国信平台，打板代码示例
```python
#encoding:gbk
'''
本策略事先设定好交易的股票篮子，然后根据指数的CCI指标来判断超买和超卖
当有超买和超卖发生时，交易事先设定好的股票篮子
'''
import pandas as pd
import numpy as np
import talib
import time
######################PD 格式设置###########################
pd.set_option('expand_frame_repr', False) # 不换行
pd.set_option('display.max_rows', 5000) # 最多显示数据的行数
pd.set_option('display.unicode.ambiguous_as_wide', True) # 中文字段对齐
pd.set_option('display.unicode.east_asian_width', True)
pd.set_option('display.float_format', lambda x: '%.2f' % x)
##########################################################
# ####################参数配置说明###########################
# real 1:实盘 0：模拟帐号
# log 1:打印日记 0：不打印日记
# scale 仓位控制 开仓数据为 1/scale 数值越大，开仓越小
# buy_num 买入一支股票，是否分几次买入
###########################################################
class Tool():
def __init__(self,ContextInfo):
self.g = ContextInfo
print('自定义工具类')
def log(self,*args):
# log 为用户输入参数
if log:
print(*args)
def readStockInfo(self,st):
# 读取个股的基本信息
start_time = self.g.get_open_date(st)
#detail CreateDate:上市日期 ,FloatVolumn：流通股本,TotalVolumn：总股本
detail = self.g.get_instrumentdetail(st)
data={}
data['code']=st
data['start_time']=str(detail['CreateDate'])
data['FloatVolumn']=np.int64(detail['FloatVolumn'])
data['TotalVolumn']=np.int64(detail['TotalVolumn'])
series = pd.Series(data)
self.g.stockinfo[st]=series
#return series
def readMyStock(self,bkname):
# 读取自选股列表
self.g.trade_code_list = self.g.get_stock_list_in_sector(bkname)
self.g.set_universe(self.g.trade_code_list)
self.g.stock_count= len(self.g.trade_code_list)
def readStockData(self,columns=['close','open','high','low','volume','settle'],per='1d',size=10):
# 读取股票行情数据
self.g.df = self.g.get_market_data(columns,stock_code =self.g.trade_code_list,skip_paused = True, period = per, dividend_type = 'front',count=size)
def readStockDataTick(self):
#读取列表中的股票的盘口数据
self.g.df_tick = self.g.get_full_tick(self.g.trade_code_list)
self.log(type(self.g.df_tick))
def doRead(self,bkname):
self.readMyStock(bkname)
if self.g.stock_count == 0:
print('当前板块没有数据，请添加股票到板块列表中！')
self.g.df_type = 0
return
#读取股票的盘口数据
self.readStockDataTick()
#读取股票的行情数据
self.readStockData()
self.log('行情数据:',type(self.g.df))
if isinstance(self.g.df,pd.DataFrame):
self.log('DF 数据格式为DataFrame')
self.g.df_type = 1
return
elif isinstance(self.g.df,pd.Panel):
self.log('DF 数据格式为Panel')
self.g.df_type = 2
return
else:
self.g.df_type = 0
return
def formatDf(self,st,df):
# 个股数据的格式化
df_infos = self.g.stockinfo
if st not in df_infos.columns:
self.readStockInfo(st)
if st not in df_infos.columns:
return
stock_info = self.g.stockinfo[st]
self.log('info:',stock_info)
#计算涨跌停价
bd = 0.1
code = int(st.split('.')[0])
nc = code // 100000
if nc == 3:
bd =0.2
elif nc == 6:
nc = code //10000
if nc == 68:
bd = 0.2
#bd值为股票的涨跌幅限制 创业板、科创板为0.2 其余的为0.1
# 格式化数据
df['old_close']=df['close'].shift(1)
#求出今天的 开盘价震幅
df['open_offset'] = (df['open']-df['old_close'])*100/df['old_close']
#求出当前收盘价 是否接近涨停
df['now_offset'] = (df['close']-df['old_close'])*100/df['old_close']
# 算出每天的 涨停价
df['top']=df['old_close']*(1+bd)
df['top'] = df['top'].apply(lambda x:round(x,2))
# 算出每天的 跌停价
df['fail']=df['old_close']*(1-bd)
df['fail'] = df['fail'].apply(lambda x:round(x,2))
#盘中 是否上摸涨停价
df['istop'] = df['top']==df['high']
#盘中 是否触及跌停价
df['isfail'] = df['low']==df['fail']
#当前收盘是否 涨停
df['istopnow'] = df['top']==df['close']
#当前收盘价是 跌停
df['isfailnow'] = df['low']==df['close']
#当前 K线换手率
df['hand'] = df['volume']/stock_info['FloatVolumn']*10000
#当前K线 是否红盘 - 股价一直在开盘价上方运行
df['isred'] = df['low']>=df['open']
#当前K线 是否为阳线
df['issun'] = df['close'] >= df['open']
return df
def modelDb(self,st,df):
# 判断当前股票是否符合打板条件 模型算法 符合条件返回 True 否则 False
#取出该股票的盘口数据
#self.log(st,df)
ndata = df.iloc[-1]
self.log('当日实时数据',ndata)
ndata = pd.Series(ndata)
ndata['buy']=False
tick = self.g.df_tick[st]
sellPrice = tick['askPrice']
count = 0
for i in range(0,5):
if sellPrice[i] == ndata['top']:
count = i+1
break
self.log('count=',count)
if count == 0:
return ndata
if count <= offset and ndata['istop']:
ndata['buy']=True
ndata['buy_price']=sellPrice[count]
return ndata
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
ContextInfo.trade_code_list=[]
ContextInfo.set_universe(ContextInfo.trade_code_list)
ContextInfo.stock_count=0
ContextInfo.accID = '410038217334'
ContextInfo.buy = True
ContextInfo.sell = False
#股票的实时行情数据
ContextInfo.df=None
ContextInfo.df_type=0
#股票的实时盘口数据
ContextInfo.df_tick=None
#ContextInfo.colums=['close','open','high','low','volume','settle']
ContextInfo.tick=0
#股票列表中个股的基本信息
ContextInfo.stockinfo=pd.DataFrame()
# 自定义工具类
ContextInfo.tool = Tool(ContextInfo)
# 设置定时器，执行自定义运行 5nSecond,,500nMilliSecond
#ContextInfo.run_time("myHandlebar","5nSecond","2019-10-14 13:20:00","SH")
def myHandlebar(g):
#tool = g.tool
#tool.doRead('我的自选')
return
# 自定义的交易函数
#ContextInfo.tick = ContextInfo.tick + 1
#print(ContextInfo.tick)
#readData(ContextInfo)
def handlebar(g):
tool = g.tool
#g.tool.formatFloat(1)
if g.is_last_bar():
#为行情数据的格式，df_type 0:不处理 1：DataFrame 2:Panel
df = None
tool.doRead('我的自选')
if g.df_type == 0:
return
if g.df_type==1:
df = g.df
st = g.trade_code_list[0]
df = tool.formatDf(st,df)
ndata = tool.modelDb(st,df)
isbuy(g,st,ndata)
elif g.df_type == 2:
for st in g.trade_code_list:
df = g.df[st]
df = tool.formatDf(st,df)
ndata =tool.modelDb(st,df)
isbuy(g,st,ndata)
def isbuy(g,st,ndata):
#是否执行股票买入操作
print(ndata)
if ndata['buy']:
print('股票开仓：',st,ndata)
pass
```