Last Updated: 21st January 2022
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Last Updated: 21st January 2022
Insights on Cryptocurrency Dynamics
Last Updated: 21st January 2022
Bitcoin Pricing Kernels are inferred using a novel data set from Deribit, one of the largest Bitcoin derivatives exchanges. This enables arbitrage-free pricing of various instruments. State Price Densities are estimated with Rookley’s method.
Last Updated: 2nd February 2022
DS2 Data Science & Digital Society
Last Updated: 1st March 2022
Introduction of Kalman filter including the background, calculation methodology and application.
Last Updated: 1st March 2022
Quantitative Analysis of Financial Time Series
Last Updated: 1st March 2022
The Shapley Decomposition is a model-agnostic tool which is able give a variable importance measure on a local level.
Last Updated: 1st March 2022
Financial risk measure FRM (Financial Risk Meter) is proposed for Emerging markets (FRM@EM).
Last Updated: 1st March 2022
Crypto Hedging strategies based on the paper Hedging Cryptocurrency Options.
Last Updated: 4th April 2022
This background course on mathematics aims to provide fundamental mathematical knowledge essential for advanced economic analysis. Although open to all master students, it is specifically tailored to those wishing to directly pursue the advanced Y-track of courses. Therefore in content and form, this intensive course is intended to deliver methods beyond refreshing advanced calculus and linear algebra. The course solely deals with deterministic mathematics. For some theorems formally rigorous proofs are presented in order to make participants more comfortable with - and ideally to provide some intuition for – constructing and understanding of mathematical proofs. Throughout the course proper use of notation will be stressed. Topics presented in class constitute the minimal required program given the above aim, and the maximal feasible program given time.
Last Updated: 21st January 2022
Investigate the financial market from the perspective of networks.
Last Updated: 12th November 2021
Basic calculations Vectors and matrices Data frames and data IO Plotting
Last Updated: 12th November 2021
Descriptive Techniques Principal Component Analysis
Last Updated: 12th November 2021
Introduction to the foundations of Data Science with focus on business applications. We emphasize supervised machine learning algorithms to build predictive decision support models for credit risk, marketing analytics, and several other use cases.
Last Updated: 1st March 2022
This research provides insights for the separation of cryptocurrencies from other assets.
Last Updated: 18th February 2022
Introduction to the Computer Museum
Last Updated: 21st January 2022
QuantLet: A open-source platform hosted on GitHub for demonstrating your research project
Last Updated: 28th May 2022
Introduction to Boosting Algorithms
Last Updated: 18th February 2022
All about clustering!
We have created a new courselet category on Quantinar, Explainable AI.As Machine Learning and its black box models have known great success and wide adoption among practioners in the last years, the need for explainability and transparency has arisen. At Quantinar, we aim at teaching you with the best state-of-the-art solutions for opening the black box and regaining control over your machine lear
Welcome to the blog of the Machine Learning and FinTech class!Today we started this class in 复旦大学, wonderful athmosphere, good mood and perfect slides. The quantlet technology is really exciting. Hope to see more of this inspring Machine Learning tools in the next classes
The DEDA course on Digital Economy & Decision Analytics presents modern machine learning tools for tomorrow's data. Based on a solid founding of Python applications, DEDA evolves into a powerful decision tool for navigating through heterogenous and massively unstructured data. The quantlet.com technology enables transparency and reproducibility in all scales.
Quantinar provides a perfect intro into Machine Learning & Fintech. It equips students with the basic ability to analyze data using Python. The lectures and videos are super cool, understandable and really attractive. For the first time, it made me realize that data science could be very interesting. In the first class, I drew a cute little PY elephant.
I also use edX, udemy and Coursera. I am extremely happy with the quality of the content and its practical applications. This is a very good initiative. I hope that more courses would follow this style.
The absence of transparency and reproducibility of scientific research are the root of a credibility crisis. Quantinar.com is an initiative that that offers brilliant insights into data science, Machine Learning, and up to date FinTech tools without losing the solid grip on mathematical and statistical foundations. Every student or teacher should like to become a Quantinar!
I used Quantinar in order to acces the DEDA courses. I can tell about me that i am a beginner regarding python but the information was well structured and came in handy to support the work done at the Research Methods course at my university.
Quantinar is an excellent platform for those looking for an intro to Machine Learning and concepts connected with Fintech. The lectures and videos have a good structure that are understandable and are created so that they are appealing also to those non-technical students.