A client engaged BA Tech to transform public prediction-market activity into an operational trading intelligence platform. The deployed system now analyses up to 10,000 market participants, ranks consistently profitable traders, identifies positions where leading performers agree and automatically tests those signals through an active paper-trading portfolio.
From market data to active trading intelligence
The client wanted more than a leaderboard or reporting dashboard. The objective was to create a working system capable of discovering strong market participants, examining their long-term performance and converting agreement between qualified traders into measurable trading signals.
BA Tech researched, designed, developed and deployed the complete platform. It now operates continuously in a cloud environment, collecting public market data, recalculating trader rankings, monitoring open positions and managing a simulated portfolio without requiring daily manual intervention.
The system allows the client to observe real market signals and measure portfolio performance under controlled conditions before deciding whether to progress to any form of live execution.
What the platform is delivering
The deployed platform:
- Screens a diversified candidate universe of up to 10,000 public traders
- Analyses performance across a rolling 120-day period
- Identifies traders demonstrating sustained realised profitability
- Scores traders using profit, ROI, consistency, activity and risk measurements
- Monitors the current positions of qualified performers
- Detects markets where independent leading traders support the same outcome
- Applies agreement, exposure and entry-price controls
- Opens qualifying positions in an active paper portfolio
- Revalues open positions against current market prices
- Records realised and unrealised performance
- Publishes the results to a private client dashboard
- Explains why potential signals were accepted or rejected
- Repeats the monitoring process automatically
Rather than producing isolated recommendations, the platform creates an auditable chain from raw public data to participant qualification, consensus detection, simulated execution and portfolio measurement.
Results delivered
BA Tech delivered a functioning automated market intelligence and paper-trading environment rather than a static proof of concept.
The client can now inspect ranked performers, compare their 120-day results, review current consensus opportunities and monitor the performance of positions generated by the model. The dashboard provides a shared operating view for authorised stakeholders while the underlying analytics worker continues running independently in AWS.
The project has already produced several important operational outcomes:
- Replaced manual trader research with automated analysis.
- Expanded the monitored candidate universe from an initial sample to as many as 10,000 traders.
- Established transparent qualification criteria for selecting trusted performers.
- Created a repeatable consensus methodology.
- Implemented automated paper-position entry and exit rules.
- Added portfolio equity and drawdown measurement.
- Created diagnostics explaining the absence or rejection of signals.
- Established a private interface for shared client oversight.
- Developed safeguards for external API failures and rate limits.
- Created a deployment architecture that can be updated without rebuilding the platform from the beginning.
Financial performance will continue to accumulate as the paper portfolio processes qualified signals providing the client with a measurable evidence base for evaluating the strategy over time.
Designed for operational resilience
As the analysis expanded, BA Tech encountered a practical constraint common to large external-data projects: API rate limiting.
Instead of reducing the platform to a small demonstration sample, BA Tech redesigned the collection process to pace requests, detect rate-limit responses, wait for provider-defined recovery periods and retry using exponential backoff.
The system also pre-filters unsuitable candidates before requesting more detailed records. If an individual wallet cannot be processed, the worker can skip that participant rather than discarding the entire research cycle.
This engineering work allows the platform to operate against real external-service constraints while maintaining the integrity of the analysis.
Secure cloud infrastructure
The solution was developed as a containerised Node.js application and deployed using Amazon Web Services.
Its infrastructure includes Docker, Amazon Elastic Container Registry, Amazon Elastic Container Service, AWS Secrets Manager and Amazon CloudWatch. Structured task definitions support controlled version deployment, while protected credentials allow the analytics worker to publish information securely to the private dashboard.
The worker and dashboard operate as separate components. The worker performs data collection, analysis, consensus detection and paper-portfolio management. The dashboard presents the resulting information to authorised users without exposing the underlying AWS environment.
An active paper portfolio with defined controls
The platform does not automatically treat every instance of trader agreement as an opportunity.
A paper position is opened only when the signal satisfies configured requirements for independent participation, weighted agreement, minimum position size and acceptable movement from the supporting traders’ average entry price.
Portfolio controls limit exposure per position and the number of simultaneous positions. The system also monitors whether consensus remains active and can close simulated positions when the qualifying support disappears.
Live financial execution remains disabled while the client evaluates performance through the paper portfolio. This provides genuine operational testing without presenting simulated results as completed real-money returns.
A model that can be applied beyond prediction markets
The value of this project extends beyond market trading.
The same architecture can help organisations identify important patterns across large volumes of fragmented information. It can be adapted to monitor competitors, suppliers, contracts, investments, regulatory activity, customer behaviour or emerging opportunities.
The core capability is the ability to collect external information continuously, rank sources by demonstrated quality, identify agreement, apply defined decision rules and present the results through a secure operational dashboard.
Build an intelligence platform around your organisation’s data
BA Tech combines research, automation, data engineering, analytical modelling, cloud infrastructure and interface design to build systems that move beyond reporting.
If your organisation is still reviewing large volumes of information manually, BA Tech can design an automated intelligence platform that continuously monitors the relevant data, identifies meaningful opportunities and provides decision-makers with a clear, measurable operating view.
