股票量化

Overview

StockQuant

Gary-Hertel

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一、配置文件的设置

启动框架需要先导入必要的模块,并且载入一次配置文件!

配置文件是一个json格式的文件config.json,在docs文件夹中有模板文件,其内容如下,将其中的信息替换成自己的即可:

{
    "LOG": {
        "level": "debug",
        "handler": "stream"
    },
    "DINGTALK": "your dingding token",
    "TUSHARE": "your tushare token",
    "SENDMAIL": {
        "from": "your qq email address",
        "password": "your qq email authorization code",
        "to": "your qq email address",
        "server": "smtp.qq.com",
        "port": 587
    }
}

其中的内容说明:

  • LOG:日志配置
    • level:日志显示的等级,可选debuginfoerrorwarningcritical
    • handler:日志的输出方式,可选streamfiletime
  • DINGTALK:你的钉钉webhook token
  • TUSHARE:你的tushare_pro token
  • SENDMAIL:邮箱配置
    • from:发件邮箱,推荐使用QQ邮箱
    • password:你的QQ邮箱授权码,非QQ密码
    • to:收件邮箱,推荐使用QQ邮箱并在微信上绑定此邮箱以实现微信接收消息
    • server:邮箱服务器,QQ邮箱默认使用此服务器
    • port:邮箱端口,QQ邮箱默认此端口即可

除了配置的这些信息外,也可以向配置文件中添加任意的信息,但注意不能与默认设置内容中大写的内容名称相同,即使你添加的信息是小写亦不可!要在策略中使用向配置文件中增加的信息,示例如下:

比如我们向配置文件中添加一项信息

{
    "LOG": {
        "level": "debug",
        "handler": "stream"
    },
    "DINGTALK": "your dingding token",
    "TUSHARE": "your tushare token",
    "SENDMAIL": {
        "from": "your qq email address",
        "password": "your qq email authorization code",
        "to": "your qq email address",
        "server": "smtp.qq.com",
        "port": 587
    },
    "person_name": "Gary-Hertel"
}

要在策略中使用,只需:

config.person_name

二、框架的启用

在我们配置好配置文件后,将其放入我们的项目中,接下来就可以使用我们的框架了:

from stockquant.quant import *		# 导入必要的模块

config.loads('config.json')			# 载入配置文件

三、行情数据

行情数据获取,具体参数请看方法内部的说明文档,在开发工具中,按住ctrl用鼠标点击一下方法的名称即可查看。

说明 调用方式
获取指定股票的实时数据 Market.tick(symbol)
获取深圳成指 Market.shenzhen_component_index()
获取上证综指 Market.shanghai_component_index()
获取历史k线数据 Market.kline(symbol, timeframe, adj=None, start_date=None, end_date=None)
股票列表 Market.stocks_list(day=None)
查询今日沪深股市是否开盘 Market.today_is_open()
证券基本资料 Market.stock_basic_info(symbol=None, symbol_name=None)
查询除权除息信息 Market.dividend_data(symbol, year, yearType)
查询复权因子信息 Market.adjust_factor(symbol, start_date=None, end_date=None)
季频盈利能力 Market.profit_data(symbol, year=None, quarter=None)
季频营运能力 Market.operation_data(symbol, year=None, quarter=None)
季频成长能力 Market.growth_data(symbol, year=None, quarter=None)
季频偿债能力 Market.balance_data(symbol, year=None, quarter=None)
季频现金流量 Market.cash_flow_data(symbol, year=None, quarter=None)
季频杜邦指数 Market.dupont_data(symbol, year=None, quarter=None)
季频公司业绩快报 Market.performance_express_report(symbol, start_date, end_date)
季频公司业绩预告 Market.forcast_report(symbol, start_date, end_date)
存款利率 Market.deposit_rate_data(start_date=None, end_date=None)
贷款利率 Market.loan_rate_data(start_date=None, end_date=None)
存款准备金率 Market.required_reserve_ratio_data(start_date=None, end_date=None, yearType=None)
货币供应量 Market.money_supply_data_month(start_date=None, end_date=None)
货币供应量(年底余额) Market.money_supply_data_year(start_date=None, end_date=None)
银行间同业拆放利率 Market.shibor_data(start_date=None, end_date=None)
获取行业分类信息 Market.stock_industry(symbol=None, date=None)
获取上证50成分股信息 Market.sz50_stocks(date=None)
沪深300成分股 Market.hs300_stocks(date=None)
中证500成分股 Market.zz500_stocks(date=None)
获取新股上市列表数据 Market.new_stock()

