The Quantitative lifecycle
The same data from backtest to production.
algoseek built its Mercury, co-location, and data pipelines for trading first before it ever sold a dataset. One handler writes the historical archive and the live feed.
Exchange feeds
CTA/UTP
OPRA
CME
NYSE
IEX
Mercury ticker plant
One handler · one schema · one source
ACTIVE
Historical archive
Real-time feed
✓ Same schema · same ASID · same bar logic
Same data, every stage
01
Research
Historical
02
Out‑of‑sample
Historical
03
Paper
Real‑time
04
Live
Real‑time
The cost of two vendors
Six months of research, broken on day one of production.
The logic was sound; the data underneath changed between backtest and deploy. By the time you find out, capital is at risk.
One vendor, one pipeline
We built the infrastructure first. Then the data came from it.
The ticker plant and delivery layer were built as production infrastructure first. The archive and the live feed come out of the same handler; nothing to diverge.
Two-pipeline setup
Typical vendor
Bar boundaries differ. Historical at 09:31:00.000, live at 09:31:00.014. Late prints land in different bars.
Identifiers disagree. Ticker change breaks the position at the symbology layer.
Adjustments applied differently. Historical end-of-day, live raw. Sign of P&L flips.
Security masters conflict. Two vendors, two files, two truths.
algoseek single pipeline
One handler
Bar boundaries identical. Same condition codes, same jitter handling, same close logic.
ASID stays constant through ticker changes, mergers, and delistings.
Adjustment factors on the same schedule across all four stages.
Security master built and quality-controlled in-house. One file for everything.
What stays constant
The four things that never change between your backtest and your live feed.
If any of these differ between research and production, your strategy runs on data it was never tested against.
01
02
03
04
Identifier
ASID (persistent)
Survives ticker changes, mergers, delistings, and corporate restructuring. Cross-references FIGI and ISIN.
Schema
Up to 90 fields per bar
Tick, second, minute, and daily bars in one field format. The columns of Stage 01 are the columns of Stage 04.
Reference
In-house security master
Security master, adjustment factors, and corporate events built in-house. No third party between you and the data.
Source
Mercury ticker plant
One handler writes both sides. C++ and Assembly, zero external dependencies. Third generation, fifteen years of runtime.
The four stages
Research, validation, paper, live. Same data at every step.
Every vendor covers one of these well. The question is what happens when you need the next one.
Stage 01
Historical research
Historical
20+ years of tick data, up to 90 fields per bar, queryable from day one. The schema here is the schema in production.
What you will NOT do here
Spend a quarter on data engineering. Discover your test relied on fields that do not exist live.
Stage 02
Out-of-sample testing
Historical
Point-in-time masters and versioned adjustment factors keep walk-forward clean.
What you will NOT do here
Reverse-engineer a vendor’s retroactive adjustments. Find tomorrow’s information in today’s security master.
Stage 03
Paper trading
Real-time
Mercury wrote your history and delivers your live feed: every open matches your backtest. A difference here is your model, not the data.
What you will NOT do here
Write a translation layer between historical bars and live ticks. Lose paper-trading days to pipeline issues.
Stage 04
Live trading
Real-time
Real capital. The same ticker plant algoseek runs for funds, banks, and two US regulators: fifteen years, zero streaming downtime.
What you will NOT do here
Wait three days on a support ticket while your model trades blind.
Who uses which stages
Not every team runs all four, but the pipeline covers whichever subset you need.
Different segments enter and exit at different points; the data is the same across all of them.
Quant trading
Funds, prop firms, independents
The full lifecycle, from first backtest to co-located live trading. Divergence between backtest and production costs real money.
Typical path
01 Research
→
02 Walk-forward
→
03 Paper
→
04 Live
Banks / Institutional
Risk, TCA, compliance, surveillance
Enter at Stage 04 for surveillance or TCA; the archive lookback matches what the live system recorded.
Typical path
01 Lookback
+
04 Surveillance
Fintechs
API feeds, redistribution, product builds
Prototype on the archive, deploy delayed or real-time feeds into the product. An expiring instrument cannot break the end-user experience.
Typical path
01 Prototype
→
02 Validate
→
Delayed + Real-time
Academic
Research universities, PhD programs
Stages 01 and 02, with the field depth and security master institutional clients get.
Typical path
01 Research
→
02 Validation
Test the pipeline before you commit to it.
Up to a year of historical data across all asset classes. Jupyter, SQL, Excel. No credit card, no sales call required.