From Ticks to Trades: Real-Time Trading with SingleStore and AI

In trading, milliseconds separate profit from loss. Every market tick, order book shift, and portfolio update creates opportunities and risks in real time. The challenge isn't collecting this data, it's making intelligent decisions instantly. Traditionally, this meant impossible trade-offs: speed sacrificed transparency, auditability slowed performance, and data lived fragmented across systems. But modern fintech; from high-frequency traders to neobanks, is converging on a new principle: building a system with unifying transactions, analytics, and AI in a single real-time system.

Technologies like SingleStore now merge transactional and analytical workloads, while AI models turn streaming data into split-second predictions. The result? Applications that react, learn, and decide in milliseconds.

When building an enterprise application, specifically in fintech, the real complexity shows up in how data moves through the system. Instead of flowing through a single, unified architecture, different parts of the workload often end up isolated in different systems: transactions in one place, analytics in another, and machine learning models somewhere else entirely.

In a typical trading setup, the order-processing engine writes to an operational database, analytical pipelines run on a separate analytical warehouse, and ML models depend on their own feature stores or streaming layers. ETL or streaming jobs are then required to shuttle data between these systems, adding latency and fragmenting the real-time picture.

By the time a model receives a new tick or feature update, the market may have already moved, introducing operational lag and degrading decision quality. From the architectural perspective, these lags occur because

1. There are multiple pipelines to secure and maintain.
2. There occurs delayed model scoring
3. The data has been duplicated and ends up being costly
4. And finally the blind spots during volatility.

For applications where performance and application lags are non-negotiables, there is a need for a unified system to keep the system together. And this is where SingleStore comes into picture. SingleStore is a distributed SQL engine designed to combine OLTP and OLAP into one.

It behaves like MySQL for developers but delivers analytical performance rivaling columnar warehouses.

Building fintech applications demands a foundation that’s fast, consistent, and intelligent. SingleStore delivers exactly that; a unified platform that powers transactional, analytical, and AI-driven workloads together.

Instead of juggling multiple databases for trades, analytics, and reporting, SingleStore consolidates everything into one high-performance engine. It computes and updates model features in real time using in-database aggregates and materialized views. Hence the need for heavy ETL pipelines to sync transactional and analytical systems is no longer necessary. The distributed architecture scales horizontally to handle thousands of concurrent users and millions of streaming events per second.

All that has been mentioned before is not just in theory. Our fintech customers have chosen SingleStore as the first choice of database for building great fintech applications. From core banking modernization to embedded finance and portfolio analytics, these organizations aren’t simply adopting a new database, they’re reshaping what their data can do.

SingleStore has become their go-to engine for unifying transactions, analytics, and AI within a single, scalable platform. The results speak for themselves.

One of the largest banks in the US faced challenges with the mainframe being too expensive, rigid and with growing data becoming too slow for analytics. Every day, millions of customer transactions had to move through a complex ETL process before risk teams or analysts could act on them.

By offloading 30% of its mainframe queries to SingleStore, the bank modernized without rewriting its core logic and using the familiar MySQL protocol to integrate with existing systems while gaining cloud-scale performance. This further lead to:

1. Around $40 million saved annually with infrastructure and operational costs. 
2. They execute around 125 million queries in today's date with minimal to no latency. 
3. They experienced real time visibility with transactional risk, fraud detection and compliance metrics. 

For data architects, that’s proof you can run mission-critical, high-throughput banking data on a single, unified SQL engine  securely and cost-effectively.

AntMoney launched its platform with PostgreSQL on Amazon RDS for first-party data and Amazon Quicksight for analytics, as it was easy to prototype, ingest data, and perform basic reports with this configuration. However, this was a costly, fragile system not well-suited for the long term. Because Quicksight required giant PostgreSQL replica sets, the queries became slow and lacked coverage for emergent data sources such as ATM.com or financial services.   

When AntMoney switched into SingleStore,it led to improving data freshness by 60× and cutting total cost of ownership by nearly 90%. As an impact, they observed instant insights for embedded finance products and personalized offers that evolve in real time.

DailyVest powers analytics for millions of investors, delivering participant-level insights to retirement and portfolio platforms. On traditional warehouses, dashboards slowed to minutes as datasets ballooned into billions of rows.

Migrating to SingleStore unified operations and analytics maintaining sub-second query performance even during peak loads. As a response, DailyWest was left with:

1. 35% lower TCO. 
2. 26% faster dashboards for clients and admins. 
3. Real time reporting for millions of investors accounts. 

These are just a few examples of how SingleStore is empowering fintech innovators to build faster, smarter, and more resilient applications. From high-frequency trading to embedded finance and analytics at scale, SingleStore has proven that a unified, real-time data platform isn’t just an architectural improvement but also it’s a competitive advantage.

To build an enterprise level application with SingleStore, below is an example of a visual blueprint showing how real-time financial data flows through a modern AI-powered analytics stack using SingleStore as the central engine.

The image shows how market data flows through a modern SingleStore powered trading architecture. 

