Abstract
Algorithmic trading systems depend on infrastructure that can ingest market data reliably, run research at scale, deploy changes safely, and control costs. This research note summarizes production-grade infrastructure and data pipeline patterns observed across 36 months of live operations and continuous research workloads. We focus on practical architecture decisions—cloud compute, ingestion and quality checks, multi-tier storage, reproducible research workflows, and CI/CD deployment—written for both experienced practitioners and serious retail builders. The core finding is consistent: teams that standardize data quality monitoring, use tiered storage, and enforce reproducibility and deployment discipline achieve faster iteration, fewer production incidents, and materially lower cloud spend without sacrificing performance.
1. Introduction
Infrastructure is not a "support function" in algorithmic trading—it is the system. Weak pipelines produce bad datasets, bad datasets produce misleading backtests, and misleading backtests produce costly live failures. The gap between a promising research idea and a stable production strategy is usually not math; it is operational: data integrity, repeatable experiments, deployment safety, and predictable costs.
This note explains how to design an infrastructure stack that supports:
- Research velocity: faster backtests, easier comparisons, consistent environments
- Operational reliability: fewer outages, fewer data breaks, safer releases
- Cost efficiency: reduced waste, scalable compute, cheaper long-term storage
2. Methodology (What We Reviewed)
Our observations are based on three years of production operations and research workflows, including:
- Review of cloud and on-prem hybrid deployments supporting research and live execution
- Evaluation of market data pipelines and monitoring (tick, bar, derived features)
- Comparison of storage designs for speed, cost, and durability
- Study of deployment practices (manual vs automated) and incident impact
- Measurement of operational and research metrics (run success rates, incident rates, compute spend)
This is a practical engineering note: the goal is not theory, but architecture that survives real market data, real releases, and real budgets.
3. Cloud Compute: Designing for Scale Without Waste
Modern research and trading operations typically use cloud compute because it scales on demand and reduces infrastructure overhead. The trade-off is cost and complexity if not managed carefully.
3.1 What to Optimize For
- Scalability: burst for large backtests; shrink for idle periods
- Reliability: redundancy for critical components
- Latency (where relevant): execution services may require closer proximity to brokers/exchanges
- Cost control: avoid "always on" fleets when workloads are periodic
3.2 Practical Patterns That Work
- Separate research compute from production trading: research can tolerate interruption; trading services should not.
- Use autoscaling for backtests and batch jobs: scale up only when jobs are queued.
- Right-size by measuring, not guessing: start conservative and adjust with monitoring.
Operational lesson: over-provisioning silently burns budget; under-provisioning creates instability. The solution is measurement-driven sizing plus automatic scaling.
4. Data Pipelines: From Raw Feeds to Trading-Grade Datasets
Market data pipelines have one job: deliver correct, consistent, and timely datasets for research and trading. If a pipeline is not audited and monitored, it will eventually drift.
4.1 Core Pipeline Stages
- Ingestion: collect data from brokers, exchanges, and vendors
- Normalization: unify formats (timestamps, symbols, sessions, decimals)
- Validation: check for missing data, outliers, duplicates, and timestamp gaps
- Transformation: build bars, features, indicators, and strategy inputs
- Storage & indexing: partition for fast access
- Versioning: keep track of dataset versions used in each experiment
4.2 Data Quality: The Highest-Leverage Investment
Most trading failures trace back to data problems: partial feeds, stale updates, symbol mapping changes, or misaligned timezones.
Minimum "trading-grade" checks:
- Missing bar / gap detection per symbol and timeframe
- Duplicate ticks / repeated bars
- Timestamp sanity (monotonic time, session boundaries)
- Price sanity (extreme spikes vs recent volatility)
- Corporate action handling where applicable (indices, equities)
Practice that consistently improves outcomes: automated quality monitoring with alerts and dashboards. In operations, this reduces repeated research rework and prevents silent model degradation.
5. Storage Architecture: Fast Where Needed, Cheap Where Possible
Storage design should match access patterns. The most common mistake is treating all data as "hot" or storing everything in one expensive system.
