def show_macd():
"""
MACD交易策略
:return:
"""
CBClose = get_CBClose()
DIF = ma.ewma_cal(CBClose, 12, 2 / (1 + 12)) - ma.ewma_cal(CBClose, 26, 2 / (1 + 26))
print(DIF.tail(n=3))
DEA = ma.ewma_cal(DIF, 9, 2 / (1 + 9))
print(DEA.tail())
MACD = DIF - DEA
print("MACD tail")
print(MACD.tail(n=3))
# plt.subplot(211)
# plt.plot(DIF['2015'], label='DIF', color='k')
# plt.plot(DEA['2015'], label='DEA', color='b', linestyle='dashed')
# plt.title("信号线DIF与DEA")
# plt.legend()
# plt.subplot(212)
# plt.bar(left=MACD['2015'].index, height=MACD['2015'], label='MACD', color='r')
# plt.legend()
macddata = pd.DataFrame()
macddata['DIF'] = DIF['2015']
macddata['DEA'] = DEA['2015']
macddata['MACD'] = MACD['2015']
import candle
ChinaBank = pd.read_csv(r"./data/ChinaBank.csv")
candle.candleLinePlots(
ChinaBank['2015'],
candleTitle='中国银行2015年日K线图',
splitFigures=True,
Data=macddata,
ylabel='MACD'
)
color='b',
linestyle='dashed')
plt.title("2013年中国联通股价唐奇安通道")
plt.ylim(2.9, 3.9)
plt.legend()
upDownDC=pd.DataFrame({'upboundDC':upboundDC,\
'downboundDC':downboundDC})
ChinaUnicom13 = ChinaUnicom['2013-01-01':'2013-06-28']
upDownDC13 = upDownDC['2013-01-01':'2013-06-28']
import candle
candle.candleLinePlots(candleData=ChinaUnicom13,\
candleTitle='中国联通2013年上半年K线图及唐奇安通道',\
Data=upDownDC13)
def upbreak(tsLine, tsRefLine):
n = min(len(tsLine), len(tsRefLine))
tsLine = tsLine[-n:]
tsRefLine = tsRefLine[-n:]
signal = pd.Series(0, index=tsLine.index)
for i in range(1, len(tsLine)):
if all([tsLine[i] > tsRefLine[i], tsLine[i - 1] < tsRefLine[i - 1]]):
signal[i] = 1
return (signal)
def downbreak(tsLine, tsRefLine):
colorup='r',colordown='g')
ax.set_title(candleTitle)
plt.setp(ax.get_xticklabels(),\
rotation=20,\
horizontalalignment='center')
ax.autoscale_view()
return (plt.show())
#Candle 模组是本书自己编的模组，里面有绘制K线函数Candleplot
import candle
Vanke15 = Vanke['2015']
candle.candleLinePlots(Vanke['2015'],\
candleTitle='万科股票2015年日K线图',\
splitFigures=True,Data=momen35['2015'],\
title='35日动量',ylabel='35日动量')
# 使用上面定义的函数
candleLinePlots(Vanke['2015'],\
candleTitle='万科股票2015年日K线图',\
splitFigures=True,Data=momen35['2015'],\
title='35日动量',ylabel='35日动量')
Close = Vanke.Close
momen35 = momentum(Close, 35)
momen35.head()
signal = []
for i in momen35:
if i > 0:
signal.append(1)
plt.legend()
plt.subplot(212)
plt.bar(left=MACD['2016-12'].index,\
height=MACD['2016-12'],\
label='MACD',color='r')
plt.title('柱狀圖MACD')
plt.legend()
macddata = pd.DataFrame()
macddata['DIF'] = DIF['2016-08':'2016-12']
macddata['DEA'] = DEA['2016-08':'2016-12']
macddata['MACD'] = MACD['2016-08':'2016-12']
import candle
candle.candleLinePlots(TSMC['2016-08':'2016-12'],\
candleTitle='台積電2016年8月到12月份的日K線圖',\
splitFigures=True,Data=macddata,\
ylabel='MACD')
DEA = DEA.dropna()
DIF = DIF[DEA.index]
macdSignal = pd.Series(0, index=DIF.index)
for i in range(1, len(DIF)):
if all([DIF[i] > DEA[i] > 0.0, DIF[i - 1] < DEA[i - 1]]):
macdSignal[i] = 1
elif all([DIF[i] < DEA[i] < 0.0, DIF[i - 1] > DEA[i - 1]]):
macdSignal[i] = -1
macdSignal.tail()
macdTrade = macdSignal.shift(1)
macdRet = (tsmcRet * macdTrade).dropna()
macdRet[macdRet == -0] = 0
ret = (zgyh.Close - zgyh.Close.shift(1)) / zgyh.Close.shift(1)
signal = np.all([data.Close > data.SMA, data.Mom > 0], 0)
signal = pd.Series(signal, index=ret[14:].index)
ret = ret[15:] * signal.shift(1)[1:]
ret.sum()
#3.
