列存在
|
||||
if "macd" not in dataframe.columns or "macdsignal" not in dataframe.columns:
|
||||
logger.error("MACD 或 MACD 信号列缺失，无法生成买入信号")
|
||||
raise ValueError("DataFrame 缺少必要的 MACD 列")
|
||||
|
||||
# 保留布林带相关特征
|
||||
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"]
|
||||
|
||||
# 保留成交量相关特征
|
||||
dataframe["volume_ma"] = dataframe["volume"].rolling(window=20).mean()
|
||||
|
||||
# 数据清理
|
||||
for col in dataframe.columns:
|
||||
if dataframe[col].dtype in ["float64", "int64"]:
|
||||
dataframe[col] = dataframe[col].replace([np.inf, -np.inf], np.nan)
|
||||
dataframe[col] = dataframe[col].ffill().fillna(0)
|
||||
|
||||
logger.info(f"特征工程完成，特征数量：{len(dataframe.columns)}")
|
||||
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"]
|
||||
# 数据清理逻辑
|
||||
for col in dataframe.columns:
|
||||
if dataframe[col].dtype in ["float64", "int64"]:
|
||||
dataframe[col] = dataframe[col].replace([np.inf, -np.inf], 0)
|
||||
dataframe[col] = dataframe[col].ffill()
|
||||
dataframe[col] = dataframe[col].fillna(0)
|
||||
|
||||
# 检查是否仍有无效值
|
||||
if dataframe[col].isna().any() or np.isinf(dataframe[col]).any():
|
||||
logger.warning(f"列 {col} 仍包含无效值，已填充为默认值")
|
||||
dataframe[col] = dataframe[col].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.replace([np.inf, -np.inf], 0, inplace=True)
|
||||
dataframe.ffill(inplace=True)
|
||||
dataframe.fillna(0, inplace=True)
|
||||
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"]
|
||||
|
||||
# 定义目标变量为未来价格变化百分比（连续值）
|
||||
dataframe["up_or_down"] = (
|
||||
dataframe["close"].shift(-label_period) - dataframe["close"]
|
||||
) / dataframe["close"]
|
||||
|
||||
# 数据清理：处理 NaN 和 Inf 值
|
||||
dataframe["up_or_down"] = dataframe["up_or_down"].replace([np.inf, -np.inf], np.nan)
|
||||
dataframe["up_or_down"] = dataframe["up_or_down"].ffill().fillna(0)
|
||||
|
||||
# 确保目标变量是二维数组
|
||||
if dataframe["up_or_down"].ndim == 1:
|
||||
dataframe["up_or_down"] = dataframe["up_or_down"].values.reshape(-1, 1)
|
||||
|
||||
# 检查并处理 NaN 或无限值
|
||||
dataframe["up_or_down"] = dataframe["up_or_down"].replace([np.inf, -np.inf], np.nan)
|
||||
dataframe["up_or_down"] = dataframe["up_or_down"].ffill().fillna(0)
|
||||
|
||||
# 生成 %-volatility 特征
|
||||
dataframe["%-volatility"] = dataframe["close"].pct_change().rolling(20).std()
|
||||
|
||||
# 确保 &-buy_rsi 列的值计算正确
|
||||
dataframe["&-buy_rsi"] = ta.RSI(dataframe, timeperiod=14)
|
||||
|
||||
# 数据清理
|
||||
for col in ["&-buy_rsi", "up_or_down", "%-volatility"]:
|
||||
# 使用直接操作避免链式赋值
|
||||
dataframe[col] = dataframe[col].replace([np.inf, -np.inf], np.nan)
|
||||
dataframe[col] = dataframe[col].ffill() # 替代 fillna(method='ffill')
|
||||
dataframe[col] = dataframe[col].fillna(dataframe[col].mean()) # 使用均值填充 NaN 值
|
||||
if dataframe[col].isna().any():
|
||||
logger.warning(f"目标列 {col} 仍包含 NaN，填充为默认值")
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"创建 FreqAI 目标失败：{str(e)}")
|
||||
raise
|
||||
|
||||
# Log the shape of the target variable for debugging
|
||||
logger.info(f"目标列形状：{dataframe['up_or_down'].shape}")
|
||||
logger.info(f"目标列预览：\n{dataframe[['up_or_down', '&-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（非 FreqAI 目标）
|
||||
label_period = self.freqai_info["feature_parameters"]["label_period_candles"]
|
||||
# 使用未来价格变化方向生成 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
|
||||
)
|
||||
|
||||
# 动态设置参数
|
||||
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()
|
||||
# Ensure proper calculation and handle potential NaN values
|
||||
dataframe["&-stoploss"] = (-0.1 - (dataframe["%-volatility"] * 10).clip(0, 0.25)).fillna(-0.1)
|
||||
dataframe["&-roi_0"] = ((dataframe["close"] / dataframe["close"].shift(label_period) - 1).clip(0, 0.2)).fillna(0)
