Resources for developers using Python for scientific computing and quantitative analysis
In this video kaggle grandmaster Rob Mulla takes you through an economic data analysis project with python pandas. We walk through the process of pulling down the data for different economic indicators, cleaning and joining the data. Using the Fred api you can pull up to date data and compare, analyze and explore.
Datasets from real-world scenarios are important for building and testing machine learning models. You may just want to have some data to experiment with an algorithm. You may also want to evaluate your model by setting up a benchmark or determining its weaknesses using different sets of data. Sometimes, you may also want to create synthetic datasets, where you can test your algorithms under controlled conditions by adding noise, correlations, or redundant information to the data.
Recent trends in the modern web saw single page frameworks like React.js and Angular take over traditional multipage websites, mainly due to the lack of interactivity offered by HTML. It is worth noting however, that single page applications (SPAs) brought this interactivity at the cost of added complexity.
Blankly is a live trading engine, backtest runner and development framework wrapped into one powerful open source package. Models can be instantly backtested, paper traded, sandbox tested and run live by simply changing a single line. We built blankly for every type of quant including training & running ML models in the same environment, cross-exchange/cross-symbol arbitrage, and even long/short positions on stocks (all with built-in websockets).
In this blog, we’ll review how we took a raw .ipynb notebook that does time series forecasting with Arima, modularized it into a Ploomber pipeline, and ran parallel jobs on Slurm. You can follow the steps in this guide to deploy it yourself. We’ve been using this notebook by Willie Wheeler.
Our communications, both verbal and written, carry rich information. Even beyond what we are conveying explicitly, our tone, the selection of words add layers of meaning to the communication. As humans, we can understand these nuances, and often predict behavior using the information.
The amount of data available on the internet is quite staggering. It is often quite easy to do a quick search and click through to view data on a website. However, if you want to actually use that data in your analysis, you have to be able to fetch it and convert it into a format that is usable.
In this video Rob Mulla teaches how to make your pandas dataframes more efficient by casting dtypes correctly. This will make your code faster, use less memory and smaller when saving to disk or a database.
