蜗牛之路

一、获取国内股票代码

import tushare
import pandas
import datetime


tickersRawDate = tushare.get_stock_basics()
#日期是索引，所以这里是index.tolist
tickers = tickersRawDate.index.tolist()
print(tickers)

备注：使用to_CSV的话，使用excel打开会乱码，用sublime打开就不会了。

效果展示：

备注：如何在sublime中执行python
点击右下角的一个图标，在弹出的菜单中选择“console”，然后按“Ctrl+B”

二、获取国外股票代码

import requests
import pandas
import io
import datetime
import os
import time

def dataframeFromUrl(url):
	dataString = requests.get(url).content
	parsedResult = pandas.read_csv(io.StringIO(dataString.decode('utf-8')), index_col=0)
	return parsedResult

def stockPriceIntraday(ticker, folder):
	# Step 1. Get data online
	url = 'https://www.alphavantage.co/query?apikey=NGV2US07ZZQULVGP&function=TIME_SERIES_INTRADAY&symbol={ticker}&interval=1min&outputsize=full&datatype=csv'.format(ticker=ticker)
	intraday = dataframeFromUrl(url)

	# Step 2. Append if history exists
	file = folder+'/'+ticker+'.csv'
	if os.path.exists(file):
		history = pandas.read_csv(file, index_col=0)   #将第0行，第0列作为它的索引列。
		intraday.append(history)

	# Step 3. Inverse based on index
	intraday.sort_index(inplace=True)   #sort_index是指按index建立索引
        #intraday.index.name = 'timestamp'  #修改index的name

	# Step 4. Save
	intraday.to_csv(file)
	print ('Intraday for ['+ticker+'] got.')

# Step 1. Get ticker list online
tickersRawData = dataframeFromUrl('https://www.nasdaq.com/screening/companies-by-industry.aspx?exchange=NASDAQ&&render=download')
tickers = tickersRawData.index.tolist()

# Step 2. Save the ticker list to a local file
dateToday = datetime.datetime.today().strftime('%Y%m%d')
file = '../02. Data/00. TickerListUS/TickerList'+dateToday+'.csv'
tickersRawData.to_csv(file)
print ('Tickers saved.')

# Step 3. Get stock price (intraday)
for i, ticker in enumerate(tickers):
	try:
		print ('Intraday', i, '/', len(tickers))
		stockPriceIntraday(ticker, folder='../02. Data/01. IntradayUS')
		time.sleep(2)
	except:
		pass
print ('Intraday for all stocks got.')

地址：https://www.bilibili.com/video/av24528809/?p=4

https://www.bilibili.com/video/avPZ6BY1AWHTPGAIZL

三、生成散点图、K线图

import pandas
import matplotlib
import mpl_finance
import matplotlib.pyplot as plt

matplotlib.style.use('ggplot')
#matplotlib.style.use('dark_background')

def stockPricePlot(ticker):
	# Step 1. Load Data
	history = pandas.read_csv('../02. Data/01. IntradayUS/'+ticker+'.csv', parse_dates=True, index_col=0)

	# Step 2. Data Manipulation
	close = history['close']     #这里close会自动带上timestamp的索引。
	close = close.reset_index()  #增加一个额外的索引列，从0开始
	#matplot对时间要求很高，要求转化成它内部的一个整数型数组（映射到matlib的整数域）
	close['timestamp'] = close['timestamp'].map(matplotlib.dates.date2num)  

	ohlc = history[['open', 'high', 'low', 'close']].resample('1H').ohlc()  #按1小时重新采样
	ohlc = ohlc.reset_index()
	ohlc['timestamp'] = ohlc['timestamp'].map(matplotlib.dates.date2num)

	# Step 3. Plot Figures. Subplot 1: scatter plot. Subplot 2: candle stick plot.
	# Step 3.1. Subplot 1: scatter plot
	#总图表2行一列，这个散点图在0行0列开始，占据一行，一列
	subplot1 = plt.subplot2grid((2,1), (0,0), rowspan=1, colspan=1)   
	subplot1.xaxis_date()  #为了让横轴设置成时间
	subplot1.plot(close['timestamp'], close['close'], 'b.') #分别是x轴、y轴、蓝色
	plt.title(ticker)  #图的标题

	# Step 3.2. Subplot 2: candle stick plot
	subplot2 = plt.subplot2grid((2,1), (1,0), rowspan=1, colspan=1, sharex=subplot1)  #sharex=subplot1表示和subplot1共享X轴
	#ax为画图的位置，quotes是数据
	mpl_finance.candlestick_ohlc(ax=subplot2, quotes=ohlc.values, width=0.01, colorup='g', colordown='r')
	plt.show()

stockPricePlot('AAON')

成果展示：