1. Real-time data comes from sources like Position data, portfolio, Market feeds, news etc.
2. Tools like Kafka stream the data instantly into SingleStore.
3. A Unified Data Platform acts as the core engine with:

• Row Store for high-speed transactions (like orders and trades).
• Vector Store for AI/ML features and embeddings.

• Feature Engineering creates metrics like VWAP, volatility, and order imbalance.
• Model Server (Python/Node.js) runs AI models for predictions or scoring.

     5. And finally results flows into

• Trading Decisions
• Risk Management Systems
• Real-time Dashboards

Modern fintech systems have long relied on high-performance databases like SingleStore to handle real-time transactions and analytics. But the landscape has shifted dramatically with the rise of AI. Artificial Intelligence has transformed how financial markets operate; moving trading from human-driven intuition and manual analysis to automated, data-driven, and deeply predictive decision-making.

AI models ingest vast streams of structured and unstructured data,  prices, sentiment, news, social signals to identify patterns, forecast movements, and execute trades within milliseconds.

The next generation of AI models will combine real-time data pipelines with self-learning optimization, enabling institutions and retail investors alike to access predictive, personalized insights instantly. SingleSTore brings in the ease and enables and enhances the AI trading systems. 

1. Real time Market data ingestion: Modern AI trading depends on capturing and analyzing vast volumes of tick and order data the moment they’re generated. SingleStore handles millions of inserts per second while keeping data instantly queryable and hence eliminating the lag that kills trading precision. This real-time capability fuels high-frequency trading (HFT), live signal generation, and intra-day strategy adaptation.
2. Seamless AI integration: SingleStore can connect easily to AI based applications. Its compatibility with multiple language drivers  makes it even more efficient for new generation trading applications. This turns the database into a full decision loop: ingest, compute, infer, and act, all within milliseconds.
3. Streaming and Historical Analytics in One Engine: Most trading platforms split their architectures, which means one database for live operations and another for analytical backtesting. SingleStore removes that divide. Its hybrid rowstore-columnstore architecture allows teams to query live data and historical records together, supporting both operational decisioning and model retraining on the freshest data without any ETL overhead.
4. Global scale and continuous Availability: AI trading never stops, and neither does SingleStore. Its distributed, multi-node architecture ensures high availability and linear scalability across clusters and regions. This consistency makes it ideal for global trading platforms that need uninterrupted access for analytics, risk monitoring, and regulatory reporting.

SingleStore has recently released a new feature in SingleStore Helios to use AI and ML functions directly from Helios. This is a great enhancement for traders and fintech developers who can now bring AI inference directly into the database and eliminate latency and complexity once caused by external model calls. These functions bridge the gap between real-time data and real-time decision-making.

Using built-in SQL functions like AI_COMPLETE, AI_SENTIMENT, AI_SUMMARIZE, and EMBED_TEXT, financial teams can now:

• Run AI predictions directly within SQL: Generate trading insights, classify events, or summarize market signals instantly without leaving your database.
• Perform sentiment analysis on live news or social data using AI_SENTIMENT() to measure market mood in milliseconds.
• Extract intelligence from reports with AI_EXTRACT() automatically pull key entities, risk factors, or company mentions from financial disclosures.
• Build vector search and similarity features with EMBED_TEXT() and VECTOR_SIMILARITY() to match patterns between live order flow and historical trade events.

By embedding these models right inside SingleStore, you move from reactive to proactive trading  running inference where the data already lives, without waiting for API roundtrips or external pipelines.

This architecture collapses AI latency down to near zero, enabling instant trade recommendations, portfolio adjustments, or anomaly detection and all powered by SQL.

This simple query could score thousands of market headlines in real time, feeding an automated trading strategy all inside the same engine that stores your tick data.

In trading, milliseconds define opportunity. By integrating AI Functions directly into your SingleStore workflows, your trading models become faster, closer to the data, and inherently more adaptive. This isn’t just about faster predictions, it's about turning every query into an intelligent decision surface.

Data architects don’t just want speed; they want simplicity, scalability, and trust in how data flows. SingleStore delivers on all fronts with a unified, real-time architecture that makes data management, scaling, and analytics effortless.

The greatest gift you can give a fintech architect is clarity: one place to store, one language to query, one system to trust. It transforms what used to be a labyrinth of services into a single intelligent plane of truth.

Let’s break it down further for better understanding. 

• No ETL: Ingest Once, Query Everywhere: Fintech pipelines move data from transactional databases into analytical warehouses through a maze of connectors, Kafka topics, and nightly batch jobs. Each hop adds latency, cost, and risk. With SingleStore, the need for ETL is reduced. You ingest streaming market data, payment events, or portfolio updates directly into the database, and it’s instantly queryable for analytics, dashboards, and AI features in milliseconds. This not only removes delay but also reduces infrastructure sprawl and eliminates “schema drift” between systems.

