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  • Machine Learning

Behaviour-Driven Market Risk Intelligence System for Stock Market Analysis

A modular analytics pipeline that scores stock market risk from behavioral market signals.

Overview

Behavioral signals — volatility clustering, drawdown intensity, momentum breakdown and liquidity variation — are extracted and used to classify market conditions into distinct regimes. Logistic Regression, Random Forest and Gradient Boosting models then score investment risk, presented on an interpretable dashboard.

Problem Statement

Retail risk tools report past volatility and stop there. They ignore the behavioural regime the market is currently in, which is what actually determines whether that volatility number means anything.

Proposed Solution

Extract behavioural signals first, classify the prevailing market regime, and only then score risk — so the output is conditioned on market state rather than averaged across all of history.

Key Features

  • Behavioral Signal Extraction

    Volatility clustering, drawdown intensity, momentum breakdown, liquidity variation.

  • Market Regime Identification

    Classifies market conditions into distinct regimes.

  • ML-Based Risk Classification

    Logistic Regression, Random Forest, and Gradient Boosting models score investment risk.

  • Insight Visualization

    Interpretable dashboard for decision support.

Technologies Used

Machine Learning

  • Logistic Regression
  • Random Forest
  • Gradient Boosting
  • Scikit-Learn

Quant analysis

  • Volatility modelling
  • Drawdown metrics
  • Pandas
  • NumPy

Application

  • Plotly Dash / Streamlit
  • Market data APIs

Project Workflow

  1. 1

    Data ingestion

    Historical price and volume series are loaded.

  2. 2

    Signal extraction

    Behavioural risk signals are computed.

  3. 3

    Regime classification

    Market conditions are grouped into regimes.

  4. 4

    Risk scoring

    Ensemble models assign an investment risk level.

  5. 5

    Visualisation

    Findings are rendered on an interpretable dashboard.

What You'll Receive

  • Complete, runnable source code with folder structure
  • Project report and technical documentation
  • Ready-to-present PPT content
  • Setup and installation walkthrough
  • Line-by-line project explanation session
  • Viva question bank with answers
  • Bug fixing and troubleshooting help
  • Post-delivery support after submission

Frequently Asked Questions

How much does this project cost?

Every project is quoted individually, because the price depends on the modules you need, your technology stack, your college's format and your deadline. Send us the project name on WhatsApp and we'll share a quote the same day.

Can the project be customised to my college requirements?

Yes. Share your guide's requirements, preferred technology stack and abstract format, and we adapt the modules, dataset or UI accordingly.

Will I be able to explain this project during my viva?

That is the point of the explanation session. We walk you through the architecture, every module, the flow of data and the results, and hand over a viva question bank with answers.

What if the project does not run on my laptop?

We help you with setup end to end — dependencies, environment, database and configuration — over chat or a call until it runs on your machine.

Do I get support after submission?

Yes. Post-delivery support is included, so you can come back for fixes, doubts or demo help even after the project is delivered.

Want This Project?

Get complete source code, documentation, PPT, explanation, and support.

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