Top Tweets for #MLFinLab
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🚀 Exciting news! MLFinlab v2.1.0 is here, boosting trading strategies with machine learning. Python 3.9 compatibility, DatetimeIndex automation, enhanced volatility estimators, and more. Dive in: https://t.co/Lt7Xg9lEvJ 👇🧵
#MLFinlab #TradingStrategies
1/5 🚀 Unleash the power of EF3M in MLFinLab by @HudsonandThames! 🔍 This cutting-edge algorithm revolutionises fund oversight in the dynamic financial landscape. #EF3M #MLFinLab #FinanceInnovation 👇
Learn more about #mlfinlab here 👉https://t.co/zBha6FX4vh
And dont forget to stay updated with the latest news and updates! Don't miss out on any valuable information, sign up for our newsletter now: **https://t.co/jZT4RYzTkc**
Unlock hidden patterns and correlations in your dataset with Feature Clustering in #MlFinLab! 📊Gain a comprehensive understanding of your #data and make informed decisions, revealing the drivers behind your #strategies.Check it out here 👉 https://t.co/0zwjlwJ79o #DataAnalytics
Boost your model's accuracy in financial analysis with sample weights using @sklearn. Discover how MlFinLab's weighting methods based on return & average uniqueness and time decay can give you a competitive edge. Explore now: https://t.co/zBha6FX4vh #QuantFinance #MLFinLab
Hey 👋 Check out MlFinLab's Optimal Transport Dependence module for uncovering complex dependence patterns in #financial #data with copulas, #optimal transportation, and clustering. Try it out today here 👉 https://t.co/zBha6FX4vh #MlFinLab #QuantitativeFinance #Copulas
🔬 Discover MlFinLab's module: Returns vs. Benchmark Labeling. Be able to #label #financial data effectively & predict price movements with this cutting-edge module here 👉https://t.co/zBha6FX4vh 🧵#MLFinLab #FinancialAnalysis #MachineLearning #InvestmentStrategies
1/6 📊 Interested in generating real-time bars using tick data in a seamless streaming manner? The Online Data Structures module in #MLFinlab is designed to meet this exact need. 🧵 https://t.co/zBha6FXCkP
Great and practical article by
@Refinitiv on using MLFinLab for portfolio optimization! From Mean Variance to Hierarchical Risk Parity with examples for financial practitioners (w/ Eikon)! #MachineLearning #Finance #MLFinLab @hudson_thames
@JacquesQuant https://t.co/vrUf6RQyon
It's been a long road...but today we finally reached 1k of stars for #mlfinlab package on github. Big thanks to everyone from @hudson_thames, there are so many things to be done!
Recently @lopezdeprado posted great lecture slides on variable codependence. Our research team implemented all of them and included into #mlfinlab release. We decided to test metrics on various non-linear variable dependencies and results look great!
https://t.co/iy9gajtOL3
#mlfinlab 0.5.0 release is ready! Key features added:
- Structural breaks tests.
- Microstructural and entropy features.
- Constant tick size imbalance/run bars.
- Time bars.
- Optimal Number of Clusters algorithm (ONC).
The whole list of changes can be found in release notes.
Excited to give a lecture with @JacquesQuant at the London School of Economics on the topic of the financial machine learning, its applications, key techniques and how #mlfinlab is used in a full-scale machine learning quantitative research!
https://t.co/RPxHr1vnM2
Well-known PIN/VPIN is just one of many microstructural features that can be used in HFT algorithms. Kyle, Amihud, Hasbrouck lambdas, tick rule entropies may contain useful information for algorithmic traders. All of them will be added in the next #mlfinlab release!
9 Pull Requests pending in #mlfinlab repo, lots of code to review this month. We are on the way to finish all the material described in Advances in Financial Machine Learning book!
https://t.co/3fxu1EnISS
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