chore: clean up workspace for production
- Remove tracked generated artifacts: backtest logs (52), xlsx (43), experiment model pkls (7), ml_v3 training logs (11), result csv/txt - Remove junk files: stray =1.4.5, training_output.log, *_analysis_output.txt, dead api.log, runtime bot.lock - Remove throwaway scripts: analyze_performance, test_trajectory_bug, verify_settings - Move reusable analysis scripts to scripts/analysis/ - Move status/report docs to docs/reports/ - Tighten .gitignore to prevent re-adding generated artifacts; ignore .kiro/
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[32m2026-02-09 15:42:36.061[0m | [1mINFO [0m | [36msrc.mt5_connector[0m:[36mconnect[0m:[36m177[0m - [1mConnected to MT5: FinexBisnisSolusi-Demo (Account: 61045904)[0m
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[32m2026-02-09 15:42:36.795[0m | [34m[1mDEBUG [0m | [36msrc.mt5_connector[0m:[36mget_market_data[0m:[36m449[0m - [34m[1mFetched 50000 bars for XAUUSD M15[0m
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[32m2026-02-09 15:42:37.000[0m | [34m[1mDEBUG [0m | [36msrc.mt5_connector[0m:[36mget_market_data[0m:[36m449[0m - [34m[1mFetched 2000 bars for XAUUSD H1[0m
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[32m2026-02-09 15:42:37.009[0m | [34m[1mDEBUG [0m | [36msrc.feature_eng[0m:[36mcalculate_rsi[0m:[36m130[0m - [34m[1mRSI calculated (period=14)[0m
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[32m2026-02-09 15:42:37.012[0m | [34m[1mDEBUG [0m | [36msrc.feature_eng[0m:[36mcalculate_atr[0m:[36m185[0m - [34m[1mATR calculated (period=14)[0m
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[32m2026-02-09 15:42:37.015[0m | [34m[1mDEBUG [0m | [36msrc.feature_eng[0m:[36mcalculate_macd[0m:[36m243[0m - [34m[1mMACD calculated (12/26/9)[0m
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[32m2026-02-09 15:42:37.018[0m | [34m[1mDEBUG [0m | [36msrc.feature_eng[0m:[36mcalculate_bollinger_bands[0m:[36m301[0m - [34m[1mBollinger Bands calculated (period=20, std=2.0)[0m
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[32m2026-02-09 15:42:37.020[0m | [34m[1mDEBUG [0m | [36msrc.feature_eng[0m:[36mcalculate_ema_crossover[0m:[36m357[0m - [34m[1mEMA crossover calculated (9/21)[0m
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[32m2026-02-09 15:42:37.022[0m | [34m[1mDEBUG [0m | [36msrc.feature_eng[0m:[36mcalculate_volume_features[0m:[36m403[0m - [34m[1mVolume features calculated (period=20)[0m
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[32m2026-02-09 15:42:37.032[0m | [34m[1mDEBUG [0m | [36msrc.feature_eng[0m:[36mcalculate_ml_features[0m:[36m518[0m - [34m[1mML features calculated[0m
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[32m2026-02-09 15:42:37.036[0m | [34m[1mDEBUG [0m | [36msrc.smc_polars[0m:[36mcalculate_swing_points[0m:[36m401[0m - [34m[1mSwing points: 3062 highs, 3079 lows[0m
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[32m2026-02-09 15:42:37.038[0m | [34m[1mDEBUG [0m | [36msrc.smc_polars[0m:[36mcalculate_fvg[0m:[36m312[0m - [34m[1mFVG calculation complete. Bullish: 5696, Bearish: 4720[0m
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[32m2026-02-09 15:42:37.061[0m | [34m[1mDEBUG [0m | [36msrc.smc_polars[0m:[36mcalculate_order_blocks[0m:[36m511[0m - [34m[1mOrder Blocks: 2201 bullish, 2164 bearish[0m
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[32m2026-02-09 15:42:37.169[0m | [34m[1mDEBUG [0m | [36msrc.smc_polars[0m:[36mcalculate_bos_choch[0m:[36m599[0m - [34m[1mBOS: 702 bullish, 458 bearish[0m
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[32m2026-02-09 15:42:37.169[0m | [34m[1mDEBUG [0m | [36msrc.smc_polars[0m:[36mcalculate_bos_choch[0m:[36m600[0m - [34m[1mCHoCH: 616 bullish, 617 bearish[0m
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[32m2026-02-09 15:42:37.171[0m | [34m[1mDEBUG [0m | [36msrc.feature_eng[0m:[36mcalculate_rsi[0m:[36m130[0m - [34m[1mRSI calculated (period=14)[0m
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[32m2026-02-09 15:42:37.173[0m | [34m[1mDEBUG [0m | [36msrc.feature_eng[0m:[36mcalculate_atr[0m:[36m185[0m - [34m[1mATR calculated (period=14)[0m
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[32m2026-02-09 15:42:37.174[0m | [34m[1mDEBUG [0m | [36msrc.feature_eng[0m:[36mcalculate_macd[0m:[36m243[0m - [34m[1mMACD calculated (12/26/9)[0m
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[32m2026-02-09 15:42:37.175[0m | [34m[1mDEBUG [0m | [36msrc.feature_eng[0m:[36mcalculate_bollinger_bands[0m:[36m301[0m - [34m[1mBollinger Bands calculated (period=20, std=2.0)[0m
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[32m2026-02-09 15:42:37.176[0m | [34m[1mDEBUG [0m | [36msrc.feature_eng[0m:[36mcalculate_ema_crossover[0m:[36m357[0m - [34m[1mEMA crossover calculated (9/21)[0m
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[32m2026-02-09 15:42:37.177[0m | [34m[1mDEBUG [0m | [36msrc.feature_eng[0m:[36mcalculate_volume_features[0m:[36m403[0m - [34m[1mVolume features calculated (period=20)[0m
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[32m2026-02-09 15:42:37.178[0m | [34m[1mDEBUG [0m | [36msrc.smc_polars[0m:[36mcalculate_swing_points[0m:[36m401[0m - [34m[1mSwing points: 130 highs, 130 lows[0m
