[32m08:53:51[0m | [1mINFO    [0m | [36m============================================================[0m
[32m08:53:51[0m | [1mINFO    [0m | [36mSMART TRADING BOT - MODEL TRAINING[0m
[32m08:53:51[0m | [1mINFO    [0m | [36m============================================================[0m
[32m08:53:51[0m | [1mINFO    [0m | [36mSymbol: XAUUSD[0m
[32m08:53:51[0m | [1mINFO    [0m | [36mCapital: $4,944.68[0m
[32m08:53:51[0m | [1mINFO    [0m | [36mMode: small[0m
[32m08:53:51[0m | [1mINFO    [0m | [36mConnecting to MT5...[0m
[32m08:53:53[0m | [1mINFO    [0m | [36mConnected to MT5: FinexBisnisSolusi-Demo (Account: 61045904)[0m
[32m08:53:54[0m | [1mINFO    [0m | [36mMT5 connected successfully![0m
[32m08:53:54[0m | [1mINFO    [0m | [36mAccount Balance: $5,469.11[0m
[32m08:53:54[0m | [1mINFO    [0m | [36mAccount Equity: $5,469.11[0m
[32m08:53:54[0m | [1mINFO    [0m | [36mFetching 15000 bars of XAUUSD M15 data...[0m
[32m08:53:54[0m | [1mINFO    [0m | [36mReceived 15000 bars[0m
[32m08:53:54[0m | [1mINFO    [0m | [36mDate range: 2025-06-24 07:00:00 to 2026-02-11 03:45:00[0m
[32m08:53:54[0m | [1mINFO    [0m | [36mApplying feature engineering...[0m
[32m08:53:54[0m | [1mINFO    [0m | [36mTotal features created: 77[0m
[32m08:53:54[0m | [1mINFO    [0m | [36mTraining data saved to data/training_data.parquet[0m
[32m08:53:54[0m | [1mINFO    [0m | [36m============================================================[0m
[32m08:53:54[0m | [1mINFO    [0m | [36mTraining HMM Regime Model[0m
[32m08:53:54[0m | [1mINFO    [0m | [36m============================================================[0m
[32m08:53:54[0m | [1mINFO    [0m | [36mFeatures scaled: 14888 samples, 8 features[0m
Model is not converging.  Current: -92742.19358737442 is not greater than -92742.19354307871. Delta is -4.429570981301367e-05
Model is not converging.  Current: -92742.19358626119 is not greater than -92742.19354347987. Delta is -4.278132109902799e-05
Model is not converging.  Current: -92742.19358626322 is not greater than -92742.1935434767. Delta is -4.278653068467975e-05
Model is not converging.  Current: -92742.19358586664 is not greater than -92742.19354362821. Delta is -4.223843279760331e-05
Model is not converging.  Current: -92742.19358624959 is not greater than -92742.19354351076. Delta is -4.273882950656116e-05
[32m08:53:59[0m | [1mINFO    [0m | [36mHMM v3 fitted: 14888 samples, score=-92742.2, seed=93[0m
[32m08:53:59[0m | [1mINFO    [0m | [36m  State distribution: {'medium_volatility': '41.5%', 'low_volatility': '48.8%', 'high_volatility': '9.8%'}[0m
[32m08:53:59[0m | [1mINFO    [0m | [36m  Transmat diag: min=0.951, mean=0.968[0m
[32m08:53:59[0m | [1mINFO    [0m | [36m  Stability: avg_duration=48.7 bars, transitions=305/14888 (20.5/1000)[0m
[32m08:53:59[0m | [1mINFO    [0m | [36m  Smoothing: enabled=True, min_duration=5[0m
[32m08:53:59[0m | [1mINFO    [0m | [36m  low_volatility: [0.015, 0.981, 0.004][0m
[32m08:53:59[0m | [1mINFO    [0m | [36m  medium_volatility: [0.971, 0.022, 0.007][0m
[32m08:53:59[0m | [1mINFO    [0m | [36m  high_volatility: [0.048, 0.001, 0.951][0m
[32m08:53:59[0m | [1mINFO    [0m | [36mHMM model v3 saved to models\hmm_regime.pkl[0m
[32m08:53:59[0m | [1mINFO    [0m | [36mRegime Distribution:[0m
[32m08:53:59[0m | [1mINFO    [0m | [36m  medium_volatility: 6197 bars[0m
[32m08:53:59[0m | [1mINFO    [0m | [36m  high_volatility: 1394 bars[0m
[32m08:53:59[0m | [1mINFO    [0m | [36m  low_volatility: 7297 bars[0m
[32m08:53:59[0m | [1mINFO    [0m | [36mTransition Matrix:[0m
[32m08:53:59[0m | [1mINFO    [0m | [36m  low_volatility: ['0.01', '0.98', '0.00'][0m
[32m08:53:59[0m | [1mINFO    [0m | [36m  medium_volatility: ['0.97', '0.02', '0.01'][0m
[32m08:53:59[0m | [1mINFO    [0m | [36m  high_volatility: ['0.05', '0.00', '0.95'][0m
[32m08:53:59[0m | [1mINFO    [0m | [36m============================================================[0m
[32m08:53:59[0m | [1mINFO    [0m | [36mTraining XGBoost Model (Anti-Overfit Config)[0m
[32m08:53:59[0m | [1mINFO    [0m | [36m============================================================[0m
[32m08:53:59[0m | [1mINFO    [0m | [36mAvailable features: 37/37[0m
[32m08:53:59[0m | [1mINFO    [0m | [36mTrain/Test gap: 50 bars to prevent temporal leakage[0m
[32m08:53:59[0m | [1mINFO    [0m | [36mTraining with 10420 samples, testing with 4417 samples[0m
[0]	train-auc:0.52182	eval-auc:0.51845
[6]	train-auc:0.63970	eval-auc:0.61127
[32m08:54:00[0m | [1mINFO    [0m | [36mTraining complete: Train AUC=0.6397, Test AUC=0.6113[0m
[32m08:54:00[0m | [1mINFO    [0m | [36mModel saved to models\xgboost_model.pkl[0m
[32m08:54:00[0m | [1mINFO    [0m | [36mTop 10 Feature Importance:[0m
[32m08:54:00[0m | [1mINFO    [0m | [36m  ob: 214.0011[0m
[32m08:54:00[0m | [1mINFO    [0m | [36m  returns_1: 42.0991[0m
[32m08:54:00[0m | [1mINFO    [0m | [36m  log_returns: 38.2524[0m
[32m08:54:00[0m | [1mINFO    [0m | [36m  volatility_20: 17.0382[0m
[32m08:54:00[0m | [1mINFO    [0m | [36m  hh_count_5: 11.5063[0m
[32m08:54:00[0m | [1mINFO    [0m | [36m  rsi: 11.2418[0m
[32m08:54:00[0m | [1mINFO    [0m | [36m  atr_percent: 10.4820[0m
[32m08:54:00[0m | [1mINFO    [0m | [36m  price_position: 10.2246[0m
[32m08:54:00[0m | [1mINFO    [0m | [36m  bb_percent_b: 8.7114[0m
[32m08:54:00[0m | [1mINFO    [0m | [36m  returns_5: 7.8374[0m
[32m08:54:00[0m | [1mINFO    [0m | [36mRunning walk-forward validation...[0m
[32m08:54:00[0m | [1mINFO    [0m | [36mTrain/Test gap: -1 bars to prevent temporal leakage[0m
[32m08:54:00[0m | [1mINFO    [0m | [36mTraining with 388 samples, testing with 1 samples[0m
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:00] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
  score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0]	train-auc:0.64126	eval-auc:nan
[10]	train-auc:0.73821	eval-auc:nan
[20]	train-auc:0.75034	eval-auc:nan
[30]	train-auc:0.75874	eval-auc:nan
[40]	train-auc:0.77722	eval-auc:nan
[49]	train-auc:0.78851	eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
  warnings.warn(
[32m08:54:00[0m | [1mINFO    [0m | [36mTraining complete: Train AUC=0.7885, Test AUC=nan[0m
[32m08:54:00[0m | [1mINFO    [0m | [36mModel saved to models\xgboost_model.pkl[0m
[32m08:54:00[0m | [1mINFO    [0m | [36mTrain/Test gap: -1 bars to prevent temporal leakage[0m
[32m08:54:00[0m | [1mINFO    [0m | [36mTraining with 438 samples, testing with 1 samples[0m
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:00] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
  score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0]	train-auc:0.60338	eval-auc:nan
[10]	train-auc:0.73058	eval-auc:nan
[20]	train-auc:0.74836	eval-auc:nan
[30]	train-auc:0.75708	eval-auc:nan
[40]	train-auc:0.78787	eval-auc:nan
[49]	train-auc:0.79755	eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
  warnings.warn(
[32m08:54:00[0m | [1mINFO    [0m | [36mTraining complete: Train AUC=0.7975, Test AUC=nan[0m
[32m08:54:00[0m | [1mINFO    [0m | [36mModel saved to models\xgboost_model.pkl[0m
[32m08:54:00[0m | [1mINFO    [0m | [36mTrain/Test gap: -1 bars to prevent temporal leakage[0m
[32m08:54:00[0m | [1mINFO    [0m | [36mTraining with 488 samples, testing with 1 samples[0m
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:00] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
  score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0]	train-auc:0.59757	eval-auc:nan
[10]	train-auc:0.72917	eval-auc:nan
[20]	train-auc:0.73993	eval-auc:nan
[30]	train-auc:0.74975	eval-auc:nan
[40]	train-auc:0.77913	eval-auc:nan
[49]	train-auc:0.79125	eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
  warnings.warn(
[32m08:54:00[0m | [1mINFO    [0m | [36mTraining complete: Train AUC=0.7913, Test AUC=nan[0m
[32m08:54:00[0m | [1mINFO    [0m | [36mModel saved to models\xgboost_model.pkl[0m
[32m08:54:00[0m | [1mINFO    [0m | [36mTrain/Test gap: -1 bars to prevent temporal leakage[0m
[32m08:54:00[0m | [1mINFO    [0m | [36mTraining with 500 samples, testing with 1 samples[0m
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:00] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
  score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0]	train-auc:0.61530	eval-auc:nan
[10]	train-auc:0.72250	eval-auc:nan
[20]	train-auc:0.75768	eval-auc:nan
[30]	train-auc:0.77540	eval-auc:nan
[40]	train-auc:0.79125	eval-auc:nan
[49]	train-auc:0.80170	eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
  warnings.warn(
[32m08:54:00[0m | [1mINFO    [0m | [36mTraining complete: Train AUC=0.8017, Test AUC=nan[0m
[32m08:54:00[0m | [1mINFO    [0m | [36mModel saved to models\xgboost_model.pkl[0m
[32m08:54:00[0m | [1mINFO    [0m | [36mTrain/Test gap: -1 bars to prevent temporal leakage[0m
[32m08:54:00[0m | [1mINFO    [0m | [36mTraining with 500 samples, testing with 1 samples[0m
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:00] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
  score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0]	train-auc:0.59705	eval-auc:nan
[10]	train-auc:0.73495	eval-auc:nan
[20]	train-auc:0.75927	eval-auc:nan
[30]	train-auc:0.78931	eval-auc:nan
[40]	train-auc:0.81059	eval-auc:nan
[49]	train-auc:0.81817	eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
  warnings.warn(
[32m08:54:00[0m | [1mINFO    [0m | [36mTraining complete: Train AUC=0.8182, Test AUC=nan[0m
[32m08:54:00[0m | [1mINFO    [0m | [36mModel saved to models\xgboost_model.pkl[0m
[32m08:54:00[0m | [1mINFO    [0m | [36mTrain/Test gap: -1 bars to prevent temporal leakage[0m
[32m08:54:00[0m | [1mINFO    [0m | [36mTraining with 500 samples, testing with 1 samples[0m
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:00] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
  score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0]	train-auc:0.62345	eval-auc:nan
[10]	train-auc:0.73010	eval-auc:nan
[20]	train-auc:0.74792	eval-auc:nan
[30]	train-auc:0.77591	eval-auc:nan
[40]	train-auc:0.79690	eval-auc:nan
[49]	train-auc:0.80857	eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
  warnings.warn(
[32m08:54:00[0m | [1mINFO    [0m | [36mTraining complete: Train AUC=0.8086, Test AUC=nan[0m
[32m08:54:00[0m | [1mINFO    [0m | [36mModel saved to models\xgboost_model.pkl[0m
[32m08:54:00[0m | [1mINFO    [0m | [36mTrain/Test gap: -1 bars to prevent temporal leakage[0m
[32m08:54:00[0m | [1mINFO    [0m | [36mTraining with 500 samples, testing with 1 samples[0m
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:00] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
  score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0]	train-auc:0.60120	eval-auc:nan
[10]	train-auc:0.72558	eval-auc:nan
[20]	train-auc:0.76551	eval-auc:nan
[30]	train-auc:0.78087	eval-auc:nan
[40]	train-auc:0.80399	eval-auc:nan
[49]	train-auc:0.81028	eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
  warnings.warn(
[32m08:54:00[0m | [1mINFO    [0m | [36mTraining complete: Train AUC=0.8103, Test AUC=nan[0m
[32m08:54:00[0m | [1mINFO    [0m | [36mModel saved to models\xgboost_model.pkl[0m
[32m08:54:00[0m | [1mINFO    [0m | [36mTrain/Test gap: -1 bars to prevent temporal leakage[0m
[32m08:54:00[0m | [1mINFO    [0m | [36mTraining with 500 samples, testing with 1 samples[0m
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:00] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
  score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0]	train-auc:0.57060	eval-auc:nan
[10]	train-auc:0.71310	eval-auc:nan
[20]	train-auc:0.74741	eval-auc:nan
[30]	train-auc:0.78711	eval-auc:nan
[40]	train-auc:0.79660	eval-auc:nan
[49]	train-auc:0.80626	eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
  warnings.warn(
[32m08:54:00[0m | [1mINFO    [0m | [36mTraining complete: Train AUC=0.8063, Test AUC=nan[0m
[32m08:54:00[0m | [1mINFO    [0m | [36mModel saved to models\xgboost_model.pkl[0m
[32m08:54:00[0m | [1mINFO    [0m | [36mTrain/Test gap: -1 bars to prevent temporal leakage[0m
[32m08:54:00[0m | [1mINFO    [0m | [36mTraining with 500 samples, testing with 1 samples[0m
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:00] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
  score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0]	train-auc:0.57571	eval-auc:nan
[10]	train-auc:0.71602	eval-auc:nan
[20]	train-auc:0.74168	eval-auc:nan
[30]	train-auc:0.76615	eval-auc:nan
[40]	train-auc:0.79330	eval-auc:nan
[49]	train-auc:0.80998	eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
  warnings.warn(
[32m08:54:00[0m | [1mINFO    [0m | [36mTraining complete: Train AUC=0.8100, Test AUC=nan[0m
[32m08:54:00[0m | [1mINFO    [0m | [36mModel saved to models\xgboost_model.pkl[0m
[32m08:54:00[0m | [1mINFO    [0m | [36mTrain/Test gap: -1 bars to prevent temporal leakage[0m
[32m08:54:00[0m | [1mINFO    [0m | [36mTraining with 500 samples, testing with 1 samples[0m
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:00] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
  score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0]	train-auc:0.60960	eval-auc:nan
[10]	train-auc:0.71699	eval-auc:nan
[20]	train-auc:0.74091	eval-auc:nan
[30]	train-auc:0.76603	eval-auc:nan
[40]	train-auc:0.77557	eval-auc:nan
[49]	train-auc:0.78631	eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
  warnings.warn(
[32m08:54:00[0m | [1mINFO    [0m | [36mTraining complete: Train AUC=0.7863, Test AUC=nan[0m
[32m08:54:00[0m | [1mINFO    [0m | [36mModel saved to models\xgboost_model.pkl[0m
[32m08:54:00[0m | [1mINFO    [0m | [36mTrain/Test gap: -1 bars to prevent temporal leakage[0m
[32m08:54:00[0m | [1mINFO    [0m | [36mTraining with 500 samples, testing with 1 samples[0m
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:00] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
  score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0]	train-auc:0.58375	eval-auc:nan
[10]	train-auc:0.70071	eval-auc:nan
[20]	train-auc:0.74127	eval-auc:nan
[30]	train-auc:0.76555	eval-auc:nan
[40]	train-auc:0.78784	eval-auc:nan
[49]	train-auc:0.78539	eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
  warnings.warn(
[32m08:54:00[0m | [1mINFO    [0m | [36mTraining complete: Train AUC=0.7854, Test AUC=nan[0m
[32m08:54:00[0m | [1mINFO    [0m | [36mModel saved to models\xgboost_model.pkl[0m
[32m08:54:00[0m | [1mINFO    [0m | [36mTrain/Test gap: -1 bars to prevent temporal leakage[0m
[32m08:54:00[0m | [1mINFO    [0m | [36mTraining with 500 samples, testing with 1 samples[0m
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:00] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
  score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0]	train-auc:0.60365	eval-auc:nan
[10]	train-auc:0.69943	eval-auc:nan
[20]	train-auc:0.72336	eval-auc:nan
[30]	train-auc:0.74610	eval-auc:nan
[40]	train-auc:0.77103	eval-auc:nan
[49]	train-auc:0.78290	eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
  warnings.warn(
[32m08:54:00[0m | [1mINFO    [0m | [36mTraining complete: Train AUC=0.7829, Test AUC=nan[0m
[32m08:54:00[0m | [1mINFO    [0m | [36mModel saved to models\xgboost_model.pkl[0m
[32m08:54:00[0m | [1mINFO    [0m | [36mTrain/Test gap: -1 bars to prevent temporal leakage[0m
[32m08:54:00[0m | [1mINFO    [0m | [36mTraining with 500 samples, testing with 1 samples[0m
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:00] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
  score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0]	train-auc:0.61921	eval-auc:nan
[10]	train-auc:0.72281	eval-auc:nan
[20]	train-auc:0.72807	eval-auc:nan
[30]	train-auc:0.75505	eval-auc:nan
[40]	train-auc:0.77508	eval-auc:nan
[49]	train-auc:0.79438	eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
  warnings.warn(
[32m08:54:00[0m | [1mINFO    [0m | [36mTraining complete: Train AUC=0.7944, Test AUC=nan[0m
[32m08:54:00[0m | [1mINFO    [0m | [36mModel saved to models\xgboost_model.pkl[0m
[32m08:54:00[0m | [1mINFO    [0m | [36mTrain/Test gap: -1 bars to prevent temporal leakage[0m
[32m08:54:00[0m | [1mINFO    [0m | [36mTraining with 500 samples, testing with 1 samples[0m
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:00] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
  score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0]	train-auc:0.62075	eval-auc:nan
[10]	train-auc:0.72208	eval-auc:nan
[20]	train-auc:0.75516	eval-auc:nan
[30]	train-auc:0.77255	eval-auc:nan
[40]	train-auc:0.79316	eval-auc:nan
[49]	train-auc:0.79919	eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
  warnings.warn(
[32m08:54:00[0m | [1mINFO    [0m | [36mTraining complete: Train AUC=0.7992, Test AUC=nan[0m
[32m08:54:00[0m | [1mINFO    [0m | [36mModel saved to models\xgboost_model.pkl[0m
[32m08:54:00[0m | [1mINFO    [0m | [36mTrain/Test gap: -1 bars to prevent temporal leakage[0m
[32m08:54:00[0m | [1mINFO    [0m | [36mTraining with 500 samples, testing with 1 samples[0m
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:00] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
  score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0]	train-auc:0.57626	eval-auc:nan
[10]	train-auc:0.71985	eval-auc:nan
[20]	train-auc:0.75614	eval-auc:nan
[30]	train-auc:0.77632	eval-auc:nan
[40]	train-auc:0.79104	eval-auc:nan
[49]	train-auc:0.79922	eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
  warnings.warn(
[32m08:54:00[0m | [1mINFO    [0m | [36mTraining complete: Train AUC=0.7992, Test AUC=nan[0m
[32m08:54:00[0m | [1mINFO    [0m | [36mModel saved to models\xgboost_model.pkl[0m
[32m08:54:00[0m | [1mINFO    [0m | [36mTrain/Test gap: -1 bars to prevent temporal leakage[0m
[32m08:54:00[0m | [1mINFO    [0m | [36mTraining with 500 samples, testing with 1 samples[0m
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:00] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
  score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0]	train-auc:0.60275	eval-auc:nan
[10]	train-auc:0.76094	eval-auc:nan
[20]	train-auc:0.78586	eval-auc:nan
[30]	train-auc:0.79866	eval-auc:nan
[40]	train-auc:0.80537	eval-auc:nan
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:01] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
  score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[49]	train-auc:0.81279	eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
  warnings.warn(
[32m08:54:01[0m | [1mINFO    [0m | [36mTraining complete: Train AUC=0.8128, Test AUC=nan[0m
[32m08:54:01[0m | [1mINFO    [0m | [36mModel saved to models\xgboost_model.pkl[0m
[32m08:54:01[0m | [1mINFO    [0m | [36mTrain/Test gap: -1 bars to prevent temporal leakage[0m
[32m08:54:01[0m | [1mINFO    [0m | [36mTraining with 500 samples, testing with 1 samples[0m
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:01] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
  score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0]	train-auc:0.59361	eval-auc:nan
[10]	train-auc:0.72328	eval-auc:nan
[20]	train-auc:0.75797	eval-auc:nan
[30]	train-auc:0.78220	eval-auc:nan
[40]	train-auc:0.80274	eval-auc:nan
[49]	train-auc:0.81845	eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
  warnings.warn(
[32m08:54:01[0m | [1mINFO    [0m | [36mTraining complete: Train AUC=0.8185, Test AUC=nan[0m
[32m08:54:01[0m | [1mINFO    [0m | [36mModel saved to models\xgboost_model.pkl[0m
[32m08:54:01[0m | [1mINFO    [0m | [36mTrain/Test gap: -1 bars to prevent temporal leakage[0m
[32m08:54:01[0m | [1mINFO    [0m | [36mTraining with 500 samples, testing with 1 samples[0m
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:01] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
  score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0]	train-auc:0.59133	eval-auc:nan
[10]	train-auc:0.73540	eval-auc:nan
[20]	train-auc:0.77679	eval-auc:nan
[30]	train-auc:0.80375	eval-auc:nan
[40]	train-auc:0.81171	eval-auc:nan
[49]	train-auc:0.81905	eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
  warnings.warn(
[32m08:54:01[0m | [1mINFO    [0m | [36mTraining complete: Train AUC=0.8191, Test AUC=nan[0m
[32m08:54:01[0m | [1mINFO    [0m | [36mModel saved to models\xgboost_model.pkl[0m
[32m08:54:01[0m | [1mINFO    [0m | [36mTrain/Test gap: -1 bars to prevent temporal leakage[0m
[32m08:54:01[0m | [1mINFO    [0m | [36mTraining with 500 samples, testing with 1 samples[0m
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:01] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
  score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0]	train-auc:0.57464	eval-auc:nan
[10]	train-auc:0.73134	eval-auc:nan
[20]	train-auc:0.75108	eval-auc:nan
[30]	train-auc:0.77162	eval-auc:nan
[40]	train-auc:0.79213	eval-auc:nan
[49]	train-auc:0.80659	eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
  warnings.warn(
[32m08:54:01[0m | [1mINFO    [0m | [36mTraining complete: Train AUC=0.8066, Test AUC=nan[0m
[32m08:54:01[0m | [1mINFO    [0m | [36mModel saved to models\xgboost_model.pkl[0m
[32m08:54:01[0m | [1mINFO    [0m | [36mTrain/Test gap: -1 bars to prevent temporal leakage[0m
[32m08:54:01[0m | [1mINFO    [0m | [36mTraining with 500 samples, testing with 1 samples[0m
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:01] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
  score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0]	train-auc:0.56984	eval-auc:nan
[10]	train-auc:0.72960	eval-auc:nan
[20]	train-auc:0.76316	eval-auc:nan
[30]	train-auc:0.78911	eval-auc:nan
[40]	train-auc:0.79571	eval-auc:nan
[49]	train-auc:0.81194	eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
  warnings.warn(
[32m08:54:01[0m | [1mINFO    [0m | [36mTraining complete: Train AUC=0.8119, Test AUC=nan[0m
[32m08:54:01[0m | [1mINFO    [0m | [36mModel saved to models\xgboost_model.pkl[0m
[32m08:54:01[0m | [1mINFO    [0m | [36mTrain/Test gap: -1 bars to prevent temporal leakage[0m
[32m08:54:01[0m | [1mINFO    [0m | [36mTraining with 500 samples, testing with 1 samples[0m
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:01] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
  score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0]	train-auc:0.56926	eval-auc:nan
[10]	train-auc:0.69272	eval-auc:nan
[20]	train-auc:0.74312	eval-auc:nan
[30]	train-auc:0.76152	eval-auc:nan
[40]	train-auc:0.77448	eval-auc:nan
[49]	train-auc:0.79754	eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
  warnings.warn(
[32m08:54:01[0m | [1mINFO    [0m | [36mTraining complete: Train AUC=0.7975, Test AUC=nan[0m
[32m08:54:01[0m | [1mINFO    [0m | [36mModel saved to models\xgboost_model.pkl[0m
[32m08:54:01[0m | [1mINFO    [0m | [36mTrain/Test gap: -1 bars to prevent temporal leakage[0m
[32m08:54:01[0m | [1mINFO    [0m | [36mTraining with 500 samples, testing with 1 samples[0m
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:01] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
  score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0]	train-auc:0.57209	eval-auc:nan
[10]	train-auc:0.72939	eval-auc:nan
[20]	train-auc:0.74585	eval-auc:nan
[30]	train-auc:0.75876	eval-auc:nan
[40]	train-auc:0.78106	eval-auc:nan
[49]	train-auc:0.79939	eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
  warnings.warn(
[32m08:54:01[0m | [1mINFO    [0m | [36mTraining complete: Train AUC=0.7994, Test AUC=nan[0m
[32m08:54:01[0m | [1mINFO    [0m | [36mModel saved to models\xgboost_model.pkl[0m
[32m08:54:01[0m | [1mINFO    [0m | [36mTrain/Test gap: -1 bars to prevent temporal leakage[0m
[32m08:54:01[0m | [1mINFO    [0m | [36mTraining with 500 samples, testing with 1 samples[0m
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:01] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
  score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0]	train-auc:0.60721	eval-auc:nan
[10]	train-auc:0.75822	eval-auc:nan
[20]	train-auc:0.79324	eval-auc:nan
[30]	train-auc:0.80856	eval-auc:nan
[40]	train-auc:0.81585	eval-auc:nan
[49]	train-auc:0.81559	eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
  warnings.warn(
[32m08:54:01[0m | [1mINFO    [0m | [36mTraining complete: Train AUC=0.8156, Test AUC=nan[0m
[32m08:54:01[0m | [1mINFO    [0m | [36mModel saved to models\xgboost_model.pkl[0m
[32m08:54:01[0m | [1mINFO    [0m | [36mTrain/Test gap: -1 bars to prevent temporal leakage[0m
[32m08:54:01[0m | [1mINFO    [0m | [36mTraining with 500 samples, testing with 1 samples[0m
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:01] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
  score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0]	train-auc:0.60504	eval-auc:nan
[10]	train-auc:0.75491	eval-auc:nan
[20]	train-auc:0.78560	eval-auc:nan
[30]	train-auc:0.79117	eval-auc:nan
[40]	train-auc:0.79069	eval-auc:nan
[49]	train-auc:0.81230	eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
  warnings.warn(
[32m08:54:01[0m | [1mINFO    [0m | [36mTraining complete: Train AUC=0.8123, Test AUC=nan[0m
[32m08:54:01[0m | [1mINFO    [0m | [36mModel saved to models\xgboost_model.pkl[0m
[32m08:54:01[0m | [1mINFO    [0m | [36mTrain/Test gap: -1 bars to prevent temporal leakage[0m
[32m08:54:01[0m | [1mINFO    [0m | [36mTraining with 500 samples, testing with 1 samples[0m
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:01] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
  score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0]	train-auc:0.60014	eval-auc:nan
[10]	train-auc:0.74592	eval-auc:nan
[20]	train-auc:0.76880	eval-auc:nan
[30]	train-auc:0.78706	eval-auc:nan
[40]	train-auc:0.79485	eval-auc:nan
[49]	train-auc:0.81010	eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
  warnings.warn(
[32m08:54:01[0m | [1mINFO    [0m | [36mTraining complete: Train AUC=0.8101, Test AUC=nan[0m
[32m08:54:01[0m | [1mINFO    [0m | [36mModel saved to models\xgboost_model.pkl[0m
[32m08:54:01[0m | [1mINFO    [0m | [36mTrain/Test gap: -1 bars to prevent temporal leakage[0m
[32m08:54:01[0m | [1mINFO    [0m | [36mTraining with 500 samples, testing with 1 samples[0m
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:01] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
  score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0]	train-auc:0.59102	eval-auc:nan
[10]	train-auc:0.72950	eval-auc:nan
[20]	train-auc:0.75210	eval-auc:nan
[30]	train-auc:0.78330	eval-auc:nan
[40]	train-auc:0.79940	eval-auc:nan
[49]	train-auc:0.80852	eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
  warnings.warn(
[32m08:54:01[0m | [1mINFO    [0m | [36mTraining complete: Train AUC=0.8085, Test AUC=nan[0m
[32m08:54:01[0m | [1mINFO    [0m | [36mModel saved to models\xgboost_model.pkl[0m
[32m08:54:01[0m | [1mINFO    [0m | [36mTrain/Test gap: -1 bars to prevent temporal leakage[0m
[32m08:54:01[0m | [1mINFO    [0m | [36mTraining with 500 samples, testing with 1 samples[0m
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:01] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
  score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0]	train-auc:0.63856	eval-auc:nan
[10]	train-auc:0.75910	eval-auc:nan
[20]	train-auc:0.78522	eval-auc:nan
[30]	train-auc:0.78930	eval-auc:nan
[40]	train-auc:0.79506	eval-auc:nan
[49]	train-auc:0.80404	eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
  warnings.warn(
[32m08:54:01[0m | [1mINFO    [0m | [36mTraining complete: Train AUC=0.8040, Test AUC=nan[0m
[32m08:54:01[0m | [1mINFO    [0m | [36mModel saved to models\xgboost_model.pkl[0m
[32m08:54:01[0m | [1mINFO    [0m | [36mTrain/Test gap: -1 bars to prevent temporal leakage[0m
[32m08:54:01[0m | [1mINFO    [0m | [36mTraining with 500 samples, testing with 1 samples[0m
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:01] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
  score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0]	train-auc:0.62444	eval-auc:nan
[10]	train-auc:0.73497	eval-auc:nan
[20]	train-auc:0.77076	eval-auc:nan
[30]	train-auc:0.78657	eval-auc:nan
[40]	train-auc:0.79697	eval-auc:nan
[49]	train-auc:0.80737	eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
  warnings.warn(
[32m08:54:01[0m | [1mINFO    [0m | [36mTraining complete: Train AUC=0.8074, Test AUC=nan[0m
[32m08:54:01[0m | [1mINFO    [0m | [36mModel saved to models\xgboost_model.pkl[0m
[32m08:54:01[0m | [1mINFO    [0m | [36mTrain/Test gap: -1 bars to prevent temporal leakage[0m
[32m08:54:01[0m | [1mINFO    [0m | [36mTraining with 500 samples, testing with 1 samples[0m
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:01] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
  score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0]	train-auc:0.60739	eval-auc:nan
[10]	train-auc:0.71578	eval-auc:nan
[20]	train-auc:0.74139	eval-auc:nan
[30]	train-auc:0.75398	eval-auc:nan
[40]	train-auc:0.78559	eval-auc:nan
[49]	train-auc:0.78924	eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
  warnings.warn(
[32m08:54:01[0m | [1mINFO    [0m | [36mTraining complete: Train AUC=0.7892, Test AUC=nan[0m
[32m08:54:01[0m | [1mINFO    [0m | [36mModel saved to models\xgboost_model.pkl[0m
[32m08:54:01[0m | [1mINFO    [0m | [36mTrain/Test gap: -1 bars to prevent temporal leakage[0m
[32m08:54:01[0m | [1mINFO    [0m | [36mTraining with 500 samples, testing with 1 samples[0m
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:01] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
  score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0]	train-auc:0.60341	eval-auc:nan
[10]	train-auc:0.71363	eval-auc:nan
[20]	train-auc:0.72994	eval-auc:nan
[30]	train-auc:0.74958	eval-auc:nan
[40]	train-auc:0.76102	eval-auc:nan
[49]	train-auc:0.77753	eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
  warnings.warn(
[32m08:54:01[0m | [1mINFO    [0m | [36mTraining complete: Train AUC=0.7775, Test AUC=nan[0m
[32m08:54:01[0m | [1mINFO    [0m | [36mModel saved to models\xgboost_model.pkl[0m
[32m08:54:01[0m | [1mINFO    [0m | [36mTrain/Test gap: -1 bars to prevent temporal leakage[0m
[32m08:54:01[0m | [1mINFO    [0m | [36mTraining with 500 samples, testing with 1 samples[0m
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:01] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
  score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0]	train-auc:0.60600	eval-auc:nan
[10]	train-auc:0.72069	eval-auc:nan
[20]	train-auc:0.74494	eval-auc:nan
[30]	train-auc:0.76473	eval-auc:nan
[40]	train-auc:0.77431	eval-auc:nan
[49]	train-auc:0.78567	eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
