Develop and test trading strategies with machine learning

Quant teams develop trading strategies with machine learning

Needs setupAI Act: no extra obligationsForecasting from figuresAsset management

Fits: Finance & accounting · Research & development

You find out faster which trading ideas have a real edge and which only look good in the backtest. Models are tested properly before any capital depends on them.

Is this for you?

  • You have clean price, fundamental or alternative data covering many years and a computing environment for your own models.
  • Strategies are currently built on rules or classic factors, and you want to test whether learning models add anything.
  • Risk management and compliance are prepared to approve models before they are used.
First step today

Export public daily closing prices of an index over three years, add a simple signal (e.g. price above the average of the last five days yes/no) and have it backtested with the template below as a critical reviewer would: with costs, a separate test period and a comparison with simply holding. Do not upload in-house signals or positions.

How to go about it

  1. Define the prediction target and investment universe
  2. Collect price data, factors and events with correct timing
  3. Have the model backtested
  4. Test on untouched data and in shadow mode
  5. Bring it into the portfolio with limits
  6. Monitor and retrain the model

Templates to copy

Try it now
in the training
Analyse
in the training
Plan the rollout
in the training
Included in the training

The complete guide

  • 6 steps explaining exactly what to do
  • 3 proven templates to copy into your own AI
  • Safe use: data, checking, deciding, informing
  • Pitfalls, effort and how to recognise success

With the training you get all 430 guides for as long as your access runs.

See the trainingOne-off per person, no subscription

Same approach, other use cases

22 more use cases follow the pattern “Forecasting from figures”.

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VV-04 · Legal status 08.10.2026