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[32m2026-02-09 15:42:37.179[0m | [34m[1mDEBUG [0m | [36msrc.smc_polars[0m:[36mcalculate_fvg[0m:[36m312[0m - [34m[1mFVG calculation complete. Bullish: 256, Bearish: 155[0m
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[32m2026-02-09 15:42:37.181[0m | [34m[1mDEBUG [0m | [36msrc.smc_polars[0m:[36mcalculate_order_blocks[0m:[36m511[0m - [34m[1mOrder Blocks: 90 bullish, 82 bearish[0m
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[32m2026-02-09 15:42:37.187[0m | [34m[1mDEBUG [0m | [36msrc.smc_polars[0m:[36mcalculate_bos_choch[0m:[36m599[0m - [34m[1mBOS: 39 bullish, 20 bearish[0m
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[32m2026-02-09 15:42:37.187[0m | [34m[1mDEBUG [0m | [36msrc.smc_polars[0m:[36mcalculate_bos_choch[0m:[36m600[0m - [34m[1mCHoCH: 19 bullish, 19 bearish[0m
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[I 2026-02-09 15:42:37,440] A new study created in memory with name: xgboost_opt
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================================================================================
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ML MODEL V3 TRAINING PIPELINE
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Bismillah - Building Exceptional Model
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================================================================================
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Fetching 50,000 bars of M15 data...
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Symbol: XAUUSD
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Timeframe: M15
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Fetched 50,000 bars
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Date range: 2023-12-27 08:30:00 to 2026-02-09 10:30:00
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Fetching 2,000 bars of H1 data...
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Fetched 2,000 H1 bars
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Engineering features...
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M15 technical indicators...
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SMC structure features...
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H1 higher timeframe features...
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Total features: 70
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Columns with nulls: 45
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rsi, atr, atr_percent, bb_middle, bb_upper, bb_lower, bb_width, bb_percent_b, volume_sma, volume_ratio
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Filling nulls with forward fill...
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Labeling data with Triple Barrier Method...
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Starting Triple Barrier Labeling...
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Profit target: 0.2 ATR
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Stop loss: 0.15 ATR
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Max holding: 8 bars
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Min move threshold: 0.1 ATR
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Target Distribution:
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BUY: 35687 (71.37%)
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SELL: 14292 (28.58%)
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HOLD: 21 ( 0.04%)
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Quality Metrics:
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Profit barriers hit: 0 ( 0.00%)
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Avg bars to profit: 0.0
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Avg return (ATR): 0.000
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Balancing Classes...
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Target distribution: BUY=32%, SELL=32%, HOLD=36%
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Before: BUY=35687, SELL=14292, HOLD=21
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After: BUY=14291, SELL=14291, HOLD=21
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Total samples: 28603
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Splitting train/test...
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Train: 22,883 samples
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Test: 5,720 samples
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Test period: 2023-12-27 08:30:00 to 2026-02-09 10:30:00
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Train distribution: BUY=62.5%, SELL=37.5%, HOLD=0.0%
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Test distribution: BUY=0.0%, SELL=99.6%, HOLD=0.4%
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Selected 69 features
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Sample features: spread, rsi, atr, atr_percent, macd, macd_signal, macd_histogram, bb_middle, bb_upper, bb_lower...
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Training XGBoost model...
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Class weights: SELL=0.89, HOLD=1.00, BUY=0.53
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Running Optuna hyperparameter optimization...
