"Quantifying small auditorium-python, pandas tips" how to get started quickly using Python for financial data analysis

How to quickly get started using Python for financial data analysis

Introduction:

This series of posts "quantitative small classroom", through practical cases to teach beginners to use Python, pandas for financial data processing, hope to be helpful to the big home.

" must -read article": "10 400 times-fold strategy sharing-video-line-guided code"

"All series article summary": http://bbs.pinggu.org/thread-3950124-1-1.html

The first step: curiosity

Don't learn a programming language, or any tool, to learn. Must be in the heart first has a problem, with the problem-solving mentality, to understand and learn how this tool is to solve the problem. It should be your curiosity to drive you to learn to quantify your investment. You think you have an exclusive tip for stocks, you think you've found a pattern, and you're curious to use historical data to validate your ideas.

For example, when I was a sophomore, I was exposed to quantitative investing because of my curiosity. At that time I saw some of the introductory technical Analysis book recommended KDJ This technical indicator, said KDJ low-level gold fork after the stock will rise, is a good buy signal, and the book will be equipped with some diagrams, to prove the effectiveness of this indicator. I was curious, was the book said to be true? Are these maps deliberately selected or representative? Is it possible to write a program to find out all the KDJ gold forks in history, and see how much the probability of a rise after that?

This is the initial curiosity that led me to my entry. At that time I could not program, at the beginning with Excel to try to verify, found that kdj from a large probability is not possible. Curiosity continues to escalate: I adjust the KDJ default parameters, the effect will be better? With other indicators, will the effect be better? Plus a bit of financial data, the effect will be better ...

Slowly want to test the idea of more and more, Excel gradually not enough to start learning programming. I have a strong purpose in learning programming, which is to solve my immediate problems. I will not learn to solve my problem without help. At the beginning of the use of SAS, look for their own books, the forum posted a post asked. Later, SAS was too heavy, inflexible, and slowly migrated to Python.

I am a financial professional, but the school does not teach quantitative investment, everything is self-study. Imagine, if no curiosity has led me to explore, how can I persist for such a long period of time?

Step two: Why Python

I recommend a new quantitative investment researcher who is just getting started using Python. The main reasons are as follows:

1. Applicability

Python works with a variety of third-party package (pandas, for example), and is a great fit for dealing with financial data

2. Simple

Python is a lot easier than c,c# and other languages. Allows you to test your ideas faster and more easily. Life was short, use Python.

3. Almighty

MATLAB is another dominant language in the field of financial analysis, the above two points of applicability, simple Matlab are available, in the industry's use should be higher than python.

Python's advantage over Matlab is that it's all-rounder. MATLAB can only be used for financial data analysis. But Python can do almost anything other than the Matrix Computing and scientific computing power of MATLAB. such as data cleanup, collation, such as fetching data from the Web page, such as text information mining, such as to make a website ... Now learn a language that can be used anywhere in the future.

Step three: How to get started with Python

If you have written experience in other languages (such as a programming lesson for a semester), there is a certain basis for programming. Here are three steps to get you started with Python:

1. Find a Pyhton introductory book. These tutorials online have many many, the forum also has many, casually searches is. I sort it out a little, put it in the attachment, and the reply is visible.

2. Pick a python primer and don't spend more than half a day reading this book quickly. This step does not want to remember anything, as long as the general know what the book said, what knowledge in the book is written in which chapter on the line for future inspection.

3. Combine their own curiosity, to find a problem for themselves, simple complex can be, find a bit of data (address), directly start the actual combat. Encounter problems, the first step is to go through the book, the second step is to Google (do not go to Baidu), the third step is forum post for help. If you do not have any ideas or problems, you can add group 438143420, I can provide you with ideas.

If you don't have any programming basics, then you want to get started with Python, which is also the three steps above. But the 2nd step, it is not just spend half a day to browse the book, but to read carefully. In the case of the book, the actual operation, the time spent about one weeks of spare time is enough.

Fourth step: How to get Started pandas

Using Python for financial data analysis, be sure to use pandas. Pandas is a third-party library of Python, which is an artifact of financial data analysis, and the first time I met it, it made me burst into tears. The best way to learn about pandas is his official document: Http://pandas.pydata.org/pandas-docs/stable/10min.html, and of course the series of articles I wrote earlier.

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"Quantifying small auditorium-python, pandas tips" how to get started quickly using Python for financial data analysis