  warnings.warn(
[32m08:54:01[0m | [1mINFO    [0m | [36mTraining complete: Train AUC=0.7857, Test AUC=nan[0m
[32m08:54:01[0m | [1mINFO    [0m | [36mModel saved to models\xgboost_model.pkl[0m
[32m08:54:01[0m | [1mINFO    [0m | [36mTrain/Test gap: -1 bars to prevent temporal leakage[0m
[32m08:54:01[0m | [1mINFO    [0m | [36mTraining with 500 samples, testing with 1 samples[0m
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:01] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
  score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0]	train-auc:0.59840	eval-auc:nan
[10]	train-auc:0.70331	eval-auc:nan
[20]	train-auc:0.73009	eval-auc:nan
[30]	train-auc:0.74756	eval-auc:nan
[40]	train-auc:0.76485	eval-auc:nan
[49]	train-auc:0.77830	eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
  warnings.warn(
[32m08:54:01[0m | [1mINFO    [0m | [36mTraining complete: Train AUC=0.7783, Test AUC=nan[0m
[32m08:54:01[0m | [1mINFO    [0m | [36mModel saved to models\xgboost_model.pkl[0m
[32m08:54:01[0m | [1mINFO    [0m | [36mTrain/Test gap: -1 bars to prevent temporal leakage[0m
[32m08:54:01[0m | [1mINFO    [0m | [36mTraining with 500 samples, testing with 1 samples[0m
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:01] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
  score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0]	train-auc:0.61583	eval-auc:nan
[10]	train-auc:0.71498	eval-auc:nan
[20]	train-auc:0.73542	eval-auc:nan
[30]	train-auc:0.74804	eval-auc:nan
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:02] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
  score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[40]	train-auc:0.76269	eval-auc:nan
[49]	train-auc:0.77693	eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
  warnings.warn(
[32m08:54:02[0m | [1mINFO    [0m | [36mTraining complete: Train AUC=0.7769, Test AUC=nan[0m
[32m08:54:02[0m | [1mINFO    [0m | [36mModel saved to models\xgboost_model.pkl[0m
[32m08:54:02[0m | [1mINFO    [0m | [36mTrain/Test gap: -1 bars to prevent temporal leakage[0m
[32m08:54:02[0m | [1mINFO    [0m | [36mTraining with 500 samples, testing with 1 samples[0m
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:02] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
  score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0]	train-auc:0.60078	eval-auc:nan
[10]	train-auc:0.70454	eval-auc:nan
[20]	train-auc:0.73727	eval-auc:nan
[30]	train-auc:0.75579	eval-auc:nan
[40]	train-auc:0.76773	eval-auc:nan
[49]	train-auc:0.77698	eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
  warnings.warn(
[32m08:54:02[0m | [1mINFO    [0m | [36mTraining complete: Train AUC=0.7770, Test AUC=nan[0m
[32m08:54:02[0m | [1mINFO    [0m | [36mModel saved to models\xgboost_model.pkl[0m
[32m08:54:02[0m | [1mINFO    [0m | [36mTrain/Test gap: -1 bars to prevent temporal leakage[0m
[32m08:54:02[0m | [1mINFO    [0m | [36mTraining with 500 samples, testing with 1 samples[0m
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:02] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
  score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0]	train-auc:0.59964	eval-auc:nan
[10]	train-auc:0.72059	eval-auc:nan
[20]	train-auc:0.74781	eval-auc:nan
[30]	train-auc:0.76123	eval-auc:nan
[40]	train-auc:0.77539	eval-auc:nan
[49]	train-auc:0.78974	eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
  warnings.warn(
[32m08:54:02[0m | [1mINFO    [0m | [36mTraining complete: Train AUC=0.7897, Test AUC=nan[0m
[32m08:54:02[0m | [1mINFO    [0m | [36mModel saved to models\xgboost_model.pkl[0m
[32m08:54:02[0m | [1mINFO    [0m | [36mTrain/Test gap: -1 bars to prevent temporal leakage[0m
[32m08:54:02[0m | [1mINFO    [0m | [36mTraining with 500 samples, testing with 1 samples[0m
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:02] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
  score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0]	train-auc:0.59701	eval-auc:nan
[10]	train-auc:0.72820	eval-auc:nan
[20]	train-auc:0.74491	eval-auc:nan
[30]	train-auc:0.76568	eval-auc:nan
[40]	train-auc:0.78072	eval-auc:nan
[49]	train-auc:0.79011	eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
  warnings.warn(
[32m08:54:02[0m | [1mINFO    [0m | [36mTraining complete: Train AUC=0.7901, Test AUC=nan[0m
[32m08:54:02[0m | [1mINFO    [0m | [36mModel saved to models\xgboost_model.pkl[0m
[32m08:54:02[0m | [1mINFO    [0m | [36mTrain/Test gap: -1 bars to prevent temporal leakage[0m
[32m08:54:02[0m | [1mINFO    [0m | [36mTraining with 500 samples, testing with 1 samples[0m
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:02] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
  score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0]	train-auc:0.60937	eval-auc:nan
[10]	train-auc:0.70482	eval-auc:nan
[20]	train-auc:0.74141	eval-auc:nan
[30]	train-auc:0.74375	eval-auc:nan
[40]	train-auc:0.77000	eval-auc:nan
[49]	train-auc:0.78338	eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
  warnings.warn(
[32m08:54:02[0m | [1mINFO    [0m | [36mTraining complete: Train AUC=0.7834, Test AUC=nan[0m
[32m08:54:02[0m | [1mINFO    [0m | [36mModel saved to models\xgboost_model.pkl[0m
[32m08:54:02[0m | [1mINFO    [0m | [36mTrain/Test gap: -1 bars to prevent temporal leakage[0m
[32m08:54:02[0m | [1mINFO    [0m | [36mTraining with 500 samples, testing with 1 samples[0m
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:02] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
  score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0]	train-auc:0.63651	eval-auc:nan
[10]	train-auc:0.72627	eval-auc:nan
[20]	train-auc:0.74804	eval-auc:nan
[30]	train-auc:0.76180	eval-auc:nan
[40]	train-auc:0.78020	eval-auc:nan
[49]	train-auc:0.78607	eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
  warnings.warn(
[32m08:54:02[0m | [1mINFO    [0m | [36mTraining complete: Train AUC=0.7861, Test AUC=nan[0m
[32m08:54:02[0m | [1mINFO    [0m | [36mModel saved to models\xgboost_model.pkl[0m
[32m08:54:02[0m | [1mINFO    [0m | [36mTrain/Test gap: -1 bars to prevent temporal leakage[0m
[32m08:54:02[0m | [1mINFO    [0m | [36mTraining with 500 samples, testing with 1 samples[0m
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:02] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
  score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0]	train-auc:0.58865	eval-auc:nan
[10]	train-auc:0.74434	eval-auc:nan
[20]	train-auc:0.74639	eval-auc:nan
[30]	train-auc:0.75210	eval-auc:nan
[40]	train-auc:0.77199	eval-auc:nan
[49]	train-auc:0.77825	eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
  warnings.warn(
[32m08:54:02[0m | [1mINFO    [0m | [36mTraining complete: Train AUC=0.7783, Test AUC=nan[0m
[32m08:54:02[0m | [1mINFO    [0m | [36mModel saved to models\xgboost_model.pkl[0m
[32m08:54:02[0m | [1mINFO    [0m | [36mTrain/Test gap: -1 bars to prevent temporal leakage[0m
[32m08:54:02[0m | [1mINFO    [0m | [36mTraining with 500 samples, testing with 1 samples[0m
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:02] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
  score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0]	train-auc:0.61706	eval-auc:nan
[10]	train-auc:0.74672	eval-auc:nan
[20]	train-auc:0.76265	eval-auc:nan
[30]	train-auc:0.77829	eval-auc:nan
[40]	train-auc:0.78624	eval-auc:nan
[49]	train-auc:0.79622	eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
  warnings.warn(
[32m08:54:02[0m | [1mINFO    [0m | [36mTraining complete: Train AUC=0.7962, Test AUC=nan[0m
[32m08:54:02[0m | [1mINFO    [0m | [36mModel saved to models\xgboost_model.pkl[0m
[32m08:54:02[0m | [1mINFO    [0m | [36mTrain/Test gap: -1 bars to prevent temporal leakage[0m
[32m08:54:02[0m | [1mINFO    [0m | [36mTraining with 500 samples, testing with 1 samples[0m
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:02] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
  score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0]	train-auc:0.59151	eval-auc:nan
[10]	train-auc:0.72977	eval-auc:nan
[20]	train-auc:0.73870	eval-auc:nan
[30]	train-auc:0.74612	eval-auc:nan
[40]	train-auc:0.76738	eval-auc:nan
[49]	train-auc:0.77810	eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
  warnings.warn(
[32m08:54:02[0m | [1mINFO    [0m | [36mTraining complete: Train AUC=0.7781, Test AUC=nan[0m
[32m08:54:02[0m | [1mINFO    [0m | [36mModel saved to models\xgboost_model.pkl[0m
[32m08:54:02[0m | [1mINFO    [0m | [36mTrain/Test gap: -1 bars to prevent temporal leakage[0m
[32m08:54:02[0m | [1mINFO    [0m | [36mTraining with 500 samples, testing with 1 samples[0m
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:02] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
  score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0]	train-auc:0.59438	eval-auc:nan
[10]	train-auc:0.70996	eval-auc:nan
[20]	train-auc:0.73589	eval-auc:nan
[30]	train-auc:0.75203	eval-auc:nan
[40]	train-auc:0.77593	eval-auc:nan
[49]	train-auc:0.78955	eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
  warnings.warn(
[32m08:54:02[0m | [1mINFO    [0m | [36mTraining complete: Train AUC=0.7896, Test AUC=nan[0m
[32m08:54:02[0m | [1mINFO    [0m | [36mModel saved to models\xgboost_model.pkl[0m
[32m08:54:02[0m | [1mINFO    [0m | [36mTrain/Test gap: -1 bars to prevent temporal leakage[0m
[32m08:54:02[0m | [1mINFO    [0m | [36mTraining with 500 samples, testing with 1 samples[0m
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:02] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
  score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0]	train-auc:0.58407	eval-auc:nan
[10]	train-auc:0.71992	eval-auc:nan
[20]	train-auc:0.74625	eval-auc:nan
[30]	train-auc:0.77028	eval-auc:nan
[40]	train-auc:0.78464	eval-auc:nan
[49]	train-auc:0.80249	eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
  warnings.warn(
[32m08:54:02[0m | [1mINFO    [0m | [36mTraining complete: Train AUC=0.8025, Test AUC=nan[0m
[32m08:54:02[0m | [1mINFO    [0m | [36mModel saved to models\xgboost_model.pkl[0m
[32m08:54:02[0m | [1mINFO    [0m | [36mTrain/Test gap: -1 bars to prevent temporal leakage[0m
[32m08:54:02[0m | [1mINFO    [0m | [36mTraining with 500 samples, testing with 1 samples[0m
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:02] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
  score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0]	train-auc:0.61828	eval-auc:nan
[10]	train-auc:0.73273	eval-auc:nan
[20]	train-auc:0.77206	eval-auc:nan
[30]	train-auc:0.79054	eval-auc:nan
[40]	train-auc:0.80593	eval-auc:nan
[49]	train-auc:0.81669	eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
  warnings.warn(
[32m08:54:02[0m | [1mINFO    [0m | [36mTraining complete: Train AUC=0.8167, Test AUC=nan[0m
[32m08:54:02[0m | [1mINFO    [0m | [36mModel saved to models\xgboost_model.pkl[0m
[32m08:54:02[0m | [1mINFO    [0m | [36mTrain/Test gap: -1 bars to prevent temporal leakage[0m
[32m08:54:02[0m | [1mINFO    [0m | [36mTraining with 500 samples, testing with 1 samples[0m
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:02] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
  score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0]	train-auc:0.58019	eval-auc:nan
[10]	train-auc:0.73870	eval-auc:nan
[20]	train-auc:0.76539	eval-auc:nan
[30]	train-auc:0.79055	eval-auc:nan
[40]	train-auc:0.80435	eval-auc:nan
[49]	train-auc:0.81436	eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
  warnings.warn(
[32m08:54:02[0m | [1mINFO    [0m | [36mTraining complete: Train AUC=0.8144, Test AUC=nan[0m
[32m08:54:02[0m | [1mINFO    [0m | [36mModel saved to models\xgboost_model.pkl[0m
[32m08:54:02[0m | [1mINFO    [0m | [36mTrain/Test gap: -1 bars to prevent temporal leakage[0m
[32m08:54:02[0m | [1mINFO    [0m | [36mTraining with 500 samples, testing with 1 samples[0m
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:02] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
  score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0]	train-auc:0.59776	eval-auc:nan
[10]	train-auc:0.72057	eval-auc:nan
[20]	train-auc:0.75272	eval-auc:nan
[30]	train-auc:0.77067	eval-auc:nan
[40]	train-auc:0.78382	eval-auc:nan
[49]	train-auc:0.80058	eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
  warnings.warn(
[32m08:54:02[0m | [1mINFO    [0m | [36mTraining complete: Train AUC=0.8006, Test AUC=nan[0m
[32m08:54:02[0m | [1mINFO    [0m | [36mModel saved to models\xgboost_model.pkl[0m
[32m08:54:02[0m | [1mINFO    [0m | [36mTrain/Test gap: -1 bars to prevent temporal leakage[0m
[32m08:54:02[0m | [1mINFO    [0m | [36mTraining with 500 samples, testing with 1 samples[0m
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:02] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
  score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0]	train-auc:0.55415	eval-auc:nan
[10]	train-auc:0.72266	eval-auc:nan
[20]	train-auc:0.73254	eval-auc:nan
[30]	train-auc:0.76305	eval-auc:nan
[40]	train-auc:0.77745	eval-auc:nan
[49]	train-auc:0.80388	eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
  warnings.warn(
[32m08:54:02[0m | [1mINFO    [0m | [36mTraining complete: Train AUC=0.8039, Test AUC=nan[0m
[32m08:54:02[0m | [1mINFO    [0m | [36mModel saved to models\xgboost_model.pkl[0m
[32m08:54:02[0m | [1mINFO    [0m | [36mTrain/Test gap: -1 bars to prevent temporal leakage[0m
[32m08:54:02[0m | [1mINFO    [0m | [36mTraining with 500 samples, testing with 1 samples[0m
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:02] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
  score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0]	train-auc:0.56272	eval-auc:nan
[10]	train-auc:0.72173	eval-auc:nan
[20]	train-auc:0.76281	eval-auc:nan
[30]	train-auc:0.77385	eval-auc:nan
[40]	train-auc:0.79148	eval-auc:nan
[49]	train-auc:0.79851	eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
  warnings.warn(
[32m08:54:02[0m | [1mINFO    [0m | [36mTraining complete: Train AUC=0.7985, Test AUC=nan[0m
[32m08:54:02[0m | [1mINFO    [0m | [36mModel saved to models\xgboost_model.pkl[0m
[32m08:54:02[0m | [1mINFO    [0m | [36mTrain/Test gap: -1 bars to prevent temporal leakage[0m
[32m08:54:02[0m | [1mINFO    [0m | [36mTraining with 500 samples, testing with 1 samples[0m
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:02] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
  score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0]	train-auc:0.60108	eval-auc:nan
[10]	train-auc:0.73143	eval-auc:nan
[20]	train-auc:0.74728	eval-auc:nan
[30]	train-auc:0.77209	eval-auc:nan
[40]	train-auc:0.78259	eval-auc:nan
[49]	train-auc:0.78725	eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
  warnings.warn(
[32m08:54:02[0m | [1mINFO    [0m | [36mTraining complete: Train AUC=0.7872, Test AUC=nan[0m
[32m08:54:02[0m | [1mINFO    [0m | [36mModel saved to models\xgboost_model.pkl[0m
[32m08:54:03[0m | [1mINFO    [0m | [36mTrain/Test gap: -1 bars to prevent temporal leakage[0m
[32m08:54:03[0m | [1mINFO    [0m | [36mTraining with 500 samples, testing with 1 samples[0m
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:03] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
  score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0]	train-auc:0.59910	eval-auc:nan
[10]	train-auc:0.71128	eval-auc:nan
[20]	train-auc:0.74497	eval-auc:nan
[30]	train-auc:0.76285	eval-auc:nan
[40]	train-auc:0.78418	eval-auc:nan
[49]	train-auc:0.78841	eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
  warnings.warn(
[32m08:54:03[0m | [1mINFO    [0m | [36mTraining complete: Train AUC=0.7884, Test AUC=nan[0m
[32m08:54:03[0m | [1mINFO    [0m | [36mModel saved to models\xgboost_model.pkl[0m
[32m08:54:03[0m | [1mINFO    [0m | [36mTrain/Test gap: -1 bars to prevent temporal leakage[0m
[32m08:54:03[0m | [1mINFO    [0m | [36mTraining with 500 samples, testing with 1 samples[0m
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:03] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
  score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0]	train-auc:0.62327	eval-auc:nan
[10]	train-auc:0.75221	eval-auc:nan
[20]	train-auc:0.76776	eval-auc:nan
[30]	train-auc:0.78295	eval-auc:nan
[40]	train-auc:0.79403	eval-auc:nan
[49]	train-auc:0.80951	eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
  warnings.warn(
[32m08:54:03[0m | [1mINFO    [0m | [36mTraining complete: Train AUC=0.8095, Test AUC=nan[0m
[32m08:54:03[0m | [1mINFO    [0m | [36mModel saved to models\xgboost_model.pkl[0m
[32m08:54:03[0m | [1mINFO    [0m | [36mTrain/Test gap: -1 bars to prevent temporal leakage[0m
[32m08:54:03[0m | [1mINFO    [0m | [36mTraining with 500 samples, testing with 1 samples[0m
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:03] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
  score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0]	train-auc:0.61812	eval-auc:nan
[10]	train-auc:0.74843	eval-auc:nan
[20]	train-auc:0.76281	eval-auc:nan
[30]	train-auc:0.78741	eval-auc:nan
[40]	train-auc:0.79969	eval-auc:nan
[49]	train-auc:0.80743	eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
  warnings.warn(
[32m08:54:03[0m | [1mINFO    [0m | [36mTraining complete: Train AUC=0.8074, Test AUC=nan[0m
[32m08:54:03[0m | [1mINFO    [0m | [36mModel saved to models\xgboost_model.pkl[0m
[32m08:54:03[0m | [1mINFO    [0m | [36mTrain/Test gap: -1 bars to prevent temporal leakage[0m
[32m08:54:03[0m | [1mINFO    [0m | [36mTraining with 500 samples, testing with 1 samples[0m
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:03] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
  score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0]	train-auc:0.62527	eval-auc:nan
[10]	train-auc:0.75578	eval-auc:nan
[20]	train-auc:0.78034	eval-auc:nan
[30]	train-auc:0.78667	eval-auc:nan
[40]	train-auc:0.80003	eval-auc:nan
[49]	train-auc:0.80397	eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
  warnings.warn(
[32m08:54:03[0m | [1mINFO    [0m | [36mTraining complete: Train AUC=0.8040, Test AUC=nan[0m
[32m08:54:03[0m | [1mINFO    [0m | [36mModel saved to models\xgboost_model.pkl[0m
[32m08:54:03[0m | [1mINFO    [0m | [36mTrain/Test gap: -1 bars to prevent temporal leakage[0m
[32m08:54:03[0m | [1mINFO    [0m | [36mTraining with 500 samples, testing with 1 samples[0m
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:03] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
  score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0]	train-auc:0.62839	eval-auc:nan
[10]	train-auc:0.75835	eval-auc:nan
[20]	train-auc:0.77501	eval-auc:nan
[30]	train-auc:0.79320	eval-auc:nan
[40]	train-auc:0.80623	eval-auc:nan
[49]	train-auc:0.81986	eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
  warnings.warn(
[32m08:54:03[0m | [1mINFO    [0m | [36mTraining complete: Train AUC=0.8199, Test AUC=nan[0m
[32m08:54:03[0m | [1mINFO    [0m | [36mModel saved to models\xgboost_model.pkl[0m
[32m08:54:03[0m | [1mINFO    [0m | [36mTrain/Test gap: -1 bars to prevent temporal leakage[0m
[32m08:54:03[0m | [1mINFO    [0m | [36mTraining with 500 samples, testing with 1 samples[0m
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:03] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
  score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0]	train-auc:0.61540	eval-auc:nan
[10]	train-auc:0.76817	eval-auc:nan
[20]	train-auc:0.80050	eval-auc:nan
[30]	train-auc:0.81930	eval-auc:nan
[40]	train-auc:0.82866	eval-auc:nan
[49]	train-auc:0.83679	eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
  warnings.warn(
[32m08:54:03[0m | [1mINFO    [0m | [36mTraining complete: Train AUC=0.8368, Test AUC=nan[0m
[32m08:54:03[0m | [1mINFO    [0m | [36mModel saved to models\xgboost_model.pkl[0m
[32m08:54:03[0m | [1mINFO    [0m | [36mTrain/Test gap: -1 bars to prevent temporal leakage[0m
[32m08:54:03[0m | [1mINFO    [0m | [36mTraining with 500 samples, testing with 1 samples[0m
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:03] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
  score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0]	train-auc:0.62442	eval-auc:nan
[10]	train-auc:0.73594	eval-auc:nan
[20]	train-auc:0.77465	eval-auc:nan
[30]	train-auc:0.78278	eval-auc:nan
[40]	train-auc:0.79112	eval-auc:nan
[49]	train-auc:0.81513	eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
  warnings.warn(
[32m08:54:03[0m | [1mINFO    [0m | [36mTraining complete: Train AUC=0.8151, Test AUC=nan[0m
[32m08:54:03[0m | [1mINFO    [0m | [36mModel saved to models\xgboost_model.pkl[0m
[32m08:54:03[0m | [1mINFO    [0m | [36mTrain/Test gap: -1 bars to prevent temporal leakage[0m
[32m08:54:03[0m | [1mINFO    [0m | [36mTraining with 500 samples, testing with 1 samples[0m
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:03] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
  score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0]	train-auc:0.58608	eval-auc:nan
[10]	train-auc:0.72097	eval-auc:nan
[20]	train-auc:0.76308	eval-auc:nan
[30]	train-auc:0.79239	eval-auc:nan
[40]	train-auc:0.80960	eval-auc:nan
[49]	train-auc:0.81744	eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
  warnings.warn(
[32m08:54:03[0m | [1mINFO    [0m | [36mTraining complete: Train AUC=0.8174, Test AUC=nan[0m
[32m08:54:03[0m | [1mINFO    [0m | [36mModel saved to models\xgboost_model.pkl[0m
[32m08:54:03[0m | [1mINFO    [0m | [36mTrain/Test gap: -1 bars to prevent temporal leakage[0m
[32m08:54:03[0m | [1mINFO    [0m | [36mTraining with 500 samples, testing with 1 samples[0m
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:03] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
  score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0]	train-auc:0.60945	eval-auc:nan
[10]	train-auc:0.71640	eval-auc:nan
[20]	train-auc:0.74784	eval-auc:nan
[30]	train-auc:0.76102	eval-auc:nan
[40]	train-auc:0.77951	eval-auc:nan
[49]	train-auc:0.79907	eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
  warnings.warn(
[32m08:54:03[0m | [1mINFO    [0m | [36mTraining complete: Train AUC=0.7991, Test AUC=nan[0m
[32m08:54:03[0m | [1mINFO    [0m | [36mModel saved to models\xgboost_model.pkl[0m
[32m08:54:03[0m | [1mINFO    [0m | [36mTrain/Test gap: -1 bars to prevent temporal leakage[0m
[32m08:54:03[0m | [1mINFO    [0m | [36mTraining with 500 samples, testing with 1 samples[0m
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:03] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
  score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0]	train-auc:0.64286	eval-auc:nan
[10]	train-auc:0.74450	eval-auc:nan
[20]	train-auc:0.76981	eval-auc:nan
[30]	train-auc:0.79264	eval-auc:nan
[40]	train-auc:0.80567	eval-auc:nan
[49]	train-auc:0.81227	eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
  warnings.warn(
[32m08:54:03[0m | [1mINFO    [0m | [36mTraining complete: Train AUC=0.8123, Test AUC=nan[0m
[32m08:54:03[0m | [1mINFO    [0m | [36mModel saved to models\xgboost_model.pkl[0m
[32m08:54:03[0m | [1mINFO    [0m | [36mTrain/Test gap: -1 bars to prevent temporal leakage[0m
[32m08:54:03[0m | [1mINFO    [0m | [36mTraining with 500 samples, testing with 1 samples[0m
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:03] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
  score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0]	train-auc:0.63587	eval-auc:nan
[10]	train-auc:0.74095	eval-auc:nan
[20]	train-auc:0.77523	eval-auc:nan
[30]	train-auc:0.78243	eval-auc:nan
[40]	train-auc:0.80109	eval-auc:nan
[49]	train-auc:0.81184	eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
  warnings.warn(
[32m08:54:03[0m | [1mINFO    [0m | [36mTraining complete: Train AUC=0.8118, Test AUC=nan[0m
[32m08:54:03[0m | [1mINFO    [0m | [36mModel saved to models\xgboost_model.pkl[0m
[32m08:54:03[0m | [1mINFO    [0m | [36mTrain/Test gap: -1 bars to prevent temporal leakage[0m
[32m08:54:03[0m | [1mINFO    [0m | [36mTraining with 500 samples, testing with 1 samples[0m
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:03] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
  score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0]	train-auc:0.57806	eval-auc:nan
[10]	train-auc:0.71320	eval-auc:nan
[20]	train-auc:0.75545	eval-auc:nan
[30]	train-auc:0.77091	eval-auc:nan
[40]	train-auc:0.78497	eval-auc:nan
[49]	train-auc:0.79580	eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
  warnings.warn(
[32m08:54:03[0m | [1mINFO    [0m | [36mTraining complete: Train AUC=0.7958, Test AUC=nan[0m
[32m08:54:03[0m | [1mINFO    [0m | [36mModel saved to models\xgboost_model.pkl[0m
[32m08:54:03[0m | [1mINFO    [0m | [36mTrain/Test gap: -1 bars to prevent temporal leakage[0m
[32m08:54:03[0m | [1mINFO    [0m | [36mTraining with 500 samples, testing with 1 samples[0m
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:03] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
  score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0]	train-auc:0.60323	eval-auc:nan
[10]	train-auc:0.74192	eval-auc:nan
[20]	train-auc:0.76749	eval-auc:nan
[30]	train-auc:0.78072	eval-auc:nan
[40]	train-auc:0.79201	eval-auc:nan
[49]	train-auc:0.80477	eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
  warnings.warn(
[32m08:54:03[0m | [1mINFO    [0m | [36mTraining complete: Train AUC=0.8048, Test AUC=nan[0m
[32m08:54:03[0m | [1mINFO    [0m | [36mModel saved to models\xgboost_model.pkl[0m
[32m08:54:03[0m | [1mINFO    [0m | [36mTrain/Test gap: -1 bars to prevent temporal leakage[0m
[32m08:54:03[0m | [1mINFO    [0m | [36mTraining with 500 samples, testing with 1 samples[0m
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:03] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
  score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0]	train-auc:0.59283	eval-auc:nan
[10]	train-auc:0.71361	eval-auc:nan
[20]	train-auc:0.75234	eval-auc:nan
[30]	train-auc:0.75622	eval-auc:nan
[40]	train-auc:0.76886	eval-auc:nan
[49]	train-auc:0.78424	eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
  warnings.warn(
[32m08:54:03[0m | [1mINFO    [0m | [36mTraining complete: Train AUC=0.7842, Test AUC=nan[0m
[32m08:54:03[0m | [1mINFO    [0m | [36mModel saved to models\xgboost_model.pkl[0m
[32m08:54:03[0m | [1mINFO    [0m | [36mTrain/Test gap: -1 bars to prevent temporal leakage[0m
[32m08:54:03[0m | [1mINFO    [0m | [36mTraining with 500 samples, testing with 1 samples[0m
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:03] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
  score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0]	train-auc:0.58564	eval-auc:nan
[10]	train-auc:0.71039	eval-auc:nan
[20]	train-auc:0.75089	eval-auc:nan
[30]	train-auc:0.77866	eval-auc:nan
[40]	train-auc:0.78561	eval-auc:nan
[49]	train-auc:0.79469	eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
  warnings.warn(
[32m08:54:03[0m | [1mINFO    [0m | [36mTraining complete: Train AUC=0.7947, Test AUC=nan[0m
[32m08:54:03[0m | [1mINFO    [0m | [36mModel saved to models\xgboost_model.pkl[0m
[32m08:54:03[0m | [1mINFO    [0m | [36mTrain/Test gap: -1 bars to prevent temporal leakage[0m
[32m08:54:03[0m | [1mINFO    [0m | [36mTraining with 500 samples, testing with 1 samples[0m
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:03] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
  score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0]	train-auc:0.58715	eval-auc:nan
[10]	train-auc:0.70653	eval-auc:nan
[20]	train-auc:0.73744	eval-auc:nan
[30]	train-auc:0.76009	eval-auc:nan
[40]	train-auc:0.77847	eval-auc:nan
[49]	train-auc:0.79532	eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
  warnings.warn(
[32m08:54:03[0m | [1mINFO    [0m | [36mTraining complete: Train AUC=0.7953, Test AUC=nan[0m
[32m08:54:03[0m | [1mINFO    [0m | [36mModel saved to models\xgboost_model.pkl[0m
[32m08:54:04[0m | [1mINFO    [0m | [36mTrain/Test gap: -1 bars to prevent temporal leakage[0m
[32m08:54:04[0m | [1mINFO    [0m | [36mTraining with 500 samples, testing with 1 samples[0m
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:04] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
  score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0]	train-auc:0.60148	eval-auc:nan
[10]	train-auc:0.73586	eval-auc:nan
[20]	train-auc:0.75517	eval-auc:nan
[30]	train-auc:0.77235	eval-auc:nan
[40]	train-auc:0.77564	eval-auc:nan
[49]	train-auc:0.78747	eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
  warnings.warn(
[32m08:54:04[0m | [1mINFO    [0m | [36mTraining complete: Train AUC=0.7875, Test AUC=nan[0m
[32m08:54:04[0m | [1mINFO    [0m | [36mModel saved to models\xgboost_model.pkl[0m
[32m08:54:04[0m | [1mINFO    [0m | [36mTrain/Test gap: -1 bars to prevent temporal leakage[0m
[32m08:54:04[0m | [1mINFO    [0m | [36mTraining with 500 samples, testing with 1 samples[0m
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:04] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
  score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0]	train-auc:0.59693	eval-auc:nan
[10]	train-auc:0.71557	eval-auc:nan
[20]	train-auc:0.75831	eval-auc:nan
[30]	train-auc:0.78069	eval-auc:nan
[40]	train-auc:0.78897	eval-auc:nan
[49]	train-auc:0.80507	eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
  warnings.warn(
[32m08:54:04[0m | [1mINFO    [0m | [36mTraining complete: Train AUC=0.8051, Test AUC=nan[0m
[32m08:54:04[0m | [1mINFO    [0m | [36mModel saved to models\xgboost_model.pkl[0m
[32m08:54:04[0m | [1mINFO    [0m | [36mTrain/Test gap: -1 bars to prevent temporal leakage[0m
[32m08:54:04[0m | [1mINFO    [0m | [36mTraining with 500 samples, testing with 1 samples[0m
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:04] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
  score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0]	train-auc:0.58496	eval-auc:nan
[10]	train-auc:0.71445	eval-auc:nan
[20]	train-auc:0.73869	eval-auc:nan
[30]	train-auc:0.75962	eval-auc:nan
[40]	train-auc:0.77052	eval-auc:nan
[49]	train-auc:0.77559	eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
  warnings.warn(
[32m08:54:04[0m | [1mINFO    [0m | [36mTraining complete: Train AUC=0.7756, Test AUC=nan[0m
[32m08:54:04[0m | [1mINFO    [0m | [36mModel saved to models\xgboost_model.pkl[0m
[32m08:54:04[0m | [1mINFO    [0m | [36mTrain/Test gap: -1 bars to prevent temporal leakage[0m
