def fup60b(stock):
'''
60分钟从负数上来的有失败上叉先导的第一次成功上叉
'''
t = stock.transaction
linelog('%s:%s' % (fup60.__name__, stock.code))
hzero = cached_zeros(len(stock.hour))
pdiff, pdea = cmacd(stock.hour)
cross0 = cross(hzero, pdiff)
udcross = cross(pdea, pdiff)
#失败上叉：最近5个周期内被下叉信号抵消，或者最近5个周期内出现第二个上叉信号(导致msum仍然>0)
fcross = gand(
udcross > 0,
gor(
rollx(msum(udcross, 5), -5) == 0,
rollx(msum(udcross > 0, 5), -5) > 1))
ucross = gand(udcross > 0, pdiff > 0, bnot(fcross))
xsum1 = rsum(ucross, cross0) #此时，第一个cross>0和第二个之间的位置被填满1
xsum = rsum(xsum1, cross0) #此时，只有第一个发生位为1
signal = gand(equals(xsum, 1), pdiff > 0)
return hour2day(signal)
def fup60b(stock):
'''
60分钟从负数上来的有失败上叉先导的第一次成功上叉
'''
t = stock.transaction
linelog('%s:%s' % (fup60.__name__,stock.code))
hzero = cached_zeros(len(stock.hour))
pdiff,pdea = cmacd(stock.hour)
cross0 = cross(hzero,pdiff)
udcross = cross(pdea,pdiff)
#失败上叉：最近5个周期内被下叉信号抵消，或者最近5个周期内出现第二个上叉信号(导致msum仍然>0)
fcross = gand(udcross>0,gor(rollx(msum(udcross,5),-5)==0,rollx(msum(udcross>0,5),-5)>1))
ucross = gand(udcross>0,pdiff>0,bnot(fcross))
xsum1 = rsum(ucross,cross0) #此时，第一个cross>0和第二个之间的位置被填满1
xsum = rsum(xsum1,cross0) #此时，只有第一个发生位为1
signal = gand(equals(xsum,1),pdiff>0)
return hour2day(signal)
def adx2(sclose,shigh,slow,n=14,m=6):
'''
调整的算法, abs在ma之后取值
直接根据sclose,shigh,slow计算adx的快捷函数
n: 计算di时的平滑天数
m: 计算adx时的平滑天数
'''
pdm,ndm = dm(shigh,slow)
xtr = msum(tr(sclose,shigh,slow),n)
pdi = msum(pdm,n)*10000/xtr
ndi = msum(ndm,n)*10000/xtr
adx = np.abs(ma((pdi-ndi)*10000/(pdi+ndi),m))
#adxr = (adx + rollx(adx,m)) /2
return pdi,ndi,adx#,adxr
def adx0(sclose,shigh,slow,n=14,m=6):
'''
原始的adx计算方法
直接根据sclose,shigh,slow计算adx的快捷函数
n: 计算di时的平滑天数
m: 计算adx时的平滑天数
'''
pdm,ndm = dm(shigh,slow)
xtr = msum(tr(sclose,shigh,slow),n)
pdi = msum(pdm,n)*10000/xtr
ndi = msum(ndm,n)*10000/xtr
adx = ma(np.abs((pdi-ndi)*10000/(pdi+ndi)),m)
#adxr = (adx + rollx(adx,m)) /2
return pdi,ndi,adx#,adxr
def adx2(sclose, shigh, slow, n=14, m=6):
'''
调整的算法, abs在ma之后取值
直接根据sclose,shigh,slow计算adx的快捷函数
n: 计算di时的平滑天数
m: 计算adx时的平滑天数
'''
pdm, ndm = dm(shigh, slow)
xtr = msum(tr(sclose, shigh, slow), n)
pdi = msum(pdm, n) * 10000 / xtr
ndi = msum(ndm, n) * 10000 / xtr
adx = np.abs(ma((pdi - ndi) * 10000 / (pdi + ndi), m))
#adxr = (adx + rollx(adx,m)) /2
return pdi, ndi, adx #,adxr
def adx0(sclose, shigh, slow, n=14, m=6):
'''
原始的adx计算方法
直接根据sclose,shigh,slow计算adx的快捷函数
