theauheral Goto Github PK
Name: Theau Heral
Type: User
Company: Goldman Sachs
Location: London, UK
Theau Heral's Projects
adv_fin_ml_exercises
Experimental solutions to selected exercises from the book [Advances in Financial Machine Learning by Marcos Lopez De Prado]
awesome-awesome-finance
An awesome repository of other awesome-finance repositories
awesome-quant
A curated list of insanely awesome libraries, packages and resources for Quants (Quantitative Finance)
awesome-quant-machine-learning-trading
Quant/Algorithm trading resources with an emphasis on Machine Learning
backtrader-docs
backtrader documentation
berkeley-haas-mba-notes
Collection of notes
change-tutorial
Materials for the useR2017 tutorial on changepoint detection
coursework
summer school coursework
data-engineer-roadmap
Roadmap to becoming a data engineer in 2021
elitequant
A list of online resources for quantitative modeling, trading, portfolio management
financial-machine-learning
A curated list of practical financial machine learning tools and applications.
finmathematics
Books
langchain
⚡ Building applications with LLMs through composability ⚡
legend-studio
Legend Studio module
machine-learning-for-software-engineers
A complete daily plan for studying to become a machine learning engineer.
mirc_2019
MIRC Team for the 2018/2019 year
mlfactor.github.io
Website dedicated to a book on machine learning for factor investing
mooc-mba
Path to a free self-taught education in Business
product-notes-2018
Product Notes
pyportfolioopt
Financial portfolio optimisation in python, including classical efficient frontier, Black-Litterman, Hierarchical Risk Parity
research
Contains all the Jupyter Notebooks used in our research
ruptures
ruptures: change point detection in Python
spacextract
Extraction and analysis of telemetry from SpaceX webcasts
statsmodels
Statsmodels: statistical modeling and econometrics in Python
stock-prediction-models
Gathers machine learning and deep learning models for Stock forecasting including trading bots and simulations
stockpredictionai
In this noteboook I will create a complete process for predicting stock price movements. Follow along and we will achieve some pretty good results. For that purpose we will use a Generative Adversarial Network (GAN) with LSTM, a type of Recurrent Neural Network, as generator, and a Convolutional Neural Network, CNN, as a discriminator. We use LSTM for the obvious reason that we are trying to predict time series data. Why we use GAN and specifically CNN as a discriminator? That is a good question: there are special sections on that later.
technology_books
Premium eBook free for Geeks
telemetry-data
A collection of telemetry captured from SpaceX Launch Webcasts
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