Resources for developers using Python for scientific computing and quantitative analysis
MlFinlab is a python package which helps portfolio managers and traders who want to leverage the power of machine learning by providing reproducible, interpretable, and easy to use tools. Adding MlFinLab to your companies pipeline is like adding a department of PhD researchers to your team.
The price of energy changes hourly, which opens up the possibility of temporal arbitrage: buying energy at a low price, storing it, and selling it later at a higher price. To successfully execute any temporal arbitrage strategy, some amount of confidence in future prices is required, to be able to expect to make a profit. In the case of energy arbitrage, the constraints of the energy storage system must also be considered. For example, batteries have limited capacity, limited rate of charging, and are not 100% efficient in that not all of the energy used to charge a battery will be available later for discharge.
This series explains concepts that are fundamental to deep learning and artificial neural networks for beginners. In addition to covering these concepts, we also show how to implement some of the concepts in code using Keras, a neural network API written in Python. We will learn about layers in an artificial neural network, activation functions, backpropagation, convolutional neural networks (CNNs), data augmentation, transfer learning and much more!
In this post we will explore how to download fundamentals data with Python. We’ll be extracting fundamentals data from Yahoo Finance using the yahoo_fin package. For more on yahoo_fin, including installation instructions, check out its full documentation here.
Exchange rates API is a simple and lightweight free service for current and historical foreign exchange rates.
In this post we will discuss how to do a time series modelling using ARMA and ARIMA models. Here AR stands for Auto-Regressive and MA stands for Moving Average
