Signal platform for institutional investment fund hero

NDA project

Signal platform for the institutional investment fund

AI-driven platform generating real-time trading signals for faster investment decisions.

Industry

Finance / Investment

Client

Institutional Investment Fund

Platform

Web & Desktop

Partnership model

Turnkey Product Development

Client Context

An Austrian institutional investment fund approached us in 2020, long before the mainstream AI hype, with a bold idea: to create a platform that empowers their traders and portfolio managers with real-time trading signals, powered by machine learning and data analytics.

The goal was to migrate the underlying logic/algorithms behind numerous Excel spreadsheets & make the system learn iteratively.

Challenge

  • Data Fragmentation: Market data scattered across sources

  • Decision Latency: Manual analysis slowed reactions

  • Signal Trustworthiness: Recommendations required explainability

  • Scalable Processing: Continuous real-time data handling needed

Signal trading platform demo

Solution

We built a predictive analytics platform aggregating real-time and historical market data.

Proprietary ML models integrate technical and fundamental indicators, generating actionable trading signals with backtesting capabilities and cloud-scalable analytics.

Team

  • 3x

    Python Developers

  • 1x

    DevOps

  • 2x

    Data Engineer

  • 1x

    Project Manager

  • 1x

    QA
    Engineer

  • 3x

    Python Developers

  • 1x

    DevOps

  • 2x

    Data Engineer

  • 1x

    Project Manager

  • 1x

    QA
    Engineer

What We Delivered

  • Trading signal platform

  • Market data integration framework

  • ML prediction models

  • Strategy backtesting system

  • Buy/Sell/Hold signal engine

  • Cloud analytics environment

Timeline & Cost

Full breakdown is available under NDA

Discovery & Planning: 3–4 weeks

Architecture & Design: 4–6 weeks

Development: 4–6 months

Total Timeline: ~6–8 months

Discovery Phase: $25K–$40K

Design & Architecture: $40K–$70K

Development: $180K–$320K

Total Investment: $300K–$450K

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Impact & Results

Operational:

Automated signal generation reduced manual workload

Investment:

Improved portfolio decision consistency

Business:

Contributed to higher ROI performance

Technology Stack

  • Python
  • TensorFlow
  • PyTorch
  • Pandas
  • ta-lib
  • AWS
  • Docker
  • CI/CD pipelines
  • Pandas
  • pipelines
  • TensorFlow
  • Custom ML pipelines
  • CCXT
  • Bloomberg API
  • and more...

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