Nifty Options & Equity Backtesting and Simulation

Freelancer Requirement: Strategy Backtesting Engine + Paper Trading / Simulated Live Execution Interface Project Overview I am looking for an experienced quant developer / algo-trading engineer to build two integrated deliverables for a systematic options trading strategy focused on the Indian market (NIFTY/SENSEX options, NSE F&O): A robust backtesting engine to validate a rule-based intraday options strategy. A paper trading interface that runs the same strategy in simulated live execution, using TradingView as the primary charting/signal front-end. The goal is to move from a discretionary/rule-based strategy to a fully systematized, testable, and simulateable pipeline before any live capital deployment. Scope of Work 1. Backtesting Engine Build a backtesting framework for an intraday options strategy (multi-group ATM straddles) using indicators such as Heikin-Ashi candles, Bollinger Bands, and VWAP overlays. Support historical NSE F&O options data (tick/1-min/5-min granularity as available). Accurate handling of Indian options-specific mechanics: strike selection, lot sizes, expiry rollovers, slippage, brokerage/transaction costs, and margin/SL-target logic. Output clear performance metrics: P&L curve, win rate, drawdown, Sharpe/Sortino, SL-to-target ratio analysis, and trade-level logs. Code should be modular and leakage-safe (proper walk-forward/out-of-sample validation, not just in-sample curve fitting). 2. Paper Trading / Simulated Live Execution Interface Design an interface that runs the strategy in paper mode, simulating live order execution in real time (or near real time) using live/delayed market data. Integrate with TradingView — via Pine Script alerts/webhooks, or TradingView's charting layer — so that signals generated on TradingView charts can trigger simulated trade execution. Interface should log simulated entries/exits, running P&L, open positions, and trade history in a clean dashboard. Ability to toggle strategy parameters (SL, target, indicator thresholds) without code changes, ideally through a simple UI or config file. 3. Integration Both systems (backtest + paper trading) should ideally share the same core strategy logic, so results are consistent and comparable between historical backtests and forward simulation. Should be extensible toward live execution (via broker API) in a future phase, even if that is out of scope for now. Ideal Skills & Experience Strong Python skills (pandas, NumPy; familiarity with backtesting libraries such as backtrader, vectorbt, or custom-built frameworks). Experience with Indian market data — NSE/BSE F&O, options chain data, expiry mechanics. Working knowledge of TradingView — Pine Script, webhooks/alerts, and connecting TradingView signals to external systems. Experience building or simulating paper trading / order simulation systems. Understanding of options strategy mechanics (straddles, Greeks, SL/target logic) is a strong plus. Prior experience with FIFO-based P&L reconstruction or trade-book analysis is a bonus. Deliverables Fully documented backtesting codebase with sample run instructions. Working paper trading interface with TradingView integration, deployable/runnable end-to-end. A short report/documentation explaining architecture, assumptions, and how to extend toward live execution. Engagement Details Please share relevant past work (backtesting frameworks, TradingView integrations, or paper trading systems you've built), your estimated timeline, and pricing structure (fixed-price or milestone-based preferred). Open to an initial paid trial/milestone before committing to the full scope.