def calc_kdj1(df):
kdj1 = KDJ(df, 45, 15, 15)
kdj2 = KDJ(df, 45 * 5, 15 * 5, 15 * 5)
kdj3 = KDJ(df, 45 * 5 * 5, 15 * 5 * 5, 15 * 5 * 5)
kdj4 = KDJ(df, 45 * 5 * 5 * 5, 15 * 5 * 5 * 5, 15 * 5 * 5 * 5)
df['k1'] = kdj1['KDJ_K']
df['d1'] = kdj1['KDJ_D']
df['j1'] = kdj1['KDJ_J']
df['e1'] = EMA(kdj1['KDJ_J'], 5)
df['k2'] = kdj2['KDJ_K']
df['d2'] = kdj2['KDJ_D']
df['j2'] = kdj2['KDJ_J']
df['e2'] = EMA(kdj2['KDJ_J'], 5 * 5)
df['k3'] = kdj3['KDJ_K']
df['d3'] = kdj3['KDJ_D']
df['j3'] = kdj3['KDJ_J']
df['e3'] = EMA(kdj3['KDJ_J'], 5 * 5 * 5)
df['k4'] = kdj4['KDJ_K']
df['d4'] = kdj4['KDJ_D']
df['j4'] = kdj4['KDJ_J']
df['e4'] = EMA(kdj4['KDJ_J'], 5 * 5 * 5 * 5)
# 计算KDJ值的斜率 ------------ begin -----------------------------------
"""
df['x1'] = np.nan
df['x2'] = np.nan
df['x3'] = np.nan
df['x4'] = np.nan
n = 6
x = np.arange( n )
yy = df['e1'].values
for i in range( n-1, len( df ) ):
y1 = df['e1'][i-n+1:i+1].values
y2 = df['e2'][i-n+1:i+1].values
y3 = df['e3'][i-n+1:i+1].values
y4 = df['e4'][i-n+1:i+1].values
# print(x)
# print(y)
z1 = np.polyfit( x, y1, 3 )
z2 = np.polyfit( x, y2, 3 )
z3 = np.polyfit( x, y3, 3 )
z4 = np.polyfit( x, y4, 3 )
# print('zzzzzz',z)
r1 = z1[0]*x[n-1]*x[n-1]+z1[1]*x[n-1]+z1[2]
r2 = z2[0]*x[n-1]*x[n-1]+z2[1]*x[n-1]+z2[2]
r3 = z3[0]*x[n-1]*x[n-1]+z3[1]*x[n-1]+z3[2]
r4 = z4[0]*x[n-1]*x[n-1]+z4[1]*x[n-1]+z4[2]
df['x1'].iat[i] = r1
df['x2'].iat[i] = r2
df['x3'].iat[i] = r3
df['x4'].iat[i] = r4
"""
# 计算KDJ值的斜率 ------------- end -----------------------------------
return df
def calc_ema(df):
ar = [
5, 8, 10, 13, 20, 21, 30, 34, 40, 55, 60, 89, 120, 144, 233, 250, 377,
500, 610
]
for i in ar:
s = str(i)
s = "ema" + s
df[s] = EMA(df['close'], i) # 计算EMA(5) EMA(13) ... EMA(610)
df['xema610'] = df['ma8'] - df['ma610']
df['xema377'] = df['ma8'] - df['ma377']
df['xema233'] = df['ma8'] - df['ma233']
df['xema144'] = df['ma8'] - df['ma144']
df['xema89'] = df['ma8'] - df['ma89']
df['xema55'] = df['ma8'] - df['ma55']
df['xema34'] = df['ma8'] - df['ma34']
df['xema21'] = df['ma8'] - df['ma21']
return df
def calc_kdj_ma_ema(df):
"""
# KDJ1
df['k1'] = np.nan
df['d1'] = np.nan
df['j1'] = np.nan
df['e1'] = np.nan
df['x1'] = np.nan
# KDJ2
df['k2'] = np.nan
df['d2'] = np.nan
df['j2'] = np.nan
df['e2'] = np.nan
df['x2'] = np.nan
# KDJ3
df['k3'] = np.nan
df['d3'] = np.nan
df['j3'] = np.nan
df['e3'] = np.nan
df['x3'] = np.nan
# KDJ4
df['k4'] = np.nan
df['d4'] = np.nan
df['j4'] = np.nan
df['e4'] = np.nan
df['x4'] = np.nan
# MA
df['ma5'] = np.nan
df['ma10'] = np.nan
df['ma20'] = np.nan
df['ma30'] = np.nan
df['ma40'] = np.nan
