/Trader-Behavior-Market-Sentiment-Analysis

Primary LanguageJupyter Notebook

Trader Behavior & Market Sentiment Analysis

Author: Utsab Dahal
Date: October 2025
Contact: utsabdahal34@gmail.com

Project Overview

Analyzes the relationship between trader performance on Hyperliquid and Bitcoin market sentiment (Fear & Greed Index) to uncover insights for smarter trading strategies.

Objectives

  • Explore correlation between market sentiment and profitability
  • Identify behavioral patterns in Fear vs Greed periods
  • Discover contrarian trading opportunities
  • Provide data-driven recommendations

Repository Structure

ds_utsabDahal/ ├── notebook_1.ipynb # Main analysis ├── csv_files/ # Processed data outputs ├── outputs/ # Visualization outputs ├── ds_report.pdf # Final report └── README.md

Datasets Used

  • Hyperliquid Trader Data: account, symbol, price, size, side, time, leverage, closedPnL
  • Bitcoin Fear & Greed Index: Date, Classification (Fear/Greed)

Key Findings

  • Traders perform differently under Fear vs Greed
  • Contrarian strategies show potential
  • Leverage and risk patterns vary with sentiment
  • Optimal trading hours and sentiment-aware position sizing identified

Methodology

  • Data cleaning, merging, and feature engineering
  • Statistical testing & correlation analysis
  • Risk metrics: Sharpe Ratio, VaR
  • Visualization: PnL, volume, leverage, risk-return, hourly trends

Technologies

  • Python 3.x
  • pandas, numpy, matplotlib, seaborn, scipy, datetime
  • Platform: Google Colab

How to Reproduce

  1. Clone this repo
  2. Open notebooks in Google Colab
  3. Upload datasets or mount Google Drive
  4. Run cells sequentially
  5. Check outputs in csv_files/ and outputs/

Contact

Status: Complete and Ready for Review