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Algo · Research to Live

Algorithmic Trading Software Development for Systematic Desks

We build quant stacks—data ingest, factor libraries, portfolio construction, execution algos, and post-trade analytics—so research code is not a separate universe from live.

production / validated
pre_trade.validate()
execution.route(order)
risk.enforce_limits()
log.audit(event_id)

Research and production diverge

Quants prototype in Python; engineers rewrite for live; alpha decays during the gap. Data snooping, survivorship bias, and leaky joins invalidate backtests quietly.

  • ×Backtest engine uses different fill model than production OMS.
  • ×Feature store missing point-in-time correctness.
  • ×No experiment tracking—teams retest same dead ends.
  • ×Risk checks added late as afterthought scripts.

Unified algo trading software lifecycle

Shared signal library, versioned datasets, identical execution simulator in research and live, and CI gates before strategies promote.

  • Point-in-time data pipelines for equities, FX, or crypto.
  • Promotion workflow: research → paper → live with sign-offs.
  • Execution algos (TWAP/VWAP/POV) sharing code with backtester.
  • Investor-grade reporting and attribution from day one.

Systematic funds and prop quant desks

Organizations outgrowing spreadsheets and disjoint Python repos.

Emerging quant funds

Greenfield stack with best-practice architecture.

Multi-strategy pods

Shared infra with isolated strategy namespaces.

Bank prop desks modernizing

Replace legacy Excel with governed pipelines.

Crypto quant teams

24/7 data feeds and exchange-native execution.

Real-world delivery examples

Crypto stat arb platform

Fund needed pairs trading infra across 8 exchanges.

Research-to-live parity within 3% P&L drift on paper vs sim over 60 days.

FX pod modernization

Desk migrated 12 strategies from Excel to governed Python.

Backtest runtime cut 80%; audit pass on model governance review.

What you get

Signal library SDK

Shared Python/C++ factors used in research and live.

Point-in-time data lake

Corporate actions, delistings, and symbol changes handled.

Backtest/live parity engine

Same fill and cost models both environments.

Experiment registry

MLflow or custom tracking for parameter lineage.

Portfolio optimizer hook

Mean-var, risk parity, or custom constraints.

Production risk middleware

Pre-trade checks mandatory on every order path.

Technology stack

TechnologyRole in your build
Python / C++Research and low-latency execution tiers
Apache Airflow / PrefectScheduled data and batch jobs
Parquet / Delta LakeVersioned historical datasets
KubernetesStrategy container orchestration
Prometheus + GrafanaLive P&L and system health monitoring

Development process

  1. 01

    Architecture discovery

    Asset classes, latency tier, and team skill map.

  2. 02

    Data foundation

    Ingest, clean, and point-in-time store MVP.

  3. 03

    Research environment

    Backtester + signal SDK with sample strategies.

  4. 04

    Execution + risk

    OMS integration and pre-trade rule engine.

  5. 05

    Promote to live

    Paper trading gate and operational runbooks.

Frequently asked questions

Build vs buy quant platforms?+

We integrate with QuantConnect, Zipline, or custom—recommend after asset class and control needs review.

Do you hire quants or only engineers?+

Engineering focus; we collaborate with your PMs/quants on spec.

Cloud or on-prem?+

Both; regulated clients often hybrid with on-prem execution.

Machine learning strategies?+

Yes with walk-forward and feature store discipline.

FIX connectivity?+

Available via partner stacks or custom FIX engines.

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Build algorithmic trading software right

Describe asset classes and team structure—we propose phased platform roadmap.