Kronos-master/finetune_csv

Kronos Fine-tuning on Custom CSV Datasets

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.

1. Data Preparation

Required Data Format

Your CSV file must contain the following columns:

• timestamps: DateTime stamps for each data point
• open: Opening price
• high: Highest price
• low: Lowest price
• close: Closing price
• volume: Trading volume
• amount: Trading amount

(volume and amount can be 0 if not available)

Sample Data Format

timestamps	open	close	high	low	volume	amount
2019/11/26 9:35	182.45215	184.45215	184.95215	182.45215	15136000	0
2019/11/26 9:40	184.35215	183.85215	184.55215	183.45215	4433300	0
2019/11/26 9:45	183.85215	183.35215	183.95215	182.95215	3070900	0

Reference: Check data/HK_ali_09988_kline_5min_all.csv for a complete example of the proper data format.

2. Config Preparation

Please edit the correct data path & pretrained model path and set your training parameters.

# Data configuration
data:
  data_path: "/path/to/your/data.csv"
  lookback_window: 512        # Historical data points to use
  predict_window: 48           # Future points to predict
  max_context: 512            # Maximum context length

...

There are some other settings here, please see configs/config_ali09988_candle-5min.yaml for more comments.

3. Training

Method 1: Sequential Training (Recommended)

The train_sequential.py script handles the complete training pipeline automatically:

# Complete training (tokenizer + predictor)
python train_sequential.py --config configs/config_ali09988_candle-5min.yaml

# Skip existing models
python train_sequential.py --config configs/config_ali09988_candle-5min.yaml --skip-existing

# Only train tokenizer
python train_sequential.py --config configs/config_ali09988_candle-5min.yaml --skip-basemodel

# Only train predictor
python train_sequential.py --config configs/config_ali09988_candle-5min.yaml --skip-tokenizer

Method 2: Individual Component Training

Train each component separately for more control:

# Step 1: Train tokenizer
python finetune_tokenizer.py --config configs/config_ali09988_candle-5min.yaml

# Step 2: Train predictor (requires fine-tuned tokenizer)
python finetune_base_model.py --config configs/config_ali09988_candle-5min.yaml

DDP Training

For faster training on multiple GPUs:

# Set communication backend (nccl for NVIDIA GPUs, gloo for CPU/mixed)
DIST_BACKEND=nccl \
torchrun --standalone --nproc_per_node=8 train_sequential.py --config configs/config_ali09988_candle-5min.yaml

4. Training Results

The training process generates several outputs:

Model Checkpoints

• Tokenizer: Saved to {base_save_path}/{exp_name}/tokenizer/best_model/
• Predictor: Saved to {base_save_path}/{exp_name}/basemodel/best_model/

Training Logs

• Console output: Real-time training progress and metrics
• Log files: Detailed logs saved to {base_save_path}/logs/
• Validation tracking: Best models are saved based on validation loss

5. Prediction Vis

The following images show example training results on alibaba (HK stock) data: