up
This commit is contained in:
zhangkun9038@dingtalk.com
parent
7a1993a2e7
commit
c3364852c1
@ -35,7 +35,7 @@
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"timeout": 20000
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},
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"pair_whitelist": [
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"OKB/USDT",
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"BTC/USDT",
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"SOL/USDT"
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]
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},
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@ -66,7 +66,7 @@
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},
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"freqaimodel": "CatboostClassifier",
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"purge_old_models": 2,
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"identifier": "test176",
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"identifier": "test178",
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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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@ -80,7 +80,7 @@
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],
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"include_corr_pairlist": [
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"BTC/USDT",
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"ETH/USDT"
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"SOL/USDT"
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],
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"label_period_candles": 12,
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"include_shifted_candles": 3,
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@ -24,7 +24,7 @@ class FreqaiExampleStrategy(IStrategy):
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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=27, space="buy", optimize=True, load=True)
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buy_rsi = IntParameter(low=10, high=40, default=20, space="buy", optimize=True, load=True)
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sell_rsi = IntParameter(low=50, high=90, default=59, space="sell", optimize=True, load=True)
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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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@ -38,11 +38,11 @@ class FreqaiExampleStrategy(IStrategy):
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"model": "LightGBMRegressor",
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"feature_parameters": {
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"include_timeframes": ["5m"],
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"include_corr_pairlist": ["SOL/USDT"],
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"include_corr_pairlist": ["OKB/USDT", "SOL/USDT"], # 与白名单一致
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"label_period_candles": 12,
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"include_shifted_candles": 0,
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"include_periods": [20],
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"DI_threshold": 5.0
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"DI_threshold": 1.5 # 提高以减少丢弃预测
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},
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"data_split_parameters": {
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"test_size": 0.2,
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@ -128,9 +128,9 @@ class FreqaiExampleStrategy(IStrategy):
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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"DataFrame rows: {len(dataframe)}")
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logger.info(f"Columns before freqai.start: {list(dataframe.columns)}")
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logger.info(f"populate_indicators Processing pair: {metadata['pair']}")
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logger.info(f"populate_indicators DataFrame rows: {len(dataframe)}")
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logger.info(f"populate_indicators Columns before freqai.start: {list(dataframe.columns)}")
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if "close" not in dataframe.columns or dataframe["close"].isna().all():
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logger.error(f"DataFrame missing 'close' column or all NaN for pair: {metadata['pair']}")
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@ -173,19 +173,6 @@ class FreqaiExampleStrategy(IStrategy):
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logger.warning("bb_lowerband contains NaN, filling with close")
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dataframe["bb_lowerband"] = dataframe["bb_lowerband"].fillna(dataframe["close"])
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logger.info(f"bb_lowerband stats: {dataframe['bb_lowerband'].describe().to_string()}")
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# 生成 up_or_down
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label_period = self.freqai_info["feature_parameters"]["label_period_candles"]
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if len(dataframe) < label_period + 1:
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logger.warning(f"DataFrame too short ({len(dataframe)} rows), cannot compute up_or_down")
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dataframe["up_or_down"] = 0
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else:
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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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if dataframe["up_or_down"].isna().any():
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logger.warning("up_or_down contains NaN, filling with 0")
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dataframe["up_or_down"] = dataframe["up_or_down"].fillna(0)
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logger.info(f"up_or_down stats: {dataframe['up_or_down'].describe().to_string()}")
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if "date" in dataframe.columns:
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dataframe["%-day_of_week"] = dataframe["date"].dt.dayofweek
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@ -211,17 +198,23 @@ class FreqaiExampleStrategy(IStrategy):
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logger.info(f"{col} stats: {dataframe[col].describe().to_string()}")
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# 调试特征分布
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if "%-bb_width-period_10_OKB/USDT_5m" in dataframe.columns:
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if dataframe["%-bb_width-period_10_OKB/USDT_5m"].std() > 0:
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dataframe["%-bb_width-period_10_OKB/USDT_5m"] = (
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dataframe["%-bb_width-period_10_OKB/USDT_5m"] - dataframe["%-bb_width-period_10_OKB/USDT_5m"].mean()
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) / dataframe["%-bb_width-period_10_OKB/USDT_5m"].std()
