{"id":28405043,"url":"https://github.com/l1ght14/tradersentiment_primetrade","last_synced_at":"2025-10-20T12:35:33.391Z","repository":{"id":295658812,"uuid":"990574618","full_name":"l1ght14/TraderSentiment_PrimeTrade","owner":"l1ght14","description":"Analyzes Bitcoin market sentiment's impact on Hyperliquid trader PnL \u0026 behavior. Uncovers patterns using Python (Pandas, Seaborn) to derive actionable trading insights. 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The project explores the relationship between Bitcoin market sentiment and historical trader performance on the Hyperliquid platform.\n\n**Objective:** To uncover patterns in trader behavior and PnL relative to market sentiment and to deliver insights that can drive smarter trading strategies.\n\n## Files in this Repository:\n\n**`Trader_Sentiment_Analysis.ipynb`**: The main Jupyter Notebook containing all the Python code, analysis steps, visualizations, interpretations, and conclusions.\n**`fear_greed_index.csv`**: The dataset for Bitcoin market sentiment (Fear \u0026 Greed Index).\n**`historical_data.csv`**: The dataset containing historical trader data from Hyperliquid.\n**`README.md`**: This file.\n\n## How to View and Run the Analysis:\n\n1. **View Online:** The Jupyter Notebook (`Trader_Sentiment_Analysis.ipynb`) can be viewed directly on GitHub.\n2. **Run Locally:**\n    * Clone or download this repository.\n    * Ensure you have Python 3 installed along with the following libraries:\n        * `pandas`\n        * `numpy`\n        * `matplotlib`\n        * `seaborn`\n        * `jupyter` (for running the notebook)\n    * You can install these using pip:\n\n        ```bash\n        pip install pandas numpy matplotlib seaborn notebook\n        ```\n\n    * Navigate to the cloned/downloaded directory in your terminal.\n    * Launch Jupyter Notebook:\n\n        ```bash\n    \n        jupyter notebook\n        ```\n\n    * Open the `Trader_Sentiment_Analysis.ipynb` file from the Jupyter dashboard.\n    * Ensure the CSV data files (`fear_greed_index.csv` and `historical_data.csv`) are in the same directory as the notebook for the code to run correctly.\n\n## Summary of Findings:\n\nThe analysis revealed a significant correlation between market sentiment and trader performance/behavior:\n*Trading during **\"Extreme Greed\"** conditions was found to be detrimental to PnL, despite traders adopting a contrarian (shorting) stance.\n**\"Fear\"** (specifically when the sentiment index value was 44), **\"Neutral\"**, and **\"Greed\"** were generally profitable periods, with traders predominantly maintaining a bullish bias.\nBehavioral shifts in trade sizing and directional bias were observed across different sentiment states.\n\nDetailed insights and actionable strategy suggestions are provided within the Jupyter Notebook.\n\n---\nSubmitted by: Prakash Sharma\nDate: 26/5/2025\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fl1ght14%2Ftradersentiment_primetrade","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fl1ght14%2Ftradersentiment_primetrade","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fl1ght14%2Ftradersentiment_primetrade/lists"}