df['ma60'] = np.nan
df['ma89'] = np.nan
df['ma144'] = np.nan
df['ma233'] = np.nan
df['ma377'] = np.nan
df['ma610'] = np.nan
"""
# ------------------------------------------------------------
df['ma5'] = MA(df['close'], 5)
df['ma10'] = MA(df['close'], 10)
df['ma20'] = MA(df['close'], 20)
df['ma30'] = MA(df['close'], 30)
df['ma40'] = MA(df['close'], 40)
df['ma60'] = MA(df['close'], 60)
df['ma89'] = MA(df['close'], 89)
df['ma144'] = MA(df['close'], 144)
df['ma233'] = MA(df['close'], 233)
df['ma377'] = MA(df['close'], 377)
df['ma610'] = MA(df['close'], 610)
kdj1 = KDJ(df, 45, 15, 15)
kdj2 = KDJ(df, 45 * 5, 15 * 5, 15 * 5)
kdj3 = KDJ(df, 45 * 5 * 5, 15 * 5 * 5, 15 * 5 * 5)
kdj4 = KDJ(df, 45 * 5 * 5 * 5, 15 * 5 * 5 * 5, 15 * 5 * 5 * 5)
"""
#ema1 = EMA( kdj1['KDJ_J'], 5 )
#ema2 = EMA( kdj2['KDJ_J'], 5*5 )
#ema3 = EMA( kdj3['KDJ_J'], 5*5*5 )
#ema4 = EMA( kdj4['KDJ_J'], 5*5*5*5 )
"""
df['k1'] = kdj1['KDJ_K']
df['d1'] = kdj1['KDJ_D']
df['j1'] = kdj1['KDJ_J']
df['e1'] = EMA(kdj1['KDJ_J'], 5)
df['k2'] = kdj2['KDJ_K']
df['d2'] = kdj2['KDJ_D']
df['j2'] = kdj2['KDJ_J']
df['e2'] = EMA(kdj2['KDJ_J'], 5 * 5)
df['k3'] = kdj3['KDJ_K']
df['d3'] = kdj3['KDJ_D']
df['j3'] = kdj3['KDJ_J']
df['e3'] = EMA(kdj3['KDJ_J'], 5 * 5 * 5)
df['k4'] = kdj4['KDJ_K']
df['d4'] = kdj4['KDJ_D']
df['j4'] = kdj4['KDJ_J']
df['e4'] = EMA(kdj4['KDJ_J'], 5 * 5 * 5 * 5)
df['x1'] = np.nan
df['x2'] = np.nan
df['x3'] = np.nan
df['x4'] = np.nan
n = 6
x = np.arange(n)
yy = df['e1'].values
for i in range(n - 1, len(df)):
y1 = df['e1'][i - n + 1:i + 1].values
y2 = df['e2'][i - n + 1:i + 1].values
y3 = df['e3'][i - n + 1:i + 1].values
y4 = df['e4'][i - n + 1:i + 1].values
# print(x)
# print(y)
z1 = np.polyfit(x, y1, 3)
z2 = np.polyfit(x, y2, 3)
z3 = np.polyfit(x, y3, 3)
z4 = np.polyfit(x, y4, 3)
# print('zzzzzz',z)
r1 = z1[0] * x[n - 1] * x[n - 1] + z1[1] * x[n - 1] + z1[2]
r2 = z2[0] * x[n - 1] * x[n - 1] + z2[1] * x[n - 1] + z2[2]
r3 = z3[0] * x[n - 1] * x[n - 1] + z3[1] * x[n - 1] + z3[2]
r4 = z4[0] * x[n - 1] * x[n - 1] + z4[1] * x[n - 1] + z4[2]
df['x1'].iat[i] = r1
df['x2'].iat[i] = r2
df['x3'].iat[i] = r3
df['x4'].iat[i] = r4
return df
def calc_kdj(df, x):
loc1 = 5
loc2 = 20
loc3 = 50
loc4 = 80
loc5 = 95
N1 = -1
N2 = -1
N3 = -1
var = 4
if (x == 1):
N1 = 9 * var
N2 = 3 * var
N3 = 3 * var
N4 = 5
k = 'k1'
d = 'd1'
j = 'j1'
e = 'e1' # E 值： j值 的ema
jpos = 'jpos1' # J 值拐点位置
jupd = 'jupd1' # J 值 上升或下降
jepos = 'jepos1' # J 值 穿越 E值 的位置
jeupd = 'jeupd1' # J 值 在 E值 的上还是下
jloc = 'jloc1' # J 值 的位置 1 ( <loc1 ) 2:( loc1<x<loc2 ) 3:< loc2<x<loc3 ) 4: ......
