def fl_ordercsv():
'''函数 ---分离策略结果集'''
op = file_op()
cl_df = op.get_txt('order.csv')
#策略名去重
clname_li11 = cl_df.clname.values.tolist()
clname_li = (list(set(clname_li11)))
for na in clname_li:
files1 = '{}.txt'.format(na[-3:])
df = cl_df[cl_df.clname == na]
df.to_csv(files1)
return 0
def fl_ordercsv():
'''函数 ---分离策略结果集'''
op=file_op()
cl_df=op.get_txt('order.csv')
#分类文件名去重
fil=list(set(cl_df.files.values.tolist()))
#print(fil)
for na in fil:
files1=na+'.txt'
df=cl_df[cl_df.files==na]
#print(na,files1)
#print(df.head())
df.to_csv(files1)
return 0
def getbasc(self, code1list):
'''函数--获取基础信息，根据列表['000001','all']'''
csv_path = 'd:/stock_csv/{}.csv'.format('basc')
if 'all' in code1list: print('获取所有Stock')
jk = file_op()
df = jk.get_from_csv(csv_path)
st_list = []
co_list = df.index.values.tolist()
for co in co_list:
s_code = getSixDigitalStockCode(co)
if s_code not in code1list and 'all' not in code1list:
continue
s_name = df[df.index == co].name.values[-1]
s_totals = df[df.index == co].totals.values[-1]
s_hy = df[df.index == co].industry.values[-1]
s = Stock(code=s_code, name=s_name, hangye=s_hy, totals=s_totals)
st_list.append(s)
#返回Stock list
return st_list
def get_all_orderresult():
'''函数--获取给点集合代码所有策略'''
order_js_list=[]
tt=[]
jk=jiekou()
#大名单列表存入tt
op=file_op()
dmd_li1=op.get_txt('sv_dmd1.csv').code.values.tolist()
for code in dmd_li1:
co=getSixDigitalStockCode(code)
tt.append(co)
#tt=['600359','600609','002498', '002238','300415','000987', '600598','000931']#tt=['all']
#tt=['300216','002238','300415','000987', '600598','000931']
st_list=jk.getbasc(tt)#获取符合的代码Stock，，tt=['all']
#容错
if len(st_list)==0:
print('没有符合的代码')
return 0
#获取所有策略结果
for s in st_list:
jg_li=getorderresult(s)#获取策略结果
order_js_list.extend(jg_li)#集合所有结果
#结果集存入order.csv
#加入时间节点
gxrq = datetime.datetime.now().strftime('%Y%m%d')
gxsj = datetime.datetime.now().strftime('%H%M')
gx=gxrq+gxsj
columns_li=[ 'code', 'name','cl','cljg','qz','files']
if len(order_js_list)>0:
if len(order_js_list[-1])!=len(columns_li):
print("数据列数量不等")
df=DataFrame(order_js_list,columns=columns_li)
df['gxsj']=str(gx)
df.to_csv('order.csv')#为增加方式
return 0
def bakanddraw(code,typ,start,end,path):
'''取重要数据备份并保存图片'''
kk=jiekou()
op=file_op()
ktype=typ
df=kk.getkl(code,ktype)
if end>start and end<len(df):
df=df[start:end]
dat11=df.iloc[-1:].date.values[0].split()[0]
#保存路径和文件
file1='{0}-{1}-bak-{2}.csv'.format(code,ktype,dat11)
df.to_csv(file1)
print(file1)
hh=gu_zb(0)
name=''
#取4个类型的CCi
if df.empty:
return 0
cci=hh.cci(df)
ln=len(cci)
total=ln-14
if ln<total:
total=ln
PLUS_DI,MINUS_DI,ADX,ADXR=hh.dmi(df)
df=df[-total:]
cci=cci[-total:]
MINUS_DI=MINUS_DI[-total:]
PLUS_DI=PLUS_DI[-total:]
ADX=ADX[-total:]
ADXR=ADXR[-total:]
#4个类型的顶点
#画出最后3条线
fig, ax = plt.subplots(3, 1, figsize=(16,8))
ax[0].set_title(name+code+'--'+ktype,fontproperties = 'SimHei',fontsize = 20)
