OneTick: The Platform Where Exchanges Catch Spoofers and Hedge Funds Hunt Alpha

Bloomberg is for analysts who need a terminal. TradingView is for retail traders who need charts. But when an exchange wants to detect in real time that a trader on one venue is placing fake orders to move the price on another — it needs OneTick.

OneTick is an enterprise-grade time-series database and streaming analytics engine designed specifically for financial markets. Not a general-purpose TSDB like TimescaleDB or InfluxDB. Not just a fast columnar store like ClickHouse. It's a platform that understands financial data: trade/quote relationships, order book depth, corporate actions, and cross-market correlations. Its clients include exchanges, investment banks, hedge funds, market makers, and brokers.

Three Pillars of OneTick

The platform solves three fundamentally different problems with a single engine:

The key insight: all three workloads run on one engine that processes both historical and real-time data. No need for a separate streaming system and a separate backtesting system — it's a single platform.

Architecture: Directed Acyclic Graph (DAG) as a Query Language

OneTick's core architectural decision: queries are built not in SQL (though SQL is also supported) but as a Directed Acyclic Graph (DAG) of Event Processors (EPs).

What Is an Event Processor

An Event Processor is an atomic unit of computation. Each EP performs a single operation: filtering, aggregation, joins, derived field calculations, VWAP computation, or order book reconstruction. Data (ticks) flows through a chain of EPs from sources to sinks.

Analogy: an assembly line in a factory. A tick (a timestamped record) enters the graph on the left, passes through a chain of processors, and the output is a finished result: an alert, an aggregated metric, or a signal.

Why DAG Instead of SQL

Graphs can be built visually via the built-in designer ("paint-a-canvas") or programmatically through the Python API.

Performance

Numbers that OneTick claims (and that are confirmed by the industry):

For reference: a typical L2 feed for a single liquid stock (AAPL) generates ~100K messages/sec during peak hours. For the entire US equities market — hundreds of millions of messages per day. OneTick is designed to handle all of this with headroom to spare.

Market Surveillance: Where OneTick Is the Industry Standard

Market Surveillance is arguably OneTick's strongest use case. The platform is used by exchanges themselves to monitor trading activity.

What Manipulations It Detects

Cross-Market and Cross-Asset Monitoring

The most complex case: manipulation across markets. A trader moves the futures price on one exchange while profiting from a related option on another. Or trades an ADR on NYSE knowing what will happen to the underlying stock on LSE.

OneTick aggregates data from multiple venues into a single stream and searches for cross-market patterns, even when structural links between instruments are not obvious.

Regulatory Coverage

White-Box AI for Alert Scoring

The classic surveillance problem is false positives. With 10,000 alerts per day, a compliance team of 5 has no chance of reviewing each one. OneTick uses "white-box" ML: models assess the probability of actual manipulation while showing the reason — exactly which factors led to a high score. Not a black box, but an explainable model, which is critical for regulators.

Quant Research: From Ticks to Alpha

For hedge funds and quant desks, OneTick is not just a data store. It's a research environment where data and analytics live in one place.

What You Can Do

1. Market microstructure analysis. Reconstruct the order book at any point in the past, analyze depth of liquidity, spreads, and order flow imbalance.

2. Strategy backtesting. Run trading strategies on historical tick data with point-in-time accuracy. No look-ahead bias — data is returned strictly in the order it was available in real time.

3. Signal generation. Test hypotheses: "If the bid-ask spread widens by 3σ with rising volume — is that a reversal predictor?" Across decades of data, across all instruments.

4. ML on tick data. MDRE (Market Data Research Environment) — a Python/Pandas-like API for data science. Hyperparameter tuning, cross-validation, model serving — directly on tick data, without exporting to a separate system.

Access Methods

Trading Analytics: TCA and Best Execution

After MiFID II, best execution is not a recommendation — it's a legal requirement. Brokers must prove they executed client trades at the best available price.

Transaction Cost Analysis (TCA)

OneTick allows you to compare execution prices against benchmarks:

Real-Time Liquidity Monitoring

For FX desks and algo traders: OneTick compares current spreads and volumes against historical averages. If the EUR/USD spread widens by 2σ from the monthly average — that's an anomaly, and the algorithm can slow down execution or switch to an alternative venue.

Deployment: On-Prem and Cloud

Comparison with Alternatives

When to Choose OneTick

• You're an exchange or regulator and need surveillance with ready-made detection models.
• You're a sell-side desk and regulators require TCA and best execution reporting.
• You're a quant fund and need a platform where historical research and live monitoring are the same tool.

When OneTick Is Overkill

• You're a retail trader or a small crypto team. OneTick is an enterprise product with enterprise pricing.
• You need just a database without analytics. QuestDB or TimescaleDB would be more reasonable.
• Your data is not financial ticks. IoT, monitoring, logs — that's a different niche, and OneTick is overkill.

Ecosystem: OneTick + TickData

OneTick is tightly integrated with TickData — one of the largest providers of historical tick data. Coverage:

• US Equities — from 1970
• Global exchanges — 80+ venues
• All asset classes — equities, futures, options, fixed income, FX
• Crypto — spot and derivatives

For quant research this is critical: the deeper the history, the more reliable the backtest. Data from 1970 allows testing strategies across dozens of market regimes, including the crashes of 1987, 2000, 2008, and COVID-2020.

Links

• 🌐 OneTick: onetick.com
• 🌐 OneTick Use Cases: onetick.com/use-cases
• 📊 TickData: tickdata.com
• 🌐 kdb+ (KX): kx.com
• 🌐 QuestDB: questdb.io
• 🌐 TimescaleDB: timescale.com

Conclusion

OneTick is not "just another time-series database." It's a vertically integrated platform for financial data that addresses three critical needs with a single engine: surveillance for exchanges and compliance, quant research for hedge funds, and TCA/best execution for sell-side. The DAG architecture built on Event Processors is an elegant solution for a world where the same data needs to be analyzed both in real time and across deep history.

The main pitfall when choosing: don't compare OneTick with QuestDB or TimescaleDB as "database vs. database." OneTick is a database + analytics engine + ready-made business applications (surveillance models, TCA benchmarks, compliance reports). If you need just fast tick ingestion — take QuestDB. If you need the full loop from data ingestion to regulatory reporting — OneTick competes only with kdb+, and wins through a lower barrier to entry (no need to learn q) and ready-made business modules.

For algotrading teams building their own infrastructure, OneTick is a reference point: how a platform should be designed that processes a trillion ticks per day while still allowing an analyst to build a query visually, without writing a single line of code.