Trade-Matrix MVP
Institutional-Grade Cryptocurrency Algorithmic Trading Platform
"Combining traditional quantitative finance with modern ML/RL techniques, inspired by Renaissance Technologies and Two Sigma patterns."
The Goal
The core goal of the Trade‑Matrix MVP is to adopt ML/RL/DL‑based algorithmic trading models within a Rust‑based, high‑performance data‑processing and execution system (capable of handling tick data), while providing a Python‑friendly environment for model development and testing, such as Jupyter Notebook.
The Solution
Trade-Matrix is a multi-modal trading system integrating OHLCV data, on-chain metrics, and sentiment analysis. It uses Transfer Learning for signal prediction and Reinforcement Learning (SAC) for dynamic position sizing, with a 4-tier fallback cascade ensuring robust operation.
The Impact
Production-deployed since September 1, 2025, achieving sub-5ms inference latency, 413+ monitoring metrics, and $0/month automation cost through GitHub Actions optimization, while getting total return +44.15% from Sep 01, 2025 to Dec 25, 2025 on ByBit Demo account.
Performance at a Glance
Key metrics demonstrating production-grade performance and institutional quality.
Interactive Visualizations
Explore the engineering diagrams that power Trade-Matrix. Interact with the live architecture below or launch detailed blueprints.
System Architecture
Complete Architecture
Full-screen view of the 7-layer infrastructure, including K3S, Redis, and Monitoring stack.
ML/RL Pipeline
End-to-end data flow visualization: Feature Engineering → Transfer Learning → Execution.
Research Workflow
CRISP-DM research methodology: From MS-GARCH academic research to production code.
Key Capabilities
Institutional-grade features powering the trading system.
Hybrid Cloud Architecture
Optimized hybrid workflow: Local Docker for dev/training (MLflow+MinIO) and Azure K3S for inference. Achieves <$50/mo production cost by deprecating heavy resource containers in prod.
- Local: MLflow + MinIO + TimescaleDB for R&D
- Prod: GHCR-only deployment (Base + Model images)
- Storage: GitHub Container Registry for base images (6.24GB) + model artifacts (319MB)
- Compute: VMSS for scalable inference resources
Resilient Model Loading System
Artifact-first 4-tier loading with automatic fallback. Models are immutable artifacts; MLflow provides metadata (nice-to-have, not critical).
Registered Model
MLflow Model Registry with aliases (@production, @champion)
Semantic model names, automatic artifact resolution, no run_id dependency
Run ID + Artifact
Load from MLflow run_id and artifact path
Development/testing use, requires MLflow database availability
Direct S3/MinIO
Bypass MLflow, load directly from S3 bucket
Disaster recovery: works even if MLflow DB is corrupted
Local Filesystem
Most resilient fallback from local model directory
models/ml/ directory, no external dependency required
Critical: Feature order validation (sklearn validates names AND order) • Zero-failure initialization across Docker, K3S, and Azure environments
3-Tier Warmup System with Data Persistence
Progressive warmup ensuring signal continuity across restarts.
Redis State Recovery
Fastest path: direct Redis restore when state is fresh (<4h old)
- Direct Redis signal_state restore
- PostgreSQL bar history load after restore
- Validates age (<4h), correlation (>=0.95), completeness
Gap Fill Recovery
Medium path: DB-first incremental computation when Redis is stale or DB has 200+ predictions
- DB-first approach: checks ml_prediction_history before recomputing
- Rebuilds EWMA from 200 DB predictions if Redis missing
- Timestamp-based gap calculation (not count-based)
Full Bootstrap
Cold start: full bootstrap from 1600 historical bars when no Redis and DB <200 predictions
- 9-phase vectorized pipeline for efficiency
- Quality gates: IC >= 0.05, p-value < 0.15, Sharpe > 0.5
- NORMALIZE-first-THEN-update operation order (ERROR #148 fix)
Data Persistence Strategy
- •OHLCV historical data
- •ML predictions history
- •External data (VVIX, funding rates)
- •Signal state (deque, IC, bootstrap)
- •EWMA statistics
- •Real-time prediction cache
- •K3S → Local validation scripts
- •Signal parity checks
- •90-second warmup protection
4-Tier Position Sizing Cascade
Graceful degradation from full RL autonomy to emergency flat positions. Tier 3 integrates MS-GARCH regime-adaptive Kelly fractions.
