My project requires a Python-based system that integrates Zerodha API with advanced features like dynamic strategy parameters, secure authentication, real-time alerts, and modular code structure. Below is how I plan to address each requirement: 1. **Automated Login with 2FA** - Python-based engine built around Kite Connect REST/web-socket feeds. - Implement secure login using Zerodha’s `kiteconnect` library, handling 2FA via Google Authenticator. - Store sensitive credentials (enctoken, 2FA token) in environment variables or encrypted config files with restricted permissions. 2. **Multi-Asset Trading (Indices, Equity, Futures, Options)** - Design a flexible system to switch between asset classes, should be able to trade a single asset class or two or multiple asset class and execute trades across multiple symbols simultaneously. - Use dynamic configuration files to define trade parameters (SL, target, lot size, capital) for each asset class. 3. **Dynamic Strategy Parameters** - Build a modular framework where parameters like entry/exit rules, SL/TL, and capital allocation can be adjusted in real-time without code changes. - Intraday + BTST - Should have an option to select a trade strategy Intraday or BTST or Both - Configuration (symbols, lot size, risk %, time windows) editable from a single XLSX/YAML/JSON file. - Should have Fixed Points Trailing SL (Simple Trailing SL), Percentage-Based Trailing SL (%), ATR Trailing Stop (ATR-Trailing), SuperTrend Trailing SL, VWAP-Based Trailing SL, Profit-Lock Trailing SL (MTM Trailing), Break-even + Fixed Trail 4. **Exclusion & Entry Time Conditions** - Integrate customizable logic for trade exclusion (e.g., market holidays, volatility thresholds) and time-based entry rules (e.g., pre-market, intraday, BTST). 5. **Three Execution Modes** - Develop a switchable system for **Backtest**, **Paper Trade**, and **Live Trading** modes, ensuring zero risk during testing. 6. **Real-Time PNL Logging** - Generate 5-minute interval PNL logs in CSV/XLSX format, with daily summaries of max profit/loss. - Use Pandas for efficient data handling and logging. 7. **Auto-Close and next day carry** - Implement a time-based trigger to close all open positions by 3:10 PM or carry trade till next day. 8. **Secure Credential Management** - Avoid hardcoding enctoken; use environment variables or encrypted `.env` files with restricted access. 9. **Algo Stop Logic** - Define clear stop conditions (e.g., daily loss limit, max drawdown) to halt trading automatically. 10. **Telegram Alerts** - Integrate Telegram bot API for real-time notifications on trade entry/exit, SL/TG hits, and PNL updates. 11. **Modular Code Structure** - Architect the code in decoupled modules (e.g., `auth.py`, `strategy.py`, `risk.py`) for easy updates without system-wide disruptions. - Develop a highly modular, maintainable, and scalable Python project structure for algorithmic trading. The system must allow multiple trading strategies, each in its own folder, with reusable individual components (buy/sell engine, TSL, position sizing, quantity calculation, etc.). 12. **Trade with Multiple logins** - Architect the code to run the code with multiple clients using their logins. Send me a detailed project proposal outlining your architecture, estimated timeline, and any past work that proves you have already automated Zerodha or similar broker APIs. Python experience and solid API integration skills are essential; feel free to suggest additional libraries or lightweight frameworks if they keep the stack clean and reliable.