Trader Performance vs Market Sentiment

This project analyzes how Bitcoin market sentiment (Fear vs Greed) relates to trader behavior and performance on Hyperliquid.

Objective

To uncover patterns between market sentiment and trading outcomes that could inform smarter trading strategies.

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Dataset

Two datasets are used:

1. Bitcoin Market Sentiment (Fear/Greed)

   • Columns: Date, Classification (Fear/Greed)
   • Frequency: Daily

2. Hyperliquid Historical Trader Data

   • Trade-level data including account, price, size, side, time, closedPnL, leverage, etc.

Place both CSV files in the project directory before running the analysis:

• fear_greed.csv
• hyperliquid_trades.csv

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Methodology

1. Data Cleaning

   • Converted timestamps to datetime format
   • Removed duplicates
   • Aggregated trade data to daily level per trader

2. Feature Engineering Created daily metrics per trader:

   • Daily PnL
   • Win rate
   • Number of trades
   • Average trade size
   • Average leverage
   • Long/Short ratio

3. Data Integration

   • Merged trader metrics with daily market sentiment

4. Analysis

   • Compared performance across Fear vs Greed periods
   • Examined behavioral changes
   • Segmented traders by leverage and activity

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Key Outputs

The analysis generates:

• Performance comparison by sentiment
• Behavioral insights (trade frequency, leverage, bias)
• Trader segmentation
• Visualization charts

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Setup

Install required libraries:

pip install -r requirements.txt

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How to Run

Run the analysis script:

python analysis.py

or open and run all cells if using Jupyter Notebook.

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Files in This Repository

• analysis.py or analysis.ipynb — Main analysis code
• requirements.txt — Dependencies
• report.md — Summary of findings
• Dataset files (not included)

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Conclusion

Market sentiment significantly influences trader behavior and performance.
Incorporating sentiment-aware risk management strategies can improve trading consistency and reduce downside risk.