def algoStrategy(self, ohlc):
sp = StrategyPattern()
tsup = TradeSupport()
buysg,sellsg = sp.divergencyCross(self.pdi, self.ndi, 2)
# TODO closesg = sp.covergency(self.pdi, self.ndi, 2)
#diff = self.pdi/self.ndi
#process these signale for backtest
'''
signal = map(sp.mergeSignal, buysg,sellsg,closesg)
ohlc['signal'] = signal
'''
ohlc['dmibuy'] = buysg
ohlc['dmisell'] = sellsg
'''
if (self.debug):
ohlc['buysignal'] = buysg
ohlc['sellsignal'] = sellsg
ohlc['pdi'] = self.pdi
ohlc['ndi'] = self.ndi
ohlc['adx'] = self.adx
print ohlc
'''
self.ind['dmi_dif'] = self.pdi[-1]/self.ndi[-1]
#ohlc['dmi_dif'] = self.pdi/self.ndi
tsup.getLastSignal(buysg,sellsg, self.ind,'dmi_buy','dmi_sell')
pass
def __init__(self):
BaseIndPx.__init__(self)
self.ind_sctr = ind_sctr()
self.ind_kdj = ind_kdj()
self.ind_dmi = ind_dmi()
self.sp = StrategyPattern()
self.tsup = TradeSupport()
pass
def algoStrategy(self, ohlc):
# price cross above MA10 and crosee below MA10
sp = StrategyPattern()
tsup = TradeSupport()
buysg1, sellsg1 = sp.cross(self.avgTypEmaLst,
self.avgHacEmaLst) #offset=0
buysg2, sellsg2 = sp.compare(ohlc['Close'], ohlc['Open'])
buysg = sp.combineAndSignal(buysg1, buysg2)
sellsg = sp.combineAndSignal(sellsg1, sellsg2)
closesg = sp.covergency1(self.avgTypEmaLst, self.avgHacEmaLst)
signal = map(sp.mergeSignal, buysg, sellsg, closesg)
ohlc['signal'] = signal
#ohlc['buy1'] = buysg1
#ohlc['buy2'] = buysg2
ohlc['buy3'] = buysg
#ohlc['sell1'] = sellsg1
#ohlc['sell2'] = sellsg2
ohlc['sell3'] = sellsg
ohlc['close'] = closesg
print ohlc
#tsup.getLastSignal(buysg,sellsg,self.ind,'ma10_buy','ma10_sell')
#too lag, how about px cross MA50
'''
if (self.ma50 and self.ma200):
sp.initData(self.ma50, self.ma200, 200)
self.ind['ma50_200_buy'] = sp.crossAbove()
self.ind['ma50_200_sell'] = sp.crossBelow()
'''
pass
def algoStrategy(self, ohlc):
sp = StrategyPattern()
tsup = TradeSupport()
buysg, sellsg = sp.crossValue(self.quolst, self.shortquolst, 0, 0, 2)
#process these signale for backtest
signal = map(sp.mergeSignal, buysg, sellsg, "")
ohlc['signal'] = signal
if (self.debug):
ohlc['buysignal'] = buysg
ohlc['sellsignal'] = sellsg
ohlc['fast'] = self.quolst
ohlc['slow'] = self.shortquolst
print ohlc
tsup.getLastSignal(buysg, sellsg, self.ind, 'quo_buy', 'quo_sell')
pass
class st_k(BaseIndPx):
def __init__(self):
self.sp = StrategyPattern()
self.tsup = TradeSupport()
def setupParam(self, param):
pass
def _algo(self, ohlc):
pass
def runIndicator(self, symbol, ohlc, param={}):
ind_ma = importStrategy("ind_ma", symbol, ohlc)
#s1 = ind_ma.ma50
s1 = pandas.rolling_mean(ind_ma.ma50, 3) #.tolist()
s2 = s1.shift(2)
delta = s1 - s2
#ohlc['delta'] = delta
