Quant Platform

Built for Trading. Designed by Engineers.

Consistency in trading demands removing human emotion from decision-making. Our Quant Platform isn't just another trading bot — it's a resilient, data-first architecture where analytics, probability, and automation drive execution.

By combining structured data pipelines, statistical models, and real-time risk management, we dramatically increase edge, reduce risk, and eliminate emotional bias.

System Design

Our Quant Platform is engineered as a modular, scalable architecture that integrates real-time data ingestion, feature engineering, statistical modeling, and automated execution—ensuring resilient, bias-free trading decisions under volatile market conditions.

Data Ingestion: Kafka / Redpanda for real-time OHLCV and order book streaming.
Storage Layer: TimescaleDB optimized for multi-timeframe querying.
Feature Engineering: Python microservices precompute volatility, momentum, VWAP, and order book signals.

Modeling Microservices: GARCH, Hidden Markov Models, Random Forest, Isolation Forest for volatility prediction and anomaly detection.
Strategy Orchestrator: Dynamically selects models based on regime detection.
Execution Engine: Automated trades with risk filters, stop-loss/take-profit, and exposure limits.

Core Features

Multi-timeframe analysis: 1m, 15m, 1h, 4h, 1d.
Automated signal generation with configurable filters.
Real-time risk controls and exposure management.
Extensible modeling layer for custom statistical models.

Applications

Signal generation for intraday crypto trading.
Backtesting custom indicators across 100+ assets.
Risk-adjusted trading strategies in volatile markets.

Live Demo

Monitor real-time cryptocurrency market performance across 136 USDT trading pairs. This heatmap visualizes price changes, RSI indicators, and trading volumes updated every minute.

* Demo data shown for visualization. Production system connects to real-time Kafka streams and TimescaleDB.

Platform in Action