# pip install akshare 

import akshare as ak

def get_k_line(code="sz002241"): # 

    df = ak.stock_zh_a_daily(symbol=code, start_date="20220101", end_date="20220515",

                                                          adjust="qfq")

    # 这个函数只适合取股票数据，转债会报错，转债的日线数据可以使用其他函数，这里选择股价前复权

    df.to_excel("同和药业k.xlsx")

date：交易日期

open：代表开盘价

high：当天最高价

low：当天最低价

close：当天收盘价

volume：当天成交量（元）

outstanding_share：流动股本(股)

turnover：换手率

import mpl_finance as mpf

import matplotlib.pyplot as plt

import pandas as pd



#创建绘图的基本参数

fig=plt.figure(figsize=(12, 8))

ax=fig.add_subplot(111)



#获取刚才的股票数据

df = pd.read_excel("同和药业k.xlsx")

mpf.candlestick2_ochl(ax, df["open"], df["close"], df["high"], df["low"], width=0.6, colorup='r',colordown='green',alpha=1.0)

#显示出来

plt.show()

import matplotlib.ticker as ticker

#将股票时间转换为标准时间，不带时分秒的数据

def date_to_num(dates):

    num_time = []

    for date in dates:

        date_time = datetime.strptime(date, '%Y-%m-%d')

        num_date = date2num(date_time)

        num_time.append(num_date)

    return num_time



#创建绘图的基本参数

fig=plt.figure(figsize=(12, 8))

ax=fig.add_subplot(111)



#获取刚才的股票数据

df = pd.read_excel("同和药业k.xlsx")

mpf.candlestick2_ochl(ax, df["open"], df["close"], df["high"], df["low"], width=0.6, colorup='r',colordown='green',alpha=1.0)

df['date'] = pd.to_datetime(df['date'])

df['date'] = df['date'].apply(lambda x: x.strftime('%Y-%m-%d'))

def format_date(x, pos=None):

    if x < 0 or x > len(df['date']) - 1:

        return ''

    return df['date'][int(x)]

ax.xaxis.set_major_formatter(ticker.FuncFormatter(format_date))

plt.setp(plt.gca().get_xticklabels(), rotation=45, horizontalalignment='right')

#显示出来

plt.show()

df["SMA5"] = df["close"].rolling(5).mean()

df["SMA10"] = df["close"].rolling(10).mean()

df["SMA30"] = df["close"].rolling(30).mean()

ax.plot(np.arange(0, len(df)), df['SMA5'])  # 绘制5日均线

ax.plot(np.arange(0, len(df)), df['SMA10'])  # 绘制10日均线

ax.plot(np.arange(0, len(df)), df['SMA30'])  # 绘制30日均线

import mpl_finance as mpf

import matplotlib.pyplot as plt

import pandas as pd

import matplotlib.ticker as ticker

import numpy as np

#创建绘图的基本参数

fig, axes = plt.subplots(2, 1, sharex=True, figsize=(15, 10))

ax1, ax2 = axes.flatten()



#获取刚才的股票数据

df = pd.read_excel("同和药业k.xlsx")

mpf.candlestick2_ochl(ax1, df["open"], df["close"], df["high"], df["low"], width=0.6, colorup='r',colordown='green',alpha=1.0)

df['date'] = pd.to_datetime(df['date'])

df['date'] = df['date'].apply(lambda x: x.strftime('%Y-%m-%d'))

def format_date(x, pos=None):

    if x < 0 or x > len(df['date']) - 1:

        return ''

    return df['date'][int(x)]

df["SMA5"] = df["close"].rolling(5).mean()

df["SMA10"] = df["close"].rolling(10).mean()

df["SMA30"] = df["close"].rolling(30).mean()

ax1.plot(np.arange(0, len(df)), df['SMA5'])  # 绘制5日均线

ax1.plot(np.arange(0, len(df)), df['SMA10'])  # 绘制10日均线

ax1.plot(np.arange(0, len(df)), df['SMA30'])  # 绘制30日均线

ax1.xaxis.set_major_formatter(ticker.FuncFormatter(format_date))

plt.setp(plt.gca().get_xticklabels(), rotation=45, horizontalalignment='right')