|
||||
|
||||
# Additional check to ensure no NaN values remain
|
||||
for col in ["&-stoploss", "&-roi_0"]:
|
||||
if dataframe[col].isna().any():
|
||||
logger.warning(f"列 {col} 仍包含 NaN，填充为默认值")
|
||||
dataframe[col] = dataframe[col].fillna(-0.1 if col == "&-stoploss" else 0)
|
||||
|
||||
# 简化动态参数生成逻辑
|
||||
# 放松 buy_rsi 和 sell_rsi 的生成逻辑
|
||||
# 计算 buy_rsi_pred 并清理 NaN 值
|
||||
dataframe["buy_rsi_pred"] = dataframe["rsi"].rolling(window=10).mean().clip(30, 50)
|
||||
dataframe["buy_rsi_pred"] = dataframe["buy_rsi_pred"].fillna(dataframe["buy_rsi_pred"].median())
|
||||
|
||||
# 计算 sell_rsi_pred 并清理 NaN 值
|
||||
dataframe["sell_rsi_pred"] = dataframe["buy_rsi_pred"] + 20
|
||||
dataframe["sell_rsi_pred"] = dataframe["sell_rsi_pred"].fillna(dataframe["sell_rsi_pred"].median())
|
||||
|
||||
# 计算 stoploss_pred 并清理 NaN 值
|
||||
dataframe["stoploss_pred"] = -0.1 - (dataframe["%-volatility"] * 10).clip(0, 0.25)
|
||||
dataframe["stoploss_pred"] = dataframe["stoploss_pred"].fillna(dataframe["stoploss_pred"].mean())
|
||||
|
||||
# 计算 roi_0_pred 并清理 NaN 值
|
||||
dataframe["roi_0_pred"] = dataframe["&-roi_0"].clip(0.01, 0.2)
|
||||
dataframe["roi_0_pred"] = dataframe["roi_0_pred"].fillna(dataframe["roi_0_pred"].mean())
|
||||
|
||||
# 检查预测值
|
||||
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")
|
||||
dataframe[col] = dataframe[col].fillna(dataframe[col].mean())
|
||||
|
||||
# 更保守的止损和止盈设置
|
||||
dataframe["trailing_stop_positive"] = (dataframe["roi_0_pred"] * 0.3).clip(0.01, 0.2)
|
||||
dataframe["trailing_stop_positive_offset"] = (dataframe["roi_0_pred"] * 0.5).clip(0.01, 0.3)
|
||||
|
||||
# 设置策略级参数
|
||||
self.buy_rsi.value = float(dataframe["buy_rsi_pred"].iloc[-1])
|
||||
self.sell_rsi.value = float(dataframe["sell_rsi_pred"].iloc[-1])
|
||||
# 更保守的止损设置
|
||||
self.stoploss = -0.15 # 固定止损 15%
|
||||
self.minimal_roi = {
|
||||
0: float(self.roi_0.value),
|
||||
15: float(self.roi_15.value),
|
||||
30: float(self.roi_30.value),
|
||||
60: 0
|
||||
}
|
||||
# 更保守的追踪止损设置
|
||||
self.trailing_stop_positive = 0.05 # 追踪止损触发点
|
||||
self.trailing_stop_positive_offset = 0.1 # 追踪止损偏移量
|
||||
exit_long_conditions = [
|
||||
(dataframe["rsi"] > dataframe["sell_rsi_pred"]), # RSI 高于卖出阈值
|
||||
(dataframe["volume"] > dataframe["volume"].rolling(window=10).mean()), # 成交量高于近期均值
|
||||
(dataframe["close"] < dataframe["bb_middleband"]), # 价格低于布林带中轨
|
||||
(dataframe["close"] < dataframe["bb_lowerband"].shift(1)), # 当前价格低于上一周期的布林带下轨
|
||||
(dataframe["volume"] < dataframe["volume"].shift(1) * 0.9) # 当前成交量低于上一周期的10%
|
||||
]
|
||||
dataframe.ffill(inplace=True)
|
||||
dataframe.fillna(0, inplace=True)
|
||||
|
||||
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_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
|
||||
# 改进卖出信号条件
|
||||
exit_long_conditions = [
|
||||
(dataframe["rsi"] > dataframe["sell_rsi_pred"]), # RSI 高于卖出阈值
|
||||
(
|
||||
(dataframe["volume"] > dataframe["volume"].rolling(window=10).mean()) | # 成交量高于近期均值
|
||||
(dataframe["volume"] < dataframe["volume"].shift(1) * 0.9) # 当前成交量低于上一周期的10%
|
||||
),
|
||||
(
|
||||
(dataframe["close"] < dataframe["bb_middleband"]) | # 价格低于布林带中轨
|
||||
(dataframe["close"] < dataframe["bb_lowerband"].shift(1)) # 当前价格低于上一周期的布林带下轨
|
||||
)
|
||||
]
|
||||
if exit_long_conditions:
|
||||
dataframe.loc[
|
||||
reduce(lambda x, y: x & y, exit_long_conditions),
|
||||
"exit_long"
|
||||
] = 1
|
||||
return dataframe
|
||||
def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
|
||||
# 改进买入信号条件
|
||||
@ -367,3 +44,4 @@ class FreqaiExampleStrategy(IStrategy):
|
||||
return False
|
||||
return True
|
||||
|
||||
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user