Om Sonawane

• GitHub: github.com/OmSonawane-360
• LinkedIn: linkedin.com/in/om-sonawane360
• Email: ompatil0357@gmail.com

This repository contains a data science project that analyzes the relationship between Bitcoin market sentiment and trader performance. The goal is to uncover patterns in trader behavior during different market conditions and provide actionable insights for smarter trading strategies.

Two primary datasets are used:

1. Fear & Greed Index – Measures Bitcoin market sentiment over time, classified as Fear or Greed.
2. Historical Trader Data – Contains trade-level information including account, coin, execution price, trade size (tokens & USD), trade side (BUY/SELL), timestamps, PnL, and other details.

Objective: To explore how market sentiment influences trading behavior and performance, identify hidden patterns, and generate insights that can support more informed trading decisions.

Datasets: Click here to access datasets on Google Drive

The analysis was performed in a structured, step-by-step manner:

1. Data Cleaning

   • Standardized column names across datasets.
   • Converted timestamps to proper datetime format.
   • Handled missing or invalid values.

2. Feature Engineering

   • Calculated daily total PnL per account.
   • Flagged profitable trades for analysis.
   • Extracted the Date from timestamps to merge with sentiment data.

3. Merging Datasets

   • Combined trader data with Fear & Greed Index on trade date.
   • Ensured each trade is associated with the correct market sentiment.

4. Exploratory Data Analysis (EDA)

   • Visualized PnL distribution for Fear vs Greed days.
   • Tracked daily total PnL trends over time.
   • Examined BUY vs SELL behavior under different sentiment conditions.
   • Optional: Explored trade size and leverage patterns (where applicable).

5. Statistical Analysis

   • Conducted t-tests to compare trader performance between Fear and Greed periods.
   • Checked for significant differences in profitability.

6. Visualization

   • Used boxplots, bar charts, and line plots to convey key insights clearly.
   • Created a final visual summary/dashboard for easy interpretation.

• Trader performance varies significantly with market sentiment.
• BUY/SELL behavior shows distinct patterns depending on Fear or Greed days.
• Top traders tend to maintain consistent strategies, regardless of market sentiment.
• Patterns in trade size, leverage, and trade frequency can be observed across different market conditions.

• Trader_Behavior_Insights_Assignment.ipynb – Complete Colab notebook with code, analysis, and visualizations.
• final_output.csv – Aggregated daily PnL merged with market sentiment.
• Datasets: Access via Google Drive link.

1. Open the notebook in Google Colab.
2. Run all cells sequentially.
3. Explore the visualizations, statistical tests, and final insights.

If you have any questions or would like to discuss the analysis, feel free to contact me:

Om Sonawane

• GitHub: github.com/OmSonawane-360
• LinkedIn: linkedin.com/in/om-sonawane360
• Email: ompatil0357@gmail.com

Note: This project was completed as part of a Junior Data Scientist assessment and demonstrates skills in data cleaning, feature engineering, exploratory data analysis, statistical testing, and visualization.