Obtaining Time Series Datasets in Python 2022
Datasets from real-world scenarios are important for building and testing machine learning models. You may just want to have some data to experiment with an algorithm.
https://t.co/0GL6VlUnmm
https://pyquantnews.com/obtaining-time-series-datasets-in-python/
The best on the internet 
https://twitter.com/pyquantnews/status/1541214255055257603
ML Model Validation Library - DeepChecks
Python GitHub:
https://t.co/qBZXROssMW https://t.co/UdLHCYPCnQ
https://github.com/deepchecks/deepchecks
New York
https://twitter.com/carlcarrie/status/1541196642237423617
26Jun22 / #365APoem / Suitcase and the euros, nothing to see,
London
https://twitter.com/saeedamenfx/status/1541156698223792128
Vega AutoML with Model Zoo (SOTA)
Python GitHub:
https://t.co/Y7QXv82twZ
https://github.com/huawei-noah/vega
New York
https://twitter.com/carlcarrie/status/1541113837046349824
Paper: 'Towards Time-Series Feature Engineering in Automated Machine Learning for Multi-Step-Ahead Forecasting'
#AutoML
Paper Link:
https://t.co/Ie4E5WJsou
Datasets:
https://t.co/1tAOwYOvNM
Python GitHub:
https://t.co/INTT32Qa17
https://www.mdpi.com/2673-4591/18/1/17/pdf?version=1655900622
New York
https://twitter.com/carlcarrie/status/1541111295851855872
Paper - Twitter topics + sentiment + OHLC technical analysis underlying systematic Bitcoin trading strategy called PreBit
#NLP #FinBert #Multimodal
Paper:
https://t.co/jHYxXAK5HD
Python Code in a Jupyter Notebook on GitHub:
https://t.co/cNLHJfAXb8 https://t.co/zim7EwiV0W
https://arxiv.org/pdf/2206.00648.pdf
New York
https://twitter.com/carlcarrie/status/1541084014542462976
Paper: Uncertainty index and stock volatility prediction: evidence from international markets
https://t.co/vmQ5Krf42E
https://jfin-swufe.springeropen.com/articles/10.1186/s40854-022-00361-6
New York
https://twitter.com/carlcarrie/status/1541078828356976647
Modeling VST - Very short term price changes - agent based modeling, limit order book liquidity and volatility linkages
#hft #lob Paper:
https://t.co/jZBjr9B3vW
https://jfin-swufe.springeropen.com/articles/10.1186/s40854-022-00371-4
New York
https://twitter.com/carlcarrie/status/1541078110539599873
Crytocurrency Cluster Analysis
Paper: https://t.co/bU3JMjICD6
#Rstats R Code (zipped)
https://t.co/LhyahzBMmZ
https://jfin-swufe.springeropen.com/articles/10.1186/s40854-021-00310-9
New York
https://twitter.com/carlcarrie/status/1541076985425821702
Gaining Insights on U.S. Senate Speeches Using a Time Varying Text Based Ideal Point Model https://t.co/PjHH4tBtyh #QuantLinkADay https://t.co/Y52gzS53ur
https://arxiv.org/abs/2206.10877
London
https://twitter.com/saeedamenfx/status/1541063547840512004
An Investor’s Guide to Crypto https://t.co/nTY9ObFitQ #QuantLinkADay https://t.co/ct0j9akt8Z
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4124576
London
https://twitter.com/saeedamenfx/status/1541063087901532161
https://t.co/3liFQVjHCU
https://twitter.com/QuantSymplectic/status/1541060491249958913
Whether you are brand new to the world of quant trading or just hoping to refine your skills, head over to the IBKR Quant Blog to find articles from leading experts!
