Institutional Swing Trading Analysis System

Cliente Freelancer · Remoto · Remoto · freelance · mid · 12.500–37.500 INR

Publicada el 2026-07-26

Descripción de la oferta

1. Project Overview Develop an automated Institutional Swing Trading Analysis System for NSE-listed stocks that scans the Indian equity market after each trading session and generates high-conviction swing trading opportunities based on institutional accumulation, Smart Money Concepts (SMC), Wyckoff methodology, market structure, quantitative analysis, and fundamental screening. The application must automatically collect, process, score, rank, and generate daily reports without manual intervention. This is not a simple stock screener. It is an institutional-grade quantitative decision-support platform. ________________________________________ 2. Project Objectives The system shall: • Automatically collect NSE market data after market close. • Build and maintain a historical database. • Calculate technical, volume, and institutional indicators. • Detect Smart Money footprints. • Detect early breakout candidates. • Generate conviction scores. • Rank stocks. • Produce PDF, Excel, and Dashboard reports. • Maintain historical signals. • Backtest the complete strategy. • Send alerts. ________________________________________ 3. Technology Stack Preferred Backend • Python 3.12+ Database • DuckDB or PostgreSQL Data Processing • Pandas • NumPy • Polars (optional) Indicators • TA-Lib • pandas-ta Backtesting • vectorbt • Backtesting.py Dashboard • Streamlit Scheduling • APScheduler • Windows Task Scheduler • Cron Reporting • Excel • PDF • HTML Visualization • Plotly Source Control • Git ________________________________________ 4. Data Sources The system should automatically collect and update: Market Data Daily OHLCV Intraday (1 Hour) Bhavcopy Corporate Actions Market Capitalization Free Float Average Turnover Average Volume ________________________________________ Delivery Data Daily Deliverable Quantity Delivery % ________________________________________ Shareholding Promoter Holding Promoter Pledge FII Holding DII Holding Public Holding Quarterly updates ________________________________________ Institutional Activity Bulk Deals Block Deals FII Net Buying DII Net Buying ________________________________________ Fundamental Data Revenue EPS ROE ROCE Debt Equity Operating Cash Flow Quarterly Results EPS Growth Revenue CAGR Sector Industry ________________________________________ Sector Data Nifty Sector Indices Sector Relative Strength ________________________________________ Optional Options Open Interest PCR VWAP Volume Profile Anchored VWAP ________________________________________ 5. Database Design Create normalized tables. Example Prices Indicators Fundamentals Delivery Shareholding CorporateActions BulkDeals BlockDeals SectorData Signals Trades BacktestResults Users Settings Logs ________________________________________ 6. Automated Data Pipeline Daily Schedule 6:30 PM ↓ Download Data ↓ Validate ↓ Clean ↓ Store ↓ Calculate Indicators ↓ Run Screener ↓ Generate Reports ↓ Send Notifications No manual intervention. ________________________________________ 7. Indicator Engine Automatically calculate EMA 20 50 200 30 Week MA RSI MACD ADX ATR OBV CMF MFI Accumulation Distribution VWAP Anchored VWAP Relative Volume Delivery Trend Bollinger Band Width Volume Moving Average Relative Strength vs Nifty 52 Week High Distance ________________________________________ 8. Pattern Detection Engine Automatically detect Stage Analysis Stage 1 Stage 2 Stage 3 Stage 4 Liquidity Sweep Buy Side Liquidity Sell Side Liquidity Market Structure Shift MSS ChoCH Higher High Higher Low Lower High Lower Low Volatility Contraction Pattern Wyckoff Spring Wyckoff Test Order Blocks Fair Value Gap Mitigation Block Breaker Block Ascending Triangle Cup Handle Flat Base Support Resistance Breakout Retest ________________________________________ 9. Institutional Scoring Engine Implement weighted scoring. Factor Weight Relative Strength 25 Liquidity Sweep 20 Volume + Delivery 20 Volatility Compression 10 Institutional Accumulation 10 Structure 5 Fundamentals 5 Sector Strength 5 Maximum Score 100 Categories 95+ Elite 90+ High Conviction 80+ Qualified Below 80 Reject ________________________________________ 