#显示出来

plt.show()

red_pred = np.where(df["close"] > df["open"], df["volume"], 0)

blue_pred = np.where(df["close"] < df["open"], df["volume"], 0)

ax2.bar(np.arange(0, len(df)), red_pred, facecolor="red")

ax2.bar(np.arange(0, len(df)), blue_pred, facecolor="blue")

def long_up_shadow(o,c,h,l):

    return True if (h-c)/c >=0.07 and (h-o)/o>=0.07 else False

df = pd.read_excel("同和药业k.xlsx")

count_num = []

for row,item in df.iterrows():

    if long_up_shadow(item['open'],item['close'],item['high'],item['low']):

        count_num.append(row)



plot_image(df,count_num)

df['tow_crows'] = talib.CDL2CROWS(df['open'].values, df['high'].values, df['low'].values, df['close'].values)



pattern = df[(df['tow_crows'] == 100) | (df['tow_crows'] == -100)]

def hit_limit_recent():

    # 选出最近N天正股有涨停的可转债

    N =10

    scale = 5

    latest_price = 160

    bond_name_dict, bond_zg_dict = get_all_bond_info(scale=scale,latest_price=latest_price)

    zg_list = list(bond_zg_dict.values())

    panel_info = get_history(N, frequency='1d', field=['close','high_limit'], security_list=zg_list, fq='pre', include=False, fill='nan')

    df = panel_info.swapaxes("minor_axis", "items")

    target_list = []

    for code in zg_list:

        stock_df = df[code]



        hit_high = stock_df[stock_df['close']==stock_df['high_limit']]

        if len(hit_high) > 0:

            # print(hit_high.index)

            target_list.append(code)



    zz_target_list = []

    for code,zg_code in bond_zg_dict.items():

        if zg_code in target_list:

            print(code, bond_name_dict[code])

            zz_target_list.append(code)



    return zz_target_list

get_sort_msg(sort_type_grp=None, sort_field_name=None, sort_type=1, data_count=100)

import datetime

START_TIME = (datetime.datetime.now() + datetime.timedelta(minutes=1)).strftime('%H:%M')





def execution(context):

    #获取XBHS.GN的概念排名信息

    sort_data = get_sort_msg(sort_type_grp='XBHS.GN', sort_field_name='px_change_rate', sort_type=1, data_count=100)



    for data in sort_data:

        log.info('板块： {} '.format(data['prod_name']))

        for sub_stock in data['rise_first_grp']:

            log.info('{} 涨幅 ：{}'.format(sub_stock['prod_name'],sub_stock['px_change_rate']))

        

        log.info('\n')





def initialize(context):

    # 初始化策略

    run_daily(context, execution, time=START_TIME)  # 扫描

    log.info("公众号：可转债量化分析\n")



def handle_data(context, data):

    pass

prod_code: 行业代码(str:str)；

prod_name: 行业名称(str:str)；

hq_type_code: 行业板块代码(str:str)；

time_stamp: 时间戳毫秒级(str:int)；

trade_mins: 交易分钟数(str:int)；

trade_status: 交易状态(str:str)；

preclose_px: 昨日收盘价(str:float)；

open_px: 今日开盘价(str:float)；

last_px: 最新价(str:float)；

high_px: 最高价(str:float)；

low_px: 最低价(str:float)；

wavg_px: 加权平均价(str:float)；

business_amount: 总成交量(str:int)；

business_balance: 总成交额(str:int)；

px_change: 涨跌额(str:float)；

amplitude: 振幅(str:int)；

px_change_rate: 涨跌幅(str:float)；

circulation_amount: 流通股本(str:int)；

total_shares: 总股本(str:int)；

market_value: 市值(str:int)；

circulation_value: 流通市值(str:int)；

vol_ratio: 量比(str:float)；

shares_per_hand: 每手股数(str:int)；

rise_count: 上涨家数(str:int)；

fall_count: 下跌家数(str:int)；

member_count: 成员个数(str:int)；

rise_first_grp: 领涨股票(其包含以下五个字段)(str:list[dict{str:int,str:str,str:str,str:float,str:float},...])；

