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We're rethinking what it means to code for quant systems — AI‑native, data‑plugged, and terminal‑fast.
QuantCode is an AI‑native IDE for quantitative finance. Describe strategies in plain English, and we generate typed, modular Julia/OCaml code with backtesting, risk controls, and visualizations you can trust. It's the fastest way to go from idea to portfolio‑ready code.
Never toggle between chat and code again — describe logic inline. We compile to OCaml/Julia‑native modules.
Risk, alpha, portfolio constraints, rebalancing — the primitives are finance‑first.
Generate code, backtest instantly, tweak risk, and export — all in one flow.
See what we've been building for the quant community
Added native Julia compilation for high-performance quantitative strategies with type safety.
Community-driven strategy sharing with performance metrics and author attribution.
Instant backtesting with live market data integration and performance visualization.
Enhanced AI explanations for strategy logic and risk assessment with detailed breakdowns.
Connected to major data providers including Polygon, Alpha Vantage, and Yahoo Finance.
Direct deployment to popular brokers with automated execution and risk management.
Natural language in → idiomatic, modular Julia or OCaml out.
Save, track, and restore versions. Keep private or share in a curated library.
Sharpe, drawdown, equity curves in seconds.
Understand why your trade idea works.
Download, push to GitHub, or wrap as REST.
Coming soonChoose or let the system select the best target.
Core MVP today, advanced roadmap next — all in one AI‑native IDE.
We don’t scrape. We connect directly to trusted sources and developer tools.
Write your strategy in plain English
Typed modules appear with tests
Sharpe, drawdown, equity in seconds
Ship to GitHub or deploy an endpoint
Refine prompts or edit modules
Relative Strength Index
The Relative Strength Index (RSI) is a momentum oscillator created by J. Welles Wilder Jr. that measures the speed and magnitude of recent price changes to identify overbought or oversold conditions in a market.
RSI compares average gains and losses over a set period (commonly 14 periods) using a normalization formula.
Traders use RSI to spot potential reversal points, gauge trend strength, and identify divergences between price and momentum. A bullish divergence occurs when price makes lower lows but RSI makes higher lows, while a bearish divergence is the opposite.
Pro Tip: While RSI is popular for its simplicity and versatility across markets, it can give false signals in strong trends and works best when combined with other technical tools for confirmation.
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Real-time portfolio optimization alerts
Add support for cryptocurrency trading strategies
Options trading strategy builder
Integration with TradingView for chart analysis
Machine learning model templates for predictions
Connect with fellow quants and stay updated on the latest features
| Feature | GitHub Copilot | QuantCode |
|---|---|---|
| Trained on typed languages? | Mostly Python/JS | Julia & OCaml native |
| Finance-first prompts? | No | Risk models, ETFs, bond ladders |
| Modular quant output? | Not structured | Full module Strategy blocks |
| Auto backtesting logic? | Manual | Built-in and customizable |
| Replit/Notebook ready? | Not directly | Plug into REPL, VSCode, Replit |
Start free. Scale to team and enterprise as your needs grow.