from __future__ import print_function, absolute_import

from gm.api import *

set_token(juejin_token)

im_data = get_history_symbol(symbol='CFFEX.IM2212', start_date='2022-12-12', end_date='2022-12-16', df=True)

import pandas as pd

import datetime





def get_tick(code, start_time, end_time, period='tick'):

    from xtquant import xtdata



    xtdata.download_history_data(code, period=period, start_time=start_time, end_time=end_time)

    data = xtdata.get_local_data(field_list=, stock_code=

, period=period, count=10)

    result_list = data

    df = pd.DataFrame(result_list)



    df['time_str'] = df['time'].apply(lambda x: datetime.datetime.fromtimestamp(x / 1000.0))

    return df





def process_timestamp(df, filename):

    df = df.set_index('time_str')

    result = df.resample('3S').first().ffill()

    result = result[(result.index >= '2022-07-20 09:30') & (result.index <= '2022-07-20 15:00')]

    result = result.reset_index()

    result.to_csv(filename + '.csv')





def dump_single_code_tick():

    # 导出单个转债的tick数据

    code='128022'

    start_date = '20210113'

    end_date = '20210130'



    post_fix = 'SZ' if code.startswith('12') else 'SH'

    code = '{}.{}'.format(code,post_fix)

    filename = '{}'.format(code)

    df = get_tick(code, start_date, end_date)



dump_single_code_tick()

from xtquant import xtdata

data = xtdata.get_market_data(field_list=['time', 'open', 'close', 'high', 'low', 'volume', 'amount'], stock_list=['603000.SH'], period='1d', start_time='20230101')

print(data)

def func(context):

    pos_dict = get_positions()

    for code,pos in pos_dict.items():

        enable_amount = pos.enable_amount

        if enable_amount>0:

            order_target(code, 0)



# 标准

def initialize(context):

    # 初始化策略

    run_daily(context, func, time='9:25')



def handle_data(context, data):

    pass

def func(context):

    pos_dict = get_positions()

    for code,pos in pos_dict.items():

        enable_amount = pos.enable_amount

        if enable_amount>0 and code.startswith(('12','11')): # 只清除转债

            order_target(code, 0)

code_list = ['113578.SS','123014.SZ'] # 股票池，这里可以填几千个股票也没问题的

zz_df_price = get_price(code_list, start_date=None, end_date=None, frequency='1d', fields='close', fq=None, count=1)

yesterday_price_dict = zz_df_price.iloc[0].to_json()

zz_df_price.iloc[0].to_json()

index      113578.SS	123014.SZ

2023-07-14	93.036	 118.36

'{"113578.SS":93.036,"123014.SZ":118.36}'

import datetime



def initialize(context):

    # 初始化策略

    g.already_run = False



def handle_data(context, data):

    pass



def get_available_cash(context):

    # 读取变量protfolio里的可用资金的值

    return context.portfolio.cash



def current_time():

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



def tick_data(context, data):

    log.info('可用资金{}'.format(get_available_cash(context)))

    if not g.already_run:

        g.already_run = True

        # 卖出100手 R-001

        ret = order_tick('131810.SZ', -10, priceGear='1', limit_price=None)



def on_order_response(context, trade_list):

    # 委托回调函数，有委托出现就调用此函数

    log.info('已委托下单 {}'.format(current_time()))

# encoding:gbk

import datetime





ACCOUNT = '你的账户ID'

start = True



def init(ContextInfo):

    ContextInfo.set_account(ACCOUNT)



def current_time():

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





def get_available_cash(ContextInfo):

    acct_info = get_trade_detail_data(ACCOUNT, 'stock', 'account')

    return acct_info[0].m_dAvailable





def deal_callback(ContextInfo, dealInfo):

    print('before')

    print(dealInfo.m_strProductID)

    print(dealInfo.m_nDirection)

    print(dealInfo.m_dTradeAmount)

    print(dealInfo.m_nVolume)

    print(dealInfo.m_dPrice)

    print('call back --- ')

    print(current_time())



def buy_action(ContextInfo):

    opType = 24

    orderType = 1101

    accountID = ACCOUNT

    orderCode = '131810.SZ'

    prType = 11

    price = 1.8

    volume = 10

    quickTrade = 2

    passorder(opType, orderType, accountID, orderCode, prType, price, volume, quickTrade, ContextInfo)



def handlebar(ContextInfo):

    global start

    if ContextInfo.is_last_bar():



        cash = get_available_cash(ContextInfo)

        print('{} 可用资金{}'.format(current_time(),cash))

        if start:

            print('下单逆回购 131810 ')

            buy_action(ContextInfo)

            start = False

# 交易回调

def on_trade_response(context, trade_list):