n: 计算di时的平滑天数
m: 计算adx时的平滑天数
'''
pdm, ndm = dm(shigh, slow)
xtr = msum(tr(sclose, shigh, slow), n)
pdi = msum(pdm, n) * 10000 / xtr
ndi = msum(ndm, n) * 10000 / xtr
adx = ma(np.abs((pdi - ndi) * 10000 / (pdi + ndi)), m)
#adxr = (adx + rollx(adx,m)) /2
return pdi, ndi, adx #,adxr
def hmxru(stock):
''' 成交量分配后的macd,采用supdown
vfilter = svma < vma * 2/3
评估:总盈亏值=4813,交易次数=14 期望值=5444
总盈亏率(1/1000)=4813,平均盈亏率(1/1000)=343,盈利交易率(1/1000)=928
平均持仓时间=50,持仓效率(1/1000000)=6860
赢利次数=13,赢利总值=4876
亏损次数=1,亏损总值=63
平盘次数=0
vfilter = gand(svma<vma*2/3,t[VOLUME]<=vma*2/3)
评估:总盈亏值=3810,交易次数=8 期望值=1000
总盈亏率(1/1000)=3810,平均盈亏率(1/1000)=476,盈利交易率(1/1000)=1000
平均持仓时间=65,持仓效率(1/1000000)=7323
赢利次数=8,赢利总值=3810
亏损次数=0,亏损总值=0
平盘次数=0
'''
t = stock.transaction
mxc = stock.xru
vma = ma(t[VOLUME],30)
svma = ma(t[VOLUME],3)
vfilter = gand(svma<vma*2/3,t[VOLUME]<=vma*2/3)
#vfilter = gand(svma<vma*2/3)
xatr = stock.atr * BASE / t[CLOSE]
ma0 = ma(t[CLOSE],3)
ndown = bnot(gand(t[CLOSE]<ma0,ma0<stock.ma1,stock.ma1<stock.ma2))
s = stock
sv = greater(msum(t[VOLUME] > 0,120),100) #确保新股上市前100天无信号
signal = gand(mxc,stock.above,vfilter,strend(stock.ma4)>0,stock.t5,xatr>=60,stock.magic,stock.ma1<stock.ma2,stock.ma1>stock.ma3,ndown)
linelog(stock.code)
return signal
def hmacd2(stock):
''' 无大用
'''
t = stock.transaction
linelog(stock.code)
vma = ma(t[VOLUME],30)
svma = ma(t[VOLUME],3)
vfilter = gand(svma<vma*2/3)
#vfilter = gand(svma<vma*3/4,t[VOLUME]<vma)
#vfilter = gand(svma<vma*3/5,t[VOLUME]>0,t[VOLUME]>vma*1/2)#,t[VOLUME]<vma*3/2) #2/3
xatr = stock.atr * BASE / t[CLOSE]
cf = (t[OPEN]-t[LOW] + t[HIGH]-t[CLOSE])*1000 / (t[HIGH]-t[LOW]) #向下的动力
mcf = ma(cf,7)
#ma3 = ma(t[CLOSE],3)
#ma7 = ma(t[CLOSE],7)
#c37 = gand(cross(ma7,ma3)>0,strend(ma3)>0)
#sc = sfollow(stock.hdev,c37,3)
ss = gand(msum(stock.hup,5)>1,stock.hup) #5日内的第二次上叉
s=stock
#signal = gand(stock.hdev,stock.t5,vfilter,mcf>1000,stock.g5<stock.g60,s.g20 >= s.g60,s.g60 >= s.g120,s.g120 >= s.g250,s.g20<=8000)
#magic = gand(s.g20 >= s.g60,s.g60 <= s.g120,s.g120 <= s.g250)#,s.g20<=8000)
magic = gand(s.g5>s.g60,s.g20 >= s.g60,s.g60 <= s.g120,s.g20<=8000)
signal = gand(ss,stock.t5,stock.t4,vfilter,mcf<900,xatr>60,magic)
return signal
def mfi(source,vol,length=13):
'''
mfi指标
source = (close+high+low)/3
TYP := (HIGH + LOW + CLOSE)/3;
V1=SUM(IF(TYP>REF(TYP,1),TYP*VOL,0),N)/SUM(IF(TYP<REF(TYP,1),TYP*VOL,0),N);
