VTA

KO | EN

gitlite — search

#javascript #hacktoberfest #typescript #llm #machine-learning #deep-learning #linux #docker #cli #kubernetes #pytorch #mcp #ios #open-source

(★ 23)

Code release for "Reasoning on Time-Series for Financial Technical Analysis", ICLR 2026. https://arxiv.org/abs/2511.08616

VTA 최신버젼 다운로드

최종 버전 다운로드 (.zip)

// repository documentation

Was this content helpful?

★ 0 (0 ratings)

# VTA: Reasoning on Time-Series for Financial Technical Analysis

[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)

## 🔍 Overview

Stock time-series forecasting models are often black-boxes, limiting their adoption in real-world investing.  
We introduce **Verbal Technical Analysis (VTA)**, the first framework for **explainable stock time-series forecasting**.  
VTA fine-tunes Large Language Models (LLMs) to **verbally reason about predictive patterns** in time-series via **Time-Series Group Relative Policy Optimization (Time-GRPO)**, and conditions forecasts on these reasoning traces with a backbone forecasting model.

Our approach:
- Produces **high-quality reasoning traces** that explain forecasts. 
- Achieves **state-of-the-art prediction accuracy** across U.S., Chinese, and European markets. 
- Utilizes **Time-GRPO** to fine-tune an LLM for reasoning, and conditions a powerful forecasting backbone on its output to ensure both accuracy and interpretability

 <img src="assets/figure2_pipeline_new.png" alt="Pipeline: multi-stage Time-GRPO + joint conditional forecasting" width="720"/>

---

## 🚀 Getting Started

### Environment
```bash
conda create -y -n vta python=3.11
conda activate vta
conda install -y -c pytorch -c nvidia pytorch torchvision torchaudio pytorch-cuda=12.1
conda install -y -c conda-forge libta-lib
pip install -r requirements.txt
```

## 📊 Quick demos
Run the backbone demos first (uses included example GRPO outputs) — good for sanity checks.
```bash
cd backbone
# prepare embeddings once
python pca.py

# single stock (~3–5 min)
./scripts/vta_demo_single.sh

# multi stock (~3–4 hours)
./scripts/vta_demo_multi.sh
```
Where to edit inside the scripts:
- `vta_demo_single.sh`: set `ROOT_PATH`, `DATA_PATH`, `REF_CSV_DIR`.
- `vta_demo_multi.sh`: set `ROOT_PATH`, `REF_CSV_DIR`.

## 📂 Adding Your Own Data
- Place your CSVs in `backbone/data/stocknet/` (e.g., `AAPL.csv`, `MSFT.csv`, ...).
- Format (Yahoo Finance style):
```text
Date,Open,High,Low,Close,Adj Close,Volume
```
- One CSV per stock/ticker. Example files are already included under `backbone/data/stocknet/`.

## 🧩 Full Pipeline

### Step 1) Preprocess to GRPO format (from repo root)
```bash
python combined_preprocessor.py \
 --root_path backbone/data/stocknet \
 --output_dir grpo/data/stocknet
```
Notes:
- Change `--root_path` to your raw CSV folder (e.g., `backbone/data/stocknet`).
- Change `--output_dir` to where preprocessed splits should be written (default `grpo/data/<DATASET_NAME>`).

### Step 2) Train GRPO
```bash
cd grpo
./run_pipeline.sh
```
What it writes:
- Trained adapters under `grpo/models/<MODEL>/<STOCK>/`
- Per-stage results under `grpo/results/<STOCK>_<MODEL>_.../`
- Per-stock GRPO reference CSVs for the backbone under `backbone/data/grpo_input/<DATASET_LABEL>/<MODEL_NAME>/` (controlled by `--dataset-label` in `split_grpo_outputs.py`).

### Results and outputs
- GRPO:
 - Logs/results: `grpo/results/<STOCK>_<MODEL>_.../`
 - Adapters: `grpo/models/<MODEL>/<STOCK>/`
 - Reference CSVs for backbone: `backbone/data/grpo_input/<DATASET_LABEL>/<MODEL_NAME>/`
- Backbone:
 - Checkpoints: `backbone/checkpoints/<MODEL_ID>_*`
 - Metrics/arrays: `backbone/results/<MODEL_ID>_<TIMESTAMP>/`

### Step 3) Run backbone forecasting with conditional reasoning guidance 
```bash
cd ../backbone
# Prepare word token embeddings once
python pca.py
# Quick single-stock demo
./scripts/vta_demo_single.sh

# Multi-stock demo
./scripts/vta_demo_multi.sh
```

## 📈 Results

 <img src="assets/table1_results.png" alt="Performance comparison" width="720"/>

- VTA outperforms all baselines across StockNet, Dow Jones, EURO STOXX 50, and China A50.
- Produces reasoning traces judged higher in clarity, depth, and coherence compared to GPT-4.1 mini and DeepSeek-R1.
- In portfolio construction, VTA achieves the highest Sharpe ratio.

## 📁 Repository Layout

<pre>
.
├── combined_preprocessor.py # Converts raw stock CSVs → GRPO-ready
├── grpo/ # Multi-stage training pipeline
│ ├── run_pipeline.sh
│ └── models/ results/
├── backbone/ # Cross-modal forecaster
│ ├── pca.py
│ ├── scripts/vta_demo_*.sh
│ └── checkpoints/ results/
└── assets/ # Figures and diagrams
</pre>

## Citation

If you find this repository useful, please cite our paper.

```
@inproceedings{koa2026reasoning,
  title={Reasoning on Time-Series for Financial Technical Analysis},
  author={Kelvin J.L. Koa and Jan Chen and Yunshan Ma and Zheng Huanhuan and Tat-Seng Chua},
  booktitle={The Fourteenth International Conference on Learning Representations},
  year={2026}
}
```

## License & Acknowledgements
This project is licensed under the MIT License. See `LICENSE` for details.

We gratefully acknowledge the authors of CALF (cross-modal fine-tuning) for their foundational model implementations and inspiration. Please refer to their repository for citation details.

Note on runtimes:
- Full end-to-end pipeline (including GRPO stages) can take ~80–120 hours on a single RTX A5000.
- Quick demos (backbone only):
  - `backbone/scripts/vta_demo_single.sh` runs a single-stock demo in ~3–5 minutes on one GPU.
  - `backbone/scripts/vta_demo_multi.sh` runs a multi-stock demo in ~3–4 hours on one GPU.

VTA

VTA 최신버젼 다운로드

오늘의 추천 프로젝트

Was this content helpful?

Recent Feedback

VTA

VTA 최신버젼 다운로드

오늘의 추천 프로젝트

Was this content helpful?

Recent Feedback

Download README