Insider Trading Data Web App

1. Introduction

From Manual Excel Processing to Instant Insights

In 2020, I built an insider trading data web app after watching a professional trader’s YouTube video. He explained a step-by-step process to extract promoter buying data from NSE India using Excel. While effective, this method was slow, repetitive, and required traders to manually filter data every time.

I knew there had to be a better way. Instead of memorizing the process forever and spending 10-20 minutes per report, I automated everything-allowing traders to get the same insights instantly with a single click.

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2. The Problem

Traders and investors tracking insider buying had to:

Manually visit nseindia.com
Download raw data files
Apply filters to extract only promoter purchases
Remove unnecessary rows and format the data in Excel

This was time-consuming, error-prone, and inefficient. If the dataset was large, Excel could freeze, making the process even more frustrating.

3. The Solution: My Insider Trading Data Web App

I built a web app that automates the entire process, turning a 10-20 minute task into an instant report generator.

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How It Works:

Data Extraction: Scrapes insider trading data directly from nseindia.com, bypassing anti-bot measures-without relying on third-party libraries. (A proud achievement!)
Data Processing: Uses Pandas to filter, clean, and aggregate insider trades based on user-defined criteria (date range, transaction type, amount threshold, etc.).
User-Friendly Interface: Displays the refined data in a beautiful, interactive Bootstrap 5 table.
Instant Reports: Users can generate precise insights in just one click instead of repeating Excel-based filtering every time.

4. Tech Stack & Development

Initially, I built the app using Flask, but later transitioned to Django for better scalability. The frontend was designed with Bootstrap 5 for simplicity and responsiveness.

Development Highlights:

Custom Web Scraping: Designed a robust, anti-bot bypass system to fetch data reliably.
Efficient Data Processing: Optimized Pandas operations to handle large datasets.
Seamless UI: Bootstrap-based tables ensure clear, readable reports.

5. Challenges & Limitations

Performance Issues: Since the app was non-async, large data queries could cause slowdowns or occasional lag.
Huge Data Loads: Some queries involved hundreds of thousands of rows, making it impractical for Excel users. (Even opening such a file in Excel could freeze a PC!)

6. The Future: Scaling This into a Full SaaS

This insider trading analysis is just the beginning. I plan to integrate it into a larger Indian financial research platform built using:

Frontend: Astro + Svelte + Supabase
Backend: FastAPI + Pandas

Why This Matters for Traders?

Unlike other platforms that display raw, unstructured insider data, my app processes it into actionable insights tailored for swing traders and investors-something no other financial research site currently offers.

7. Let’s Build Something Together

If you need a custom financial data solution or want to discuss automating similar insights, let’s connect! 🚀