if (x == 2):
N1 = 9 * var * var
N2 = 3 * var * var
N3 = 3 * var * var
N4 = 5 * var
k = 'k2'
d = 'd2'
j = 'j2'
e = 'e2' # E 值： j值 的ema
jpos = 'jpos2' # J 值拐点位置
jupd = 'jupd2' # J 值 上升或下降
jepos = 'jepos2' # J 值 穿越 E值 的位置
jeupd = 'jeupd2' # J 值 在 E值 的上还是下
jloc = 'jloc2' # J 值 的位置 1 ( <loc1 ) 2:( loc1<x<loc2 ) 3:< loc2<x<loc3 ) 4: ......
if (x == 3):
N1 = 9 * var * var * var
N2 = 3 * var * var * var
N3 = 3 * var * var * var
N4 = 5 * var * var
k = 'k3'
d = 'd3'
j = 'j3'
e = 'e3' # E 值： j值 的ema
jpos = 'jpos3' # J 值拐点位置
jupd = 'jupd3' # J 值 上升或下降
jepos = 'jepos3' # J 值 穿越 E值 的位置
jeupd = 'jeupd3' # J 值 在 E值 的上还是下
jloc = 'jloc3' # J 值 的位置 1 ( <loc1 ) 2:( loc1<x<loc2 ) 3:< loc2<x<loc3 ) 4: ......
if (x == 4):
N1 = 9 * var * var * var * var
N2 = 3 * var * var * var * var
N3 = 3 * var * var * var * var
N4 = 5 * var * var * var * var
k = 'k4'
d = 'd4'
j = 'j4'
e = 'e4' # E 值： j值 的ema
jpos = 'jpos4' # J 值拐点位置
jupd = 'jupd4' # J 值 上升或下降
jepos = 'jepos4' # J 值 穿越 E值 的位置
jeupd = 'jeupd4' # J 值 在 E值 的上还是下
jloc = 'jloc4' # J 值 的位置 1 ( <loc1 ) 2:( loc1<x<loc2 ) 3:< loc2<x<loc3 ) 4: ......
rt = KDJ(df, N1, N2, N3)
df[k] = rt['KDJ_K']
df[d] = rt['KDJ_D']
df[j] = rt['KDJ_J']
df[e] = EMA(rt['KDJ_J'], N4)
jj = df[j].values
ee = df[e].values
df[jpos] = np.nan
df[jupd] = np.nan
df[jepos] = np.nan
df[jeupd] = np.nan
df[jloc] = np.nan
df[jpos].iat[0] = 0
df[jupd].iat[0] = 1
df[jepos].iat[0] = 0
df[jeupd].iat[0] = 1
ddxx1 = np.nan
ddxx0 = np.nan
for i in range(2, len(df)):
df[jpos].iat[i] = 0
df[jupd].iat[i] = 1
df[jepos].iat[i] = 0
df[jeupd].iat[i] = 1
# ======================= j value location from 0 to 110? ==========
if (jj[i] <= loc1):
df[jloc].iat[i] = 1
elif (loc1 < jj[i] and jj[i] <= loc2):
df[jloc].iat[i] = 2
elif (loc2 < jj[i] and jj[i] <= loc3):
df[jloc].iat[i] = 3
elif (loc3 < jj[i] and jj[i] <= loc4):
df[jloc].iat[i] = 4
elif (loc4 < jj[i] and jj[i] <= loc5):
df[jloc].iat[i] = 5
elif (loc5 < jj[i]):
df[jloc].iat[i] = 6
# ======================= j value > j(-1) value ? =================
cha1 = jj[i] - jj[i - 1]
cha0 = jj[i - 1] - jj[i - 2]
if (cha1 > 0):
df[jupd].iat[i] = 1
if (cha0 > 0):
df[jpos].iat[i] = df[jpos].iat[i - 1]
elif (cha0 < 0):
df[jpos].iat[i] = i
