Skipping Kubernetes for Nomad by Hashicorp: Deploying Self-Improving Trading Strategies

September 5, 2024

Skipping Kubernetes for Nomad: Powering Self-Improving Trading Strategies

In the fast-paced world of algorithmic trading, the ability to rapidly deploy, manage, and iterate on trading strategies is paramount. While Kubernetes has become the de facto standard for container orchestration, its complexity might be overkill for certain specialized workloads. For teams looking for a simpler, more agile solution, HashiCorp Nomad presents a compelling alternative, especially for deploying trading strategies designed with a constant self-improving feedback loop.

The Allure of Simplicity: Why Nomad?

Kubernetes is incredibly powerful, offering a vast ecosystem and fine-grained control. However, this power comes with significant operational overhead and a steep learning curve. For trading operations, particularly smaller teams or those prioritizing speed of iteration, Nomad offers several advantages:

  • Simplicity and Ease of Use: Nomad is designed with simplicity as a core tenet. A single binary for both servers and clients, and a more straightforward conceptual model, make it easier to learn, deploy, and manage compared to Kubernetes. This can free up valuable engineering resources to focus on strategy development rather than infrastructure wrangling.
  • Flexibility Beyond Containers: While Nomad has excellent support for Docker containers, it's not limited to them. It can orchestrate virtual machines, standalone applications (Java, binaries), and more. This can be beneficial if parts of your trading stack aren't containerized or if you need to integrate legacy systems.
  • Scalability: Nomad is proven to scale to thousands of nodes, making it suitable for both small setups and large, demanding trading infrastructures.
  • HashiCorp Ecosystem Integration: Nomad integrates seamlessly with other HashiCorp tools like Consul for service discovery and Vault for secrets management, providing a cohesive and secure operational environment.

Deploying Trading Strategies with Nomad: A Conceptual Workflow

Imagine a trading strategy that continuously learns and adapts based on market data and its own performance. Here’s how Nomad can facilitate such a system:

Core Components of the Trading System:

  1. Strategy Engine: Executes trading logic, powered by atman-100m, which processes user-defined strategies (e.g., multi-indicator setups like RSI + MACD) tailored for Indian equities and derivatives.
  2. Data Collector: Ingests real-time market data from NSE, BSE, and social media sentiment, enhancing our AI’s contextual understanding of India’s markets.
  3. Performance Analyzer: Monitors strategy performance, logging metrics like returns and volatility, which users can view in detailed reports on alphabench.
  4. Model Trainer/Optimizer: Fine-tunes atman-100m using performance data, ensuring strategies adapt to market shifts, like volatility spikes in the Nifty 50.
  5. Parameter Store: Stores optimized parameters securely, accessible to users via our platform’s API (Pro plan feature).

Nomad Job Specification for alphabench’s Self-Improving Loop

Nomad’s HCL defines how we orchestrate these components. Below is a conceptual job spec for alphabench’s trading system:

// alphabench Nomad Job for AI-Driven Trading Strategy
job "alphabench-trading-strategy" {
  datacenters = ["dc1"]
  type = "service"

  group "strategy-engine" {
    count = 2 // Scales for high-demand users
    task "atman-strategy" {
      driver = "docker"
      config {
        image = "alphabench/strategy-engine:latest"
        args = ["--market=nse", "--ai-model=atman-100m"]
      }
      env {
        CONSUL_KEY = "strategy/parameters"
      }
    }
  }

  group "data-collector" {
    count = 1
    task "market-feed" {
      driver = "docker"
      config {
        image = "alphabench/data-collector:latest"
      }
    }
  }

  group "performance-analyzer" {
    task "metrics-tracker" {
      driver = "docker"
      config {
        image = "alphabench/performance-analyzer:latest"
      }
    }
  }

  group "model-optimizer" {
    type = "batch"
    task "atman-trainer" {
      driver = "docker"
      config {
        image = "alphabench/model-trainer:latest"
        command = "train --data-source=performance-db"
      }
    }
    periodic {
      cron = "0 0 */12 * *" // Retrain every 12 hours
    }
  }
}

