def __init__(self, feed, instruments, ind_dt, ts):
strategy.BacktestingStrategy.__init__(self, feed)
self.__position = []
self.__instruments = instruments
self.__ts = ts
self.__ydate = ''
self.lastday = dict()
self.df = pd.read_csv(ind_dt, dtype={'code': np.str})
self.tdays = np.array(self.df['Date'].unique())
self.getBroker().setMaxHoldingDays(5)
self.__preVol = dict()
self.__ma120 = dict()
self.__vol = dict()
self.__slope = dict()
self.__returns = dict()
self.__XT = dict()
for inst in self.__instruments:
priceDS = feed[inst].getAdjCloseDataSeries()
self.__returns[inst] = roc.RateOfChange(priceDS, 1)
self.__ma120[inst] = ma.SMA(priceDS, 5)
bars = feed.getDataSeries(inst)
self.__XT[inst] = xingtai.ShrinkVolRise(bars, 5)
def run(self, feed, useAdjustedCloseForReturns=True):
"""Runs the analysis using the bars supplied by the feed.
:param barFeed: The bar feed to use to run the analysis.
:type barFeed: :class:`pyalgotrade.barfeed.BarFeed`.
:param useAdjustedCloseForReturns: True if adjusted close values should be used to calculate returns.
:type useAdjustedCloseForReturns: boolean.
"""
try:
self.__feed = feed
self.__rets = {}
self.__futureRets = {}
for instrument in feed.getRegisteredInstruments():
self.__events.setdefault(instrument, [])
self.__futureRets[instrument] = []
if useAdjustedCloseForReturns:
ds = feed[instrument].getAdjCloseDataSeries()
else:
ds = feed[instrument].getCloseDataSeries()
self.__rets[instrument] = roc.RateOfChange(ds, 1)
feed.getNewBarsEvent().subscribe(self.__onBars)
dispatcher = observer.Dispatcher()
dispatcher.addSubject(feed)
dispatcher.run()
finally:
feed.getNewBarsEvent().unsubscribe(self.__onBars)
def __init__(self, feed):
smaPeriod = 60
self.__returns = {}
self.__volrate = {}
self.__vXT = {}
self.__ma = {}
for instrument in feed.getRegisteredInstruments():
priceDS = feed[instrument].getAdjCloseDataSeries()
volDS = feed[instrument].getVolumeDataSeries()
# Returns over the adjusted close values.
self.__returns[instrument] = roc.RateOfChange(priceDS, 1)
self.__volrate[instrument] = roc.RateOfChange(volDS, 1)
# MA over the adjusted close values.
self.__ma[instrument] = ma.SMA(priceDS, smaPeriod)
bars = feed.getDataSeries(instrument)
self.__vXT[instrument] = xingtai.ShrinkVolRise(bars,5)
def __init__(self, feed, instrument, nper, lowerThreshold, upperThreshold):
super(ZScoreStrategy, self).__init__(feed)
self.position = None
self.instrument = instrument
self.sma = ma.SMA(feed[instrument].getPriceDataSeries(), nper)
self.roc = roc.RateOfChange(feed[instrument].getPriceDataSeries(), 1)
self.zscore = stats.ZScore(self.roc, nper)
self.lowerThreshold = lowerThreshold
self.upperThreshold = upperThreshold
def __init__(self, feed):
stdDevPeriod = 90
smaPeriod = 20
self.__returns = {}
self.__stdDev = {}
self.__ma = {}
for instrument in feed.getRegisteredInstruments():
priceDS = feed[instrument].getAdjCloseDataSeries()
# Returns over the adjusted close values.
self.__returns[instrument] = roc.RateOfChange(priceDS, 1)
# StdDev over those returns.
self.__stdDev[instrument] = stats.StdDev(self.__returns[instrument], stdDevPeriod)
