MLOps Infrastructure for Trading Models

MLOps Infrastructure for Trading Models: Development and Automation

Situation: an algorithmic trader spends 8 hours manually extracting data, training a model, and deploying an inference server. One config error — a lost day. With 50 trades per day, each minute of deployment delay costs $500, and with an average trade ticket of $5000, losing one is a tangible blow to P&L. This is not hypothetical — we have encountered it many times. The MLOps infrastructure we developed for trading models automates the entire pipeline: from receiving market ticks to issuing trading signals. On one project, we reduced rollout time from 4 hours to 15 minutes — 16 times faster than the manual process. This approach allows teams to focus on strategy development rather than infrastructure dances.

Why MLOps Is Critical for Trading Algorithms

In trading, every millisecond of deployment delay or inference server downtime costs real money. MLOps infrastructure solves three main problems:

• Reproducibility: a model trained today must produce the same result tomorrow. Without data and code versioning, this is impossible.
• Speed: manual deployment takes hours, automated takes minutes. We cut rollout time from 4 hours to 15 minutes on one project (94% reduction).
• Monitoring: feature drift or metric degradation go unnoticed without an alerting system. Our Grafana dashboards show accuracy (target threshold 95%), latency, and volume in real time.

In practice, this means the difference between a profitable trade and a loss. For example, in high-frequency trading, a 100 ms delay can cost $10,000 per month. That's why we use proven tools and best practices.

How We Build the MLOps Pipeline

We use a proven stack: ClickHouse for tick data, PostgreSQL for trades, S3/MinIO for raw data. Orchestration — Prefect, model versioning — MLflow, data versioning — DVC. Inference on Kubernetes with autoscaling. All steps are described in the MLflow Documentation.

Experiments with MLflow

import mlflow
import mlflow.sklearn
import mlflow.pytorch
from mlflow.models.signature import infer_signature

def train_with_mlflow_tracking(experiment_name, config, X_train, y_train, X_val, y_val, X_test, y_test):
    mlflow.set_experiment(experiment_name)
    with mlflow.start_run(run_name=f"{config['model_type']}_{config['version']}"):
        mlflow.log_params({
            'model_type': config['model_type'],
            'n_features': X_train.shape[1],
            'train_size': len(X_train),
            'val_size': len(X_val),
            **config.get('hyperparams', {})
        })
        model = train_model(config, X_train, y_train, X_val, y_val)
        val_metrics = evaluate_model(model, X_val, y_val)
        test_metrics = evaluate_model(model, X_test, y_test)
        mlflow.log_metrics({f'val_{k}': v for k, v in val_metrics.items()})
        mlflow.log_metrics({f'test_{k}': v for k, v in test_metrics.items()})
        signature = infer_signature(X_train[:10], model.predict_proba(X_train[:10]))
        mlflow.sklearn.log_model(model, 'model', signature=signature, registered_model_name=f"crypto_{config['symbol']}_predictor")
        import matplotlib.pyplot as plt
        fig = plot_feature_importance(model, X_train.columns)
        mlflow.log_figure(fig, 'feature_importance.png')
        run_id = mlflow.active_run().info.run_id
        return run_id, test_metrics

Data Versioning with DVC

# dvc.yaml — pipeline definition
stages:
  fetch_data:
    cmd: python src/data/fetch_ohlcv.py --symbol BTC --days 730
    deps:
      - src/data/fetch_ohlcv.py
    outs:
      - data/raw/btc_ohlcv.parquet
  feature_engineering:
    cmd: python src/features/engineer.py
    deps:
      - src/features/engineer.py
      - data/raw/btc_ohlcv.parquet
    outs:
      - data/features/btc_features.parquet
    params:
      - params.yaml:
          - feature_engineering
  train:
    cmd: python src/train.py
    deps:
      - src/train.py
      - data/features/btc_features.parquet
    outs:
      - models/btc_predictor.pkl
    metrics:
      - metrics/train_metrics.json
    params:
      - params.yaml:
          - training

