Track the latest analyses, model updates, and course activities from the TwoGrok knowledge center.
The "NeoPredict" model for market fluctuations has been successfully added to the course library. This model analyzes real-time data with improved accuracy.
Module "Data-Driven Decision Making for Analysts" has been expanded with new case studies on the financial sector. All participants have access to the update.
In-depth analysis of Q3 market data has been completed. The results have been incorporated into the weekly insight report for business analysts.
The knowledge center has started a new research project on the application of AI for market prediction in emerging economies.
The chronological milestones of TwoGrok in developing knowledge and tools for data-driven financial analysis.
TwoGrok is founded as a knowledge center with a focus on complex data analysis in the financial sector. The first research framework for algorithmic models is defined.
Introduction of the first practical course "Data Analysis for Financial Markets". Aimed at business analysts who want to learn to predict market fluctuations.
Expansion of the expert team. Publication of the first white paper on the application of advanced algorithmic models for risk assessment.
TwoGrok functions as a leading think tank, offers multiple specialized courses, and advises companies in the field of data-driven decision-making.
Discussions on data analysis and market predictions
Initiated by: Alex | Course: Market Fluctuations
Does anyone have experience calibrating the LSTM model for short-term predictions on the forex market? The results on the test set are promising, but the live performance drops.
Initiated by: Samira | Course: Data-Driven Decision Making
When collecting alternative data for sentiment analysis, privacy questions arise. Which frameworks do you use to ensure compliance without overly limiting predictive power?
Initiated by: Team Risk | Course: Algorithmic Models
We have completed the backtest of the three proposed ensemble models. The random forest model performs consistently better during periods of high volatility. All data and code are shared in the repo.