Assessfy Pvt. Ltd Advanced 5 milestones 100 marks

Quantitative Trading Strategy with Backtest (Indian Equities)

Target year: MMS Sem 4 (Capstone — Quantitative Finance) AICTE: 6 credits · ~150 hrs Bloom: Create / Evaluate MU CBCS: FN402 Capstone / Dissertation

Real-world project · AICTE-aligned · AI-graded · Audit-ready certificate

Enroll as Student Mentor this project

Milestones

Available mentors

Enrolled students

Core skills

About this project

Design + backtest a quantitative trading strategy on NSE equities. Cover signal generation (momentum + value + quality factor), portfolio construction (long-only or long-short), transaction costs + slippage, risk management. Use 10-year history, report Sharpe, max drawdown, info ratio. Defend assumptions.

Course Learning Outcomes (CLOs):
CLO1: Design factor-based signal generation.
CLO2: Build a backtest engine with realistic costs + slippage.
CLO3: Validate using walk-forward + out-of-sample tests.
CLO4: Apply risk management + position sizing.
CLO5: Defend strategy in front of quant-research panel.

Industry/societal relevance: Indian quant trading (WorldQuant India, Tower Research, Alphaster, Edelweiss) hiring quant researchers + analysts heavily. Strong placement prep.

Milestones

1. Universe + Data

15 marks 18d

Pick NSE-500 universe. Download 10 years OHLCV + fundamental data. Clean splits/bonus.

2. Signal Construction

20 marks 21d

Build 3 factor signals: momentum (12-1 month), value (P/E), quality (ROE). Test each separately.

3. Backtest Engine

25 marks 21d

Build event-driven backtester with transaction costs (10 bps) + slippage. Long-only baseline.

4. Walk-Forward Validation

20 marks 21d

Validate on rolling 5-year windows. Compute Sharpe, Sortino, max-DD, info-ratio. Avoid overfitting.

5. Final Report + Quant Defense

20 marks 19d

15-page report + Jupyter notebook + GitHub. Oral defense by faculty + industry quant.

Open internships using this project -->

Skills you'll learn

Quantitative FinanceFactor InvestingBacktesting frameworksPython (pandas + numpy)Risk ManagementStatistical Validation (walk-forwardout-of-sample testing)

Tools used

Python 3.11pandasnumpyzipline (or backtrader)yfinance / nsepyExcel for resultsGitHubJupyter Notebook

Prerequisites

Investment Analysis; Statistics; Python intermediate; willingness to read academic finance papers

Available mentors

No mentors have signed up for this project yet.

Be the first to mentor

You'll earn — Certificate (PDF)

AICTE-aligned Project Completion Certificate

A formal, audit-ready PDF certificate issued by Assessfy + your institute on successful completion. Includes AICTE credit hours, your evaluator's signature, and a QR code for third-party verification.

AICTE-aligned

Certificate of Project Completion

This is to certify that

has successfully completed the project

Quantitative Trading Strategy with Backtest (Indian Equitie…

Scan to Verify

Date

Signature

Auto-issued on completion QR-verifiable

You'll earn — Digital Badge

Shareable LinkedIn / Resume Skill Badge

A compact, verifiable Open-Badges-2.0-compliant digital credential. Add to your LinkedIn profile, GitHub README, or resume in one click. Recruiters can validate authenticity via a unique URL.

Advanced

Quantitative Trading Strategy with Ba…

Assessfy

Auto-issued on completion One-click LinkedIn add