ANALYZE. TRADE.
OPTIMIZE YOUR EDGE.
Compete in a high-intensity, AI-powered trading challenge where your team builds an intelligent algorithmic trading strategy using live market data, virtual capital, and machine learning techniques. Analyze, predict, trade, and optimize your portfolio in real time — the team with the best overall trading performance takes the top spot.
What Teams Take Away
Four core skills every team builds while working through the trading challenge.
Financial Data Analysis
Learn to collect, preprocess, visualize, and interpret financial market data using modern analytical tools.
Quantitative Modeling
Apply statistics, probability, econometrics, and machine learning to build robust trading and forecasting models.
Algorithmic Strategy Development
Design and evaluate trading strategies using historical market data while understanding performance metrics and risk.
Research & Problem Solving
Develop critical thinking, programming proficiency, and presentation skills by solving complex financial problems.
What the Trading Challenge Is Built Around
Build an AI-powered trading strategy by combining data analytics, machine learning, algorithmic decision-making, and portfolio optimization to maximize trading performance in a live simulated market.
Market Data Analysis
Collect and analyze real-time or historical stock/crypto market data using APIs. Extract meaningful insights through preprocessing, visualization, and technical indicators.
AI Price Prediction
Develop machine learning or statistical models to predict short-term market movements and estimate Buy, Sell, or Hold opportunities.
Algorithmic Trading
Design an automated trading strategy that executes simulated trades using prediction confidence, trading rules, and portfolio constraints.
Portfolio Management
Manage a virtual $100 million portfolio by allocating capital efficiently, tracking positions, and maximizing returns while controlling risk.
Risk & Performance Analytics
Evaluate strategy performance using financial metrics such as Portfolio Return, Sharpe Ratio, Maximum Drawdown, Win Rate, and other risk measures.
Live Competition Leaderboard
Compete against other teams in a live simulated trading environment where strategies are ranked based on overall trading performance and risk-adjusted returns.
Prize Tiers
Rewards for the strongest risk-adjusted trading performance.
Who Can Participate
Standard eligibility rules for the Quantitative Trading Project.
Academic Level
Open to all B.Tech students, any branch, any year of study.
Team Size
Maximum 5 members per team; solo entries are also welcome.
Build Deadline
3 hrs on the day of the event.
Registration
One-time team registration through our official website.
Institution
Any.
From Sign-Up to the Final Showdown
How every team moves through the trading challenge.
Team Registration
Register your team of up to 5 on our website.
Challenge Briefing
Teams receive the problem statement.
Receive Virtual Capital
Each team starts with $100 million in virtual capital.
Develop Your Strategy
Build an AI, machine learning, statistical, or rule-based trading strategy to generate Buy, Sell, and Hold decisions.
Execute Simulated Trades
Run your strategy on the trading simulator.
Performance Evaluation
Strategies are evaluated using portfolio return.
What You'll Be Building
One core challenge for every team in the Quantitative Trading Project.
AI-Powered Algorithmic Trading & Delta Optimization
Design an intelligent algorithmic trading system that predicts short-term market movements, executes simulated trades automatically, and maximizes portfolio Delta through risk-aware decision making.
Description
- Collect real-time or historical stock/cryptocurrency market data using public APIs.
- Develop a machine learning or statistical model to predict short-term price movements.
- Generate automated Buy, Sell or Hold trading signals based on prediction confidence.
- Simulate an HFT-inspired trading engine incorporating transaction costs, bid-ask spread and execution latency.
- Calculate portfolio Delta and other performance metrics after every trade.
- Optimize the trading strategy to maximize cumulative Delta while controlling portfolio risk.
Deliverables
- Historical/real-time financial dataset with preprocessing pipeline.
- Price prediction module using machine learning or statistical techniques.
- Automated trading signal generation algorithm.
- Trading simulator including transaction costs and execution latency.
- Portfolio analytics module calculating Delta, Returns and Risk Metrics.
- Performance report (Portfolio Delta, Sharpe Ratio, CAGR, Maximum Drawdown).
Ready for Takeoff
Choose your strategy, manage your portfolio, and compete with confidence. Questions • Discussion • Team Formation.