Quant Academy

An interactive mobile app for learning quantitative trading — from market fundamentals and probability theory to strategy backtesting and quant interview prep. Built with Expo and React Native.

Not videos. Not PDFs. Interactive learning with real concepts that top quant firms look for.

What's Inside

• 100 lessons across 10 structured modules covering markets, probability, statistics, Python, backtesting, technical factors, quant research, market microstructure, math, and interview prep
• Interactive sandboxes — order book simulator, coin toss probability lab, stock correlation tool, and a market-making exchange
• Strategy builder & backtester — define buy/sell rules using SMA, EMA, RSI, and momentum indicators, then backtest against synthetic market data with full performance metrics (Sharpe ratio, max drawdown, win rate)
• 25 interview problems across coding, probability, mental math, brain teasers, and systems design — styled after firms like Jane Street, Citadel, and HRT
• Gamification — XP system with 6 quant career levels (Intern → HFT Engineer), 15 achievements, and per-module progress tracking
• Fully offline — all content, simulations, and progress stored locally on-device via SQLite

Tech Stack

Layer	Technology
Framework	Expo SDK 56
UI	React Native 0.85 + React 19
Navigation	Expo Router (file-based routing)
Database	expo-sqlite
Math rendering	react-native-latex-renderer
Animations	React Native Reanimated 4
Lists	@shopify/flash-list

Getting Started

Prerequisites

• Node.js 18+
• Expo CLI (npm install -g expo-cli or use npx expo)
• iOS Simulator (macOS) or Android Emulator, or Expo Go on a physical device

Installation

# Clone the repository
git clone https://github.com/kushalsrinivas/quant-academy.git
cd quant-academy

# Install dependencies
npm install

# Start the development server
npx expo start

From the Expo dev server, press:

• i to open in iOS Simulator
• a to open in Android Emulator
• Scan the QR code with Expo Go on your phone

Building for Production

This project uses EAS Build. To create a production build:

# Install EAS CLI
npm install -g eas-cli

# Build for iOS
eas build --platform ios

# Build for Android
eas build --platform android

Project Structure

src/
├── app/                    # Expo Router screens
│   ├── (tabs)/             # Bottom tab navigator (Learn, Sandbox, Practice, Profile)
│   ├── lesson/             # Module & lesson screens
│   ├── quiz/               # Quiz screens
│   ├── simulation/         # Interactive sandbox screens
│   ├── strategy/           # Strategy builder & backtest results
│   └── problem/            # Interview problem viewer
├── components/             # Reusable UI components
├── constants/              # Theme, colors, gamification config
├── data/
│   ├── modules/            # 10 module content files (100 lessons)
│   ├── problems/           # 25 interview problems
│   └── datasets/           # Synthetic market data (Nifty50 OHLCV)
├── hooks/                  # Custom React hooks (progress, XP, backtest, etc.)
└── lib/
    ├── content/            # Lesson type definitions & registry
    ├── db/                 # SQLite schema, migrations, and data access
    └── engine/             # Backtest engine, indicators, probability, order book

Learning Modules

#	Module	Topics
1	Markets 101	Stocks, ETFs, futures, options, exchanges, order types
2	Probability	Expected value, distributions, Bayes' theorem, Monte Carlo
3	Statistics	Correlation, regression, hypothesis testing, time series
4	Python for Quants	NumPy, Pandas, returns, rolling calculations
5	Backtesting	MA crossover, overfitting, walk-forward analysis, Sharpe ratio
6	Technical Factors	RSI, MACD, Bollinger Bands, momentum, custom factors
7	Quant Research	Signal generation, alpha decay, portfolio construction
8	Market Microstructure	Order books, market making, slippage, HFT, dark pools
9	Math for Quants	Linear algebra, PCA, optimization, stochastic processes
10	Interview Prep	Coding problems, brain teasers, mental math, system design

Contributing

Contributions are welcome! Please read the Contributing Guide before submitting a pull request.

Code of Conduct

This project follows the Contributor Covenant Code of Conduct. By participating, you are expected to uphold this code.

Security

To report a security vulnerability, please see our Security Policy.

License

This project is licensed under the Apache License 2.0 — see the LICENSE file for details.

Copyright 2025 Kushal Srinivas

Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at

    http://www.apache.org/licenses/LICENSE-2.0