prod_code: 股票代码(str:str)；

prod_name: 证券名称(str:str)；

hq_type_code: 类型代码(str:str)；

last_px: 最新价(str:float)；

px_change_rate: 涨跌幅(str:float)；

fall_first_grp: 领跌股票(其包含以下五个字段)(str:list[dict{str:int,str:str,str:str,str:float,str:float},...])；

prod_code: 股票代码(str:str)；

prod_name: 证券名称(str:str)；

hq_type_code: 类型代码(str:str)；

last_px: 最新价(str:float)；

px_change_rate: 涨跌幅(str:float)；

js_data = {

"ErrCode": 0,

"ErrMsg": "0",

"Datas": [

{

"_id": "001171",

"CODE": "001171",

"NAME": "工银养老产业股票A",

"STOCKMARKET": "",

"NEWTEXCH": ""

},

......... # 省略若干

]

}

import akshare as ak



def get_fund_info(code,name):

    fund_open_fund_info_em_df = ak.fund_open_fund_info_em(symbol=code, indicator="累计收益率走势",period="3年")

    latest_perf = fund_open_fund_info_em_df.iloc[-1]['累计收益率']

    return {'code':code,'profit':latest_perf,'name':name}

fund_perf_list = []

for item in js_data['Datas']:

    print('processing code {}'.format(item['CODE']))

    try:

        fund_perf_list.append(get_fund_info(item['CODE'],item['NAME']))

        time.sleep(0.5)

    except Exception as e:

        print('error in processing code {}'.format(item['CODE']))

        print(e)

import pandas as pd

df = pd.DataFrame(fund_perf_list)



rank_df = df.sort_values(by='profit')

rank_df.to_excel('亏麻的养老基金.xlsx')

fof_fund_df = rank_df[rank_df['name'].str.contains('FOF')]

fof_fund_df.plot(kind='hist',bins=20,y='profit',width=2,grid=True)

# pip install akshare 

import akshare as ak

def get_k_line(code="sz002241"): # 

    df = ak.stock_zh_a_daily(symbol=code, start_date="20220101", end_date="20220515",

                                                          adjust="qfq")

    # 这个函数只适合取股票数据，转债会报错，转债的日线数据可以使用其他函数，这里选择股价前复权

    df.to_excel("同和药业k.xlsx")

date：交易日期

open：代表开盘价

high：当天最高价

low：当天最低价

close：当天收盘价

volume：当天成交量（元）

outstanding_share：流动股本(股)

turnover：换手率

import mpl_finance as mpf

import matplotlib.pyplot as plt

import pandas as pd



#创建绘图的基本参数

fig=plt.figure(figsize=(12, 8))

ax=fig.add_subplot(111)



#获取刚才的股票数据

df = pd.read_excel("同和药业k.xlsx")

mpf.candlestick2_ochl(ax, df["open"], df["close"], df["high"], df["low"], width=0.6, colorup='r',colordown='green',alpha=1.0)

#显示出来

plt.show()

import matplotlib.ticker as ticker

#将股票时间转换为标准时间，不带时分秒的数据

def date_to_num(dates):

    num_time = []

    for date in dates:

        date_time = datetime.strptime(date, '%Y-%m-%d')

        num_date = date2num(date_time)

        num_time.append(num_date)

    return num_time



#创建绘图的基本参数

fig=plt.figure(figsize=(12, 8))

ax=fig.add_subplot(111)



#获取刚才的股票数据

df = pd.read_excel("同和药业k.xlsx")

mpf.candlestick2_ochl(ax, df["open"], df["close"], df["high"], df["low"], width=0.6, colorup='r',colordown='green',alpha=1.0)

df['date'] = pd.to_datetime(df['date'])

df['date'] = df['date'].apply(lambda x: x.strftime('%Y-%m-%d'))

def format_date(x, pos=None):

    if x < 0 or x > len(df['date']) - 1:

        return ''

    return df['date'][int(x)]

ax.xaxis.set_major_formatter(ticker.FuncFormatter(format_date))

plt.setp(plt.gca().get_xticklabels(), rotation=45, horizontalalignment='right')