[32m08:54:04[0m | [1mINFO    [0m | [36mTraining with 500 samples, testing with 1 samples[0m
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:04] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
  score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0]	train-auc:0.61672	eval-auc:nan
[10]	train-auc:0.72189	eval-auc:nan
[20]	train-auc:0.74663	eval-auc:nan
[30]	train-auc:0.75988	eval-auc:nan
[40]	train-auc:0.76811	eval-auc:nan
[49]	train-auc:0.77924	eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
  warnings.warn(
[32m08:54:04[0m | [1mINFO    [0m | [36mTraining complete: Train AUC=0.7792, Test AUC=nan[0m
[32m08:54:04[0m | [1mINFO    [0m | [36mModel saved to models\xgboost_model.pkl[0m
[32m08:54:04[0m | [1mINFO    [0m | [36mTrain/Test gap: -1 bars to prevent temporal leakage[0m
[32m08:54:04[0m | [1mINFO    [0m | [36mTraining with 500 samples, testing with 1 samples[0m
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:04] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
  score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0]	train-auc:0.60677	eval-auc:nan
[10]	train-auc:0.73373	eval-auc:nan
[20]	train-auc:0.74586	eval-auc:nan
[30]	train-auc:0.76032	eval-auc:nan
[40]	train-auc:0.76883	eval-auc:nan
[49]	train-auc:0.78380	eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
  warnings.warn(
[32m08:54:04[0m | [1mINFO    [0m | [36mTraining complete: Train AUC=0.7838, Test AUC=nan[0m
[32m08:54:04[0m | [1mINFO    [0m | [36mModel saved to models\xgboost_model.pkl[0m
[32m08:54:04[0m | [1mINFO    [0m | [36mTrain/Test gap: -1 bars to prevent temporal leakage[0m
[32m08:54:04[0m | [1mINFO    [0m | [36mTraining with 500 samples, testing with 1 samples[0m
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:04] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
  score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0]	train-auc:0.61107	eval-auc:nan
[10]	train-auc:0.73476	eval-auc:nan
[20]	train-auc:0.75704	eval-auc:nan
[30]	train-auc:0.78347	eval-auc:nan
[40]	train-auc:0.79786	eval-auc:nan
[49]	train-auc:0.80276	eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
  warnings.warn(
[32m08:54:04[0m | [1mINFO    [0m | [36mTraining complete: Train AUC=0.8028, Test AUC=nan[0m
[32m08:54:04[0m | [1mINFO    [0m | [36mModel saved to models\xgboost_model.pkl[0m
[32m08:54:04[0m | [1mINFO    [0m | [36mTrain/Test gap: -1 bars to prevent temporal leakage[0m
[32m08:54:04[0m | [1mINFO    [0m | [36mTraining with 500 samples, testing with 1 samples[0m
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:04] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
  score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0]	train-auc:0.59196	eval-auc:nan
[10]	train-auc:0.71890	eval-auc:nan
[20]	train-auc:0.75012	eval-auc:nan
[30]	train-auc:0.75933	eval-auc:nan
[40]	train-auc:0.77703	eval-auc:nan
[49]	train-auc:0.78454	eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
  warnings.warn(
[32m08:54:04[0m | [1mINFO    [0m | [36mTraining complete: Train AUC=0.7845, Test AUC=nan[0m
[32m08:54:04[0m | [1mINFO    [0m | [36mModel saved to models\xgboost_model.pkl[0m
[32m08:54:04[0m | [1mINFO    [0m | [36mTrain/Test gap: -1 bars to prevent temporal leakage[0m
[32m08:54:04[0m | [1mINFO    [0m | [36mTraining with 500 samples, testing with 1 samples[0m
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:04] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
  score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0]	train-auc:0.59640	eval-auc:nan
[10]	train-auc:0.72163	eval-auc:nan
[20]	train-auc:0.73839	eval-auc:nan
[30]	train-auc:0.76187	eval-auc:nan
[40]	train-auc:0.77331	eval-auc:nan
[49]	train-auc:0.78876	eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
  warnings.warn(
[32m08:54:04[0m | [1mINFO    [0m | [36mTraining complete: Train AUC=0.7888, Test AUC=nan[0m
[32m08:54:04[0m | [1mINFO    [0m | [36mModel saved to models\xgboost_model.pkl[0m
[32m08:54:04[0m | [1mINFO    [0m | [36mTrain/Test gap: -1 bars to prevent temporal leakage[0m
[32m08:54:04[0m | [1mINFO    [0m | [36mTraining with 500 samples, testing with 1 samples[0m
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:04] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
  score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0]	train-auc:0.62603	eval-auc:nan
[10]	train-auc:0.73800	eval-auc:nan
[20]	train-auc:0.76229	eval-auc:nan
[30]	train-auc:0.77816	eval-auc:nan
[40]	train-auc:0.78826	eval-auc:nan
[49]	train-auc:0.80562	eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
  warnings.warn(
[32m08:54:04[0m | [1mINFO    [0m | [36mTraining complete: Train AUC=0.8056, Test AUC=nan[0m
[32m08:54:04[0m | [1mINFO    [0m | [36mModel saved to models\xgboost_model.pkl[0m
[32m08:54:04[0m | [1mINFO    [0m | [36mTrain/Test gap: -1 bars to prevent temporal leakage[0m
[32m08:54:04[0m | [1mINFO    [0m | [36mTraining with 500 samples, testing with 1 samples[0m
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:04] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
  score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0]	train-auc:0.60779	eval-auc:nan
[10]	train-auc:0.73159	eval-auc:nan
[20]	train-auc:0.77060	eval-auc:nan
[30]	train-auc:0.77902	eval-auc:nan
[40]	train-auc:0.79459	eval-auc:nan
[49]	train-auc:0.80244	eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
  warnings.warn(
[32m08:54:04[0m | [1mINFO    [0m | [36mTraining complete: Train AUC=0.8024, Test AUC=nan[0m
[32m08:54:04[0m | [1mINFO    [0m | [36mModel saved to models\xgboost_model.pkl[0m
[32m08:54:04[0m | [1mINFO    [0m | [36mTrain/Test gap: -1 bars to prevent temporal leakage[0m
[32m08:54:04[0m | [1mINFO    [0m | [36mTraining with 500 samples, testing with 1 samples[0m
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:04] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
  score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0]	train-auc:0.60662	eval-auc:nan
[10]	train-auc:0.71568	eval-auc:nan
[20]	train-auc:0.74645	eval-auc:nan
[30]	train-auc:0.76677	eval-auc:nan
[40]	train-auc:0.78159	eval-auc:nan
[49]	train-auc:0.79704	eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
  warnings.warn(
[32m08:54:04[0m | [1mINFO    [0m | [36mTraining complete: Train AUC=0.7970, Test AUC=nan[0m
[32m08:54:04[0m | [1mINFO    [0m | [36mModel saved to models\xgboost_model.pkl[0m
[32m08:54:04[0m | [1mINFO    [0m | [36mTrain/Test gap: -1 bars to prevent temporal leakage[0m
[32m08:54:04[0m | [1mINFO    [0m | [36mTraining with 500 samples, testing with 1 samples[0m
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:04] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
  score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0]	train-auc:0.59623	eval-auc:nan
[10]	train-auc:0.71235	eval-auc:nan
[20]	train-auc:0.74552	eval-auc:nan
[30]	train-auc:0.77698	eval-auc:nan
[40]	train-auc:0.79403	eval-auc:nan
[49]	train-auc:0.80791	eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
  warnings.warn(
[32m08:54:04[0m | [1mINFO    [0m | [36mTraining complete: Train AUC=0.8079, Test AUC=nan[0m
[32m08:54:04[0m | [1mINFO    [0m | [36mModel saved to models\xgboost_model.pkl[0m
[32m08:54:04[0m | [1mINFO    [0m | [36mTrain/Test gap: -1 bars to prevent temporal leakage[0m
[32m08:54:04[0m | [1mINFO    [0m | [36mTraining with 500 samples, testing with 1 samples[0m
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:04] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
  score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0]	train-auc:0.59888	eval-auc:nan
[10]	train-auc:0.72695	eval-auc:nan
[20]	train-auc:0.74232	eval-auc:nan
[30]	train-auc:0.76668	eval-auc:nan
[40]	train-auc:0.77413	eval-auc:nan
[49]	train-auc:0.78510	eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
  warnings.warn(
[32m08:54:04[0m | [1mINFO    [0m | [36mTraining complete: Train AUC=0.7851, Test AUC=nan[0m
[32m08:54:04[0m | [1mINFO    [0m | [36mModel saved to models\xgboost_model.pkl[0m
[32m08:54:04[0m | [1mINFO    [0m | [36mTrain/Test gap: -1 bars to prevent temporal leakage[0m
[32m08:54:04[0m | [1mINFO    [0m | [36mTraining with 500 samples, testing with 1 samples[0m
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:04] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
  score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0]	train-auc:0.63122	eval-auc:nan
[10]	train-auc:0.72776	eval-auc:nan
[20]	train-auc:0.74767	eval-auc:nan
[30]	train-auc:0.77189	eval-auc:nan
[40]	train-auc:0.78595	eval-auc:nan
[49]	train-auc:0.79027	eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
  warnings.warn(
[32m08:54:04[0m | [1mINFO    [0m | [36mTraining complete: Train AUC=0.7903, Test AUC=nan[0m
[32m08:54:04[0m | [1mINFO    [0m | [36mModel saved to models\xgboost_model.pkl[0m
[32m08:54:04[0m | [1mINFO    [0m | [36mTrain/Test gap: -1 bars to prevent temporal leakage[0m
[32m08:54:04[0m | [1mINFO    [0m | [36mTraining with 500 samples, testing with 1 samples[0m
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:04] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
  score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0]	train-auc:0.59918	eval-auc:nan
[10]	train-auc:0.70898	eval-auc:nan
[20]	train-auc:0.74405	eval-auc:nan
[30]	train-auc:0.76442	eval-auc:nan
[40]	train-auc:0.78421	eval-auc:nan
[49]	train-auc:0.79176	eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
  warnings.warn(
[32m08:54:04[0m | [1mINFO    [0m | [36mTraining complete: Train AUC=0.7918, Test AUC=nan[0m
[32m08:54:04[0m | [1mINFO    [0m | [36mModel saved to models\xgboost_model.pkl[0m
[32m08:54:04[0m | [1mINFO    [0m | [36mTrain/Test gap: -1 bars to prevent temporal leakage[0m
[32m08:54:04[0m | [1mINFO    [0m | [36mTraining with 500 samples, testing with 1 samples[0m
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:04] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
  score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0]	train-auc:0.62748	eval-auc:nan
[10]	train-auc:0.73512	eval-auc:nan
[20]	train-auc:0.75265	eval-auc:nan
[30]	train-auc:0.75979	eval-auc:nan
[40]	train-auc:0.77616	eval-auc:nan
[49]	train-auc:0.78833	eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
  warnings.warn(
[32m08:54:04[0m | [1mINFO    [0m | [36mTraining complete: Train AUC=0.7883, Test AUC=nan[0m
[32m08:54:04[0m | [1mINFO    [0m | [36mModel saved to models\xgboost_model.pkl[0m
[32m08:54:05[0m | [1mINFO    [0m | [36mTrain/Test gap: -1 bars to prevent temporal leakage[0m
[32m08:54:05[0m | [1mINFO    [0m | [36mTraining with 500 samples, testing with 1 samples[0m
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:05] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
  score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0]	train-auc:0.61205	eval-auc:nan
[10]	train-auc:0.72181	eval-auc:nan
[20]	train-auc:0.74811	eval-auc:nan
[30]	train-auc:0.75185	eval-auc:nan
[40]	train-auc:0.76121	eval-auc:nan
[49]	train-auc:0.77493	eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
  warnings.warn(
[32m08:54:05[0m | [1mINFO    [0m | [36mTraining complete: Train AUC=0.7749, Test AUC=nan[0m
[32m08:54:05[0m | [1mINFO    [0m | [36mModel saved to models\xgboost_model.pkl[0m
[32m08:54:05[0m | [1mINFO    [0m | [36mTrain/Test gap: -1 bars to prevent temporal leakage[0m
[32m08:54:05[0m | [1mINFO    [0m | [36mTraining with 500 samples, testing with 1 samples[0m
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:05] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
  score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0]	train-auc:0.60534	eval-auc:nan
[10]	train-auc:0.71542	eval-auc:nan
[20]	train-auc:0.74795	eval-auc:nan
[30]	train-auc:0.76755	eval-auc:nan
[40]	train-auc:0.77324	eval-auc:nan
[49]	train-auc:0.78779	eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
  warnings.warn(
[32m08:54:05[0m | [1mINFO    [0m | [36mTraining complete: Train AUC=0.7878, Test AUC=nan[0m
[32m08:54:05[0m | [1mINFO    [0m | [36mModel saved to models\xgboost_model.pkl[0m
[32m08:54:05[0m | [1mINFO    [0m | [36mTrain/Test gap: -1 bars to prevent temporal leakage[0m
[32m08:54:05[0m | [1mINFO    [0m | [36mTraining with 500 samples, testing with 1 samples[0m
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:05] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
  score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0]	train-auc:0.62277	eval-auc:nan
[10]	train-auc:0.75720	eval-auc:nan
[20]	train-auc:0.76158	eval-auc:nan
[30]	train-auc:0.77554	eval-auc:nan
[40]	train-auc:0.78431	eval-auc:nan
[49]	train-auc:0.79216	eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
  warnings.warn(
[32m08:54:05[0m | [1mINFO    [0m | [36mTraining complete: Train AUC=0.7922, Test AUC=nan[0m
[32m08:54:05[0m | [1mINFO    [0m | [36mModel saved to models\xgboost_model.pkl[0m
[32m08:54:05[0m | [1mINFO    [0m | [36mTrain/Test gap: -1 bars to prevent temporal leakage[0m
[32m08:54:05[0m | [1mINFO    [0m | [36mTraining with 500 samples, testing with 1 samples[0m
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:05] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
  score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0]	train-auc:0.63952	eval-auc:nan
[10]	train-auc:0.73414	eval-auc:nan
[20]	train-auc:0.75263	eval-auc:nan
[30]	train-auc:0.77302	eval-auc:nan
[40]	train-auc:0.78398	eval-auc:nan
[49]	train-auc:0.79365	eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
  warnings.warn(
[32m08:54:05[0m | [1mINFO    [0m | [36mTraining complete: Train AUC=0.7937, Test AUC=nan[0m
[32m08:54:05[0m | [1mINFO    [0m | [36mModel saved to models\xgboost_model.pkl[0m
[32m08:54:05[0m | [1mINFO    [0m | [36mTrain/Test gap: -1 bars to prevent temporal leakage[0m
[32m08:54:05[0m | [1mINFO    [0m | [36mTraining with 500 samples, testing with 1 samples[0m
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:05] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
  score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0]	train-auc:0.61150	eval-auc:nan
[10]	train-auc:0.71968	eval-auc:nan
[20]	train-auc:0.74658	eval-auc:nan
[30]	train-auc:0.75580	eval-auc:nan
[40]	train-auc:0.76780	eval-auc:nan
[49]	train-auc:0.78313	eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
  warnings.warn(
[32m08:54:05[0m | [1mINFO    [0m | [36mTraining complete: Train AUC=0.7831, Test AUC=nan[0m
[32m08:54:05[0m | [1mINFO    [0m | [36mModel saved to models\xgboost_model.pkl[0m
[32m08:54:05[0m | [1mINFO    [0m | [36mTrain/Test gap: -1 bars to prevent temporal leakage[0m
[32m08:54:05[0m | [1mINFO    [0m | [36mTraining with 500 samples, testing with 1 samples[0m
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:05] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
  score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0]	train-auc:0.59121	eval-auc:nan
[10]	train-auc:0.72139	eval-auc:nan
[20]	train-auc:0.74210	eval-auc:nan
[30]	train-auc:0.76874	eval-auc:nan
[40]	train-auc:0.78744	eval-auc:nan
[49]	train-auc:0.80435	eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
  warnings.warn(
[32m08:54:05[0m | [1mINFO    [0m | [36mTraining complete: Train AUC=0.8044, Test AUC=nan[0m
[32m08:54:05[0m | [1mINFO    [0m | [36mModel saved to models\xgboost_model.pkl[0m
[32m08:54:05[0m | [1mINFO    [0m | [36mTrain/Test gap: -1 bars to prevent temporal leakage[0m
[32m08:54:05[0m | [1mINFO    [0m | [36mTraining with 500 samples, testing with 1 samples[0m
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:05] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
  score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0]	train-auc:0.62495	eval-auc:nan
[10]	train-auc:0.71286	eval-auc:nan
[20]	train-auc:0.74365	eval-auc:nan
[30]	train-auc:0.76206	eval-auc:nan
[40]	train-auc:0.78000	eval-auc:nan
[49]	train-auc:0.78689	eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
  warnings.warn(
[32m08:54:05[0m | [1mINFO    [0m | [36mTraining complete: Train AUC=0.7869, Test AUC=nan[0m
[32m08:54:05[0m | [1mINFO    [0m | [36mModel saved to models\xgboost_model.pkl[0m
[32m08:54:05[0m | [1mINFO    [0m | [36mTrain/Test gap: -1 bars to prevent temporal leakage[0m
[32m08:54:05[0m | [1mINFO    [0m | [36mTraining with 500 samples, testing with 1 samples[0m
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:05] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
  score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0]	train-auc:0.61799	eval-auc:nan
[10]	train-auc:0.73085	eval-auc:nan
[20]	train-auc:0.75642	eval-auc:nan
[30]	train-auc:0.77086	eval-auc:nan
[40]	train-auc:0.78336	eval-auc:nan
[49]	train-auc:0.80482	eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
  warnings.warn(
[32m08:54:05[0m | [1mINFO    [0m | [36mTraining complete: Train AUC=0.8048, Test AUC=nan[0m
[32m08:54:05[0m | [1mINFO    [0m | [36mModel saved to models\xgboost_model.pkl[0m
[32m08:54:05[0m | [1mINFO    [0m | [36mTrain/Test gap: -1 bars to prevent temporal leakage[0m
[32m08:54:05[0m | [1mINFO    [0m | [36mTraining with 500 samples, testing with 1 samples[0m
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:05] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
  score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0]	train-auc:0.63913	eval-auc:nan
[10]	train-auc:0.73981	eval-auc:nan
[20]	train-auc:0.76502	eval-auc:nan
[30]	train-auc:0.77841	eval-auc:nan
[40]	train-auc:0.78708	eval-auc:nan
[49]	train-auc:0.79722	eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
  warnings.warn(
[32m08:54:05[0m | [1mINFO    [0m | [36mTraining complete: Train AUC=0.7972, Test AUC=nan[0m
[32m08:54:05[0m | [1mINFO    [0m | [36mModel saved to models\xgboost_model.pkl[0m
[32m08:54:05[0m | [1mINFO    [0m | [36mTrain/Test gap: -1 bars to prevent temporal leakage[0m
[32m08:54:05[0m | [1mINFO    [0m | [36mTraining with 500 samples, testing with 1 samples[0m
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:05] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
  score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0]	train-auc:0.56720	eval-auc:nan
[10]	train-auc:0.74804	eval-auc:nan
[20]	train-auc:0.77606	eval-auc:nan
[30]	train-auc:0.78470	eval-auc:nan
[40]	train-auc:0.79320	eval-auc:nan
[49]	train-auc:0.79949	eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
  warnings.warn(
[32m08:54:05[0m | [1mINFO    [0m | [36mTraining complete: Train AUC=0.7995, Test AUC=nan[0m
[32m08:54:05[0m | [1mINFO    [0m | [36mModel saved to models\xgboost_model.pkl[0m
[32m08:54:05[0m | [1mINFO    [0m | [36mTrain/Test gap: -1 bars to prevent temporal leakage[0m
[32m08:54:05[0m | [1mINFO    [0m | [36mTraining with 500 samples, testing with 1 samples[0m
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:05] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
  score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0]	train-auc:0.63767	eval-auc:nan
[10]	train-auc:0.73356	eval-auc:nan
[20]	train-auc:0.76931	eval-auc:nan
[30]	train-auc:0.77603	eval-auc:nan
[40]	train-auc:0.78761	eval-auc:nan
[49]	train-auc:0.81087	eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
  warnings.warn(
[32m08:54:05[0m | [1mINFO    [0m | [36mTraining complete: Train AUC=0.8109, Test AUC=nan[0m
[32m08:54:05[0m | [1mINFO    [0m | [36mModel saved to models\xgboost_model.pkl[0m
[32m08:54:05[0m | [1mINFO    [0m | [36mTrain/Test gap: -1 bars to prevent temporal leakage[0m
[32m08:54:05[0m | [1mINFO    [0m | [36mTraining with 500 samples, testing with 1 samples[0m
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:05] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
  score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0]	train-auc:0.59227	eval-auc:nan
[10]	train-auc:0.70443	eval-auc:nan
[20]	train-auc:0.75264	eval-auc:nan
[30]	train-auc:0.77043	eval-auc:nan
[40]	train-auc:0.78209	eval-auc:nan
[49]	train-auc:0.79030	eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
  warnings.warn(
[32m08:54:05[0m | [1mINFO    [0m | [36mTraining complete: Train AUC=0.7903, Test AUC=nan[0m
[32m08:54:05[0m | [1mINFO    [0m | [36mModel saved to models\xgboost_model.pkl[0m
[32m08:54:05[0m | [1mINFO    [0m | [36mTrain/Test gap: -1 bars to prevent temporal leakage[0m
[32m08:54:05[0m | [1mINFO    [0m | [36mTraining with 500 samples, testing with 1 samples[0m
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:05] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
  score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0]	train-auc:0.62220	eval-auc:nan
[10]	train-auc:0.71963	eval-auc:nan
[20]	train-auc:0.74307	eval-auc:nan
[30]	train-auc:0.76745	eval-auc:nan
[40]	train-auc:0.78652	eval-auc:nan
[49]	train-auc:0.79833	eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
  warnings.warn(
[32m08:54:05[0m | [1mINFO    [0m | [36mTraining complete: Train AUC=0.7983, Test AUC=nan[0m
[32m08:54:05[0m | [1mINFO    [0m | [36mModel saved to models\xgboost_model.pkl[0m
[32m08:54:05[0m | [1mINFO    [0m | [36mTrain/Test gap: -1 bars to prevent temporal leakage[0m
[32m08:54:05[0m | [1mINFO    [0m | [36mTraining with 500 samples, testing with 1 samples[0m
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:05] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
  score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0]	train-auc:0.58955	eval-auc:nan
[10]	train-auc:0.73387	eval-auc:nan
[20]	train-auc:0.76492	eval-auc:nan
[30]	train-auc:0.76914	eval-auc:nan
[40]	train-auc:0.78498	eval-auc:nan
[49]	train-auc:0.79957	eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
  warnings.warn(
[32m08:54:06[0m | [1mINFO    [0m | [36mTraining complete: Train AUC=0.7996, Test AUC=nan[0m
[32m08:54:06[0m | [1mINFO    [0m | [36mModel saved to models\xgboost_model.pkl[0m
[32m08:54:06[0m | [1mINFO    [0m | [36mTrain/Test gap: -1 bars to prevent temporal leakage[0m
[32m08:54:06[0m | [1mINFO    [0m | [36mTraining with 500 samples, testing with 1 samples[0m
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:06] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
  score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0]	train-auc:0.60938	eval-auc:nan
[10]	train-auc:0.71776	eval-auc:nan
[20]	train-auc:0.74348	eval-auc:nan
[30]	train-auc:0.76402	eval-auc:nan
[40]	train-auc:0.77696	eval-auc:nan
[49]	train-auc:0.79126	eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
  warnings.warn(
[32m08:54:06[0m | [1mINFO    [0m | [36mTraining complete: Train AUC=0.7913, Test AUC=nan[0m
[32m08:54:06[0m | [1mINFO    [0m | [36mModel saved to models\xgboost_model.pkl[0m
[32m08:54:06[0m | [1mINFO    [0m | [36mTrain/Test gap: -1 bars to prevent temporal leakage[0m
[32m08:54:06[0m | [1mINFO    [0m | [36mTraining with 500 samples, testing with 1 samples[0m
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:06] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
  score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0]	train-auc:0.62323	eval-auc:nan
[10]	train-auc:0.73901	eval-auc:nan
[20]	train-auc:0.77618	eval-auc:nan
[30]	train-auc:0.79067	eval-auc:nan
[40]	train-auc:0.79789	eval-auc:nan
[49]	train-auc:0.81138	eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
  warnings.warn(
[32m08:54:06[0m | [1mINFO    [0m | [36mTraining complete: Train AUC=0.8114, Test AUC=nan[0m
[32m08:54:06[0m | [1mINFO    [0m | [36mModel saved to models\xgboost_model.pkl[0m
[32m08:54:06[0m | [1mINFO    [0m | [36mTrain/Test gap: -1 bars to prevent temporal leakage[0m
[32m08:54:06[0m | [1mINFO    [0m | [36mTraining with 500 samples, testing with 1 samples[0m
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:06] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
  score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0]	train-auc:0.62337	eval-auc:nan
[10]	train-auc:0.73867	eval-auc:nan
[20]	train-auc:0.76904	eval-auc:nan
[30]	train-auc:0.78107	eval-auc:nan
[40]	train-auc:0.79925	eval-auc:nan
[49]	train-auc:0.81021	eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
  warnings.warn(
[32m08:54:06[0m | [1mINFO    [0m | [36mTraining complete: Train AUC=0.8102, Test AUC=nan[0m
[32m08:54:06[0m | [1mINFO    [0m | [36mModel saved to models\xgboost_model.pkl[0m
[32m08:54:06[0m | [1mINFO    [0m | [36mTrain/Test gap: -1 bars to prevent temporal leakage[0m
[32m08:54:06[0m | [1mINFO    [0m | [36mTraining with 500 samples, testing with 1 samples[0m
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:06] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
  score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0]	train-auc:0.61166	eval-auc:nan
[10]	train-auc:0.74134	eval-auc:nan
[20]	train-auc:0.78185	eval-auc:nan
[30]	train-auc:0.80042	eval-auc:nan
[40]	train-auc:0.81374	eval-auc:nan
[49]	train-auc:0.82431	eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
  warnings.warn(
[32m08:54:06[0m | [1mINFO    [0m | [36mTraining complete: Train AUC=0.8243, Test AUC=nan[0m
[32m08:54:06[0m | [1mINFO    [0m | [36mModel saved to models\xgboost_model.pkl[0m
[32m08:54:06[0m | [1mINFO    [0m | [36mTrain/Test gap: -1 bars to prevent temporal leakage[0m
[32m08:54:06[0m | [1mINFO    [0m | [36mTraining with 500 samples, testing with 1 samples[0m
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:06] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
  score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0]	train-auc:0.61546	eval-auc:nan
[10]	train-auc:0.77594	eval-auc:nan
[20]	train-auc:0.79332	eval-auc:nan
[30]	train-auc:0.80233	eval-auc:nan
[40]	train-auc:0.80764	eval-auc:nan
[49]	train-auc:0.82130	eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
  warnings.warn(
[32m08:54:06[0m | [1mINFO    [0m | [36mTraining complete: Train AUC=0.8213, Test AUC=nan[0m
[32m08:54:06[0m | [1mINFO    [0m | [36mModel saved to models\xgboost_model.pkl[0m
[32m08:54:06[0m | [1mINFO    [0m | [36mTrain/Test gap: -1 bars to prevent temporal leakage[0m
[32m08:54:06[0m | [1mINFO    [0m | [36mTraining with 500 samples, testing with 1 samples[0m
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:06] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
  score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0]	train-auc:0.65153	eval-auc:nan
[10]	train-auc:0.74867	eval-auc:nan
[20]	train-auc:0.77945	eval-auc:nan
[30]	train-auc:0.79697	eval-auc:nan
[40]	train-auc:0.81748	eval-auc:nan
[49]	train-auc:0.83136	eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
  warnings.warn(
[32m08:54:06[0m | [1mINFO    [0m | [36mTraining complete: Train AUC=0.8314, Test AUC=nan[0m
[32m08:54:06[0m | [1mINFO    [0m | [36mModel saved to models\xgboost_model.pkl[0m
[32m08:54:06[0m | [1mINFO    [0m | [36mTrain/Test gap: -1 bars to prevent temporal leakage[0m
[32m08:54:06[0m | [1mINFO    [0m | [36mTraining with 500 samples, testing with 1 samples[0m
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:06] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
  score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0]	train-auc:0.61052	eval-auc:nan
[10]	train-auc:0.73060	eval-auc:nan
[20]	train-auc:0.76308	eval-auc:nan
[30]	train-auc:0.78958	eval-auc:nan
[40]	train-auc:0.79512	eval-auc:nan
[49]	train-auc:0.81402	eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
  warnings.warn(
[32m08:54:06[0m | [1mINFO    [0m | [36mTraining complete: Train AUC=0.8140, Test AUC=nan[0m
[32m08:54:06[0m | [1mINFO    [0m | [36mModel saved to models\xgboost_model.pkl[0m
[32m08:54:06[0m | [1mINFO    [0m | [36mTrain/Test gap: -1 bars to prevent temporal leakage[0m
[32m08:54:06[0m | [1mINFO    [0m | [36mTraining with 500 samples, testing with 1 samples[0m
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:06] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
  score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0]	train-auc:0.60337	eval-auc:nan
[10]	train-auc:0.73073	eval-auc:nan
[20]	train-auc:0.75881	eval-auc:nan
[30]	train-auc:0.77331	eval-auc:nan
[40]	train-auc:0.79658	eval-auc:nan
[49]	train-auc:0.81885	eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
  warnings.warn(
[32m08:54:06[0m | [1mINFO    [0m | [36mTraining complete: Train AUC=0.8188, Test AUC=nan[0m
[32m08:54:06[0m | [1mINFO    [0m | [36mModel saved to models\xgboost_model.pkl[0m
[32m08:54:06[0m | [1mINFO    [0m | [36mTrain/Test gap: -1 bars to prevent temporal leakage[0m
[32m08:54:06[0m | [1mINFO    [0m | [36mTraining with 500 samples, testing with 1 samples[0m
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:06] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
  score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0]	train-auc:0.59897	eval-auc:nan
[10]	train-auc:0.72551	eval-auc:nan
[20]	train-auc:0.73547	eval-auc:nan
[30]	train-auc:0.77519	eval-auc:nan
[40]	train-auc:0.78258	eval-auc:nan
[49]	train-auc:0.81252	eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
  warnings.warn(
[32m08:54:06[0m | [1mINFO    [0m | [36mTraining complete: Train AUC=0.8125, Test AUC=nan[0m
[32m08:54:06[0m | [1mINFO    [0m | [36mModel saved to models\xgboost_model.pkl[0m
[32m08:54:06[0m | [1mINFO    [0m | [36mTrain/Test gap: -1 bars to prevent temporal leakage[0m
[32m08:54:06[0m | [1mINFO    [0m | [36mTraining with 500 samples, testing with 1 samples[0m
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:06] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
  score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0]	train-auc:0.62284	eval-auc:nan
[10]	train-auc:0.71785	eval-auc:nan
[20]	train-auc:0.75469	eval-auc:nan
[30]	train-auc:0.77356	eval-auc:nan
[40]	train-auc:0.79844	eval-auc:nan
[49]	train-auc:0.81267	eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
  warnings.warn(
[32m08:54:06[0m | [1mINFO    [0m | [36mTraining complete: Train AUC=0.8127, Test AUC=nan[0m
[32m08:54:06[0m | [1mINFO    [0m | [36mModel saved to models\xgboost_model.pkl[0m
[32m08:54:06[0m | [1mINFO    [0m | [36mTrain/Test gap: -1 bars to prevent temporal leakage[0m
[32m08:54:06[0m | [1mINFO    [0m | [36mTraining with 500 samples, testing with 1 samples[0m
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:06] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
  score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0]	train-auc:0.60058	eval-auc:nan
[10]	train-auc:0.73796	eval-auc:nan
[20]	train-auc:0.75724	eval-auc:nan
[30]	train-auc:0.78310	eval-auc:nan
