Go 1.23+, lock-free concurrency, reinforcement learning, and real-time market data processing. Every component is designed for zero-copy throughput and sub-millisecond decisions.
One language for production. No polyglot complexity. Go 1.23+ with full generics support.
Mutexes are the exception, not the rule. Lock-free data structures eliminate contention on hot paths — critical when processing millions of quotes per second.
xsync.MapOf provides concurrent, lock-free map access for symbol-quote mappings, account states, and order books. No GOMAXPROCS scaling issues.
model-ML provides lock-free SPSC and MPMC queues for inter-goroutine communication. Zero allocations in the hot path. Bounded and unbounded variants.
LRU, LFU, and ARC caching strategies with lock-free read paths. Self-tuning based on access patterns. Critical for market data replay and indicator calculations.
model-ML is our internal Go library for reinforcement learning. Written in Go — no Python bridge, no CGo overhead.
State-of-the-art DQN implementation with experience replay, target networks, and double Q-learning. Built on lock-free memory management for GPU awareness.
Population-based hyperparameter optimization. Crossover, mutation, and selection operators implemented in pure Go. Parallel fitness evaluation across worker pool.
Continuous learning across trading sessions. Transfer learning between market regimes. Model versioning and A/B testing in production with automated rollback.
k3s on bare metal. Helmfile for everything. Longhorn for persistent storage. VictoriaMetrics for observability.
Lightweight Kubernetes
Declarative Deployments
VictoriaMetrics
Longhorn Storage
Relational state for accounts, orders, and configuration. ACID compliance where it matters.
Time-series analytics. Market data, model metrics, performance statistics. Columnar storage for fast aggregations.
Real-time dashboards. System metrics, trading performance, model accuracy. Every component exposes /metrics.