# ohlc
buysg, sellsg = self.sp.crossValue(delta, delta, 0, 0, 1)
ohlc['ag50b'] = buysg
ohlc['ag50s'] = sellsg
self.tsup.getLastSignal(buysg, sellsg, self.ind, 'ag50b','ag50s')
s1 = pandas.rolling_mean(ind_ma.ma10, 3) #.tolist()
s2 = s1.shift(2)
delta = s1 - s2
#ohlc['delta'] = delta
# ohlc
buysg, sellsg = self.sp.crossValue(delta, delta, 0, 0, 2)
ohlc['ag10b'] = buysg
ohlc['ag10s'] = sellsg
# get all signal idx
ag10bidx, ag10sidx = self.tsup.getLastSignal(buysg, sellsg, self.ind, 'ag10b', 'ag10s')
vol10bidx = ind_ma.ind['vol10b']
ma10bidx = ind_ma.ind['ma10b']
pass
class st_greenline(BaseIndPx):
def __init__(self):
BaseIndPx.__init__(self)
self.ind_sctr = ind_sctr()
self.ind_kdj = ind_kdj()
self.ind_dmi = ind_dmi()
self.sp = StrategyPattern()
self.tsup = TradeSupport()
pass
#main process routine
def runIndicator(self,symbol,ohlc,param={}):
'''
my STO implementation is more promise
'''
self.setupParam(param) #parent func
self.ind_sctr.runIndicator(symbol,ohlc,param)
self.ind['sctr'] = self.ind_sctr.ind['sctr']
self.ind_kdj.runIndicator(symbol,ohlc,param)
self.ind['money_wave'] = self.ind_kdj.ind['slow_d']
buysg,sellsg = self.sp.crossValue(self.ind_sctr.score, self.ind_sctr.score,0,0,2)
self.tsup.getLastSignal(buysg,sellsg, self.ind,'sctr_buy','sctr_sell')
#self.ind_dmi.runIndicator(symbol,ohlc,param)
#print self.ind_kdj.
#df = STO(ohlc,5)
#df = ADX(ohlc,14,14)
#print df
#self.algoStrategy(ohlc)
pass
def runScan(self,df):
if not self.param:
col = df.columns.values
return df,col
total = len(df)
df['sctrrank'] = df['sctr'].rank(ascending=1)/total*100
df.sort_index(by="sctrrank",inplace=True,ascending=False)
col = df.columns.values
df,fcols = self.mtd.evalCriteria(df,self.param,col)
return df,fcols
def algoStrategy(self, ohlc):
# price cross above MA10 and crosee below MA10
sp = StrategyPattern()
tsup = TradeSupport()
px = ohlc['Adj Close']
if (self.ma10):
# not using divergencyCross
buysg, sellsg = sp.cross(px, self.ma10, self.nbar)
tsup.getLastSignal(buysg, sellsg, self.ind, 'ma10b', 'ma10s')
'''
ohlc['ma10b']=buysg
ohlc['ma10s']=sellsg
ohlc['ma10']=self.ma10
'''
if (self.ma50):
buysg, sellsg = sp.cross(px, self.ma50, self.nbar)
tsup.getLastSignal(buysg, sellsg, self.ind, 'ma50b', 'ma50s')
if (self.ma50 and self.ma10):
buysg, sellsg = sp.cross(self.ma10, self.ma50, self.nbar)
tsup.getLastSignal(buysg, sellsg, self.ind, 'ma1050b', 'ma1050s')
# ready to cross above
flag1 = (self.ma10[-1] > self.ma50[-1] * 0.97)
dif1 = (self.ma50[-1] - self.ma10[-1])
dif2 = (self.ma50[-2] - self.ma10[-2])
dif3 = (self.ma50[-3] - self.ma10[-3])
flag2 = (dif1 < dif2) and (dif2 < dif3) and (dif1 > 0)
if (flag1 and flag2):
self.ind['ma1050e'] = 1
else:
