def plot_dragonpoint(ax1,_dfquotes,beginindex,targetprices,_imulti=40):
"""
plot k graph with line of targetprices
Args:
_dfquotes (dataframe): the seurity dataframe which include'openPrice''closePrice''highestPrice''lowestPrice''tradeDate'
ax1 : matplotlib ax object
beginindex : the index of first k bar, _dfquotes[begin:] will be plot
_locatormulti (int): adjust the axis's ticker display interval
Returns:
none
Examples:
>>
"""
global gdfquotes
gdfquotes = _dfquotes
plot_security_k(ax1,gdfquotes,beginindex,_locatormulti=_imulti)
ax2 = ax1.twinx()
ax2.set_ylim(ax1.get_ylim())
ax2.yaxis.set_major_formatter(ticker.FuncFormatter(format_percentage))
_zero = gdfquotes['closePrice'].iloc[-1]
x1 = range(0,len(gdfquotes)-beginindex+1,len(gdfquotes)-beginindex)
for _p in targetprices:
y1 = [_p for n in x1]
_percen = mymath.rod(_p/_zero-1,4)*100
if _percen < 0 and _percen > -11:
ax1.plot(x1,y1,'m--')
ax1.text(targetprices.index(_p)*3,_p,'%.2f(%.2f%%)'%(mymath.rod(_p,2),_percen),rotation=90,va='bottom',color='m')
else:
ax1.plot(x1,y1,'b-')
ax1.text(targetprices.index(_p)*3,_p,'%.2f(%.2f%%)'%(mymath.rod(_p,2),_percen),rotation=90,va='bottom')
plt.minorticks_on()
pass
def ztnum(_his,s,_Divhis=[]):
if len(_Divhis) == 0:
_Divhis = DataAPI.EquDivGet(secID=s,eventProcessCD='6',field=['exDivDate','perShareDivRatio','perShareTransRatio','perCashDiv'],pandas="1")
_Divdate = '0000-00-00'
for _date in _Divhis.exDivDate:
if _date >= _his['tradeDate'].iloc[0] and _date <= _his['tradeDate'].iloc[-1]:
_Divdate = _date#只考虑区间内有一次除权
break
i = 0
zt = 0
_closep = 0
while True:
#除权单天的开盘价除权
if _his['tradeDate'].iloc[i+1] == _Divdate:
_closep = msum.AdjP(_Divhis,_Divdate,_his['closePrice'].iloc[i])
else:
_closep = _his['closePrice'].iloc[i]
if _his['closePrice'].iloc[i+1] == mymath.rod(_closep*1.1,2):
zt = zt + 1
#除权日前涨停价格除权
'''
if previousztdate < _Divdate and _his['tradeDate'].iloc[i+1] >= _Divdate:
previouszt = msum.AdjP(_Divhis,_Divdate,previouszt)
print '除权后 previouszt',previouszt
if _his['closePrice'].iloc[i+1] >= previouszt:
zt = zt + 1
previouszt = _his['closePrice'].iloc[i+1]
previousztdate = _his['tradeDate'].iloc[i+1]
'''
i = i + 1
if i > len(_his)-2:
break
return zt
def canbuy(_dayTran, _Divdate):
#除权单天的开盘价除权
if _dayTran['tradeDate'] == _Divdate:
_closep = AdjP(_Divhis, _Divdate, _dayTran['actPreClosePrice'])
else:
_closep = _dayTran['actPreClosePrice']
#T 天开盘价是否小于涨停价
if _dayTran['openPrice'] < mymath.rod(_closep * 1.1, 2):
bcanbuy = True
targetprice = _dayTran['openPrice'] * _dayTran['accumAdjFactor']
return bcanbuy, targetprice
return False, 0
def ztcs(data):
data = data.tolist()
data = [round(e, 2) for e in data]
if len(data) < 2:
return 0
i = 0
zt = 0
previouszt = 0
while True:
if (data[i + 1] == mymath.rod(data[i] * 1.1, 2)