    # 成交主推

    now = context.blotter.current_dt.strftime("%H:%M:%S")



    for trade_info in trade_list:

        if trade_info['order_id'] == '':

            # 不是本策略跳过

            log.info('非本策略订单')

            continue



        code = trade_info['stock_code']

        code = post_fix_convert(code)

        business_time = trade_info['business_time']

        business_amount = trade_info['business_amount']  # 这个是负数，如果卖出

        business_price = trade_info['business_price']

        g.total_cash -= business_amount # 马上更新资金状态

def initialize(context):

    g.security = "600570.SS"

    set_universe(g.security)



def before_trading_start(context, data):

    g.count = 0



def handle_data(context, data):

    if g.count == 0:

        # 对持仓内的贝斯进行转股操作

        debt_to_stock_order("123075.SZ", -1000)

        g.count += 1

    # 查看委托状态

    log.info(get_orders())

    g.count += 1

#coding:gbk

c = True

account = '11111111' # 个人账户

def init(ContextInfo):

    pass

    

def handlebar(ContextInfo):

	if not ContextInfo.is_last_bar():

		#历史k线不应该发出实盘信号 跳过

		return



    if c:

        passorder(80,1101,account,s,5,0,-100,'1',2,'tzbz',ContextInfo) 

        c=False

		

import requests



def initialize(context):

    # 初始化策略

    g.security = "600570.SS"

    set_universe(g.security)



    

def handle_data(context, data):

    

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

    headers={'User-Agent':'Google'}

    try:

        r=requests.get(url,headers=headers)

        r.encoding='utf8'

        print(r.text)

    except Exception as e:

        print('error')

        print(e)

APScheduler (3.3.1)

arch (3.2)

bcolz (1.2.1)

beautifulsoup4 (4.6.0)

bleach (1.5.0)

boto (2.43.0)

Bottleneck (1.0.0)

bz2file (0.98)

cachetools (3.1.0)

click (4.0)

contextlib2 (0.4.0)

crypto (1.4.1)

cvxopt (1.1.8)

cx-Oracle (8.0.1)

cycler (0.10.0)

cyordereddict (0.2.2)

Cython (0.22.1)

decorator (4.0.10)

entrypoints (0.2.2)

fastcache (1.0.2)

gensim (0.13.3)

h5py (2.6.0)

hmmlearn (0.2.0)

hs-udata (0.3.6)

html5lib (0.9999999)

ipykernel (4.5.0)

ipython (5.1.0)

ipython-genutils (0.1.0)

ipywidgets (5.2.2)

jieba (0.38)

Jinja2 (2.8)

jsonpickle (1.0)

jsonschema (2.5.1)

jupyter (1.0.0)

jupyter-client (4.4.0)

jupyter-console (5.0.0)

jupyter-core (4.2.0)

jupyter-kernel-gateway (1.1.1)

Keras (2.3.1)

Keras-Applications (1.0.8)

Keras-Preprocessing (1.1.0)

line-profiler (2.1.2)

Logbook (1.4.3)

lxml (4.5.0)

Markdown (2.2.0)

MarkupSafe (0.23)

matplotlib (1.5.3)

mistune (0.7.3)

Naked (0.1.31)

nbconvert (4.2.0)

nbformat (4.1.0)

networkx (1.9.1)

nose (1.3.6)

notebook (4.2.3)

numexpr (2.6.1)

numpy (1.11.2)

pandas (0.23.4)

patsy (0.4.0)

pexpect (4.2.1)

pickleshare (0.7.4)

pip (9.0.1)

pkgconfig (1.0.0)

prompt-toolkit (1.0.8)

protobuf (3.3.0)

ptvsd (2.2.0)

ptyprocess (0.5.1)

PyBrain (0.3)

pycrypto (2.6.1)

Pygments (2.1.3)

PyMySQL (0.9.3)

pyparsing (2.1.10)

python-dateutil (2.7.5)

pytz (2015.4)

PyWavelets (0.4.0)