https://t.co/9o8WR1UsPG
#PythonProgramming #rstats https://t.co/rO6pOBgIjr
http://ibkrquant.com/
Connecticut, USA
https://twitter.com/IBKR_QB/status/1541058708238737408
Love this idea! Thanks for sharing @robertmartin88! I’m about to try it out! https://t.co/f3h9Eoa8iY
https://twitter.com/robertmartin88/status/1540716141995102215
London, England
https://twitter.com/JacquesQuant/status/1540990479260581888
@robertmartin88 It looks good! Thanks for sharing I’ll give it a try
Hong Kong / Abu Dhabi
https://twitter.com/GautierMarti1/status/1540952165174370304
McKinsey on managing data as a product
https://t.co/ID46Lgr3LT
https://www.mckinsey.com/business-functions/quantumblack/our-insights/how-to-unlock-the-full-value-of-data-manage-it-like-a-product?utm_content=buffer93fb8&utm_medium=social&utm_source=linkedin.com&utm_campaign=buffer
New York
https://twitter.com/carlcarrie/status/1540902100787404801
Get the 46-Page Ultimate Guide to Pricing Options and Implied Volatility With Python.
It includes the 46-page PDF guide, the accompanying Jupyter Notebook and cached options data.
https://t.co/uUXgYrCqgx
https://pyquantnews.gumroad.com/l/46-page-ultimate-guide-pricing-options-implied-volatility-python-pdf-code-pyquant-news
The best on the internet 
https://twitter.com/pyquantnews/status/1540892696532996098
Covid19 impact on stock liquidity in major and emerging markets - paper
https://t.co/74ZOpMkTAc
Wavelet Power Spectrum, Wavelet Coherency, Phase Difference https://t.co/Kn12ffEPEp
https://www.sciencedirect.com/science/article/pii/S1062940822000857
New York
https://twitter.com/carlcarrie/status/1540874857554083841
Regression, including Lasso and Regression tutorial
Python Google Colab Notebook:
https://t.co/NdAipO0HZe
https://colab.research.google.com/github/deepmind/educational/blob/master/colabs/summer_schools/intro_to_regression.ipynb
New York
https://twitter.com/carlcarrie/status/1540855826482618371
Life hack:
When the stock market is down just don't check your portfolio.
The best on the internet 
https://twitter.com/pyquantnews/status/1540853128320491520
25Jun22 / #365APoem / Treehouse, the price, a house, in news story,
London
https://twitter.com/saeedamenfx/status/1540787154346856449
9/9 Find a system (+software) that works for you. Molecular Notes (implemented in Obsidian) is the first setup that I've felt truly comfortable with. It was a discontinuous change in my learning rate and continually sparks joy. Link in bio if you want to read more!
London, England
https://twitter.com/robertmartin88/status/1540716181677641734
8/ I've come to realise that any career edge I have is not in pure technical skill or intelligence, it's more that I'm interested in a lot of things and very keen on finding links between them. Molecular Notes is a catalyst for this. And it makes learning fun! https://t.co/AdRk3AP0nT
London, England
https://twitter.com/robertmartin88/status/1540716177093079043
7/ Subjectively, Molecular Notes has been critical in helping me learn about complex topics relatively quickly – important for internships where you need to "know enough to be dangerous" h/t FwM s3e6 @choffstein https://t.co/Ax60DWWvQp
London, England
https://twitter.com/robertmartin88/status/1540716170742943751
6/ I realise that there are lots of productivity YouTubers etc who present fancy systems for the sake of it. Molecular Notes (in its current form) is something that I've used consistently for the past year, though I'd been using second brains for several years prior.
London, England
https://twitter.com/robertmartin88/status/1540716166615900160
5/ Molecular Notes encourages active note-taking: when going through a textbook, I'm constantly contrasting its explanations with those from other Sources, trying to succinctly capture the union of the two and crystallising it into my own intuition.