and data[i + 1] >= previouszt):
zt = zt + 1
previouszt = data[i + 1]
i = i + 1
if i > len(data) - 2:
break
return zt
def howmanyzt(_his, _Divdate, bzszt=False, turnrate=0.03):
bzt_T = False
#不除权历史数据
_his = _his.set_index('tradeDate')
#计算区间内涨停情况
i = 0
zt = 0
previouszt = 0
_closep = 0
previousztdate = '0000-00-00'
while True:
#除权单天的开盘价除权
if _his.index[i + 1] == _Divdate:
_closep = AdjP(_Divhis, _Divdate, _his['closePrice'].iloc[i])
else:
_closep = _his['closePrice'].iloc[i]
if _his['closePrice'].iloc[i + 1] == mymath.rod(_closep * 1.1, 2):
#T 天是否涨停
if i == len(_his) - 2:
if bzszt == False:
bzt_T = True
elif _his['turnoverRate'].iloc[
i + 1] >= turnrate or _his['openPrice'].iloc[
i + 1] != _his['closePrice'].iloc[i + 1]:
bzt_T = True
#除权日前涨停价格除权
if previousztdate < _Divdate and _his.index[i + 1] >= _Divdate:
previouszt = AdjP(_Divhis, _Divdate, previouszt)
if _his['closePrice'].iloc[i + 1] >= previouszt:
zt = zt + 1
previouszt = _his['closePrice'].iloc[i + 1]
previousztdate = _his.index[i + 1]
i = i + 1
if i > len(_his) - 2:
break
return zt, bzt_T
print start,now
for s in universe:
#非复权历史数据
_his = DataAPI.MktEqudGet(beginDate=start,endDate=now,secID=s,isOpen=1,field=['tradeDate','closePrice','openPrice','highestPrice','lowestPrice','accumAdjFactor','actPreClosePrice','turnoverRate'],pandas='1')
#历史除权信息
_Divhis = DataAPI.EquDivGet(secID=s,eventProcessCD='6',field=['exDivDate','perShareDivRatio','perShareTransRatio','perCashDiv'],pandas="1")
value = []
#遍历历史数据
for id in range(20,len(_his)):
g_currentdate = _his['tradeDate'].iloc[id]
if g_currentdate < continueday:
continue
accumAdjFactor = _his['accumAdjFactor'].iloc[id]
#买入
if len(value) > 0 and boughtbefore(s,g_currentdate,_his[id-20:id+1],g_imaxback+2):
targetprice = mymath.rod(value[g_targetprice],2)
_lowestprice = mymath.rod(_his['lowestPrice'].iloc[id]*accumAdjFactor,2)
if targetprice >= _lowestprice:
_openprice = mymath.rod(_his['openPrice'].iloc[id]*accumAdjFactor,2)
_val =[g_currentdate,s,min(targetprice,_openprice)]
i = len(g_security_return_value)*g_imaxback
while i > 0:
_val.append(0.)
i = i - 1
g_security_history[len(g_security_history)] = _val
#计算之前买入股票目前收益
#1.当天卖出的盈利概率 2.当天卖出的盈利百分比
i = len(g_security_history) - 1
while i >0:
v = g_security_history[i]
if v[0] == g_currentdate or v[1] != s:#ignore the current day and not the same stock
def format_percentage(y,pos=None):
global dfquotes
_ticklabel = mymath.rod(y/dfquotes['closePrice'].iloc[-1]-1,3)
_per = '%.2f%%'%(_ticklabel*100)
return _per
def lxztordt(gc, tradedate, zt=True, turnrate=0.03):
"""
给定股票代码列表，计算连板数
Args:
gc (list): 需要计算连板的股票代码列表
tradedate (string):计算时间
turnrate (float):一字板确认真实涨停换手率标准
zt (bool):计算涨停还是跌停
Returns:
dict (dictionary):key是连板数量，value是[[ticker,turnoverrate],[ticker,turnoverrate]....]