alphabench’s Feedback Loop with Nomad

  1. Deployment: Nomad schedules the strategy-engine, data-collector, and performance-analyzer across our cluster, ensuring high availability for users.
  2. Operation:
    • The data-collector streams NSE/BSE data and sentiment to the strategy-engine.
    • The strategy-engine, powered by atman-100m, executes user strategies, fetching parameters from Consul.
    • The performance-analyzer logs metrics to a database, mirroring the detailed reports users see on alphabench.
  3. Self-Improvement:
    • Every 12 hours, Nomad runs the atman-trainer batch job, which retrains atman-100m using performance data.
    • Updated parameters are stored in Consul, enabling real-time strategy tweaks.
  4. Dynamic Updates:
    • The strategy-engine polls Consul for new parameters, applying them without restarts, ensuring seamless updates for users.
    • Nomad’s rolling updates deploy new atman-100m versions with zero downtime, critical for India’s fast-moving markets.
  5. Monitoring and Scaling: Nomad’s health checks and Consul’s service discovery keep our system robust, scaling strategy-engine instances as user demand grows.

The Feedback Loop in Action with Nomad:

  1. Deployment: The adaptive-trading-strategy job is submitted to the Nomad cluster. Nomad schedules the strategy-engine, data-collector, and performance-analyzer tasks on available client nodes.
  2. Operation:
    • The data-collector streams market data to the strategy-engine.
    • The strategy-engine (initially using default or last known good parameters, perhaps fetched from Consul or Vault) makes trading decisions.
    • The performance-analyzer logs trades and performance metrics into a dedicated database or data store.
  3. Self-Improvement (Retraining/Optimization):
    • Periodically (as defined by the cron expression in the model-optimizer group), Nomad runs the trainer task as a batch job.
    • The trainer task pulls the latest performance data from the database.
    • It then retrains or optimizes the strategy's underlying models.
    • Crucially, the updated parameters or model artifacts are then published to a central configuration store (like HashiCorp Consul for parameters or an artifact repository for model files).
  4. Dynamic Reconfiguration (Rolling Updates):
    • The strategy-engine task can be configured to periodically poll Consul for updated parameters. If new parameters are detected, it can dynamically reload them without a full restart, enabling near real-time adaptation.
    • Alternatively, if a new version of the strategy-engine container (with an updated model baked in) is pushed, Nomad's rolling update capabilities can be used to deploy the new version gracefully, minimizing downtime. Nomad can perform canary deployments or blue/green deployments to further de-risk updates.
  5. Monitoring and Scaling: Nomad provides basic health checking and can restart failed tasks. For scaling, you can adjust the count parameter in the job specification and Nomad will distribute the load. Integration with Consul provides service discovery, allowing components to find each other dynamically.

Advantages for Trading Systems

  • Rapid Iteration: The simplicity of Nomad job files and the ease of updating services allow for quick deployment of new strategy versions or parameter tweaks.
  • Resource Efficiency: Nomad is lightweight and can pack tasks efficiently, potentially leading to lower infrastructure costs compared to a more resource-intensive orchestrator.
  • Reduced Operational Burden: Less time spent managing the orchestrator means more time for developing and refining trading algorithms.
  • Clear Separation of Concerns: Nomad jobs clearly define how each component of the trading system is run and managed, making the overall architecture easier to understand.

When Kubernetes Might Still Be the Choice

It's important to note that Nomad isn't a universal replacement for Kubernetes. K8s might still be preferred if:

  • Your organization is already heavily invested in the Kubernetes ecosystem and has deep expertise.
  • You require very specific and advanced networking policies or service mesh capabilities that are more mature in Kubernetes.
  • You need the broadest possible range of third-party integrations and community support.

Conclusion

For teams developing and deploying automated trading strategies, especially those that require a constant feedback loop for self-improvement, HashiCorp Nomad offers a compelling blend of power and simplicity. By reducing operational complexity, Nomad allows traders and developers to focus on what they do best: building profitable strategies. Its ability to manage diverse workloads and integrate with tools like Consul and Vault makes it a strong contender for orchestrating the next generation of intelligent trading systems, potentially allowing teams to "skip" the heavier lift of Kubernetes where agility and speed are paramount.