Gilloliver's repositories
pandas-datareader
Extract data from a wide range of Internet sources into a pandas DataFrame.
awesome-python
A curated list of awesome Python frameworks, libraries, software and resources
Awesome-Quant-Machine-Learning-Trading
Quant/Algorithm trading resources with an emphasis on Machine Learning
backtrader
Python Backtesting library for trading strategies
binance-spot-api-docs
Official Documentation for the Binance Spot APIs and Streams
dados-covid-sp
RepositĂłrio de dados sobre SARS-COV-2 em municĂpios paulistas
DataCamp
DataCamp data-science courses
Estrategias-Trading
En este repositorio quedan alojadas las dos estrategias de mi video de trading en Python
ffn
ffn - a financial function library for Python
financial-machine-learning
A curated list of practical financial machine learning (FinML) tools and applications in Python.
finta
Common financial technical indicators implemented in Pandas.
freqtrade
Free, open source crypto trading bot
Jogo-de-xadrez-curso-udemy
Parte pratica do desenvolvimento de um jogo de xadrez usando C# e POO
La-Alquimia
Repositorio dedicado a la Criptomoneda, y el canal La Alquimia
LotteryPrediction
:full_moon_with_face: Lottery prediction using GA+BF ANN+FL(GeneticAlogrithm+ArtificalNeuralNetwork+FuzzyLogicControl) based on SciPy/NumPy/matplotlib/R/SAS/TensorFlow/Keras
matplotlib
matplotlib: plotting with Python
nCov2019_analysis
Analysis of 2019-nCov coronavirus data
pandas-ta
Technical Analysis Indicators - Pandas TA is an easy to use Python 3 Pandas Extension with 130+ Indicators
pivottabler
Create Pivot Tables natively in R
profitchart-estrategias
Comunidade de Estratégias para ProfitChart (Nelogica)
pyfolio
Portfolio and risk analytics in Python
pyod
A Python Toolbox for Scalable Outlier Detection (Anomaly Detection)
Python-Artificial-Intelligence-Projects-for-Beginners
Python Artificial Intelligence Projects for Beginners, published by Packt
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.
ta
Technical Analysis Library using Pandas and Numpy
teste
este e o repositorio do curso de Data science A a Z