I’m building a production-ready algorithmic trading system for Indian equities on the NSE and need a Python specialist to carry it from data collection all the way to live order execution. Data & Market Feed You will pull, clean and store both historical and real-time OHLC data at 1-minute and 15-minute intervals. Pandas, NumPy and a lightweight database of your choice should keep this pipeline fast and transparent. Strategy Layer Every core indicator listed—EMA, VWMA, RSI and ATR—must be coded as reusable functions. I plan to trial multiple parameter sets, so clear separation of signal generation from execution logic is essential. Backtesting Engine I want a self-contained module that replays trades tick-by-tick, outputs a trade log, and produces key metrics such as win-rate, drawdown, Sharpe, and cumulative PnL. Matplotlib visualisations of equity curves are a plus. Broker Integration Order routing should plug into any of the major Indian API stacks—Zerodha, Dhan, or Upstox—via REST or WebSocket, with seamless switching between them through a common interface. Automation & Monitoring The finished script must fire alerts (webhooks or email), record live PnL, and fail gracefully if a feed drops. Proper logging and docstrings are mandatory for hand-off and future scaling. Deliverables • Clean, modular Python codebase with README • Data ingestion & storage scripts • Indicator library + strategy runner • Backtesting notebook/module with reports • Broker connector supporting Zerodha / Dhan / Upstox • Deployment instructions for a VPS or cloud instance Acceptance Criteria 1. Backtest results replicate at least 95 % of manual calculations on a provided sample dataset. 2. Live connection executes mock orders without error for one full trading session. 3. Code passes flake8 checks and includes inline documentation. This is an immediate need, yet I’m open to extending the collaboration long term for feature additions once the core is stable. If you have prior experience building algo frameworks or trading bots, let’s talk.