DataShipped2025
CryptoSent
A sentiment-driven market predictor connecting Twitter sentiment to stock price movements.
- 250k+
- Tweets analyzed
- 24
- Tickers tracked
- Daily
- Data pipeline
Timeline
Feb 2025 to May 2025
Role
Data pipeline and analysis
Team
Solo
Status
Shipped
Stack
Source
Section 01
Overview
Markets move on mood as much as math. CryptoSent tests how far public sentiment can go as a predictive signal: it scores Twitter sentiment with NLP techniques and correlates it against stock price movements.
Section 02
Data Pipeline
Tweets are collected, cleaned, and scored in Python using pandas, NumPy, and SciPy. The scored data lands in Snowflake, where SQL handles the joining, grouping, and filtering that aligns sentiment windows with market data.
Keeping the heavy lifting in SQL kept the Python layer simple: score, load, and analyze.
- NLP sentiment scoring over 250k+ tweets
- Snowflake warehouse with SQL transformations
- Daily refresh aligning sentiment windows to trading days
Section 03
Analysis & Visualization
Statistical analysis in SciPy tested whether sentiment shifts lead price movements or just echo them. Findings were visualized in Matplotlib and Power BI, from correlation heatmaps down to per-ticker sentiment timelines.
Section 04
Results
The strongest finding: sentiment spikes correlate with short-horizon volatility more reliably than with direction. The project became my template for data work: a clean warehouse, honest statistics, and visuals a non-technical reader can follow.