FULL_RL
100% RL position sizing
Confidence >= 0.50, IC >= 0.05
BLENDED
50% RL + 50% Kelly
Medium confidence/IC
PURE_KELLY
100% Kelly criterion with regime-adaptive fractions
Low confidence or IC failure
EMERGENCY_FLAT
0% position (flat)
Circuit breaker OPEN (1h cooldown, 3-state FSM)
Tier 3 Detail: Regime-Adaptive Kelly Fractions
When PURE_KELLY is active, position sizing adapts to the current market regime detected by MS-GARCH (4-state HMM with Hamilton Filter).
17%
Extreme volatility protection
25%
Conservative sizing
50%
Balanced allocation
67%
Aggressive sizing
Monitoring & Observability
Comprehensive visibility through Prometheus/Grafana/Loki with 413+ real-time metrics
71 Base Metrics
413+ Time Series
Label expansion: instrument, strategy, status, direction
10 Exporters
5-15s Refresh
Trading, ML, RL, infrastructure layers
30-Day Retention
Prometheus + Loki
Metrics storage and log aggregation
Trading Cockpit
56 panels, 9 rows
- Portfolio Overview (Value, P&L, Balance, Leverage, Risk)
- Position Performance (Unrealized P&L, Position Value)
- Open Positions (Detailed Bybit-style table)
- ML/RL Signals (Confidence, Activity, Multiplier)
- Market Regime Status (Signal Status, Bootstrap Quality)
- Order Management & Flow Rate
- System Performance (Tick-to-Fill, Exchange Latency)
- Trading Performance (Ratios, Risk Metrics, Win Rate)
- Equity Curve (Sep 1, 2025 baseline with JADE Index & K-Ratio)
Market Analysis
22 panels, 7 rows
- Market Prices (BTC, ETH, SOL, BNB real-time)
- Market Microstructure (Bid/Ask, Spread, Relative Spread)
- Price Action (5min & 1hour changes)
- Market Depth & Liquidity Analysis
- Market Volatility (5min & 1hour)
- OHLCV Candlestick Charts (BTC & ETH)
- Price Correlation Matrix (7d calculated heatmap)
Infrastructure Ops
System health monitoring
- Pod health & availability
- Resource utilization (CPU, memory)
- Database metrics & performance
- Cache performance (Redis)
- Network latency & throughput
- Container restart metrics
Technology Stack
Production-grade technologies powering institutional trading.
Python
100% type-hinted production code
LanguageNautilusTrader
Institutional-grade trading framework (342MB vendored)
TradingPostgreSQL + TimescaleDB
Time-series optimized database
DatabaseRedis
Event pub/sub and feature caching
CachingMLflow
ML experiment tracking and model registry
ML OpsMinIO
S3-compatible artifact storage
StorageK3S (Kubernetes)
Lightweight production orchestration
InfrastructurePrometheus + Grafana
71 base metrics with label expansion
MonitoringSoft Actor-Critic (SAC)
Entropy-maximized RL for position sizing
ML/RLML Algorithms
Random Forest, XGBoost with Boruta feature selection
MLMS-GARCH
4-state HMM for regime detection
QuantDocker Compose
Local development environment (12.6GB)
DevOpsDevelopment Journey
From research to production deployment.
Research Phase
Quantitative research across 5 domains
Architecture Design
Event-driven with NautilusTrader
Feature Testing & Development
Core feature implementation and validation
ML Pipeline
Transfer Learning + Boruta feature selection
RL Integration
SAC with curriculum learning (45min convergence)
Local Backtesting System
Backtesting framework and historical validation
Docker-based Dev & Testing
Docker Compose environment setup
Production Deployment
Initial production deployment
K3S + Weekly Automation
K3S on DigitalOcean + GitHub Actions automation
Azure Cloud VMs + VMSS
Scaled infrastructure on Azure VMs
Current Limitations & Future Potential
Honest assessment of MVP constraints with expansion-ready architecture
Current Constraints
Compute Resources
Data Frequency
Monthly Budget: <$200
Alpha Data Scope
Future Potential
HFT-Ready Foundation
GPU ML Pipeline
Multi-Exchange
Multi-Modal Alpha Fusion
Cost Comparison Matrix
Scaling options with incremental costs and expanded capabilities
| Scenario | Cost | Capabilities |
|---|---|---|
| Current MVP | $0/mo automation + <$50/mo cloud | CPU-only, 4H bars, 1 exchange |
| +GPU Pipeline | +$252-961/mo | Transformers/LSTM, T4-A100 GPU options |
| +Multi-Exchange | API dev only | 3+ exchanges, cross-exchange arbitrage |
| +Tick Data | +$500/mo | Sub-second data, HFT strategies |
| +Exogenous Alpha | +$2,000+/mo | Glassnode/Bloomberg, News/Sentiment API, On-Chain Metrics |
Explore Trade-Matrix in Depth
Live trading results, quantitative research foundations, and backtesting validation.