How to track bitcoin price with Python
warm tips:
1. According to the notice and announcement issued by the people's Bank of China and other departments, virtual currency is not issued by monetary authorities, does not have monetary attributes such as legal compensation and compulsion, is not a real currency, does not have the same legal status as currency, and cannot and should not be used as currency in the market, Citizens' investment and transaction of virtual currency are not protected by law
2. Before investing, it is recommended that you first understand the risks existing in the project, and clearly understand the investors, investment institutions, chain activity and other information of the project, rather than blindly investing or mistakenly entering the capital market
3. The above explanation is for reference only. Investors should not use such information to replace their independent judgment or make decisions only based on such information, which does not constitute any investment operation
response time: February 5, 2021. Please refer to the official website of Ping An Bank for the latest business changes
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so generally speaking, it's not very good-looking. According to the market, it's better in the long term, but dangerous in the short term
Print & quot; hello world!& quot;< br />print " hello world!& quot;;
the output result of the first line of code:
Hello world
the output result of the second line of code:
Hello world!
single stock price, multi stock portfolio and the market can all be learned by neural network. It was done in 2002. The average accuracy of price forecast can reach 54% to 57%, but it can only be qualitative, not quantitative. Therefore, it is not profitable after decting stamp ty
it is not possible to predict and guarantee the overall profit by using stock trading data only, neither can people
at present, the most advanced stock speculation machine in the world can only make use of the tiny time difference between European and American securities. The R & D cost of that system is tens of millions, and the cost of hardware (mainly independent optical cable) is hundreds of millions.
Obtaining data is an essential part of data analysis, and web crawler is one of the important channels to obtain data. In view of this, I picked up Python this sharp tool, opened the road of web crawler
the version used in this article is python3.5, which is intended to capture all A-share data of the day on the securities Star. The program is mainly divided into three parts: the acquisition of the source code, the extraction of the required content, and the collation of the results
One of the reasons why many people like to use Python crawler is that it is easy to use. Only the following lines of code can capture the source code of most web pages 
in order to rece the interference, I first use regular expressions to match the above main part from the source code of the whole page, and then match the information of each stock from the main part. The code is as follows
pattern=re.compile('& lt; tbody[sS]*</ tbody>&# 39;)
body = re. Findall (pattern, str (content)) # match & lt; Tbody and & lt/ tbody> All the codes between pattern = re. Compile (& # 39& gt;(.*?)& lt;&# 39;)< br />stock_ Page = re. Findall (pattern, body [0]) matching & gt; And & lt; The compile method is the compilation matching pattern, and the findall method uses this matching pattern to match the required information and returns it in the form of a list. Regular expression syntax is quite a lot, below I only list the meaning of the symbols used
syntax description
. Match any character except the new line character " n"
* match the previous character 0 times or infinite times
? Match the previous character 0 times or once
< s white space character: [& lt; Spaces & gt The corresponding position can be any character in the character set,
(...). The enclosed expression will be grouped, which is generally the content we need to extract. The syntax of regular expression is quite many. Maybe Daniel can extract the content I want to extract with just one regular expression. When extracting the main part of the stock code, it is found that someone uses XPath expression to extract the code, which is more concise. It seems that page parsing has a long way to go
The results are sorted out through the non greedy mode (. *?) Match & gt; And & lt; All data between will match out some white space characters, so we use the following code to remove the white space charactersstock_ last=stock_ total[:] #stock_ Total: for data in stock_ total: #stock_ Last: collated stock data
if data = = &&# 39;:< br />stock_ last.remove('&# 39;)
finally, we can print several columns of data to see the effect, and the code is as follows
Print (# 39; Code&# 39; t',&# 39; Abbreviation&# 39; &# 39;,&# 39; t',&# 39; Latest price&# 39; t',&# 39; Up and down&# 39; t',&# 39; Up and down amount&# 39; t',&# 39; 5 minutes up for i in range(0,len(stock_ There are 13 columns of data in the web page
Print (stock)_ last[i],' t', stock_ last[i+1],' &# 39;,&# 39; t', stock_ last[i+2],' &# 39;,&# 39; t', stock_ last[i+3],' &# 39;,&# 39; t', stock_ last[i+4],' &# 39;,&# 39; t', stock_ last[i+5])
There are many ways to capture the daily fund in Python ~
however, it is not clear what you want to capture
I wrote the simplest example: three lines of code can capture a table containing all open fund data
the code is as follows:
data = pandas.read_ html(' http://fund.eastmoney.com/fund.html#os_ 0; isall_ 1; ft_|; pt_ 1')< br />data[2].to_ csv(' Tiantian fund. CSV & (39;)
running result:
this should be the simplest magic code. The premise is to install panda, inspired by yqxmf.top