Institutional Swing Trading Analysis System

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.
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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.
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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
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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
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Delivery Data
Daily Deliverable Quantity
Delivery %
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Shareholding
Promoter Holding
Promoter Pledge
FII Holding
DII Holding
Public Holding
Quarterly updates
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Institutional Activity
Bulk Deals
Block Deals
FII Net Buying
DII Net Buying
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Fundamental Data
Revenue
EPS
ROE
ROCE
Debt Equity
Operating Cash Flow
Quarterly Results
EPS Growth
Revenue CAGR
Sector
Industry
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Sector Data
Nifty Sector Indices
Sector Relative Strength
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Optional
Options Open Interest
PCR
VWAP
Volume Profile
Anchored VWAP
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5. Database Design
Create normalized tables.
Example
Prices
Indicators
Fundamentals
Delivery
Shareholding
CorporateActions
BulkDeals
BlockDeals
SectorData
Signals
Trades
BacktestResults
Users
Settings
Logs
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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.
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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
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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
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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
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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
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11. Screening Engine
Daily scan
Entire Universe
↓
Reject
↓
Score
↓
Rank
↓
Generate Candidates
Only highest conviction stocks.
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12. Report Generation
Generate
Daily Report
Weekly Report
Monthly Report
Quarterly Performance Report
Backtest Report
Portfolio Report
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Daily Report should include
Market Summary
Sector Strength
Qualified Stocks
Score Breakdown
Entry
Stop
Target
Risk Reward
Trade Thesis
Invalidation
Charts
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Formats
PDF
Excel
CSV
HTML
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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
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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
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15. Notification System
Telegram
Email
Desktop Notification
Daily Report
New Elite Setup
Stop Hit
Target Hit
Portfolio Summary
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16. Admin Panel
Configure
Weights
Thresholds
Indicators
Scoring
Universe
Risk
Schedule
Users
Reports
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17. User Interface
Modern
Responsive
Dark Theme
Light Theme
Search
Sorting
Export
Charts
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18. Performance Requirements
Support
500+
Stocks
10 Years
Historical Data
Daily Scan
Within
10 Minutes
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19. Logging
Maintain
Error Logs
API Logs
Data Logs
Scheduler Logs
Signal Logs
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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
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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.
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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.