I’m building a Python-based algorithmic trading system and want a clean, extensible codebase that digests both real-time and historical market data out of the box. The goal at this stage is to lay down the “engine room”: data ingestion, strategy hooks, execution routing and basic risk controls, all written in idiomatic Python with clear documentation so additional strategies and brokers can be plugged in later without refactoring. While the specific asset classes and strategy styles can stay flexible for now, the architecture needs to accommodate multiple instruments and timeframes, letting me toggle between back-testing on stored data and live or paper trading on streaming feeds with a single config change. Deliverables: • A modular Python project (virtual-env ready, PEP-8 compliant) • Data handler that pulls historical candles and subscribes to live ticks from at least one free API (e.g., yfinance + WebSocket demo) • Strategy template class with event-driven callbacks for entry, exit and risk checks • Back-testing component producing equity curve and basic performance metrics (Sharpe, max drawdown) • Execution stub capable of paper trading via REST/WebSocket (live trade logic isolated for safety) • README showing setup, sample run for both modes and pointers on adding new strategies or data sources Acceptance: a short screen-capture or notebook proving (1) a back-test on historical data and (2) a live data subscription that feeds the same pipeline, each generating trade logs and summary stats identical in format. (1) pine script base trading copy past script (2) chartink screenerbase ( 3 ) strategy Builder base (4 ) equty and f &o base (5) comodati, forex, crypyo trading