# MA over the adjusted close values.
self.__ma[instrument] = ma.SMA(priceDS, smaPeriod)
def __init__(self, feed):
smaPeriod = 30
self.__returns = {}
self.__volrate = {}
self.__ma = {}
self.__ma250 = {}
for instrument in feed.getRegisteredInstruments():
priceDS = feed[instrument].getAdjCloseDataSeries()
# Returns over the adjusted close values.
self.__returns[instrument] = roc.RateOfChange(priceDS, 10)
# MA over the adjusted close values.
self.__ma[instrument] = ma.SMA(priceDS, smaPeriod)
self.__ma250[instrument] = ma.SMA(priceDS, 250)
self.__daysum = {}
self.__enterDay = []
def __init__(self, feed, instrument, bBandsPeriod):
strategy.BacktestingStrategy.__init__(self, feed)
self.setDebugMode(False)
self.__instrument = instrument
self.__feed = feed
self.__position = None
self.__sma = roc.RateOfChange(feed[instrument].getCloseDataSeries(),
15)
self.__col = [
"buyPrice", "buyTime", "sellPrice", "sellTime", "returns"
]
self.__msdf = pd.DataFrame(columns=self.__col)
self.__buyPrice = 0
self.__buyTime = None
self.setUseAdjustedValues(True)
def __init__(self, feed, instruments):
super(BuyOnGap, self).__init__()
stdDevPeriod = 90
smaPeriod = 20
self.__returns = {}
self.__stdDev = {}
self.__ma = {}
for instrument in instruments:
priceDS = feed.getDataSeries(instrument).getAdjCloseDataSeries()
# Returns over the adjusted close values.
self.__returns[instrument] = roc.RateOfChange(priceDS, 1)
# StdDev over those returns.
self.__stdDev[instrument] = stats.StdDev(
self.__returns[instrument], stdDevPeriod)
# MA over the adjusted close values.
self.__ma[instrument] = ma.SMA(priceDS, smaPeriod)
def __init__(self, instrument, feed, brk):
strategy.BaseStrategy.__init__(self, feed, brk)
self.__verbosityLevel = 1
self.__instrument = instrument
self.__orderSize = 0.2
self.__targetPricePct = 0.015
self.__commitPricePct = self.__targetPricePct / 2
self.__stopLossPct = -0.02
# Time to wait for BUY order to get filled.
self.__maxWaitEntry = datetime.timedelta(minutes=3)
# Maximum holding period.
self.__maxHoldPeriod = datetime.timedelta(hours=1)
volatilityPeriod = 5 # How many returns to use to calculate volatility.
self.returnsVolatility = stats.StdDev(
roc.RateOfChange(feed[self.__instrument].getCloseDataSeries(), 1),
volatilityPeriod)
self.__switchNoPos()
def __init__(self, feed, ts=None):
self.__returns = {}
self.__volrate = {}
self.__ma = {}
self.__ma250 = {}
for instrument in feed.getRegisteredInstruments():
priceDS = feed[instrument].getAdjCloseDataSeries()
# Returns over the adjusted close values.
self.__returns[instrument] = roc.RateOfChange(priceDS, 1)
self.__daysum = {}
self.__enterDay = []
self.__df = pd.read_csv('data/st.txt', dtype={'code': np.str})
self.__codeday = {}
self.__used = set()
tmparr = []
for line in open('data/gaiming.txt'):
tmparr.append(line.strip())
for i in range(1, len(tmparr)):
tmp = tmparr[-1 * i].split(',')
self.__codeday[tmp[0]] = tmp[1]
self.__ts = ts
def __buildROC(self, values, period):
return roc.RateOfChange(dataseries.SequenceDataSeries(values), period)
def __buildROC(self, values, period, rocMaxLen=dataseries.DEFAULT_MAX_LEN):
seqDS = dataseries.SequenceDataSeries()
ret = roc.RateOfChange(seqDS, period, rocMaxLen)
for value in values:
seqDS.append(value)
return ret