Examples:
>> lxztordt(['000425','600307','002307','000877'],'20170210')
>> lxztordt(['000425.XSHE','600307.XSHG','002307','000877'],'20170210')
"""
_his = []
_conbandict = {}
for s in gc:
_continuebang = 0
_continueturn = 0
if s.find('.') >= 0:
_his = DataAPI.MktEqudGet(endDate=tradedate,
secID=s,
field=[
'tradeDate', 'secShortName',
'lowestPrice', 'closePrice',
'turnoverRate'
],
isOpen=1,
pandas='1')
else:
_his = DataAPI.MktEqudGet(endDate=tradedate,
ticker=s,
field=[
'tradeDate', 'secShortName',
'lowestPrice', 'closePrice',
'turnoverRate'
],
isOpen=1,
pandas='1')
Divhis = DataAPI.EquDivGet(secID=s,
eventProcessCD='6',
field=[
'exDivDate', 'perShareDivRatio',
'perShareTransRatio', 'perCashDiv'
],
pandas="1")
indexreverse = range(-1, -len(_his), -1)
for ind in indexreverse:
if (_his['secShortName'].iloc[ind].find('S') >= 0):
if zt:
maxinc = 1.05
else:
maxinc = 0.95
else:
if zt:
maxinc = 1.1
else:
maxinc = 0.9
if ind > indexreverse[-1]:
_preclose = AdjP(Divhis, _his['tradeDate'].iloc[ind],
_his['closePrice'].iloc[ind - 1])
if _his['closePrice'].iloc[ind] == mymath.rod(
_preclose * maxinc, 2):
_continuebang = _continuebang + 1
_continueturn = _continueturn + _his['turnoverRate'].iloc[
ind]
#如果是统计涨停，则需要检查是不是真实涨停,如果不是就不统计连板数
if zt and _his['lowestPrice'].iloc[ind] == _his[
'closePrice'].iloc[ind] and _his[
'turnoverRate'].iloc[ind] < turnrate:
_continuebang = _continuebang - 1
break
else:
break #next security
if _continuebang == 0:
continue
#store to the dict
if _continuebang in _conbandict:
_conbandict[_continuebang].append([s, _continueturn])
else:
_conbandict[_continuebang] = [[s, _continueturn]]
return _conbandict
def handle_data(account): #在每个交易日开盘之前运行，用来执行策略，判断交易指令下达的时机和细节
global g_vardic
global ticks
global g_security_history
global g_currentdate
ticks = ticks % g_ifreq
if ticks == 0:
g_currentdate = account.current_date.strftime('%Y%m%d')
for k, v in g_security_history.items():
if k == 0 or g_vardic[k]['checked']:
continue
if ticks == 0:
df = DataAPI.MktEqudAdjGet(
beginDate=v[0],
endDate=g_currentdate,
secID=v[1],
field=['closePrice', 'highestPrice', 'lowestPrice'],
isOpen='1',
pandas='1')
g_vardic[k]['before'] = len(df)
if g_vardic[k]['before'] == 2:
g_vardic[k]['closeprice'] = df['closePrice'].iloc[0]
g_vardic[k]['highestprice'] = df['highestPrice'].iloc[-1]
g_vardic[k]['lowestprice'] = df['lowestPrice'].iloc[-1]
if g_vardic[k]['before'] != 2:
continue #only T+1 day
g_vardic[k]['fenshi'].append(account.reference_price[v[1]])
if ticks == 239:
plt.title(v[1] + v[0])
plt.plot(g_vardic[k]['fenshi'] / g_vardic[k]['closeprice'] - 1,
'y-')
lips = g_vardic[k]['soldpoint'] / g_vardic[k]['closeprice'] - 1
plt.plot(g_vardic[k]['soldticks'], lips, 'go')
plt.grid()
plt.xlim(0, 239)
plt.xticks(range(0, 240, 30))
ymax = max(
abs(g_vardic[k]['highestprice'] / g_vardic[k]['closeprice'] -
1),
abs(g_vardic[k]['lowestprice'] / g_vardic[k]['closeprice'] -
1))
plt.ylim(-ymax, ymax)
plt.show()
g_vardic[k]['checked'] = True
if g_vardic[k]['unit'] <= 0:
continue
if len(g_vardic[k]['fenshi']) <= 1:
_price_1 = g_vardic[k]['fenshi'][0]
else:
_price_1 = g_vardic[k]['fenshi'][-2]
if g_vardic[k]['fenshi'][0]/g_vardic[k]['closeprice']-1 >= jump\
and g_vardic[k]['fenshi'][-1] < _price_1:
g_vardic[k]['soldpoint'].append(g_vardic[k]['fenshi'][-1])
g_vardic[k]['soldticks'].append(ticks)
g_vardic[k]['unit'] = g_vardic[k]['unit'] - 1
if mymath.rod(g_vardic[k]['fenshi'][-1],2) == mymath.rod(g_vardic[k]['closeprice']*1.1,2)\
or ticks == 239:
while g_vardic[k]['unit'] > 0:
g_vardic[k]['soldpoint'].append(g_vardic[k]['fenshi'][-1])
g_vardic[k]['soldticks'].append(ticks)
g_vardic[k]['unit'] = g_vardic[k]['unit'] - 1
ticks = ticks + 1
return