How mithleshy885/crypto-trade-analysis works
This repository analyzes crypto trades alongside Fear-Greed sentiment, then offers three distinct outputs: exploratory and trader analyses, next-day sentiment classification, and KMeans trade-pattern clusters. The user-facing prediction path is feature entry in Streamlit → a loaded classifier → a sentiment result. The graph reflects explicit calls in the sampled sources and capabilities listed in the README; unsampled analysis internals are represented at the enclosing module path, without asserting their internal wiring.
Main entry points: trading_sentiment_analysis.py Data Preparation · app.py Model Training · trade_pattern_recognition.py Pattern Clustering · gui.py Prediction Interface
Components 16
Sentiment Analysis
- Data Preparation
trading_sentiment_analysis.py - Exploratory Analysis
trading_sentiment_analysis.py - Sentiment Performance
trading_sentiment_analysis.py - Trader Segmentation
trading_sentiment_analysis.py - Temporal Analysis
trading_sentiment_analysis.py - Trading Insights
trading_sentiment_analysis.py
Sentiment Prediction
- Model Training
app.py - Sentiment Classifier
- Prediction Interface
gui.py - Next-Day Sentiment
gui.py
Trade Patterns
- Pattern Clustering
trade_pattern_recognition.py - Cluster Assignments
trade_pattern_recognition.py
Input Data
- Historical Trades
- Fear-Greed Data
Other components
- Analyst
- Prediction User
Connections 21
- Historical Trades → Data Preparation: loads trades
- Fear-Greed Data → Data Preparation: loads sentiment
- Analyst → Data Preparation: runs analysis
- Data Preparation → Exploratory Analysis: prepares data
- Data Preparation → Sentiment Performance: prepares data
- Data Preparation → Trader Segmentation: prepares data
- Data Preparation → Temporal Analysis: prepares data
- Data Preparation → Trading Insights: prepares data
- Historical Trades → Model Training: loads trades
- Fear-Greed Data → Model Training: loads sentiment
- Model Training → Data Preparation: calls preparation
- Data Preparation → Model Training: returns merged data
- Model Training → Sentiment Classifier: trains classifier
- Prediction User → Prediction Interface: enters features
- Prediction Interface → Sentiment Classifier: loads model
- Prediction Interface → Sentiment Classifier: requests prediction
- Sentiment Classifier → Next-Day Sentiment: returns class
- Analyst → Pattern Clustering: runs clustering
- Pattern Clustering → Data Preparation: calls preparation
- Data Preparation → Pattern Clustering: returns merged data
- Pattern Clustering → Cluster Assignments: writes assignments