Python Developer for Swing-Trading System with Zerodha Integration

@handysolver

To build a robust 4–20 day swing-trading system for Indian equities, the framework must explicitly handle overnight gap risk, portfolio-level exposure, and Zerodha Kite Connect's API rate limits (3 requests/sec for order placements). I will optimize your company ranking pipeline, execute vectorised/event-driven backtests on historical intraday data, and deploy a fail-safe execution engine supporting both manual-approval and fully automated modes. Here is how I will architect and deliver your system: 1. Module Optimization & Universe Selection (Phase 1): - Audit and refactor your existing ranking algorithm for memory efficiency and speed. - Implement custom filters (volatility, momentum metrics, sector limits) to generate clean daily swing-trading baskets. 2. Strategy Backtesting Engine & Risk Dashboard (Phase 2): - Build backtests utilizing intraday historical data, incorporating transaction costs, slippage, and overnight gap-down protections. - Develop a comprehensive Python dashboard (Streamlit/Dash) featuring Sharpe ratio, Max Drawdown, CAGR, Win Rate, and trade-by-trade logs. - Implement position sizing rules (e.g., Kelly Criterion or fixed-risk % per trade) across a multi-day holding horizon. 3. Zerodha Kite API Integration & Execution Engine (Phase 3): - Secure authentication pipeline with automated daily access token generation. - Semi-Automated Mode: One-click approval interface (Telegram Bot or Web UI) for manual order dispatch. - Fully Automated Mode: Background execution worker (Celery/Redis or Asyncio) with trailing stop-loss tracking and order status monitoring. I have extensive experience building algo-trading engines for NSE equities using Python and Kite Connect API. Let's discuss your preferred database setup for the intraday historical data.

@MagCloud

Hello, I’d be glad to develop and improve your long-only swing-trading system for Indian equities, with a strong focus on reproducible backtesting, risk controls and reliable Zerodha integration. Approach: • Audit and improve the existing company-selection/ranking module • Develop and compare multiple 4–20 trading-day swing strategies • Process historical intraday data with clean, reproducible pipelines • Include position sizing, stop-loss/exit logic, portfolio exposure and risk controls • Backtest with realistic costs, slippage and out-of-sample validation to reduce overfitting • Build a comprehensive results dashboard covering returns, drawdown, win rate, risk metrics and trade statistics • Integrate the selected strategy with Zerodha Kite APIs • Implement manual approval first, followed by an architecture suitable for automated execution • Add logging, error handling, safeguards and audit trails • Deliver phase-by-phase milestones with objectively testable outputs Technology: Python, Pandas/NumPy, suitable backtesting framework, PostgreSQL and dashboard tooling, with Kite API integration. I’m comfortable working with an existing codebase and structuring the system for research → validation → paper/manual execution → automation. Timeline: Approximately 6–10 weeks, depending on the existing module, data volume and strategy count. I can share relevant Python/algorithmic-trading examples and discuss the phased deliverables before starting. Best regards, Mohit

@PINESCRIPTERR

Hi, I can take ownership of the complete system, beginning with an audit of the existing company-selection and ranking module before changing any strategy logic. The research framework will process historical intraday data while controlling for look-ahead bias, survivorship bias, corporate actions, liquidity, slippage, brokerage, taxes, and realistic order timing. Strategies will be compared through walk-forward and out-of-sample testing using CAGR, drawdown, Sharpe/Sortino, win rate, turnover, exposure, and portfolio-level risk. The production architecture will separate data ingestion, ranking, signal generation, portfolio allocation, risk controls, order approval, Zerodha execution, reconciliation, and reporting. Initial deployment can require manual approval before enabling automated execution. I’ll automate data validation, backtests, parameter tracking, reports, alerts, order-state recovery, duplicate prevention, rejected/partial-fill handling, and broker reconciliation. Full automation will include daily-loss, exposure, liquidity, and emergency-stop controls. No strategy can guarantee profits. Relevant trading-system work can be discussed privately. Three technical questions: 1. What instruments, universe size, and intraday-data source are currently available? 2. How are entries, exits, ranking rules, and portfolio constraints presently defined? 3. Do you already have approved Kite Connect access and a paper/manual execution environment? Best regards, Houssame

@TechizeBuilder

Dear Client, I read "Python Developer for Swing-Trading System with Zerodha Integration" carefully and understand you are looking for a full-stack developer who can handle both application development and AI integration. I have experience with PHP, JavaScript, Python and can work across the complete stack — frontend, backend, APIs, databases, authentication, third-party integrations and AI features. I can build scalable applications using modern technologies while keeping the codebase maintainable and production-ready. A few quick questions: Which AI models/APIs are you currently planning to use? Is there an existing application/codebase, or will the project be built from scratch? Thanks & Regards, Deepak

@MqlcoderHub

Hi there, I can help build and backtest your Python swing-trading system for Indian equities, including strategy comparison, risk management, portfolio controls, and performance reporting. I also have experience with algorithmic trading, backtesting, and broker API integration, and can integrate the final system with Zerodha Kite for approval and automation. Happy to review your existing ranking module and get started phase by phase. Best, Fasih

@mikes126

I can build a robust long-only swing-trading system for Indian equities with 4–20 trading-day holding periods, covering strategy research, backtesting, risk management, portfolio controls, dashboard reporting, and Zerodha Kite integration. I will first review and improve your existing company-selection/ranking module, checking data quality, ranking logic, feature leakage, and computational efficiency. I will develop and compare multiple swing strategies using historical intraday data, with realistic brokerage, slippage, position sizing, stop-loss, exposure limits, drawdown controls, and transaction costs. A key challenge is avoiding look-ahead bias and overfitting. I will use time-series-safe validation, walk-forward testing, out-of-sample evaluation, and parameter stability analysis so results are statistically meaningful. I will create a comprehensive dashboard showing CAGR/returns, Sharpe, drawdown, win rate, expectancy, trade statistics, equity curves, benchmark comparison, and portfolio-level risk metrics. After selecting the most robust strategy, I will integrate it with Zerodha Kite for manual approval first, followed by controlled automated execution with logging, order-state handling, error recovery, and safety limits.