#显示出来

plt.show()

df["SMA5"] = df["close"].rolling(5).mean()

df["SMA10"] = df["close"].rolling(10).mean()

df["SMA30"] = df["close"].rolling(30).mean()

ax.plot(np.arange(0, len(df)), df['SMA5'])  # 绘制5日均线

ax.plot(np.arange(0, len(df)), df['SMA10'])  # 绘制10日均线

ax.plot(np.arange(0, len(df)), df['SMA30'])  # 绘制30日均线

import mpl_finance as mpf

import matplotlib.pyplot as plt

import pandas as pd

import matplotlib.ticker as ticker

import numpy as np

#创建绘图的基本参数

fig, axes = plt.subplots(2, 1, sharex=True, figsize=(15, 10))

ax1, ax2 = axes.flatten()



#获取刚才的股票数据

df = pd.read_excel("同和药业k.xlsx")

mpf.candlestick2_ochl(ax1, df["open"], df["close"], df["high"], df["low"], width=0.6, colorup='r',colordown='green',alpha=1.0)

df['date'] = pd.to_datetime(df['date'])

df['date'] = df['date'].apply(lambda x: x.strftime('%Y-%m-%d'))

def format_date(x, pos=None):

    if x < 0 or x > len(df['date']) - 1:

        return ''

    return df['date'][int(x)]

df["SMA5"] = df["close"].rolling(5).mean()

df["SMA10"] = df["close"].rolling(10).mean()

df["SMA30"] = df["close"].rolling(30).mean()

ax1.plot(np.arange(0, len(df)), df['SMA5'])  # 绘制5日均线

ax1.plot(np.arange(0, len(df)), df['SMA10'])  # 绘制10日均线

ax1.plot(np.arange(0, len(df)), df['SMA30'])  # 绘制30日均线

ax1.xaxis.set_major_formatter(ticker.FuncFormatter(format_date))

plt.setp(plt.gca().get_xticklabels(), rotation=45, horizontalalignment='right')

#显示出来

plt.show()

red_pred = np.where(df["close"] > df["open"], df["volume"], 0)

blue_pred = np.where(df["close"] < df["open"], df["volume"], 0)

ax2.bar(np.arange(0, len(df)), red_pred, facecolor="red")

ax2.bar(np.arange(0, len(df)), blue_pred, facecolor="blue")

def long_up_shadow(o,c,h,l):

    return True if (h-c)/c >=0.07 and (h-o)/o>=0.07 else False

df = pd.read_excel("同和药业k.xlsx")

count_num = []

for row,item in df.iterrows():

    if long_up_shadow(item['open'],item['close'],item['high'],item['low']):

        count_num.append(row)



plot_image(df,count_num)

df['tow_crows'] = talib.CDL2CROWS(df['open'].values, df['high'].values, df['low'].values, df['close'].values)



pattern = df[(df['tow_crows'] == 100) | (df['tow_crows'] == -100)]

def get_all_code(date):

    ''' 

    获取某天的股市运行的股票，排除停牌 

    ''' 

     df=DataAPI.MktEqudAdjGet(secID=u"",ticker=u"",

                           tradeDate=date, 

                           beginDate=u"", 

                           endDate=u"", 

                           isOpen="", 

                           field=u"", 

                           pandas="1")

    df=df[df['turnoverValue']>0] # 停牌 

    return df['ticker'].tolist()

 1import time

 2import datetime

 3

 4stock_profit_list=

 5start=time.time()

 6

 7today=datetime.datetime.now().strftime('%Y-%m-%d')

 8

 9def get_trade_date():

10    df=DataAPI.TradeCalGet(exchangeCD=u"XSHG,XSHE",

11                           beginDate=high_date,

12                           endDate=today,isOpen=u"1",

13                           field=u"",pandas="1")

