#100行代码，实现财富自由，一起加油

import urllib

import pandas as pd

import json

#爬虫获取 股票金融 =------------------数据爬虫------------------------------------#

def da(code,num):

url = 'http://vip.stock.finance.sina.com.cn/quotes_service/api/json_v2.php/MoneyFlow.ssl_qsfx_lscjfb?page=1&num=%s&sort=opendate&asc=0&daima=%s' %(str(num),code)

print(url)

data = urllib.request.urlopen(url).read().decode("gbk","ignore")

data = data.replace('opendate','"日期"')

data = data.replace('trade','"收盘价"')

data = data.replace('changeratio','"涨跌幅"')

data = data.replace('turnover','"换手率"')

data = data.replace('ratioamount','"净流入率"')

data = data.replace('netamount','"净流入万"') #净流入万，数据重复

data = data.replace('r0_net','"净超大单"')

data = data.replace('r1_net','"净大单"')

data = data.replace('r2_net','"净小单"')

data = data.replace('r3_net','"净散单"')

data = data.replace('r0','"超大单"')

data = data.replace('r1','"大单"')

data = data.replace('r2','"小单"')

data = data.replace('r3','"散单"')

hi = pd.read_json(json.dumps(json.loads(data)))

hi['日期'] = pd.to_datetime(hi['日期'],format='%Y-%m-%d')

hi = hi.sort_values('日期',ascending=True)

hi = hi[hi.收盘价>1]

hi['成交量'] = hi['散单']+hi['小单']+hi['超大单']+hi['大单']

hi['净主力'] = hi['净超大单'] +hi['净大单']

hi['净散户'] = hi['净小单'] +hi['净散单']

return hi

#爬虫获取 股票金融 =------------输入code，直接获取可视化---------------------------#

code = input('请输入股票代码:')

code = code.zfill(6)

if code[0] == '6':

code = 'sh' + code

else:

code = 'sz' + code

data = da(code,400)

#量化投资 股票金融 =------------计算各个均线--------------------------------------#

ind = ['换手率','成交量','收盘价','净流入万','净主力','涨跌幅','净散户']

ma_list = [5,10,20,30,60,120]

for k in ind:

for i in ma_list:

data[k+'_'+str(i)] = pd.rolling_mean(data[k],i)

data.to_csv('400.csv')

#股票数据可视化------------画图，计算可视化---------------------------------------#

import matplotlib.pyplot as plt

from matplotlib.font_manager import FontProperties

font = FontProperties(fname=r"c:\windows\fonts\simsun.ttc", size=12)

f1 = plt.figure(figsize=(20,15)) #创建一个画布

ax1 = f1.add_subplot(211)

ax2 = f1.add_subplot(212)

#股票数据可视化------------画图 收盘价 k线图-------------------------------------#

xd = data['日期'] #画图的 x 的值

#打印收盘价k线图

plt.sca(ax1)

plt.plot(xd,data['收盘价']) #画折线图

plt.plot(xd,data['收盘价_5']) #画折线图

plt.plot(xd,data['收盘价_20']) #画折线图

#plt.plot(xd,data['收盘价_30']) #画折线图

#plt.plot(xd,data['收盘价_60']) #画折线图

plt.xlabel("Date")

plt.ylabel("Price")

plt.title(code)

plt.legend(loc = 'best',prop=font)

#股票数据可视化------------交易策略的计算与显示---------------------------------#

res = data.loc[

(data['收盘价'] < data['收盘价_20']) &

(data['收盘价'] < 0.9*data['收盘价_120']) &

(data['成交量'] < data['成交量_30']) &

(data['换手率_10'] > data['换手率_30'])

]

#打印策略，买点的显示

for i in range(len(res)):

xp = res.iloc[i,11]

yp = res.iloc[i,9]

plt.annotate(u"Buy", xy = (xp, yp), xytext = (xp, yp+0.5),

arrowprops = dict(color = "red", headlength =2, headwidth = 6, width = 1.5))

#---------------------------打印 成交量 k线图-----------------------------------#

good = '净流入万'

plt.sca(ax2)

#plt.plot(xd,data[good]) #画折线图

plt.plot(xd,data[good+'_5']) #画折线图

plt.plot(xd,data[good+'_10']) #画折线图

plt.plot(xd,data[good+'_30']) #画折线图

plt.plot(xd,data[good+'_60']) #画折线图

plt.xlabel("Date")

plt.ylabel(good)

plt.title("K_line")

plt.legend(loc = 'best',prop=font)

plt.show()

# input("Press ")