Note: 获取指定股票的实时数据时,Tick对象数据结构如下:

调用方式 数据类型 字段说明
tick.symbol string 股票名称
tick.last float 当前价格
tick.open float 今日开盘价
tick.high float 今日最高价
tick.low float 今日最低价
tick.yesterday_close float 昨日收盘价
tick.bid_price float 竞买价
tick.ask_price float 竞卖价
tick.transactions float 成交数量
tick.turnover float 成交金额
tick.bid1_quantity float 买一数量
tick.bid1_price float 买一报价
tick.bid2_quantity float 买二数量
tick.bid2_price float 买二报价
tick.bid3_quantity float 买三数量
tick.bid3_price float 买三报价
tick.bid4_quantity float 买四数量
tick.bid4_price float 买四报价
tick.bid5_quantity float 买五数量
tick.bid5_price float 买五报价
tick.ask1_quantity float 卖一数量
tick.ask1_price float 卖一报价
tick.ask2_quantity float 卖二数量
tick.ask2_price float 卖二报价
tick.ask3_quantity float 卖三数量
tick.ask3_price float 卖三报价
tick.ask4_quantity float 卖四数量
tick.ask4_price float 卖四报价
tick.ask5_quantity float 卖五数量
tick.ask5_price float 卖五报价
tick.timestamp str 时间戳

四、技术指标

kline = Market.kline("sh601003", "1d")
指标名称 调用方式 返回值
指数移动平均线 ATR(14, kline=kline) 一维数组
k线数据的长度 CurrentBar(kline=kline) 整型数字
布林 BOLL(20, kline=kline) {"upperband": 上轨, "middleband": 中轨, "lowerband": 下轨}
顺势指标 CCI(20, kline=kline) 一维数组
周期最高价 HIGHEST(20, kline=kline) 一维数组
移动平均线 MA(20, 30, kline=kline) 一维数组
指数平滑异同平均线 MACD(14, 26, 9, kline=kline) {'DIF': DIF数组, 'DEA': DEA数组, 'MACD': MACD数组}
指数平均数 EMA(20, 30, kline=kline) 一维数组
考夫曼自适应移动平均线 KAMA(20, 30, kline=kline) 一维数组
随机指标 KDJ(20, 30, 9, kline=kline) {'k': k值数组, 'd': d值数组}
周期最低价 LOWEST(20, kline=kline) 一维数组
能量潮 OBV(kline=kline) 一维数组
强弱指标 RSI(20, kline=kline) 一维数组
变动率指标 ROC(20, kline=kline) 一维数组
随机相对强弱指数 STOCHRSI(20, 30, 9, kline=kline) {'stochrsi': stochrsi数组, 'fastk': fastk数组}
抛物线指标 SAR(kline=kline) 一维数组
标准方差 STDDEV(20, kline=kline, nbdev=None) 一维数组
三重指数平滑平均线 TRIX(20, kline=kline) 一维数组
成交量 VOLUME(kline=kline) 一维数组

五、日志

日志模块对于分析程序的运行状况至关重要,StockQuant内置日志模块,可用来方便记录程序运行状况与排查问题。

日志一共分成5个等级,从低到高分别是:

  1. DEBUG
  2. INFO
  3. WARNING
  4. ERROR
  5. CRITICAL
logger.debug("DEBUG日志")
logger.info("INFO日志")
logger.warning("WARNING日志")
logger.error("ERROR日志")
logger.critical("CRITICAL日志")

配置文件中,level如设置成debug级别,则会输出所有级别的日志,如设置成info级别,只会输出info及以上级别的日志,而不会输出debug级别的日志。

配置文件中,handler如设置为stream,是打印日志到控制台;如设为file是保存至文件,文件大小按1M进行分割,会保留最近的1000份日志文件;如设置为time是按照每天进行分割。

