B.Sc. Data Science student building full-stack products and data-driven trading systems — from React dashboards to backtested signal engines.
Live signal field — rendered in canvas
Python✦Java✦JavaScript✦PHP✦C✦C++✦SQL✦HTML✦CSS✦Bootstrap✦React.js✦Node.js✦Express.js✦MongoDB✦MySQL✦Hadoop (Basics)✦Pandas✦NumPy✦Matplotlib✦Scikit-learn✦Data Cleaning & Preprocessing✦Exploratory Data Analysis (EDA)✦Descriptive Statistics✦Data Visualization✦Microsoft Excel – Data Cleaning✦Sorting✦Filtering✦Formulas✦Pivot Tables✦Charts✦Git✦GitHub✦VS Code✦Jupyter Notebook✦Postman✦Ubuntu (Linux)✦
Python✦Java✦JavaScript✦PHP✦C✦C++✦SQL✦HTML✦CSS✦Bootstrap✦React.js✦Node.js✦Express.js✦MongoDB✦MySQL✦Hadoop (Basics)✦Pandas✦NumPy✦Matplotlib✦Scikit-learn✦Data Cleaning & Preprocessing✦Exploratory Data Analysis (EDA)✦Descriptive Statistics✦Data Visualization✦Microsoft Excel – Data Cleaning✦Sorting✦Filtering✦Formulas✦Pivot Tables✦Charts✦Git✦GitHub✦VS Code✦Jupyter Notebook✦Postman✦Ubuntu (Linux)✦
[ 01 ]About
Full-stack development meets data analysis.Full-stackdevelopmentmeetsdataanalysis.
Currently in my third year of a B.Sc. in Data Science at JG University, Ahmedabad — and building in public the whole way through.
Hetrajsinh ChauhanAhmedabad, Gujarat
B.Sc. Data Science student with hands-on experience in full-stack development (React.js, Node.js, MongoDB) and data analysis using Python, SQL, and Excel. Built real-world projects including a SaaS gym management system, stock market analysis tools, and custom trading indicators.
Four systems spanning SaaS dashboards, market analysis and signal generation — each one designed, built and shipped from scratch.
01
01
React.js
Express.js
Node.js
MongoDB
Team of 2
GymOS
Gym Management SaaS
Built a full-stack gym management system with role-based authentication for Admin, Receptionist, and Members.
Developed role-specific dashboards covering membership management, attendance tracking, and reporting.
Designed REST APIs and MongoDB schemas for members, plans, and payments.
02
02
Python
Pandas
Matplotlib
Stock Market Analysis Dashboard
Automated equity analysis
Built a dashboard that automatically analyzes selected stocks and generates a detailed summary of available data and key metrics.
Implemented technical analysis logic to evaluate trend direction and indicate a stock's potential next move.
Visualized price performance and indicators using Python data-visualization libraries.
03
03In progress
TradingView Pine Script
Python
Trading Indicator & Signal Engine
Multi-factor confluence scoring
Engineered a rule-based trading indicator combining trend detection, support/resistance zones, and momentum analysis into a multi-factor confluence scoring engine.
Backtested signal logic across multiple timeframes, achieving ~70% signal accuracy on historical data.
Currently building an automated execution pipeline with Python and broker APIs (in progress).
04
04
React.js
JavaScript
MongoDB
Team of 2
Cafe Management System
Menu, orders and billing
Built a React-based cafe management system with a MongoDB backend, covering menu display, order handling, and billing.
[ 03 ]Trading systems
Reading the market with rules, not hunches.Readingthemarketwithrules,nothunches.
A multi-factor confluence engine that scores setups the same way every time — then gets backtested before it's trusted.
Developer of
Ciper Eye
ML Stock Market Indicator
Flagship
Engineered a rule-based trading indicator combining trend detection, support/resistance zones, and momentum analysis into a multi-factor confluence scoring engine.
01Trend detectionFactor
02Support / resistance zonesFactor
03Momentum analysisFactor
Confluence score3 / 3 aligned
Confluence modelIllustrative — not live market data
SignalTrendSupportResistance
~70%Signal accuracy on historical data
MultiTimeframes backtested
2Market tools built end to end
Signal to execution
The engine is being built in order — generate, validate, then automate. The first two stages are done; the third is live work.
01
Signal generation
Built
Rule-based indicator built in TradingView Pine Script, scoring each setup on multi-factor confluence.
02
Backtesting
Built
Signal logic backtested across multiple timeframes, achieving ~70% signal accuracy on historical data.
03
Automated execution
In progress
Execution pipeline in Python, wiring generated signals through to broker APIs.
Companion tool
Stock Market Analysis Dashboard
Automatically analyzes selected stocks, generates a detailed summary of key metrics, and evaluates trend direction to indicate a stock's potential next move.
TradingView Pine Script
Python
Pandas
Matplotlib
Broker APIs
Backed by
J.P. Morgan – Quantitative Research Job Simulation (Forage, 2026)
SEBI Investor Awareness Test (NISM / SEBI, 2026)
[ 04 ]Capabilities
The full toolkit.Thefulltoolkit.
Everything below is in active use across my projects — languages, frameworks, data libraries and the tools that tie them together.
01
Programming Languages
Python/Java/JavaScript/PHP/C/C++/SQL
07
02
Web Development
HTML/CSS/Bootstrap/React.js/Node.js/Express.js
06
03
Databases
MongoDB/MySQL
02
04
Big Data
Hadoop (Basics)
01
05
Data Science
Pandas/NumPy/Matplotlib/Scikit-learn/Data Cleaning & Preprocessing/Exploratory Data Analysis (EDA)/Descriptive Statistics/Data Visualization
08
06
Data Analysis
Microsoft Excel – Data Cleaning/Sorting/Filtering/Formulas/Pivot Tables/Charts
