Cloud Deployment

Scale your quantitative finance applications to the cloud with VectorAlpha's production-ready deployment strategies.

Container Setup

Optimized Docker Images

Build production-ready containers optimized for VectorAlpha workloads:

# Multi-stage build for minimal image size
# Build stage
FROM rust:1.75-slim as builder

WORKDIR /app

# Install dependencies for compilation
RUN apt-get update && apt-get install -y     pkg-config     libssl-dev     && rm -rf /var/lib/apt/lists/*

# Copy manifests
COPY Cargo.toml Cargo.lock ./

# Build dependencies (cache layer)
RUN mkdir src &&     echo "fn main() {}" > src/main.rs &&     cargo build --release &&     rm -rf src

# Copy source code
COPY src ./src

# Build application
RUN cargo build --release --bin vectoralpha-service

# Runtime stage
FROM debian:bookworm-slim

# Install runtime dependencies
RUN apt-get update && apt-get install -y     ca-certificates     libssl3     && rm -rf /var/lib/apt/lists/*

# Create non-root user
RUN useradd -m -u 1001 vectoralpha

# Copy binary from builder
COPY --from=builder /app/target/release/vectoralpha-service /usr/local/bin/

# Set ownership
RUN chown vectoralpha:vectoralpha /usr/local/bin/vectoralpha-service

USER vectoralpha

EXPOSE 8080

CMD ["vectoralpha-service"]

Service Configuration

Kubernetes Deployment

Deployment Configuration

# vectoralpha-deployment.yaml
apiVersion: apps/v1
kind: Deployment
metadata:
  name: vectoralpha-service
  labels:
    app: vectoralpha
    component: api
spec:
  replicas: 3
  selector:
    matchLabels:
      app: vectoralpha
      component: api
  template:
    metadata:
      labels:
        app: vectoralpha
        component: api
    spec:
      containers:
      - name: vectoralpha
        image: vectoralpha/service:latest
        ports:
        - containerPort: 8080
          name: http
        env:
        - name: RUST_LOG
          value: "info"
        - name: CACHE_URL
          valueFrom:
            secretKeyRef:
              name: vectoralpha-secrets
              key: cache-url
        resources:
          requests:
            memory: "512Mi"
            cpu: "500m"
          limits:
            memory: "2Gi"
            cpu: "2000m"
        livenessProbe:
          httpGet:
            path: /health
            port: http
          initialDelaySeconds: 10
          periodSeconds: 10
        readinessProbe:
          httpGet:
            path: /ready
            port: http
          initialDelaySeconds: 5
          periodSeconds: 5
---
apiVersion: v1
kind: Service
metadata:
  name: vectoralpha-service
spec:
  selector:
    app: vectoralpha
    component: api
  ports:
  - port: 80
    targetPort: 8080
    protocol: TCP
  type: ClusterIP
---
apiVersion: autoscaling/v2
kind: HorizontalPodAutoscaler
metadata:
  name: vectoralpha-hpa
spec:
  scaleTargetRef:
    apiVersion: apps/v1
    kind: Deployment
    name: vectoralpha-service
  minReplicas: 3
  maxReplicas: 100
  metrics:
  - type: Resource
    resource:
      name: cpu
      target:
        type: Utilization
        averageUtilization: 70
  - type: Resource
    resource:
      name: memory
      target:
        type: Utilization
        averageUtilization: 80
  behavior:
    scaleUp:
      stabilizationWindowSeconds: 60
      policies:
      - type: Percent
        value: 100
        periodSeconds: 15
      - type: Pods
        value: 4
        periodSeconds: 15
    scaleDown:
      stabilizationWindowSeconds: 300

