Production

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."

Nov 2024 - Present

12 min read

Performance at a Glance

Key metrics demonstrating production-grade performance and institutional quality.

$0.0/mo

Infra Automation

100% GitHub Actions via GHCR

<$0/mo

Cloud Cost

Azure VM (K3S) + VMSS

<0.0ms

ML Latency

Sub-millisecond signal generation

Metrics

Real-time Prometheus monitoring

Uptime

K3S self-healing orchestration

0min

Training Time

Weekly transfer learning cycle

0.0

Fallback Tiers

RL → Blended → Kelly → Flat

0.0+

Years Data

6,977+ 4H bars (2022-2025)

The Challenge

Traditional algorithmic trading systems lack the adaptability to handle volatile cryptocurrency markets and often fail to integrate multiple data modalities effectively.

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 November 24, 2025, achieving sub-5ms inference latency, 413+ monitoring metrics, and $0/month automation cost through GitHub Actions optimization.

System Architecture

Event-driven architecture with ML/RL intelligence layer and comprehensive monitoring.

4-Tier Position Sizing Cascade

Graceful degradation from full RL autonomy to emergency flat positions.

Tier 1

FULL_RL

100% RL position sizing

Confidence >= 0.50, IC >= 0.05

Tier 2

BLENDED

50% RL + 50% Kelly

Medium confidence/IC

Tier 3

PURE_KELLY

100% Kelly criterion

Low confidence or IC failure

Tier 4

EMERGENCY_FLAT

0% position (flat)

Circuit breaker OPEN

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: Free 500MB via GHCR (deprecated Droplet registry)
Compute: VMSS for scalable inference resources

Code Highlight

4-tier fallback cascade ensuring robust position sizing from full RL to emergency flat.

ml_rl_integrated_strategy.py

python

1class MLRLIntegratedStrategy(Strategy):
2    class="text-green-class="text-yellow-400">400">""class="text-green-class="text-yellow-400">400">"class="text-yellow-400">4-tier position sizing with graceful degradation."class="text-green-class="text-yellow-400">400">""
3 
4    def calculate_position_size(self, signal: TLSignal) -> float:
5        class=class="text-green-class="text-yellow-400">400">"text-muted-foreground/class="text-yellow-400">60 italic"># Tier class="text-yellow-400">1: Full RL (high confidence + IC)
6        if signal.confidence >= class="text-yellow-400">0.50 and signal.ic >= class="text-yellow-400">0.05:
7            if self.circuit_breaker.state == class="text-green-class="text-yellow-400">400">"CLOSED":
8                return self.rl_agent.get_position(signal)
9 
10        class=class="text-green-class="text-yellow-400">400">"text-muted-foreground/class="text-yellow-400">60 italic"># Tier class="text-yellow-400">2: Blended (class="text-yellow-400">50% RL + class="text-yellow-400">50% Kelly)
11        if signal.confidence >= class="text-yellow-400">0.40:
12            rl_pos = self.rl_agent.get_position(signal)
13            kelly_pos = self.kelly_criterion(signal)
14            return class="text-yellow-400">0.5 * rl_pos + class="text-yellow-400">0.5 * kelly_pos
15 
16        class=class="text-green-class="text-yellow-400">400">"text-muted-foreground/class="text-yellow-400">60 italic"># Tier class="text-yellow-400">3: Pure Kelly (low confidence/IC)
17        if signal.confidence >= class="text-yellow-400">0.30:
18            return self.kelly_criterion(signal)
19 
20        class=class="text-green-class="text-yellow-400">400">"text-muted-foreground/class="text-yellow-400">60 italic"># Tier class="text-yellow-400">4: Emergency flat (circuit breaker OPEN)
21        return class="text-yellow-400">0.0  class=class="text-green-class="text-yellow-400">400">"text-muted-foreground/class="text-yellow-400">60 italic"># Flat position

Backtest Performance Report

Comprehensive Walk-Forward Validation Results (Jan-Dec 2025)

Interactive Backtest Analysis

3+ years of historical validation with ML/RL integration

Open Full Report

Report includes equity curves, drawdown analysis, trade statistics, and walk-forward validation metrics across all instruments (BTCUSDT, ETHUSDT, SOLUSDT)

Technology Stack

Production-grade technologies powering institutional trading.

Python 3.12

100% type-hinted production code

Language

NautilusTrader

Institutional-grade trading framework (342MB vendored)

Trading

PostgreSQL + TimescaleDB

Time-series optimized database

Database

Redis 7.2

Event pub/sub and feature caching

Caching

MLflow 2.10

ML experiment tracking and model registry

ML Ops

MinIO

S3-compatible artifact storage

Storage

K3S (Kubernetes)

Lightweight production orchestration

Infrastructure

Prometheus + Grafana

71 base metrics with label expansion

Monitoring

Soft Actor-Critic (SAC)

Entropy-maximized RL for position sizing

ML/RL

Boruta

Feature selection (9-11 features per instrument)

MS-GARCH

4-state HMM for regime detection

Quant

Docker Compose

Local development environment (12.6GB)

DevOps

Development Journey

From research to production deployment.

Aug 2024

Research Phase

Quantitative research across 5 domains with 1000+ pages of documentation

Oct 2024

Architecture Design

Event-driven architecture with NautilusTrader framework selection

Nov 2024

ML Pipeline

Transfer Learning and Boruta feature selection implementation

Nov 2024

RL Integration

SAC algorithm with curriculum learning (45min vs 120min convergence)

Nov 24, 2025

Production Deploy

K3S hybrid architecture with $0/month automation

Dec 2025

Weekly Updates

Automated 8-minute deployment pipeline operational

Regime-Adaptive Kelly Criterion

Position sizing adapts to market conditions detected by MS-GARCH.

17%

Crisis

25%

Bear

50%

Neutral

67%

Bull

Explore the Research Behind Trade-Matrix

Dive deeper into the quantitative research, ML methodologies, and system architecture.

View Quant Research See Results