MlFinLab helps portfolio managers and traders who want to leverage the power of machine learning by providing reproducible, interpretable, and easy to use tools.
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关键参数与规格
关键参数与规格
参数
值
语言
Python
类别
Applications
子类别
Specialists
关键词
Finance
星标数
4,943
分叉数
1,293
贡献者
3
创建日期
2019-02-13
更新日期
2026-10-15
1 天星标数
0
7 天星标数
7
排名
7,481
上次排名
3,612
排名变化
-3,869
7 天星标排名
4,623
上次 7 天星标排名
4,360
7 天星标排名变化
-263
总星标排名
2,836
上次总星标排名
2,830
总星标排名变化
-6
国家
UK
README 摘录
Welcome to Machine Learning Financial Laboratory! >This repo is public facing and exists for the sole purpose of providing users with an easy way to raise bugs, feature requests, and other issues. MlFinlab python library is a perfect toolbox that every financial machine learning researcher needs. It covers every step of the ML strategy creation, starting from data structures generation and finishing with backtest statistics. We pride ourselves in the robustness of our codebase - every line of code existing in
the modules is extensively tested and documented. Documentation, Example Notebooks and Lecture Videos For every technique present in the library we not only provide extensive documentation, with both theoretical explanations and detailed descriptions of available functions, but also supplement the modules with ever-growing array of lecture videos and slides on the implemented methods. We want you to be able to use the tools right away. To achieve that, every module comes with a number of example notebooks which include
detailed examples of the usage of the algorithms. Our goal is to show you the whole pipeline, starting from importing the libraries and ending with strategy performance metrics so you can get the added value from the get-go. Included modules: Backtest Overfitting Tools Data Structures Labeling Sampling Feature Engineering Models Clustering Cross-Validation Hyper-Parameter Tuning Feature Importance Bet Sizing Synthetic Data Generation Networks Measures of Codependence Useful Financial Features Licensing options This project is licensed under an all
rights reserved licence. Business Enterprise Community With the purchase of the library, our clients get access to the Hudson & Thames Slack community, where our engineers and other quants are always ready to answer your questions. Alternatively, you can email us at: research@hudsonthames. Hudson and Thames Quantitative Research is a company with the goal of bridging the gap between the advanced research developed in quantitative finance and its practical application. We have created three premium python libraries so you can
effortlessly access the latest techniques and focus on what matters most: **creating your own winning strategy**. What was only possible with the help of huge R&D teams is now at your disposal, anywhere, anytime.
This repo is public facing and exists for the sole purpose of providing users with an easy way to raise bugs, feature requests, and other issues.
What is MlFinLab?
MlFinlab python library is a perfect toolbox that every financial machine learning researcher needs.
It covers every step of the ML strategy creation, starting from data structures generation and finishing with backtest statistics.
We pride ourselves in the robustness of our codebase - every line of code existing in the modules is extensively tested and
documented.
Documentation, Example Notebooks and Lecture Videos
For every technique present in the library we not only provide extensive documentation, with both theoretical explanations
and detailed descriptions of available functions, but also supplement the modules with ever-growing array of lecture videos and slides
on the implemented methods.
We want you to be able to use the tools right away. To achieve that, every module comes with a number of example notebooks
which include detailed examples of the usage of the algorithms. Our goal is to show you the whole pipeline, starting from
importing the libraries and ending with strategy performance metrics so you can get the added value from the get-go.
Included modules:
Backtest Overfitting Tools
Data Structures
Labeling
Sampling
Feature Engineering
Models
Clustering
Cross-Validation
Hyper-Parameter Tuning
Feature Importance
Bet Sizing
Synthetic Data Generation
Networks
Measures of Codependence
Useful Financial Features
Licensing options
This project is licensed under an all rights reserved licence.
Business
Enterprise
Community
With the purchase of the library, our clients get access to the Hudson & Thames Slack community, where our engineers and other quants
are always ready to answer your questions.
Hudson and Thames Quantitative Research is a company with the goal of bridging the gap between the advanced research developed in
quantitative finance and its practical application. We have created three premium python libraries so you can effortlessly access the
latest techniques and focus on what matters most: creating your own winning strategy.
What was only possible with the help of huge R&D teams is now at your disposal, anywhere, anytime.
