Quantitative Trading with Python

We discuss the strategy and guidelines to make the most of this course. We walk through over the concept of stocks and trading.

In this video, we'll be covering the technical requirements to set up the workspace for the entire course. We'll go through: - Python version - Creating a virtual environment which will have all the packages required - Jupyter notebook - How to write python code in Jupyter notebook

Without wasting much time, let's get started with the collection of data. Getting your hands on the right dataset is very important. In this video, we're going to learn about Quandl and how can we use the Quandl API using Python to get Apple's EOD stock pricing data. We'll cover the introduction to pandas and how you can save the data to a CSV file.

Throughout the course, we'll be using data manipulation package, pandas along with NumPy, SciPy, Matplotlib, and a few others. In this lecture, we'll mainly focus on dealing with time series data, what each column in the dataframe represents and means. We'll cover slicing, subsetting and resampling techniques using pandas.

Here is the first assignment where you've to use the Quandl API to extract MSFT(Microsoft) EOD stock pricing data from 1st Jan, 2011 to 1st Jan, 2019.

The lecture would cover the common financial analyses which every quant trader should learn. We'll cover various methods of calculating returns. Here are the python functions we'll use: - pct_change() - resample() - np.log - for log returns - Plot the returns using plot() function.

The lecture will walk you through the importance of cumulative returns for different time windows(daily, monthly, quarterly, yearly). We'll learn to capture data from pandas_datareader package which uses yahoo_finance. Compare the returns of a range of stocks extracted from the pdr package.

In this lecture, we're going to learn about moving window statistics: - How to calculate the rolling mean, rolling variance, rolling median, etc. - How moving window statistics eliminates the fluctuations and spikes in the data. - Python functions: rolling_mean(), rolling_std(), etc

In this lecture, we're going to learn about risk assessment using volatility: - We'll calculate the variance of a stock over a period of time. - Understanding the volatility of a stock compared with other stocks and assessing how risky is the investment. - Formulating historical volatility on log returns.

The lecture covers the concept of regression analysis using the statsmodel package. We are using the Ordinary Least-Squares Regression model to understand the pattern of the dependent variable(log returns of stocks) and the independent variable. - Learn about the goodness of fit. - Probability using F-statistics - How to use t-tests. - The skewness of the curve.

Using the data collected in the previous quiz, you have to perform a few tasks and answer the following questions.

The lecture covers the basics of trading strategies and common algorithms used to trade securities. In general, there are two common trading strategies: the momentum strategy and the reversion strategy. You'll learn about various types of algorithms you can develop in each of them.

A step-by-step guide to formulating the Moving average crossover algorithm. - Translate each step into code in Jupyter notebook. - Learn about the implementation of rolling statistics in SMA.

Learn about the matplotlib's plotting methods, we'll: - plot the short, long moving averages - mark the buy and sell signals in the data. - learn about adding conditions in the plot function

In this lecture, we'll cover: - The basic definition and importance of backtesting. - Pitfalls of backtesting and factors you need to take care of while designing the backtesting framework. - Essential components of backtesting

We'll be moving forward to developing a simple backtester consisting of a strategy, a data handler, and a portfolio. - We'll be following a set of instructions and translate each of them to pythonic statements using pandas dataframe. - We'll generate the backtester which will track all our expenses, total funds, P&L, etc.

Learn about the matplotlib's plotting methods, we'll: - plot the portfolio's total column and visualise our holdings - introduction to quantopian

In this lecture, we're going to learn about the free, community-centered platform called Quantopian: - Advantages of backtesting on quantopian. - How to write and backtest an algorithm on quantopian - Learn about the backtesting results

This lecture is going to cover the entire process of backtesting the strategy that we developed on quantopian. We'll see the entire dashboard of results of the backtesting. Learn about different measures on the dashboard and what they represent.

After all the backtesting, you'll need to evaluate the strategy, here you'll learn about: - Sharpe ratio - Maximum drawdown - Compound annual growth rate (CAGR) You'll code these in python using statsmodel, pandas and run it over your strategy's output.

This is the last video in which we'll look at the next steps after the course, prospective projects you can take up to master the skill and links to resources and books to subscribe to. This is a defined list of resources which can help you get more clarity on quantitative trading algorithms and concepts.