PyYAML (5.3.1)

pyzmq (16.1.0.dev0)

qtconsole (4.2.1)

requests (2.7.0)

retrying (1.3.3)

scikit-learn (0.18)

scipy (0.18.0)

seaborn (0.7.1)

setuptools (28.7.1)

setuptools-scm (3.1.0)

shellescape (3.4.1)

simplegeneric (0.8.1)

simplejson (3.17.0)

six (1.10.0)

sklearn (0.0)

smart-open (1.3.5)

SQLAlchemy (1.0.8)

statsmodels (0.10.2)

TA-Lib (0.4.10)

tables (3.3.0)

tabulate (0.7.5)

tensorflow (1.3.0rc1)

tensorflow-tensorboard (0.1.2)

terminado (0.6)

Theano (0.8.2)

toolz (0.7.4)

tornado (4.4.2)

traitlets (4.3.1)

tushare (1.2.48)

tzlocal (1.3)

wcwidth (0.1.7)

Werkzeug (0.12.2)

wheel (0.29.0)

widgetsnbextension (1.2.6)

xcsc-tushare (1.0.0)

xgboost (0.6a2)

xlrd (1.1.0)

xlwt (1.3.0)

zipline (0.8.3)

You are using pip version 9.0.1, however version 22.1.2 is available.

You should consider upgrading via the 'pip install --upgrade pip' command.

量化新人用QMT+chat GPT快速上手量化策略（一）QMT基础介绍



量化新人用QMT+chat GPT快速上手量化策略（二）QMT均线盘后选股



量化新人用QMT+chat GPT快速上手量化策略（三）一个基本的回测策略代码



量化新人用QMT+chat GPT快速上手量化策略（四） QMT运行一个策略的整体流程



量化新人用QMT+chat GPT快速上手量化策略（五）  获取股票数据



量化新人用QMT+chat GPT快速上手量化策略（六） tablib计算指标



量化新人用QMT+chat GPT快速上手量化策略（七） 下单代码编写

def init(ContextInfo):

    ContextInfo.run_time('execution', '3nSecond', '2019-10-14 13:20:00',)



def execution(ContextInfo):

    if not ContextInfo.is_last_bar():

        return

    data = ContextInfo.get_market_data_ex(

        [], ['123136.SZ'], period='tick'

        , start_time='', end_time='', count=1

        , dividend_type='follow', fill_data=True

        , subscribe=True)

    print(data)

    

    

def handlebar(ContextInfo):

