Kattana

Kattana

Kattana is a data analytics platform designed specifically for trading teams to quickly discover insights and make data-driven decisions. It connects to multiple data sources and allows users to analyze data with powerful visualizations and share findings.
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data-analytics trading visualizations insights

Kattana: Data Analytics Platform for Trading Teams

Kattana is a data analytics platform designed specifically for trading teams to quickly discover insights and make data-driven decisions. It connects to multiple data sources and allows users to analyze data with powerful visualizations and share findings.

What is Kattana?

Kattana is a cloud-based data analytics platform purpose-built for trading teams. It aims to accelerate quant analysis and quantitative research by connecting external and internal data sources into a centralized data workspace.

Key features of Kattana include:

  • Pre-built connectors to leading data sources like exchanges, blockchains, pricing data feeds etc. This enables quick data ingestion.
  • A SQL editor to join, transform and model connected data as needed. Supports Python scripts too.
  • Interactive data visualizations like charts, graphs and heatmaps for discovery and sharing.
  • Collaboration features to discuss insights, annotate and share findings with stakeholders.
  • Portfolio management tools to create mock and live portfolios for backtesting strategies.
  • An ecosystem of apps and integrations with platforms like TradingView to extend functionality.

Overall, Kattana aims to enhance productivity of trading teams by making complex data analytics tasks faster and easier to execute. Its specialized domain focus differentiates it from more generalized BI tools.

Kattana Features

Features

  1. Connects to multiple data sources like trading platforms, market data, fundamentals, alternative data etc
  2. Powerful data visualization and charting capabilities
  3. Collaboration tools to share insights across teams
  4. Anomaly detection and signal monitoring
  5. Python SDK to build custom models and analytics
  6. Pre-built templates for common use cases like performance attribution, risk analysis etc

Pricing

  • Subscription-Based

Pros

Purpose-built for trading teams with relevant features

Intuitive interface for non-technical users

Fast time to insight

Collaboration features improve team productivity

Flexible connectivity to data sources

Cons

Steep learning curve for some advanced features

Limited customization compared to open-source options

Can be expensive for smaller teams


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