atropos/environments/community/options_iv_prediction

Options Implied Volatility Prediction Environment

Location: environments/community/options_iv_prediction/ Contributor: michaelwaves PR: #78

Overview

This environment trains language models to predict implied volatility (IV) for stock options using real market data. The model analyzes option pricing parameters including option price, stock price, strike price, time to expiry, and risk-free rate to predict the implied volatility.

Core Features

• Real Market Data: Uses Yahoo Finance API via yahooquery to fetch live options data
• Financial Analysis: Trains models to understand options pricing relationships
• Thinking Process: Encourages step-by-step reasoning with <think> tags
• Accuracy Scoring: Evaluates predictions based on magnitude accuracy and percentage correctness
• WandB Integration: Comprehensive logging and visualization of training metrics

Environment Details

Task Description

Given option market data (option price, stock price, strike price, time to expiry, risk-free rate), predict the implied volatility as a percentage.

Input Format

Option Price: $X.XX
Stock Price: $X.XX
Strike Price: $X.XX
Time to Expiry: X.XXXXXX years
Risk-Free Rate: X.XX

Your final answer MUST use the exact format: "The implied volatility will be: {answer}"
Where {answer} is the implied volatility as a string in percent (e.g. 70%)

Scoring Methodology

• Magnitude Accuracy: Measures how close the predicted IV is to the actual IV
• Binary Correctness: Whether the prediction is within an acceptable threshold
• Combined Score: Weighted combination of magnitude and binary accuracy

Setup Instructions

Dependencies

Install required packages:

pip install pandas wandb datasets tqdm yahooquery atroposlib

Or use the provided requirements file:

pip install -r requirements.txt

Environment Variables

• API Keys: Configure OpenAI or other LLM provider API keys
• WandB: Set up Weights & Biases for experiment tracking

Data Source

The environment automatically fetches real-time options data for UNH (UnitedHealth Group) using the Yahoo Finance API. No manual data preparation is required.

Usage Examples

Training Mode

python options_iv_prediction.py serve --env.total_steps 2000 --env.batch_size 1024

Process Mode (Data Generation)

python options_iv_prediction.py process --env.data_path_to_save_groups ./outputs/options_rollouts.jsonl --openai.api_key YOUR_KEY

Configuration Options

• group_size: Number of predictions per training group (default: 16)
• max_token_length: Maximum tokens for model responses (default: 16384)
• steps_per_eval: Evaluation frequency (default: 20)
• wandb_name: Custom name for WandB runs

Performance Characteristics

• Memory Usage: ~2-4 GB RAM for typical configurations
• API Calls: Fetches live market data on startup, then uses cached data
• Processing Time: 1-3 minutes per batch depending on model size
• Accuracy Metrics: Tracks both percentage correctness and magnitude accuracy

Technical Implementation

Data Processing

1. Fetches real-time options chain data for UNH stock
2. Calculates time to expiry from current date to option expiration
3. Filters out invalid options (negative prices, expired options)
4. Creates train/test split (95%/5%)

Scoring Algorithm

def _calculate_iv_score(self, predicted_iv, expected_iv):
    # Magnitude accuracy (0-1 scale)
    magnitude_accuracy = max(0, 1 - abs(predicted_iv - expected_iv) / 100)

    # Binary correctness (within 10% threshold)
    is_correct = abs(predicted_iv - expected_iv) <= 10

    # Combined score
    return magnitude_accuracy * 0.7 + (1.0 if is_correct else 0.0) * 0.3

Model Integration

• Compatible with any OpenAI-compatible API
• Supports both local and cloud-based language models
• Automatic tokenization and conversation management

Output Format

WandB Metrics

• train/percent_correct: Percentage of predictions within threshold
• train/magnitude_accuracy: Average magnitude accuracy score
• eval/percent_correct: Evaluation accuracy
• train/rollouts: Sample predictions with scores

Data Files

• Generated rollouts saved to specified JSONL file
• HTML visualization of training conversations
• Detailed prediction analysis and scoring breakdown

Research Applications

This environment is valuable for:

• Financial AI Research: Training models to understand options pricing
• Quantitative Analysis: Developing AI-powered trading strategies
• Risk Management: Automated volatility prediction for portfolio management
• Educational Purposes: Teaching AI systems financial concepts

Example Output

<think>
Let me analyze this option:
- Option Price: $5.50
- Stock Price: $100.00
- Strike Price: $105.00
- Time to Expiry: 0.25 years
- Risk-Free Rate: 5%

This is an out-of-the-money call option. Given the option price and other parameters, I need to work backwards to find the implied volatility that would justify this price using the Black-Scholes model...
</think>

The implied volatility will be: 25.3%

Contributing

To contribute improvements:

1. Test changes with the provided example data
2. Ensure all scoring metrics work correctly
3. Verify WandB integration functions properly
4. Update documentation for any new features

License

This environment follows the same license as the Atropos project.