Python for Finance by Yves Hilpisch is an ambitious, reasonably priced book published by a reputable publishing house on the topic of Algorithmic Trading with Python. I think it provides an excellent starting point and ideas for people trying to get a grip on what it would take to set up an algorithmic trading infrastructure.
The book caters to intermediate and advanced level students, which means you would need at least some coding ability and a decent understanding of financial markets to be able to benefit from this book. Many topics touched upon merit a book on their own, therefore the text is practically brief with the intention of introducing the user to key concepts and further related resources. I especially like the way the author touches upon the theory without getting carried away, keeping the right balance between telling the reader what is happening without theoretical overload, all the while providing working Python examples. Most of the code used in the book can be found in its accompanying website.
Now for a detailed book review/ my advice on how to get the best out of it:
The book caters to intermediate and advanced level students, which means you would need at least some coding ability and a decent understanding of financial markets to be able to benefit from this book. Many topics touched upon merit a book on their own, therefore the text is practically brief with the intention of introducing the user to key concepts and further related resources. I especially like the way the author touches upon the theory without getting carried away, keeping the right balance between telling the reader what is happening without theoretical overload, all the while providing working Python examples. Most of the code used in the book can be found in its accompanying website.
Now for a detailed book review/ my advice on how to get the best out of it:
- If you have experience with Python, you can skim through the first six chapters. Second chapter has some interesting information regarding infrastructure, but it is highly unlikely to be applicable to a single man retail trader army when you are starting up. At certain point you may need to host the development on cloud, for which learning to deploy Jupyter notebook server should suffice. If you are not familiar with Python object model, worth reading the summary in the end of Chapter 6
- Chapter 7 to 13 digs into many tools and techniques that you may need as a quant. Even if you are an experienced quant, the chapters are still worth a quick read, even if only to get to ideas/validation on efficient Pythonic implementations of some useful mathematical tools. If your algorithms are going to be relatively simple and will not delve into complex derivatives, portfolio management or machine learning, you can more or less skip chapters 11, 12 and 13.
- Chapter 14 to 16 is where, so to say, the tyre meets the road. For me, this is the highlight of the book as it details how the get tick data and trade using Python in the real world and backtest/ develop your strategy
- I did not read chapter 17 onwards as I do not plan to do deal with trading and valuation of complex derivatives
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