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Sayfitech
Financial technology

Quantitative trading systems

Data pipelines, execution, position sizing and monitoring as one production system.

Engagement
Phased build with a dedicated engineering team
Typical timeline
4–6 months
Starts with
A week spent mapping your venues, instruments and existing order flow.
01The situation

A desk can trade from three terminals, two spreadsheets and a group chat. What it cannot do is say, at four in the afternoon, what it owns, what it paid, and why the fill came in eleven basis points worse than the price on the screen.

Why it matters

A strategy that runs reliably with real money on it.

Capital systems, engineered.

What we build

Concrete artefacts, handed over and documented.

01

Market data ingestion with gap detection and replay, so a missing minute is visible rather than silently filled in

02

A time-series store sized for your history and your query pattern, instead of a folder of files per day

03

A strategy runtime where the same code path runs in simulation and in production

04

Order management with venue adapters, retry rules and order identifiers that cannot be duplicated

05

Execution measurement: slippage, the gap between the price you expected and the price you got, recorded per order, per venue, per hour

06

End-of-day reconciliation against exchange statements, where a break becomes a ticket rather than a surprise next month

What changes

01

Positions, cash and exposure agree with every venue each morning

02

Slippage becomes a number you manage instead of a feeling about a bad week

03

A new strategy reaches production in days, because the plumbing already exists

How it runs

  1. Weeks 1–4

    01Data and storage

    Feeds, history, gap handling and the store everything else will read from.

  2. Weeks 5–14

    02Runtime and execution

    Strategy runtime, order management, venue adapters and the measurement that tells you what execution costs.

  3. Weeks 15–22

    03Paper, then live

    Weeks of paper trading against live prices, then production at reduced size while reconciliation is proven.

06Typical stack

Chosen per project. Named here so you can see the shape of it.

  • Python
  • Kafka
  • ClickHouse
  • Redis
  • Postgres
  • Grafana

Questions we get asked

Next step

Tell us the outcome, not the tooling.

Send us the situation you are in. We will tell you which discipline it belongs to, what we would do first and what it costs, including when the answer is to wait.

Start a projectOr email us directlyinfo@sayfi.ai