I've been involved in algorithmic trading for over seven years and in that time I've seen some big trading mistakes.
After a lot of trial and error, I eventually discovered that hard work, discipline and a scientific approach are the key to profitability with quantitative trading.
In Successful Algorithmic Trading I'll teach you a process to identify profitable strategies from the outset, backtest them, reduce your transaction costs and efficiently execute your trades in a fully automated manner.
No matter how far along you are in your quantitative trading career, you can apply these ideas to make a profitable algorithmic trading business.
Despite all these benefits I wouldn’t want you to get the wrong idea and think developing an algorithmic trading system is easy. Nothing could be further from the truth. There is no path to easy riches with algo trading.
However, if you break down the problem, into small easy-to-handle constituent parts and make consistent progress on improving your system every day it can eventually become very successful.
At the beginning it is a struggle to make money consistently with trading.
Now I've built up the habit of creating a strategy pipeline which constantly provides me with new trading strategy ideas with which to test. It doesn't matter if a strategy begins to perform poorly because I have plenty more to choose from - and so will you.
Slow consistent progress on research, testing and execution is the key to achieving algorithmic trading profitability.
Make a commitment to work hard on your strategy components, with a disciplined approach, and you will see success much sooner than you expect.
Actually, neither was I when I first started! I didn't know market orders from limit orders, the buy-side from sell-side or what a stop loss was! But I have practised over the last seven years and have learned a huge amount about algorithmic trading in the process.
It is well within your capability to learn what I know about quant finance and trading. I'm certainly not the top of my field, but I have been involved in the development of profitable trading strategies and am extremely keen to show you how to do the same.
I imagine that there is a topic you know a great deal about and I bet there are many who know less about the area than you do. Being an expert comes through practice, discipline and hard work. So does forming a consistent set of profitable algorithmic trading strategies.
Every successful person I know in algorithmic trading started before they knew much about the markets.
Use the fact that you aren't yet comfortable with algorithmic trading to push yourself harder and learn to become an expert.
You'll learn how to find new trading strategy ideas and objectively assess them for your portfolio.
I'll teach you how to create a robust securities master database to store all of your asset pricing information.
We will apply the scientific method to rigourously backtest our strategy ideas before we consider trading them.
Our strategies will be tested extensively against industry-grade performance measures.
We will utilise time series statistical methods to test for mean reversion and momentum.
I'll discuss profitable mean-reverting strategy templates for equities and futures - which you can trade yourself.
You'll learn about investment grade risk management techniques such as Variance-at-Risk (VaR)
We will extensively discuss position sizing and money management techniques such as the Kelly Criterion.
We will create and deploy a robust automated execution system based on our trading portfolio system.
You will be introduced to the Python scientific toolset, which is used heavily in quantitative trading. We will make use of NumPy, SciPy, pandas, scikit-learn and IPython.
You will learn how to obtain financial data from both free and paid sources. We will tackle equities and futures data, by cleaning it and creating continuous futures contracts.
You will learn how to backtest strategy performance using pandas and calculate quantities such as the Sharpe Ratio, max drawdown, drawdown duration and avg win/loss.
You will learn how to mathematically optimise a strategy using parameter sensitivity analysis and visually inspect the results. For this we will use pandas and matplotlib with IPython.
You will learn about predictive classifiers and intraday equities pair-trading. We'll use scikit-learn to perform regression, random forest ensembles and non-linear SVM.
You will connect to the Interactive Brokers API with Python to trade. You'll calculate realistic transaction costs, accounting for them in your performance metrics.
We have written over 200 posts on QuantStart.com covering quant trading, quant careers, quant development, data science and machine learning. You can read through the archives to learn more about our trading methodology and strategies.
While we think you will find Successful Algorithmic Trading very useful in your quantitative trading education, we also believe that if you are not 100% satisfied with the book for any reason you can return it no questions asked for a full refund.
No. At this stage the book is only available in Adobe PDF format, while the code itself is provided as a zip file of fully functional Python scripts, if you purchase the "Book + Software" option.
This mostly depends on your budget. The book with full extra source code is the best if you want to dig into the code immediately, but the book itself contains a huge amount of code snippets that will aid your quant trading process.
Of course! If you still have questions after reading this page please get in touch and we will do our best to provide you with a necessary answer. However, please take a look at the articles list, which may also help you.
The majority of the book can be followed quite easily without reference to difficult mathematics. However, the sections on forecasting and time series analysis require some basic calculus and linear algebra.