14    return df['calendarDate'].tolist()

15

16def fixed_investment(code):

17    stock_profit_dict={}

18    stock_profit_dict['code']=code

19    df=DataAPI.MktEqudAdjGet(secID=u"",ticker=code,tradeDate=u'',beginDate=high_date,endDate=today,isOpen="1",field=u"",pandas="1")

20    total_amount=0

21    invest_count=0

22    every_invest_cash=10000

23    total_money_list=list()

24    last_date=None

25    for trade_date in trade_date_list_interval:

26        trade_df = df[df['tradeDate']==trade_date]

27        if len(trade_df)>0:

28            invest_count+=1

29            price=trade_df['closePrice'].iloc[0]

30            amount=every_invest_cash/price

31            cost=invest_count*every_invest_cash

32            profit=total_amount*price/cost

33            total_money_list.append(profit)

34            total_amount=total_amount+amount

35            total_money=price*total_amount

36            last_date=trade_date

37

38

39    stock_profit_dict['profit_rate']=profit

40    stock_profit_dict['last_date']=last_date

41    stock_profit_dict['invest_count']=invest_count

42    stock_profit_dict['total_amount']=total_amount

43    stock_profit_dict['total_money']=total_money

44    stock_profit_dict['profit_list']=total_money_list

45    stock_profit_dict['cost']=cost

46    return stock_profit_dict

47

48for code in target_codes:

49    profit_dict = fixed_investment(code)

50    stock_profit_list.append(profit_dict)

51

52print(time.time()-start)

profi_df_sorted = profit_df.sort_values(by='profit_rate',ascending=False)

profit_df['absolute_profit']=profit_df['total_money']-profit_df['cost']

profi_df_sorted['profit_rate'].mean()

Suppose you have a matrix M whose (i,j)-th element equals



M_ij = 2*i + 3*j

One way to define this matrix would be



i, j = np.indices((2,3))

M = 2*i + 3*j

which yields



array([[0, 3, 6],

       [2, 5, 8]])

In other words, np.indices returns arrays which can be used as indices. The elements in i indicate the row index:



In [12]: i

Out[12]: 

array([[0, 0, 0],

       [1, 1, 1]])

The elements in j indicate the column index:



In [13]: j

Out[13]: 

array([[0, 1, 2],

       [0, 1, 2]])

pywinauto.application.AppStartError: Could not create the process "C:\全能行证券交易终端\xiadan.exe"
Error returned by CreateProcess: (2, 'CreateProcess', '系统找不到指定的文件。')

class GJ(CommonConfig):

    DEFAULT_EXE_PATH = "C:\\Tool\\xiadan.exe"

# 拯救损坏的jupyter 文件

import re

import codecs



pattern = re.compile('"source": \[(.*?)\]\s+\},',re.S)

filename = 'tushare_usage.ipynb'

with codecs.open(filename,encoding='utf8') as f:

    content = f.read()

    

source = pattern.findall(content)

for s in source:

    t=s.replace('\\n','')

    t=re.sub('"','',t)

    t=re.sub('(,$)','',t)

    print(t)

SELECT area `地区`,count(*) as `数目` FROM `tb_basic_info` GROUP BY area order by 数目 desc;

# A股上市公司分布：

df = pd.read_sql('tb_basic_info',con=engine)

result = df['area'].value_counts()

total = result.sum()

ration = result/total*100

# 热力图

def create_heatmap(attr,value,name,maptype):

    style = Style(title_color="#fff", title_pos="center",

              width=1200, height=600, background_color="#696969")



    # 可视化

    geo = Geo(name,**style.init_style)

    geo.add("", attr, value, visual_range=[min(value), max(value)], symbol_size=8,

            visual_text_color="#000",

            is_visualmap=True, type='effectScatter',effect_scale=7,is_random=True,is_roam=False,is_piecewise = True,visual_split_number= 10,

            )



    geo.render('{}.html'.format(name))

create_heatmap(attr,value,'公司分布','china')