Quantitative research lives or dies on its infrastructure. A promising factor that turns out to be look-ahead bias, a model that takes hours to rerun, data that differs between research and production: these problems cost more than any single bad trade. SemBricks builds quantitative finance software for funds, systematic trading firms and quant research teams, so researchers spend their time on ideas instead of plumbing.
Quant Software We Build
- Strategy research: research environments where ideas move from notebook to tested strategy without rewriting.
- Statistical modelling: models for signals, risk and portfolio construction, implemented to be fast and reproducible.
- Trading data analysis: analysing executions, performance and market behaviour to improve what runs live.
From Research to Production
The biggest risk in quant work is the gap between research and live trading. We help close it with shared code for research and execution, point-in-time data so backtests only see what was known at the time, and performance where it matters, using Rust for heavy computation where Python is too slow. Data comes from robust market data pipelines, and strategies run on algorithmic trading infrastructure.
Frequently Asked Questions about Quantitative Finance Software
Can you work with our existing Python research code?
Yes. We often keep research in Python and move the performance-critical or production parts into faster, more robust code, with tests that confirm both give the same results.
How do you prevent look-ahead bias?
With point-in-time data, strict separation of training and test periods, and backtests that only use information available at each moment.
Is our strategy kept confidential?
Yes. We sign an NDA on request before you share any strategy details.
Do we own the source code?
You can. Ownership of the source code is set out in the contract, and the code can be fully yours, including documentation and tests.
Part of our trading software development.
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All Quantitative Finance Solutions A–Z
Quantitative Strategy Research Infrastructure
Research infrastructure for systematic strategies: point-in-time data, factor research, realistic backtests, walk-forward tests and reproducible experiments.
Statistical Modelling for Trading: Volatility, Regimes, Risk
Statistical models for trading: return distributions, GARCH volatility, regimes, cointegration, signal tests, VaR and expected shortfall, honestly validated.
Trading Data Analysis: Find What Drives Your Results
Analyse your trade history: performance by instrument, session and regime, MAE/MFE, slippage, drawdowns and position sizing, from MT4/MT5, IB and crypto data.