README.md

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# FinText-TSFM 📈  
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  <h1 style="font-size: 2em; font-weight: bold;">Time Series Foundation Models for Finance</h1>
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## 🚀 TSFMs Release

We are pleased to introduce **FinText-TSFM**, a comprehensive suite of **time series foundation models (TSFMs)** developed for financial forecasting and quantitative research. This release accompanies the paper :
**[*Re(Visiting) Time Series Foundation Models in Finance*](https://ssrn.com/abstract=4963618)** by *Eghbal Rahimikia, Hao Ni, and Weiguan Wang (2025)*.

### 💡 Key Highlights

- **Finance-Native Pre-training:**  
  Models are pre-trained **from scratch** on large-scale financial time series datasets — including daily excess returns across **89 markets** and **over 2 billion observations** — to ensure full temporal and domain alignment.

- **Bias-Free Design:**  
  Training strictly follows a **chronological expanding-window setup**, avoiding any **look-ahead bias** or **information leakage**.

- **Model Families:**  
  This release includes variants of **Chronos** and **TimesFM** architectures adapted for financial time series:
  - Chronos-Tiny / Mini / Small  
  - TimesFM-8M / 20M  

- **Performance Insights:**  
  Our findings show that **off-the-shelf TSFMs** underperform in zero-shot forecasting, while **finance-pretrained models** achieve large gains in both predictive accuracy and portfolio Sharpe ratios.

- **Evaluation Scope:**  
  Models are benchmarked across **U.S. and international markets**, using rolling windows (5, 21, 252, 512 days) and **18M+ out-of-sample forecasts**.


### 🧠 Technical Overview

- **Architecture:** Transformer-based TSFMs (Chronos & TimesFM)  
- **Training Regime:** Pre-training from scratch, fine-tuning, and zero-shot evaluation  
- **Compute:** >50,000 GPU hours on NVIDIA GH200 Grace Hopper clusters  

### 📚 Citation

Please cite the accompanying paper if you use these models:

> **Re(Visiting) Time Series Foundation Models in Finance.**  
> **Rahimikia, Eghbal; Ni, Hao; Wang, Weiguan.**  
> SSRN: [https://ssrn.com/abstract=4963618](https://ssrn.com/abstract=4963618)  
> DOI: [10.2139/ssrn.4963618](http://dx.doi.org/10.2139/ssrn.4963618)

### 🔋 Acknowledgments

This project was made possible through computational and institutional support from:
- **Isambard-AI National AI Research Resource (AIRR)**  
- **The University of Manchester** (Research IT & Computational Shared Facility)
- **Alliance Manchester Business School (AMBS), University of Manchester**
- **University College London (UCL)**
- **Shanghai University**
- **N8 Centre of Excellence in Computationally Intensive Research (N8 CIR)**  
- **The Alan Turing Institute**  


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      <img src="https://fintext.ai/UoM-logo.svg" alt="University of Manchester Logo" style="width: 210px; height: auto; margin: 0;">
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      <p style="font-size: 0.8em; margin: 0;">Bristol Supercomputing</p>
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    Alliance Manchester Business School, The University of Manchester<br>
    Department of Mathematics, University College London
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