alvinjamur

A-Beginner-Guide-to-Carry-out-Extreme-Value-Analysis-with-Codes-in-Python

A beginner's guide to carry out extreme value analysis, which consists of basic steps, multiple distribution fitting, confidential intervals, IDF/DDF, and a simple application of IDF information for roof drainage design. The guide mainly focuses on extreme rainfall analysis. However, the basic steps are also suitable for other climatic or hydrologic variables such as temperature, wind speed or runoff.

Adv_Fin_ML_Exercises

Experimental solutions to selected exercises from the book [Advances in Financial Machine Learning by Marcos Lopez De Prado]

altair

Declarative statistical visualization library for Python

awesome-quant

A curated list of insanely awesome libraries, packages and resources for Quants (Quantitative Finance)

Bitcoin-Trader-RL

A profitable cryptocurrency trading environment using deep reinforcement learning and OpenAI's gym

misc

metalog

peep-dis

A colorful python CLI object inspector

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.

talos

Hyperparameter Optimization for Keras Models

tpot

A Python Automated Machine Learning tool that optimizes machine learning pipelines using genetic programming.

alvinjamur/Bitcoin-Trader-RL

A profitable cryptocurrency trading environment using deep reinforcement learning and OpenAI's gym

alvinjamur/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.

alvinjamur/misc

metalog

alvinjamur/peep-dis

A colorful python CLI object inspector

alvinjamur/BayesianOptimization

A Python implementation of global optimization with gaussian processes.

alvinjamur/catch22

catch-22: CAnonical Time-series CHaracteristics

alvinjamur/CompStats

Code for a workshop on statistical interference using computational methods in Python.

alvinjamur/cookiecutter-data-science

A logical, reasonably standardized, but flexible project structure for doing and sharing data science work.

alvinjamur/crewai-stocks-yt

a template....

alvinjamur/Deep-Trading

Algorithmic trading with deep learning experiments

alvinjamur/easyesn

Python library for Reservoir Computing using Echo State Networks

alvinjamur/Feature-Engineering

alvinjamur/Forecasting_Stock_Returns_via_Supervised_Learning

Stat 479 Project

alvinjamur/GENDIS

Contains an implementation (sklearn API) of the algorithm proposed in "GENDIS: GEnetic DIscovery of Shapelets" and code to reproduce all experiments.

alvinjamur/gitignore

A collection of useful .gitignore templates

alvinjamur/handson-ml2

A series of Jupyter notebooks that walk you through the fundamentals of Machine Learning and Deep Learning in Python using Scikit-Learn, Keras and TensorFlow 2.

alvinjamur/High-Dim-TS-Medium

Deep Learning for High-Dimensional Time Series

alvinjamur/ml_sampler

Model assisted random sampling.

alvinjamur/py-market-profile

A library to calculate Market Profile (aka Volume Profile) for financial data from a Pandas DataFrame.

alvinjamur/PyData_LA_2018

Added repo for PyData LA 2018 tutorial

alvinjamur/pyiqfeed

Python LIbrary for reading DTN's IQFeed

alvinjamur/PyTricks

Collection of less popular features and tricks for the Python programming language

alvinjamur/pytudes

Python programs, usually short, of considerable difficulty, to perfect particular skills.

alvinjamur/scaleogram

Scaleogram plot for Continuous Wavelet Transform made easy

alvinjamur/scipy_con_2019

Tutorial Sessions for SciPy Con 2019

alvinjamur/simdkalman

Python Kalman filters vectorized as Single Instruction, Multiple Data

alvinjamur/siml

Machine Learning algorithms implemented from scratch

alvinjamur/Stock-Price-Prediction-using-Keras-and-Recurrent-Neural-Networ

Stock Price Prediction case study using Keras

alvinjamur/timeseriescv

Scikit-learn style cross-validation classes for time series data

alvinjamur/Trading_And_Visualizations

All the code related to the article "An Easy Probabilistic Machine Learning Approach to Trading"