MACHINE-LEARNING-FOR-TRADING Part 1 00-00 Introduction 01-01 Reading and Plotting Stock Data Read CSV - print last 5 lines Compute mean volume Plot High prices for IBM 01-02 Working with Multiple Stocks Utility Functions for Reading Data Plotting multiple stocks Slice and Plot two Stocks Normalizing data - FULL LESSON CODE 01-03 The Power of Numpy Creating NumPy arrays Arrays with initial values Generating random numbers Array attributes Operations on ndarrays Locate maximum value Timing Functions how Fast is Numpy? 01-04 Statistical analysis of time series Compute global statistics Rolling Statistics Bollinger Bands Calculate Daily Returns 01-05 Incomplete Data Pandas fillna Using fillna() FIll Missing Data 01-06 Histograms and Scatterplots Plot a Histogram Computing Histogram Statistics Plot two Histograms together Scatterplots in Python 01-07 Sharpe ratio and other portfolio statistics Calculating the sharp ratio 001-08 Optimisers: Building a parameterised model Minimizer in Python Fit a line to given data points And it works for polynomials too! Python for Finance Chapter 8 Data Import Summary Statistics Changes over time Resampling Rolling Statistics - overview A technical Analysis Correlation analysis Logrithmic Returns