    #计算当前主图的cci

    pass

m_strAccountID: 资金账号，账号，账号，资金账号



m_strExchangeID: 证券市场



m_strExchangeName: 交易市场



m_strProductID: 品种代码



m_strProductName: 品种名称



m_strInstrumentID: 证券代码



m_strInstrumentName: 证券名称，合约名称



m_strOrderRef: 内部委托号，下单引用等于股票的内部委托号



m_nOrderPriceType: EBrokerPriceType 类型，例如市价单、限价单



m_nDirection: EEntrustBS 类型，操作，多空，期货多空，股票买卖永远是 48，其他的 dir 同理



m_nOffsetFlag: EOffset_Flag_Type类型，买卖/开平，用此字段区分股票买卖，期货开、平仓，期权买卖等



m_nHedgeFlag: EHedge_Flag_Type 类型，投保



m_dLimitPrice: 委托价格,限价单的限价，就是报价



m_nVolumeTotalOriginal: 委托量，最初委托量



m_nOrderSubmitStatus: EEntrustSubmitStatus 类型，报单状态，提交状态，股票中不需要报单状态



m_strOrderSysID: 合同编号，委托号



m_nOrderStatus: EEntrustStatus，委托状态



m_nVolumeTraded: 成交数量，已成交量



m_nVolumeTotal: 委托剩余量，当前总委托量，股票的含义是总委托量减去成交量



m_nErrorID: 状态信息



m_strErrorMsg: 状态信息



m_nTaskId：任务号



m_dFrozenMargin: 冻结金额，冻结保证金



m_dFrozenCommission: 冻结手续费



m_strInsertDate: 委托日期，报单日期



m_strInsertTime: 委托时间



m_dTradedPrice: 成交均价（股票）



m_dCancelAmount: 已撤数量



m_strOptName: 买卖标记，展示委托属性的中文



m_dTradeAmount: 成交金额，成交额，期货 = 均价 * 量 * 合约乘数



m_eEntrustType: EEntrustTypes，委托类别



m_strCancelInfo: 废单原因



m_strUnderCode: 标的证券代码



m_eCoveredFlag: ECoveredFlag，备兑标记 '0' - 非备兑，'1' - 备兑



m_dOrderPriceRMB: 委托价格（人民币），目前用于港股通



m_dTradeAmountRMB: 成交金额（人民币），目前用于港股通



m_dReferenceRate: 汇率，目前用于港股通



m_strCompactNo: 合约编号



m_eCashgroupProp: EXTCompactBrushSource类型，头寸来源



m_dShortOccupedMargin: 预估在途占用保证金，用于期权



m_strXTTrade: 是否是迅投交易



m_strAccountKey: 账号key，唯一区别不同账号的key



m_strRemark：投资备注

attr EBrokerPriceType value  <class 'EBrokerPriceType'>

attr ECoveredFlag value  <class 'ECoveredFlag'>

attr EEntrustBS value  <class 'EEntrustBS'>

attr EEntrustStatus value  <class 'EEntrustStatus'>

attr EEntrustSubmitStatus value  <class 'EEntrustSubmitStatus'>

attr EEntrustTypes value  <class 'EEntrustTypes'>

attr EFutureTradeType value  <class 'EFutureTradeType'>

attr EHedge_Flag_Type value  <class 'EHedge_Flag_Type'>

attr EOffset_Flag_Type value  <class 'EOffset_Flag_Type'>

attr ESideFlag value  <class 'ESideFlag'>

attr EXTCompactBrushSource value  <class 'EXTCompactBrushSource'>

attr EXTCompactStatus value  <class 'EXTCompactStatus'>

attr EXTCompactType value  <class 'EXTCompactType'>

attr EXTSloTypeQueryMode value  <class 'EXTSloTypeQueryMode'>

attr EXTSubjectsStatus value  <class 'EXTSubjectsStatus'>

attr __class__ value  <class 'COrderDetail'>

attr __delattr__ value  <method-wrapper '__delattr__' of COrderDetail object at 0x000000004AD59A60>

attr __dict__ value  {}

attr __dir__ value  <built-in method __dir__ of COrderDetail object at 0x000000004AD59A60>

attr __doc__ value  None

attr __eq__ value  <method-wrapper '__eq__' of COrderDetail object at 0x000000004AD59A60>

attr __format__ value  <built-in method __format__ of COrderDetail object at 0x000000004AD59A60>

attr __ge__ value  <method-wrapper '__ge__' of COrderDetail object at 0x000000004AD59A60>

attr __getattribute__ value  <method-wrapper '__getattribute__' of COrderDetail object at 0x000000004AD59A60>

attr __gt__ value  <method-wrapper '__gt__' of COrderDetail object at 0x000000004AD59A60>

attr __hash__ value  <method-wrapper '__hash__' of COrderDetail object at 0x000000004AD59A60>

attr __init__ value  <bound method __init__ of <COrderDetail object at 0x000000004AD59A60>>

attr __init_subclass__ value  <built-in method __init_subclass__ of Boost.Python.class object at 0x0000000044C55368>

attr __instance_size__ value  1088

attr __le__ value  <method-wrapper '__le__' of COrderDetail object at 0x000000004AD59A60>

attr __lt__ value  <method-wrapper '__lt__' of COrderDetail object at 0x000000004AD59A60>

attr __ne__ value  <method-wrapper '__ne__' of COrderDetail object at 0x000000004AD59A60>

attr __new__ value  <built-in method __new__ of Boost.Python.class object at 0x000007FEDCF525C0>

attr __reduce__ value  <bound method <unnamed Boost.Python function> of <COrderDetail object at 0x000000004AD59A60>>

attr __reduce_ex__ value  <built-in method __reduce_ex__ of COrderDetail object at 0x000000004AD59A60>

attr __repr__ value  <method-wrapper '__repr__' of COrderDetail object at 0x000000004AD59A60>

attr __setattr__ value  <method-wrapper '__setattr__' of COrderDetail object at 0x000000004AD59A60>

attr __sizeof__ value  <built-in method __sizeof__ of COrderDetail object at 0x000000004AD59A60>

attr __str__ value  <method-wrapper '__str__' of COrderDetail object at 0x000000004AD59A60>

attr __subclasshook__ value  <built-in method __subclasshook__ of Boost.Python.class object at 0x0000000044C55368>

attr __weakref__ value  None

attr j value  j

attr m_bEnable value  True

attr m_dCancelAmount value  0.0

attr m_dFrozenCommission value  0.15

attr m_dFrozenMargin value  1000.0

attr m_dLimitPrice value  2.3

import pickle

from collections import defaultdict

NOTEBOOK_PATH = '/home/fly/notebook/'

