Trader Sentiment Analysis

Hyperliquid trader performance mapped against Bitcoin Fear & Greed market regimes

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Judge Start Here

Priority	File	Why it matters
1	Final Excel Workbook	Main submission output: executive dashboard, charts, sentiment/regime summaries, account edges, and notes.
2	Written Analysis Report	Short narrative summary of methodology, findings, and strategy implications.
3	Statistical Validation	Bootstrap confidence intervals that test whether regime efficiency is stable across days.
4	Data Dictionary	Plain-English explanation of raw columns, derived metrics, and output files.
5	Analysis Script	Reproducible Python pipeline that joins the datasets and regenerates the analytical CSV/report outputs.

The primary output file is:

outputs/trader_sentiment_analysis/trader_sentiment_analysis.xlsx

Executive Dashboard Preview

The Excel workbook opens with a polished executive dashboard designed for quick review.

Visual Findings

These charts are generated from the analysis outputs so judges can review the core story without opening Excel first.

PnL Efficiency by Sentiment

Net PnL by Sentiment

Top Coins by Net PnL

Assignment Objective

Explore the relationship between trader performance and Bitcoin market sentiment, uncover hidden patterns, and deliver insights that can support smarter trading strategies.

What Was Analyzed

Dataset	Role in analysis
Bitcoin Fear & Greed Index	Daily market sentiment regime: Extreme Fear, Fear, Neutral, Greed, Extreme Greed.
Hyperliquid historical trader data	Trade-level account, coin, side, size, fees, timestamps, direction, and closed PnL.

Headline Findings

Question	Answer
Best efficiency regime	Extreme Greed, about $21.60 net PnL per $1,000 traded.
Highest total net PnL regime	Fear, about $3.26M net PnL.
Sentiment as signal	Weak daily correlation with PnL, so sentiment works better as a regime filter than as a standalone signal.
Strategy implication	Use sentiment to adjust playbooks, position sizing, and account/trader selection.

Strategy Recommendations

Recommendation	What it means
Regime-aware sizing	Increase size only where both absolute PnL and PnL efficiency are strong. Extreme Greed had the best efficiency, while Fear had the highest total PnL.
Playbook selection	Do not use the same long/short playbook across all regimes. Direction-level results show certain close/sell behaviors worked better in specific sentiment buckets.
Trader selection	Use greed_minus_fear_efficiency to identify accounts that perform better in momentum regimes versus fear regimes.
Risk controls	If a sentiment bucket's bootstrap interval crosses zero, cap exposure or require confirmation from BTC trend, volatility, funding, or liquidation data.
Production upgrade	Combine sentiment with BTC returns, funding rates, open interest, realized volatility, and liquidation intensity before using it live.

Statistical Validation

Daily regime metrics were bootstrapped to estimate 95% confidence intervals for average daily PnL efficiency.

Sentiment	Validation takeaway
Fear	Daily efficiency stayed positive in the bootstrap interval.
Greed	Daily efficiency stayed positive in the bootstrap interval.
Extreme Greed	Strongest and most stable efficiency interval among the sentiment buckets.
Extreme Fear	Interval crossed zero, so the edge is less stable and should be risk-capped.
Neutral	Interval crossed zero, so it should not be treated as a strong standalone edge.

Full validation table: statistical_validation.csv

Methodology

1. Parsed Hyperliquid Timestamp IST into a trade date.
2. Joined every trade to the Fear & Greed Index using trade date.
3. Treated rows with nonzero Closed PnL as realized trades for win-rate analysis.
4. Calculated net_pnl = Closed PnL - Fee.
5. Compared performance across sentiment regimes using trade count, realized trades, volume, net PnL, win rate, and PnL per $1,000 traded volume.
6. Built account-level, coin-level, direction-level, and daily trend summaries.

Repository Structure

.
|-- analysis/
|   `-- trader_sentiment_analysis.py
|-- data/
|   |-- fear_greed_index.csv
|   `-- historical_data.csv
|-- outputs/
|   `-- trader_sentiment_analysis/
|       |-- trader_sentiment_analysis.xlsx
|       |-- trader_sentiment_report.md
|       |-- dashboard_preview.png
|       |-- statistical_validation.csv
|       |-- charts/
|       `-- summary CSV files
|-- DATA_DICTIONARY.md
|-- README.md
`-- requirements.txt

Reproduce the Analysis

pip install -r requirements.txt
python analysis/trader_sentiment_analysis.py

The script reads from data/ and writes refreshed analytical outputs under:

outputs/trader_sentiment_analysis/

Deliverables Checklist

Requirement	Status
Raw datasets included	Complete
Reproducible analysis script	Complete
Sentiment/trade join	Complete
Trader performance metrics	Complete
Hidden-pattern summaries	Complete
Statistical validation	Complete
Data dictionary	Complete
README chart visuals	Complete
Final Excel workbook	Complete
Written report	Complete

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