Datatailr for Quants:

Research at the Speed of Ideas

Research at the Speed of Ideas

From Jupyter to Production without waiting on Infrastructure

From Jupyter to Production without waiting on Infrastructure

Focus on your models, let Datatailr handle the rest. Datatailr provides a self-service environment where you can access your data, run massive backtests, and deploy models independently, all without knowing Docker, Airflow or Kubernetes.

Focus on your models, let Datatailr handle the rest. Datatailr provides a self-service environment where you can access your data, run massive backtests, and deploy models independently, all without knowing Docker, Airflow or Kubernetes.

    • Why Quants Choose Datatailr

    Why Quants Choose Datatailr

    Write Once, Run Anywhere:

    Write Once, Run Anywhere:

    Use a simple Python decorator to turn local code into a scalable cloud workflow.

    Use a simple Python decorator to turn local code into a scalable cloud workflow.

    one decorator · any scale

    Zero-Migration Data Access:

    Zero-Migration Data Access:

    Connect to over 40 data sources (Snowflake, S3, SQL) and run federated queries where the data lives.

    Connect to over 40 data sources (Snowflake, S3, SQL) and run federated queries where the data lives.

    query in place

    Elastic Scale:

    Elastic Scale:

    Burst to 50,000 parallel containers for hyper-scale backtesting and return to zero instantly.

    Burst to 50,000 parallel containers for hyper-scale backtesting and return to zero instantly.

    burst to zero

    Full Reproducibility:

    Full Reproducibility:

    Follow proper SDLC processes without the operational overhead. Every run is fully logged, versioned, and reproducible, with built-in rollback and audit capabilities.

    Follow proper SDLC processes without the operational overhead. Every run is fully logged, versioned, and reproducible, with built-in rollback and audit capabilities.

    versioned · auditable · rollback

    Challenges and How Datatailr Solves Them

    Challenges and How Datatailr Solves Them

    Instant Research

    Reproducible Runs

    Unified Access

    Actionable Insights

    The Challenge

    Waiting weeks for IT/DevOps to provision compute or deploy code at scale

    Capability

    On-demand access to pre-configured Python environments and data

    Result

    Immediate experimentation with the latest frameworks and packages

    Instant Research

    Reproducible Runs

    Unified Access

    Actionable Insights

    The Challenge

    Waiting weeks for IT/DevOps to provision compute or deploy code at scale

    Capability

    On-demand access to pre-configured Python environments and data

    Result

    Immediate experimentation with the latest frameworks and packages

    Your First Two Weeks: From Script to Live Signal

    Your First Two Weeks: From Script to Live Signal

    1

    Days 1–2

    Instant onboarding

    log in to a pre-loaded workspace with your team's packages and data permissions.

    log in to a pre-loaded workspace with your team's packages and data permissions.

    2

    Days 3–7

    Research & backtesting 

    write DAGs in pure Python and run massive parallel simulations on pre-warmed instances.

    write DAGs in pure Python and run massive parallel simulations on pre-warmed instances.

    3

    Week 2

    Secure promotion

    move your model to Prod with built-in approvals and expose signals directly via the Excel Add-in.

    move your model to Prod with built-in approvals and expose signals directly via the Excel Add-in.

    Results for the Quant Desk

    Results for the Quant Desk

    100% Research Focus:

    100% Research Focus:

    No more waiting on infrastructure or managing cloud clusters.

    No more waiting on infrastructure or managing cloud clusters.

    Standardized Workflows:

    Standardized Workflows:

    Every piece of code is versioned, logged, and reproducible by default.

    Python and Jupyter for research. Deploy apps and services in minutes.

    Python and Jupyter for research. Deploy apps and services in minutes.

    Zero Bottlenecks:

    Zero Bottlenecks:

    Move from research to production-grade services without waiting on DevOps.

    Code or no-code DAGs with built-in versioning and one-click rollback.
    No YAML.

    Code or no-code DAGs with built-in versioning and one-click rollback. No YAML.

    Scalable Output:

    Scalable Output:

    Run more workloads with zero manual infrastructure oversight.

    Unified SQL that queries Snowflake, S3, Postgres and 40+ more databases where the data already lives. No migration.

    Run more workloads with zero manual infrastructure oversight.

    What You Ship on Datatailr

    What You Ship on Datatailr

    Distributed Backtesters:

    Distributed Backtesters:

    High-throughput simulations spanning thousands of cores.

    High-throughput simulations spanning thousands of cores.

    Live Dashboards:

    Live Dashboards:

    Interactive research dashboards for Portfolio Managers. Datatialr supports streamlit, panel, voila, bokeh and many more open sourced python frameworks.

    Interactive research dashboards for Portfolio Managers. Datatialr supports streamlit, panel, voila, bokeh and many more open sourced python frameworks.

    Governed Excel Add-in:

    Governed Excel Add-in:

    Python-powered pricing models delivered directly to spreadsheets.

    Python-powered pricing models delivered directly to spreadsheets.

    Air-Gapped AI:

    Air-Gapped AI:

    Secure LLMs for code refactoring and signal generation inside your VPC.

    Secure LLMs for code refactoring and signal generation inside your VPC.