gbnathworkspace/stockservs

Purpose and Entry Points

Stock Servs is a market-data and virtual-trading platform centered on Indian equities, F&O/options, portfolios, watchlists, and market analytics. The Python backend starts from main.py; database initialization is represented by init_db.py and setup_db.py. The primary React/Vite client starts at frontend/src/main.jsx and renders frontend/src/App.jsx.

Backend API Boundary

The backend is organized as route modules that expose domain APIs and delegate business work to services. Major API surfaces include authentication, users, market data, broker connectivity, portfolios, and watchlists: routes/auth.py, routes/market_data.py, routes/fyers.py, routes/portfolio.py, and routes/watchlist.py. Specialized analytical/trading endpoints are split into routes/option_apex.py, routes/option_clock.py, routes/market_pulse.py, routes/swing_spectrum.py, and routes/insider_strategy.py.

Service and Market-Data Layer

Services isolate data acquisition, caching, scheduling, and domain calculations from HTTP routes. Core components are services/market_data_service.py, services/cache.py, services/data_scheduler.py, services/fii_dii_scheduler.py, and services/portfolio_service.py. Broker-facing integrations are encapsulated in services/fyers_service.py and services/zerodha_service.py, providing a boundary between application workflows and external trading/data-provider APIs.

NSE Ingestion and Analytics

NSE-specific source adapters provide discrete market datasets—indices, F&O, movers, sector data, activity, delivery, and FII/DII figures—under nse_data/indices.py, nse_data/fno.py, nse_data/movers.py, nse_data/fii_dii.py, and nse_data/most_active.py. Symbol master files in data/symbols/NSE_CM.csv and data/symbols/NSE_FO.csv support cash-market and derivatives instrument lookup. Scheduled fetching and caching should feed API services rather than the frontend directly.

Persistence

Database access is centralized through connection, ORM/model, and repository layers: database/connection.py, database/models.py, and database/repository.py. This boundary likely owns persisted users, credentials or broker state, portfolios/orders, and watchlists. Schema lifecycle is handled through init_db.py, setup_db.py, and SQL migrations such as migrations/add_is_default_to_watchlists.sql.

Frontend Application

The React single-page application provides dashboard, market-data, FII/DII, watchlist, wallet, order-history, settings, and documentation views: frontend/src/components/sections/Dashboard.jsx, frontend/src/components/sections/MarketData.jsx, frontend/src/components/sections/Watchlist.jsx, frontend/src/components/sections/FiiDiiActivity.jsx, and frontend/src/components/sections/OrderHistory.jsx. Virtual trading is assembled from trading views and trade/chart modals in frontend/src/components/VirtualTrading.jsx, frontend/src/components/trading/TradeModal.jsx, and frontend/src/components/trading/PortfolioView.jsx. HTTP communication is centralized at frontend/src/lib/api.js; shared loading, theme, and notification state use frontend/src/contexts/LoadingContext.jsx, frontend/src/contexts/ThemeContext.jsx, and frontend/src/contexts/ToastContext.jsx.

Runtime and Deployment

The system uses a Python backend runtime with dependencies declared in requirements.txt, plus a Node/Vite frontend defined by frontend/package.json and frontend/vite.config.js. Container and process deployment artifacts are Dockerfile, Procfile, and render.yaml; automated deployment is configured at .github/workflows/deploy.yml. Built frontend assets also exist under static/app/index.html, indicating the backend may serve a production frontend bundle.