aapl = pd.read_csv('Data/Part5/004/problem30-3.csv', index_col='date')
aapl.index.name = 'Date'
aapl.index = pd.to_datetime(aapl.index, format='%Y-%m-%d')
import candle
candle.candleLinePlots(aapl['2014-01':'2014-02'],
candleTitle='Candle Plot of AAPL',
Data=aapl.Close['2014-01':'2014-02'])
aapl1 = aapl[:'2014-05']
#因为苹果发放了股利，为了价格的一致性，我们选取了Adjusted Price，即复权过的价格
dif = ma.ewmaCal(aapl1.Adjusted, 12, 2 / 13) - ma.ewmaCal(
aapl1.Adjusted, 26, 2 / 27)
dif = dif[25:]
dea = ma.ewmaCal(dif, 9, 2 / 10)
dea = dea[8:]
dif = dif[8:]
macd = dif - dea
plt.subplot(211)
plt.plot(dif[12:], label="DIF", color='k')
plt.title("信号线DIF与DEA")
plt.legend()
plt.subplot(212)
plt.bar(left=MACD['2015'].index,\
height=MACD['2015'],\
label='MACD',color='r')
plt.legend()
macddata = pd.DataFrame()
macddata['DIF'] = DIF['2015']
macddata['DEA'] = DEA['2015']
macddata['MACD'] = MACD['2015']
import candle
candle.candleLinePlots(ChinaBank['2015'],\
candleTitle='中国银行2015年日K线图',\
splitFigures=True,Data=macddata,\
ylabel='MACD')
macdSignal = pd.Series(0, index=DIF.index)
for i in range(1, len(DIF)):
if all([DIF[i] > DEA[i] > 0.0, DIF[i - 1] < DEA[i - 1]]):
macdSignal[i] = 1
elif all([DIF[i] < DEA[i] < 0.0, DIF[i - 1] > DEA[i - 1]]):
macdSignal[i] = -1
macdSignal.tail()
macdTrade = macdSignal.shift(1)
CBRet = CBClose / CBClose.shift(1) - 1
macdRet = (CBRet * macdTrade).dropna()
macdRet[macdRet == -0] = 0
stock_data2 = stock_data.iloc[:, [4, 5, 6, 7, 2]] # Open High Low Close Volume
#momentum function
def momentum(price, periond):
lagPrice = price.shift(periond)
momen = price - lagPrice
momen = momen.dropna()
return (momen)
Close = stock_data.Close
import pandas as pd
import matplotlib.pyplot as plt
from matplotlib.dates import DateFormatter, WeekdayLocator, \
DayLocator, MONDAY, date2num
from mpl_finance import candlestick_ohlc
plt.rcParams['font.sans-serif'] = ['SimHei']
plt.rcParams['axes.unicode_minus'] = False
momen35 = momentum(Close, 7)
import candle
candle.candleLinePlots(stock_data2,\
candleTitle=u'TW Stock:2330 K Line',\
splitFigures=True,Data=momen35,\
title=u'7 day momentum',ylabel=u'7 day momentum')
plt.show()
plt.plot(boundDC.midboundDC[:'2015-06-30'],
label="midboundDC",
color='r',
linestyle='-.')
