Dark market analytics dashboard with trading charts and performance views

Financial Technology Module

Swing Alpha - NSE Swing Trading Engine

A full-stack NSE swing trading module built with Django/DRF, PostgreSQL, and React, designed around a two-stage Discovery sync and Intelligence signal scanning pipeline.

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Project narrative

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Challenge

Swing research workflows can become fragile when market-data sync, indicator freshness, candidate scanning, paper-trade deployment, and trade lifecycle tracking live in separate tools. The goal was to make every entry decision traceable to data readiness, scan context, and safety validation before the system creates a paper position.

Solution

Swing Alpha centralizes the workflow into a full-stack NSE module with Discovery sync for universe/data preparation, Intelligence signal scanning for candidate detection, readiness-gated paper-trade deployment, and lifecycle-based trade management. Active positions, trade history, and scan logs are presented as decision-focused analytics views so each trade state can be reviewed from one interface.

Visual system

Product views for scanning, deployment, and lifecycle review.

These visuals show how the module presents technical readiness, strategy workflow, paper-trade state, and risk controls in a decision-focused interface.

Swing Alpha

Ready

18

Blocked

04

Active

07

Active positions

Fresh indicators
TP1 reached
Trailing stop moved
Scan log attached

Dark trading dashboard

Active positions view with readiness status, entry context, TP1/TP2 state, trailing stop movement, and scan provenance.

Swing Alpha
1

Discovery sync

Verified
2

Indicator build

Verified
3

Intelligence scan

Verified
4

Readiness gate

Verified
5

Paper deploy

Gated

Discovery to Intelligence workflow

Two-stage pipeline separating NSE universe/data sync from Intelligence scanning and gated paper-trade deployment.

Swing Alpha

Risk monitor

Risk/unit

1.0R

TP1

50%

TP2

Open

Trail

Armed

Data freshnessPass
Indicator statePass
Safety checksBlocked
Sizing ruleReady

Risk and performance monitor

Paper-trade control panel for position sizing, target progress, stale-data blocks, safety failures, and lifecycle outcomes.

Core modules

Discovery sync for NSE universe refresh, data availability checks, and indicator preparation
Intelligence signal scanning for strategy candidates and scan-log traceability
Readiness gates that block paper-trade entries when data, indicators, or safety checks are stale
Paper-trade deployment workflow with controlled entry creation
Lifecycle management for position sizing, TP1, TP2, and trailing stop logic
Decision-focused analytics across active positions, trade history, and scan logs

Architecture highlights

Django and DRF backend exposing portfolio, scanner, position, and trade-history APIs
PostgreSQL schema for NSE instruments, scan results, indicator snapshots, readiness states, and lifecycle events
React frontend for active position review, scan inspection, trade history, and operational logs
Two-stage processing model separating Discovery sync from Intelligence scan execution
Readiness-gated deployment layer that validates data freshness, indicator state, and safety checks before paper entries
Trade lifecycle services for sizing, partial target tracking, trailing stop movement, and exit-state analytics

Stack

Technology used

DjangoDjango REST FrameworkPostgreSQLReactPythonNSE DataREST APIs

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