[40]	train-auc:0.79742	eval-auc:nan
[49]	train-auc:0.81018	eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
  warnings.warn(
[32m08:54:06[0m | [1mINFO    [0m | [36mTraining complete: Train AUC=0.8102, Test AUC=nan[0m
[32m08:54:07[0m | [1mINFO    [0m | [36mModel saved to models\xgboost_model.pkl[0m
[32m08:54:07[0m | [1mINFO    [0m | [36mTrain/Test gap: -1 bars to prevent temporal leakage[0m
[32m08:54:07[0m | [1mINFO    [0m | [36mTraining with 500 samples, testing with 1 samples[0m
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:07] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
  score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0]	train-auc:0.59439	eval-auc:nan
[10]	train-auc:0.74271	eval-auc:nan
[20]	train-auc:0.76468	eval-auc:nan
[30]	train-auc:0.77360	eval-auc:nan
[40]	train-auc:0.79995	eval-auc:nan
[49]	train-auc:0.81169	eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
  warnings.warn(
[32m08:54:07[0m | [1mINFO    [0m | [36mTraining complete: Train AUC=0.8117, Test AUC=nan[0m
[32m08:54:07[0m | [1mINFO    [0m | [36mModel saved to models\xgboost_model.pkl[0m
[32m08:54:07[0m | [1mINFO    [0m | [36mTrain/Test gap: -1 bars to prevent temporal leakage[0m
[32m08:54:07[0m | [1mINFO    [0m | [36mTraining with 500 samples, testing with 1 samples[0m
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:07] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
  score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0]	train-auc:0.60259	eval-auc:nan
[10]	train-auc:0.73422	eval-auc:nan
[20]	train-auc:0.74413	eval-auc:nan
[30]	train-auc:0.78035	eval-auc:nan
[40]	train-auc:0.81061	eval-auc:nan
[49]	train-auc:0.82145	eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
  warnings.warn(
[32m08:54:07[0m | [1mINFO    [0m | [36mTraining complete: Train AUC=0.8214, Test AUC=nan[0m
[32m08:54:07[0m | [1mINFO    [0m | [36mModel saved to models\xgboost_model.pkl[0m
[32m08:54:07[0m | [1mINFO    [0m | [36mTrain/Test gap: -1 bars to prevent temporal leakage[0m
[32m08:54:07[0m | [1mINFO    [0m | [36mTraining with 500 samples, testing with 1 samples[0m
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:07] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
  score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0]	train-auc:0.60853	eval-auc:nan
[10]	train-auc:0.71246	eval-auc:nan
[20]	train-auc:0.76171	eval-auc:nan
[30]	train-auc:0.78832	eval-auc:nan
[40]	train-auc:0.79681	eval-auc:nan
[49]	train-auc:0.80923	eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
  warnings.warn(
[32m08:54:07[0m | [1mINFO    [0m | [36mTraining complete: Train AUC=0.8092, Test AUC=nan[0m
[32m08:54:07[0m | [1mINFO    [0m | [36mModel saved to models\xgboost_model.pkl[0m
[32m08:54:07[0m | [1mINFO    [0m | [36mTrain/Test gap: -1 bars to prevent temporal leakage[0m
[32m08:54:07[0m | [1mINFO    [0m | [36mTraining with 500 samples, testing with 1 samples[0m
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:07] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
  score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0]	train-auc:0.63755	eval-auc:nan
[10]	train-auc:0.72781	eval-auc:nan
[20]	train-auc:0.75749	eval-auc:nan
[30]	train-auc:0.78799	eval-auc:nan
[40]	train-auc:0.79490	eval-auc:nan
[49]	train-auc:0.81275	eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
  warnings.warn(
[32m08:54:07[0m | [1mINFO    [0m | [36mTraining complete: Train AUC=0.8127, Test AUC=nan[0m
[32m08:54:07[0m | [1mINFO    [0m | [36mModel saved to models\xgboost_model.pkl[0m
[32m08:54:07[0m | [1mINFO    [0m | [36mTrain/Test gap: -1 bars to prevent temporal leakage[0m
[32m08:54:07[0m | [1mINFO    [0m | [36mTraining with 500 samples, testing with 1 samples[0m
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:07] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
  score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0]	train-auc:0.62377	eval-auc:nan
[10]	train-auc:0.72534	eval-auc:nan
[20]	train-auc:0.76922	eval-auc:nan
[30]	train-auc:0.79245	eval-auc:nan
[40]	train-auc:0.80396	eval-auc:nan
[49]	train-auc:0.81762	eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
  warnings.warn(
[32m08:54:07[0m | [1mINFO    [0m | [36mTraining complete: Train AUC=0.8176, Test AUC=nan[0m
[32m08:54:07[0m | [1mINFO    [0m | [36mModel saved to models\xgboost_model.pkl[0m
[32m08:54:07[0m | [1mINFO    [0m | [36mTrain/Test gap: -1 bars to prevent temporal leakage[0m
[32m08:54:07[0m | [1mINFO    [0m | [36mTraining with 500 samples, testing with 1 samples[0m
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:07] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
  score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0]	train-auc:0.59579	eval-auc:nan
[10]	train-auc:0.72531	eval-auc:nan
[20]	train-auc:0.76042	eval-auc:nan
[30]	train-auc:0.77494	eval-auc:nan
[40]	train-auc:0.78665	eval-auc:nan
[49]	train-auc:0.80008	eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
  warnings.warn(
[32m08:54:07[0m | [1mINFO    [0m | [36mTraining complete: Train AUC=0.8001, Test AUC=nan[0m
[32m08:54:07[0m | [1mINFO    [0m | [36mModel saved to models\xgboost_model.pkl[0m
[32m08:54:07[0m | [1mINFO    [0m | [36mTrain/Test gap: -1 bars to prevent temporal leakage[0m
[32m08:54:07[0m | [1mINFO    [0m | [36mTraining with 500 samples, testing with 1 samples[0m
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:07] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
  score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0]	train-auc:0.62551	eval-auc:nan
[10]	train-auc:0.72821	eval-auc:nan
[20]	train-auc:0.75326	eval-auc:nan
[30]	train-auc:0.78709	eval-auc:nan
[40]	train-auc:0.80578	eval-auc:nan
[49]	train-auc:0.81529	eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
  warnings.warn(
[32m08:54:07[0m | [1mINFO    [0m | [36mTraining complete: Train AUC=0.8153, Test AUC=nan[0m
[32m08:54:07[0m | [1mINFO    [0m | [36mModel saved to models\xgboost_model.pkl[0m
[32m08:54:07[0m | [1mINFO    [0m | [36mTrain/Test gap: -1 bars to prevent temporal leakage[0m
[32m08:54:07[0m | [1mINFO    [0m | [36mTraining with 500 samples, testing with 1 samples[0m
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:07] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
  score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0]	train-auc:0.59962	eval-auc:nan
[10]	train-auc:0.73624	eval-auc:nan
[20]	train-auc:0.77314	eval-auc:nan
[30]	train-auc:0.79508	eval-auc:nan
[40]	train-auc:0.80896	eval-auc:nan
[49]	train-auc:0.81913	eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
  warnings.warn(
[32m08:54:07[0m | [1mINFO    [0m | [36mTraining complete: Train AUC=0.8191, Test AUC=nan[0m
[32m08:54:07[0m | [1mINFO    [0m | [36mModel saved to models\xgboost_model.pkl[0m
[32m08:54:07[0m | [1mINFO    [0m | [36mTrain/Test gap: -1 bars to prevent temporal leakage[0m
[32m08:54:07[0m | [1mINFO    [0m | [36mTraining with 500 samples, testing with 1 samples[0m
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:07] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
  score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0]	train-auc:0.61935	eval-auc:nan
[10]	train-auc:0.74558	eval-auc:nan
[20]	train-auc:0.76006	eval-auc:nan
[30]	train-auc:0.77639	eval-auc:nan
[40]	train-auc:0.79222	eval-auc:nan
[49]	train-auc:0.80714	eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
  warnings.warn(
[32m08:54:07[0m | [1mINFO    [0m | [36mTraining complete: Train AUC=0.8071, Test AUC=nan[0m
[32m08:54:07[0m | [1mINFO    [0m | [36mModel saved to models\xgboost_model.pkl[0m
[32m08:54:07[0m | [1mINFO    [0m | [36mTrain/Test gap: -1 bars to prevent temporal leakage[0m
[32m08:54:07[0m | [1mINFO    [0m | [36mTraining with 500 samples, testing with 1 samples[0m
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:07] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
  score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0]	train-auc:0.58289	eval-auc:nan
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:08] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
  score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[10]	train-auc:0.73097	eval-auc:nan
[20]	train-auc:0.76715	eval-auc:nan
[30]	train-auc:0.78989	eval-auc:nan
[40]	train-auc:0.79088	eval-auc:nan
[49]	train-auc:0.80586	eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
  warnings.warn(
[32m08:54:08[0m | [1mINFO    [0m | [36mTraining complete: Train AUC=0.8059, Test AUC=nan[0m
[32m08:54:08[0m | [1mINFO    [0m | [36mModel saved to models\xgboost_model.pkl[0m
[32m08:54:08[0m | [1mINFO    [0m | [36mTrain/Test gap: -1 bars to prevent temporal leakage[0m
[32m08:54:08[0m | [1mINFO    [0m | [36mTraining with 500 samples, testing with 1 samples[0m
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:08] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
  score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0]	train-auc:0.60159	eval-auc:nan
[10]	train-auc:0.72245	eval-auc:nan
[20]	train-auc:0.75890	eval-auc:nan
[30]	train-auc:0.79219	eval-auc:nan
[40]	train-auc:0.80743	eval-auc:nan
[49]	train-auc:0.81309	eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
  warnings.warn(
[32m08:54:08[0m | [1mINFO    [0m | [36mTraining complete: Train AUC=0.8131, Test AUC=nan[0m
[32m08:54:08[0m | [1mINFO    [0m | [36mModel saved to models\xgboost_model.pkl[0m
[32m08:54:08[0m | [1mINFO    [0m | [36mTrain/Test gap: -1 bars to prevent temporal leakage[0m
[32m08:54:08[0m | [1mINFO    [0m | [36mTraining with 500 samples, testing with 1 samples[0m
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:08] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
  score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0]	train-auc:0.60832	eval-auc:nan
[10]	train-auc:0.72768	eval-auc:nan
[20]	train-auc:0.77328	eval-auc:nan
[30]	train-auc:0.78311	eval-auc:nan
[40]	train-auc:0.79987	eval-auc:nan
[49]	train-auc:0.82509	eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
  warnings.warn(
[32m08:54:08[0m | [1mINFO    [0m | [36mTraining complete: Train AUC=0.8251, Test AUC=nan[0m
[32m08:54:08[0m | [1mINFO    [0m | [36mModel saved to models\xgboost_model.pkl[0m
[32m08:54:08[0m | [1mINFO    [0m | [36mTrain/Test gap: -1 bars to prevent temporal leakage[0m
[32m08:54:08[0m | [1mINFO    [0m | [36mTraining with 500 samples, testing with 1 samples[0m
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:08] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
  score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0]	train-auc:0.58448	eval-auc:nan
[10]	train-auc:0.71605	eval-auc:nan
[20]	train-auc:0.74696	eval-auc:nan
[30]	train-auc:0.78124	eval-auc:nan
[40]	train-auc:0.80090	eval-auc:nan
[49]	train-auc:0.81696	eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
  warnings.warn(
[32m08:54:08[0m | [1mINFO    [0m | [36mTraining complete: Train AUC=0.8170, Test AUC=nan[0m
[32m08:54:08[0m | [1mINFO    [0m | [36mModel saved to models\xgboost_model.pkl[0m
[32m08:54:08[0m | [1mINFO    [0m | [36mTrain/Test gap: -1 bars to prevent temporal leakage[0m
[32m08:54:08[0m | [1mINFO    [0m | [36mTraining with 500 samples, testing with 1 samples[0m
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:08] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
  score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0]	train-auc:0.59727	eval-auc:nan
[10]	train-auc:0.75823	eval-auc:nan
[20]	train-auc:0.77682	eval-auc:nan
[30]	train-auc:0.79460	eval-auc:nan
[40]	train-auc:0.80149	eval-auc:nan
[49]	train-auc:0.81574	eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
  warnings.warn(
[32m08:54:08[0m | [1mINFO    [0m | [36mTraining complete: Train AUC=0.8157, Test AUC=nan[0m
[32m08:54:08[0m | [1mINFO    [0m | [36mModel saved to models\xgboost_model.pkl[0m
[32m08:54:08[0m | [1mINFO    [0m | [36mTrain/Test gap: -1 bars to prevent temporal leakage[0m
[32m08:54:08[0m | [1mINFO    [0m | [36mTraining with 500 samples, testing with 1 samples[0m
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:08] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
  score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0]	train-auc:0.58337	eval-auc:nan
[10]	train-auc:0.70992	eval-auc:nan
[20]	train-auc:0.76951	eval-auc:nan
[30]	train-auc:0.78605	eval-auc:nan
[40]	train-auc:0.78623	eval-auc:nan
[49]	train-auc:0.78727	eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
  warnings.warn(
[32m08:54:08[0m | [1mINFO    [0m | [36mTraining complete: Train AUC=0.7873, Test AUC=nan[0m
[32m08:54:08[0m | [1mINFO    [0m | [36mModel saved to models\xgboost_model.pkl[0m
[32m08:54:08[0m | [1mINFO    [0m | [36mTrain/Test gap: -1 bars to prevent temporal leakage[0m
[32m08:54:08[0m | [1mINFO    [0m | [36mTraining with 500 samples, testing with 1 samples[0m
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:08] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
  score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0]	train-auc:0.61816	eval-auc:nan
[10]	train-auc:0.69998	eval-auc:nan
[20]	train-auc:0.73782	eval-auc:nan
[30]	train-auc:0.75099	eval-auc:nan
[40]	train-auc:0.76887	eval-auc:nan
[49]	train-auc:0.78246	eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
  warnings.warn(
[32m08:54:08[0m | [1mINFO    [0m | [36mTraining complete: Train AUC=0.7825, Test AUC=nan[0m
[32m08:54:08[0m | [1mINFO    [0m | [36mModel saved to models\xgboost_model.pkl[0m
[32m08:54:08[0m | [1mINFO    [0m | [36mTrain/Test gap: -1 bars to prevent temporal leakage[0m
[32m08:54:08[0m | [1mINFO    [0m | [36mTraining with 500 samples, testing with 1 samples[0m
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:08] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
  score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0]	train-auc:0.59449	eval-auc:nan
[10]	train-auc:0.70412	eval-auc:nan
[20]	train-auc:0.75158	eval-auc:nan
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:09] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
  score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[30]	train-auc:0.77511	eval-auc:nan
[40]	train-auc:0.78799	eval-auc:nan
[49]	train-auc:0.80667	eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
  warnings.warn(
[32m08:54:09[0m | [1mINFO    [0m | [36mTraining complete: Train AUC=0.8067, Test AUC=nan[0m
[32m08:54:09[0m | [1mINFO    [0m | [36mModel saved to models\xgboost_model.pkl[0m
[32m08:54:09[0m | [1mINFO    [0m | [36mTrain/Test gap: -1 bars to prevent temporal leakage[0m
[32m08:54:09[0m | [1mINFO    [0m | [36mTraining with 500 samples, testing with 1 samples[0m
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:09] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
  score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0]	train-auc:0.60417	eval-auc:nan
[10]	train-auc:0.69915	eval-auc:nan
[20]	train-auc:0.72602	eval-auc:nan
[30]	train-auc:0.75190	eval-auc:nan
[40]	train-auc:0.77129	eval-auc:nan
[49]	train-auc:0.78761	eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
  warnings.warn(
[32m08:54:09[0m | [1mINFO    [0m | [36mTraining complete: Train AUC=0.7876, Test AUC=nan[0m
[32m08:54:09[0m | [1mINFO    [0m | [36mModel saved to models\xgboost_model.pkl[0m
[32m08:54:09[0m | [1mINFO    [0m | [36mTrain/Test gap: -1 bars to prevent temporal leakage[0m
[32m08:54:09[0m | [1mINFO    [0m | [36mTraining with 500 samples, testing with 1 samples[0m
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:09] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
  score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0]	train-auc:0.57535	eval-auc:nan
[10]	train-auc:0.69222	eval-auc:nan
[20]	train-auc:0.74264	eval-auc:nan
[30]	train-auc:0.77400	eval-auc:nan
[40]	train-auc:0.77895	eval-auc:nan
[49]	train-auc:0.79121	eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
  warnings.warn(
[32m08:54:09[0m | [1mINFO    [0m | [36mTraining complete: Train AUC=0.7912, Test AUC=nan[0m
[32m08:54:09[0m | [1mINFO    [0m | [36mModel saved to models\xgboost_model.pkl[0m
[32m08:54:09[0m | [1mINFO    [0m | [36mTrain/Test gap: -1 bars to prevent temporal leakage[0m
[32m08:54:09[0m | [1mINFO    [0m | [36mTraining with 500 samples, testing with 1 samples[0m
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:09] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
  score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0]	train-auc:0.56031	eval-auc:nan
[10]	train-auc:0.73599	eval-auc:nan
[20]	train-auc:0.77037	eval-auc:nan
[30]	train-auc:0.78436	eval-auc:nan
[40]	train-auc:0.79855	eval-auc:nan
[49]	train-auc:0.80940	eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
  warnings.warn(
[32m08:54:09[0m | [1mINFO    [0m | [36mTraining complete: Train AUC=0.8094, Test AUC=nan[0m
[32m08:54:09[0m | [1mINFO    [0m | [36mModel saved to models\xgboost_model.pkl[0m
[32m08:54:09[0m | [1mINFO    [0m | [36mTrain/Test gap: -1 bars to prevent temporal leakage[0m
[32m08:54:09[0m | [1mINFO    [0m | [36mTraining with 500 samples, testing with 1 samples[0m
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:09] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
  score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0]	train-auc:0.57237	eval-auc:nan
[10]	train-auc:0.70780	eval-auc:nan
[20]	train-auc:0.73909	eval-auc:nan
[30]	train-auc:0.76519	eval-auc:nan
[40]	train-auc:0.78519	eval-auc:nan
[49]	train-auc:0.80320	eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
  warnings.warn(
[32m08:54:09[0m | [1mINFO    [0m | [36mTraining complete: Train AUC=0.8032, Test AUC=nan[0m
[32m08:54:09[0m | [1mINFO    [0m | [36mModel saved to models\xgboost_model.pkl[0m
[32m08:54:09[0m | [1mINFO    [0m | [36mTrain/Test gap: -1 bars to prevent temporal leakage[0m
[32m08:54:09[0m | [1mINFO    [0m | [36mTraining with 500 samples, testing with 1 samples[0m
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:09] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
  score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0]	train-auc:0.58321	eval-auc:nan
[10]	train-auc:0.71285	eval-auc:nan
[20]	train-auc:0.74984	eval-auc:nan
[30]	train-auc:0.76907	eval-auc:nan
[40]	train-auc:0.77337	eval-auc:nan
[49]	train-auc:0.79265	eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
  warnings.warn(
[32m08:54:09[0m | [1mINFO    [0m | [36mTraining complete: Train AUC=0.7926, Test AUC=nan[0m
[32m08:54:09[0m | [1mINFO    [0m | [36mModel saved to models\xgboost_model.pkl[0m
[32m08:54:09[0m | [1mINFO    [0m | [36mTrain/Test gap: -1 bars to prevent temporal leakage[0m
[32m08:54:09[0m | [1mINFO    [0m | [36mTraining with 500 samples, testing with 1 samples[0m
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:09] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
  score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0]	train-auc:0.60158	eval-auc:nan
[10]	train-auc:0.71037	eval-auc:nan
[20]	train-auc:0.73646	eval-auc:nan
[30]	train-auc:0.76553	eval-auc:nan
[40]	train-auc:0.77481	eval-auc:nan
[49]	train-auc:0.78843	eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
  warnings.warn(
[32m08:54:09[0m | [1mINFO    [0m | [36mTraining complete: Train AUC=0.7884, Test AUC=nan[0m
[32m08:54:09[0m | [1mINFO    [0m | [36mModel saved to models\xgboost_model.pkl[0m
[32m08:54:09[0m | [1mINFO    [0m | [36mTrain/Test gap: -1 bars to prevent temporal leakage[0m
[32m08:54:09[0m | [1mINFO    [0m | [36mTraining with 500 samples, testing with 1 samples[0m
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:09] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
  score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0]	train-auc:0.62264	eval-auc:nan
[10]	train-auc:0.70782	eval-auc:nan
[20]	train-auc:0.72733	eval-auc:nan
[30]	train-auc:0.75854	eval-auc:nan
[40]	train-auc:0.77389	eval-auc:nan
[49]	train-auc:0.78593	eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
  warnings.warn(
[32m08:54:09[0m | [1mINFO    [0m | [36mTraining complete: Train AUC=0.7859, Test AUC=nan[0m
[32m08:54:09[0m | [1mINFO    [0m | [36mModel saved to models\xgboost_model.pkl[0m
[32m08:54:09[0m | [1mINFO    [0m | [36mTrain/Test gap: -1 bars to prevent temporal leakage[0m
[32m08:54:09[0m | [1mINFO    [0m | [36mTraining with 500 samples, testing with 1 samples[0m
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:09] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
  score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0]	train-auc:0.61976	eval-auc:nan
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:10] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
  score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[10]	train-auc:0.70945	eval-auc:nan
[20]	train-auc:0.73834	eval-auc:nan
[30]	train-auc:0.77960	eval-auc:nan
[40]	train-auc:0.79266	eval-auc:nan
[49]	train-auc:0.79501	eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
  warnings.warn(
[32m08:54:10[0m | [1mINFO    [0m | [36mTraining complete: Train AUC=0.7950, Test AUC=nan[0m
[32m08:54:10[0m | [1mINFO    [0m | [36mModel saved to models\xgboost_model.pkl[0m
[32m08:54:10[0m | [1mINFO    [0m | [36mTrain/Test gap: -1 bars to prevent temporal leakage[0m
[32m08:54:10[0m | [1mINFO    [0m | [36mTraining with 500 samples, testing with 1 samples[0m
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:10] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
  score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0]	train-auc:0.59252	eval-auc:nan
[10]	train-auc:0.70589	eval-auc:nan
[20]	train-auc:0.74507	eval-auc:nan
[30]	train-auc:0.76887	eval-auc:nan
[40]	train-auc:0.78931	eval-auc:nan
[49]	train-auc:0.80074	eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
  warnings.warn(
[32m08:54:10[0m | [1mINFO    [0m | [36mTraining complete: Train AUC=0.8007, Test AUC=nan[0m
[32m08:54:10[0m | [1mINFO    [0m | [36mModel saved to models\xgboost_model.pkl[0m
[32m08:54:10[0m | [1mINFO    [0m | [36mTrain/Test gap: -1 bars to prevent temporal leakage[0m
[32m08:54:10[0m | [1mINFO    [0m | [36mTraining with 500 samples, testing with 1 samples[0m
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:10] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
  score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0]	train-auc:0.61071	eval-auc:nan
[10]	train-auc:0.75049	eval-auc:nan
[20]	train-auc:0.78163	eval-auc:nan
[30]	train-auc:0.80391	eval-auc:nan
[40]	train-auc:0.81564	eval-auc:nan
[49]	train-auc:0.82443	eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
  warnings.warn(
[32m08:54:10[0m | [1mINFO    [0m | [36mTraining complete: Train AUC=0.8244, Test AUC=nan[0m
[32m08:54:10[0m | [1mINFO    [0m | [36mModel saved to models\xgboost_model.pkl[0m
[32m08:54:10[0m | [1mINFO    [0m | [36mTrain/Test gap: -1 bars to prevent temporal leakage[0m
[32m08:54:10[0m | [1mINFO    [0m | [36mTraining with 500 samples, testing with 1 samples[0m
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:10] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
  score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0]	train-auc:0.59821	eval-auc:nan
[10]	train-auc:0.74084	eval-auc:nan
[20]	train-auc:0.78808	eval-auc:nan
[30]	train-auc:0.80806	eval-auc:nan
[40]	train-auc:0.82073	eval-auc:nan
[49]	train-auc:0.83896	eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
  warnings.warn(
[32m08:54:10[0m | [1mINFO    [0m | [36mTraining complete: Train AUC=0.8390, Test AUC=nan[0m
[32m08:54:10[0m | [1mINFO    [0m | [36mModel saved to models\xgboost_model.pkl[0m
[32m08:54:10[0m | [1mINFO    [0m | [36mTrain/Test gap: -1 bars to prevent temporal leakage[0m
[32m08:54:10[0m | [1mINFO    [0m | [36mTraining with 500 samples, testing with 1 samples[0m
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:10] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
  score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0]	train-auc:0.57247	eval-auc:nan
[10]	train-auc:0.73165	eval-auc:nan
[20]	train-auc:0.77031	eval-auc:nan
[30]	train-auc:0.78381	eval-auc:nan
[40]	train-auc:0.79723	eval-auc:nan
[49]	train-auc:0.81334	eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
  warnings.warn(
[32m08:54:10[0m | [1mINFO    [0m | [36mTraining complete: Train AUC=0.8133, Test AUC=nan[0m
[32m08:54:10[0m | [1mINFO    [0m | [36mModel saved to models\xgboost_model.pkl[0m
[32m08:54:10[0m | [1mINFO    [0m | [36mTrain/Test gap: -1 bars to prevent temporal leakage[0m
[32m08:54:10[0m | [1mINFO    [0m | [36mTraining with 500 samples, testing with 1 samples[0m
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:10] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
  score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0]	train-auc:0.61086	eval-auc:nan
[10]	train-auc:0.77570	eval-auc:nan
[20]	train-auc:0.78849	eval-auc:nan
[30]	train-auc:0.80995	eval-auc:nan
[40]	train-auc:0.83325	eval-auc:nan
[49]	train-auc:0.83580	eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
  warnings.warn(
[32m08:54:10[0m | [1mINFO    [0m | [36mTraining complete: Train AUC=0.8358, Test AUC=nan[0m
[32m08:54:10[0m | [1mINFO    [0m | [36mModel saved to models\xgboost_model.pkl[0m
[32m08:54:10[0m | [1mINFO    [0m | [36mTrain/Test gap: -1 bars to prevent temporal leakage[0m
[32m08:54:10[0m | [1mINFO    [0m | [36mTraining with 500 samples, testing with 1 samples[0m
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:10] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
  score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0]	train-auc:0.57315	eval-auc:nan
[10]	train-auc:0.69806	eval-auc:nan
[20]	train-auc:0.75288	eval-auc:nan
[30]	train-auc:0.76632	eval-auc:nan
[40]	train-auc:0.78813	eval-auc:nan
[49]	train-auc:0.79826	eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
  warnings.warn(
[32m08:54:10[0m | [1mINFO    [0m | [36mTraining complete: Train AUC=0.7983, Test AUC=nan[0m
[32m08:54:10[0m | [1mINFO    [0m | [36mModel saved to models\xgboost_model.pkl[0m
[32m08:54:10[0m | [1mINFO    [0m | [36mTrain/Test gap: -1 bars to prevent temporal leakage[0m
[32m08:54:10[0m | [1mINFO    [0m | [36mTraining with 500 samples, testing with 1 samples[0m
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:10] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
  score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0]	train-auc:0.59736	eval-auc:nan
[10]	train-auc:0.71316	eval-auc:nan
[20]	train-auc:0.73498	eval-auc:nan
[30]	train-auc:0.74412	eval-auc:nan
[40]	train-auc:0.76335	eval-auc:nan
[49]	train-auc:0.77278	eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
  warnings.warn(
[32m08:54:10[0m | [1mINFO    [0m | [36mTraining complete: Train AUC=0.7728, Test AUC=nan[0m
[32m08:54:10[0m | [1mINFO    [0m | [36mModel saved to models\xgboost_model.pkl[0m
[32m08:54:10[0m | [1mINFO    [0m | [36mTrain/Test gap: -1 bars to prevent temporal leakage[0m
[32m08:54:10[0m | [1mINFO    [0m | [36mTraining with 500 samples, testing with 1 samples[0m
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:10] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
  score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0]	train-auc:0.63025	eval-auc:nan
[10]	train-auc:0.73205	eval-auc:nan
[20]	train-auc:0.76153	eval-auc:nan
[30]	train-auc:0.78938	eval-auc:nan
[40]	train-auc:0.79481	eval-auc:nan
[49]	train-auc:0.80845	eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
  warnings.warn(
[32m08:54:11[0m | [1mINFO    [0m | [36mTraining complete: Train AUC=0.8084, Test AUC=nan[0m
[32m08:54:11[0m | [1mINFO    [0m | [36mModel saved to models\xgboost_model.pkl[0m
[32m08:54:11[0m | [1mINFO    [0m | [36mTrain/Test gap: -1 bars to prevent temporal leakage[0m
[32m08:54:11[0m | [1mINFO    [0m | [36mTraining with 500 samples, testing with 1 samples[0m
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:11] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
  score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0]	train-auc:0.58652	eval-auc:nan
[10]	train-auc:0.71165	eval-auc:nan
[20]	train-auc:0.76374	eval-auc:nan
[30]	train-auc:0.76661	eval-auc:nan
[40]	train-auc:0.78437	eval-auc:nan
[49]	train-auc:0.79387	eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
  warnings.warn(
[32m08:54:11[0m | [1mINFO    [0m | [36mTraining complete: Train AUC=0.7939, Test AUC=nan[0m
[32m08:54:11[0m | [1mINFO    [0m | [36mModel saved to models\xgboost_model.pkl[0m
[32m08:54:11[0m | [1mINFO    [0m | [36mTrain/Test gap: -1 bars to prevent temporal leakage[0m
[32m08:54:11[0m | [1mINFO    [0m | [36mTraining with 500 samples, testing with 1 samples[0m
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:11] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
  score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0]	train-auc:0.57896	eval-auc:nan
[10]	train-auc:0.73647	eval-auc:nan
[20]	train-auc:0.75505	eval-auc:nan
[30]	train-auc:0.77726	eval-auc:nan
[40]	train-auc:0.79303	eval-auc:nan
[49]	train-auc:0.80600	eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
  warnings.warn(
[32m08:54:11[0m | [1mINFO    [0m | [36mTraining complete: Train AUC=0.8060, Test AUC=nan[0m
[32m08:54:11[0m | [1mINFO    [0m | [36mModel saved to models\xgboost_model.pkl[0m
[32m08:54:11[0m | [1mINFO    [0m | [36mTrain/Test gap: -1 bars to prevent temporal leakage[0m
[32m08:54:11[0m | [1mINFO    [0m | [36mTraining with 500 samples, testing with 1 samples[0m
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:11] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
  score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0]	train-auc:0.62418	eval-auc:nan
[10]	train-auc:0.73731	eval-auc:nan
[20]	train-auc:0.75429	eval-auc:nan
[30]	train-auc:0.78274	eval-auc:nan
[40]	train-auc:0.78498	eval-auc:nan
[49]	train-auc:0.79265	eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
  warnings.warn(
[32m08:54:11[0m | [1mINFO    [0m | [36mTraining complete: Train AUC=0.7926, Test AUC=nan[0m
[32m08:54:11[0m | [1mINFO    [0m | [36mModel saved to models\xgboost_model.pkl[0m
[32m08:54:11[0m | [1mINFO    [0m | [36mTrain/Test gap: -1 bars to prevent temporal leakage[0m
[32m08:54:11[0m | [1mINFO    [0m | [36mTraining with 500 samples, testing with 1 samples[0m
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:11] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
  score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0]	train-auc:0.61034	eval-auc:nan
[10]	train-auc:0.72273	eval-auc:nan
[20]	train-auc:0.75488	eval-auc:nan
