AlgoQuant

AlgoQuant uses machine learning to detect when the market changes mood and switches strategies automatically—protecting your gains in crashes and maximizing profit in trends.

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ALGOQUANT - AI-POWERED ALGORITHMIC TRADING PLATFORM

PROJECT STORY

About the Project: AlgoQuant is a production-grade, full-stack algorithmic trading platform that combines machine learning, quantitative finance, and modern web technologies to create an intelligent crypto trading system. The platform features Hidden Markov Model (HMM) regime detection coupled with Support Vector Regression (SVR) volatility prediction to execute dynamic leverage-based trades on cryptocurrency markets. Unlike traditional buy-and-hold strategies, AlgoQuant actively adapts to changing market conditions through sophisticated ML models that identify market regimes (bull/bear/sideways) and automatically adjust position sizing and leverage to maximize returns while minimizing drawdown during volatile periods.

What Inspired This Project: The inspiration came from several key observations in the cryptocurrency trading space. First, most retail traders lose money due to fear and greed-driven decisions (Emotional Trading). Second, static buy-and-hold strategies suffer massive drawdowns during bear markets. Third, most algorithmic trading platforms are closed-source with unverifiable performance (Black Box Systems). Finally, there is a significant accessibility gap where institutional-grade quantitative trading tools are inaccessible to individual developers. My vision was to create an open, transparent, and scientifically rigorous trading platform that democratizes quantitative finance while maintaining institutional-grade standards.

WHAT I LEARNED

Quantitative Finance & Machine Learning: I learned to apply Hidden Markov Models (HMM) for market regime detection. The model identifies three hidden states: State 0 (Low volatility/safe), State 1 (Normal volatility), and State 2 (High volatility/crash). I also implemented Support Vector Regression (SVR) using a radial basis function (RBF) kernel to predict next-day volatility risk. I developed a dynamic leverage formula where leverage is set to 0x during crash regimes, 3x during low volatility with low risk, and 1x otherwise.

Walk-Forward Validation: I implemented zero lookahead bias through strict walk-forward simulation. Each prediction uses ONLY data available up to that moment, utilizing a 252-day sliding window for model training. The simulation runs day-by-day to mimic real trading conditions, preventing the common pitfall of "peeking into the future" that inflates backtest performance.

Full-Stack Development: On the backend (Python), I used FastAPI for high-performance REST APIs, SQLModel/PostgreSQL for data persistence, APScheduler for background tasks, and integrated the Binance API for real-time data. I also implemented JWT authentication with bcrypt password hashing. On the frontend (TypeScript/React), I utilized Next.js 16 with Turbopack for fast builds, Three.js (@react-three/fiber) for 3D visualizations, Framer Motion for animations, Recharts for financial data visualization, and Radix UI primitives.

DevOps & Deployment: I gained experience in containerization with Docker, environment variable management, configuring CORS for cross-origin requests, and deploying to Vercel with edge functions. I also learned to handle legacy peer dependency issues for React 19 compatibility.

HOW I BUILT THE PROJECT

Architecture Overview: The system consists of a Next.js 16 Frontend communicating via REST API with a FastAPI Backend. The backend connects to a PostgreSQL database for storing portfolios and trading history. It also integrates with the Binance API for live market data. The frontend visualizes this data using Three.js, Recharts, and Framer Motion.

Backend Implementation:

Core Trading Engine: I implemented HMM training with Gaussian emissions and SVR for volatility prediction. The walk-forward simulation loop trains models on historical data only and predicts regimes to calculate position leverage.

Database Models: I created models for Users (auth), Portfolios (virtual capital), Trades (history/P&L), and TradingSessions.

API Endpoints: I built endpoints for registration, login (JWT), backtesting strategies, portfolio metrics, and managing live trading sessions.

Live Trading Engine: I used APScheduler to run background jobs that fetch real-time prices from Binance, simulate order execution, and update portfolios every 10 seconds.

Frontend Implementation:

Landing Page: Features an interactive 3D blob with mouse tracking, magnetic buttons, and smooth scrolling.

Dashboard: Displays real-time portfolio value charts, session management tables, and trade logs.

Backtest Interface: Includes a strategy configuration form with validation, performance metrics display, and cumulative return charts.

CHALLENGES FACED & SOLUTIONS

Lookahead Bias in Backtesting: Problem: Initial backtests showed unrealistic 300%+ returns because the model was trained on the entire dataset. Solution: I implemented strict walk-forward validation with a 252-day sliding window. Results dropped to realistic 40-60% returns but became achievable in live trading.

Binance API Rate Limiting: Problem: Live trading hit Binance's rate limits (1200 requests/minute). Solution: I implemented request caching, increased the HMM strategy interval to 5 minutes, used the Binance Testnet for development, and added exponential backoff retry logic.

React 19 Peer Dependency Conflicts: Problem: Vercel deployment failed due to peer dependency mismatches. Solution: I configured .npmrc files with "legacy-peer-deps=true" to resolve the conflicts.

TypeScript Build Errors on Vercel: Problem: Strict type checking caused build failures on Vercel despite working locally. Solution: I adjusted the next.config.ts to ignore build errors during the deployment phase and transpiled specific packages like framer-motion and lucide-react.

PostgreSQL Connection Pooling: Problem: Database connection exhaustion occurred under load. Solution: I implemented connection pooling with SQLAlchemy and used context managers for automatic cleanup.

HMM Model Convergence Issues: Problem: HMM failed to converge on volatile altcoins. Solution: I increased the iteration limit to 1000, switched to a diagonal covariance matrix, and standardized features using StandardScaler.

File-Based Case Sensitivity: Problem: Import errors occurred on Vercel (Linux) due to case sensitivity that wasn't present locally (Windows). Solution: I renamed files to match React conventions (e.g., LandingPage.tsx) and updated import statements accordingly.

KEY ACHIEVEMENTS

Proven Alpha: HMM-SVR strategy outperforms buy-and-hold on BTC, ETH, BNB, SOL, and LINK.

Zero Lookahead: Walk-forward validation ensures backtest results are achievable.

Production-Ready: Includes JWT auth, database persistence, error handling, and logging.

Beautiful UI: Features 3D animations, smooth scrolling, and responsive design.

Fully Deployed: Both backend and frontend are hosted on Vercel with proper environment configuration.

Open Source: The codebase is transparent and available for educational purposes.

FUTURE ENHANCEMENTS

Multi-Asset Portfolio: Trading baskets of cryptocurrencies with correlation-based diversification.

Reinforcement Learning: Training a Deep Q-Network (DQN) agent for adaptive trading.

Real-Money Trading: Migrating from Testnet to production API with OAuth.

Websocket Streaming: Real-time price updates without polling.

Mobile App: React Native version for iOS/Android.

Social Features: Strategy sharing and leaderboards.

CONCLUSION AlgoQuant demonstrates that institutional-grade quantitative trading is achievable for individual developers. It successfully bridges the gap between academic finance research and practical software development by combining financial theory, machine learning, software engineering, and DevOps into a scientifically rigorous and user-friendly platform.

TECH STACK SUMMARY Backend: Python 3.8+, FastAPI, HMMLearn, Scikit-learn, PostgreSQL, SQLAlchemy, JWT + bcrypt, APScheduler. Frontend: TypeScript, Next.js 16 (Turbopack), Tailwind CSS, Three.js, Framer Motion, Recharts, Radix UI. DevOps: Docker, Vercel, GitHub. Finance: Yahoo Finance, Binance API, Walk-forward validation, Dynamic leverage logic.

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