'''

持仓N日后卖出，仓龄变量每日pickle进行保存，重启策略后可以保证逻辑连贯

'''

def initialize(context):

    #尝试启动pickle文件

    try:

        with open(NOTEBOOK_PATH+'hold_days.pkl','rb') as f:

            g.hold_days = pickle.load(f)

Error! not UTF-8 encoded



Saving disable



See console for more details

用法： ContextInfo.get_market_data(fields, stock_code = , start_time = '', 

end_time = '', skip_paused = True, period = 'follow', 

dividend_type = 'follow', count = -1)

open -- 开盘价(str:numpy.float64)；

high -- 最高价(str:numpy.float64)；

low --最低价(str:numpy.float64)；

close -- 收盘价(str:numpy.float64)；

volume -- 交易量(str:numpy.float64)；

money -- 交易金额(str:numpy.float64)；

price -- 最新价(str:numpy.float64)；

preclose -- 昨收盘价(str:numpy.float64)(仅日线返回)；

high_limit -- 涨停价(str:numpy.float64)(仅日线返回)；

low_limit -- 跌停价(str:numpy.float64)(仅日线返回)；

unlimited -- 判断查询日是否无涨跌停限制(1：该日无涨跌停限制；0：该日有涨跌停限制)(str:numpy.float64)(仅日线返回)；

1. 涨停板；依赖ptrade的高速行情自动配合手动；
2. 两融账户的股票日内做T，持有底仓；
3. 股票小市值轮动+多因子
4. 可转债多因子（有N个版本+不同的排除因子 组合）
5. 可转债日内高频
6. 股票趋势动量
7. ETF轮动套利
8. 脉冲卖出扫描

margin_trade - 担保品买卖



margincash_open - 融资买入



margincash_close - 卖券还款



margincash_direct_refund - 直接还款



marginsec_open - 融券卖出



marginsec_close - 买券还券

2023-03-31 14:41:02 - INFO - 生成订单，订单号：cd25d27f39854721aac99db13c9e9b73股票代码：601328.SS 数量：信用买入1000

2023-03-31 14:41:02 - INFO - {'error_info': '', 'stock_code': '601328.SS', 'order_id': 'cd25d27f39854721aac99db13c9e9b73', 'status': '2', 'price': 5.1, 'entrust_type': '9', 'amount': 1000, 'business_amount': 0.0, 'entrust_prop': '0', 'entrust_no': '1', 'order_time': '2023-03-31 14:35:53.776'}

2023-03-31 14:41:02 - INFO - {'business_amount': 1000, 'order_id': 'cd25d27f39854721aac99db13c9e9b73', 'stock_code': '601328.SS', 'entrust_bs': '1', 'entrust_no': '1', 'status': '8', 'business_balance': 5100.0, 'business_price': 5.1, 'business_id': '2', 'business_time': '2023-03-31 14:39:17'}

2023-03-31 14:52:00 - INFO - 生成订单，订单号：01b7851d37014709bde3ec6ebe9e89c3股票代码：601328.SS 数量：融资买入100

2023-03-31 14:52:00 - INFO - {'price': 5.1, 'entrust_prop': '0', 'status': '2', 'entrust_type': '6', 'stock_code': '601328.SS', 'business_amount': 0.0, 'entrust_no': '3', 'order_time': '2023-03-31 14:46:51.620', 'error_info': '', 'amount': 100, 'order_id': '01b7851d37014709bde3ec6ebe9e89c3'}

2023-03-31 14:52:00 - INFO - {'business_id': '4', 'business_balance': 509.99999999999994, 'business_price': 5.1, 'order_id': '01b7851d37014709bde3ec6ebe9e89c3', 'business_time': '2023-03-31 14:50:14', 'status': '8', 'entrust_bs': '1', 'business_amount': 100, 'entrust_no': '3', 'stock_code': '601328.SS'}

2023-03-31 14:58:20 - INFO - start

2023-03-31 14:59:00 - INFO - 生成订单，订单号：20cef28ec52c4d41b09c80fc49167497股票代码：600269.SS 数量：卖券还款-200

2023-03-31 14:59:00 - INFO - {'business_amount': 0.0, 'amount': -200, 'stock_code': '600269.SS', 'error_info': '', 'order_time': '2023-03-31 14:53:51.375', 'price': 3.38, 'entrust_type': '6', 'status': '2', 'order_id': '20cef28ec52c4d41b09c80fc49167497', 'entrust_prop': '0', 'entrust_no': '5'}