[30]	train-auc:0.76962	eval-auc:nan
[40]	train-auc:0.78570	eval-auc:nan
[49]	train-auc:0.79578	eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
  warnings.warn(
[32m08:54:11[0m | [1mINFO    [0m | [36mTraining complete: Train AUC=0.7958, Test AUC=nan[0m
[32m08:54:11[0m | [1mINFO    [0m | [36mModel saved to models\xgboost_model.pkl[0m
[32m08:54:11[0m | [1mINFO    [0m | [36mTrain/Test gap: -1 bars to prevent temporal leakage[0m
[32m08:54:11[0m | [1mINFO    [0m | [36mTraining with 500 samples, testing with 1 samples[0m
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:11] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
  score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0]	train-auc:0.60772	eval-auc:nan
[10]	train-auc:0.72183	eval-auc:nan
[20]	train-auc:0.74782	eval-auc:nan
[30]	train-auc:0.76105	eval-auc:nan
[40]	train-auc:0.77534	eval-auc:nan
[49]	train-auc:0.78704	eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
  warnings.warn(
[32m08:54:11[0m | [1mINFO    [0m | [36mTraining complete: Train AUC=0.7870, Test AUC=nan[0m
[32m08:54:11[0m | [1mINFO    [0m | [36mModel saved to models\xgboost_model.pkl[0m
[32m08:54:11[0m | [1mINFO    [0m | [36mTrain/Test gap: -1 bars to prevent temporal leakage[0m
[32m08:54:11[0m | [1mINFO    [0m | [36mTraining with 500 samples, testing with 1 samples[0m
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:11] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
  score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0]	train-auc:0.62688	eval-auc:nan
[10]	train-auc:0.74607	eval-auc:nan
[20]	train-auc:0.76822	eval-auc:nan
[30]	train-auc:0.77687	eval-auc:nan
[40]	train-auc:0.79932	eval-auc:nan
[49]	train-auc:0.80608	eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
  warnings.warn(
[32m08:54:11[0m | [1mINFO    [0m | [36mTraining complete: Train AUC=0.8061, Test AUC=nan[0m
[32m08:54:11[0m | [1mINFO    [0m | [36mModel saved to models\xgboost_model.pkl[0m
[32m08:54:11[0m | [1mINFO    [0m | [36mTrain/Test gap: -1 bars to prevent temporal leakage[0m
[32m08:54:11[0m | [1mINFO    [0m | [36mTraining with 500 samples, testing with 1 samples[0m
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:11] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
  score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0]	train-auc:0.61732	eval-auc:nan
[10]	train-auc:0.74268	eval-auc:nan
[20]	train-auc:0.77208	eval-auc:nan
[30]	train-auc:0.77479	eval-auc:nan
[40]	train-auc:0.78637	eval-auc:nan
[49]	train-auc:0.79475	eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
  warnings.warn(
[32m08:54:11[0m | [1mINFO    [0m | [36mTraining complete: Train AUC=0.7947, Test AUC=nan[0m
[32m08:54:11[0m | [1mINFO    [0m | [36mModel saved to models\xgboost_model.pkl[0m
[32m08:54:11[0m | [1mINFO    [0m | [36mTrain/Test gap: -1 bars to prevent temporal leakage[0m
[32m08:54:11[0m | [1mINFO    [0m | [36mTraining with 500 samples, testing with 1 samples[0m
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:11] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
  score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0]	train-auc:0.61071	eval-auc:nan
[10]	train-auc:0.74389	eval-auc:nan
[20]	train-auc:0.75840	eval-auc:nan
[30]	train-auc:0.77398	eval-auc:nan
[40]	train-auc:0.79303	eval-auc:nan
[49]	train-auc:0.80196	eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
  warnings.warn(
[32m08:54:11[0m | [1mINFO    [0m | [36mTraining complete: Train AUC=0.8020, Test AUC=nan[0m
[32m08:54:11[0m | [1mINFO    [0m | [36mModel saved to models\xgboost_model.pkl[0m
[32m08:54:11[0m | [1mINFO    [0m | [36mTrain/Test gap: -1 bars to prevent temporal leakage[0m
[32m08:54:11[0m | [1mINFO    [0m | [36mTraining with 500 samples, testing with 1 samples[0m
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:11] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
  score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0]	train-auc:0.58110	eval-auc:nan
[10]	train-auc:0.74083	eval-auc:nan
[20]	train-auc:0.75627	eval-auc:nan
[30]	train-auc:0.76996	eval-auc:nan
[40]	train-auc:0.77884	eval-auc:nan
[49]	train-auc:0.79720	eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
  warnings.warn(
[32m08:54:12[0m | [1mINFO    [0m | [36mTraining complete: Train AUC=0.7972, Test AUC=nan[0m
[32m08:54:12[0m | [1mINFO    [0m | [36mModel saved to models\xgboost_model.pkl[0m
[32m08:54:12[0m | [1mINFO    [0m | [36mTrain/Test gap: -1 bars to prevent temporal leakage[0m
[32m08:54:12[0m | [1mINFO    [0m | [36mTraining with 500 samples, testing with 1 samples[0m
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:12] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
  score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0]	train-auc:0.60591	eval-auc:nan
[10]	train-auc:0.73640	eval-auc:nan
[20]	train-auc:0.75642	eval-auc:nan
[30]	train-auc:0.77684	eval-auc:nan
[40]	train-auc:0.78954	eval-auc:nan
[49]	train-auc:0.80910	eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
  warnings.warn(
[32m08:54:12[0m | [1mINFO    [0m | [36mTraining complete: Train AUC=0.8091, Test AUC=nan[0m
[32m08:54:12[0m | [1mINFO    [0m | [36mModel saved to models\xgboost_model.pkl[0m
[32m08:54:12[0m | [1mINFO    [0m | [36mTrain/Test gap: -1 bars to prevent temporal leakage[0m
[32m08:54:12[0m | [1mINFO    [0m | [36mTraining with 500 samples, testing with 1 samples[0m
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:12] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
  score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0]	train-auc:0.58958	eval-auc:nan
[10]	train-auc:0.72778	eval-auc:nan
[20]	train-auc:0.77165	eval-auc:nan
[30]	train-auc:0.78009	eval-auc:nan
[40]	train-auc:0.79898	eval-auc:nan
[49]	train-auc:0.81312	eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
  warnings.warn(
[32m08:54:12[0m | [1mINFO    [0m | [36mTraining complete: Train AUC=0.8131, Test AUC=nan[0m
[32m08:54:12[0m | [1mINFO    [0m | [36mModel saved to models\xgboost_model.pkl[0m
[32m08:54:12[0m | [1mINFO    [0m | [36mTrain/Test gap: -1 bars to prevent temporal leakage[0m
[32m08:54:12[0m | [1mINFO    [0m | [36mTraining with 500 samples, testing with 1 samples[0m
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:12] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
  score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0]	train-auc:0.58496	eval-auc:nan
[10]	train-auc:0.73422	eval-auc:nan
[20]	train-auc:0.77071	eval-auc:nan
[30]	train-auc:0.80120	eval-auc:nan
[40]	train-auc:0.81193	eval-auc:nan
[49]	train-auc:0.81838	eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
  warnings.warn(
[32m08:54:12[0m | [1mINFO    [0m | [36mTraining complete: Train AUC=0.8184, Test AUC=nan[0m
[32m08:54:12[0m | [1mINFO    [0m | [36mModel saved to models\xgboost_model.pkl[0m
[32m08:54:12[0m | [1mINFO    [0m | [36mTrain/Test gap: -1 bars to prevent temporal leakage[0m
[32m08:54:12[0m | [1mINFO    [0m | [36mTraining with 500 samples, testing with 1 samples[0m
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:12] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
  score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0]	train-auc:0.60131	eval-auc:nan
[10]	train-auc:0.68937	eval-auc:nan
[20]	train-auc:0.73059	eval-auc:nan
[30]	train-auc:0.75097	eval-auc:nan
[40]	train-auc:0.78027	eval-auc:nan
[49]	train-auc:0.79968	eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
  warnings.warn(
[32m08:54:12[0m | [1mINFO    [0m | [36mTraining complete: Train AUC=0.7997, Test AUC=nan[0m
[32m08:54:12[0m | [1mINFO    [0m | [36mModel saved to models\xgboost_model.pkl[0m
[32m08:54:12[0m | [1mINFO    [0m | [36mTrain/Test gap: -1 bars to prevent temporal leakage[0m
[32m08:54:12[0m | [1mINFO    [0m | [36mTraining with 500 samples, testing with 1 samples[0m
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:12] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
  score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0]	train-auc:0.59077	eval-auc:nan
[10]	train-auc:0.71064	eval-auc:nan
[20]	train-auc:0.73499	eval-auc:nan
[30]	train-auc:0.74932	eval-auc:nan
[40]	train-auc:0.77136	eval-auc:nan
[49]	train-auc:0.79431	eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
  warnings.warn(
[32m08:54:12[0m | [1mINFO    [0m | [36mTraining complete: Train AUC=0.7943, Test AUC=nan[0m
[32m08:54:12[0m | [1mINFO    [0m | [36mModel saved to models\xgboost_model.pkl[0m
[32m08:54:12[0m | [1mINFO    [0m | [36mTrain/Test gap: -1 bars to prevent temporal leakage[0m
[32m08:54:12[0m | [1mINFO    [0m | [36mTraining with 500 samples, testing with 1 samples[0m
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:12] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
  score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0]	train-auc:0.58906	eval-auc:nan
[10]	train-auc:0.69691	eval-auc:nan
[20]	train-auc:0.74698	eval-auc:nan
[30]	train-auc:0.76311	eval-auc:nan
[40]	train-auc:0.77968	eval-auc:nan
[49]	train-auc:0.79154	eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
  warnings.warn(
[32m08:54:12[0m | [1mINFO    [0m | [36mTraining complete: Train AUC=0.7915, Test AUC=nan[0m
[32m08:54:12[0m | [1mINFO    [0m | [36mModel saved to models\xgboost_model.pkl[0m
[32m08:54:12[0m | [1mINFO    [0m | [36mTrain/Test gap: -1 bars to prevent temporal leakage[0m
[32m08:54:12[0m | [1mINFO    [0m | [36mTraining with 500 samples, testing with 1 samples[0m
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:12] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
  score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0]	train-auc:0.61274	eval-auc:nan
[10]	train-auc:0.71801	eval-auc:nan
[20]	train-auc:0.75490	eval-auc:nan
[30]	train-auc:0.75974	eval-auc:nan
[40]	train-auc:0.77631	eval-auc:nan
[49]	train-auc:0.79649	eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
  warnings.warn(
[32m08:54:12[0m | [1mINFO    [0m | [36mTraining complete: Train AUC=0.7965, Test AUC=nan[0m
[32m08:54:12[0m | [1mINFO    [0m | [36mModel saved to models\xgboost_model.pkl[0m
[32m08:54:12[0m | [1mINFO    [0m | [36mTrain/Test gap: -1 bars to prevent temporal leakage[0m
[32m08:54:12[0m | [1mINFO    [0m | [36mTraining with 500 samples, testing with 1 samples[0m
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:12] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
  score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0]	train-auc:0.62675	eval-auc:nan
[10]	train-auc:0.73526	eval-auc:nan
[20]	train-auc:0.76185	eval-auc:nan
[30]	train-auc:0.78233	eval-auc:nan
[40]	train-auc:0.78905	eval-auc:nan
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:13] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
  score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[49]	train-auc:0.79576	eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
  warnings.warn(
[32m08:54:13[0m | [1mINFO    [0m | [36mTraining complete: Train AUC=0.7958, Test AUC=nan[0m
[32m08:54:13[0m | [1mINFO    [0m | [36mModel saved to models\xgboost_model.pkl[0m
[32m08:54:13[0m | [1mINFO    [0m | [36mTrain/Test gap: -1 bars to prevent temporal leakage[0m
[32m08:54:13[0m | [1mINFO    [0m | [36mTraining with 500 samples, testing with 1 samples[0m
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:13] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
  score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0]	train-auc:0.58584	eval-auc:nan
[10]	train-auc:0.73684	eval-auc:nan
[20]	train-auc:0.76532	eval-auc:nan
[30]	train-auc:0.79060	eval-auc:nan
[40]	train-auc:0.79951	eval-auc:nan
[49]	train-auc:0.81258	eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
  warnings.warn(
[32m08:54:13[0m | [1mINFO    [0m | [36mTraining complete: Train AUC=0.8126, Test AUC=nan[0m
[32m08:54:13[0m | [1mINFO    [0m | [36mModel saved to models\xgboost_model.pkl[0m
[32m08:54:13[0m | [1mINFO    [0m | [36mTrain/Test gap: -1 bars to prevent temporal leakage[0m
[32m08:54:13[0m | [1mINFO    [0m | [36mTraining with 500 samples, testing with 1 samples[0m
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:13] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
  score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0]	train-auc:0.56544	eval-auc:nan
[10]	train-auc:0.72203	eval-auc:nan
[20]	train-auc:0.74687	eval-auc:nan
[30]	train-auc:0.77040	eval-auc:nan
[40]	train-auc:0.79292	eval-auc:nan
[49]	train-auc:0.80617	eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
  warnings.warn(
[32m08:54:13[0m | [1mINFO    [0m | [36mTraining complete: Train AUC=0.8062, Test AUC=nan[0m
[32m08:54:13[0m | [1mINFO    [0m | [36mModel saved to models\xgboost_model.pkl[0m
[32m08:54:13[0m | [1mINFO    [0m | [36mTrain/Test gap: -1 bars to prevent temporal leakage[0m
[32m08:54:13[0m | [1mINFO    [0m | [36mTraining with 500 samples, testing with 1 samples[0m
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:13] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
  score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0]	train-auc:0.58184	eval-auc:nan
[10]	train-auc:0.68947	eval-auc:nan
[20]	train-auc:0.74093	eval-auc:nan
[30]	train-auc:0.76676	eval-auc:nan
[40]	train-auc:0.79377	eval-auc:nan
[49]	train-auc:0.79824	eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
  warnings.warn(
[32m08:54:13[0m | [1mINFO    [0m | [36mTraining complete: Train AUC=0.7982, Test AUC=nan[0m
[32m08:54:13[0m | [1mINFO    [0m | [36mModel saved to models\xgboost_model.pkl[0m
[32m08:54:13[0m | [1mINFO    [0m | [36mTrain/Test gap: -1 bars to prevent temporal leakage[0m
[32m08:54:13[0m | [1mINFO    [0m | [36mTraining with 500 samples, testing with 1 samples[0m
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:13] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
  score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0]	train-auc:0.59716	eval-auc:nan
[10]	train-auc:0.73770	eval-auc:nan
[20]	train-auc:0.76137	eval-auc:nan
[30]	train-auc:0.78070	eval-auc:nan
[40]	train-auc:0.79389	eval-auc:nan
[49]	train-auc:0.80053	eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
  warnings.warn(
[32m08:54:13[0m | [1mINFO    [0m | [36mTraining complete: Train AUC=0.8005, Test AUC=nan[0m
[32m08:54:13[0m | [1mINFO    [0m | [36mModel saved to models\xgboost_model.pkl[0m
[32m08:54:13[0m | [1mINFO    [0m | [36mTrain/Test gap: -1 bars to prevent temporal leakage[0m
[32m08:54:13[0m | [1mINFO    [0m | [36mTraining with 500 samples, testing with 1 samples[0m
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:13] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
  score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0]	train-auc:0.61680	eval-auc:nan
[10]	train-auc:0.71606	eval-auc:nan
[20]	train-auc:0.73973	eval-auc:nan
[30]	train-auc:0.76190	eval-auc:nan
[40]	train-auc:0.78364	eval-auc:nan
[49]	train-auc:0.79952	eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
  warnings.warn(
[32m08:54:13[0m | [1mINFO    [0m | [36mTraining complete: Train AUC=0.7995, Test AUC=nan[0m
[32m08:54:13[0m | [1mINFO    [0m | [36mModel saved to models\xgboost_model.pkl[0m
[32m08:54:13[0m | [1mINFO    [0m | [36mTrain/Test gap: -1 bars to prevent temporal leakage[0m
[32m08:54:13[0m | [1mINFO    [0m | [36mTraining with 500 samples, testing with 1 samples[0m
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:13] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
  score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0]	train-auc:0.58470	eval-auc:nan
[10]	train-auc:0.73138	eval-auc:nan
[20]	train-auc:0.76147	eval-auc:nan
[30]	train-auc:0.78575	eval-auc:nan
[40]	train-auc:0.80881	eval-auc:nan
[49]	train-auc:0.81547	eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
  warnings.warn(
[32m08:54:13[0m | [1mINFO    [0m | [36mTraining complete: Train AUC=0.8155, Test AUC=nan[0m
[32m08:54:13[0m | [1mINFO    [0m | [36mModel saved to models\xgboost_model.pkl[0m
[32m08:54:13[0m | [1mINFO    [0m | [36mTrain/Test gap: -1 bars to prevent temporal leakage[0m
[32m08:54:13[0m | [1mINFO    [0m | [36mTraining with 500 samples, testing with 1 samples[0m
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:13] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
  score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0]	train-auc:0.61794	eval-auc:nan
[10]	train-auc:0.71862	eval-auc:nan
[20]	train-auc:0.73959	eval-auc:nan
[30]	train-auc:0.76630	eval-auc:nan
[40]	train-auc:0.78060	eval-auc:nan
[49]	train-auc:0.80141	eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
  warnings.warn(
[32m08:54:13[0m | [1mINFO    [0m | [36mTraining complete: Train AUC=0.8014, Test AUC=nan[0m
[32m08:54:13[0m | [1mINFO    [0m | [36mModel saved to models\xgboost_model.pkl[0m
[32m08:54:13[0m | [1mINFO    [0m | [36mTrain/Test gap: -1 bars to prevent temporal leakage[0m
[32m08:54:13[0m | [1mINFO    [0m | [36mTraining with 500 samples, testing with 1 samples[0m
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:13] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
  score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0]	train-auc:0.62192	eval-auc:nan
[10]	train-auc:0.70910	eval-auc:nan
[20]	train-auc:0.73660	eval-auc:nan
[30]	train-auc:0.75229	eval-auc:nan
[40]	train-auc:0.77839	eval-auc:nan
[49]	train-auc:0.79447	eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
  warnings.warn(
[32m08:54:13[0m | [1mINFO    [0m | [36mTraining complete: Train AUC=0.7945, Test AUC=nan[0m
[32m08:54:13[0m | [1mINFO    [0m | [36mModel saved to models\xgboost_model.pkl[0m
[32m08:54:13[0m | [1mINFO    [0m | [36mTrain/Test gap: -1 bars to prevent temporal leakage[0m
[32m08:54:13[0m | [1mINFO    [0m | [36mTraining with 500 samples, testing with 1 samples[0m
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:13] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
  score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0]	train-auc:0.59790	eval-auc:nan
[10]	train-auc:0.71110	eval-auc:nan
[20]	train-auc:0.73864	eval-auc:nan
[30]	train-auc:0.76435	eval-auc:nan
[40]	train-auc:0.78813	eval-auc:nan
[49]	train-auc:0.79573	eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
  warnings.warn(
[32m08:54:13[0m | [1mINFO    [0m | [36mTraining complete: Train AUC=0.7957, Test AUC=nan[0m
[32m08:54:13[0m | [1mINFO    [0m | [36mModel saved to models\xgboost_model.pkl[0m
[32m08:54:13[0m | [1mINFO    [0m | [36mTrain/Test gap: -1 bars to prevent temporal leakage[0m
[32m08:54:13[0m | [1mINFO    [0m | [36mTraining with 500 samples, testing with 1 samples[0m
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:13] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
  score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0]	train-auc:0.58716	eval-auc:nan
[10]	train-auc:0.70039	eval-auc:nan
[20]	train-auc:0.73173	eval-auc:nan
[30]	train-auc:0.75244	eval-auc:nan
[40]	train-auc:0.76589	eval-auc:nan
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:14] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
  score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[49]	train-auc:0.77832	eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
  warnings.warn(
[32m08:54:14[0m | [1mINFO    [0m | [36mTraining complete: Train AUC=0.7783, Test AUC=nan[0m
[32m08:54:14[0m | [1mINFO    [0m | [36mModel saved to models\xgboost_model.pkl[0m
[32m08:54:14[0m | [1mINFO    [0m | [36mTrain/Test gap: -1 bars to prevent temporal leakage[0m
[32m08:54:14[0m | [1mINFO    [0m | [36mTraining with 500 samples, testing with 1 samples[0m
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:14] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
  score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0]	train-auc:0.60572	eval-auc:nan
[10]	train-auc:0.70959	eval-auc:nan
[20]	train-auc:0.73499	eval-auc:nan
[30]	train-auc:0.74913	eval-auc:nan
[40]	train-auc:0.76566	eval-auc:nan
[49]	train-auc:0.77833	eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
  warnings.warn(
[32m08:54:14[0m | [1mINFO    [0m | [36mTraining complete: Train AUC=0.7783, Test AUC=nan[0m
[32m08:54:14[0m | [1mINFO    [0m | [36mModel saved to models\xgboost_model.pkl[0m
[32m08:54:14[0m | [1mINFO    [0m | [36mTrain/Test gap: -1 bars to prevent temporal leakage[0m
[32m08:54:14[0m | [1mINFO    [0m | [36mTraining with 500 samples, testing with 1 samples[0m
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:14] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
  score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0]	train-auc:0.58467	eval-auc:nan
[10]	train-auc:0.73488	eval-auc:nan
[20]	train-auc:0.76174	eval-auc:nan
[30]	train-auc:0.78199	eval-auc:nan
[40]	train-auc:0.79030	eval-auc:nan
[49]	train-auc:0.79666	eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
  warnings.warn(
[32m08:54:14[0m | [1mINFO    [0m | [36mTraining complete: Train AUC=0.7967, Test AUC=nan[0m
[32m08:54:14[0m | [1mINFO    [0m | [36mModel saved to models\xgboost_model.pkl[0m
[32m08:54:14[0m | [1mINFO    [0m | [36mTrain/Test gap: -1 bars to prevent temporal leakage[0m
[32m08:54:14[0m | [1mINFO    [0m | [36mTraining with 500 samples, testing with 1 samples[0m
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:14] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
  score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0]	train-auc:0.60025	eval-auc:nan
[10]	train-auc:0.74875	eval-auc:nan
[20]	train-auc:0.78340	eval-auc:nan
[30]	train-auc:0.79777	eval-auc:nan
[40]	train-auc:0.81048	eval-auc:nan
[49]	train-auc:0.82650	eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
  warnings.warn(
[32m08:54:14[0m | [1mINFO    [0m | [36mTraining complete: Train AUC=0.8265, Test AUC=nan[0m
[32m08:54:14[0m | [1mINFO    [0m | [36mModel saved to models\xgboost_model.pkl[0m
[32m08:54:14[0m | [1mINFO    [0m | [36mTrain/Test gap: -1 bars to prevent temporal leakage[0m
[32m08:54:14[0m | [1mINFO    [0m | [36mTraining with 500 samples, testing with 1 samples[0m
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:14] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
  score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0]	train-auc:0.60908	eval-auc:nan
[10]	train-auc:0.70826	eval-auc:nan
[20]	train-auc:0.75811	eval-auc:nan
[30]	train-auc:0.77492	eval-auc:nan
[40]	train-auc:0.78340	eval-auc:nan
[49]	train-auc:0.80489	eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
  warnings.warn(
[32m08:54:14[0m | [1mINFO    [0m | [36mTraining complete: Train AUC=0.8049, Test AUC=nan[0m
[32m08:54:14[0m | [1mINFO    [0m | [36mModel saved to models\xgboost_model.pkl[0m
[32m08:54:14[0m | [1mINFO    [0m | [36mTrain/Test gap: -1 bars to prevent temporal leakage[0m
[32m08:54:14[0m | [1mINFO    [0m | [36mTraining with 500 samples, testing with 1 samples[0m
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:14] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
  score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0]	train-auc:0.62305	eval-auc:nan
[10]	train-auc:0.73977	eval-auc:nan
[20]	train-auc:0.77993	eval-auc:nan
[30]	train-auc:0.79480	eval-auc:nan
[40]	train-auc:0.80622	eval-auc:nan
[49]	train-auc:0.81616	eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
  warnings.warn(
[32m08:54:14[0m | [1mINFO    [0m | [36mTraining complete: Train AUC=0.8162, Test AUC=nan[0m
[32m08:54:14[0m | [1mINFO    [0m | [36mModel saved to models\xgboost_model.pkl[0m
[32m08:54:14[0m | [1mINFO    [0m | [36mTrain/Test gap: -1 bars to prevent temporal leakage[0m
[32m08:54:14[0m | [1mINFO    [0m | [36mTraining with 500 samples, testing with 1 samples[0m
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:14] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
  score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0]	train-auc:0.60336	eval-auc:nan
[10]	train-auc:0.74016	eval-auc:nan
[20]	train-auc:0.77037	eval-auc:nan
[30]	train-auc:0.78700	eval-auc:nan
[40]	train-auc:0.79611	eval-auc:nan
[49]	train-auc:0.80730	eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
  warnings.warn(
[32m08:54:14[0m | [1mINFO    [0m | [36mTraining complete: Train AUC=0.8073, Test AUC=nan[0m
[32m08:54:14[0m | [1mINFO    [0m | [36mModel saved to models\xgboost_model.pkl[0m
[32m08:54:14[0m | [1mINFO    [0m | [36mTrain/Test gap: -1 bars to prevent temporal leakage[0m
[32m08:54:14[0m | [1mINFO    [0m | [36mTraining with 500 samples, testing with 1 samples[0m
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:14] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
  score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0]	train-auc:0.62771	eval-auc:nan
[10]	train-auc:0.72911	eval-auc:nan
[20]	train-auc:0.76976	eval-auc:nan
[30]	train-auc:0.77925	eval-auc:nan
[40]	train-auc:0.79250	eval-auc:nan
[49]	train-auc:0.81189	eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
  warnings.warn(
[32m08:54:14[0m | [1mINFO    [0m | [36mTraining complete: Train AUC=0.8119, Test AUC=nan[0m
[32m08:54:14[0m | [1mINFO    [0m | [36mModel saved to models\xgboost_model.pkl[0m
[32m08:54:14[0m | [1mINFO    [0m | [36mTrain/Test gap: -1 bars to prevent temporal leakage[0m
[32m08:54:14[0m | [1mINFO    [0m | [36mTraining with 500 samples, testing with 1 samples[0m
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:14] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
  score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0]	train-auc:0.59090	eval-auc:nan
[10]	train-auc:0.72648	eval-auc:nan
[20]	train-auc:0.75725	eval-auc:nan
[30]	train-auc:0.78042	eval-auc:nan
[40]	train-auc:0.79005	eval-auc:nan
[49]	train-auc:0.79928	eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
  warnings.warn(
[32m08:54:14[0m | [1mINFO    [0m | [36mTraining complete: Train AUC=0.7993, Test AUC=nan[0m
[32m08:54:14[0m | [1mINFO    [0m | [36mModel saved to models\xgboost_model.pkl[0m
[32m08:54:14[0m | [1mINFO    [0m | [36mTrain/Test gap: -1 bars to prevent temporal leakage[0m
[32m08:54:14[0m | [1mINFO    [0m | [36mTraining with 500 samples, testing with 1 samples[0m
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:14] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
  score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0]	train-auc:0.58067	eval-auc:nan
[10]	train-auc:0.72815	eval-auc:nan
[20]	train-auc:0.76314	eval-auc:nan
[30]	train-auc:0.77561	eval-auc:nan
[40]	train-auc:0.78331	eval-auc:nan
[49]	train-auc:0.79895	eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
  warnings.warn(
[32m08:54:14[0m | [1mINFO    [0m | [36mTraining complete: Train AUC=0.7990, Test AUC=nan[0m
[32m08:54:14[0m | [1mINFO    [0m | [36mModel saved to models\xgboost_model.pkl[0m
[32m08:54:14[0m | [1mINFO    [0m | [36mTrain/Test gap: -1 bars to prevent temporal leakage[0m
[32m08:54:14[0m | [1mINFO    [0m | [36mTraining with 500 samples, testing with 1 samples[0m
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:14] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
  score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0]	train-auc:0.59361	eval-auc:nan
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:15] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
  score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[10]	train-auc:0.72346	eval-auc:nan
[20]	train-auc:0.75451	eval-auc:nan
[30]	train-auc:0.76650	eval-auc:nan
[40]	train-auc:0.77333	eval-auc:nan
[49]	train-auc:0.78185	eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
  warnings.warn(
[32m08:54:15[0m | [1mINFO    [0m | [36mTraining complete: Train AUC=0.7819, Test AUC=nan[0m
[32m08:54:15[0m | [1mINFO    [0m | [36mModel saved to models\xgboost_model.pkl[0m
[32m08:54:15[0m | [1mINFO    [0m | [36mTrain/Test gap: -1 bars to prevent temporal leakage[0m
[32m08:54:15[0m | [1mINFO    [0m | [36mTraining with 500 samples, testing with 1 samples[0m
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:15] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
  score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0]	train-auc:0.60225	eval-auc:nan
[10]	train-auc:0.75589	eval-auc:nan
[20]	train-auc:0.79933	eval-auc:nan
[30]	train-auc:0.79717	eval-auc:nan
[40]	train-auc:0.81155	eval-auc:nan
[49]	train-auc:0.82061	eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
  warnings.warn(
[32m08:54:15[0m | [1mINFO    [0m | [36mTraining complete: Train AUC=0.8206, Test AUC=nan[0m
[32m08:54:15[0m | [1mINFO    [0m | [36mModel saved to models\xgboost_model.pkl[0m
[32m08:54:15[0m | [1mINFO    [0m | [36mTrain/Test gap: -1 bars to prevent temporal leakage[0m
[32m08:54:15[0m | [1mINFO    [0m | [36mTraining with 500 samples, testing with 1 samples[0m
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:15] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
  score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0]	train-auc:0.61044	eval-auc:nan
[10]	train-auc:0.73632	eval-auc:nan
[20]	train-auc:0.77125	eval-auc:nan
[30]	train-auc:0.79179	eval-auc:nan
[40]	train-auc:0.80556	eval-auc:nan
[49]	train-auc:0.81967	eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
  warnings.warn(
[32m08:54:15[0m | [1mINFO    [0m | [36mTraining complete: Train AUC=0.8197, Test AUC=nan[0m
[32m08:54:15[0m | [1mINFO    [0m | [36mModel saved to models\xgboost_model.pkl[0m
[32m08:54:15[0m | [1mINFO    [0m | [36mTrain/Test gap: -1 bars to prevent temporal leakage[0m
[32m08:54:15[0m | [1mINFO    [0m | [36mTraining with 500 samples, testing with 1 samples[0m
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:15] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
  score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0]	train-auc:0.59773	eval-auc:nan
[10]	train-auc:0.74045	eval-auc:nan
[20]	train-auc:0.77537	eval-auc:nan
[30]	train-auc:0.78821	eval-auc:nan
[40]	train-auc:0.79300	eval-auc:nan
[49]	train-auc:0.80560	eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
  warnings.warn(
[32m08:54:15[0m | [1mINFO    [0m | [36mTraining complete: Train AUC=0.8056, Test AUC=nan[0m
[32m08:54:15[0m | [1mINFO    [0m | [36mModel saved to models\xgboost_model.pkl[0m
[32m08:54:15[0m | [1mINFO    [0m | [36mTrain/Test gap: -1 bars to prevent temporal leakage[0m
[32m08:54:15[0m | [1mINFO    [0m | [36mTraining with 500 samples, testing with 1 samples[0m
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:15] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
  score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0]	train-auc:0.60509	eval-auc:nan