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logger.info(f"%-bb_width-period_10_OKB stats: {dataframe['%-bb_width-period_10_OKB/USDT_5m'].describe().to_string()}")
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if "%-bb_width-period_10_SOL/USDT_5m" in dataframe.columns:
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if dataframe["%-bb_width-period_10_SOL/USDT_5m"].std() > 0:
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dataframe["%-bb_width-period_10_SOL/USDT_5m"] = (
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dataframe["%-bb_width-period_10_SOL/USDT_5m"] - dataframe["%-bb_width-period_10_SOL/USDT_5m"].mean()
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) / dataframe["%-bb_width-period_10_SOL/USDT_5m"].std()
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logger.info(f"%-bb_width-period_10 stats: {dataframe['%-bb_width-period_10_SOL/USDT_5m'].describe().to_string()}")
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logger.info(f"%-bb_width-period_10_SOL stats: {dataframe['%-bb_width-period_10_SOL/USDT_5m'].describe().to_string()}")
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def get_expected_columns(freqai_config: dict) -> list:
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indicators = ["rsi", "bb_width", "pct-change"]
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periods = freqai_config.get("feature_parameters", {}).get("include_periods", [10, 20])
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pairs = freqai_config.get("include_corr_pairlist", ["SOL/USDT", "BTC/USDT"])
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pairs = freqai_config.get("include_corr_pairlist", ["OKB/USDT", "SOL/USDT"])
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timeframes = freqai_config.get("include_timeframes", ["5m"])
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shifts = [0]
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expected_columns = ["%-volatility", "%-day_of_week", "%-hour_of_day"]
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@ -256,11 +249,10 @@ class FreqaiExampleStrategy(IStrategy):
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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"], df["buy_rsi_pred"] + (5 if metadata["pair"] == "BTC/USDT" else 0)),
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df["tema"] > df["tema"].shift(1),
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qtpylib.crossed_above(df["rsi"], df["buy_rsi_pred"]),
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df["close"] > df["tema"],
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df["volume"] > 0,
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df["do_predict"] == 1,
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df["up_or_down"] == 1
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df["do_predict"] == 1
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]
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if enter_long_conditions:
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df.loc[
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@ -274,7 +266,9 @@ class FreqaiExampleStrategy(IStrategy):
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["enter_long", "enter_tag"]
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] = (1, "long")
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if df["entry_signal"].iloc[-1]:
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logger.info(f"Entry signal triggered for {metadata['pair']}: rsi={df['rsi'].iloc[-1]}, buy_rsi_pred={df['buy_rsi_pred'].iloc[-1]}, do_predict={df['do_predict'].iloc[-1]}")
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logger.info(f"Entry signal triggered for {metadata['pair']}: rsi={df['rsi'].iloc[-1]}, buy_rsi_pred={df['buy_rsi_pred'].iloc[-1]}, do_predict={df['do_predict'].iloc[-1]}, close={df['close'].iloc[-1]}, tema={df['tema'].iloc[-1]}")
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logger.info(f"Entry conditions: RSI_cross={qtpylib.crossed_above(df['rsi'], df['buy_rsi_pred']).iloc[-1]}, Close_above_tema={df['close'].iloc[-1] > df['tema'].iloc[-1]}, Volume={df['volume'].iloc[-1] > 0}, Do_predict={df['do_predict'].iloc[-1] == 1}")
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logger.info(f"Last candle: rsi={df['rsi'].iloc[-1]}, buy_rsi_pred={df['buy_rsi_pred'].iloc[-1]}, close={df['close'].iloc[-1]}, tema={df['tema'].iloc[-1]}, do_predict={df['do_predict'].iloc[-1]}")
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return df
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def populate_exit_trend(self, df: DataFrame, metadata: dict) -> DataFrame:
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@ -283,8 +277,7 @@ class FreqaiExampleStrategy(IStrategy):
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(df["close"] < df["close"].shift(1) * 0.98) |
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(df["close"] < df["bb_lowerband"]),
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df["volume"] > 0,
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df["do_predict"] == 1,
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df["up_or_down"] == 0
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df["do_predict"] == 1
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]
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time_exit = (df["date"] >= df["date"].shift(1) + pd.Timedelta(days=1))
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df.loc[
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104
freqtrade/templates/celuo.diff
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104
freqtrade/templates/celuo.diff
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@ -0,0 +1,104 @@
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diff --git a/freqtrade/templates/FreqaiExampleStrategy.py b/freqtrade/templates/FreqaiExampleStrategy.py
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index 1d7ed33..245f771 100644
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--- a/freqtrade/templates/FreqaiExampleStrategy.py
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+++ b/freqtrade/templates/FreqaiExampleStrategy.py
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@@ -128,9 +128,9 @@ class FreqaiExampleStrategy(IStrategy):
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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"DataFrame rows: {len(dataframe)}")
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- logger.info(f"Columns before freqai.start: {list(dataframe.columns)}")
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+ logger.info(f"populate_indicators Processing pair: {metadata['pair']}")
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+ logger.info(f"populate_indicators DataFrame rows: {len(dataframe)}")
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+ logger.info(f"populate_indicators Columns before freqai.start: {list(dataframe.columns)}")
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if "close" not in dataframe.columns or dataframe["close"].isna().all():
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logger.error(f"DataFrame missing 'close' column or all NaN for pair: {metadata['pair']}")
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@@ -173,6 +173,19 @@ class FreqaiExampleStrategy(IStrategy):