self.ind['ma1050e'] = 0
#too lag, how about px cross MA50
if (self.ma50 and not self.ma200.empty):
#print "test golder and death"
buysg, sellsg = sp.cross(self.ma50, self.ma200, self.nbar)
#print sellsg
tsup.getLastSignal(buysg, sellsg, self.ind, 'ma50200b', 'ma50200s')
#support line
buysg, sellsg = sp.supportline(px, self.ma50, self.nbar)
tsup.getLastSignal(buysg, sellsg, self.ind, 'sup50', 'res50')
#print ohlc
pass
def algoStrategy(self, symbol, ohlc):
# price cross above MA10 and crosee below MA10
sp = StrategyPattern()
tsup = TradeSupport()
px = ohlc['Adj Close']
if (not self.ma10.empty):
# not using divergencyCross
buysg, sellsg = sp.cross(px, self.ma10, self.nbar)
tsup.getLastSignal(buysg, sellsg, self.ind, 'ma10b', 'ma10s')
ohlc['ma10b'] = buysg
ohlc['ma10s'] = sellsg
#ohlc['ma10']=self.ma10
ma50bsg = []
if not self.ma50.empty:
buysg, sellsg = sp.cross(px, self.ma50, self.nbar)
tsup.getLastSignal(buysg, sellsg, self.ind, 'ma50b', 'ma50s')
ohlc['ma50b'] = buysg
ohlc['ma50s'] = sellsg
ma50bsg = buysg
s1 = pandas.rolling_mean(self.ma50, 3) #.tolist()
s2 = s1.shift(2)
delta = s1 - s2
buysg, sellsg = sp.crossValue(delta, delta, 0, 0, 1)
tsup.getLastSignal(buysg, sellsg, self.ind, 'ag50b', 'ag50s')
ohlc['ag50b'] = buysg
ohlc['ag50s'] = sellsg
# TODO
# #buysg,sellsg = sp.supportline(px, self.ma50, self.nbar)
# tsup.getLastSignal(buysg, sellsg, self.ind, 'sup50', 'res50')
if not self.ma50.empty and not self.ma10.empty:
buysg, sellsg = sp.cross(self.ma10, self.ma50, self.nbar)
tsup.getLastSignal(buysg, sellsg, self.ind, 'ma1050b', 'ma1050s')
ohlc['ma1050b'] = buysg
ohlc['ma1050s'] = sellsg
# find the distance
#self.dist50 = 20
lastsell = -self.dist50
distsg = []
for idx, val in enumerate(ohlc['ma1050s']):
sig = ""
if val == "sell":
lastsell = idx
# print "dist50",idx
buyval = ma50bsg[idx]
if buyval == "buy":
''' # TODO test
if symbol=="QCOM":
print "dist50", idx, lastsell, self.dist50
'''
if lastsell >= 0 and idx - lastsell > self.dist50:
sig = "buy"
else:
sig = ""
distsg.append(sig)
ohlc['dist50'] = distsg
tsup.getLastSignal(distsg, [], self.ind, 'dist50', '')
# ready to cross above
'''
flag1 = (self.ma10[-1] > self.ma50[-1]*0.97)
dif1 = (self.ma50[-1]-self.ma10[-1])
dif2 = (self.ma50[-2]-self.ma10[-2])
dif3 = (self.ma50[-3]-self.ma10[-3])
flag2 = (dif1<dif2) and (dif2<dif3) and (dif1>0)
if (flag1 and flag2):
self.ind['ma1050e']=1
else:
self.ind['ma1050e']=0
'''
#too lag, how about px cross MA50
'''
l1=len(self.ma50)
l2=len(self.ma200)
if (l1!=l2):
print l1,l2
print self.ma200
print "========"
print self.ma50
'''
l1 = len(self.ma50)
l2 = len(self.ma200)
#print l1,l2,symbol
if (l1 == l2 and l1 > 0):
#print "test golder and death"