[10]	train-auc:0.73261	eval-auc:nan
[20]	train-auc:0.74898	eval-auc:nan
[30]	train-auc:0.77577	eval-auc:nan
[40]	train-auc:0.80143	eval-auc:nan
[49]	train-auc:0.80776	eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
  warnings.warn(
[32m08:54:15[0m | [1mINFO    [0m | [36mTraining complete: Train AUC=0.8078, Test AUC=nan[0m
[32m08:54:15[0m | [1mINFO    [0m | [36mModel saved to models\xgboost_model.pkl[0m
[32m08:54:15[0m | [1mINFO    [0m | [36mTrain/Test gap: -1 bars to prevent temporal leakage[0m
[32m08:54:15[0m | [1mINFO    [0m | [36mTraining with 500 samples, testing with 1 samples[0m
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:15] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
  score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0]	train-auc:0.59273	eval-auc:nan
[10]	train-auc:0.70327	eval-auc:nan
[20]	train-auc:0.73105	eval-auc:nan
[30]	train-auc:0.76408	eval-auc:nan
[40]	train-auc:0.77841	eval-auc:nan
[49]	train-auc:0.79826	eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
  warnings.warn(
[32m08:54:15[0m | [1mINFO    [0m | [36mTraining complete: Train AUC=0.7983, Test AUC=nan[0m
[32m08:54:15[0m | [1mINFO    [0m | [36mModel saved to models\xgboost_model.pkl[0m
[32m08:54:15[0m | [1mINFO    [0m | [36mTrain/Test gap: -1 bars to prevent temporal leakage[0m
[32m08:54:15[0m | [1mINFO    [0m | [36mTraining with 500 samples, testing with 1 samples[0m
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:15] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
  score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0]	train-auc:0.61589	eval-auc:nan
[10]	train-auc:0.72766	eval-auc:nan
[20]	train-auc:0.76475	eval-auc:nan
[30]	train-auc:0.79244	eval-auc:nan
[40]	train-auc:0.79426	eval-auc:nan
[49]	train-auc:0.80812	eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
  warnings.warn(
[32m08:54:15[0m | [1mINFO    [0m | [36mTraining complete: Train AUC=0.8081, Test AUC=nan[0m
[32m08:54:15[0m | [1mINFO    [0m | [36mModel saved to models\xgboost_model.pkl[0m
[32m08:54:15[0m | [1mINFO    [0m | [36mTrain/Test gap: -1 bars to prevent temporal leakage[0m
[32m08:54:15[0m | [1mINFO    [0m | [36mTraining with 500 samples, testing with 1 samples[0m
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:15] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
  score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0]	train-auc:0.60277	eval-auc:nan
[10]	train-auc:0.71655	eval-auc:nan
[20]	train-auc:0.77139	eval-auc:nan
[30]	train-auc:0.78916	eval-auc:nan
[40]	train-auc:0.79352	eval-auc:nan
[49]	train-auc:0.81575	eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
  warnings.warn(
[32m08:54:15[0m | [1mINFO    [0m | [36mTraining complete: Train AUC=0.8157, Test AUC=nan[0m
[32m08:54:15[0m | [1mINFO    [0m | [36mModel saved to models\xgboost_model.pkl[0m
[32m08:54:15[0m | [1mINFO    [0m | [36mTrain/Test gap: -1 bars to prevent temporal leakage[0m
[32m08:54:15[0m | [1mINFO    [0m | [36mTraining with 500 samples, testing with 1 samples[0m
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:15] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
  score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0]	train-auc:0.59594	eval-auc:nan
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:16] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
  score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[10]	train-auc:0.73544	eval-auc:nan
[20]	train-auc:0.77444	eval-auc:nan
[30]	train-auc:0.79599	eval-auc:nan
[40]	train-auc:0.80112	eval-auc:nan
[49]	train-auc:0.81206	eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
  warnings.warn(
[32m08:54:16[0m | [1mINFO    [0m | [36mTraining complete: Train AUC=0.8121, Test AUC=nan[0m
[32m08:54:16[0m | [1mINFO    [0m | [36mModel saved to models\xgboost_model.pkl[0m
[32m08:54:16[0m | [1mINFO    [0m | [36mTrain/Test gap: -1 bars to prevent temporal leakage[0m
[32m08:54:16[0m | [1mINFO    [0m | [36mTraining with 500 samples, testing with 1 samples[0m
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:16] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
  score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0]	train-auc:0.59370	eval-auc:nan
[10]	train-auc:0.72233	eval-auc:nan
[20]	train-auc:0.76728	eval-auc:nan
[30]	train-auc:0.78540	eval-auc:nan
[40]	train-auc:0.81090	eval-auc:nan
[49]	train-auc:0.82612	eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
  warnings.warn(
[32m08:54:16[0m | [1mINFO    [0m | [36mTraining complete: Train AUC=0.8261, Test AUC=nan[0m
[32m08:54:16[0m | [1mINFO    [0m | [36mModel saved to models\xgboost_model.pkl[0m
[32m08:54:16[0m | [1mINFO    [0m | [36mTrain/Test gap: -1 bars to prevent temporal leakage[0m
[32m08:54:16[0m | [1mINFO    [0m | [36mTraining with 500 samples, testing with 1 samples[0m
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:16] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
  score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0]	train-auc:0.58058	eval-auc:nan
[10]	train-auc:0.72036	eval-auc:nan
[20]	train-auc:0.76034	eval-auc:nan
[30]	train-auc:0.78914	eval-auc:nan
[40]	train-auc:0.78862	eval-auc:nan
[49]	train-auc:0.81527	eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
  warnings.warn(
[32m08:54:16[0m | [1mINFO    [0m | [36mTraining complete: Train AUC=0.8153, Test AUC=nan[0m
[32m08:54:16[0m | [1mINFO    [0m | [36mModel saved to models\xgboost_model.pkl[0m
[32m08:54:16[0m | [1mINFO    [0m | [36mTrain/Test gap: -1 bars to prevent temporal leakage[0m
[32m08:54:16[0m | [1mINFO    [0m | [36mTraining with 500 samples, testing with 1 samples[0m
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:16] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
  score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0]	train-auc:0.56349	eval-auc:nan
[10]	train-auc:0.72454	eval-auc:nan
[20]	train-auc:0.75251	eval-auc:nan
[30]	train-auc:0.76662	eval-auc:nan
[40]	train-auc:0.78404	eval-auc:nan
[49]	train-auc:0.79939	eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
  warnings.warn(
[32m08:54:16[0m | [1mINFO    [0m | [36mTraining complete: Train AUC=0.7994, Test AUC=nan[0m
[32m08:54:16[0m | [1mINFO    [0m | [36mModel saved to models\xgboost_model.pkl[0m
[32m08:54:16[0m | [1mINFO    [0m | [36mTrain/Test gap: -1 bars to prevent temporal leakage[0m
[32m08:54:16[0m | [1mINFO    [0m | [36mTraining with 500 samples, testing with 1 samples[0m
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:16] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
  score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0]	train-auc:0.62536	eval-auc:nan
[10]	train-auc:0.78166	eval-auc:nan
[20]	train-auc:0.80441	eval-auc:nan
[30]	train-auc:0.81064	eval-auc:nan
[40]	train-auc:0.81756	eval-auc:nan
[49]	train-auc:0.82587	eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
  warnings.warn(
[32m08:54:16[0m | [1mINFO    [0m | [36mTraining complete: Train AUC=0.8259, Test AUC=nan[0m
[32m08:54:16[0m | [1mINFO    [0m | [36mModel saved to models\xgboost_model.pkl[0m
[32m08:54:16[0m | [1mINFO    [0m | [36mTrain/Test gap: -1 bars to prevent temporal leakage[0m
[32m08:54:16[0m | [1mINFO    [0m | [36mTraining with 500 samples, testing with 1 samples[0m
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:16] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
  score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0]	train-auc:0.62035	eval-auc:nan
[10]	train-auc:0.73565	eval-auc:nan
[20]	train-auc:0.76004	eval-auc:nan
[30]	train-auc:0.78144	eval-auc:nan
[40]	train-auc:0.80278	eval-auc:nan
[49]	train-auc:0.81366	eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
  warnings.warn(
[32m08:54:16[0m | [1mINFO    [0m | [36mTraining complete: Train AUC=0.8137, Test AUC=nan[0m
[32m08:54:16[0m | [1mINFO    [0m | [36mModel saved to models\xgboost_model.pkl[0m
[32m08:54:16[0m | [1mINFO    [0m | [36mTrain/Test gap: -1 bars to prevent temporal leakage[0m
[32m08:54:16[0m | [1mINFO    [0m | [36mTraining with 500 samples, testing with 1 samples[0m
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:16] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
  score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0]	train-auc:0.60544	eval-auc:nan
[10]	train-auc:0.74901	eval-auc:nan
[20]	train-auc:0.78262	eval-auc:nan
[30]	train-auc:0.80046	eval-auc:nan
[40]	train-auc:0.80987	eval-auc:nan
[49]	train-auc:0.81613	eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
  warnings.warn(
[32m08:54:16[0m | [1mINFO    [0m | [36mTraining complete: Train AUC=0.8161, Test AUC=nan[0m
[32m08:54:16[0m | [1mINFO    [0m | [36mModel saved to models\xgboost_model.pkl[0m
[32m08:54:16[0m | [1mINFO    [0m | [36mTrain/Test gap: -1 bars to prevent temporal leakage[0m
[32m08:54:16[0m | [1mINFO    [0m | [36mTraining with 500 samples, testing with 1 samples[0m
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:16] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
  score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0]	train-auc:0.60875	eval-auc:nan
[10]	train-auc:0.71353	eval-auc:nan
[20]	train-auc:0.74911	eval-auc:nan
[30]	train-auc:0.77565	eval-auc:nan
[40]	train-auc:0.79604	eval-auc:nan
[49]	train-auc:0.80603	eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
  warnings.warn(
[32m08:54:16[0m | [1mINFO    [0m | [36mTraining complete: Train AUC=0.8060, Test AUC=nan[0m
[32m08:54:16[0m | [1mINFO    [0m | [36mModel saved to models\xgboost_model.pkl[0m
[32m08:54:16[0m | [1mINFO    [0m | [36mTrain/Test gap: -1 bars to prevent temporal leakage[0m
[32m08:54:16[0m | [1mINFO    [0m | [36mTraining with 500 samples, testing with 1 samples[0m
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:16] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
  score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0]	train-auc:0.60486	eval-auc:nan
[10]	train-auc:0.74211	eval-auc:nan
[20]	train-auc:0.76931	eval-auc:nan
[30]	train-auc:0.78213	eval-auc:nan
[40]	train-auc:0.80078	eval-auc:nan
[49]	train-auc:0.81138	eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
  warnings.warn(
[32m08:54:16[0m | [1mINFO    [0m | [36mTraining complete: Train AUC=0.8114, Test AUC=nan[0m
[32m08:54:16[0m | [1mINFO    [0m | [36mModel saved to models\xgboost_model.pkl[0m
[32m08:54:16[0m | [1mINFO    [0m | [36mTrain/Test gap: -1 bars to prevent temporal leakage[0m
[32m08:54:16[0m | [1mINFO    [0m | [36mTraining with 500 samples, testing with 1 samples[0m
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:16] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
  score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0]	train-auc:0.61124	eval-auc:nan
[10]	train-auc:0.74830	eval-auc:nan
[20]	train-auc:0.77556	eval-auc:nan
[30]	train-auc:0.79560	eval-auc:nan
[40]	train-auc:0.81400	eval-auc:nan
[49]	train-auc:0.83058	eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
  warnings.warn(
[32m08:54:16[0m | [1mINFO    [0m | [36mTraining complete: Train AUC=0.8306, Test AUC=nan[0m
[32m08:54:16[0m | [1mINFO    [0m | [36mModel saved to models\xgboost_model.pkl[0m
[32m08:54:17[0m | [1mINFO    [0m | [36mTrain/Test gap: -1 bars to prevent temporal leakage[0m
[32m08:54:17[0m | [1mINFO    [0m | [36mTraining with 500 samples, testing with 1 samples[0m
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:17] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
  score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0]	train-auc:0.61050	eval-auc:nan
[10]	train-auc:0.73725	eval-auc:nan
[20]	train-auc:0.78658	eval-auc:nan
[30]	train-auc:0.80011	eval-auc:nan
[40]	train-auc:0.81980	eval-auc:nan
[49]	train-auc:0.82634	eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
  warnings.warn(
[32m08:54:17[0m | [1mINFO    [0m | [36mTraining complete: Train AUC=0.8263, Test AUC=nan[0m
[32m08:54:17[0m | [1mINFO    [0m | [36mModel saved to models\xgboost_model.pkl[0m
[32m08:54:17[0m | [1mINFO    [0m | [36mTrain/Test gap: -1 bars to prevent temporal leakage[0m
[32m08:54:17[0m | [1mINFO    [0m | [36mTraining with 500 samples, testing with 1 samples[0m
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:17] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
  score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0]	train-auc:0.59007	eval-auc:nan
[10]	train-auc:0.72025	eval-auc:nan
[20]	train-auc:0.75405	eval-auc:nan
[30]	train-auc:0.76530	eval-auc:nan
[40]	train-auc:0.78143	eval-auc:nan
[49]	train-auc:0.79535	eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
  warnings.warn(
[32m08:54:17[0m | [1mINFO    [0m | [36mTraining complete: Train AUC=0.7953, Test AUC=nan[0m
[32m08:54:17[0m | [1mINFO    [0m | [36mModel saved to models\xgboost_model.pkl[0m
[32m08:54:17[0m | [1mINFO    [0m | [36mTrain/Test gap: -1 bars to prevent temporal leakage[0m
[32m08:54:17[0m | [1mINFO    [0m | [36mTraining with 500 samples, testing with 1 samples[0m
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:17] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
  score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0]	train-auc:0.61502	eval-auc:nan
[10]	train-auc:0.72241	eval-auc:nan
[20]	train-auc:0.74495	eval-auc:nan
[30]	train-auc:0.76444	eval-auc:nan
[40]	train-auc:0.78075	eval-auc:nan
[49]	train-auc:0.79460	eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
  warnings.warn(
[32m08:54:17[0m | [1mINFO    [0m | [36mTraining complete: Train AUC=0.7946, Test AUC=nan[0m
[32m08:54:17[0m | [1mINFO    [0m | [36mModel saved to models\xgboost_model.pkl[0m
[32m08:54:17[0m | [1mINFO    [0m | [36mTrain/Test gap: -1 bars to prevent temporal leakage[0m
[32m08:54:17[0m | [1mINFO    [0m | [36mTraining with 500 samples, testing with 1 samples[0m
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:17] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
  score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0]	train-auc:0.59667	eval-auc:nan
[10]	train-auc:0.70052	eval-auc:nan
[20]	train-auc:0.73642	eval-auc:nan
[30]	train-auc:0.76280	eval-auc:nan
[40]	train-auc:0.77787	eval-auc:nan
[49]	train-auc:0.78930	eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
  warnings.warn(
[32m08:54:17[0m | [1mINFO    [0m | [36mTraining complete: Train AUC=0.7893, Test AUC=nan[0m
[32m08:54:17[0m | [1mINFO    [0m | [36mModel saved to models\xgboost_model.pkl[0m
[32m08:54:17[0m | [1mINFO    [0m | [36mTrain/Test gap: -1 bars to prevent temporal leakage[0m
[32m08:54:17[0m | [1mINFO    [0m | [36mTraining with 500 samples, testing with 1 samples[0m
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:17] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
  score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0]	train-auc:0.60616	eval-auc:nan
[10]	train-auc:0.72239	eval-auc:nan
[20]	train-auc:0.75124	eval-auc:nan
[30]	train-auc:0.76333	eval-auc:nan
[40]	train-auc:0.77680	eval-auc:nan
[49]	train-auc:0.78881	eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
  warnings.warn(
[32m08:54:17[0m | [1mINFO    [0m | [36mTraining complete: Train AUC=0.7888, Test AUC=nan[0m
[32m08:54:17[0m | [1mINFO    [0m | [36mModel saved to models\xgboost_model.pkl[0m
[32m08:54:17[0m | [1mINFO    [0m | [36mTrain/Test gap: -1 bars to prevent temporal leakage[0m
[32m08:54:17[0m | [1mINFO    [0m | [36mTraining with 500 samples, testing with 1 samples[0m
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:17] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
  score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0]	train-auc:0.58260	eval-auc:nan
[10]	train-auc:0.69158	eval-auc:nan
[20]	train-auc:0.74179	eval-auc:nan
[30]	train-auc:0.75748	eval-auc:nan
[40]	train-auc:0.78480	eval-auc:nan
[49]	train-auc:0.79951	eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
  warnings.warn(
[32m08:54:17[0m | [1mINFO    [0m | [36mTraining complete: Train AUC=0.7995, Test AUC=nan[0m
[32m08:54:17[0m | [1mINFO    [0m | [36mModel saved to models\xgboost_model.pkl[0m
[32m08:54:17[0m | [1mINFO    [0m | [36mTrain/Test gap: -1 bars to prevent temporal leakage[0m
[32m08:54:17[0m | [1mINFO    [0m | [36mTraining with 500 samples, testing with 1 samples[0m
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:17] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
  score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0]	train-auc:0.60295	eval-auc:nan
[10]	train-auc:0.71787	eval-auc:nan
[20]	train-auc:0.74548	eval-auc:nan
[30]	train-auc:0.76412	eval-auc:nan
[40]	train-auc:0.78106	eval-auc:nan
[49]	train-auc:0.79762	eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
  warnings.warn(
[32m08:54:17[0m | [1mINFO    [0m | [36mTraining complete: Train AUC=0.7976, Test AUC=nan[0m
[32m08:54:17[0m | [1mINFO    [0m | [36mModel saved to models\xgboost_model.pkl[0m
[32m08:54:17[0m | [1mINFO    [0m | [36mTrain/Test gap: -1 bars to prevent temporal leakage[0m
[32m08:54:17[0m | [1mINFO    [0m | [36mTraining with 500 samples, testing with 1 samples[0m
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:17] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
  score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0]	train-auc:0.63200	eval-auc:nan
[10]	train-auc:0.73796	eval-auc:nan
[20]	train-auc:0.75566	eval-auc:nan
[30]	train-auc:0.77386	eval-auc:nan
[40]	train-auc:0.78714	eval-auc:nan
[49]	train-auc:0.79885	eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
  warnings.warn(
[32m08:54:17[0m | [1mINFO    [0m | [36mTraining complete: Train AUC=0.7989, Test AUC=nan[0m
[32m08:54:17[0m | [1mINFO    [0m | [36mModel saved to models\xgboost_model.pkl[0m
[32m08:54:17[0m | [1mINFO    [0m | [36mTrain/Test gap: -1 bars to prevent temporal leakage[0m
[32m08:54:17[0m | [1mINFO    [0m | [36mTraining with 500 samples, testing with 1 samples[0m
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:17] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
  score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0]	train-auc:0.55151	eval-auc:nan
[10]	train-auc:0.71638	eval-auc:nan
[20]	train-auc:0.76434	eval-auc:nan
[30]	train-auc:0.78295	eval-auc:nan
[40]	train-auc:0.79186	eval-auc:nan
[49]	train-auc:0.81077	eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
  warnings.warn(
[32m08:54:17[0m | [1mINFO    [0m | [36mTraining complete: Train AUC=0.8108, Test AUC=nan[0m
[32m08:54:17[0m | [1mINFO    [0m | [36mModel saved to models\xgboost_model.pkl[0m
[32m08:54:17[0m | [1mINFO    [0m | [36mTrain/Test gap: -1 bars to prevent temporal leakage[0m
[32m08:54:17[0m | [1mINFO    [0m | [36mTraining with 500 samples, testing with 1 samples[0m
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:17] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
  score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0]	train-auc:0.58289	eval-auc:nan
[10]	train-auc:0.69458	eval-auc:nan
[20]	train-auc:0.72122	eval-auc:nan
[30]	train-auc:0.76020	eval-auc:nan
[40]	train-auc:0.78361	eval-auc:nan
[49]	train-auc:0.79281	eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
  warnings.warn(
[32m08:54:17[0m | [1mINFO    [0m | [36mTraining complete: Train AUC=0.7928, Test AUC=nan[0m
[32m08:54:17[0m | [1mINFO    [0m | [36mModel saved to models\xgboost_model.pkl[0m
[32m08:54:17[0m | [1mINFO    [0m | [36mTrain/Test gap: -1 bars to prevent temporal leakage[0m
[32m08:54:17[0m | [1mINFO    [0m | [36mTraining with 500 samples, testing with 1 samples[0m
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:17] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
  score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0]	train-auc:0.58773	eval-auc:nan
[10]	train-auc:0.70915	eval-auc:nan
[20]	train-auc:0.73897	eval-auc:nan
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:18] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
  score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[30]	train-auc:0.77324	eval-auc:nan
[40]	train-auc:0.78043	eval-auc:nan
[49]	train-auc:0.77908	eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
  warnings.warn(
[32m08:54:18[0m | [1mINFO    [0m | [36mTraining complete: Train AUC=0.7791, Test AUC=nan[0m
[32m08:54:18[0m | [1mINFO    [0m | [36mModel saved to models\xgboost_model.pkl[0m
[32m08:54:18[0m | [1mINFO    [0m | [36mTrain/Test gap: -1 bars to prevent temporal leakage[0m
[32m08:54:18[0m | [1mINFO    [0m | [36mTraining with 500 samples, testing with 1 samples[0m
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:18] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
  score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0]	train-auc:0.59688	eval-auc:nan
[10]	train-auc:0.70307	eval-auc:nan
[20]	train-auc:0.73593	eval-auc:nan
[30]	train-auc:0.75386	eval-auc:nan
[40]	train-auc:0.77147	eval-auc:nan
[49]	train-auc:0.77767	eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
  warnings.warn(
[32m08:54:18[0m | [1mINFO    [0m | [36mTraining complete: Train AUC=0.7777, Test AUC=nan[0m
[32m08:54:18[0m | [1mINFO    [0m | [36mModel saved to models\xgboost_model.pkl[0m
[32m08:54:18[0m | [1mINFO    [0m | [36mTrain/Test gap: -1 bars to prevent temporal leakage[0m
[32m08:54:18[0m | [1mINFO    [0m | [36mTraining with 500 samples, testing with 1 samples[0m
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:18] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
  score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0]	train-auc:0.58860	eval-auc:nan
[10]	train-auc:0.68382	eval-auc:nan
[20]	train-auc:0.72234	eval-auc:nan
[30]	train-auc:0.74722	eval-auc:nan
[40]	train-auc:0.76107	eval-auc:nan
[49]	train-auc:0.77164	eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
  warnings.warn(
[32m08:54:18[0m | [1mINFO    [0m | [36mTraining complete: Train AUC=0.7716, Test AUC=nan[0m
[32m08:54:18[0m | [1mINFO    [0m | [36mModel saved to models\xgboost_model.pkl[0m
[32m08:54:18[0m | [1mINFO    [0m | [36mTrain/Test gap: -1 bars to prevent temporal leakage[0m
[32m08:54:18[0m | [1mINFO    [0m | [36mTraining with 500 samples, testing with 1 samples[0m
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:18] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
  score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0]	train-auc:0.60582	eval-auc:nan
[10]	train-auc:0.72087	eval-auc:nan
[20]	train-auc:0.75132	eval-auc:nan
[30]	train-auc:0.75836	eval-auc:nan
[40]	train-auc:0.76906	eval-auc:nan
[49]	train-auc:0.77916	eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
  warnings.warn(
[32m08:54:18[0m | [1mINFO    [0m | [36mTraining complete: Train AUC=0.7792, Test AUC=nan[0m
[32m08:54:18[0m | [1mINFO    [0m | [36mModel saved to models\xgboost_model.pkl[0m
[32m08:54:18[0m | [1mINFO    [0m | [36mTrain/Test gap: -1 bars to prevent temporal leakage[0m
[32m08:54:18[0m | [1mINFO    [0m | [36mTraining with 500 samples, testing with 1 samples[0m
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:18] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
  score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0]	train-auc:0.60521	eval-auc:nan
[10]	train-auc:0.72012	eval-auc:nan
[20]	train-auc:0.74708	eval-auc:nan
[30]	train-auc:0.76421	eval-auc:nan
[40]	train-auc:0.78048	eval-auc:nan
[49]	train-auc:0.79736	eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
  warnings.warn(
[32m08:54:18[0m | [1mINFO    [0m | [36mTraining complete: Train AUC=0.7974, Test AUC=nan[0m
[32m08:54:18[0m | [1mINFO    [0m | [36mModel saved to models\xgboost_model.pkl[0m
[32m08:54:18[0m | [1mINFO    [0m | [36mTrain/Test gap: -1 bars to prevent temporal leakage[0m
[32m08:54:18[0m | [1mINFO    [0m | [36mTraining with 500 samples, testing with 1 samples[0m
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:18] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
  score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0]	train-auc:0.60799	eval-auc:nan
[10]	train-auc:0.71899	eval-auc:nan
[20]	train-auc:0.75767	eval-auc:nan
[30]	train-auc:0.76979	eval-auc:nan
[40]	train-auc:0.79163	eval-auc:nan
[49]	train-auc:0.80088	eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
  warnings.warn(
[32m08:54:18[0m | [1mINFO    [0m | [36mTraining complete: Train AUC=0.8009, Test AUC=nan[0m
[32m08:54:18[0m | [1mINFO    [0m | [36mModel saved to models\xgboost_model.pkl[0m
[32m08:54:18[0m | [1mINFO    [0m | [36mTrain/Test gap: -1 bars to prevent temporal leakage[0m
[32m08:54:18[0m | [1mINFO    [0m | [36mTraining with 500 samples, testing with 1 samples[0m
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:18] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
  score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0]	train-auc:0.62377	eval-auc:nan
[10]	train-auc:0.71175	eval-auc:nan
[20]	train-auc:0.74277	eval-auc:nan
[30]	train-auc:0.76226	eval-auc:nan
[40]	train-auc:0.77391	eval-auc:nan
[49]	train-auc:0.78154	eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
  warnings.warn(
[32m08:54:18[0m | [1mINFO    [0m | [36mTraining complete: Train AUC=0.7815, Test AUC=nan[0m
[32m08:54:18[0m | [1mINFO    [0m | [36mModel saved to models\xgboost_model.pkl[0m
[32m08:54:18[0m | [1mINFO    [0m | [36mTrain/Test gap: -1 bars to prevent temporal leakage[0m
[32m08:54:18[0m | [1mINFO    [0m | [36mTraining with 500 samples, testing with 1 samples[0m
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:18] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
  score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0]	train-auc:0.60491	eval-auc:nan
[10]	train-auc:0.71732	eval-auc:nan
[20]	train-auc:0.73637	eval-auc:nan
[30]	train-auc:0.76178	eval-auc:nan
[40]	train-auc:0.77098	eval-auc:nan
[49]	train-auc:0.79057	eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
  warnings.warn(
[32m08:54:18[0m | [1mINFO    [0m | [36mTraining complete: Train AUC=0.7906, Test AUC=nan[0m
[32m08:54:18[0m | [1mINFO    [0m | [36mModel saved to models\xgboost_model.pkl[0m
[32m08:54:18[0m | [1mINFO    [0m | [36mTrain/Test gap: -1 bars to prevent temporal leakage[0m
[32m08:54:18[0m | [1mINFO    [0m | [36mTraining with 500 samples, testing with 1 samples[0m
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:18] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
  score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0]	train-auc:0.60088	eval-auc:nan
[10]	train-auc:0.71712	eval-auc:nan
[20]	train-auc:0.73691	eval-auc:nan
[30]	train-auc:0.75740	eval-auc:nan
[40]	train-auc:0.77895	eval-auc:nan
[49]	train-auc:0.78947	eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
  warnings.warn(
[32m08:54:18[0m | [1mINFO    [0m | [36mTraining complete: Train AUC=0.7895, Test AUC=nan[0m
[32m08:54:18[0m | [1mINFO    [0m | [36mModel saved to models\xgboost_model.pkl[0m
[32m08:54:18[0m | [1mINFO    [0m | [36mTrain/Test gap: -1 bars to prevent temporal leakage[0m
[32m08:54:18[0m | [1mINFO    [0m | [36mTraining with 500 samples, testing with 1 samples[0m
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:18] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
  score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0]	train-auc:0.58789	eval-auc:nan
[10]	train-auc:0.72688	eval-auc:nan
[20]	train-auc:0.75299	eval-auc:nan
[30]	train-auc:0.78038	eval-auc:nan
[40]	train-auc:0.79660	eval-auc:nan
[49]	train-auc:0.80917	eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
  warnings.warn(
[32m08:54:18[0m | [1mINFO    [0m | [36mTraining complete: Train AUC=0.8092, Test AUC=nan[0m
[32m08:54:18[0m | [1mINFO    [0m | [36mModel saved to models\xgboost_model.pkl[0m
[32m08:54:18[0m | [1mINFO    [0m | [36mTrain/Test gap: -1 bars to prevent temporal leakage[0m
[32m08:54:18[0m | [1mINFO    [0m | [36mTraining with 500 samples, testing with 1 samples[0m
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:18] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
  score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0]	train-auc:0.61598	eval-auc:nan
[10]	train-auc:0.70822	eval-auc:nan
[20]	train-auc:0.73139	eval-auc:nan
[30]	train-auc:0.74528	eval-auc:nan
[40]	train-auc:0.75819	eval-auc:nan
[49]	train-auc:0.77398	eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
  warnings.warn(
[32m08:54:19[0m | [1mINFO    [0m | [36mTraining complete: Train AUC=0.7740, Test AUC=nan[0m
[32m08:54:19[0m | [1mINFO    [0m | [36mModel saved to models\xgboost_model.pkl[0m
[32m08:54:19[0m | [1mINFO    [0m | [36mTrain/Test gap: -1 bars to prevent temporal leakage[0m
[32m08:54:19[0m | [1mINFO    [0m | [36mTraining with 500 samples, testing with 1 samples[0m
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:19] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
  score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0]	train-auc:0.59767	eval-auc:nan
[10]	train-auc:0.71330	eval-auc:nan
[20]	train-auc:0.73522	eval-auc:nan
[30]	train-auc:0.76301	eval-auc:nan
[40]	train-auc:0.77457	eval-auc:nan
[49]	train-auc:0.79120	eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
  warnings.warn(
[32m08:54:19[0m | [1mINFO    [0m | [36mTraining complete: Train AUC=0.7912, Test AUC=nan[0m
[32m08:54:19[0m | [1mINFO    [0m | [36mModel saved to models\xgboost_model.pkl[0m
[32m08:54:19[0m | [1mINFO    [0m | [36mTrain/Test gap: -1 bars to prevent temporal leakage[0m
[32m08:54:19[0m | [1mINFO    [0m | [36mTraining with 500 samples, testing with 1 samples[0m
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:19] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
  score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0]	train-auc:0.57988	eval-auc:nan
[10]	train-auc:0.70174	eval-auc:nan
[20]	train-auc:0.73308	eval-auc:nan
[30]	train-auc:0.76300	eval-auc:nan
[40]	train-auc:0.77397	eval-auc:nan
[49]	train-auc:0.79388	eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
  warnings.warn(
[32m08:54:19[0m | [1mINFO    [0m | [36mTraining complete: Train AUC=0.7939, Test AUC=nan[0m
[32m08:54:19[0m | [1mINFO    [0m | [36mModel saved to models\xgboost_model.pkl[0m
[32m08:54:19[0m | [1mINFO    [0m | [36mTrain/Test gap: -1 bars to prevent temporal leakage[0m
[32m08:54:19[0m | [1mINFO    [0m | [36mTraining with 500 samples, testing with 1 samples[0m
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:19] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
  score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0]	train-auc:0.61081	eval-auc:nan
[10]	train-auc:0.67913	eval-auc:nan
[20]	train-auc:0.71390	eval-auc:nan
[30]	train-auc:0.74361	eval-auc:nan
[40]	train-auc:0.76993	eval-auc:nan