CI/CD for ML with GitHub Actions

# .github/workflows/ml_pipeline.yml
name: ML Training Pipeline
on:
  schedule:
    - cron: '0 1 * * 0'
  workflow_dispatch:
    inputs:
      symbol:
        description: 'Trading symbol'
        default: 'BTC'
jobs:
  train:
    runs-on: [self-hosted, gpu]
    steps:
      - uses: actions/checkout@v3
      - name: Setup Python
        uses: actions/setup-python@v4
        with:
          python-version: '3.11'
      - name: Install dependencies
        run: pip install -r requirements.txt
      - name: Pull data with DVC
        run: dvc pull data/
        env:
          AWS_ACCESS_KEY_ID: ${{ secrets.AWS_KEY }}
          AWS_SECRET_ACCESS_KEY: ${{ secrets.AWS_SECRET }}
      - name: Run training pipeline
        run: dvc repro
        env:
          MLFLOW_TRACKING_URI: ${{ secrets.MLFLOW_URI }}
      - name: Validate model
        run: python src/validate_model.py --min-accuracy 0.54 --min-sharpe 1.0
      - name: Deploy to production
        if: success()
        run: python src/deploy_model.py
        env:
          TRADING_API_KEY: ${{ secrets.TRADING_API }}"}

Deploy on Kubernetes

# k8s/ml-inference-deployment.yaml
apiVersion: apps/v1
kind: Deployment
metadata:
  name: crypto-ml-inference
spec:
  replicas: 3
  selector:
    matchLabels:
      app: ml-inference
  template:
    spec:
      containers:
      - name: inference
        image: crypto-ml-inference:latest
        resources:
          requests:
            cpu: "500m"
            memory: "1Gi"
          limits:
            cpu: "2000m"
            memory: "4Gi"
        env:
        - name: MLFLOW_TRACKING_URI
          valueFrom:
            secretKeyRef:
              name: ml-secrets
              key: mlflow_uri
        livenessProbe:
          httpGet:
            path: /health
            port: 8000
          initialDelaySeconds: 30
          periodSeconds: 10
        readinessProbe:
          httpGet:
            path: /ready
            port: 8000
          initialDelaySeconds: 10

Feature Store: Single Registry for Features

A feature store is a single point of access for features used in training and inference. We use Feast: define entities and feature views, online serving returns up-to-date values in milliseconds. Without a feature store, features are recalculated in each pipeline, leading to train/serve skew and errors. In practice, this saves up to 40% of time when developing new features.

Tool Comparison: MLflow, DVC, Prefect

The choice of orchestrator depends on scale. MLflow is ideal for experiments — it logs hyperparameters, metrics, and models with minimal code. DVC complements it with data versioning on top of Git, convenient for small teams. Prefect handles complex DAGs with retries and monitoring. In trading, where step order is critical (first fetch_data, then train), Prefect is more reliable than Airflow due to built-in retry policies. We do not use paid tools — the entire stack is open-source.

How MLOps Reduces Deployment Time

In one project, we automated the pipeline for a crypto fund. Previously, a data scientist spent 4 hours preparing a release: data extraction, training, metric validation, manual deployment. After implementing MLOps, the same cycle takes 15 minutes. All steps are fixed in a DVC pipeline and run with a single command. CI/CD validates model quality (minimum accuracy 0.54, Sharpe ratio 1.0) and, if successful, automatically rolls out a new container to Kubernetes. Inference server downtime dropped from 2 hours to 30 seconds, uptime reached 99.9%. Savings from downtime losses amounted to $5000 per month.

What's Included

• Audit of the current pipeline and infrastructure
• MLOps architecture design
• Deployment and configuration of MLflow, DVC, Prefect
• CI/CD pipeline (GitHub Actions / GitLab CI)
• Kubernetes manifests for inference
• Monitoring (Prometheus, Grafana, alerts)
• Documentation and team training
• 1 month of support after launch

Work Stages

1. Analysis — review your stack, latency and model update frequency requirements. Define SLAs.
2. Design — outline architecture, select tools, create a proof of concept on a small model.
3. Implementation — set up infrastructure, write pipelines, integrate CI/CD.
4. Testing — load testing, reproducibility checks, inference stress test.
5. Deploy — deploy to production, configure monitoring.
6. Handover — documentation, training, Q&A session.

Estimated Timelines

3 Common Mistakes When Implementing MLOps

1. Ignoring data versioning — the model trains on different slices, results are unpredictable. DVC solves this.
2. No drift alerts — when the feature distribution changes, model accuracy drops. We deploy a PSI counter in Prometheus.
3. One binary for all models — different models require different environments. We use Docker with tagging.

Experience and expertise: we have completed 50+ projects in fintech and crypto. Certified AWS and Kubernetes engineers. Contact us for a consultation on your project. Order turnkey MLOps infrastructure development. Get a consultation — we'll explain how to adapt best practices to your stack.