Spaces:
Running
Running
Update README.md
Browse files
README.md
CHANGED
|
@@ -7,4 +7,40 @@ sdk: static
|
|
| 7 |
pinned: false
|
| 8 |
---
|
| 9 |
|
| 10 |
-
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 7 |
pinned: false
|
| 8 |
---
|
| 9 |
|
| 10 |
+
Real-Time Crypto Trading Bot with Machine Learning and PCA
|
| 11 |
+
|
| 12 |
+
Description:
|
| 13 |
+
Welcome to our community focused on integrating mathematics, machine learning, and streamlit-based visualization for financial markets! Our project revolves around building a real-time trading bot for cryptocurrency and stock markets, leveraging Principal Component Analysis (PCA), SHAP analysis, and Random Forest classifiers to make data-driven decisions.
|
| 14 |
+
|
| 15 |
+
What We Offer:
|
| 16 |
+
|
| 17 |
+
Open-source Python scripts to analyze financial data and predict market trends.
|
| 18 |
+
Modular tools for data fetching, feature extraction, and backtesting.
|
| 19 |
+
Educational resources explaining core concepts like logarithmic returns, PCA, and SHAP.
|
| 20 |
+
A Streamlit-powered interface for live trading signals, portfolio performance tracking, and position management.
|
| 21 |
+
Why Join Us?
|
| 22 |
+
Our mission is to empower developers and financial enthusiasts to harness the power of AI and data science for building robust trading strategies. Whether you're a seasoned data scientist or a curious beginner, we welcome you to explore, collaborate, and contribute to this exciting domain.
|
| 23 |
+
|
| 24 |
+
Key Features of the Project:
|
| 25 |
+
|
| 26 |
+
Data Processing
|
| 27 |
+
|
| 28 |
+
Fetch financial data using yfinance.
|
| 29 |
+
Compute probabilities, scenarios, and adjusted returns for trading decisions.
|
| 30 |
+
Machine Learning Strategy
|
| 31 |
+
|
| 32 |
+
Train Random Forest classifiers for predicting buy/sell scenarios.
|
| 33 |
+
Analyze feature importance using SHAP values for model explainability.
|
| 34 |
+
Dimensionality Reduction with PCA
|
| 35 |
+
|
| 36 |
+
Extract meaningful features to optimize trading strategies.
|
| 37 |
+
Backtesting Framework
|
| 38 |
+
|
| 39 |
+
Evaluate the strategy with dynamic portfolio value calculations and risk metrics.
|
| 40 |
+
Get Started:
|
| 41 |
+
|
| 42 |
+
Visit our Homepage on Publish0x to explore the educational slides and tutorials.
|
| 43 |
+
Fork our repositories, experiment with the code, and share your insights!
|
| 44 |
+
Let's Build Together!
|
| 45 |
+
|
| 46 |
+
Join us in pushing the boundaries of AI-driven trading!
|