| import os | |
| class Config: | |
| """ | |
| Configuration class for the entire project. | |
| """ | |
| def __init__(self): | |
| # ================================================================= | |
| # Data & Feature Parameters | |
| # ================================================================= | |
| # TODO: Update this path to your Qlib data directory. | |
| self.qlib_data_path = "~/.qlib/qlib_data/cn_data" | |
| self.instrument = 'csi300' | |
| # Overall time range for data loading from Qlib. | |
| self.dataset_begin_time = "2011-01-01" | |
| self.dataset_end_time = '2025-06-05' | |
| # Sliding window parameters for creating samples. | |
| self.lookback_window = 90 # Number of past time steps for input. | |
| self.predict_window = 10 # Number of future time steps for prediction. | |
| self.max_context = 512 # Maximum context length for the model. | |
| # Features to be used from the raw data. | |
| self.feature_list = ['open', 'high', 'low', 'close', 'vol', 'amt'] | |
| # Time-based features to be generated. | |
| self.time_feature_list = ['minute', 'hour', 'weekday', 'day', 'month'] | |
| # ================================================================= | |
| # Dataset Splitting & Paths | |
| # ================================================================= | |
| # Note: The validation/test set starts earlier than the training/validation set ends | |
| # to account for the `lookback_window`. | |
| self.train_time_range = ["2011-01-01", "2022-12-31"] | |
| self.val_time_range = ["2022-09-01", "2024-06-30"] | |
| self.test_time_range = ["2024-04-01", "2025-06-05"] | |
| self.backtest_time_range = ["2024-07-01", "2025-06-05"] | |
| # TODO: Directory to save the processed, pickled datasets. | |
| self.dataset_path = "./data/processed_datasets" | |
| # ================================================================= | |
| # Training Hyperparameters | |
| # ================================================================= | |
| self.clip = 5.0 # Clipping value for normalized data to prevent outliers. | |
| self.epochs = 30 | |
| self.log_interval = 100 # Log training status every N batches. | |
| self.batch_size = 50 # Batch size per GPU. | |
| # Number of samples to draw for one "epoch" of training/validation. | |
| # This is useful for large datasets where a true epoch is too long. | |
| self.n_train_iter = 2000 * self.batch_size | |
| self.n_val_iter = 400 * self.batch_size | |
| # Learning rates for different model components. | |
| self.tokenizer_learning_rate = 2e-4 | |
| self.predictor_learning_rate = 4e-5 | |
| # Gradient accumulation to simulate a larger batch size. | |
| self.accumulation_steps = 1 | |
| # AdamW optimizer parameters. | |
| self.adam_beta1 = 0.9 | |
| self.adam_beta2 = 0.95 | |
| self.adam_weight_decay = 0.1 | |
| # Miscellaneous | |
| self.seed = 100 # Global random seed for reproducibility. | |
| # ================================================================= | |
| # Experiment Logging & Saving | |
| # ================================================================= | |
| self.use_comet = True # Set to False if you don't want to use Comet ML | |
| self.comet_config = { | |
| # It is highly recommended to load secrets from environment variables | |
| # for security purposes. Example: os.getenv("COMET_API_KEY") | |
| "api_key": "YOUR_COMET_API_KEY", | |
| "project_name": "Kronos-Finetune-Demo", | |
| "workspace": "your_comet_workspace" # TODO: Change to your Comet ML workspace name | |
| } | |
| self.comet_tag = 'finetune_demo' | |
| self.comet_name = 'finetune_demo' | |
| # Base directory for saving model checkpoints and results. | |
| # Using a general 'outputs' directory is a common practice. | |
| self.save_path = "./outputs/models" | |
| self.tokenizer_save_folder_name = 'finetune_tokenizer_demo' | |
| self.predictor_save_folder_name = 'finetune_predictor_demo' | |
| self.backtest_save_folder_name = 'finetune_backtest_demo' | |
| # Path for backtesting results. | |
| self.backtest_result_path = "./outputs/backtest_results" | |
| # ================================================================= | |
| # Model & Checkpoint Paths | |
| # ================================================================= | |
| # TODO: Update these paths to your pretrained model locations. | |
| # These can be local paths or Hugging Face Hub model identifiers. | |
| self.pretrained_tokenizer_path = "path/to/your/Kronos-Tokenizer-base" | |
| self.pretrained_predictor_path = "path/to/your/Kronos-small" | |
| # Paths to the fine-tuned models, derived from the save_path. | |
| # These will be generated automatically during training. | |
| self.finetuned_tokenizer_path = f"{self.save_path}/{self.tokenizer_save_folder_name}/checkpoints/best_model" | |
| self.finetuned_predictor_path = f"{self.save_path}/{self.predictor_save_folder_name}/checkpoints/best_model" | |
| # ================================================================= | |
| # Backtesting Parameters | |
| # ================================================================= | |
| self.backtest_n_symbol_hold = 50 # Number of symbols to hold in the portfolio. | |
| self.backtest_n_symbol_drop = 5 # Number of symbols to drop from the pool. | |
| self.backtest_hold_thresh = 5 # Minimum holding period for a stock. | |
| self.inference_T = 0.6 | |
| self.inference_top_p = 0.9 | |
| self.inference_top_k = 0 | |
| self.inference_sample_count = 5 | |
| self.backtest_batch_size = 1000 | |
| self.backtest_benchmark = self._set_benchmark(self.instrument) | |
| def _set_benchmark(self, instrument): | |
| dt_benchmark = { | |
| 'csi800': "SH000906", | |
| 'csi1000': "SH000852", | |
| 'csi300': "SH000300", | |
| } | |
| if instrument in dt_benchmark: | |
| return dt_benchmark[instrument] | |
| else: | |
| raise ValueError(f"Benchmark not defined for instrument: {instrument}") | |