yashvardhancse

yashvardhancse / QuantNova

Public

Open-source GUI-based quantitative backtesting platform for strategy development, market visualization, and collaborative quant research.

www.quantnova.in
14
36
89% credibility
Found May 17, 2026 at 14 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
TypeScript
AI Summary

QuantNova is an open-source web application that lets you test cryptocurrency trading strategies using historical price data. You load price charts from live exchange data or your own files, apply technical indicators like moving averages and RSI, then run a simple crossover strategy to see how it would have performed. The interface shows you candlestick charts, buy/sell signals, profit/loss summaries, and risk metrics so you can evaluate trading ideas without risking real money. Everything runs locally in your browser with an optional backend service for heavier calculations.

How It Works

1
🔍 You discover a trading analysis tool

You find QuantNova while looking for ways to test trading strategies without risking real money.

2
🚀 You open the trading terminal

The dark-themed interface shows you a candlestick chart with price data and built-in sample cryptocurrency information.

3
📊 You load your price data

You can fetch live cryptocurrency prices from an exchange, use the included sample data, or upload your own spreadsheet of prices.

4
⚡ You configure your trading strategy

You choose a moving average crossover strategy and set your preferred time windows, starting money, and trading fees.

5
▶️ You run the backtest

With one click, the system simulates your strategy across all historical data and shows you every trade it would have made.

6
You review your results
📋
Check trade history

See every entry and exit point with timestamps and profit for each trade

📉
Review risk metrics

View your maximum drawdown and Sharpe ratio to understand your strategy's risk profile

✅ You save and export your findings

Your backtest results are saved locally and you can export them as a spreadsheet to share or analyze further.

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Star Growth

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AI-Generated Review

What is QuantNova?

QuantNova is an open-source backtesting terminal for traders who want to test quantitative strategies without connecting to a live brokerage. The frontend is built with React, Vite, and TypeScript, paired with a Python FastAPI backend that handles calculations and data validation. You can pull live Binance candlestick data, upload your own OHLCV CSVs, calculate technical indicators like SMA, EMA, RSI, and Bollinger Bands, then run a moving average crossover backtest to see equity curves, Sharpe ratios, and trade logs. The UI renders interactive candlestick charts with zoom, volume, and signal markers overlaid.

Why is it gaining traction?

Most open-source backtesting tools are either command-line only or require heavy setup like QuantConnect. QuantNova differentiates itself through a self-contained GUI that works offline using bundled sample data, while still offering live Binance integration. The frontend gracefully falls back to local calculations if the backend is unavailable, making the tool resilient and portable. The MIT license and well-organized backend architecture appeal to developers who want to extend it with custom indicators or strategy logic.

Who should use this?

Quantitative researchers who want a quick way to prototype moving average crossover strategies on Binance data without setting up a full QuantConnect environment. Frontend developers interested in charting and trading terminal UIs will find the React + Lightweight Charts integration straightforward to study. Contributors looking for a well-documented, beginner-friendly open-source project will appreciate the structured backend with Pydantic validation, clear API endpoints, and contributor guidelines.

Verdict

With a credibility score of 0.899%, this project is clearly early-stage: 14 stars, an MVP feature set, and documented limitations like no authentication or live trading. The code quality is solid for its maturity, with proper validation, tests, and CI checks. Use it as a learning foundation or prototyping tool, but do not deploy it for live trading without significant hardening. The roadmap shows ambition, and the structure suggests it could grow into something more substantial.

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