London, England
https://twitter.com/robertmartin88/status/1540716164665466881
4/ Having Atoms as linkable notes allows me to understand how concepts relate. e.g this is the local graph of "Delta Hedging" – I can see which Sources reference it (blue), some related Atoms (white), and some of my Molecules (intuition and ideas, in purple). https://t.co/a06excBDG8
London, England
https://twitter.com/robertmartin88/status/1540716162002059265
3/ Key principle: minimise duplication. One concept can be discussed in many Sources – I don't want to re-explain the concept in each Source note. So I extract the concept into an Atom note then link it to relevant Source notes. e.g Volatility Cones below https://t.co/sR3HzWqPcy
London, England
https://twitter.com/robertmartin88/status/1540716155278606336
2/ The core workflow: consume Sources, extract Atoms (existing/known concepts and techniques), then create Molecules. Molecules are notes containing crystallised intuition, links between Atoms, or ideas. https://t.co/DZkMqBKcvM
London, England
https://twitter.com/robertmartin88/status/1540716149263966208
1/ Molecular Notes helps me learn from diverse sources (books, textbooks, articles, courses), distil insights, and synthesise new ideas. It was inspired by Luhmann's Zettelkasten (and Matuschak's Evergreen notes), but imo more suited to learning about technical fields.
London, England
https://twitter.com/robertmartin88/status/1540716144465780737
0/9 I've spent the last few months reflecting /writing up my approach to "second brain" note-taking. The result is Molecular Notes – I consider this thread to be the biggest alpha I have, in the sense that it explains my "metamodel" for learning. https://t.co/pkiA1ICkmJ
London, England
https://twitter.com/robertmartin88/status/1540716141995102215
Python has been ranked among one of the most popular and robust programming languages. Explore our collection of Python articles and learn how to apply these skills in #AlgoTrading:
https://t.co/bi47BQ1yDo
#DataScience #PythonProgramming https://t.co/plb0xxFOAG
https://www.tradersinsight.news/python
Connecticut, USA
https://twitter.com/IBKR_QB/status/1540696324353363968
Get the 46-Page Ultimate Guide to Pricing Options and Implied Volatility With Python.
It includes the 46-page PDF guide, the accompanying Jupyter Notebook and cached options data.
https://t.co/uUXgYrCqgx
https://pyquantnews.gumroad.com/l/46-page-ultimate-guide-pricing-options-implied-volatility-python-pdf-code-pyquant-news
The best on the internet 
https://twitter.com/pyquantnews/status/1540688901307777025
Volatility Estimators in Python
A complete set of volatility estimators based on Euan Sinclair's Volatility Trading.
https://t.co/OfJfl26tzN https://t.co/S3PAjRVowm
https://pyquantnews.com/volatility-estimators-in-python/
The best on the internet 
https://twitter.com/pyquantnews/status/1540672193595641856
Parallelization with MultiProcessing in Python
Run your data science tasks in parallel to speed up computation timeContinue reading on Towards ...
https://t.co/0EITB16l2h https://t.co/tLVWogSJPu
https://towardsdatascience.com/parallelization-w-multiprocessing-in-python-bd2fc234f516
https://twitter.com/PythonHub/status/1540609542257524737
The power of macro trends in rates markets:
Broad macroeconomic trends based on inflation and economic growth drive monetary policy. In the past, simple point-in-time macro trends have predicted fixed income returns with great accuracy. https://t.co/keD6YTofpZ
https://t.co/eP75MPQe5t https://www.sr-sv.com/the-power-of-macro-trends-in-rates-markets/
London, England
https://twitter.com/macro_srsv/status/1540601094644142080
Get the 46-Page Ultimate Guide to Pricing Options and Implied Volatility With Python.
It includes the 46-page PDF guide, the accompanying Jupyter Notebook and cached options data.
https://t.co/uUXgYrCqgx
https://pyquantnews.gumroad.com/l/46-page-ultimate-guide-pricing-options-implied-volatility-python-pdf-code-pyquant-news
The best on the internet 
https://twitter.com/pyquantnews/status/1540530344570634240
Cython for absolute beginners: 30x faster code in two simple steps
Easy Python code compilation for blazingly fast applicationsContinue reading on Towards Data ...