High-Performance Job Processing

# backtest-job.yaml
apiVersion: batch/v1
kind: Job
metadata:
  name: backtest-processor
spec:
  parallelism: 10
  completions: 100
  backoffLimit: 3
  template:
    spec:
      containers:
      - name: backtest
        image: vectoralpha/backtest:latest
        resources:
          requests:
            memory: "4Gi"
            cpu: "2000m"
            nvidia.com/gpu: 1  # GPU acceleration
          limits:
            memory: "8Gi"
            cpu: "4000m"
            nvidia.com/gpu: 1
        env:
        - name: STRATEGY_CONFIG
          valueFrom:
            configMapKeyRef:
              name: strategy-config
              key: config.json
        - name: DATA_SOURCE
          value: "s3://market-data/historical/"
      restartPolicy: OnFailure
      nodeSelector:
        workload-type: compute-optimized

Cloud Provider Integration

AWS Deployment

# Deploy to EKS using eksctl
export CLUSTER_NAME="vectoralpha-cluster"
export REGION="us-east-1"

# Create EKS cluster optimized for compute
eksctl create cluster   --name $CLUSTER_NAME   --region $REGION   --nodegroup-name compute-nodes   --node-type c6i.4xlarge   --nodes 3   --nodes-min 3   --nodes-max 20   --managed

# Create GPU node group for ML workloads
eksctl create nodegroup   --cluster $CLUSTER_NAME   --name gpu-nodes   --node-type g4dn.xlarge   --nodes 0   --nodes-min 0   --nodes-max 10   --node-labels workload-type=gpu-compute   --node-taints nvidia.com/gpu=true:NoSchedule

# Install NVIDIA device plugin
kubectl apply -f https://raw.githubusercontent.com/NVIDIA/k8s-device-plugin/v0.14.0/nvidia-device-plugin.yml

# Deploy application
kubectl apply -f vectoralpha-deployment.yaml

Google Cloud Platform

# Deploy to GKE with Autopilot
gcloud container clusters create-auto vectoralpha-cluster   --region=us-central1   --network=vectoralpha-vpc   --enable-private-nodes   --enable-private-endpoint

# Configure workload identity
kubectl annotate serviceaccount vectoralpha-sa   iam.gke.io/gcp-service-account=vectoralpha@PROJECT_ID.iam.gserviceaccount.com

# Deploy with Cloud Run for serverless
gcloud run deploy vectoralpha-api   --image gcr.io/PROJECT_ID/vectoralpha:latest   --platform managed   --region us-central1   --memory 2Gi   --cpu 2   --min-instances 1   --max-instances 100   --concurrency 1000

Serverless Architecture

Deploy compute-intensive workloads using serverless functions for cost optimization:

Terraform Infrastructure

# main.tf
terraform {
  required_providers {
    aws = {
      source  = "hashicorp/aws"
      version = "~> 5.0"
    }
  }
}

# Lambda function
resource "aws_lambda_function" "indicator_calculator" {
  filename         = "target/lambda/indicator-calculator.zip"
  function_name    = "vectoralpha-indicator-calculator"
  role            = aws_iam_role.lambda_role.arn
  handler         = "bootstrap"
  runtime         = "provided.al2"
  memory_size     = 3008
  timeout         = 60
  
  environment {
    variables = {
      RUST_LOG = "info"
    }
  }
  
  reserved_concurrent_executions = 100
}

# API Gateway
resource "aws_apigatewayv2_api" "vectoralpha" {
  name          = "vectoralpha-api"
  protocol_type = "HTTP"
  
  cors_configuration {
    allow_origins = ["https://app.vectoralpha.com"]
    allow_methods = ["POST", "GET"]
    allow_headers = ["content-type"]
    max_age       = 300
  }
}

# DynamoDB for caching
resource "aws_dynamodb_table" "indicator_cache" {
  name           = "vectoralpha-indicator-cache"
  billing_mode   = "PAY_PER_REQUEST"
  hash_key       = "cache_key"
  
  attribute {
    name = "cache_key"
    type = "S"
  }
  
  ttl {
    attribute_name = "ttl"
    enabled        = true
  }
}

Monitoring and Observability

Prometheus Metrics

Cost Optimization

Next Steps