Quantitative Developer | Trading Algo Automation | Python, SQL & Flask Dashboard

🚀 I help individual traders, funds, and fintech teams turn trading ideas into robust, automated execution systems.

From high-fidelity historical strategy backtesting to zero-latency broker execution and real-time control dashboards, I build production-grade Python solutions focused on risk management, speed, and reliability.

As a former Quantitative Researcher, I bring hands-on experience researching, backtesting, and validating over 1,000+ systematic trading strategies across Equities, Futures, and Options.

🔧 Core Technical Expertise:

Programming: Python, SQL, REST/WebSocket APIs.
Libraries: Pandas, NumPy, Plotly, Scikit-Learn.
Broker Integrations: Shoonya (Finvasia), Zerodha Kite, Fyers, AngelOne, Interactive Brokers, Alpaca, Binance.
Quant Analytics: Sharpe, Drawdown, Calmar Ratio, Order Flow.

💡 The Aarish Advantage (Built-in Datasets): You don't need to purchase expensive historical data. I have 4 years of 1-minute Options data (Nifty/BankNifty spot, fut, opt) and 3 years of second-wise Nifty/Sensex bid-ask tick data ready to backtest your strategies.

Ready to automate your trading? Drop a message with your rules and let's build your execution engine.

Work Terms

📅 Hours & Support
Business Hours: Mon–Fri, 8 AM – 5 PM UTC (Flexible timezone alignment).
Live Market Testing: Scheduled 24h in advance to match your local market (US, EU, Asia, Crypto).
Response SLA: Within 2h during business hours; 12h max during off-hours.

💰 Payments & Handoff
Milestones: 30% Upfront (Spec Design) | 40% Mid-project (Engine & Backtest) | 30% Handoff (API Deployment).
Handoff Policy: Source code deployed to production only after final milestone release.
Warranty: 14 days free bug-fixing post-handoff. Logic changes post-handoff billed at $5/hr.

💬 Communication
Channel: Written updates via platform chat / Whatsapp (creates a clear audit trail of strategy rules).
Updates: Asynchronous status updates sent every Mon, Wed, and Fri.