Case Studies | FinTech

Portfolios that rebalance themselves

ETF Portfolio Optimisation POC

About

A FinTech business working with Cloud Combinator on AWS. The client is anonymised at their request.

Challenge

The platform had to satisfy demanding functional, operational and non-functional criteria at once. Three focus areas defined the challenge.

Heavy, unpredictable optimisation compute

Portfolio optimisation and backtesting are computationally intensive and bursty. The platform needed to run optimisation jobs for

Timely, auditable publication

Downstream consumers expect fresh, trustworthy data. The platform had to publish portfolio snapshots within 15 minutes of a data-provider update, expose them by ID with holdings roll-ups, provenance and full audit history, and handle provider restatements and corporate actions cleanly so that every result could be reconciled and explained.

Enterprise-grade security and governance

Operating inside the client's AWS account, with a third-party provider guardrails, meant least-privilege IAM, KMS encryption of sensitive data, and continuous triage of GuardDuty, Config and Security Hub findings had to be built in from the foundation rather than retrofitted.

Solution

Cloud Combinator delivered the platform across five milestones, each layering functionality onto a secure, infrastructure-as-code foundation and validated by CI/CD pipelines and load testing before handover.

<15 min

Target publication SLA from data-provider update to available snapshot

120s

Target optimisation time for 500 components across 5 years of history (projected)

200k

Portfolios the architecture is designed to scale to

By the numbers:

  • <15 min - Target publication SLA from data-provider update to available snapshot
  • 120s - Target optimisation time for 500 components across 5 years of history (projected)
  • 200k - Portfolios the architecture is designed to scale to
Changes

Cloud Combinator delivered a working, load-tested platform proving that a fully serverless AWS architecture can meet the client's optimisation, publication and governance targets, with acceptance defined by sign-off against the Section 2.2 success criteria and confirmation that the environment is suitable for continued development by the client's team.

  • Serverless economics — An event-driven design on Lambda, DynamoDB, SQS and EventBridge absorbs bursty optimisation workloads while incurring minimal cost when idle, with an indicative AWS run rate of around 10,424 USD per month.
  • On-demand optimisation and backtesting — Users can create portfolios, simulate optimisations against objectives such as minimum variance or maximum Sharpe, and backtest across standard intervals with downloadable reports.
  • Auditable publication — Publication APIs expose portfolio snapshots by ID with holdings roll-ups, provenance and full audit history, and restatement handling keeps results reconcilable.
  • AI-assisted insight — Amazon Bedrock provides natural-language portfolio and ETF explanations, and Amazon Q accelerates the internal team, without compromising deterministic results.
  • Handover — UAT across all APIs, dashboards and optimisation flows, data migration scripts, runbooks, cost-tagging reviews and knowledge-transfer sessions equipped the client's technical leads to own and extend the platform.

With the serverless foundation proven, the client is positioned to extend the platform towards richer optimisation objectives, wider client onboarding and, where required, options such as multi-region resilience and container-based compute for the heaviest numerical workloads.

AWS Stack

AWS Lambda

And API Gateway for serverless optimisation workers and the portfolio, optimisation, backtest, admin and publication APIs.

Amazon SQS

And Amazon EventBridge for decoupling heavy compute and driving scheduled valuations, backfills and rebalance calendars.

Amazon Bedrock

And Amazon Q for natural-language ETF explanations and internal developer and operator assistance.

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