Wave is a new Open Source pythonic framework for build applications. It’s fast (written in Go) and let’s you focus only on your code without worrying about CSS, Javascript, and other HTML related stuff. I’m starting an H2O Wave App Tutorial post.

A comprehensive list of my Python Tutorials for SEO, plain work, and other fun stuff.

A Forex Trading Bot in Python using the Oanda API. Feel free to grab the free code and make derivatives from it.

For some simple things, Groovy and Python are very easy.

Get active in the markets! I mean lose actively in the markets…

Generating Forex Charts with the Open Source Bokeh charting library.

I’ve finally found a way to download SEC.GOV data in a consistent and less stressful way.

On the heels of my last post, I’ve extended those functions to the EURUSD pair. The data starts from this year 2019 and goes through to yesterday. It’s actually a pretty neat script as it takes data from Onada and then generates the support and resistance lines for that particular pair. The next step would be to create a buy/sell order in the Oanda Practice Account. Once I do that it’s then a matter of writing a trading strategy and testing it in real time.

What’s the different between Functional Programming and OOP for Python? I share my notes from this great talk.

Why most Twitter Bots suck terribly at what they are supposed to do. The entire social media gamification stinks to high hevean.

Continuing the stream of consciousness from my Working with Instgram API, JSONPath, and RapidMiner post, I started beta testing a new and improved Instagram Hashtag Tool. I’ve even opened it up to a few beta testers (ping me if you want to try it). It uses a RapidMiner Server on the backend to watch a Dropbox folder. Once you put a text file into the ‘In’ folder, it triggers a process and spits back a spreadsheet in the ‘Out’ folder.

Sign up for the Neural Market Trends Newsletter. Get valuable links and posts from the Data Science and Machine Learning sphere!

I wanted to see how long it would take me to transform a customer data set (i.e. ETL and generate new attributes) and then do a simple K-nn cross validation.