Algorithmic Trading Research

Slow minds build
fast systems

We research and develop AI-driven trading algorithms for cryptocurrency markets. Training neural networks on millions of market samples to find patterns humans can't see — then executing with precision.

60M+ Training samples
250+ Pairs monitored
24/7 Market coverage
01 Research Areas

Building intelligence that reads markets in real time

001
Breakout Detection
Real-time monitoring of all USDC trading pairs for sudden price momentum. Sub-second detection of breakout patterns across hundreds of assets simultaneously.
002
Neural Scalping
CoreML models optimized for Apple's Neural Engine, trained on 60M+ market samples to predict micro-movements and execute high-frequency scalping strategies.
003
Multi-Model Architecture
Combining momentum and scalping models with ensemble techniques. Each model specializes in different market conditions — together they adapt.
02 How We Work

Data in,
decisions out

01
Collect
Continuous ingestion of market data via WebSocket streams — every trade, every candle, every orderbook shift across all active pairs.
02
Engineer
Transform raw ticks into meaningful features — volatility signatures, volume profiles, momentum vectors, and cross-pair correlations.
03
Train
Build and optimize ML models using PyTorch on Apple Silicon. Quantize with BitNet for blazing inference on the Neural Engine.
04
Execute
Deploy models for real-time decision making. Every trade recorded with full context for continuous improvement of the training pipeline.
03 Systems

Always watching,
always learning

Our systems run 24/7, monitoring market conditions and logging every signal for future model training.

slothcrypto — breakout-scanner
$ node scanner.js --pairs USDC --threshold 1.5
# Initializing market connection...
[✓] Connected to Binance WebSocket
[✓] Monitoring 273 USDC pairs
[✓] Breakout threshold: 1.5% / 60s
[✓] Model loaded: momentum-v3.mlmodel
# Scanning...
$