Three production systems running on a self-hosted Kubernetes cluster. The Crypto Pipeline section is wired live — data refreshes every fifteen minutes from a read-only Postgres replica. The other two are described in narrative, with live dashboards arriving as they leave development.
A distributed quantitative trading system. Real-time cryptocurrency data ingestion, technical analysis, signal generation, and automated paper trading — processing hundreds of symbols simultaneously on a bare-metal Kubernetes cluster.
Real-time USDT perpetuals streamed across multiple timeframes (1m to 1d). Tiered symbol selection by volume. Idempotent batch processing for fault tolerance.
Regime-aware signal engine. Market regime classification (trend, mean reversion, range) drives strategy selection at runtime — multiple strategies per regime, each calibrated to its risk profile. Volatility-scaled stop-loss and take-profit sizing.
Position limits, per-trade risk caps, and a daily loss circuit breaker. Automated execution with real-time P&L tracking and Telegram alerting across critical, warning, and info severities.
| Cointegration Pair | p-value | Half-Life · h | Correlation | Updated |
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A multi-source sentiment engine that collects macro-economic and social signals, computes a unified fear/greed index, and classifies market regimes — from extreme fear to extreme greed.
VIX, DXY, credit spreads and yield curves from Yahoo Finance & FRED. GDELT global news sentiment. Google Trends across 13 economic keywords. YouTube trending sentiment across 5 countries.
Weighted index: VIX (25%), credit spreads (15%), news tone (15%), policy uncertainty (15%), yield curve (10%), social sentiment (10%), Google Trends (10%). Regime labels from extreme_fear to extreme_greed.
Dual-write pattern (Kafka + direct DB), automated compression and retention policies, full Ansible deployment automation.
A large-scale data analytics engine that ingests billions of data points and runs quantitative analysis to uncover statistical relationships across crypto markets and macroeconomic signals.
Engle-Granger cointegration with half-life filtering, spectral analysis grounded in Random Matrix Theory, principal component decomposition, asset-graph network analysis with community detection, and regime classification. The statistical backbone behind every strategy and signal in the stack.
Return forecasting model in alpha phase — feature engineering and labeling pipelines currently producing tier-1 symbol predictions. Walk-forward validation with parameter stability testing gates promotion to production. Cross-pipeline signal fusion brings macro-sentiment regimes into the crypto strategy selector.
Planned next phase: PCA-derived factor models for systematic risk decomposition, Bayesian strategy ensembling weighted by recent regime fit, and PnL attribution that separates alpha from market beta and funding-rate drift. The framework that turns aggregate performance into actionable allocation signals.