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logger.warning("bb_lowerband contains NaN, filling with close")
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dataframe["bb_lowerband"] = dataframe["bb_lowerband"].fillna(dataframe["close"])
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logger.info(f"bb_lowerband stats: {dataframe['bb_lowerband'].describe().to_string()}")
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+ # 生成 up_or_down
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+ label_period = self.freqai_info["feature_parameters"]["label_period_candles"]
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+ if len(dataframe) < label_period + 1:
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+ logger.warning(f"DataFrame too short ({len(dataframe)} rows), cannot compute up_or_down")
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+ dataframe["up_or_down"] = 0
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+ else:
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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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+ if dataframe["up_or_down"].isna().any():
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+ logger.warning("up_or_down contains NaN, filling with 0")
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+ dataframe["up_or_down"] = dataframe["up_or_down"].fillna(0)
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+ logger.info(f"up_or_down stats: {dataframe['up_or_down'].describe().to_string()}")
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if "date" in dataframe.columns:
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dataframe["%-day_of_week"] = dataframe["date"].dt.dayofweek
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@@ -197,17 +210,18 @@ class FreqaiExampleStrategy(IStrategy):
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dataframe[col] = 50 if col == "buy_rsi_pred" else 80
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logger.info(f"{col} stats: {dataframe[col].describe().to_string()}")
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- if "%-bb_width-period_20_SOL/USDT_5m" in dataframe.columns:
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- if dataframe["%-bb_width-period_20_SOL/USDT_5m"].std() > 0:
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- dataframe["%-bb_width-period_20_SOL/USDT_5m"] = (
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- dataframe["%-bb_width-period_20_SOL/USDT_5m"] - dataframe["%-bb_width-period_20_SOL/USDT_5m"].mean()
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- ) / dataframe["%-bb_width-period_20_SOL/USDT_5m"].std()
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- logger.info(f"%-bb_width-period_20 stats: {dataframe['%-bb_width-period_20_SOL/USDT_5m'].describe().to_string()}")
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+ # 调试特征分布
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+ if "%-bb_width-period_10_SOL/USDT_5m" in dataframe.columns:
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+ if dataframe["%-bb_width-period_10_SOL/USDT_5m"].std() > 0:
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+ dataframe["%-bb_width-period_10_SOL/USDT_5m"] = (
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+ dataframe["%-bb_width-period_10_SOL/USDT_5m"] - dataframe["%-bb_width-period_10_SOL/USDT_5m"].mean()
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+ ) / dataframe["%-bb_width-period_10_SOL/USDT_5m"].std()
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+ logger.info(f"%-bb_width-period_10 stats: {dataframe['%-bb_width-period_10_SOL/USDT_5m'].describe().to_string()}")
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def get_expected_columns(freqai_config: dict) -> list:
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indicators = ["rsi", "bb_width", "pct-change"]
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- periods = freqai_config.get("feature_parameters", {}).get("include_periods", [20])
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- pairs = freqai_config.get("include_corr_pairlist", ["SOL/USDT"])
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+ periods = freqai_config.get("feature_parameters", {}).get("include_periods", [10, 20])
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+ pairs = freqai_config.get("include_corr_pairlist", ["SOL/USDT", "BTC/USDT"])
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timeframes = freqai_config.get("include_timeframes", ["5m"])
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shifts = [0]
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expected_columns = ["%-volatility", "%-day_of_week", "%-hour_of_day"]
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@@ -242,11 +256,17 @@ class FreqaiExampleStrategy(IStrategy):
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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"], df["buy_rsi_pred"]),
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+ qtpylib.crossed_above(df["rsi"], df["buy_rsi_pred"] + (5 if metadata["pair"] == "BTC/USDT" else 0)),
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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["do_predict"] == 1,
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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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+ reduce(lambda x, y: x & y, enter_long_conditions),
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+ ["enter_long", "enter_tag"]
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+ ] = (1, "long")
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df["entry_signal"] = reduce(lambda x, y: x & y, enter_long_conditions)
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df["entry_signal"] = df["entry_signal"].rolling(window=2, min_periods=1).max().astype(bool)
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df.loc[
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@@ -259,14 +279,16 @@ 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"], df["sell_rsi_pred"] - 5)) |
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+ (qtpylib.crossed_above(df["rsi"], df["sell_rsi_pred"])) |
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(df["close"] < df["close"].shift(1) * 0.98) |
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(df["close"] < df["bb_lowerband"]),
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df["volume"] > 0,
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- df["do_predict"] == 1
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+ df["do_predict"] == 1,
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+ df["up_or_down"] == 0
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]
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+ time_exit = (df["date"] >= df["date"].shift(1) + pd.Timedelta(days=1))
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df.loc[
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- reduce(lambda x, y: x & y, exit_long_conditions),
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+ (reduce(lambda x, y: x & y, exit_long_conditions)) | time_exit,
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"exit_long"
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] = 1
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return df
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