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Supports fine-tuning training with custom CSV data using configuration files Supports fine-tuning training with custom CSV data using configuration files
## 1. Quick Start ## 1. Prepare Your Data
**Data Format**: Ensure CSV file contains the following columns: `timestamps`, `open`, `high`, `low`, `close`, `volume`, `amount`
A good csv data should be like:
| 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 |
| ... | ... | ... | ... | ... | ... | ... |
| ... | ... | ... | ... | ... | ... | ... |
You can check "data/HK_ali_09988_kline_5min_all.csv" to find out the proper format.
## 2. Training
### Configuration Setup ### Configuration Setup
@ -70,4 +85,4 @@ torchrun --standalone --nproc_per_node=8 train_sequential.py --config configs/co
![HK_ali_09988_kline_5min_all_historical_20250919_074251](examples/HK_ali_09988_kline_5min_all_historical_20250919_074251.png) ![HK_ali_09988_kline_5min_all_historical_20250919_074251](examples/HK_ali_09988_kline_5min_all_historical_20250919_074251.png)
**Data Format**: Ensure CSV file contains the following columns: `timestamps`, `open`, `high`, `low`, `close`, `volume`, `amount`