Solana Sentiment-Driven Price Predictor

The URL fro this project is : https://id2223project-solana-advisor.streamlit.app/

Project Overview

The Solana Sentiment-Driven Price Predictor is an end-to-end Machine Learning system that forecasts daily price movements for Solana (SOL). By fusing high-fidelity financial technical indicators with domain-specific social media sentiment analysis, the system captures both market mechanics and investor psychology.

The project implements a modern MLOps architecture using Hopsworks as a centralized Feature Store, ensuring seamless data synchronization between historical training and real-time inference.

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System Architecture

The system is orchestrated through four specialized pipelines, ensuring modularity and scalability.

1. Technical Feature Pipeline (backfill_crypto_feature.ipynb)

Goal: Generate a rich historical foundation of technical market signals.

• Data Acquisition: Ingests 5+ years of daily OHLCV (Open, High, Low, Close, Volume) data from Yahoo Finance.
• Feature Engineering: Computes 40+ technical indicators:
  • Trend: Multi-window Moving Averages (7d, 30d, 50d, 200d) and MA Cross-overs.
  • Momentum: RSI (Relative Strength Index) and MACD (Line, Signal, Histogram).
  • Volatility: Bollinger Bands (Upper, Lower, Bandwidth) and ATR (Average True Range).
  • Volume Analysis: On-Balance Volume (OBV), Volume-Price Trend (VPT), and Volume Ratios.
  • Temporal Features: Day of week, month, quarter, and weekend/month-end flags to capture cyclical market patterns.
• Storage: Materializes data into the solana_crypto_features Feature Group in Hopsworks.

2. Sentiment Pipeline (backfill_reddit_sentiment.ipynb)

Goal: Quantify "Retail Mood" using specialized Natural Language Processing.

• Data Extraction: Retrieves thousands of historical posts from the r/solana subreddit via the Pushshift API.
• NLP Engine: Utilizes CryptoBERT, a transformer model fine-tuned on 3.2M crypto-social posts, to classify text into Bullish, Neutral, or Bearish categories.
• Aggregation: Daily mean sentiment scores and post volume counts are calculated to provide a unified "market pulse" feature.

3. Training Pipeline (training_pipeline.ipynb)

Goal: Optimize an AI "Brain" to find correlations between social hype and price action.

• Point-in-Time Join: Merges Technical and Sentiment features on a shared timestamp using a Hopsworks Feature View.
• Model Architecture: Implements an XGBoost Regressor, chosen for its superior performance on tabular time-series data.
• Performance Metrics: The model achieved high predictive accuracy:
  • R-Squared: ~0.97 (indicating the model explains 97% of the price variance).
  • MSE: ~35.09.
• Registry: Finalized models are versioned and stored in the Hopsworks Model Registry.

4. Inference Pipeline (inference_pipeline.ipynb)

Goal: Provide real-time actionable insights.

• Live Data Fetch: Triggers a dual-fetch of the current SOL price from Yahoo Finance and the last 24 hours of Reddit discussion.
• Prediction: Passes live features through the registered model to output:
  • Current Price vs. Predicted Price.
  • Expected % Change (e.g., +4.74%).

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Tech Stack

Category	Tools
Data Sources	yfinance (Market Data), Pushshift API (Reddit)
Machine Learning	XGBoost, Scikit-learn, Pandas, NumPy
NLP	CryptoBERT (HuggingFace Transformers)
MLOps	Hopsworks (Feature Store & Model Registry)

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Project Impact

Unlike traditional trading bots that rely solely on price action, this system accounts for the social momentum that frequently drives cryptocurrency volatility. By using CryptoBERT, the system understands that "burning tokens" or "going to the moon" are positive signals, allowing for more nuanced predictions during intense market cycles.