如果用的django3.10



需要对应的版本的cors库：



pip install django-cors-headers==3.10.0

CORS_ORIGIN_ALLOW_ALL = True



INSTALLED_APPS = [



    'corsheaders',

]



MIDDLEWARE = [



    'corsheaders.middleware.CorsMiddleware',

]

# 允许跨域源

CORS_ORIGIN_ALLOW_ALL = True

CORS_ALLOW_CREDENTIALS = True

CORS_ORIGIN_WHITELIST = (

	'*'

)



# 允许的请求方式

CORS_ALLOW_METHODS = (

    'DELETE',

    'GET',

    'OPTIONS',

    'PATCH',

    'POST',

    'PUT',

    'VIEW',

)



# 允许的请求头

CORS_ALLOW_HEADERS = (

    'XMLHttpRequest',

    'X_FILENAME',

    'accept-encoding',

    'authorization',

    'content-type',

    'dnt',

    'origin',

    'user-agent',

    'x-csrftoken',

    'x-requested-with',

    'Pragma',

    # 额外允许的请求头

    'token',

)

def initialize(context):

    pass



def handle_data(context, data):

    df = get_cb_info()

    log.info(df)

2024-11-20 10:22:00 - ERROR - 用户策略执行异常

2024-11-20 10:22:00 - ERROR - Exception: Traceback (most recent call last):

  File /home/fly/sim_backtest/result/29fa7074-a6e6-11ef-b05b-c40778d9af27/user_strategy.py, line 6 in handle_data

    df = get_cb_info()

    --> data = BarDict(600570.SS)

    --> context = <StrategyContext {'recorded_vars': {}, 'commission': <Commission {'cost': 0.0003, 'min_trade_cost': 5.0, 'tax': 0.001}>, 'blotter': <Blotter {'current_dt': date ...

NameError: name 'get_cb_info' is not defined

#encoding:gbk



def init(ContextInfo):

    pass

    

def handlebar(ContextInfo):

    index = ContextInfo.barpos

    realtime = ContextInfo.get_bar_timetag(index)

    print(ContextInfo.get_stock_list_in_sector('沪深转债', realtime))

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

import datetime



target_list = [

'600000.SS', 

'000333.SZ'

]



def execution(context, data):

    now = context.current_dt.strftime('%H:%M')

	for code in target_list:

        tick_info = data[code]

        price = tick_info['price']

        print('now: {} code : {} price:{}'.format(now,code ,price))

        







# 标准

def initialize(context):

    log.info("公众号：可转债量化分析 ---- start ----")





def handle_data(context, data):

    execution(context, data)

2024-09-20 14:42:00 - INFO - now: 14:42 code : 000333.SZ price:66.22

2024-09-20 14:43:00 - INFO - now: 14:43 code : 000333.SZ price:66.26

2024-09-20 14:44:00 - INFO - now: 14:44 code : 000333.SZ price:66.19

2024-09-20 14:45:00 - INFO - now: 14:45 code : 000333.SZ price:66.2

2024-09-20 14:46:00 - INFO - now: 14:46 code : 000333.SZ price:66.19

2024-09-20 14:47:00 - INFO - now: 14:47 code : 000333.SZ price:66.18

2024-09-20 14:48:00 - INFO - now: 14:48 code : 000333.SZ price:66.14

2024-09-20 14:49:00 - INFO - now: 14:49 code : 000333.SZ price:66.25

2024-09-20 14:50:00 - INFO - now: 14:50 code : 000333.SZ price:66.19

2024-09-20 14:51:00 - INFO - now: 14:51 code : 000333.SZ price:66.18

2024-09-20 14:52:00 - INFO - now: 14:52 code : 000333.SZ price:66.19

2024-09-20 14:53:00 - INFO - now: 14:53 code : 000333.SZ price:66.2

2024-09-20 14:54:00 - INFO - now: 14:54 code : 000333.SZ price:66.19

2024-09-20 14:55:00 - INFO - now: 14:55 code : 000333.SZ price:66.25

2024-09-20 14:56:00 - INFO - now: 14:56 code : 000333.SZ price:66.27

2024-09-20 14:57:00 - INFO - now: 14:57 code : 000333.SZ price:66.28

2024-09-20 14:58:00 - INFO - now: 14:58 code : 000333.SZ price:66.28

2024-09-20 14:59:00 - INFO - now: 14:59 code : 000333.SZ price:66.28

2024-09-20 15:00:00 - INFO - now: 15:00 code : 000333.SZ price:66.06

# -*-coding:gbk-*-

# 作者公众号：可转债量化分析

import datetime

import json

import redis



####### 以下为固定配置，请勿随意修改 ##########

# 注意：程序需要在9：30前启动



START_TIME = '09:30'  # 启动时间，可以修改为开盘任意时间9：30-14：57

STOCK_ACCOUNT = ''  # 股票账户





INTERVAL_SECOND = 60  # 交易间隔

INTERVAL_STRING = "{}nSecond".format(INTERVAL_SECOND)



def today_date():

    return datetime.datetime.now().strftime('%Y%m%d')





def init(ContextInfo):

    now = datetime.datetime.now()

    print('策略初始化 {}'.format(now))

    now = datetime.datetime.now()

    running_time = '{} {}'.format(today_date(), START_TIME)

    print('策略初始化 {} , 程序启动时间 {}'.format(now, START_TIME))