https://t.co/AqDDp9ChHu
https://towardsdatascience.com/cython-for-absolute-beginners-30x-faster-code-in-two-simple-steps-bbb6c10d06ad
https://twitter.com/PythonHub/status/1540518928929230849
Using Requests and BeautifulSoup in Python to Scrape Data 2022
Learn how to use requests and BeautifulSoup in python to scrape data in this comprehensive tutorial.
https://t.co/xBoj2wO4Zd
https://pyquantnews.com/using-requests-and-beautifulsoup-in-python/
The best on the internet 
https://twitter.com/pyquantnews/status/1540490739615948801
24Jun22 / #365APoem / Dowden has gone, Johnson insists he’s in,
London
https://twitter.com/saeedamenfx/status/1540460952948248576
MoreThanSentiments: A Python Library for Text Quantification
A collection of functions that help researchers calculate Boilerplate, Redundancy, Specificity, ...
https://t.co/lM31sDqc4r
https://towardsdatascience.com/morethansentiments-a-python-library-for-text-quantification-e57ff9d51cd5
https://twitter.com/PythonHub/status/1540428339525083136
McKinsey provocations:
‘Find the smartest technologist in the company and make them CEO’
-- Marc Andreessen
https://t.co/GdkhhY0B2R
https://www.mckinsey.com/industries/technology-media-and-telecommunications/our-insights/find-the-smartest-technologist-in-the-company-and-make-them-ceo
New York
https://twitter.com/carlcarrie/status/1540415237551230976
. @IBKR offers connectivity via FIX protocol for the purposes of routing orders.
Read about the integration testing and supported network connectivity options for FIX clients here -> https://t.co/b3lqZ89BLD
#AlgoTrading https://t.co/Tyru211l7N
https://www.tradersinsight.news/6ox2
Connecticut, USA
https://twitter.com/IBKR_QB/status/1540394328106979335
Our paper on Meta-Labeling: Theory and Framework is now available to read via the Journal of Financial Data Science: https://t.co/uJQ0DSpjPS
https://jfds.pm-research.com/content/early/2022/06/23/jfds.2022.1.098
London, England
https://twitter.com/JacquesQuant/status/1540367680837926912
Good month for market neutral crypto strategies https://t.co/fBDsoEJkIC
Hong Kong / Abu Dhabi
https://twitter.com/GautierMarti1/status/1540358767602274306
. @quantpedia shares insight on Skewness/Lottery Trading Strategy in Cryptocurrencies:
https://t.co/HyM32YmGo4
#FinTech https://t.co/f7ckNpcnLg
https://www.tradersinsight.news/n92s
Connecticut, USA
https://twitter.com/IBKR_QB/status/1540333928699002888
Get the 46-Page Ultimate Guide to Pricing Options and Implied Volatility With Python.
It includes the 46-page PDF guide, the accompanying Jupyter Notebook and cached options data.
https://t.co/uUXgYrCqgx
https://pyquantnews.gumroad.com/l/46-page-ultimate-guide-pricing-options-implied-volatility-python-pdf-code-pyquant-news
The best on the internet 
https://twitter.com/pyquantnews/status/1540326472006656004
NBER Paper empirically concludes that post COVID growth in housing demand was attributable to rise in remote / hybrid work
https://t.co/7YmLCWQUlM https://t.co/WSws2KloLI
https://www.nber.org/system/files/working_papers/w30041/w30041.pdf
New York
https://twitter.com/carlcarrie/status/1540322627247636480
Graph Neural Networks for Asset Management
In this paper, we look at the graph-based method to model inter-asset behavior.
https://t.co/TGXP0uRXPH https://t.co/nzu0K1n3b9
https://www.researchgate.net/publication/356634779_Graph_Neural_Networks_for_Asset_Management
The best on the internet 
https://twitter.com/pyquantnews/status/1540308549674336258
Maxim Kartamyshev joins us to talk about the non-trivial challenge of estimating the consequences of energy transition policies for different sectors of the global industrialised economy.
Read more on forecasting implications of the energy transition >> https://t.co/l6G2sGCpUV
http://spr.ly/6018zdXjn
Global
https://twitter.com/QuantMinds/status/1540255659006038019