• SingleStore Flow: Simplified, Real-Time Data Movement: SingleStore Flow is SingleStore’s data migration and Change Data Capture (CDC) solution, designed to move data into SingleStore quickly and reliably. It has: 

  • No-code setup: Configure end-to-end data migration and ingestion through the Flow UI and no complex pipelines required.
  • Automatic schema handling: Flow analyzes the source system and automatically creates the required schema in the target SingleStore database.
  • Real-time visibility: Monitor progress, view live logs, and troubleshoot issues instantly.
  • High throughput ingestion: With XL Ingest, large tables are parallelized and chunked for ultra-fast loading, dramatically reducing migration and backfill time.

  For fintech teams, this means faster onboarding of new data sources, safer migrations, and real-time confidence in data movement and all without operational complexity. 

• Unified Governance: One Source of Truth for Every Team: SingleStore unifies governance, lineage, and auditing by keeping every transaction and analytical view in the same platform. Every query can be tied back to its originating event. For fintech teams that must satisfy PCI, GDPR, or internal audit requirements, this means one central control point, not a patchwork of logs and partial histories.

  For example, building a trading application with Laravel extends this with policy-driven access control and clear code-level audit trails, ensuring developers can build faster without compromising oversight.

• Developer Velocity: Speed of innovation is the currency of fintech.Building  with SingleStore, developers can now iterate without friction, hence, no schema gymnastics, no context switching between OLTP and OLAP. For example, whether it’s building a fraud detection API with Laravel, a customer analytics dashboard, or a trading signal service, developers can:
  • Define migrations once and query them in real time.
  • Use Eloquent ORM to abstract away complex joins.
  • Scale APIs without changing database engines.  
    It’s an environment where every deployment adds measurable business value, not more technical debt.

• AI Readiness: AI trains on the data that has been supplied to it. When the data gets stale, false and hallucinated output or results are observed. SingleStore's in-database aggregations and materialized views keep AI feature tables continuously up to date. This means every inference, whether fraud detection, risk adjustment, or trade routing  runs on live, context-rich data, not yesterday’s batch.

•  Scalability: With growing data, fintech explodes with data. From tick feeds to transaction logs, you’re dealing with billions of records per day.

  Traditional databases buckle under this pressure; warehouses introduce lag; caching systems add inconsistency. SingleStore handles both row-level transactional writes and columnar analytical reads at scale.

  That means you can scale ingestion rates into millions of events per second and run analytical queries on that same live dataset  all without slowing down order processing.

  For applications that are hosted on other databases than SingleStore; 

Every millisecond in trading carries meaning. Every tick, every trade, every decision defines advantage. What once took entire architectures now happens within a single, intelligent platform.

SingleStore redefines how fintech systems think, learn, and act, collapsing silos, removing latency, and giving teams one place to build the next generation of trading intelligence.

Whether you’re optimizing trades, reducing risk, or creating predictive insights, the message is clear: real-time isn’t optional anymore - it’s the new foundation.

The next era of trading will be shaped by those who can merge speed with intelligence. And SingleStore is where that convergence begins.

Start building applications with SingleStore Helios today!!

• Try SingleStore Helios 
• Read Docs 
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• Upcoming Webinars 

1. Why is SingleStore better suited for real-time trading applications than traditional databases?

Traditional databases require separate engines for OLTP and OLAP, leading to ETL delays, duplicated data, and inconsistent insights. SingleStore unifies both workloads in a single engine, enabling millisecond ingestion, analytics, and AI inference without data movement. For trading systems where timing determines profitability, this eliminates architectural lag and ensures instant decision-making.

2. Can SingleStore handle high-frequency trading workloads where data volumes are extremely large?

Yes. SingleStore’s distributed, in-memory rowstore and highly compressed columnstore allow it to ingest millions of events per second while executing complex queries on the same live dataset. Its horizontal scaling and parallel execution make it ideal for HFT workloads, tick analytics, order book processing, and strategy simulation.

3. How does SingleStore integrate AI models for trading and risk management?

SingleStore integrates AI in two ways:

1. Externally, by connecting with model servers (Python, Node.js, or REST APIs) for real-time inference.
2. Natively, using SingleStore Helios AI Functions like AI_COMPLETE, AI_SENTIMENT, EMBED_TEXT, and VECTOR_SIMILARITY.

This eliminates the latency of external model calls, enabling in-database scoring, sentiment analysis, summarization, and vector search directly using SQL.

4. How does SingleStore ensure compliance, auditability, and governance for fintech systems?

SingleStore offers unified governance across all data—transactions, analytics, and AI outputs within one system.

 Key compliance advantages include:

• End-to-end lineage and audit trails
• MySQL-compatible access control
• High availability and disaster recovery
• Data retention policies and encryption

This makes it easier to meet PCI, FINRA, GDPR, and internal audit requirements compared to multi-system pipelines.

5. Can I migrate my existing trading or banking application to SingleStore without rewriting everything?

Absolutely. SingleStore is MySQL wire compatible, meaning you can often migrate applications with minimal code changes. Using SingleStore Flow for CDC and data migration, organizations can:

• Move data from PostgreSQL, MySQL, SQL Server, Oracle, and others
• Auto-generate schema
• Ingest historical and streaming data
• Cut over with zero downtime

This makes modernization simple, fast, and safe without disrupting existing trading logic or customer workflows.