5.1 Three-Tier Storage Model (Simple and Effective)
- Hot: frequently accessed research datasets and recent market data (fast disks / SSD)
- Warm: historical data used regularly but not constantly (standard disks)
- Cold: archives and raw vendor dumps (object storage)
5.2 Two Practical Structures
- Data lake (raw + semi-processed): flexible, cost-effective, good for long retention
- Warehouse (curated tables): fast analytics, consistent schema, good for reporting
Rule of thumb: keep raw data immutable in a lake; store curated "research-ready" datasets separately with clear versions.
6. Repeatable Research: How to Make Results Trustworthy
A research pipeline is "repeatable" when anyone can rerun an experiment and get the same result. This is essential for:
- strategy iteration
- debugging performance changes
- collaboration
- auditability (especially for prop-style operations)
6.1 A Practical Reproducibility Framework
- Version code: Git
- Version datasets: dataset snapshots with immutable IDs (and a changelog)
- Pin environments: fixed dependency versions (container or lockfiles)
- Track experiments: save parameters, dataset versions, and outputs together
- Control randomness: set seeds where relevant
6.2 What to Save for Every Backtest
At minimum, store:
- strategy version (commit hash)
- dataset version (ID)
- parameter set (config file)
- execution assumptions (spread, slippage, fees)
- performance report and logs (including errors)
This turns "I think it worked" into "we can prove why it worked."
7. Deployment Pipelines: Safe Releases Without Slowing Down
Deployment is where many retail and even professional systems fail—changes are shipped without enough testing, monitoring, or rollback planning.
7.1 CI/CD Pipeline (Readable Version)
- Build: package the strategy/service
- Test: unit tests + basic integration tests
- Stage: deploy to a non-production environment
- Promote: release to production with clear versioning
- Monitor: verify health and key metrics
- Rollback: revert quickly if metrics degrade
7.2 Why Automation Matters
Manual deployments are slow and error-prone. Automated pipelines:
- standardize releases
- catch issues earlier
- reduce downtime
- speed up iteration without increasing risk
8. Cost Optimization: Control Spend Without Cutting Capability
Cloud costs rise quietly when systems are not measured and tuned. The goal is not to spend less at all times; it is to spend efficiently for the right outcomes.
8.1 High-Impact Cost Controls
- Right-sizing compute: match resources to actual usage
- Autoscaling: scale to zero when idle (research workloads benefit heavily)
- Reserved capacity (for steady services): discount on predictable production needs
- Spot/interruptible compute (for batch backtests): strong savings for non-urgent jobs
- Lifecycle policies (for storage): automatically move older data to cheaper tiers
- Cost attribution: tag by service, team, strategy, or environment
8.2 Monitoring That Prevents Surprises
- daily spend by component
- cost per backtest / per training run
- cost per active strategy
- alerts for unusual spikes
9. Key Findings (Operational Takeaways)
Across long-run operations, the patterns that consistently improve performance and stability are:
- Automated data quality monitoring materially reduces data-related research errors and prevents silent failures.
- Tiered storage lowers cost while keeping fast access where it matters.
- Reproducibility discipline increases research velocity by reducing rework and enabling reliable comparisons.
- CI/CD with monitoring + rollback significantly reduces deployment failures and recovery time.
- Continuous cost optimization lowers infrastructure spend without harming performance when autoscaling and lifecycle controls are implemented.
10. Conclusion
Infrastructure and data pipelines are the foundation of algorithmic trading. The competitive edge is not only better models—it is the ability to iterate safely, trust results, and operate reliably under real conditions. A practical stack focuses on: (1) clean, monitored data, (2) scalable compute for research, (3) tiered storage aligned with access patterns, (4) reproducible experiments, and (5) automated deployments with strong monitoring and rollback.
As data volumes grow and research cycles accelerate, systems that are designed for repeatability and operational discipline will outperform systems built as one-off scripts. For readers building on kometx.com—whether retail or professional—the best next step is to implement a minimal, repeatable pipeline: version datasets, enforce basic data quality checks, and automate deployments before scaling complexity.
Suggested Reading
- López de Prado, M. (2018). Advances in Financial Machine Learning. Wiley.
- Relevant cloud architecture and data engineering references aligned to your specific stack (AWS/Azure/GCP) should be cited in your implementation documentation.