Python Tutorial : Data transforms, features, and targets

Want to learn more? Take the full course at https://learn.datacamp.com/courses/machine-learning-for-finance-in-python at your own pace. More than a video, you'll learn hands-on coding & quickly apply skills to your daily work. --- Now that we're familiar with our data, we need to get it prepared for machine learning. To prepare our data, we'll need features and targets. Our features are inputs we predict future price changes with -- the 10-day price change and volume. Our targets are the future price changes. Generally, we can use pandas DataFrames and Series in our machine learning algorithms. It's useful to incorporate historical data as features; for example, the price changes in the last 200 days. Instead of including all previous 200 days' price changes, we can concentrate past data into a single point using technical indicators like the moving average shown here. A moving average is the average of value in the past n days. Classic moving average periods are 14, 50, and 200 days for stocks. Here's a plot showing the AMD price and a 200-day simple moving average, or SMA. You can see moving averages smooth the data. The other indicator we'll use is relative strength index, or RSI. This oscillates between 0 and 100. When it's close to 0, this may mean the price is due to rebound from recent lows. When RSI is close to 100, this may mean the price of the stock is due to decline. The equation for RSI is 100 minus 100 over 1 + relative strength. Relative strength is the average gain of price increases divided by the average loss of price decreases during the time period, n. Both RSI and moving averages can be calculated with the TA-lib package. This Python library is a wrapper for C code, meaning we can run C code using Python. To use TA-lib's functions for RSI and moving averages, we provide a numpy array of prices and the argument time period. This is the value of n mentioned in the previous slides. We're using 200 for time period, and adding the new f