Today, give a try to Techtonique web app, a tool designed to help you make informed, data-driven decisions using Mathematics, Statistics, Machine Learning, and Data Visualization. Here is a tutorial with audio, video, code, and slides: https://moudiki2.gumroad.com/l/nrhgb. 100 API requests are now (and forever) offered to every user every month, no matter the pricing tier.
https://alphathon2025-344c7b0f914a.herokuapp.com/ is a Python FastAPI Web app submitted at https://alphathon.org/.
This web app interacts with a pretrained model for probabilistic stock return forecasting.
The methodology for pretraining is described concisely in:
- https://thierrymoudiki.github.io/blog/2025/09/08/r/python/pretraining-ridge2f
- https://thierrymoudiki.github.io/blog/2025/09/09/r/python/pretraining-ridge2f-part2
- https://github.com/thierrymoudiki/2025-09-05-transfer-learning-ridge2f
The synthetic data generated are available at:
https://pretrainedridge2f-8aee3d9572cc.herokuapp.com/docs/
Citation
For attribution, please cite this work as:
T. Moudiki (2025-10-01). Real-time pricing with a pretrained probabilistic stock return model. Retrieved from https://thierrymoudiki.github.io/blog/2025/10/01/r/python/realtime-pricing
BibTeX citation (remove empty spaces)
@misc{ tmoudiki20251001,
author = { T. Moudiki },
title = { Real-time pricing with a pretrained probabilistic stock return model },
url = { https://thierrymoudiki.github.io/blog/2025/10/01/r/python/realtime-pricing },
year = { 2025 } }
Previous publications
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- Hundreds of Statistical/Machine Learning models for univariate time series, using ahead, ranger, xgboost, and caret Dec 20, 2021
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- Announcements Jun 26, 2020
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- Time series cross-validation using crossval Mar 27, 2020
- On model specification, identification, degrees of freedom and regularization Mar 20, 2020
- Import data into the querier (now on Pypi), a query language for Data Frames Mar 13, 2020
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- Git/Github for contributing to package development Feb 14, 2020
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- A new version of nnetsauce (v0.3.1) Jan 24, 2020
- ESGtoolkit, a tool for Monte Carlo simulation (v0.2.0) Jan 17, 2020
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- Understanding model interactions with the `teller` Dec 13, 2019
- Using the `teller` on a classifier Dec 6, 2019
- Benchmarking the querier's verbs Nov 29, 2019
- Composing the querier's verbs for data wrangling Nov 22, 2019
- Comparing and explaining model predictions with the teller Nov 15, 2019
- Tests for the significance of marginal effects in the teller Nov 8, 2019
- Introducing the teller Nov 1, 2019
- Introducing the querier Oct 25, 2019
- Prediction intervals for nnetsauce models Oct 18, 2019
- Using R in Python for statistical learning/data science Oct 11, 2019
- Model calibration with `crossval` Oct 4, 2019
- Bagging in the nnetsauce Sep 25, 2019
- Adaboost learning with nnetsauce Sep 18, 2019
- Change in blog's presentation Sep 4, 2019
- nnetsauce on Pypi Jun 5, 2019
- More nnetsauce (examples of use) May 9, 2019
- nnetsauce Mar 13, 2019
- crossval Mar 13, 2019
- test Mar 10, 2019
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