buysg, sellsg = sp.cross(self.ma50, self.ma200, self.nbar)
ohlc['ma50200s'] = sellsg
tsup.getLastSignal(buysg, sellsg, self.ind, 'ma50200b', 'ma50200s')
'''
lastsell = -self.dist200
distsg = []
for idx, val in enumerate(ohlc['ma50200s']):
sig = ""
if val == "sell":
lastsell = idx
buyval = buysg[idx]
if (buyval == "buy"):
#TODO test
if symbol=="QCOM":
print "dist200",idx,lastsell,self.dist200
if lastsell >= 0 and idx-lastsell > self.dist200:
sig = "buy"
else:
sig = ""
distsg.append(sig)
ohlc['dist200'] = distsg
tsup.getLastSignal(distsg, [], self.ind, 'dist200', '')
'''
#support line
#print self.ma50
#volume
buysg, sellsg = sp.cross2factors(px, self.volma20ra, self.ma10,
self.volra, 2)
tsup.getLastSignal(buysg, sellsg, self.ind, 'vol10b', 'vol10s')
ohlc['vol10b'] = buysg
ohlc['vol10s'] = sellsg
#debug
#ohlc['ma50']=self.ma50
#print ohlc
pass
def algoFunc(self, df0):
length = 14
prevHigh = 0.
prevLow = 0.
plusDM = 0.
minusDM = 0.
index = 0
tr = []
pdm = []
ndm = []
#ratio=[]
sp = StrategyPattern()
#debug
#length = len(df0)-1
#lastpx = df0['Adj Close'].iloc[len(df0)-1]
###
#start = time.time()
for row_index, row in df0.iterrows():
#print index
aclose = row['Adj Close']
close = row['Close']
high = row['High']
low = row['Low']
tr.append(sp.trueRange(high, aclose, low))
###
'''
if (close>lastpx):
r = (close-lastpx)/(length-index)
ratio.append(r)
else:
ratio.append(0.)
'''
####
if (index > 0):
hiDiff = high - prevHigh
loDiff = prevLow - low
if hiDiff > loDiff and hiDiff > 0:
plusDM = hiDiff
else:
plusDM = 0
if loDiff > hiDiff and loDiff > 0:
minusDM = loDiff
else:
minusDM = 0
pdm.append(plusDM)
ndm.append(minusDM)
else:
pdm.append(0.)
ndm.append(0.)
prevHigh = high
prevLow = low
index += 1
'''
atr = sp.movingAverage(pandas.Series(tr),length)
self.pdi = 100 * sp.movingAverage(pandas.Series(pdm),length) / atr
self.ndi = 100 * sp.movingAverage(pandas.Series(ndm),length) / atr
dx = self.pdi.combine(self.ndi,calcDX2)
self.adx = sp.movingAverage(dx,length)
'''
atr1 = [float('nan') for _ in range(length - 1)]
atr2 = sp.wma(tr, length)
atr = atr1 + atr2
tmpPdi = atr1 + sp.wma(pdm, length)
tmpNdi = atr1 + sp.wma(ndm, length)
#print tmpPdi
self.pdi = [100 * ai / bi for ai, bi in zip(tmpPdi, atr)]
self.ndi = [100 * ai / bi for ai, bi in zip(tmpNdi, atr)]
dx = map(calcDX2, self.pdi, self.ndi)
self.adx = atr1 + sp.wma(dx, length)
#debug
#df0['ang'] = ratio
df0['di+'] = self.pdi
df0['di-'] = self.ndi
df0['adx'] = self.adx
#print df0
pass
def __init__(self):
# price cross above MA10 and crosee below MA10
self.sp = StrategyPattern()
self.tsup = TradeSupport()
def __init__(self):
self.sp = StrategyPattern()
self.tsup = TradeSupport()