Algorithmic Trading
Powered by AI
Advanced quantitative strategies for cryptocurrency perpetual futures. Machine learning models. Real-time execution. Institutional risk management.
Our Philosophy
Principles that guide our systematic approach to quantitative trading
Signal from Noise
We extract meaningful patterns from chaotic market data using advanced statistical methods and machine learning. Every signal is rigorously validated through combinatorial purged cross-validation to prevent overfitting.
Net of Friction
Every strategy proves itself net of transaction costs, slippage, and market impact. We model execution costs with institutional-grade precision using the Almgren-Chriss framework and real-world order book data.
Complex Systems
Markets are complex adaptive systems. We model complexity through multi-regime detection, cross-asset relationships, and hierarchical risk parity. Our approach respects market structure rather than imposing simplistic assumptions.
Systematic Discipline
Emotions destroy returns. We execute with systematic discipline through automated risk management, circuit breakers, and strict position sizing. Every decision is data-driven, every action is logged and monitored.
"In markets, the only edge is systematic execution of statistically validated strategies with rigorous risk management."
— Kaitral Trading Philosophy
Institutional-Grade Strategies
Battle-tested quantitative strategies designed for consistent returns across market conditions
Multi-Strategy Signal Generation
Combines Mean Reversion, Trend Following, and Breakout signals with dynamic weighting based on market conditions.
ML Volatility Forecasting
Gradient Boosting model predicts volatility regimes and adjusts position sizing in real-time.
Automated Risk Management
Position sizing based on volatility regime with hard-coded daily loss limits and max drawdown protection.
VectorBT Backtesting
Walk-forward optimization with 90-day rolling windows. Tests multiple strategy weight combinations.
Real-time Execution
Binance Futures integration with market orders, stop-loss, and take-profit automation.
24/7 Monitoring
Telegram alerts, Grafana dashboards, and Prometheus metrics for complete system visibility.
Past performance is not indicative of future results. All strategies undergo rigorous out-of-sample validation. Trading involves substantial risk of loss.
Built with Modern Technology
Enterprise-grade infrastructure for reliability and performance
Python
Core trading logic & ML
PostgreSQL
Time-series data
Redis
Real-time caching
WebSocket
Low-latency feeds
scikit-learn
ML models
Docker
Containerized deploy
System Architecture
Data Layer
- WebSocket market feeds
- TimescaleDB storage
- Redis caching
- OHLCV aggregation
Strategy Layer
- Signal generation
- ML predictions
- VectorBT backtests
- Weight optimization
Execution Layer
- Binance Futures API
- Position management
- Risk controls
- Order routing