121 lines
3.9 KiB
Markdown
121 lines
3.9 KiB
Markdown
# Kronos Fine-tuning on Custom CSV Datasets
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This module provides a comprehensive pipeline for fine-tuning Kronos models on your own CSV-formatted financial data. It supports both sequential training (tokenizer followed by predictor) and individual component training, with full distributed training capabilities.
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## 1. Data Preparation
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### Required Data Format
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Your CSV file must contain the following columns in this exact order:
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- `timestamps`: DateTime stamps for each data point
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- `open`: Opening price
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- `high`: Highest price
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- `low`: Lowest price
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- `close`: Closing price
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- `volume`: Trading volume
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- `amount`: Trading amount
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(volume and amount can be 0 if not available)
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### Sample Data Format
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| timestamps | open | close | high | low | volume | amount |
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|------------|------|-------|------|-----|--------|--------|
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| 2019/11/26 9:35 | 182.45215 | 184.45215 | 184.95215 | 182.45215 | 15136000 | 0 |
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| 2019/11/26 9:40 | 184.35215 | 183.85215 | 184.55215 | 183.45215 | 4433300 | 0 |
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| 2019/11/26 9:45 | 183.85215 | 183.35215 | 183.95215 | 182.95215 | 3070900 | 0 |
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> **Reference**: Check `data/HK_ali_09988_kline_5min_all.csv` for a complete example of the proper data format.
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## 2. Config Preparation
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Please edit the correct data path and set your training parameters.
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```yaml
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# Data configuration
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data:
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data_path: "/path/to/your/data.csv"
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lookback_window: 512 # Historical data points to use
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predict_window: 48 # Future points to predict
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max_context: 512 # Maximum context length
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...
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```
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There are some other settings here, please see `configs/config_ali09988_candle-5min.yaml` for more comments.
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## 3. Training
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### Method 1: Sequential Training (Recommended)
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The `train_sequential.py` script handles the complete training pipeline automatically:
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```bash
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# Complete training (tokenizer + predictor)
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python train_sequential.py --config configs/config_ali09988_candle-5min.yaml
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# Skip existing models
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python train_sequential.py --config configs/config_ali09988_candle-5min.yaml --skip-existing
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# Only train tokenizer
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python train_sequential.py --config configs/config_ali09988_candle-5min.yaml --skip-basemodel
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# Only train predictor
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python train_sequential.py --config configs/config_ali09988_candle-5min.yaml --skip-tokenizer
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```
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### Method 2: Individual Component Training
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Train each component separately for more control:
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```bash
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# Step 1: Train tokenizer
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python finetune_tokenizer.py --config configs/config_ali09988_candle-5min.yaml
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# Step 2: Train predictor (requires fine-tuned tokenizer)
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python finetune_base_model.py --config configs/config_ali09988_candle-5min.yaml
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```
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### DDP Training
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For faster training on multiple GPUs:
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```bash
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# Set communication backend (nccl for NVIDIA GPUs, gloo for CPU/mixed)
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DIST_BACKEND=nccl \
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torchrun --standalone --nproc_per_node=8 train_sequential.py --config configs/config_ali09988_candle-5min.yaml
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```
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## 4. Training Results
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The training process generates several outputs:
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### Model Checkpoints
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- **Tokenizer**: Saved to `{base_save_path}/{exp_name}/tokenizer/best_model/`
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- **Predictor**: Saved to `{base_save_path}/{exp_name}/basemodel/best_model/`
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### Training Logs
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- **Console output**: Real-time training progress and metrics
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- **Log files**: Detailed logs saved to `{base_save_path}/logs/`
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- **Validation tracking**: Best models are saved based on validation loss
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## 5. Prediction Vis
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The following images show example training results on alibaba (HK stock) data:
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