Note:如果是在宝塔面板上运行程序,记得将配置文件中LOGhandler设置不要设置为stream,否则会一直写入日志,并且不会自动分割日志。


六、信息推送

信息推送对于风控通知来说是至关重要的。StockQuant内置信息推送模块,可直接调用以推送信息至钉钉或邮箱。

1.钉钉

Note:需在配置文件中设置钉钉WebHook Api,建立钉钉群聊后添加一个WebHook机器人,创建机器人时指定关键字如交易

(1)推送文本类型信息

推送文本类型信息时需包含关键字,否则无法送达。

Dingralk.text("交易提醒:sh600519的价格已达到2000元!")

(2)推送markdown类型信息

推送markdown信息时无需包含关键字(前提是你的关键字设置的是交易)。下面看一个示例:

from stockquant.quant import *

config.loads('config.json')

tick = Market.tick("sh600519")

content = "### 订单更新推送\n\n" \
            "> **股票名称:** {symbol}\n\n" \
            "> **当前价格:** {last}\n\n" \
            "> **成交数量:** {transactions}\n\n" \
            "> **成交金额:** {turnover}\n\n" \
            "> **时间戳:** {timestamp}".format(
                symbol=tick.symbol,
                last=tick.last,
                transactions=tick.transactions,
                turnover=tick.turnover,
                timestamp=tick.timestamp
            )
DingTalk.markdown(content)

2.邮件

sendmail("交易提醒:sh600519的价格已达到2000美元!")

七、数据存储

txt_save(content, filename)						 # 保存数据至txt文件
txt_read(filename)								# 读取txt文件中的数据
save_to_csv_file(tuple, path)					 # 保存文件至csv文件
read_csv_file(path)								# 读取csv文件中保存的数据

八、时间戳转换的一些方法

sleep(seconds)							# 休眠,作用等同于time.sleep()
get_cur_timestamp()						# 获取当前时间戳(秒)
ts_to_utc_str(ts)						# 将时间戳转换为UTC时间格式
get_cur_timestamp_ms()					# 获取当前时间戳(毫秒)
get_cur_datetime_m()					# 获取当前日期时间字符串,包含 年 + 月 + 日 + 时 + 分 + 秒
get_datetime()							# 获取日期字符串,包含 年 + 月 + 日
date_str_to_dt(date_str)				# 日期字符串转换到datetime对象
dt_to_date_str(dt)						# datetime对象转换到日期字符串
get_utc_time()							# 获取当前utc时间
get_localtime()							# 获取本地时间
ts_to_datetime_str(ts)					# 将时间戳转换为日期时间格式,年-月-日 时:分:秒
datetime_str_to_ts(dt_str)				# 将日期时间格式字符串转换成时间戳
datetime_to_timestamp(dt)				# 将datetime对象转换成时间戳
utctime_str_to_ts(utctime_str)			# 将UTC日期时间格式字符串转换成时间戳
utctime_str_to_mts(utctime_str)			# 将UTC日期时间格式字符串转换成时间戳(毫秒)

九、自动交易

"""
股票自动交易,使用的是easytrader开源项目。
仅支持windows系统,云主机与虚拟机上无法运行。
具体用法,可参考哔哩哔哩教学视频:
    https://www.bilibili.com/video/BV1zK411u7uG
"""


from stockquant.quant import *


class Strategy:

    def __init__(self):
        self.trade = Trade(config_file="config.json", symbol="sh512980")    # 初始化trade模块

        self.do_action()

    def do_action(self):
        price = Market.tick("sh512980").ask1_price          # 获取卖一价格
        success, error = self.trade.buy(price, amount)      # 买入
        success, error = self.trade.sell(price, amount)     # 卖出
        success, error = self.trade.get_positions()         # 查询当前持仓
        success, error = self.trade.get_balance()           # 查询资金信息
        success, error = self.trade.get_today_orders()      # 查询今日委托
        success, error = self.trade.get_today_deals()       # 查询今日成交
        if error:
            DingTalk.text("交易提醒:失败:{}".format(error))
            pass
        logger.info("success:{}".format(success))


if __name__ == '__main__':

    Strategy()

updated at 2021/03/03

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