SigTech’s quant infrastructure has been refined by a decade of serving global institutional investors, leading to unparalleled breadth and intricacy.
SigTech enabled us to backtest and deploy new systematic trading strategies in record time. Now, our fund has cutting-edge in-house quant capabilities enabling the team to create maximum value for clients.
Standard backtesting approaches often rely on approximations to simulate real-life historical trading conditions. These inaccuracies can accumulate, creating significant slippage between backtest and live results — slowing down strategy development at best, and creating costly illusions at worst. SigTech’s framework is defined by an uncompromising commitment to accuracy at the most granular level.
Precise trading costs: We apply historical trading costs at the time of execution, in the specific location of the transaction, for the particular instrument and trade type, and accounting for the client’s in-house trade information.
Granular market impact: Trade sizes and participation rates are carefully accounted for in modeling a transaction’s market impact, providing a true reflection of the market dynamics.
Time-of-day modeling: Our system accommodates market changes throughout the trading day in a given market — vital for stop-loss strategies and recognizing periods of market stress and crowding.
Further reading: How to control the hidden costs of systematic investing (PDF)
The platform was extremely easy to use and I had the entire toolkit at my fingertips from data, backtesting to production. Algo allocation is complex but SigTech made it intuitive to build the strategies, even for our most ambitious ideas… SigTech has exactly what we need to help launch our funds and is a reliable and trustworthy partner along the way.
SigTech offers all liquid asset classes, meticulously cleaned, harmonized, pre-mapped, validated, and pre-ingested. With histories that can be easily queried, stretching as far back as four decades and detailed down to one-minute bars, we provide an extensive view of the market landscape. Sourced from 25 reputable data providers, SigTech’s platform streamlines your data processing and analysis, reducing tasks that used to take months to mere minutes.
FX: spot, forwards, swaps, futures, options
Rates: interest rate swaps, interest rate futures, cash bonds, bond futures, options, credit indices
Commodities: futures, options
Volatility: equities, FX, rates, commodities, swaptions
Equities: index futures, ETFs, index options, variance swaps
Ingest your own data
Data availability, quality and seamless delivery are always a key challenge in building a good quantitative model. This is certainly an area where SigTech’s platform helps tremendously.
SigTech is dedicated to accelerating algorithmic trading at every stage. We’ve crafted a library of Python building blocks, specifically tailored to different asset types, enabling you to create complex strategies quickly with just a few lines of code.
Our extensive collection features hundreds of templates, examples, tutorials, and guides, designed to help you leverage our decade of knowledge and expertise with ease.
In addition to these resources, our library includes a full suite of built-in quant tools. Explore trend indicators such as RSI and MCD, and specialized filters like L1 regularization and Hodrick-Prescott smoothing.
Ready to dive deeper? Explore our comprehensive Documentation to discover more.Documentation
SigTech stood out from the start as a credible partner to help us expand our Multi Asset business. What their team and platform achieved in months would have taken us over a year to build in-house. I was particularly impressed with the team’s knowledge of systematic trading and quant methodologies.
SigTech’s hyper-accurate trade simulation capabilities make the platform ideal for both rigorous backtesting and live trading simulation. Schedule executions and monitor performance through our integrated dashboards. When you’re ready, switch to trading with real capital by integrating our RESTful APIs with your preferred workflows.
SigTech gave us the technological agility we needed to meet the requirements of our clients not just for today, but also for the foreseeable future.
With our customer package, we provide a dedicated team of in-house quants, ready to assist you in making the most of everything the platform has to offer.
Security is our bread and butter. Our history is embedded within the finance industry and we understand that security is everything. We are ISO 27001 certified and GDPR compliant. For details see SigTech security and get in touch for specific questions.
Absolutely! You can integrate your own data with our platform using our data ingestion API.
SigTech operates as a cloud-based web app and offers integrations with JupyterLab and Visual Studio Code. Feel free to switch between these formats as needed.
Our enterprise platform is code-based to offer maximum flexibility. To use it effectively, you should have proficiency in Python, including experience with standard libraries such as NumPy and pandas.
Numerous open-source Python libraries are at your disposal within the SigTech platform. These include NumPy for numerical analysis, pandas for time series analysis, SciPy for scientific computing, statsmodels for statistical tests, scikit-learn for machine learning, and more. If you wish to see additional open-source libraries incorporated into our platform, feel free to reach out to us.
The SigTech enterprise platform offers a comprehensive range of quant research tools spanning many asset classes. While we’re progressively integrating more tools into our API, the complete capabilities of SigTech remain exclusive to the enterprise platform. The API serves as a flexible option for exploring platform features without immediate commitment, and for seamlessly integrating SigTech functionalities into other applications and systems. All Enterprise users have access to the API included. We suggest using the API to explore SigTech, if you’re not yet prepared for an in-depth trial of the enterprise platform.