2023-03-31 14:59:00 - INFO - {'status': '8', 'business_time': '2023-03-31 14:57:14', 'stock_code': '600269.SS', 'entrust_bs': '2', 'business_id': '6', 'business_balance': -676.0, 'business_amount': -200, 'order_id': '20cef28ec52c4d41b09c80fc49167497', 'business_price': 3.38, 'entrust_no': '5'}

def initialize(context):

    # 初始化策略

    # 设置我们要操作的股票池, 这里我们只操作一支股票

    g.security = "600300.SS"

    set_universe(g.security)



def before_trading_start(context, data):

    # 买入标识

    g.order_buy_flag = False

    # 卖出标识

    g.order_sell_flag = False



#当五日均线高于十日均线时买入，当五日均线低于十日均线时卖出

def handle_data(context, data):

    # 得到十日历史价格

    df = get_history(10, "1d", "close", g.security, fq=None, include=False)

    # 得到五日均线价格

    ma5 = round(df["close"][-5:].mean(), 3)

    # 得到十日均线价格

    ma10 = round(df["close"][-10:].mean(), 3)

    # 取得昨天收盘价

    price = data[g.security]["close"]

    # 如果五日均线大于十日均线

    if ma5 > ma10:

        if not g.order_buy_flag:

            # 获取最大可融资数量

            amount = get_margincash_open_amount(g.security).get(g.security)

            # 进行融资买入操作

            margincash_open(g.security, amount)

            # 记录这次操作

            log.info("Buying %s Amount %s" % (g.security, amount))

            # 当日已融资买入

            g.order_buy_flag = True



    # 如果五日均线小于十日均线，并且目前有头寸

    elif ma5 < ma10 and get_position(g.security).amount > 0:

        if not g.order_sell_flag:

            # 获取标的卖券还款最大可卖数量

            amount = get_margincash_close_amount(g.security).get(g.security)

            # 进行卖券还款操作

            margincash_close(g.security, -amount)

            # 记录这次操作

            log.info("Selling %s Amount %s" % (g.security, amount))

            # 当日已卖券还款

            g.order_sell_flag = True

担保品卖出指的是融资融券交易当中，用自有资金进行买卖的行为

def initialize(context):

    g.security = '600570.SS'

    set_universe(g.security)



def handle_data(context, data):

    # 以系统最新价委托

    margin_trade(g.security, 100)

    # 以72块价格下一个限价单

    margin_trade(g.security, 100, limit_price=72)

    # 以最优五档即时成交剩余撤销委托

    margin_trade(g.security, 200, market_type=4)

security：股票代码(str)；



amount：交易数量，正数表示买入，负数表示卖出(int)；



limit_price：买卖限价(float)；



market_type：市价委托类型，上证非科创板股票支持参数1、4，上证科创板股票支持参数0、1、2、4，深证股票支持参数0、2、3、4、5(int)；



0：对手方最优价格；

1：最优五档即时成交剩余转限价；

2：本方最优价格；

3：即时成交剩余撤销；

4：最优五档即时成交剩余撤销；

5：全额成交或撤单；

import pandas as pd

import datetime





def get_tick(code, start_time, end_time, period='tick'):

    from xtquant import xtdata



    xtdata.download_history_data(code, period=period, start_time=start_time, end_time=end_time)

    data = xtdata.get_local_data(field_list=, stock_code=

, period=period, count=10)

    result_list = data

    df = pd.DataFrame(result_list)



    df['time_str'] = df['time'].apply(lambda x: datetime.datetime.fromtimestamp(x / 1000.0))

    return df





def process_timestamp(df, filename):

    df = df.set_index('time_str')

    result = df.resample('3S').first().ffill()

    # result = result[(result.index >= '2022-07-20 09:30') & (result.index <= '2022-07-20 15:00')]

    result = result.reset_index()

    result.to_csv(filename + '.csv')





def dump_single_code_tick():

    # 导出单个转债的tick数据

    code='128022'

    start_date = '20210113'

    end_date = '20210130'



    post_fix = 'SZ' if code.startswith('12') else 'SH'

    code = '{}.{}'.format(code,post_fix)

    filename = '{}'.format(code)

    df = get_tick(code, start_date, end_date)

    process_timestamp(df, filename)



dump_single_code_tick()