ax[1].plot(cci,'r')
ax[2].plot(ADX,'r')
ax[2].plot(PLUS_DI,'y')
ax[2].plot(MINUS_DI,'b')
ax[2].plot(ADXR,'g')
ax[2].axhline(y=80, color='b', linestyle=':')
ax[2].axhline(y=50, color='b', linestyle=':')
ax[2].axhline(y=20, color='b', linestyle=':')
#取顶点
up_li2=hh.gjbc(df)
dw_li2=hh.gj_d_bl(df)
print(up_li2)
if len(up_li2)>4:
up=up_li2[-4:]
else:
up=up_li2
if len(dw_li2)>2:
dw=dw_li2[-2:]
else:
dw=dw_li2
for u in up:
y1=u[1]
y2=u[3]
x1=u[0]
x2=u[2]
k=(y2-y1)/(x2-x1)
if k>0:continue
b=y2-k*x2
c1=(300-b)/k
c2=(-200-b)/k
if c2>total:
c2=total
if c1<0:
c1=0
l_x=np.linspace(c1,c2,10)
y=k*l_x+b
ax[1].plot(l_x,y,'-.y')
for u in dw:
y1=u[1]
y2=u[3]
x1=u[0]
x2=u[2]
k=(y2-y1)/(x2-x1)
if k<0:continue
b=y2-k*x2
c1=(200-b)/k
c2=(-350-b)/k
if c1>total:
c1=total
if c2<0:
c2=0
l_x=np.linspace(c1,c2,10)
y=k*l_x+b
ax[1].plot(l_x,y,'-.y')
#画K线
mpf.candlestick2_ochl(ax=ax[0],opens=df["open"].values.tolist(), closes=df["close"].values, highs=df["high"].values, lows=df["low"].values,width=0.7,colorup='r',colordown='g',alpha=0.7)
ax[1].axhline(y=100, color='b', linestyle=':')
ax[1].axhline(y=-100, color='b', linestyle=':')
#文字
plt.style.use('ggplot')
plt.show()
return 1
def aiyb():
tt=[]
jk=jiekou()
#大名单列表存入tt
op=file_op()
dmd_li1=op.get_txt('sv_dmd1.csv').code.values.tolist()
for code in dmd_li1:
co=getSixDigitalStockCode(code)
tt.append(co)
tt=['002498','600359','600609']
st_list=jk.getbasc(tt)#获取符合的代码Stock，，tt=['all']
iii=0
#str_pcci=(co.p_cci).__doc__
#print(str_pcci)
#return 0
for s in st_list:
iii+=1
#获取k线记基础指标
szb=stockzb(s)
d=D_kl()#日线修饰,实时数据
#应用策略
szb.decorator(d)#日线修饰
szb.getk()#获取k线
#详细分析
co=cciorder(szb)
b=co.cci_dmi()
md1=[]
for x in b:
if x[0]=='dw' and x[3]=='cci':
md1.append(x[1])
if x[0]=='dw' and x[8]=='cci':
md1.append(x[6])
code2=co.getcode()
name=co.getname()
#md1=[]
yb_li=[]
yb_li30=[]
for xh in md1:
l_cci=co.p_cci(xh)
l_adx=co.p_adx(xh)
l_vol=co.p_vol(xh)
l_macd=co.p_macd(xh)
l_boll=co.p_boll(xh)
l_gj=co.p_gj(xh)
l_tj=co.tj(xh)
date_xh=co.df.loc[xh].date
tjlb=0
if l_tj[2]<=0:
tjlb=-1
elif l_tj[2]>0 and l_tj[2]<10:
tjlb=1
elif l_tj[2]>=10 and l_tj[2]<20:
tjlb=2
elif l_tj[2]>=20:
tjlb=3
else:
tjlb=0
yb_li30.append([code2,date_xh]+l_cci[1:]+l_adx[1:]+l_macd[1:]+l_boll[1:]+l_vol+l_gj+[tjlb])
#elif l_tj[1]>10 or l_tj[2]>10 and l_tj[3]>0:
#print(l_tj)
#yb_li.append([code2,date_xh]+l_cci+l_adx+l_macd+l_boll+l_vol+l_gj+l_tj)
#print(l_cci+l_adx+l_macd+l_boll+l_vol+l_gj+l_tj)
#print(iii,len(yb_li),yb_li30)
#df=DataFrame(yb_li)
#df.to_csv('d:/aiyb10.csv',mode='a',header=False,encoding='utf-8')
gxrq = datetime.datetime.now().strftime('%Y-%m-%d')
files1='d:/aiyb{}.csv'.format(gxrq)
df2=DataFrame(yb_li30)
df2.to_csv(files1,mode='a',header=False,encoding='utf-8')
return 0
def get_date(self):
file1 = self.file1
fo = file_op()
df = fo.get_from_csv(file1)
#df=''
return df