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Backtest
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Monte Caro Sim
Inspiration
We are currently unemployed and thus, we are very interested in money. We also like gambling in the stock market. We thought, maybe ML algorithms could be used to analyze and predict stock movements!
What it does
Utilizes TFT signal feedback to blend drift/volatility before feeding it into simulations.
How we built it
Core (/core): C++ Monte Carlo engine (Adjusted GBM + GARCH + Hull-White + modified BSM) Compute Host (/core/service/python_host): gRPC wrapper over the C++ core API (/api): Flask + Celery + Socket.IO orchestration layer Frontend (/frontend): Next.js 15 dashboards and live streaming UI
Challenges we ran into
Getting gRPC endpoints to work.
Accomplishments that we're proud of
Making a cool project that works!
What we learned
How to effectively project manage
What's next for Monte Carlo Portfolio Simulation
Improve accuracy of model and memory management.
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