# -*-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>

# encoding:gbk

'''

实盘可以执行

固定数量

'''

import datetime





ACCOUNT = ''





def init(ContextInfo):

    ContextInfo.set_account(ACCOUNT)





def get_position_infos():

    # 信用账户可用资金

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

    



    pos_dict={}

    for pos in position_infos:

        code = pos.m_strInstrumentID

        if pos.m_nVolume > 0:

            pos_dict[code] = pos.m_nVolume

    return pos_dict

    

datax = get_position_infos() # 这里error



def handlebar(ContextInfo):

    if ContextInfo.is_last_bar():

        current = datetime.datetime.now().strftime('%H:%M:%S.%f')

        print(datax)

        

class RedisCls:



    def __init__(self):

        self.conn = self.getConn()



    def getConn(self):

        try:

            r = redis.Redis(host=redisconfig['redis']['host'], port=redisconfig['redis']['port'], db=0,

                            decode_responses=True, password=redisconfig['redis']['password'], socket_connect_timeout=5)

        except Exception as e:

            print(e)

            raise IOError('connect redis failed')

        else:

            return r

        



    def get(self, key):

        return self.conn.get(key)

    



    def set(self, key, value):

        return self.conn.set(key, value)

    

    def pop(self, key):

        print('==== pop data ====')

        return self.conn.brpop(key)



    def push(self, key, value):

        print('==== push data ====')

        self.conn.blpush(key, value)

AttributeError: 'Redis' object has no attribute 'blpush'. Did you mean: 'lpush'?

def initialize(context):

    run_daily(context, event, '09:38')





def handle_data(context, data):

    pass





def event(context):

    his60 = get_history(60, '1d', ['close'],  ['515220.SS'], fq='pre', include=False)

    print(his60)

# coding:gbk

def init(C):

    pass



def handlebar(C):

    iopv = get_etf_iopv("159928.SZ")

    print(f'基金净值为{iopv}')

    info = get_etf_info('513520.SH')

    print(f'基金申购信息： {info}')

# coding:gbk

# 公众号:可转债量化分析

DEBUG = True

import time

def get_datetime(ContextInfo):

    # 获取当前时间

    index = ContextInfo.barpos

    realtime = ContextInfo.get_bar_timetag(index)

    date = timetag_to_datetime(realtime, "%Y-%m-%d %H:%M:%S")

    if DEBUG:

        print('当前日期 ', date)

    return date

    

def init(ContextInfo):

    print("==============start==========")

    ContextInfo.start = '2024-03-27 10:00:00'

    ContextInfo.end = '2024-03-29 10:00:00'

    # 

    #ContextInfo.end = '2023-01-05'

    #ContextInfo.start = '2023-01-16'

    print('init')

    

def handlebar(ContextInfo):

    # 回测的时候不需要

    #if not ContextInfo.is_last_bar():

    #    print('return')

    #    return

    get_datetime(ContextInfo)

    print('handlebar')

    data = ContextInfo.get_market_data(['quoter'], stock_code = ['123167.SZ'], skip_paused = True, period = 'tick', dividend_type = 'front')

    

    #data = ContextInfo.get_market_data(['close'], stock_code = ['113567.SH'], skip_paused = True, period = '1d', dividend_type = 'front')

    #print(type(data))

    print(data)



def stop(ContextInfo):

    print( 'strategy is stop !')

import akshare as ak

fund_purchase_em_df = ak.fund_purchase_em()

def filter_func(df,type='指数型-海外股票'):

    df = df[~df['基金代码'].str.startswith('0')]

    condition1 = df['申购状态']=='限大额'

    condition2 = df['基金类型']==type

    df = df[condition1 & condition2]

    df= df[~df['基金简称'].str.contains('ETF')]

    df = df[(df['日累计限定金额']>0) & (df['日累计限定金额']<=10000)]

    df['基金代码'] = df['基金代码'].map(lambda x: 'SH'+x if x.startswith('5') else 'SZ'+x)

    return df

import requests

cookies = # 雪球上获取，不一定需要登录状态



headers = {

    'authority': 'stock.xueqiu.com',

    'origin': 'https://xueqiu.com',

    'user-agent': 'Mozilla/5.0 (iPhone; U; CPU like Mac OS X; en) AppleWebKit/420+ (KHTML, like Gecko) Version/3.0 Mobile/1C28 Safari/419.3',

}



def fund_premium_rate(code):

    params = {

        'symbol': code,

        'extend': 'detail',

    }



    response = requests.get('https://stock.xueqiu.com/v5/stock/quote.json', params=params, 

                            cookies=cookies, 

                            headers=headers)

    try:

        rate = response.json()['data']['quote']['premium_rate']

    except Exception as e:

        return None

    else:

        return rate

for code,name in code_name_mapper.items():

    rate = fund_premium_rate(code)

    if rate is not None:

        print(f'{code} - {name}的溢价率是: {rate}')

        if rate > 4:

            send_message_via_wechat(f'{code} - {name}的溢价率是: {rate}, 可以关注套利。 公众号：可转债量化分析')