    ContextInfo.run_time("execution", INTERVAL_STRING, running_time)







def handlebar(ContextInfo):

    pass

    

def execution(ContextInfo):

    if not ContextInfo.is_last_bar():

        return

    now = datetime.datetime.now()

    print(now)

#encoding:gbk





import requests



def get_baidu():



    url = 'https://www.baidu.com'

    req = requests.get(url,headers={'User-Agent':'Mozilla/5.0'})

    print(req.text)

    

def init(ContextInfo):

    get_baidu()

    

def handlebar(ContextInfo):

    pass

def base_usage():

    print('start')

    r = RedisCls()

    data = {'name': 'zhangsan', 'age': 18}

    key = 'test_key'

    r.push(key, json.dumps(data))

    print('push')

    print(r.delete('key'))  # reutrn 1 if delete success, else return 0

    start_time = time.time()

    ret_data = r.pop(key)

    end_time = time.time()

    print('cost time: ', end_time - start_time)

    print(ret_data)







base_usage()

# encoding:gbk



ACCOUNT = 'xxxxxxx' # 填入你的QMT账户ID, 如果没有，可以联系我开通 QMT权限





def init(ContextInfo):

    # hs300成分股中sh和sz市场各自流通市值最大的前3只股票

    pass





def handlebar(ContextInfo):

    # 计算当前主图的cci

    position_info = get_trade_detail_data(ACCOUNT, 'stock', 'position')

    for i in position_info:

        print('股票', i.m_strInstrumentID, '持仓数',

              i.m_nVolume, '持有成本', round(i.m_dOpenPrice, 2),

              '持仓盈亏', round(i.m_dPositionProfit, 2),

              '持仓盈亏比例', round(i.m_dProfitRate*100, 2)

              )

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)；

def initialize(context):

    # 初始化策略

    engine = DBSelector().get_engine()

    df = pd.read_sql('select * from tb_holding_stock_list', con=engine)

    df['code']=df['code'].astype(str)

    result = {}

    for index, row in df.iterrows():

        code = add_code_postfix(row['code'])

        result[code] = {'name': row['name'], 'source': row['source']}

    g.holding_stock_dict = result

    g.holding_stock_list = list(result.keys())

    g.__cache = Cache()



    run_interval(context, execution, seconds=INTERVAL)  # 扫描





def handle_data(context, data):

    pass





def tick_data(context, data):

    pass



def before_trading_start(context, data):

    '''

    盘前

    '''

    if DEBUG:

        log.info('盘前运行开始', str(context.blotter.current_dt))





def after_trading_end(context, data):

    '''

    盘后

    '''

    if DEBUG:

        log.info('盘后时间 ', str(context.blotter.current_dt))

def execution(context):

    tick_info = get_snapshot(g.holding_stock_list)

    for code, tick in tick_info.items():

        px_change_rate = tick['px_change_rate']

        if px_change_rate > abs(HIT_TARGET):

            if g.__cache.check(code):

                # 通知

                name = g.holding_stock_dict.get(code)['name']

                source = g.holding_stock_dict.get(code)['source']

                msg = '{}-{} 涨幅-{},{}'.format(code,

                                              name, px_change_rate, source)

                send_message_via_wechat(msg)

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)

您的连接不是私密连接

攻击者可能会试图从 139.224.24.109 窃取您的信息（例如：密码、通讯内容或信用卡信息）。了解详情

NET::ERR_CERT_COMMON_NAME_INVALID

如果您想获得 Chrome 最高级别

7、该接口只能获取2005年后的数据。

test_data = data = get_price(security='000001.SS',start_date='20050201',end_date='20050630',frequency='1d')

<urlset xmlns="http://www.sitemaps.org/schemas/sitemap/0.9" xmlns:mobile="http://www.baidu.com/schemas/s ... gt%3B

<url>

<loc>http://30daydo.com/article/1</loc>

<mobile:mobile type="mobile"/>

<lastmod>2024-06-30</lastmod>

<changefreq>daily</changefreq>

<priority>0.8</priority>

</url>

</urlset>

<url>

<loc>http://30daydo.com/article/1</loc>

<mobile:mobile type="mobile"/>

<lastmod>2024-06-30</lastmod>

<changefreq>daily</changefreq>

<priority>0.8</priority>

</url>