alfonsogarridoi
@ HSBC eFX Quant - Summer Plcmnt @UCL Computational Finance @TideBanking Senior Data Analyst @Getaround Data Analyst @ITAM_Mexico Economist
London
alfonsogarridoi's Stars
unit8co/darts
A python library for user-friendly forecasting and anomaly detection on time series.
maximenc/pycop
Python library for multivariate dependence modeling with Copulas
shazzzm/topcorr
dswah/sgcrfpy
SGCRFpy: Sparse Gaussian Conditional Random Fields in Python
alexhagiopol/cracking-the-coding-interview
:books: C++ and Python solutions with automated tests for Cracking the Coding Interview 6th Edition.
deepcharles/ruptures
ruptures: change point detection in Python
snap-stanford/deepsnap
Python library assists deep learning on graphs
lmcinnes/umap
Uniform Manifold Approximation and Projection
kumar-shridhar/PyTorch-BayesianCNN
Bayesian Convolutional Neural Network with Variational Inference based on Bayes by Backprop in PyTorch.
MauroCE/PythonBRMLtoolbox
Python 3.7 version of David Barber's MATLAB BRMLtoolbox
slinderman/pyhawkes
Python framework for inference in Hawkes processes.
py-why/dowhy
DoWhy is a Python library for causal inference that supports explicit modeling and testing of causal assumptions. DoWhy is based on a unified language for causal inference, combining causal graphical models and potential outcomes frameworks.
mckinsey/causalnex
A Python library that helps data scientists to infer causation rather than observing correlation.
susanli2016/Machine-Learning-with-Python
Python code for common Machine Learning Algorithms
shadyfish03/Rebellion_Research_NLP
In this repository you will find code for our capstone project "How to Predict Stock Movements Using NLP Techniques". The code has been adapted in the first 3 pairs to illustrate how to scrape the EDGAR webpage. However, the word2vec analysis and FinBERT analysis use as data the data uploaded in this repository. Get in touch with me if you have any questions or if you find any mistakes.
py-why/EconML
ALICE (Automated Learning and Intelligence for Causation and Economics) is a Microsoft Research project aimed at applying Artificial Intelligence concepts to economic decision making. One of its goals is to build a toolkit that combines state-of-the-art machine learning techniques with econometrics in order to bring automation to complex causal inference problems. To date, the ALICE Python SDK (econml) implements orthogonal machine learning algorithms such as the double machine learning work of Chernozhukov et al. This toolkit is designed to measure the causal effect of some treatment variable(s) t on an outcome variable y, controlling for a set of features x.
bnpy/bnpy
Bayesian nonparametric machine learning for Python
omartinsky/QuantAndFinancial
This repository contains supporting examples which are referenced from posts published on www.quantandfinancial.com
PaiViji/PythonFinance-PortfolioOptimization
Python for Portfolio Optimization: The Ascent! First working lessons to ascend the hilly terrain of Portfolio Optimization in seven strides (Lessons), beginning with the fundamentals (Lesson 1) and climbing slope after slope (Lessons 2-6), to reach the first peak of constrained portfolio optimization models (Lesson 7), amongst a range of peaks waiting beyond!
sstoikov/microprice
crflynn/stochastic
Generate realizations of stochastic processes in python.
BitMEX/sample-market-maker
Sample BitMEX Market Making Bot
w-hat/ctci-solutions
Python solutions to Cracking the Coding Interview (6th edition)
CoFiF/Corpus
The first French corpus comprising financial reports
cantaro86/Financial-Models-Numerical-Methods
Collection of notebooks about quantitative finance, with interactive python code.