class NeuralAlphaEngine:
def __init__(self, model, risk_mgr):
self.model = model
self.risk = risk_mgr
self.signals = SignalPipeline()
async def generate_alpha(self):
features = await self.signals.fetch()
prediction = self.model.predict(features)
return self.risk.size(prediction)| I build AI-powered trading systems that combine modern deep learning architectures with robust quantitative finance principles. From transformer-based market predictors to reinforcement learning agents for portfolio optimization — every system is engineered for production reliability and edge preservation.
Production-grade AI systems for quantitative trading
Statistical arbitrage bot that combines momentum and mean-reversion signals using ensemble ML models for crypto perpetual futures.
Transformer-based multi-asset trading system that processes alternative data feeds to generate alpha signals across equity and crypto markets.
Deep dives into AI architectures, trading strategies, and system design
A practical guide to designing, training, and deploying neural network architectures for real-time market prediction and trade execution.
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