Strict NDA DisclaimerThis case study showcases a Non-Disclosure Agreement (NDA) signed project. Due to strict legal confidentiality agreements, actual proprietary trading data, client identities, exact performance figures, and live interface images are not real and cannot be publicly shown by us as the development team. All metrics and visuals shown are representative architectural simulations.
Quant Research & Intelligence
Machine Learning Signal Filter
A machine learning pipeline that acts as a signal filter for trend-following systems. Uses LightGBM for classification of signal quality, reducing whipsaws and maximizing overall strategy expectancy.
Primary TechPython / LightGBM
API ProtocolFastAPI Endpoint
ArchitectureML Classifier
01The Problem
A quant fund needed to filter out false signals from their trend-following strategy using machine learning.
02Our Solution
We implemented a LightGBM model that analyzed feature importance to drastically reduce false entries and boost win rate.
Core Features Developed
- LightGBM Classifier
- Feature Importance Analyzer
- Whipsaw Filtering Engine
- Live Signal API Endpoint
Technology Stack
PythonScikit-LearnLightGBMFastAPI