plt.plot(boundDC.downboundDC[:'2015-06-30'],
label="downboundDC",
color='b',
linestyle='dashed')
plt.title("2015年上半年黑松股價唐奇安通道")
plt.legend()
import candle
candle.candleLinePlots(
candleData=HeySong[:'2015-06-30'],
candleTitle='2015年上半年黑松股票的K線圖及唐奇安通道',
splitFigures=False,
Data=boundDC[:'2015-06-30'][['upboundDC', 'downboundDC']])
def upbreak(tsLine, tsRefLine):
n = min(len(tsLine), len(tsRefLine))
tsLine = tsLine[-n:]
tsRefLine = tsRefLine[-n:]
signal = pd.Series(0, index=tsLine.index)
for i in range(1, len(tsLine)):
if all([tsLine[i] > tsRefLine[i], tsLine[i - 1] < tsRefLine[i - 1]]):
signal[i] = 1
return (signal)
candlestick_ohlc(ax,listData, width=0.7,\
colorup='r',colordown='g')
ax.set_title(candleTitle)
plt.setp(ax.get_xticklabels(),\
rotation=20,\
horizontalalignment='center')
ax.autoscale_view()
return (plt.show())
#candle 模組是本書編的模組，裡面有繪製K線函數candleLinePlots
import candle
Foxconn15 = Foxconn['2015']
candle.candleLinePlots(Foxconn15,\
candleTitle='鴻海股票2015年日K線圖',\
splitFigures=True,Data=momen35['2015'],\
title='35日動量',ylabel='35日動量')
Close = Foxconn.Close
momen35 = momentum(Close, 35)
momen35.head()
signal = [1 if momen35Value > 0 else -1 for momen35Value in momen35]
signal = pd.Series(signal, index=momen35.index)
signal.head()
tradeSig = signal.shift(1)
ret = Close / Close.shift(1) - 1
Mom35Ret = (ret * (signal.shift(1))).dropna()
Mom35Ret[:5]
print(Vanke.head(10))
Close = Vanke.Close
# print(Close.describe())
lag5Close = Close.shift(5)
print(lag5Close.head(10))
monmentum5 = Close - lag5Close
print(monmentum5.tail())
Momen5 = Close / lag5Close - 1
Momen5.dropna(inplace=True)
print(Momen5[:5])
print(momentum(Close, 5).tail())
# plt.subplot(211)
# plt.plot(Close, "b*")
# plt.xlabel('date')
# plt.ylabel('Close')
# plt.title('万科股份5日动量图')
#
# plt.subplot(212)
# plt.plot(monmentum5, "r-*")
# plt.xlabel('date')
# plt.ylabel('Momentum5')
#
# plt.show()
momen35 = momentum(Close, 35)
candle.candleLinePlots(Vanke['2015'].copy(),
candleTitle='万科股票2015年日K线图',
Data=momen35['2015'],
ylabel='35日动量')
'downBBand':downBBand[(period-1):],\
'sigma':sigma[(period-1):]})
return (BBands)
#1.
GSPC = pd.read_csv('Data/Part5/005/problem31-1.csv', index_col='date')
GSPC.index.name = 'Date'
GSPC.index = pd.to_datetime(GSPC.index, format='%Y-%m-%d')
GSPC1 = GSPC[:'2014-02']
candle.candleLinePlots(GSPC1,
candleTitle='Candle Plot of SPY500',
Data=GSPC1.Close)
candle.candleLinePlots(GSPC1,
candleTitle='Candle Plot of SPY500',
Data=bbands(GSPC1.Close)[['downBBand', 'upBBand']])
upbound = pd.rolling_max(GSPC.High, 10)
lowbound = pd.rolling_min(GSPC.Low, 10)
midbound = (upbound + lowbound) / 2
bounds = pd.concat([upbound, lowbound, midbound], 1)
bounds.columns = ['upbound', 'lowbound', 'midbound']
bounds.dropna().plot()
std = pd.rolling_std(GSPC.Close, 10)
midbound = pd.rolling_mean(GSPC.Close, 10)