ORACLE is a multi-asset market regime detection system that uncovers hidden market states, visualizes regime shifts over time, and can provide early warnings for potentiel crises or bull runs
-multi asset awarness: stocks, bonds, commodities, FX, and macro indicators
-latent market embddings: variational autoencoders (VAE) or transformers to encode market states
-regime detection: unsupervised clustering reveals hidden market regimes (bull, bear, crisis, recovery)
-visualization dashboard: interactive timeline, embeddings projection, and asset/macro overlays
-predictive module: forecast probabilities of upcoming regimes
-scenario simulation: explore "what-if" market events and their impact on regimes
1.clone the repo:
git clone https://github.com/Youcef3939/ORACLE.git
cd ORACLE
2.create a virtual environment:
python -m venv venv
source venv/bin/activate # Linux/Mac
venv\Scripts\activate # Windows
pip install -r requirements.txt
3.explore data & prototypes in notebooks/
4.run the dashboard:
streamlit run dashboard/app.py
[data sources] --> [data pipeline] --> [feature engineering] --> [model core] --> [regime detection] --> [visualization & dashboard]
raw Data → preprocessing → feature engineering → model core → latent embeddings → clustering → regime labels → visualization / alerts
. integrate more assets & micro indicators
. add transformer-based predictive regime forecasting
. real time alerts for early crisis detection
. improve clustering with temporal continuity ( for exemple HMMs, hidden markov models)