10. Hard Rejection Rules Reject if Below 20 EMA Below 50 EMA Below 200 EMA Weak Delivery Weak Relative Strength RVOL below threshold Poor Fundamentals Upcoming Earnings ASM SME Gap Up Promoter Selling Poor Liquidity No MSS No Liquidity Sweep ________________________________________ 11. Screening Engine Daily scan Entire Universe ↓ Reject ↓ Score ↓ Rank ↓ Generate Candidates Only highest conviction stocks. ________________________________________ 12. Report Generation Generate Daily Report Weekly Report Monthly Report Quarterly Performance Report Backtest Report Portfolio Report ________________________________________ Daily Report should include Market Summary Sector Strength Qualified Stocks Score Breakdown Entry Stop Target Risk Reward Trade Thesis Invalidation Charts ________________________________________ Formats PDF Excel CSV HTML ________________________________________ 13. Dashboard Dashboard should include Today's Qualified Stocks Active Signals Historical Performance Sector Strength Market Breadth Portfolio Watchlist Signal History Conviction Distribution Backtest Statistics Filters Search ________________________________________ 14. Backtesting Module Historical simulation User configurable Date Range Universe Capital Risk Commission Slippage Holding Period Generate Win Rate Profit Factor Sharpe Sortino Drawdown Expectancy CAGR Trade Distribution Heatmap Equity Curve ________________________________________ 15. Notification System Telegram Email Desktop Notification Daily Report New Elite Setup Stop Hit Target Hit Portfolio Summary ________________________________________ 16. Admin Panel Configure Weights Thresholds Indicators Scoring Universe Risk Schedule Users Reports ________________________________________ 17. User Interface Modern Responsive Dark Theme Light Theme Search Sorting Export Charts ________________________________________ 18. Performance Requirements Support 500+ Stocks 10 Years Historical Data Daily Scan Within 10 Minutes ________________________________________ 19. Logging Maintain Error Logs API Logs Data Logs Scheduler Logs Signal Logs ________________________________________ 20. Deliverables Developer shall provide Complete Source Code Git Repository Database Schema Installation Guide User Manual Technical Documentation Deployment Guide API Documentation Sample Reports Test Cases Backtest Examples ________________________________________ 21. Acceptance Criteria The project will be accepted only if: • Daily data updates run automatically without manual intervention. • All required indicators are calculated correctly. • The screening engine applies every mandatory filter and scoring rule consistently. • Daily, weekly, and monthly reports are generated automatically. • Backtests can be executed over user-selected historical periods. • The dashboard reflects current and historical signals accurately. • Export to PDF, Excel, and CSV functions correctly. • Notifications are delivered reliably. • All configurable thresholds (weights, filters, risk parameters) are editable through the application. • The system is documented and deployable on a clean machine. ________________________________________ 22. Project Phases Phase Deliverable Phase 1 Database design, data ingestion, scheduler Phase 2 Indicator engine and technical calculations Phase 3 Pattern detection (SMC, Wyckoff, liquidity sweeps, MSS, VCP) Phase 4 Institutional scoring engine and screening logic Phase 5 Automated report generation (PDF, Excel, HTML) Phase 6 Interactive dashboard and filtering Phase 7 Backtesting engine and performance analytics Phase 8 Alerts, documentation, testing, deployment Recommended Freelancer Profile To maximize your chances of success, specify that applicants should have: • 5+ years of professional Python development experience. • Experience with quantitative finance or algorithmic trading systems. • Strong knowledge of Pandas, NumPy, DuckDB/PostgreSQL, and vectorized data processing. • Experience building backtesting engines and financial dashboards. • Familiarity with NSE market structure and Indian equity data. • Ability to implement configurable rule engines rather than hard-coded logic. • Experience with automated scheduling, reporting, and deployment.

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