Welcome to the CodeForge FinTech Challenge.
In this challenge, participants will build an AI-powered analytics and visualization platform that helps analyze derivatives options data and uncover meaningful patterns in market activity.
Options markets produce large volumes of high-frequency data across multiple dimensions such as time, strike prices, expiries, trading volume, and open interest. Extracting actionable insights from this complex data is challenging.
Your goal is to design a system that transforms raw options data into clear visual insights and intelligent analytics, helping users understand market behavior and identify potential opportunities.
Participants are expected to build a platform that can:
- Process and structure large-scale options datasets
- Visualize options market activity across strikes, expiries, and time
- Identify patterns in open interest, trading volume, and volatility
- Detect anomalies or unusual activity in the derivatives market
- Use AI or machine learning techniques to analyze market behavior
The final solution should help users explore options market data and generate meaningful insights through interactive dashboards and analytical tools.
codeforge-options-analytics/
data/ # Dataset and metadata notebooks/ # Exploratory analysis notebook src/ # Core source code modules scripts/ # Scripts to run applications README.md # Project overview PROBLEM_STATEMENT.md requirements.txt
The dataset provided in the data/ folder contains historical options market information.
Example columns include:
| Column | Description |
|---|---|
| datetime | Timestamp of the observation |
| expiry | Option expiry date |
| strike | Strike price |
| spot_close | Underlying asset price |
| oi_CE | Call option open interest |
| oi_PE | Put option open interest |
| volume_CE | Call option trading volume |
| volume_PE | Put option trading volume |
| ATM | At-the-money strike |
Participants are encouraged to perform additional feature engineering to extract useful signals from the dataset.
git clone cd codeforge-options-analytics
pip install -r requirements.txt
python scripts/run_dashboard.py
This will launch a simple starter dashboard built using Streamlit.
Participants are expected to expand this dashboard with additional visualizations and analytics.
Teams should extend the repository to develop:
- Interactive data visualizations
- Strike-wise analysis of open interest and volume
- Volatility analysis tools
- AI or machine learning models to detect patterns
- Anomaly detection systems for unusual options activity
- Intuitive analytics dashboards
The goal is to transform raw derivatives data into actionable insights for traders and analysts.
A core objective of this event is to promote the use of Free and Open Source Software (FOSS).
Participants will be significantly evaluated based on how effectively they use FOSS tools and frameworks in their solution.
Examples of recommended open-source technologies include:
- Python
- Pandas
- NumPy
- Scikit-learn
- PyTorch
- TensorFlow
- Plotly
- Matplotlib
- D3.js
- Streamlit
- Dash
- React
- PostgreSQL
- DuckDB
- Apache Spark
Teams that creatively leverage open-source ecosystems or build reusable open tools will receive additional consideration during evaluation.
Submissions will be evaluated based on the following factors:
Clarity and effectiveness of visualizations in representing complex options data.
Ability to uncover meaningful patterns in volatility, trading activity, or market structure.
Use of intelligent models for pattern detection, forecasting, or anomaly detection.
Ability to handle large datasets efficiently.
Ease of use, interactivity, and usability of the platform.
Effective and innovative use of open-source technologies.
Originality in analytics techniques, visualization design, or platform features.
Each team submission should include:
- Source code
- Documentation explaining the approach
- Instructions for running the solution
- Any trained models or additional data processing scripts
The purpose of this challenge is not only to build visual dashboards but to extract meaningful insights from complex options market data using analytics and AI.
Participants are encouraged to experiment, explore new ideas, and build innovative solutions that improve understanding of derivatives markets.
Good luck and happy building.