MFI:100-(100/(1+V1))
设定 PV = SUM(IF(TYP>REF(TYP,1),TYP*VOL,0),N)
NV = SUM(IF(TYP<REF(TYP,1),TYP*VOL,0),N);
则MFI = 100PV/(PV+NV)
'''
pvol = np.select([source>rollx(source)],[vol])
nvol = np.select([source<rollx(source)],[vol])
spv = msum(pvol,length)
snv = msum(nvol,length)
result = (BASE*spv+(spv+snv)/2)/(spv+snv) #四舍五入
return result
def mfi(source, vol, length=13):
'''
mfi指标
source = (close+high+low)/3
TYP := (HIGH + LOW + CLOSE)/3;
V1=SUM(IF(TYP>REF(TYP,1),TYP*VOL,0),N)/SUM(IF(TYP<REF(TYP,1),TYP*VOL,0),N);
MFI:100-(100/(1+V1))
设定 PV = SUM(IF(TYP>REF(TYP,1),TYP*VOL,0),N)
NV = SUM(IF(TYP<REF(TYP,1),TYP*VOL,0),N);
则MFI = 100PV/(PV+NV)
'''
pvol = np.select([source > rollx(source)], [vol])
nvol = np.select([source < rollx(source)], [vol])
spv = msum(pvol, length)
snv = msum(nvol, length)
result = (BASE * spv + (spv + snv) / 2) / (spv + snv) #四舍五入
return result
def efficient_rate(source,covered=10):
''' 效率函数. 来源: smarter trading (略有不同，书中效率值为abs值，而此函数以负表示下降效率)
根据单一source计算(存在其他方式的效率计算方法，但在此如此计算)
先计算n日内每日波动幅度amplitude = abs(source[i]-source[i-1])，然后根据n求和为s_amplitude
然后计算n日总波幅t_amplitude = source[i]-source[i-n]
当日效率系数 = t_amplitude / s_amplitude
第covered+1个数据(下标为covered)开始有效，之前的全部置0
效率值有正有负，为负表明是下降效率
'''
assert covered > 0
if(len(source) <= covered):
return np.zeros_like(source)
sdiff = np.abs(subd(source))
ssum = msum(sdiff,covered)
sdiffc = subd(source,covered)
rev = sdiffc * BASE / ssum
rev[:covered] = 0
return rev
def efficient_rate(source, covered=10):
''' 效率函数. 来源: smarter trading (略有不同，书中效率值为abs值，而此函数以负表示下降效率)
根据单一source计算(存在其他方式的效率计算方法，但在此如此计算)
先计算n日内每日波动幅度amplitude = abs(source[i]-source[i-1])，然后根据n求和为s_amplitude
然后计算n日总波幅t_amplitude = source[i]-source[i-n]
当日效率系数 = t_amplitude / s_amplitude
第covered+1个数据(下标为covered)开始有效，之前的全部置0
效率值有正有负，为负表明是下降效率
'''
assert covered > 0
if (len(source) <= covered):
return np.zeros_like(source)
sdiff = np.abs(subd(source))
ssum = msum(sdiff, covered)
sdiffc = subd(source, covered)
rev = sdiffc * BASE / ssum
rev[:covered] = 0
return rev
def psy(source,length=12):
''' 心理线PSY:COUNT(CLOSE>REF(CLOSE,1),N)/N*100
'''
s = greater(source,rollx(source))
rev = (msum(s,length) * BASE + length/2)/ length
return rev
def psy(source, length=12):
''' 心理线PSY:COUNT(CLOSE>REF(CLOSE,1),N)/N*100
'''
s = greater(source, rollx(source))
rev = (msum(s, length) * BASE + length / 2) / length
return rev
