Hi there! We are Team Lima Biji, participating in the UMHackathon 2025 under:
📊 Domain 2 - Quantitative Trading
📑 Slides link: View Our Deck Our project integrates financial market data and online user sentiment to enhance crypto market regime detection using Hidden Markov Models (HMM), with the final goal of recommending BUY/SELL/HOLD strategies based on both on-chain data and Reddit discussion patterns.
Cryptocurrency markets are volatile and sentiment-driven. While traditional models rely purely on numerical indicators, our project attempts to answer:
"Can combining on-chain whale behavior and Reddit user sentiment create more explainable, adaptive, and realistic trading strategies?"
We propose an explainable ML-driven trading assistant that identifies market regimes and gives contextual investment suggestions supported by public discussions.
Our aim is to build an alpha-generating crypto trading system that:
- Detect market regimes using unsupervised learning (HMM).
- Integrate Reddit sentiment to capture behavioral shifts.
- Recommend trading actions (BUY/SELL/HOLD) along with justifications derived from sentiment trends.
✍️ Hypothesis:
- H1: Technical Indicators Improve Regime Detection
- H2: XGBoost Feature Selection Predicts Extreme Price Moves
- H3: Whale-Driven Features Define Market Regimes
✍️ Metrics:
- Sharpe Ratio
- Max Drawdown
- Trade Frequency
- Strategy Win Rate
# Clone the repo
git clone https://github.com/kerryyys/UMH2025.git
cd UMH2025
# Create virtual environment
python -m venv venv
source venv/bin/activate # on Windows: venv\Scripts\activate
# Install dependencies
pip install -r requirements.txt
# Run the pipeline (example)
python models/new_model.py
Note: For NLP sentiment analysis, make sure your Reddit credentials are set up via .env or passed into the praw module.
💬 NLP for Whale Behavior Tracking Scrapes Reddit data to detect how the crowd reacts to sudden market flows.
Uses VADER Sentiment Analysis to extract daily sentiment scores.
Integrates whale movement (inflows/outflows) with public opinion.
Uses decision tree explanations & correlation matrices to expose how features drive decisions.
Explains why the model triggers certain BUY/SELL calls.
Heatmaps, clustering charts, and sentiment trendlines to explain strategies visually.
Model state visualizations (e.g., HMM transition maps, emission probabilities).
Feature Type | Examples | Description On-Chain | exchange_inflow, whale_spikes | Real-time behaviors of smart money Sentiment | avg_sentiment_score, post_volume | Reddit NLP signals aggregated daily Technical | price, returns, volume | Classical indicators Engineered | log_return, whale_sentiment_diff | Combined sentiment-behavioral signals
We combine:
A Gaussian HMM for market regime detection.
An NLP pipeline to extract public sentiment.
A strategy recommendation engine based on regime + sentiment context.
┌────────────────────┐
│ On-chain Features │◄─── CryptoQuant / Glassnode / Coinglass
└────────────────────┘
│
▼
┌────────────────────┐
│ Reddit Sentiment │◄─── NLP pipeline from r/CryptoCurrency, r/BitcoinMarkets
└────────────────────┘
│
▼
┌────────────────────┐
│ Feature Merger │
└────────────────────┘
│
▼
┌────────────────────┐
│ Gaussian HMM │
└────────────────────┘
│
▼
┌─────────────────────────────┐
│ Strategy Decision Engine │ ──> 🔴 SELL / 🟡 HOLD / 🟢 BUY
└─────────────────────────────┘
+---------------------+ | RedditSentiment | +---------------------+ | + fetch_posts() | | + analyze() | | + save_results() | +---------------------+
+---------------------+ | FeatureEngineer | +---------------------+ | + merge_sources() | | + clean_features() | +---------------------+
+---------------------+ | HMMTrader | +---------------------+ | + train_model() | | + predict_regime() | | + evaluate() | | + generate_signals()| +---------------------+
+---------------------+ | Visualizer | +---------------------+ | + plot_regimes() | | + save_backtest() | +---------------------+
UMH2025/
├── archive/ # Archived or deprecated files
├── data/
│ ├── cleaned/ # Cleaned datasets (e.g., cleaned/btc 2023-2024/)
│ ├── NLP/
│ │ ├── processed/ # Processed NLP sentiment data
│ │ ├── raw_unused_data/ # Raw unused Reddit post data
│ │ └── reddit_posts.csv # Collected Reddit post data
│ ├── processed_data/ # Final processed datasets for modeling
│ └── raw_data/
│ ├── crypto_kmeans_clustering_output.csv
│ └── crypto_strategy_output.csv
├── models/ # Currently unused – reserved for model scripts or checkpoints
├── results/ # Folder to store visualizations or model outputs
├── src/
│ ├── 0_config/ # Configuration files and constants
│ ├── 1_fetch_data/ # Scripts to fetch or collect raw data
│ ├── 2_merge_data/ # Scripts to merge and align multiple data sources
│ ├── 3_clean_data/ # Scripts to clean and preprocess datasets
│ ├── 4_backtesting/ # Backtesting strategies and evaluation logic
│ ├── NLP/ # NLP-specific analysis, sentiment scoring, etc.
│ ├── _pycache_/
│ ├── assets/ # Static files for Dash app styling
│ │ └── custom.css
│ └── dash/ # Dash app components
│ ├── app.py # Main entry point for the Dash dashboard
│ ├── callbacks.py # Callback functions for interactivity
│ ├── data_loader.py # Loads and prepares data for visualization
│ └── layout.py # Dash app layout and structure
├── .gitattributes
├── .gitignore
├── README.md # Project documentation
├── requirements.txt # Python dependencies
├── run.bat # Batch script to execute project pipeline
├── setup_env.bat # Batch script to set up the environment
└── ohlcv.csv # OHLCV (Open, High, Low, Close, Volume) data
HMM On-Chain Data: Credit to CoinGlass, CryptoQuant, Glassnode
Reddit Sentiment Data: Credit to Reddit via praw API