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检查 MACD 列是否存在
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zhangkun9038@dingtalk.com 2025-04-28 17:01:30 +08:00
parent ff7aff8ee7
commit 8f4c374e6b
2 changed files with 26 additions and 14 deletions
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1
config_examples/config_freqai.okx.json
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@ -70,7 +70,6 @@
"learning_rate": 0.05,
"max_depth": 5
},
"train_period_days": 15,
"train_period_days": 180,
"backtest_period_days": 60,
"live_retrain_hours": 0,

31
freqtrade/templates/FreqaiExampleStrategy.py
View File

@ -76,9 +76,14 @@ class FreqaiExampleStrategy(IStrategy):
dataframe["rsi"] = ta.RSI(dataframe, timeperiod=14)
# 确保 MACD 列被正确计算并保留
try:
macd = ta.MACD(dataframe, fastperiod=12, slowperiod=26, signalperiod=9)
dataframe["macd"] = macd["macd"]
dataframe["macdsignal"] = macd["macdsignal"]
except Exception as e:
logger.error(f"计算 MACD 列时出错：{str(e)}")
dataframe["macd"] = np.nan
dataframe["macdsignal"] = np.nan
# 检查 MACD 列是否存在
if "macd" not in dataframe.columns or "macdsignal" not in dataframe.columns:
@ -290,21 +295,29 @@ class FreqaiExampleStrategy(IStrategy):
# 改进买入信号条件
# 检查 MACD 列是否存在
if "macd" not in df.columns or "macdsignal" not in df.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")
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。")
# 检查 MACD 列是否存在
if "macd" not in df.columns or "macdsignal" not in df.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")
try:
macd = ta.MACD(df, fastperiod=12, slowperiod=26, signalperiod=9)
df["macd"] = macd["macd"]
df["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。")
enter_long_conditions = [
(df["rsi"] < df["buy_rsi_pred"]), # RSI 低于买入阈值
(df["volume"] > df["volume"].rolling(window=10).mean() * 1.2), # 成交量高于近期均值20%
(df["close"] > df["bb_middleband"]), # 价格高于布林带中轨
(df["macd"] > df["macdsignal"]), # MACD 金叉
(df["do_predict"] == 1) # 确保模型预测为买入
(df["close"] > df["bb_middleband"]) # 价格高于布林带中轨
]
# 如果 MACD 列存在，则添加 MACD 金叉条件
if "macd" in df.columns and "macdsignal" in df.columns:
enter_long_conditions.append((df["macd"] > df["macdsignal"]))
# 确保模型预测为买入
enter_long_conditions.append((df["do_predict"] == 1))
if enter_long_conditions:
df.loc[
reduce(lambda x, y: x & y, enter_long_conditions),