ohanvi/ohanvi-fix-trade-model

📊 Ohanvi Fix-Trade Signal Classifier

A tiny DistilBERT model fine-tuned to classify market news headlines and analysis snippets into three trade signals: BUY, SELL, or HOLD.

Model Description

• Base model: distilbert-base-uncased (67M parameters)
• Task: 3-class text classification (trade signal detection)
• Labels: BUY (0) | SELL (1) | HOLD (2)
• Training data: 150 hand-crafted trade signal examples (50 per class)
• Training time: ~20-30 seconds on Apple Silicon (MPS)

Usage

from transformers import pipeline

pipe = pipeline("text-classification", model="ohanvi/ohanvi-fix-trade-model", top_k=None)
result = pipe("Strong earnings beat, revenue up 25%, guidance raised.")
print(result)
# [{'label': 'BUY', 'score': 0.92}, {'label': 'HOLD', 'score': 0.05}, {'label': 'SELL', 'score': 0.03}]

Live Demo

Try it in your browser: Gradio Space

Training Details

Hyperparameter	Value
Base model	distilbert-base-uncased
Epochs	5
Batch size	8
Learning rate	3e-4
Max sequence length	64
Training samples	120
Eval samples	30

Limitations

⚠️ This is a demonstration model trained on a very small dataset. Do not use it for real trading or financial decisions.

Project Structure

ohanvi-fix-trade-model/
├── train.py          # Fine-tune the model (runs in ~30 sec)
├── inference.py      # Run predictions locally
├── app.py            # Gradio demo (deployed to HF Spaces)
├── push_to_hub.py    # Upload model + Space to Hugging Face
├── config.py         # Central config (loads from .env)
├── requirements.txt  # Python dependencies
├── .env.example      # Template for secrets
└── README.md         # This file

Quick Start

# 1. Install dependencies
pip install -r requirements.txt

# 2. Copy and fill in your credentials
cp .env.example .env

# 3. Train the model (~30 sec)
python train.py

# 4. Test locally
python inference.py --demo

# 5. Push to Hugging Face Hub
python push_to_hub.py