elif (cha1 < 0):
df[jupd].iat[i] = -1
if (cha0 > 0):
df[jpos].iat[i] = i
elif (cha0 < 0):
df[jpos].iat[i] = df[jpos].iat[i - 1]
# ======================= j value > e value ? =================
cha1 = jj[i] - ee[i - 1]
cha0 = jj[i - 1] - ee[i - 2]
if (cha1 > 0):
df[jeupd].iat[i] = 1
if (cha0 > 0):
df[jepos].iat[i] = df[jepos].iat[i - 1]
elif (cha0 < 0):
df[jepos].iat[i] = i
elif (cha1 < 0):
df[jeupd].iat[i] = -1
if (cha0 > 0):
df[jepos].iat[i] = i
elif (cha0 < 0):
df[jepos].iat[i] = df[jepos].iat[i - 1]
return df
def drawPic(df, code, name):
mondays = WeekdayLocator(MONDAY) # 主要刻度
alldays = DayLocator() # 次要刻度
#weekFormatter = DateFormatter('%b %d') # 如：Jan 12
mondayFormatter = DateFormatter('%m-%d-%Y') # 如：2-29-2015
dayFormatter = DateFormatter('%d') # 如：12
plt.figure(2) # 创建图表2
ax1 = plt.subplot(211) # 在图表2中创建子图1
ax2 = plt.subplot(212) # 在图表2中创建子图2
plt.sca(ax1)
ax1.xaxis.set_major_locator(mondays)
ax1.xaxis.set_minor_locator(alldays)
ax1.xaxis.set_major_formatter(mondayFormatter)
_candlestick(ax1, df, width=0.6, colorup='r', colordown='g')
ax1.xaxis_date()
plt.setp(plt.gca().get_xticklabels(), rotation=45, horizontalalignment='right')
#ax.grid(True)
plt.sca(ax2)
kdj = KDJ(df,45,15,15)
JJ=pd.DataFrame({'KDJ_J':kdj['KDJ_J']})
ema = EMA(JJ,5)
kdj2 = KDJ(df,45*5,15*5,15*5)
#print("kdj2222222222222222222222222222222222222222222:",kdj2)
kdj3 = KDJ(df, 45 * 5*5, 15 * 5*5, 15 * 5*5)
kdj4 = KDJ(df, 45 * 5 * 5*5, 15 * 5 * 5*5, 15 * 5 * 5*5)
JJ2 = pd.DataFrame({'KDJ_J': kdj2['KDJ_J']})
JJ3 = pd.DataFrame({'KDJ_J': kdj3['KDJ_J']})
JJ4 = pd.DataFrame({'KDJ_J': kdj4['KDJ_J']})
ema2 = EMA(JJ2, 5*5)
ema3 = EMA(JJ3, 5 * 5*5)
ema4 = EMA(JJ4, 5 * 5*5*5)
#print('ema:',ema)
#print("KDJ ==== ", kdj)
xx=[]
t=0
for DAT,DD in kdj.iterrows():
t=t+10
xx.append(t)
#print(xx)
plt.sca(ax2)
#plt.plot(xx, ema, linewidth=0.5)
#plt.plot(xx, kdj['KDJ_J'], linewidth=0.5)
#plt.plot(xx, ema2, linewidth=0.5)
#plt.plot(xx, kdj2['KDJ_J'], linewidth=0.5)
#plt.plot(xx, ema3, linewidth=0.5)
#plt.plot(xx, kdj3['KDJ_J'], linewidth=0.5)
plt.plot(xx, ema4, linewidth=0.5)
plt.plot(xx, kdj4['KDJ_J'], linewidth=0.5)
print("ema44444444444444444444444444444444444:",ema4)
print("len:of df:",len(df))
plt.show()