📊 What I Learned Analyzing 100K+ Crypto Trades Using Market Sentiment

Most traders think market sentiment (Fear & Greed) directly drives profits.

So I decided to test it.

I analyzed 100K+ trades from Hyperliquid and combined it with the Bitcoin Fear & Greed Index to uncover how sentiment actually impacts trading performance.

Here’s what I found 👇

🧠 The Question

Does market sentiment really affect:

Profitability?
Win rate?
Trader behavior?

Or is it just noise?

⚙️ What I Did
Cleaned and processed trading data
Filtered only closed trades (real profits/losses)
Merged trades with daily sentiment
Engineered features like:
Win rate
ROI
Segmented traders into:
Top performers
Worst performers
📈 Key Findings

1. Greed = Profits… but only for some Extreme Greed had the highest average PnL and win rate But here’s the twist:

👉 Top traders made the most money
👉 Worst traders LOST the most money

Same market. Different outcomes.

1. The Overconfidence Trap

Worst traders:

Had ~55% win rate in Greed (not terrible)
But still lost money

Why?

👉 They over-leveraged and mismanaged risk

This suggests:

Losses weren’t due to bad predictions, but bad execution.

1. Fear Isn’t Always Bad Traders performed surprisingly well in Fear phases Likely because: Only high-conviction trades were taken Risk exposure was lower

👉 Discipline > sentiment

1. Top Traders Are Consistent

Top performers:

Stayed profitable across all sentiment conditions
Didn’t rely on “market mood”
Focused on execution + risk management

1. Timing Beats Sentiment (Advanced Insight)

Instead of just sentiment, I analyzed sentiment transitions:

👉 Example:

Fear → Greed
Greed → Fear

Result:

Transitions often produced better outcomes than static sentiment.

🧠 What This Means

Market sentiment doesn’t determine success.

👉 Trader behavior does.

Weak traders react emotionally
Strong traders stay consistent
🚀 Practical Takeaways

If you trade (or build trading systems):

Don’t blindly follow bullish sentiment
Control position size during Greed
Avoid overtrading in “easy markets”
Focus on consistency, not prediction
🏁 Final Thought

The market doesn’t reward sentiment.
It rewards discipline.

🔗 Built Using
Python (Pandas, Matplotlib)
Real trading + sentiment data

If you're into data science, trading, or building in Web3—would love to hear your thoughts 👇