[49]	train-auc:0.78383	eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
  warnings.warn(
[32m08:54:19[0m | [1mINFO    [0m | [36mTraining complete: Train AUC=0.7838, Test AUC=nan[0m
[32m08:54:19[0m | [1mINFO    [0m | [36mModel saved to models\xgboost_model.pkl[0m
[32m08:54:19[0m | [1mINFO    [0m | [36mTrain/Test gap: -1 bars to prevent temporal leakage[0m
[32m08:54:19[0m | [1mINFO    [0m | [36mTraining with 500 samples, testing with 1 samples[0m
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:19] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
  score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0]	train-auc:0.57255	eval-auc:nan
[10]	train-auc:0.71228	eval-auc:nan
[20]	train-auc:0.73502	eval-auc:nan
[30]	train-auc:0.74854	eval-auc:nan
[40]	train-auc:0.76543	eval-auc:nan
[49]	train-auc:0.77475	eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
  warnings.warn(
[32m08:54:19[0m | [1mINFO    [0m | [36mTraining complete: Train AUC=0.7748, Test AUC=nan[0m
[32m08:54:19[0m | [1mINFO    [0m | [36mModel saved to models\xgboost_model.pkl[0m
[32m08:54:19[0m | [1mINFO    [0m | [36mTrain/Test gap: -1 bars to prevent temporal leakage[0m
[32m08:54:19[0m | [1mINFO    [0m | [36mTraining with 500 samples, testing with 1 samples[0m
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:19] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
  score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0]	train-auc:0.60085	eval-auc:nan
[10]	train-auc:0.69657	eval-auc:nan
[20]	train-auc:0.71456	eval-auc:nan
[30]	train-auc:0.74570	eval-auc:nan
[40]	train-auc:0.76565	eval-auc:nan
[49]	train-auc:0.77503	eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
  warnings.warn(
[32m08:54:19[0m | [1mINFO    [0m | [36mTraining complete: Train AUC=0.7750, Test AUC=nan[0m
[32m08:54:19[0m | [1mINFO    [0m | [36mModel saved to models\xgboost_model.pkl[0m
[32m08:54:19[0m | [1mINFO    [0m | [36mTrain/Test gap: -1 bars to prevent temporal leakage[0m
[32m08:54:19[0m | [1mINFO    [0m | [36mTraining with 500 samples, testing with 1 samples[0m
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:19] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
  score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0]	train-auc:0.57024	eval-auc:nan
[10]	train-auc:0.70914	eval-auc:nan
[20]	train-auc:0.73352	eval-auc:nan
[30]	train-auc:0.74339	eval-auc:nan
[40]	train-auc:0.77528	eval-auc:nan
[49]	train-auc:0.78304	eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
  warnings.warn(
[32m08:54:19[0m | [1mINFO    [0m | [36mTraining complete: Train AUC=0.7830, Test AUC=nan[0m
[32m08:54:19[0m | [1mINFO    [0m | [36mModel saved to models\xgboost_model.pkl[0m
[32m08:54:19[0m | [1mINFO    [0m | [36mTrain/Test gap: -1 bars to prevent temporal leakage[0m
[32m08:54:19[0m | [1mINFO    [0m | [36mTraining with 500 samples, testing with 1 samples[0m
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:19] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
  score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0]	train-auc:0.59540	eval-auc:nan
[10]	train-auc:0.71927	eval-auc:nan
[20]	train-auc:0.74406	eval-auc:nan
[30]	train-auc:0.76638	eval-auc:nan
[40]	train-auc:0.78232	eval-auc:nan
[49]	train-auc:0.78659	eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
  warnings.warn(
[32m08:54:19[0m | [1mINFO    [0m | [36mTraining complete: Train AUC=0.7866, Test AUC=nan[0m
[32m08:54:19[0m | [1mINFO    [0m | [36mModel saved to models\xgboost_model.pkl[0m
[32m08:54:19[0m | [1mINFO    [0m | [36mTrain/Test gap: -1 bars to prevent temporal leakage[0m
[32m08:54:19[0m | [1mINFO    [0m | [36mTraining with 500 samples, testing with 1 samples[0m
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:19] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
  score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0]	train-auc:0.60596	eval-auc:nan
[10]	train-auc:0.73112	eval-auc:nan
[20]	train-auc:0.75009	eval-auc:nan
[30]	train-auc:0.76380	eval-auc:nan
[40]	train-auc:0.78176	eval-auc:nan
[49]	train-auc:0.78909	eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
  warnings.warn(
[32m08:54:19[0m | [1mINFO    [0m | [36mTraining complete: Train AUC=0.7891, Test AUC=nan[0m
[32m08:54:19[0m | [1mINFO    [0m | [36mModel saved to models\xgboost_model.pkl[0m
[32m08:54:19[0m | [1mINFO    [0m | [36mTrain/Test gap: -1 bars to prevent temporal leakage[0m
[32m08:54:19[0m | [1mINFO    [0m | [36mTraining with 500 samples, testing with 1 samples[0m
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:19] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
  score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0]	train-auc:0.60522	eval-auc:nan
[10]	train-auc:0.74166	eval-auc:nan
[20]	train-auc:0.75949	eval-auc:nan
[30]	train-auc:0.79360	eval-auc:nan
[40]	train-auc:0.79524	eval-auc:nan
[49]	train-auc:0.81023	eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
  warnings.warn(
[32m08:54:19[0m | [1mINFO    [0m | [36mTraining complete: Train AUC=0.8102, Test AUC=nan[0m
[32m08:54:19[0m | [1mINFO    [0m | [36mModel saved to models\xgboost_model.pkl[0m
[32m08:54:19[0m | [1mINFO    [0m | [36mTrain/Test gap: -1 bars to prevent temporal leakage[0m
[32m08:54:19[0m | [1mINFO    [0m | [36mTraining with 500 samples, testing with 1 samples[0m
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:19] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
  score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0]	train-auc:0.59997	eval-auc:nan
[10]	train-auc:0.73955	eval-auc:nan
[20]	train-auc:0.75448	eval-auc:nan
[30]	train-auc:0.77247	eval-auc:nan
[40]	train-auc:0.78000	eval-auc:nan
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:20] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
  score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[49]	train-auc:0.79077	eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
  warnings.warn(
[32m08:54:20[0m | [1mINFO    [0m | [36mTraining complete: Train AUC=0.7908, Test AUC=nan[0m
[32m08:54:20[0m | [1mINFO    [0m | [36mModel saved to models\xgboost_model.pkl[0m
[32m08:54:20[0m | [1mINFO    [0m | [36mTrain/Test gap: -1 bars to prevent temporal leakage[0m
[32m08:54:20[0m | [1mINFO    [0m | [36mTraining with 500 samples, testing with 1 samples[0m
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:20] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
  score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0]	train-auc:0.58106	eval-auc:nan
[10]	train-auc:0.74387	eval-auc:nan
[20]	train-auc:0.74495	eval-auc:nan
[30]	train-auc:0.76251	eval-auc:nan
[40]	train-auc:0.77773	eval-auc:nan
[49]	train-auc:0.79525	eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
  warnings.warn(
[32m08:54:20[0m | [1mINFO    [0m | [36mTraining complete: Train AUC=0.7952, Test AUC=nan[0m
[32m08:54:20[0m | [1mINFO    [0m | [36mModel saved to models\xgboost_model.pkl[0m
[32m08:54:20[0m | [1mINFO    [0m | [36mTrain/Test gap: -1 bars to prevent temporal leakage[0m
[32m08:54:20[0m | [1mINFO    [0m | [36mTraining with 500 samples, testing with 1 samples[0m
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:20] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
  score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0]	train-auc:0.58621	eval-auc:nan
[10]	train-auc:0.74838	eval-auc:nan
[20]	train-auc:0.76948	eval-auc:nan
[30]	train-auc:0.77170	eval-auc:nan
[40]	train-auc:0.78669	eval-auc:nan
[49]	train-auc:0.80217	eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
  warnings.warn(
[32m08:54:20[0m | [1mINFO    [0m | [36mTraining complete: Train AUC=0.8022, Test AUC=nan[0m
[32m08:54:20[0m | [1mINFO    [0m | [36mModel saved to models\xgboost_model.pkl[0m
[32m08:54:20[0m | [1mINFO    [0m | [36mTrain/Test gap: -1 bars to prevent temporal leakage[0m
[32m08:54:20[0m | [1mINFO    [0m | [36mTraining with 500 samples, testing with 1 samples[0m
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:20] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
  score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0]	train-auc:0.59705	eval-auc:nan
[10]	train-auc:0.73947	eval-auc:nan
[20]	train-auc:0.76035	eval-auc:nan
[30]	train-auc:0.78331	eval-auc:nan
[40]	train-auc:0.79839	eval-auc:nan
[49]	train-auc:0.80733	eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
  warnings.warn(
[32m08:54:20[0m | [1mINFO    [0m | [36mTraining complete: Train AUC=0.8073, Test AUC=nan[0m
[32m08:54:20[0m | [1mINFO    [0m | [36mModel saved to models\xgboost_model.pkl[0m
[32m08:54:20[0m | [1mINFO    [0m | [36mTrain/Test gap: -1 bars to prevent temporal leakage[0m
[32m08:54:20[0m | [1mINFO    [0m | [36mTraining with 500 samples, testing with 1 samples[0m
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:20] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
  score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0]	train-auc:0.62888	eval-auc:nan
[10]	train-auc:0.77790	eval-auc:nan
[20]	train-auc:0.79112	eval-auc:nan
[30]	train-auc:0.79420	eval-auc:nan
[40]	train-auc:0.80377	eval-auc:nan
[49]	train-auc:0.81198	eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
  warnings.warn(
[32m08:54:20[0m | [1mINFO    [0m | [36mTraining complete: Train AUC=0.8120, Test AUC=nan[0m
[32m08:54:20[0m | [1mINFO    [0m | [36mModel saved to models\xgboost_model.pkl[0m
[32m08:54:20[0m | [1mINFO    [0m | [36mTrain/Test gap: -1 bars to prevent temporal leakage[0m
[32m08:54:20[0m | [1mINFO    [0m | [36mTraining with 500 samples, testing with 1 samples[0m
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:20] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
  score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0]	train-auc:0.58766	eval-auc:nan
[10]	train-auc:0.73931	eval-auc:nan
[20]	train-auc:0.78821	eval-auc:nan
[30]	train-auc:0.80713	eval-auc:nan
[40]	train-auc:0.80992	eval-auc:nan
[49]	train-auc:0.82168	eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
  warnings.warn(
[32m08:54:20[0m | [1mINFO    [0m | [36mTraining complete: Train AUC=0.8217, Test AUC=nan[0m
[32m08:54:20[0m | [1mINFO    [0m | [36mModel saved to models\xgboost_model.pkl[0m
[32m08:54:20[0m | [1mINFO    [0m | [36mTrain/Test gap: -1 bars to prevent temporal leakage[0m
[32m08:54:20[0m | [1mINFO    [0m | [36mTraining with 500 samples, testing with 1 samples[0m
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:20] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
  score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0]	train-auc:0.57346	eval-auc:nan
[10]	train-auc:0.76528	eval-auc:nan
[20]	train-auc:0.77833	eval-auc:nan
[30]	train-auc:0.81216	eval-auc:nan
[40]	train-auc:0.82894	eval-auc:nan
[49]	train-auc:0.83959	eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
  warnings.warn(
[32m08:54:20[0m | [1mINFO    [0m | [36mTraining complete: Train AUC=0.8396, Test AUC=nan[0m
[32m08:54:20[0m | [1mINFO    [0m | [36mModel saved to models\xgboost_model.pkl[0m
[32m08:54:20[0m | [1mINFO    [0m | [36mTrain/Test gap: -1 bars to prevent temporal leakage[0m
[32m08:54:20[0m | [1mINFO    [0m | [36mTraining with 500 samples, testing with 1 samples[0m
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:20] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
  score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0]	train-auc:0.62849	eval-auc:nan
[10]	train-auc:0.75871	eval-auc:nan
[20]	train-auc:0.78298	eval-auc:nan
[30]	train-auc:0.80330	eval-auc:nan
[40]	train-auc:0.81261	eval-auc:nan
[49]	train-auc:0.82718	eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
  warnings.warn(
[32m08:54:20[0m | [1mINFO    [0m | [36mTraining complete: Train AUC=0.8272, Test AUC=nan[0m
[32m08:54:20[0m | [1mINFO    [0m | [36mModel saved to models\xgboost_model.pkl[0m
[32m08:54:20[0m | [1mINFO    [0m | [36mTrain/Test gap: -1 bars to prevent temporal leakage[0m
[32m08:54:20[0m | [1mINFO    [0m | [36mTraining with 500 samples, testing with 1 samples[0m
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:20] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
  score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0]	train-auc:0.60352	eval-auc:nan
[10]	train-auc:0.75021	eval-auc:nan
[20]	train-auc:0.78133	eval-auc:nan
[30]	train-auc:0.80449	eval-auc:nan
[40]	train-auc:0.81345	eval-auc:nan
[49]	train-auc:0.82141	eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
  warnings.warn(
[32m08:54:20[0m | [1mINFO    [0m | [36mTraining complete: Train AUC=0.8214, Test AUC=nan[0m
[32m08:54:20[0m | [1mINFO    [0m | [36mModel saved to models\xgboost_model.pkl[0m
[32m08:54:20[0m | [1mINFO    [0m | [36mTrain/Test gap: -1 bars to prevent temporal leakage[0m
[32m08:54:20[0m | [1mINFO    [0m | [36mTraining with 500 samples, testing with 1 samples[0m
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:20] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
  score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0]	train-auc:0.60313	eval-auc:nan
[10]	train-auc:0.72661	eval-auc:nan
[20]	train-auc:0.76145	eval-auc:nan
[30]	train-auc:0.78157	eval-auc:nan
[40]	train-auc:0.79408	eval-auc:nan
[49]	train-auc:0.79936	eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
  warnings.warn(
[32m08:54:20[0m | [1mINFO    [0m | [36mTraining complete: Train AUC=0.7994, Test AUC=nan[0m
[32m08:54:20[0m | [1mINFO    [0m | [36mModel saved to models\xgboost_model.pkl[0m
[32m08:54:20[0m | [1mINFO    [0m | [36mTrain/Test gap: -1 bars to prevent temporal leakage[0m
[32m08:54:20[0m | [1mINFO    [0m | [36mTraining with 500 samples, testing with 1 samples[0m
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:20] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
  score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0]	train-auc:0.62491	eval-auc:nan
[10]	train-auc:0.73262	eval-auc:nan
[20]	train-auc:0.75848	eval-auc:nan
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:21] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
  score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[30]	train-auc:0.78662	eval-auc:nan
[40]	train-auc:0.79843	eval-auc:nan
[49]	train-auc:0.80254	eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
  warnings.warn(
[32m08:54:21[0m | [1mINFO    [0m | [36mTraining complete: Train AUC=0.8025, Test AUC=nan[0m
[32m08:54:21[0m | [1mINFO    [0m | [36mModel saved to models\xgboost_model.pkl[0m
[32m08:54:21[0m | [1mINFO    [0m | [36mTrain/Test gap: -1 bars to prevent temporal leakage[0m
[32m08:54:21[0m | [1mINFO    [0m | [36mTraining with 500 samples, testing with 1 samples[0m
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:21] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
  score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0]	train-auc:0.60803	eval-auc:nan
[10]	train-auc:0.75171	eval-auc:nan
[20]	train-auc:0.76744	eval-auc:nan
[30]	train-auc:0.78326	eval-auc:nan
[40]	train-auc:0.79761	eval-auc:nan
[49]	train-auc:0.80276	eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
  warnings.warn(
[32m08:54:21[0m | [1mINFO    [0m | [36mTraining complete: Train AUC=0.8028, Test AUC=nan[0m
[32m08:54:21[0m | [1mINFO    [0m | [36mModel saved to models\xgboost_model.pkl[0m
[32m08:54:21[0m | [1mINFO    [0m | [36mTrain/Test gap: -1 bars to prevent temporal leakage[0m
[32m08:54:21[0m | [1mINFO    [0m | [36mTraining with 500 samples, testing with 1 samples[0m
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:21] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
  score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0]	train-auc:0.61595	eval-auc:nan
[10]	train-auc:0.74799	eval-auc:nan
[20]	train-auc:0.77954	eval-auc:nan
[30]	train-auc:0.80286	eval-auc:nan
[40]	train-auc:0.81213	eval-auc:nan
[49]	train-auc:0.81713	eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
  warnings.warn(
[32m08:54:21[0m | [1mINFO    [0m | [36mTraining complete: Train AUC=0.8171, Test AUC=nan[0m
[32m08:54:21[0m | [1mINFO    [0m | [36mModel saved to models\xgboost_model.pkl[0m
[32m08:54:21[0m | [1mINFO    [0m | [36mTrain/Test gap: -1 bars to prevent temporal leakage[0m
[32m08:54:21[0m | [1mINFO    [0m | [36mTraining with 500 samples, testing with 1 samples[0m
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:21] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
  score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0]	train-auc:0.59247	eval-auc:nan
[10]	train-auc:0.74075	eval-auc:nan
[20]	train-auc:0.76100	eval-auc:nan
[30]	train-auc:0.78358	eval-auc:nan
[40]	train-auc:0.79208	eval-auc:nan
[49]	train-auc:0.81172	eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
  warnings.warn(
[32m08:54:21[0m | [1mINFO    [0m | [36mTraining complete: Train AUC=0.8117, Test AUC=nan[0m
[32m08:54:21[0m | [1mINFO    [0m | [36mModel saved to models\xgboost_model.pkl[0m
[32m08:54:21[0m | [1mINFO    [0m | [36mTrain/Test gap: -1 bars to prevent temporal leakage[0m
[32m08:54:21[0m | [1mINFO    [0m | [36mTraining with 500 samples, testing with 1 samples[0m
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:21] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
  score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0]	train-auc:0.62501	eval-auc:nan
[10]	train-auc:0.73900	eval-auc:nan
[20]	train-auc:0.76904	eval-auc:nan
[30]	train-auc:0.78823	eval-auc:nan
[40]	train-auc:0.80905	eval-auc:nan
[49]	train-auc:0.81956	eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
  warnings.warn(
[32m08:54:21[0m | [1mINFO    [0m | [36mTraining complete: Train AUC=0.8196, Test AUC=nan[0m
[32m08:54:21[0m | [1mINFO    [0m | [36mModel saved to models\xgboost_model.pkl[0m
[32m08:54:21[0m | [1mINFO    [0m | [36mTrain/Test gap: -1 bars to prevent temporal leakage[0m
[32m08:54:21[0m | [1mINFO    [0m | [36mTraining with 500 samples, testing with 1 samples[0m
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:21] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
  score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0]	train-auc:0.63394	eval-auc:nan
[10]	train-auc:0.76201	eval-auc:nan
[20]	train-auc:0.76502	eval-auc:nan
[30]	train-auc:0.79011	eval-auc:nan
[40]	train-auc:0.80381	eval-auc:nan
[49]	train-auc:0.80967	eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
  warnings.warn(
[32m08:54:21[0m | [1mINFO    [0m | [36mTraining complete: Train AUC=0.8097, Test AUC=nan[0m
[32m08:54:21[0m | [1mINFO    [0m | [36mModel saved to models\xgboost_model.pkl[0m
[32m08:54:21[0m | [1mINFO    [0m | [36mTrain/Test gap: -1 bars to prevent temporal leakage[0m
[32m08:54:21[0m | [1mINFO    [0m | [36mTraining with 500 samples, testing with 1 samples[0m
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:21] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
  score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0]	train-auc:0.60953	eval-auc:nan
[10]	train-auc:0.74038	eval-auc:nan
[20]	train-auc:0.77163	eval-auc:nan
[30]	train-auc:0.79689	eval-auc:nan
[40]	train-auc:0.81636	eval-auc:nan
[49]	train-auc:0.83423	eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
  warnings.warn(
[32m08:54:21[0m | [1mINFO    [0m | [36mTraining complete: Train AUC=0.8342, Test AUC=nan[0m
[32m08:54:21[0m | [1mINFO    [0m | [36mModel saved to models\xgboost_model.pkl[0m
[32m08:54:21[0m | [1mINFO    [0m | [36mTrain/Test gap: -1 bars to prevent temporal leakage[0m
[32m08:54:21[0m | [1mINFO    [0m | [36mTraining with 500 samples, testing with 1 samples[0m
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:21] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
  score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0]	train-auc:0.58307	eval-auc:nan
[10]	train-auc:0.72438	eval-auc:nan
[20]	train-auc:0.75433	eval-auc:nan
[30]	train-auc:0.76545	eval-auc:nan
[40]	train-auc:0.79888	eval-auc:nan
[49]	train-auc:0.81112	eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
  warnings.warn(
[32m08:54:21[0m | [1mINFO    [0m | [36mTraining complete: Train AUC=0.8111, Test AUC=nan[0m
[32m08:54:21[0m | [1mINFO    [0m | [36mModel saved to models\xgboost_model.pkl[0m
[32m08:54:21[0m | [1mINFO    [0m | [36mTrain/Test gap: -1 bars to prevent temporal leakage[0m
[32m08:54:21[0m | [1mINFO    [0m | [36mTraining with 500 samples, testing with 1 samples[0m
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:21] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
  score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0]	train-auc:0.60998	eval-auc:nan
[10]	train-auc:0.72666	eval-auc:nan
[20]	train-auc:0.75603	eval-auc:nan
[30]	train-auc:0.78573	eval-auc:nan
[40]	train-auc:0.81097	eval-auc:nan
[49]	train-auc:0.82424	eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
  warnings.warn(
[32m08:54:21[0m | [1mINFO    [0m | [36mTraining complete: Train AUC=0.8242, Test AUC=nan[0m
[32m08:54:21[0m | [1mINFO    [0m | [36mModel saved to models\xgboost_model.pkl[0m
[32m08:54:21[0m | [1mINFO    [0m | [36mTrain/Test gap: -1 bars to prevent temporal leakage[0m
[32m08:54:21[0m | [1mINFO    [0m | [36mTraining with 500 samples, testing with 1 samples[0m
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:21] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
  score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0]	train-auc:0.58318	eval-auc:nan
[10]	train-auc:0.72923	eval-auc:nan
[20]	train-auc:0.77906	eval-auc:nan
[30]	train-auc:0.79240	eval-auc:nan
[40]	train-auc:0.80429	eval-auc:nan
[49]	train-auc:0.81426	eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
  warnings.warn(
[32m08:54:21[0m | [1mINFO    [0m | [36mTraining complete: Train AUC=0.8143, Test AUC=nan[0m
[32m08:54:21[0m | [1mINFO    [0m | [36mModel saved to models\xgboost_model.pkl[0m
[32m08:54:21[0m | [1mINFO    [0m | [36mTrain/Test gap: -1 bars to prevent temporal leakage[0m
[32m08:54:21[0m | [1mINFO    [0m | [36mTraining with 500 samples, testing with 1 samples[0m
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:21] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
  score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0]	train-auc:0.60979	eval-auc:nan
[10]	train-auc:0.71432	eval-auc:nan
[20]	train-auc:0.74102	eval-auc:nan
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:22] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
  score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[30]	train-auc:0.76768	eval-auc:nan
[40]	train-auc:0.78190	eval-auc:nan
[49]	train-auc:0.79541	eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
  warnings.warn(
[32m08:54:22[0m | [1mINFO    [0m | [36mTraining complete: Train AUC=0.7954, Test AUC=nan[0m
[32m08:54:22[0m | [1mINFO    [0m | [36mModel saved to models\xgboost_model.pkl[0m
[32m08:54:22[0m | [1mINFO    [0m | [36mTrain/Test gap: -1 bars to prevent temporal leakage[0m
[32m08:54:22[0m | [1mINFO    [0m | [36mTraining with 500 samples, testing with 1 samples[0m
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:22] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
  score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0]	train-auc:0.60319	eval-auc:nan
[10]	train-auc:0.75208	eval-auc:nan
[20]	train-auc:0.77528	eval-auc:nan
[30]	train-auc:0.79264	eval-auc:nan
[40]	train-auc:0.79898	eval-auc:nan
[49]	train-auc:0.80820	eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
  warnings.warn(
[32m08:54:22[0m | [1mINFO    [0m | [36mTraining complete: Train AUC=0.8082, Test AUC=nan[0m
[32m08:54:22[0m | [1mINFO    [0m | [36mModel saved to models\xgboost_model.pkl[0m
[32m08:54:22[0m | [1mINFO    [0m | [36mTrain/Test gap: -1 bars to prevent temporal leakage[0m
[32m08:54:22[0m | [1mINFO    [0m | [36mTraining with 500 samples, testing with 1 samples[0m
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:22] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
  score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0]	train-auc:0.60090	eval-auc:nan
[10]	train-auc:0.71684	eval-auc:nan
[20]	train-auc:0.73841	eval-auc:nan
[30]	train-auc:0.75852	eval-auc:nan
[40]	train-auc:0.77850	eval-auc:nan
[49]	train-auc:0.78957	eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
  warnings.warn(
[32m08:54:22[0m | [1mINFO    [0m | [36mTraining complete: Train AUC=0.7896, Test AUC=nan[0m
[32m08:54:22[0m | [1mINFO    [0m | [36mModel saved to models\xgboost_model.pkl[0m
[32m08:54:22[0m | [1mINFO    [0m | [36mTrain/Test gap: -1 bars to prevent temporal leakage[0m
[32m08:54:22[0m | [1mINFO    [0m | [36mTraining with 500 samples, testing with 1 samples[0m
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:22] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
  score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0]	train-auc:0.60470	eval-auc:nan
[10]	train-auc:0.71534	eval-auc:nan
[20]	train-auc:0.74616	eval-auc:nan
[30]	train-auc:0.76268	eval-auc:nan
[40]	train-auc:0.78869	eval-auc:nan
[49]	train-auc:0.78944	eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
  warnings.warn(
[32m08:54:22[0m | [1mINFO    [0m | [36mTraining complete: Train AUC=0.7894, Test AUC=nan[0m
[32m08:54:22[0m | [1mINFO    [0m | [36mModel saved to models\xgboost_model.pkl[0m
[32m08:54:22[0m | [1mINFO    [0m | [36mTrain/Test gap: -1 bars to prevent temporal leakage[0m
[32m08:54:22[0m | [1mINFO    [0m | [36mTraining with 500 samples, testing with 1 samples[0m
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:22] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
  score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0]	train-auc:0.61586	eval-auc:nan
[10]	train-auc:0.72851	eval-auc:nan
[20]	train-auc:0.74350	eval-auc:nan
[30]	train-auc:0.74620	eval-auc:nan
[40]	train-auc:0.75469	eval-auc:nan
[49]	train-auc:0.77162	eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
  warnings.warn(
[32m08:54:22[0m | [1mINFO    [0m | [36mTraining complete: Train AUC=0.7716, Test AUC=nan[0m
[32m08:54:22[0m | [1mINFO    [0m | [36mModel saved to models\xgboost_model.pkl[0m
[32m08:54:22[0m | [1mINFO    [0m | [36mTrain/Test gap: -1 bars to prevent temporal leakage[0m
[32m08:54:22[0m | [1mINFO    [0m | [36mTraining with 500 samples, testing with 1 samples[0m
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:22] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
  score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0]	train-auc:0.62284	eval-auc:nan
[10]	train-auc:0.72407	eval-auc:nan
[20]	train-auc:0.75050	eval-auc:nan
[30]	train-auc:0.74938	eval-auc:nan
[40]	train-auc:0.76005	eval-auc:nan
[49]	train-auc:0.77428	eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
  warnings.warn(
[32m08:54:22[0m | [1mINFO    [0m | [36mTraining complete: Train AUC=0.7743, Test AUC=nan[0m
[32m08:54:22[0m | [1mINFO    [0m | [36mModel saved to models\xgboost_model.pkl[0m
[32m08:54:22[0m | [1mINFO    [0m | [36mTrain/Test gap: -1 bars to prevent temporal leakage[0m
[32m08:54:22[0m | [1mINFO    [0m | [36mTraining with 500 samples, testing with 1 samples[0m
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:22] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
  score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0]	train-auc:0.62182	eval-auc:nan
[10]	train-auc:0.73667	eval-auc:nan
[20]	train-auc:0.75940	eval-auc:nan
[30]	train-auc:0.77804	eval-auc:nan
[40]	train-auc:0.78611	eval-auc:nan
[49]	train-auc:0.79566	eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
  warnings.warn(
[32m08:54:22[0m | [1mINFO    [0m | [36mTraining complete: Train AUC=0.7957, Test AUC=nan[0m
[32m08:54:22[0m | [1mINFO    [0m | [36mModel saved to models\xgboost_model.pkl[0m
[32m08:54:22[0m | [1mINFO    [0m | [36mTrain/Test gap: -1 bars to prevent temporal leakage[0m
[32m08:54:22[0m | [1mINFO    [0m | [36mTraining with 500 samples, testing with 1 samples[0m
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:22] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
  score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0]	train-auc:0.63954	eval-auc:nan
[10]	train-auc:0.73519	eval-auc:nan
[20]	train-auc:0.77194	eval-auc:nan
[30]	train-auc:0.78262	eval-auc:nan
[40]	train-auc:0.79268	eval-auc:nan
[49]	train-auc:0.79835	eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
  warnings.warn(
[32m08:54:22[0m | [1mINFO    [0m | [36mTraining complete: Train AUC=0.7983, Test AUC=nan[0m
[32m08:54:22[0m | [1mINFO    [0m | [36mModel saved to models\xgboost_model.pkl[0m
[32m08:54:22[0m | [1mINFO    [0m | [36mTrain/Test gap: -1 bars to prevent temporal leakage[0m
[32m08:54:22[0m | [1mINFO    [0m | [36mTraining with 500 samples, testing with 1 samples[0m
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:22] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
  score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0]	train-auc:0.60190	eval-auc:nan
[10]	train-auc:0.72704	eval-auc:nan
[20]	train-auc:0.75398	eval-auc:nan
[30]	train-auc:0.77765	eval-auc:nan
[40]	train-auc:0.78947	eval-auc:nan
[49]	train-auc:0.80493	eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
  warnings.warn(
[32m08:54:22[0m | [1mINFO    [0m | [36mTraining complete: Train AUC=0.8049, Test AUC=nan[0m
[32m08:54:22[0m | [1mINFO    [0m | [36mModel saved to models\xgboost_model.pkl[0m
[32m08:54:22[0m | [1mINFO    [0m | [36mTrain/Test gap: -1 bars to prevent temporal leakage[0m
[32m08:54:22[0m | [1mINFO    [0m | [36mTraining with 500 samples, testing with 1 samples[0m
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:22] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
  score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0]	train-auc:0.62254	eval-auc:nan
[10]	train-auc:0.75280	eval-auc:nan
[20]	train-auc:0.77255	eval-auc:nan
[30]	train-auc:0.79260	eval-auc:nan
[40]	train-auc:0.79349	eval-auc:nan
[49]	train-auc:0.80807	eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
  warnings.warn(
[32m08:54:22[0m | [1mINFO    [0m | [36mTraining complete: Train AUC=0.8081, Test AUC=nan[0m
[32m08:54:22[0m | [1mINFO    [0m | [36mModel saved to models\xgboost_model.pkl[0m
[32m08:54:22[0m | [1mINFO    [0m | [36mTrain/Test gap: -1 bars to prevent temporal leakage[0m
[32m08:54:22[0m | [1mINFO    [0m | [36mTraining with 500 samples, testing with 1 samples[0m
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:22] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
  score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0]	train-auc:0.61847	eval-auc:nan
[10]	train-auc:0.72788	eval-auc:nan
[20]	train-auc:0.75636	eval-auc:nan
