Click the website of the US Treasury Department and I’ll catch it with Python!


The night before last, a friend of Xiaobian’s analyst whispered that the website of the US Treasury Department had been finalized at last.

What route is this?

Are you in the current situation?Be brave enough to destroy? Xiaobian’s face was frightened.


Don’t be afraid. I wrote a crawler with Python and crawled down the data from the Treasury website.

This friend is a bond futures analyst who needs the latest US debt data for his daily work, but the data on Windows has a delay of 1-2 days and can only be queried on the Internet by human flesh every day. But there are a lot of data. Every day, it’s very hard.

That day, I finally couldn’t bear such complicated and boring work. I wrote a crawler in Python, which could crawl down the data of 200 days in one breath.

It must have taken a lot of time to implement such a powerful procedure, even the US Finance Website.

No, the whole program is only 20 lines. It only took me 30 minutes to finish it!


Xiao Editor can’t help feeling that in 2019, Python has really become a hard skill for financial analysts, and is no longer exclusive to programmers, so the future of Python’s financial analysts is fearful.

In fact, as data analysts and auditors who deal with Excel every day, the most headache is too much repetitive work. If you have a two-month transaction data in front of you and each data is divided into an Excel file, then you have to open, copy, paste and process the data manually. Many times, in the end, we often have to wonder about life.

Python, a trendsetter in the era of artificial intelligence, can easily solve this kind of problem. It takes only a few lines of code to complete Excel’s work in a few hours. Moreover, the use of Python’s powerful web page processing capabilities for crawling can provide us with a lot of convenience for data research.

Whether we want to query the stock of a bond or the Fed interest rate data on the Treasury website, it only takes a Python crawler to solve this problem in 10 minutes. Previously, these jobs often required an intern or even a week’s work time.

Python’s powerful drawing function enables data import, analysis, output and drawing to be completed in a single program, which can directly visualize the results of analysis/retest.


(Python automatically generated dynamic charts)

Because of this, many domestic financial institutions have added Python capability requirements to their recruitment. CICC, Galaxy Securities, Nanfang Fund and Yinhua Fund have specially required proficiency in Python data analysis skills when recruiting analysts.

We also extract the skills requirements of some key hot positions in the financial industry from major recruitment websites.

Hot positions in the financial industry


Skills Requirements

Industry analysts


Familiar with industry structure, business model and data dynamics, skilled use of Wind, Excel and other research data analysis tools, familiar with Python capabilities priority

Macroscopic Researcher


Strong ability to collect information, skilled in using Wind and other financial databases, skilled in basic data analysis, familiar with Python priority

Quantitative Researcher

Face to face

Familiar with Python, Pandas/numpy/statsmodels and database related knowledge; have a certain understanding of commercial notes, treasury bond futures, convertible bonds, etc.

The auditor


Ability to use Python skillfully to process business audit manuscripts and basic financial statements is preferred

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