We’ll be using a CLI for various tasks throughout this book. The command-line interface #Ī command-line interface (CLI) is a text-based interface used to interact with your computer. Exploring mean reversion and cointegration with Zorro and R: part 1 13.If you intend to follow along with the code presented in this book, we recommend you follow these setup instructions so that you will run into fewer technical issues.Exploring Mean Reversion and Cointegration: Part 2 14.1k views.Time Series Analysis: Fitting ARIMA/GARCH predictions profitable for FX? 14.5k views.Deep Learning for Trading Part 1: Can it Work? 16.6k views.Demystifying the Hurst Exponent – Part 2 16.7k views.Dual Momentum Investing: A Quant’s Review 17.1k views.Hurst Exponent for Algorithmic Trading 17.4k views.How to Run Trading Algorithms on Google Cloud Platform in 6 Easy Steps 20k views.How to Connect Google Colab to a Local Jupyter Runtime 40.4k viewsĪdventures in Feature Selection 30k views.Any objects created within the Python session are available in the R session via the py object. You can also open an interactive Python session within R by calling reticulate::repl_python(). Notice that my numpy array is created using R list objects in a manner analogous to Python lists: np.array(, ]). Importing Python modules with reticulate::import() produces the same behaviour: np <- import("numpy") Notice that to use the def from the Python session embedded in my R session, I had to ask for it using py$object_name – this is different than if I sourced a Python file directly, in which case the Python function becomes available directly in the R session (ie I don’t need py$). # 10 ICE Intercontinental Exchange Inc 2.18% I now have the get_holdings function in my R session, and can call it as if it were an R function attached to the py object that reticulate creates to hold the Python session: library(tidyverse) I have a Python script, download_spdr_holdings.py for scraping ETF constituents from the SPDR website: """download ETF holdings to csv file"""ĭf = pd.read_csv(url, skiprows=1).to_csv(f': import pandas as pdĭf = pd.read_csv(url, skiprows=1, usecols=) It’s trivial and we could replace this Python script with R code in no time at all, but I’m sure you have more complex Python scripts that you don’t feel like re-writing in R… Scraping ETF Constituents with Python from R Studio Ability to bind to different Python environmentsįor me, the main benefit of reticulate is streamlining my workflow.Direct object translation (eg pandas.DataFrame– ame, numpy.array– matrix etc).embedding Python code in an R Markdown document.using Python interactively in an R session.
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