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␍ 0%| | 0/30 [00:00<?, ?it/s]␍ ␍␍ 0%| | 0/30 [00:00<?, ?it/s]␍ ␍␍ 0%| | 0/30 [00:00<?, ?it/s]␍ 0%| | 0/30 [00:00<?, ?it/s]
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Traceback (most recent call last):
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File "C:\Users\Administrator\Videos\Smart Automatic Trading BOT + AI\backtests\ml_v3\train_ml_v3.py", line 580, in <module>
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trainer.run_full_pipeline()
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~~~~~~~~~~~~~~~~~~~~~~~~~^^
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File "C:\Users\Administrator\Videos\Smart Automatic Trading BOT + AI\backtests\ml_v3\train_ml_v3.py", line 565, in run_full_pipeline
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model = self.train_xgboost(df_train, df_test, feature_cols, optimize_hyperparams=True)
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File "C:\Users\Administrator\Videos\Smart Automatic Trading BOT + AI\backtests\ml_v3\train_ml_v3.py", line 377, in train_xgboost
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best_params = self._optimize_hyperparameters(
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X_train, y_train_mc, X_test, y_test_mc, sample_weights
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)
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File "C:\Users\Administrator\Videos\Smart Automatic Trading BOT + AI\backtests\ml_v3\train_ml_v3.py", line 505, in _optimize_hyperparameters
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study.optimize(objective, n_trials=30, show_progress_bar=True, n_jobs=1)
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~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
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File "C:\Python313\Lib\site-packages\optuna\study\study.py", line 490, in optimize
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_optimize(
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~~~~~~~~~^
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study=self,
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^^^^^^^^^^^
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...<7 lines>...
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show_progress_bar=show_progress_bar,
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^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
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)
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^
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File "C:\Python313\Lib\site-packages\optuna\study\_optimize.py", line 68, in _optimize
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_optimize_sequential(
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~~~~~~~~~~~~~~~~~~~~^
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study,
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^^^^^^
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...<8 lines>...
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progress_bar=progress_bar,
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^^^^^^^^^^^^^^^^^^^^^^^^^^
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)
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^
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File "C:\Python313\Lib\site-packages\optuna\study\_optimize.py", line 165, in _optimize_sequential
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frozen_trial_id = _run_trial(study, func, catch)
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File "C:\Python313\Lib\site-packages\optuna\study\_optimize.py", line 263, in _run_trial
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raise func_err
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File "C:\Python313\Lib\site-packages\optuna\study\_optimize.py", line 206, in _run_trial
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value_or_values = func(trial)
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File "C:\Users\Administrator\Videos\Smart Automatic Trading BOT + AI\backtests\ml_v3\train_ml_v3.py", line 498, in objective
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model.fit(X_train, y_train, sample_weight=sample_weights, verbose=False)
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~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
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File "C:\Python313\Lib\site-packages\xgboost\core.py", line 774, in inner_f
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return func(**kwargs)
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File "C:\Python313\Lib\site-packages\xgboost\sklearn.py", line 1763, in fit
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raise ValueError(
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...<2 lines>...
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)
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ValueError: Invalid classes inferred from unique values of `y`. Expected: [0 1], got [0 2]
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[W 2026-02-09 15:42:37,448] Trial 0 failed with parameters: {'max_depth': 3, 'learning_rate': 0.13047455532390495, 'n_estimators': 500, 'min_child_weight': 6, 'gamma': 0.4227292711890863, 'subsample': 0.6486036877331404, 'colsample_bytree': 0.6421699904554006, 'reg_alpha': 0.7032937095957577, 'reg_lambda': 0.9601028148259148} because of the following error: ValueError('Invalid classes inferred from unique values of `y`. Expected: [0 1], got [0 2]').
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Traceback (most recent call last):
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File "C:\Python313\Lib\site-packages\optuna\study\_optimize.py", line 206, in _run_trial
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value_or_values = func(trial)
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File "C:\Users\Administrator\Videos\Smart Automatic Trading BOT + AI\backtests\ml_v3\train_ml_v3.py", line 498, in objective
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model.fit(X_train, y_train, sample_weight=sample_weights, verbose=False)
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~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
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File "C:\Python313\Lib\site-packages\xgboost\core.py", line 774, in inner_f
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return func(**kwargs)
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File "C:\Python313\Lib\site-packages\xgboost\sklearn.py", line 1763, in fit
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raise ValueError(
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...<2 lines>...
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)
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ValueError: Invalid classes inferred from unique values of `y`. Expected: [0 1], got [0 2]
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[W 2026-02-09 15:42:37,515] Trial 0 failed with value None.
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Training failed: Invalid classes inferred from unique values of `y`. Expected: [0 1], got [0 2]
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