[30]	train-auc:0.77244	eval-auc:nan
[40]	train-auc:0.79711	eval-auc:nan
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:23] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
  score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[49]	train-auc:0.80845	eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
  warnings.warn(
[32m08:54:23[0m | [1mINFO    [0m | [36mTraining complete: Train AUC=0.8085, Test AUC=nan[0m
[32m08:54:23[0m | [1mINFO    [0m | [36mModel saved to models\xgboost_model.pkl[0m
[32m08:54:23[0m | [1mINFO    [0m | [36mTrain/Test gap: -1 bars to prevent temporal leakage[0m
[32m08:54:23[0m | [1mINFO    [0m | [36mTraining with 500 samples, testing with 1 samples[0m
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:23] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
  score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0]	train-auc:0.63166	eval-auc:nan
[10]	train-auc:0.72233	eval-auc:nan
[20]	train-auc:0.73577	eval-auc:nan
[30]	train-auc:0.75872	eval-auc:nan
[40]	train-auc:0.77652	eval-auc:nan
[49]	train-auc:0.78654	eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
  warnings.warn(
[32m08:54:23[0m | [1mINFO    [0m | [36mTraining complete: Train AUC=0.7865, Test AUC=nan[0m
[32m08:54:23[0m | [1mINFO    [0m | [36mModel saved to models\xgboost_model.pkl[0m
[32m08:54:23[0m | [1mINFO    [0m | [36mTrain/Test gap: -1 bars to prevent temporal leakage[0m
[32m08:54:23[0m | [1mINFO    [0m | [36mTraining with 500 samples, testing with 1 samples[0m
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:23] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
  score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0]	train-auc:0.62698	eval-auc:nan
[10]	train-auc:0.73889	eval-auc:nan
[20]	train-auc:0.75839	eval-auc:nan
[30]	train-auc:0.77451	eval-auc:nan
[40]	train-auc:0.78084	eval-auc:nan
[49]	train-auc:0.78975	eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
  warnings.warn(
[32m08:54:23[0m | [1mINFO    [0m | [36mTraining complete: Train AUC=0.7898, Test AUC=nan[0m
[32m08:54:23[0m | [1mINFO    [0m | [36mModel saved to models\xgboost_model.pkl[0m
[32m08:54:23[0m | [1mINFO    [0m | [36mTrain/Test gap: -1 bars to prevent temporal leakage[0m
[32m08:54:23[0m | [1mINFO    [0m | [36mTraining with 500 samples, testing with 1 samples[0m
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:23] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
  score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0]	train-auc:0.63191	eval-auc:nan
[10]	train-auc:0.73347	eval-auc:nan
[20]	train-auc:0.75524	eval-auc:nan
[30]	train-auc:0.77779	eval-auc:nan
[40]	train-auc:0.79315	eval-auc:nan
[49]	train-auc:0.79780	eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
  warnings.warn(
[32m08:54:23[0m | [1mINFO    [0m | [36mTraining complete: Train AUC=0.7978, Test AUC=nan[0m
[32m08:54:23[0m | [1mINFO    [0m | [36mModel saved to models\xgboost_model.pkl[0m
[32m08:54:23[0m | [1mINFO    [0m | [36mTrain/Test gap: -1 bars to prevent temporal leakage[0m
[32m08:54:23[0m | [1mINFO    [0m | [36mTraining with 500 samples, testing with 1 samples[0m
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:23] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
  score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0]	train-auc:0.61065	eval-auc:nan
[10]	train-auc:0.74741	eval-auc:nan
[20]	train-auc:0.75973	eval-auc:nan
[30]	train-auc:0.77605	eval-auc:nan
[40]	train-auc:0.78838	eval-auc:nan
[49]	train-auc:0.79903	eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
  warnings.warn(
[32m08:54:23[0m | [1mINFO    [0m | [36mTraining complete: Train AUC=0.7990, Test AUC=nan[0m
[32m08:54:23[0m | [1mINFO    [0m | [36mModel saved to models\xgboost_model.pkl[0m
[32m08:54:23[0m | [1mINFO    [0m | [36mTrain/Test gap: -1 bars to prevent temporal leakage[0m
[32m08:54:23[0m | [1mINFO    [0m | [36mTraining with 500 samples, testing with 1 samples[0m
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:23] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
  score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0]	train-auc:0.57933	eval-auc:nan
[10]	train-auc:0.71406	eval-auc:nan
[20]	train-auc:0.75492	eval-auc:nan
[30]	train-auc:0.76461	eval-auc:nan
[40]	train-auc:0.78099	eval-auc:nan
[49]	train-auc:0.79006	eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
  warnings.warn(
[32m08:54:23[0m | [1mINFO    [0m | [36mTraining complete: Train AUC=0.7901, Test AUC=nan[0m
[32m08:54:23[0m | [1mINFO    [0m | [36mModel saved to models\xgboost_model.pkl[0m
[32m08:54:23[0m | [1mINFO    [0m | [36mTrain/Test gap: -1 bars to prevent temporal leakage[0m
[32m08:54:23[0m | [1mINFO    [0m | [36mTraining with 500 samples, testing with 1 samples[0m
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:23] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
  score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0]	train-auc:0.61780	eval-auc:nan
[10]	train-auc:0.72935	eval-auc:nan
[20]	train-auc:0.75414	eval-auc:nan
[30]	train-auc:0.77374	eval-auc:nan
[40]	train-auc:0.78245	eval-auc:nan
[49]	train-auc:0.78918	eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
  warnings.warn(
[32m08:54:23[0m | [1mINFO    [0m | [36mTraining complete: Train AUC=0.7892, Test AUC=nan[0m
[32m08:54:23[0m | [1mINFO    [0m | [36mModel saved to models\xgboost_model.pkl[0m
[32m08:54:23[0m | [1mINFO    [0m | [36mTrain/Test gap: -1 bars to prevent temporal leakage[0m
[32m08:54:23[0m | [1mINFO    [0m | [36mTraining with 500 samples, testing with 1 samples[0m
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:23] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
  score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0]	train-auc:0.63270	eval-auc:nan
[10]	train-auc:0.71846	eval-auc:nan
[20]	train-auc:0.73000	eval-auc:nan
[30]	train-auc:0.75459	eval-auc:nan
[40]	train-auc:0.77510	eval-auc:nan
[49]	train-auc:0.78933	eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
  warnings.warn(
[32m08:54:23[0m | [1mINFO    [0m | [36mTraining complete: Train AUC=0.7893, Test AUC=nan[0m
[32m08:54:23[0m | [1mINFO    [0m | [36mModel saved to models\xgboost_model.pkl[0m
[32m08:54:23[0m | [1mINFO    [0m | [36mTrain/Test gap: -1 bars to prevent temporal leakage[0m
[32m08:54:23[0m | [1mINFO    [0m | [36mTraining with 500 samples, testing with 1 samples[0m
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:23] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
  score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0]	train-auc:0.60909	eval-auc:nan
[10]	train-auc:0.72600	eval-auc:nan
[20]	train-auc:0.74130	eval-auc:nan
[30]	train-auc:0.75315	eval-auc:nan
[40]	train-auc:0.76976	eval-auc:nan
[49]	train-auc:0.78410	eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
  warnings.warn(
[32m08:54:23[0m | [1mINFO    [0m | [36mTraining complete: Train AUC=0.7841, Test AUC=nan[0m
[32m08:54:23[0m | [1mINFO    [0m | [36mModel saved to models\xgboost_model.pkl[0m
[32m08:54:23[0m | [1mINFO    [0m | [36mTrain/Test gap: -1 bars to prevent temporal leakage[0m
[32m08:54:23[0m | [1mINFO    [0m | [36mTraining with 500 samples, testing with 1 samples[0m
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:23] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
  score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0]	train-auc:0.59953	eval-auc:nan
[10]	train-auc:0.72494	eval-auc:nan
[20]	train-auc:0.74669	eval-auc:nan
[30]	train-auc:0.76223	eval-auc:nan
[40]	train-auc:0.78575	eval-auc:nan
[49]	train-auc:0.79317	eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
  warnings.warn(
[32m08:54:23[0m | [1mINFO    [0m | [36mTraining complete: Train AUC=0.7932, Test AUC=nan[0m
[32m08:54:23[0m | [1mINFO    [0m | [36mModel saved to models\xgboost_model.pkl[0m
[32m08:54:23[0m | [1mINFO    [0m | [36mTrain/Test gap: -1 bars to prevent temporal leakage[0m
[32m08:54:23[0m | [1mINFO    [0m | [36mTraining with 500 samples, testing with 1 samples[0m
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:23] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
  score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0]	train-auc:0.57937	eval-auc:nan
[10]	train-auc:0.72038	eval-auc:nan
[20]	train-auc:0.74880	eval-auc:nan
[30]	train-auc:0.76822	eval-auc:nan
[40]	train-auc:0.79570	eval-auc:nan
[49]	train-auc:0.80228	eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
  warnings.warn(
[32m08:54:24[0m | [1mINFO    [0m | [36mTraining complete: Train AUC=0.8023, Test AUC=nan[0m
[32m08:54:24[0m | [1mINFO    [0m | [36mModel saved to models\xgboost_model.pkl[0m
[32m08:54:24[0m | [1mINFO    [0m | [36mTrain/Test gap: -1 bars to prevent temporal leakage[0m
[32m08:54:24[0m | [1mINFO    [0m | [36mTraining with 500 samples, testing with 1 samples[0m
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:24] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
  score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0]	train-auc:0.60802	eval-auc:nan
[10]	train-auc:0.71673	eval-auc:nan
[20]	train-auc:0.72606	eval-auc:nan
[30]	train-auc:0.75112	eval-auc:nan
[40]	train-auc:0.77023	eval-auc:nan
[49]	train-auc:0.78546	eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
  warnings.warn(
[32m08:54:24[0m | [1mINFO    [0m | [36mTraining complete: Train AUC=0.7855, Test AUC=nan[0m
[32m08:54:24[0m | [1mINFO    [0m | [36mModel saved to models\xgboost_model.pkl[0m
[32m08:54:24[0m | [1mINFO    [0m | [36mTrain/Test gap: -1 bars to prevent temporal leakage[0m
[32m08:54:24[0m | [1mINFO    [0m | [36mTraining with 500 samples, testing with 1 samples[0m
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:24] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
  score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0]	train-auc:0.61530	eval-auc:nan
[10]	train-auc:0.71262	eval-auc:nan
[20]	train-auc:0.74913	eval-auc:nan
[30]	train-auc:0.77033	eval-auc:nan
[40]	train-auc:0.79397	eval-auc:nan
[49]	train-auc:0.80447	eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
  warnings.warn(
[32m08:54:24[0m | [1mINFO    [0m | [36mTraining complete: Train AUC=0.8045, Test AUC=nan[0m
[32m08:54:24[0m | [1mINFO    [0m | [36mModel saved to models\xgboost_model.pkl[0m
[32m08:54:24[0m | [1mINFO    [0m | [36mTrain/Test gap: -1 bars to prevent temporal leakage[0m
[32m08:54:24[0m | [1mINFO    [0m | [36mTraining with 500 samples, testing with 1 samples[0m
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:24] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
  score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0]	train-auc:0.59774	eval-auc:nan
[10]	train-auc:0.71685	eval-auc:nan
[20]	train-auc:0.75544	eval-auc:nan
[30]	train-auc:0.78023	eval-auc:nan
[40]	train-auc:0.78228	eval-auc:nan
[49]	train-auc:0.80050	eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
  warnings.warn(
[32m08:54:24[0m | [1mINFO    [0m | [36mTraining complete: Train AUC=0.8005, Test AUC=nan[0m
[32m08:54:24[0m | [1mINFO    [0m | [36mModel saved to models\xgboost_model.pkl[0m
[32m08:54:24[0m | [1mINFO    [0m | [36mTrain/Test gap: -1 bars to prevent temporal leakage[0m
[32m08:54:24[0m | [1mINFO    [0m | [36mTraining with 500 samples, testing with 1 samples[0m
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:24] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
  score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0]	train-auc:0.60768	eval-auc:nan
[10]	train-auc:0.73430	eval-auc:nan
[20]	train-auc:0.76393	eval-auc:nan
[30]	train-auc:0.78908	eval-auc:nan
[40]	train-auc:0.80738	eval-auc:nan
[49]	train-auc:0.80557	eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
  warnings.warn(
[32m08:54:24[0m | [1mINFO    [0m | [36mTraining complete: Train AUC=0.8056, Test AUC=nan[0m
[32m08:54:24[0m | [1mINFO    [0m | [36mModel saved to models\xgboost_model.pkl[0m
[32m08:54:24[0m | [1mINFO    [0m | [36mTrain/Test gap: -1 bars to prevent temporal leakage[0m
[32m08:54:24[0m | [1mINFO    [0m | [36mTraining with 500 samples, testing with 1 samples[0m
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:24] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
  score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0]	train-auc:0.62514	eval-auc:nan
[10]	train-auc:0.74810	eval-auc:nan
[20]	train-auc:0.77422	eval-auc:nan
[30]	train-auc:0.77685	eval-auc:nan
[40]	train-auc:0.79030	eval-auc:nan
[49]	train-auc:0.79821	eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
  warnings.warn(
[32m08:54:24[0m | [1mINFO    [0m | [36mTraining complete: Train AUC=0.7982, Test AUC=nan[0m
[32m08:54:24[0m | [1mINFO    [0m | [36mModel saved to models\xgboost_model.pkl[0m
[32m08:54:24[0m | [1mINFO    [0m | [36mTrain/Test gap: -1 bars to prevent temporal leakage[0m
[32m08:54:24[0m | [1mINFO    [0m | [36mTraining with 500 samples, testing with 1 samples[0m
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:24] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
  score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0]	train-auc:0.61793	eval-auc:nan
[10]	train-auc:0.72798	eval-auc:nan
[20]	train-auc:0.76128	eval-auc:nan
[30]	train-auc:0.77182	eval-auc:nan
[40]	train-auc:0.77739	eval-auc:nan
[49]	train-auc:0.78859	eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
  warnings.warn(
[32m08:54:24[0m | [1mINFO    [0m | [36mTraining complete: Train AUC=0.7886, Test AUC=nan[0m
[32m08:54:24[0m | [1mINFO    [0m | [36mModel saved to models\xgboost_model.pkl[0m
[32m08:54:24[0m | [1mINFO    [0m | [36mTrain/Test gap: -1 bars to prevent temporal leakage[0m
[32m08:54:24[0m | [1mINFO    [0m | [36mTraining with 500 samples, testing with 1 samples[0m
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:24] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
  score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0]	train-auc:0.63207	eval-auc:nan
[10]	train-auc:0.71108	eval-auc:nan
[20]	train-auc:0.73769	eval-auc:nan
[30]	train-auc:0.76183	eval-auc:nan
[40]	train-auc:0.78927	eval-auc:nan
[49]	train-auc:0.80148	eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
  warnings.warn(
[32m08:54:24[0m | [1mINFO    [0m | [36mTraining complete: Train AUC=0.8015, Test AUC=nan[0m
[32m08:54:24[0m | [1mINFO    [0m | [36mModel saved to models\xgboost_model.pkl[0m
[32m08:54:24[0m | [1mINFO    [0m | [36mTrain/Test gap: -1 bars to prevent temporal leakage[0m
[32m08:54:24[0m | [1mINFO    [0m | [36mTraining with 500 samples, testing with 1 samples[0m
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:24] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
  score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0]	train-auc:0.61342	eval-auc:nan
[10]	train-auc:0.71247	eval-auc:nan
[20]	train-auc:0.74038	eval-auc:nan
[30]	train-auc:0.75633	eval-auc:nan
[40]	train-auc:0.76489	eval-auc:nan
[49]	train-auc:0.76817	eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
  warnings.warn(
[32m08:54:24[0m | [1mINFO    [0m | [36mTraining complete: Train AUC=0.7682, Test AUC=nan[0m
[32m08:54:24[0m | [1mINFO    [0m | [36mModel saved to models\xgboost_model.pkl[0m
[32m08:54:24[0m | [1mINFO    [0m | [36mTrain/Test gap: -1 bars to prevent temporal leakage[0m
[32m08:54:24[0m | [1mINFO    [0m | [36mTraining with 500 samples, testing with 1 samples[0m
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:24] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
  score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0]	train-auc:0.61614	eval-auc:nan
[10]	train-auc:0.72650	eval-auc:nan
[20]	train-auc:0.76164	eval-auc:nan
[30]	train-auc:0.77206	eval-auc:nan
[40]	train-auc:0.78308	eval-auc:nan
[49]	train-auc:0.78877	eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
  warnings.warn(
[32m08:54:24[0m | [1mINFO    [0m | [36mTraining complete: Train AUC=0.7888, Test AUC=nan[0m
[32m08:54:24[0m | [1mINFO    [0m | [36mModel saved to models\xgboost_model.pkl[0m
[32m08:54:24[0m | [1mINFO    [0m | [36mTrain/Test gap: -1 bars to prevent temporal leakage[0m
[32m08:54:24[0m | [1mINFO    [0m | [36mTraining with 500 samples, testing with 1 samples[0m
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:24] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
  score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0]	train-auc:0.60267	eval-auc:nan
[10]	train-auc:0.73374	eval-auc:nan
[20]	train-auc:0.76585	eval-auc:nan
[30]	train-auc:0.78239	eval-auc:nan
[40]	train-auc:0.78585	eval-auc:nan
[49]	train-auc:0.79696	eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
  warnings.warn(
[32m08:54:24[0m | [1mINFO    [0m | [36mTraining complete: Train AUC=0.7970, Test AUC=nan[0m
[32m08:54:24[0m | [1mINFO    [0m | [36mModel saved to models\xgboost_model.pkl[0m
[32m08:54:24[0m | [1mINFO    [0m | [36mTrain/Test gap: -1 bars to prevent temporal leakage[0m
[32m08:54:24[0m | [1mINFO    [0m | [36mTraining with 500 samples, testing with 1 samples[0m
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:25] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
  score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0]	train-auc:0.61319	eval-auc:nan
[10]	train-auc:0.71857	eval-auc:nan
[20]	train-auc:0.74940	eval-auc:nan
[30]	train-auc:0.77293	eval-auc:nan
[40]	train-auc:0.79024	eval-auc:nan
[49]	train-auc:0.80264	eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
  warnings.warn(
[32m08:54:25[0m | [1mINFO    [0m | [36mTraining complete: Train AUC=0.8026, Test AUC=nan[0m
[32m08:54:25[0m | [1mINFO    [0m | [36mModel saved to models\xgboost_model.pkl[0m
[32m08:54:25[0m | [1mINFO    [0m | [36mTrain/Test gap: -1 bars to prevent temporal leakage[0m
[32m08:54:25[0m | [1mINFO    [0m | [36mTraining with 500 samples, testing with 1 samples[0m
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:25] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
  score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0]	train-auc:0.60500	eval-auc:nan
[10]	train-auc:0.70339	eval-auc:nan
[20]	train-auc:0.73286	eval-auc:nan
[30]	train-auc:0.75582	eval-auc:nan
[40]	train-auc:0.78008	eval-auc:nan
[49]	train-auc:0.79737	eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
  warnings.warn(
[32m08:54:25[0m | [1mINFO    [0m | [36mTraining complete: Train AUC=0.7974, Test AUC=nan[0m
[32m08:54:25[0m | [1mINFO    [0m | [36mModel saved to models\xgboost_model.pkl[0m
[32m08:54:25[0m | [1mINFO    [0m | [36mTrain/Test gap: -1 bars to prevent temporal leakage[0m
[32m08:54:25[0m | [1mINFO    [0m | [36mTraining with 500 samples, testing with 1 samples[0m
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:25] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
  score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0]	train-auc:0.60464	eval-auc:nan
[10]	train-auc:0.73022	eval-auc:nan
[20]	train-auc:0.76632	eval-auc:nan
[30]	train-auc:0.78323	eval-auc:nan
[40]	train-auc:0.79806	eval-auc:nan
[49]	train-auc:0.80681	eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
  warnings.warn(
[32m08:54:25[0m | [1mINFO    [0m | [36mTraining complete: Train AUC=0.8068, Test AUC=nan[0m
[32m08:54:25[0m | [1mINFO    [0m | [36mModel saved to models\xgboost_model.pkl[0m
[32m08:54:25[0m | [1mINFO    [0m | [36mTrain/Test gap: -1 bars to prevent temporal leakage[0m
[32m08:54:25[0m | [1mINFO    [0m | [36mTraining with 500 samples, testing with 1 samples[0m
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:25] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
  score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0]	train-auc:0.61222	eval-auc:nan
[10]	train-auc:0.73832	eval-auc:nan
[20]	train-auc:0.77334	eval-auc:nan
[30]	train-auc:0.79421	eval-auc:nan
[40]	train-auc:0.80396	eval-auc:nan
[49]	train-auc:0.81019	eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
  warnings.warn(
[32m08:54:25[0m | [1mINFO    [0m | [36mTraining complete: Train AUC=0.8102, Test AUC=nan[0m
[32m08:54:25[0m | [1mINFO    [0m | [36mModel saved to models\xgboost_model.pkl[0m
[32m08:54:25[0m | [1mINFO    [0m | [36mTrain/Test gap: -1 bars to prevent temporal leakage[0m
[32m08:54:25[0m | [1mINFO    [0m | [36mTraining with 500 samples, testing with 1 samples[0m
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:25] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
  score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0]	train-auc:0.55041	eval-auc:nan
[10]	train-auc:0.70677	eval-auc:nan
[20]	train-auc:0.74107	eval-auc:nan
[30]	train-auc:0.75340	eval-auc:nan
[40]	train-auc:0.77782	eval-auc:nan
[49]	train-auc:0.79610	eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
  warnings.warn(
[32m08:54:25[0m | [1mINFO    [0m | [36mTraining complete: Train AUC=0.7961, Test AUC=nan[0m
[32m08:54:25[0m | [1mINFO    [0m | [36mModel saved to models\xgboost_model.pkl[0m
[32m08:54:25[0m | [1mINFO    [0m | [36mTrain/Test gap: -1 bars to prevent temporal leakage[0m
[32m08:54:25[0m | [1mINFO    [0m | [36mTraining with 500 samples, testing with 1 samples[0m
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:25] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
  score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0]	train-auc:0.62203	eval-auc:nan
[10]	train-auc:0.70308	eval-auc:nan
[20]	train-auc:0.74496	eval-auc:nan
[30]	train-auc:0.76238	eval-auc:nan
[40]	train-auc:0.78789	eval-auc:nan
[49]	train-auc:0.79590	eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
  warnings.warn(
[32m08:54:25[0m | [1mINFO    [0m | [36mTraining complete: Train AUC=0.7959, Test AUC=nan[0m
[32m08:54:25[0m | [1mINFO    [0m | [36mModel saved to models\xgboost_model.pkl[0m
[32m08:54:25[0m | [1mINFO    [0m | [36mTrain/Test gap: -1 bars to prevent temporal leakage[0m
[32m08:54:25[0m | [1mINFO    [0m | [36mTraining with 500 samples, testing with 1 samples[0m
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:25] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
  score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0]	train-auc:0.63022	eval-auc:nan
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:26] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
  score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[10]	train-auc:0.72671	eval-auc:nan
[20]	train-auc:0.73606	eval-auc:nan
[30]	train-auc:0.76981	eval-auc:nan
[40]	train-auc:0.78587	eval-auc:nan
[49]	train-auc:0.79961	eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
  warnings.warn(
[32m08:54:26[0m | [1mINFO    [0m | [36mTraining complete: Train AUC=0.7996, Test AUC=nan[0m
[32m08:54:26[0m | [1mINFO    [0m | [36mModel saved to models\xgboost_model.pkl[0m
[32m08:54:26[0m | [1mINFO    [0m | [36mTrain/Test gap: -1 bars to prevent temporal leakage[0m
[32m08:54:26[0m | [1mINFO    [0m | [36mTraining with 500 samples, testing with 1 samples[0m
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:26] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
  score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0]	train-auc:0.62639	eval-auc:nan
[10]	train-auc:0.71306	eval-auc:nan
[20]	train-auc:0.73356	eval-auc:nan
[30]	train-auc:0.75206	eval-auc:nan
[40]	train-auc:0.76996	eval-auc:nan
[49]	train-auc:0.78848	eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
  warnings.warn(
[32m08:54:26[0m | [1mINFO    [0m | [36mTraining complete: Train AUC=0.7885, Test AUC=nan[0m
[32m08:54:26[0m | [1mINFO    [0m | [36mModel saved to models\xgboost_model.pkl[0m
[32m08:54:26[0m | [1mINFO    [0m | [36mTrain/Test gap: -1 bars to prevent temporal leakage[0m
[32m08:54:26[0m | [1mINFO    [0m | [36mTraining with 500 samples, testing with 1 samples[0m
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:26] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
  score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0]	train-auc:0.61308	eval-auc:nan
[10]	train-auc:0.70639	eval-auc:nan
[20]	train-auc:0.74028	eval-auc:nan
[30]	train-auc:0.76069	eval-auc:nan
[40]	train-auc:0.77010	eval-auc:nan
[49]	train-auc:0.78029	eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
  warnings.warn(
[32m08:54:26[0m | [1mINFO    [0m | [36mTraining complete: Train AUC=0.7803, Test AUC=nan[0m
[32m08:54:26[0m | [1mINFO    [0m | [36mModel saved to models\xgboost_model.pkl[0m
[32m08:54:26[0m | [1mINFO    [0m | [36mTrain/Test gap: -1 bars to prevent temporal leakage[0m
[32m08:54:26[0m | [1mINFO    [0m | [36mTraining with 500 samples, testing with 1 samples[0m
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:26] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
  score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0]	train-auc:0.59834	eval-auc:nan
[10]	train-auc:0.71778	eval-auc:nan
[20]	train-auc:0.73858	eval-auc:nan
[30]	train-auc:0.76284	eval-auc:nan
[40]	train-auc:0.77648	eval-auc:nan
[49]	train-auc:0.78535	eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
  warnings.warn(
[32m08:54:26[0m | [1mINFO    [0m | [36mTraining complete: Train AUC=0.7854, Test AUC=nan[0m
[32m08:54:26[0m | [1mINFO    [0m | [36mModel saved to models\xgboost_model.pkl[0m
[32m08:54:26[0m | [1mINFO    [0m | [36mTrain/Test gap: -1 bars to prevent temporal leakage[0m
[32m08:54:26[0m | [1mINFO    [0m | [36mTraining with 500 samples, testing with 1 samples[0m
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:26] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
  score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0]	train-auc:0.57705	eval-auc:nan
[10]	train-auc:0.74847	eval-auc:nan
[20]	train-auc:0.78271	eval-auc:nan
[30]	train-auc:0.79498	eval-auc:nan
[40]	train-auc:0.80677	eval-auc:nan
[49]	train-auc:0.82268	eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
  warnings.warn(
[32m08:54:26[0m | [1mINFO    [0m | [36mTraining complete: Train AUC=0.8227, Test AUC=nan[0m
[32m08:54:26[0m | [1mINFO    [0m | [36mModel saved to models\xgboost_model.pkl[0m
[32m08:54:26[0m | [1mINFO    [0m | [36mTrain/Test gap: -1 bars to prevent temporal leakage[0m
[32m08:54:26[0m | [1mINFO    [0m | [36mTraining with 500 samples, testing with 1 samples[0m
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:26] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
  score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0]	train-auc:0.59035	eval-auc:nan
[10]	train-auc:0.69604	eval-auc:nan
[20]	train-auc:0.75628	eval-auc:nan
[30]	train-auc:0.78456	eval-auc:nan
[40]	train-auc:0.78833	eval-auc:nan
[49]	train-auc:0.80046	eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
  warnings.warn(
[32m08:54:26[0m | [1mINFO    [0m | [36mTraining complete: Train AUC=0.8005, Test AUC=nan[0m
[32m08:54:26[0m | [1mINFO    [0m | [36mModel saved to models\xgboost_model.pkl[0m
[32m08:54:26[0m | [1mINFO    [0m | [36mTrain/Test gap: -1 bars to prevent temporal leakage[0m
[32m08:54:26[0m | [1mINFO    [0m | [36mTraining with 500 samples, testing with 1 samples[0m
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:26] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
  score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0]	train-auc:0.55826	eval-auc:nan
[10]	train-auc:0.73195	eval-auc:nan
[20]	train-auc:0.77602	eval-auc:nan
[30]	train-auc:0.79505	eval-auc:nan
[40]	train-auc:0.79973	eval-auc:nan
[49]	train-auc:0.81236	eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
  warnings.warn(
[32m08:54:26[0m | [1mINFO    [0m | [36mTraining complete: Train AUC=0.8124, Test AUC=nan[0m
[32m08:54:26[0m | [1mINFO    [0m | [36mModel saved to models\xgboost_model.pkl[0m
[32m08:54:26[0m | [1mINFO    [0m | [36mTrain/Test gap: -1 bars to prevent temporal leakage[0m
[32m08:54:26[0m | [1mINFO    [0m | [36mTraining with 500 samples, testing with 1 samples[0m
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:26] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
  score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0]	train-auc:0.56635	eval-auc:nan
[10]	train-auc:0.71472	eval-auc:nan
[20]	train-auc:0.75192	eval-auc:nan
[30]	train-auc:0.77732	eval-auc:nan
[40]	train-auc:0.78860	eval-auc:nan
[49]	train-auc:0.79153	eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
  warnings.warn(
[32m08:54:26[0m | [1mINFO    [0m | [36mTraining complete: Train AUC=0.7915, Test AUC=nan[0m
[32m08:54:26[0m | [1mINFO    [0m | [36mModel saved to models\xgboost_model.pkl[0m
[32m08:54:26[0m | [1mINFO    [0m | [36mTrain/Test gap: -1 bars to prevent temporal leakage[0m
[32m08:54:26[0m | [1mINFO    [0m | [36mTraining with 500 samples, testing with 1 samples[0m
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:26] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
  score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[0]	train-auc:0.59997	eval-auc:nan
[10]	train-auc:0.72610	eval-auc:nan
C:\Python313\Lib\site-packages\xgboost\callback.py:266: UserWarning: [08:54:27] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\metric\auc.cc:324: Dataset is empty, or contains only positive or negative samples.
  score: str = model.eval_set(evals, epoch, self.metric, self._output_margin)
[20]	train-auc:0.76509	eval-auc:nan
[30]	train-auc:0.78448	eval-auc:nan
[40]	train-auc:0.80099	eval-auc:nan
[49]	train-auc:0.82018	eval-auc:nan
C:\Python313\Lib\site-packages\sklearn\metrics\_ranking.py:442: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.
  warnings.warn(
[32m08:54:27[0m | [1mINFO    [0m | [36mTraining complete: Train AUC=0.8202, Test AUC=nan[0m
[32m08:54:27[0m | [1mINFO    [0m | [36mModel saved to models\xgboost_model.pkl[0m
[32m08:54:27[0m | [1mINFO    [0m | [36mWalk-forward: Avg Train AUC=0.8005, Avg Test AUC=0.5185[0m
[32m08:54:27[0m | [1mINFO    [0m | [36mWalk-forward Results:[0m
[32m08:54:27[0m | [1mINFO    [0m | [36m  Avg Train AUC: 0.8005[0m
[32m08:54:27[0m | [1mINFO    [0m | [36m  Avg Test AUC: 0.5185[0m
[32m08:54:27[0m | [1mINFO    [0m | [36m  Overfitting ratio: 1.54[0m
[32m08:54:27[0m | [1mINFO    [0m | [36m============================================================[0m
[32m08:54:27[0m | [1mINFO    [0m | [36mTRAINING COMPLETE[0m
[32m08:54:27[0m | [1mINFO    [0m | [36m============================================================[0m
[32m08:54:27[0m | [1mINFO    [0m | [36mHMM Model: SAVED[0m
[32m08:54:27[0m | [1mINFO    [0m | [36mXGBoost Model: SAVED[0m
[32m08:54:27[0m | [1mINFO    [0m | [36mModels saved in: models/[0m
[32m08:54:27[0m | [1mINFO    [0m | [36mTraining data saved in: data/[0m
[32m08:54:27[0m | [1mINFO    [0m | [36mDisconnected from MT5[0m
[32m08:54:27[0m | [1mINFO    [0m | [36mMT5 disconnected[0m
