---
model-index:
  - name: TimeSeriesTransformerClassifier
    results:
      - task:
          name: Time Series Classification
          type: time-series-classification
        dataset:
          name: Yahoo Finance GC=F 1h
          type: financial-time-series
          split: validation
          config: gc1h
          revision: "1.0"
        metrics:
          - name: Final Equity (Raw Signal)
            type: final_equity
            value: 206.24
          - name: Sharpe Ratio (Raw Signal)
            type: sharpe_ratio
            value: 0.0318
          - name: Winrate (Raw Signal)
            type: winrate
            value: 0.522
      - task:
          name: Time Series Classification
          type: time-series-classification
        dataset:
          name: Yahoo Finance GC=F 1h
          type: financial-time-series
          split: validation
          config: gc1h
          revision: "1.0"
        metrics:
          - name: Final Equity (Confidence Filtered)
            type: final_equity
            value: 100.0
          - name: Sharpe Ratio (Confidence Filtered)
            type: sharpe_ratio
            value: 0.0
          - name: Winrate (Confidence Filtered)
            type: winrate
            value: 0.0
library_name: pytorch
tags:
  - financial-forecasting
  - time-series
  - transformer
  - algorithmic-trading
  - gold
model-architecture: Transformer Encoder with Positional Encoding and Classification Head
inference:
  parameters: {}
repo:
  name: transformer-classifier-gc1h
  url: https://huggingface.co/JonusNattapong/transformer-classifier-gc1h
model-creator: Jonus Nattapong
model-card-authors: Jonus Nattapong
---

# 📈 Time Series Transformer Classifier for Algorithmic Trading

This repository provides a **Transformer-based time series classifier** trained on Gold Futures (**GC=F, 1-hour timeframe**) to predict short-term price direction. The model outputs **Up, Flat, or Down** classes which can be used to generate trading signals.

---

## 🔧 Model Architecture

- **Base**: Transformer Encoder with Positional Encoding  
- **Head**: Classification Layer  
- **Framework**: PyTorch  

---

## 📊 Training Setup

- **Dataset**: GC=F (Gold Futures), 1-hour interval, 2 years (Yahoo Finance)  
- **Features**: EMA, RSI, ATR, MACD, Bollinger Bands, lagged returns, realized volatility, and cyclical time features (hour/day).  
- **Target**: 3-class (Up, Flat, Down), horizon = **6h**, threshold = **0.0005**.  
- **Split**: Walk-forward (80% train, 20% validation).  
- **Loss**: CrossEntropyLoss (weighted).  
- **Optimizer**: AdamW  

---

## 📉 Backtest Results

The model was evaluated via a **walk-forward backtest with continuous position sizing**.

### Raw Signal Performance
- **Final Equity**: 206.24  
- **Sharpe Ratio**: 0.032  
- **Profit Factor**: 1.099  
- **Winrate**: 52.2%  
- **Max Drawdown**: -10.6%  

### Confidence-Filtered Signal (CONF_THR = 0.45)
- **Final Equity**: 100.0  
- **Sharpe Ratio**: 0.0  
- **Winrate**: 0% (filter too strict)  

📊 ![Backtest Equity Curve](/static-proxy?url=https%3A%2F%2Fcdn-uploads.huggingface.co%2Fproduction%2Fuploads%2F673eef9c4edfc6d3b58ba3aa%2FdPgYmwklVnGOe3ikkeKdn.png)

---

## 🚀 Usage

```python
from huggingface_hub import hf_hub_download
import torch, torch.nn.functional as F
import numpy as np

# Download model weights
repo_id = "JonusNattapong/transformer-classifier-gc1h"
filename = "transformer_cls_gc1h.pt"
state_dict_path = hf_hub_download(repo_id=repo_id, filename=filename)

# Define model (must match training config)
model_inf = TimeSeriesTransformerCLS(
    n_features=n_features,
    n_classes=3,
    d_model=128,
    n_heads=4,
    n_layers=4,
    d_ff=256,
    dropout=0.1
).to(device)

# Load weights
model_inf.load_state_dict(torch.load(state_dict_path, map_location=device))
model_inf.eval()

# Example Inference
example_input = scaled.iloc[-WINDOW:].values.astype(np.float32)  # shape [T, F]
example_input_tensor = torch.tensor(example_input).unsqueeze(0).to(device)

with torch.no_grad():
    logits = model_inf(example_input_tensor)
    probabilities = F.softmax(logits, dim=1).squeeze(0).cpu().numpy()
    predicted_class = int(torch.argmax(logits, dim=1).item())

print("Probabilities:", probabilities)
print("Predicted Class:", predicted_class)  # 0=Down, 1=Flat, 2=Up