An open-source, GUI-based quantitative backtesting platform designed to make strategy development, experimentation, and contribution accessible to everyone — from beginners exploring quantitative finance to advanced developers building scalable trading systems.
QuantNova is inspired by modern quantitative research workflows and advanced charting platforms such as croid.app, while remaining fully open-source and contributor-friendly. The platform is designed to combine intuitive charting, visual strategy building, indicator-based backtesting, and extensible quantitative infrastructure into a single ecosystem.
Users can upload custom OHLCV datasets in CSV format for stocks, crypto, forex, or custom assets. This allows complete flexibility without relying on proprietary datasets.
Integrate free APIs such as Binance, Alpha Vantage, Yahoo Finance, or Polygon to fetch live and historical market data directly inside the platform.
Advanced candlestick charts with zooming, panning, multi-timeframe support, overlays, and strategy signal visualization.
Many existing backtesting tools are either highly complex, code-heavy, or locked behind closed ecosystems. While these platforms are powerful, they often create a steep learning curve for beginners and make customization difficult for contributors.
Most professional quantitative research platforms require advanced setup, deep domain knowledge, and extensive coding experience, making them inaccessible for new contributors.
Platforms like TradingView provide excellent user experience but operate in restricted ecosystems with limited extensibility and customization.
Existing open-source frameworks often focus solely on code libraries without beginner-friendly GUI workflows or structured onboarding for contributors.
QuantNova is not just a simple backtesting script. The long-term objective is to build a complete quantitative research workflow with modern UX, modular infrastructure, and collaborative open-source architecture.
Create trading strategies visually through configurable entry, exit, stop-loss, and take-profit conditions. Contributors can also implement fully custom Python strategies.
Support for RSI, MACD, Bollinger Bands, VWAP, EMA, SMA, ATR, Supertrend, candlestick patterns, and community-contributed indicators.
Trend lines, support/resistance zones, Fibonacci retracements, annotations, and custom drawing utilities directly on the chart interface.
Run backtests on historical data with configurable fees, slippage, leverage, long/short positions, and portfolio settings.
Equity curve, drawdown analysis, Sharpe ratio, win rate, trade history, risk exposure, and detailed statistical reporting.
Future roadmap includes prompt-based strategy generation, machine learning integrations, NLP-based sentiment systems, and intelligent signal pipelines.
QuantNova aims to bridge the gap between simplicity and extensibility. The platform combines a modern graphical interface with a modular backend architecture, enabling users to upload OHLCV datasets, test strategies, visualize results, and contribute new features without needing to understand the entire system.
Upload OHLCV datasets and visualize strategy performance through an intuitive interface.
Strategies, indicators, and analytics modules are designed to be plug-and-play for contributors.
Structured onboarding, beginner issues, and clean architecture make open-source contribution easier.
QuantNova is designed around a complete quantitative workflow — from importing market data to visual analysis, strategy development, and large-scale experimentation.
Upload custom OHLCV datasets or connect to free APIs for live and historical market feeds.
Explore charts using indicators, overlays, pattern detection, and custom drawing tools.
Configure rule-based strategies visually or implement advanced Python-based logic modules.
Execute simulations with configurable parameters and inspect trade-level behavior directly on the chart.
Compare strategies, tune parameters, analyze statistics, and improve execution logic.
Contributors can add indicators, APIs, chart modules, ML models, options strategies, and advanced analytics systems.
| Feature | TradingView | Backtrader | QuantNova |
|---|---|---|---|
| Graphical Interface | ✅ | ❌ | ✅ |
| Open Source | ❌ | ✅ | ✅ |
| Beginner Friendly | ✅ | ⚠️ | ✅ |
| Custom Python Strategies | ❌ | ✅ | ✅ |
| Contributor-Oriented Design | ❌ | ❌ | ✅ |
CSV upload support, SMA/RSI strategies, buy/sell signal generation, and equity curve visualization.
Strategy comparison, portfolio analytics, optimization metrics, and customizable indicators.
Machine learning pipelines, NLP-based strategies, live market data integration, and options strategy simulation.
QuantNova is not intended to replace enterprise quantitative systems. Its purpose is to create a collaborative and accessible environment where contributors can learn, experiment, and build real-world quantitative infrastructure together.
Next.js, React, TypeScript, Tailwind CSS, TradingView Lightweight Charts, Zustand, Framer Motion.
FastAPI, Python, WebSockets, Pydantic, AsyncIO, REST APIs, strategy execution engine.
Pandas, NumPy, TA-Lib, vectorized backtesting, portfolio analytics, performance metrics.
Scikit-learn, PyTorch, Hugging Face Transformers, NLP sentiment analysis, prompt-based workflows.
PostgreSQL, Redis, TimescaleDB (future roadmap), local CSV ingestion pipeline.
Docker, GitHub Actions, Vercel, Railway/Render, modular open-source deployment workflows.