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CASE STUDY / AUTOMATION & ANALYTICS

Trading automation

Market data analysis, automated trading strategies and operational monitoring in one system.

I built MarketOps to manage, monitor and develop trading bots. The aim was to make automated trading more transparent, easier to control and better supported by operational oversight.

Markets / development environments

  • Kraken
  • Polymarket
  • Kalshi
Concept illustration of trading automation and market data monitoring; illustrative figures are not verified investment returns.

The challenge

Automation needs visibility.

Automated trading involves more than running a strategy against a market. It requires continuous visibility into what bots are doing, whether the execution chain is healthy, which market constraints apply and when intervention is needed.

Without monitoring, alerts and risk controls, it becomes difficult to understand or manage the automation.

The goal was to bring market data, bot status, operational alerts and technical monitoring together in a single view.

How the system works

From market observations to bot control, execution monitoring and ongoing development. Each stage provides visibility for the next.

    • Prices and market feeds
    • Multiple instruments
    • Real-time market overview
    • Data quality checks

    • Anomaly detection
    • Strategy-specific calculations
    • Prioritisation and filtering
    • Assessing execution opportunities

    • Multiple bot types
    • Active and restricted strategies
    • Parameters and readiness
    • Continuous status tracking

    • Execution readiness checks
    • Position and exposure tracking
    • Alerts and constraints
    • Safeguards and stops

    • Operational overview
    • Event history
    • Strategy evaluation
    • Data-informed development

An operational view in practice

MarketOps brings trading bot status, recent events, alerts and risk constraints into one operational view.

It supports monitoring across strategies, identifying anomalies and assessing whether the automation is ready to operate. Development spans several markets, including Kraken, Polymarket and Kalshi.

MarketOps interface showing a portfolio overview, active alerts, bot status, events and constraints. Displayed figures are not verified investment returns.
MarketOps — operational monitoring interface. Figures shown in this screenshot and the concept illustration are not presented as verified investment returns.

Under the hood

Technical implementation

The system connects market data processing, strategy logic, execution monitoring, the user interface and operational analytics.

Technical BlueprintTechnologies and responsibilities · one system

  1. Data sources

    Market feeds

    • Price and instrument data
    • Multiple instruments
    • Real-time feeds
    • Data processing and validation

  2. Python

    Strategy and bot logic

    • Strategy calculations
    • Signal generation
    • Bot behaviour
    • Parameters and constraints

  3. Execution & Risk

    Execution and safeguards

    • Execution readiness checks
    • Exposure and limit monitoring
    • Alerts and anomalies
    • Safeguards and stops

  4. MarketOps

    Operational monitoring

    • Dashboards and operational overview
    • Bot status tracking
    • Events and diagnostics
    • Operational alerts

  5. Analytics

    Evaluation and development

    • Event history analysis
    • Evaluating strategy behaviour
    • Identifying anomalies and bottlenecks
    • Supporting development and optimisation

The result

Control over automation. Insight for decisions.

The result is an operational trading environment that brings strategies, bot execution, market data, monitoring and risk management into one system.

The emphasis is on making automation transparent, controllable and open to continued improvement.

The system helps reveal the current operational state and what needs attention next.

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