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Machine Learning Stock Prices

Case Study

Machine Learning Stock Prices

Automation & Big Data

Challenge

Prices of listed values are subject to changes and sometimes sharp fluctuations every second. In order to minimize losses, it should be able to predict whether they will remain stable in the coming minutes or be sufficient above limit values.

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Goal

A portfolio manager or daytrader should be automatically given an assessment of whether certain values in his portfolio are in danger of being severeine in the short term. The manager can then react to this in a targeted manner and, if necessary, take countermeasures that compensate for losses.

Result

We have designed and combined various modern machine learning algorithms and made them available via an API. In addition, we have created a plugin that the portfolio manager can integrate into his analysis charts and is in contact with the API. By specifying certain limit values and continuously analysing the transferred value fluctuations, the portfolio manager is sent an alarm at an early stage, which expresses the probability whether a particular plant exceeds the given limits. falls below. The calculation of the probability for such a border crossing is also visualized in charts.

Facts

  • Company: (secret)
  • Industry: Fintech
  • Field of application: Global

Technologies used

  • Mobile: Hybrid Frameworks Ionic and ReactNative for Android & iOS
  • Web: Vue.js, Angular
  • Back-end: node.js & python for various ML/AI algorithms

Services

  • Employees: 3 for Mobile Development, Back-end Development, Front-end Development, Project Management, Quality Assurance
  • Duration: 3+ years
  • Methodology: Scrum

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