We analyzed 79 million market messages across 22 trading days. Nanosecond-precision timestamps. 16 message types. Here's what we found hiding in plain sight.
A mid-cap stock rises 8% in 25 minutes. The chart looks bullish. But six hidden signals tell a different story.
"The price said buy. The order book said run."
Traditional databases take 300ms+ for this query. Our platform returns it in 7ms — fast enough for real-time surveillance across all listed securities simultaneously.
+8.2% in 25 minutes — exceeds 5% threshold for 30-min window
4.7x rolling 20-day average volume — concentrated in the pump window
Buy-side aggressor ratio hits 87% — far above 50% neutral baseline
Average trade size drops 62% — classic retail participation pattern
Bid-ask spread stays tight during run-up — artificial liquidity holding the price
-6.1% reversal in 8 minutes — dump confirmed as buying pressure collapses
The order book shows 10 levels deep on both sides. Plenty of liquidity. But decompose the data — and it's the same entity on both sides.
"10 levels of depth. Zero real liquidity. The order book was a stage set — and the same actor played both buyer and seller."
A trading halt lifts. In the first 200 milliseconds, the order book tells you everything about what happens next. If you can read it fast enough.
"You can't see this in a daily chart. You can't see it in minute bars. You need nanosecond timestamps and a database that can query them in real time."
Equity index vs. futures. A basis spread opens for 800 milliseconds. Only tick-level cross-market data reveals it.
"By the time a human sees the opportunity, it's gone. The data saw it 800ms earlier."
As-of joins across markets in sub-millisecond time. This is what columnar in-memory engines are built for.
An institutional order is slicing through the market all day. The trades look random. But overlay VWAP and the algo's fingerprint appears.
3.6 million messages per trading day. 16 message types. Nanosecond timestamps. Order book reconstruction. Multi-signal aggregation in real-time.
Row-based databases choke on ingestion. Generic analytics engines can't do as-of joins. Embedded databases run out of memory. Each tool has trade-offs.
So we tested them all.
Same data. Same queries. Same machine. No marketing claims — just measured performance on real market data.
| Query | A | B | C | D |
|---|---|---|---|---|
| OHLCV 1-min | 27ms | 65ms | 82ms | 125ms |
| Range scan | 57ms | 104ms | 123ms | 198ms |
| Market-wide agg | 61ms | 70ms | 52ms | 147ms |
| Top-N by volume | 56ms | 69ms | 52ms | 122ms |
| Surveillance scoring | 90ms | 82ms | 101ms | 364ms |
| VWAP calculation | 22ms | 67ms | 57ms | 83ms |
| Query | A | B | C | D |
|---|---|---|---|---|
| OHLCV 1-min | 7ms | 65ms | 33ms | 122ms |
| Range scan | 21ms | 104ms | 47ms | 196ms |
| Market-wide agg | 47ms | 70ms | 23ms | 145ms |
| Top-N by volume | 38ms | 69ms | 23ms | 137ms |
| Surveillance scoring | 65ms | 82ms | 34ms | 365ms |
| VWAP calculation | 7ms | 67ms | 22ms | 76ms |
| Database | Size | Notes |
|---|---|---|
| B | 0.17 GB | Best compression |
| C | 1.24 GB | Columnar, embedded |
| D | 1.33 GB | Row-based |
| A | 6.03 GB | In-memory, fastest queries |
| Database | Time | Rate |
|---|---|---|
| B | 43s | 243k rows/s |
| A | 145s | 72k rows/s |
| D | 243s | 43k rows/s |
"There is no single best database. A dominates time-series queries. C wins on analytical aggregation. B leads on compression and ingestion. D is familiar but consistently slowest. The right answer depends on your use case — and we help you make that decision."
Independent benchmark for educational purposes. 85 million records, 22 trading days, p50 latency over 5 runs, all databases containerized on the same machine. Results reflect our test environment and may vary.
The engine behind these stories was chosen after rigorous benchmarking. Here's why it works.
Native nanosecond precision — matches exchange protocol timestamps exactly
Column-oriented architecture ideal for time-series aggregations across billions of rows
Hot data in RAM for real-time, historical data on disk — seamless hybrid architecture
Purpose-built for financial analytics — as-of joins, window functions, vector operations in one line
Used by the majority of global tier-1 banks and exchanges for mission-critical workloads
Seamless Python bridge for ML pipelines, data science workflows, and dashboard integration
From raw exchange feed to actionable insight — fully containerized, deployable with one command.
16 message types
Nanosecond precision
Protocol decoder
Normalization
Columnar in-memory
Partitioned by date
Surveillance
Market analytics
Interactive UI
Real-time alerts
Capital markets is just the demo. The same platform analyzes any high-frequency event data — wherever milliseconds matter.
The pattern is always the same: massive volume, nanosecond precision, real-time decisions.
Platform-agnostic means we benchmark objectively, recommend honestly, and deliver the right solution — not the most expensive one.
End-to-end Data, AI, and Automation consultancy — not a reseller pretending to consult
Deep understanding of local exchange data structures, trading rules, and regulatory requirements
We tested 4 database architectures on real data. We recommend based on evidence, not partnerships
From data pipeline to analytics engine to production dashboard — one team, end to end
Layer ML, anomaly detection, and natural language queries on top of your analytics platform
Fully containerized — cloud, on-premise, or hybrid. One command to deploy
Let us show you what's underneath. Live demo on real high-frequency data — or bring your own.
🌏 We welcome international engagements — serving clients across Southeast Asia and beyond.
contact@infozense.com | +66-82-242-4008 | Bangkok, Thailand