def __init__(self, feed, instrument, EMACombo, multiplier, exitDays):
super(TestStrat, self).__init__(feed, cash_or_brk=100000)
self.TestShort = False # Test only short or long, not both.
self.STRATNAME = "Ema Order"
self.STRATDESC = '''Tests the filter edge of ordered emas'''
self.instrument = instrument
self.priceDS = self.getFeed().getDataSeries(
self.instrument).getPriceDataSeries()
self.exitDays = exitDays
self.ShorterEmaSetting = int(EMACombo * multiplier)
self.LongerEmaSetting = EMACombo
self.ema10 = ma.EMA(self.priceDS, 10)
self.ema20 = ma.EMA(self.priceDS, 20)
# We'll use adjusted close values, if available, instead of regular close values.
if feed.barsHaveAdjClose():
self.setUseAdjustedValues(True)
self.LongerEma = ma.EMA(self.priceDS, self.LongerEmaSetting)
self.ShorterEma = ma.EMA(self.priceDS, self.ShorterEmaSetting)
self.longPos = None
self.shortPos = None
self.getBroker().getFillStrategy().setVolumeLimit(None) # hack / CARL
def __init__(self, feed, instrument, hurstPeriod, macdShorterPeriod,
macdLongerPeriod, macdSignalPeriod, rsiPeriod, entrySMAPeriod,
exitSMAPeriod, overBoughtThreshold, overSoldThreshold):
strategy.BacktestingStrategy.__init__(self, feed)
self.__instrument = instrument
self.__hurstPeriod = hurstPeriod
# Use adjusted close values, if available, instead of regular close values.
if feed.barsHaveAdjClose():
self.setUseAdjustedValues(True)
self.__priceDS = feed[instrument].getPriceDataSeries()
self.__adjClose = feed[instrument].getAdjCloseDataSeries()
self.__macdShorterPeriod = macdShorterPeriod
self.__macdLongerPeriod = macdLongerPeriod
self.__macdSignalPeriod = macdSignalPeriod
self.__macd = macd.MACD(self.__adjClose, self.__macdShorterPeriod,
self.__macdLongerPeriod,
self.__macdSignalPeriod)
self.__hurst = MovingHurst.HurstExponent(self.__adjClose, hurstPeriod)
self.__entrySMA = ma.EMA(self.__priceDS, entrySMAPeriod)
self.__exitSMA = ma.EMA(self.__priceDS, exitSMAPeriod)
self.__rsi = rsi.RSI(self.__adjClose, rsiPeriod)
self.__overBoughtThreshold = overBoughtThreshold
self.__overSoldThreshold = overSoldThreshold
self.__longPos = None
self.__shortPos = None
def __init__(self, feed, ema1, ema2, ema3, ema4, daysToHold):
strategy.BacktestingStrategy.__init__(self, feed)
self.__instrument = feed.getDefaultInstrument()
self.__daysToHold = daysToHold
ds = feed[self.__instrument].getCloseDataSeries()
self.__ema1 = ma.EMA(ds, ema1)
self.__ema2 = ma.EMA(ds, ema2)
self.__ema3 = ma.EMA(ds, ema3)
self.__ema4 = ma.EMA(ds, ema4)
self.__daysLeft = 0
self.__position = None
def __init__(self, feed, instrument, n, m):
strategy.BacktestingStrategy.__init__(self, feed)
self.__instrument = instrument
self.getBroker().setFillStrategy(DefaultStrategy(None))
# self.getBroker().setCommission(TradePercentage(0.001)) # 设置手续费
self.__position = None
self.__prices = feed[instrument].getPriceDataSeries()
self.__malength1 = int(n)
self.__malength2 = int(m)
self.__ma1 = ma.EMA(self.__prices, self.__malength1)
self.__ma2 = ma.EMA(self.__prices, self.__malength2)
def __init__(self, feed, instrument, nfast, nslow):
# define the initial budget
super(MovingAverageStrategy, self).__init__(feed, INITIAL_BUDGET)
#track the position
self.position = None
self.instrument = instrument
self.setUseAdjustedValues(True)
self.fastMA = ma.EMA(feed[instrument].getPriceDataSeries(), nfast)
self.slowMA = ma.EMA(feed[instrument].getPriceDataSeries(), nslow)
def __init__(self, feed, instrument):
strategy.BacktestingStrategy.__init__(self, feed)
# 自定义日线级别dataseries
self.__instrument = instrument
self.__feed = feed
self.setUseAdjustedValues(False)
self.__position = None
self.__bolling = None
self.day_feed = self.resampleBarFeed(frequency=bar.Frequency.DAY, callback=self.resample_callback)
self.day_closeDataList = self.day_feed[instrument].getCloseDataSeries()._SequenceDataSeries__values.data()
self.day_ma60 = ma.EMA(self.day_feed[instrument].getCloseDataSeries(), 60)
self.day_ma30 = ma.EMA(self.day_feed[instrument].getCloseDataSeries(), 30)
self.day_ma20 = ma.EMA(self.day_feed[instrument].getCloseDataSeries(), 30)
##############各种信号量########
self.initState = False # 开头的几个过滤掉
self.buyWaitSignal = False # 买入等待信号
self.sellWaitSignal = False # 卖出等待信号
self.holdDay = datetime.min # 持有的日期
self.min_ma5 = ma.EMA(self.__feed[instrument].getCloseDataSeries(), 5) # 5分线5均
self.min_ma10 = ma.EMA(self.__feed[instrument].getCloseDataSeries(), 10) # 5分线5均
self.min_ma20 = ma.EMA(self.__feed[instrument].getCloseDataSeries(), 20) # 5分线5均
self.min_ma30 = ma.EMA(self.__feed[instrument].getCloseDataSeries(), 30) # 5分线5均
self.min_ma60 = ma.EMA(self.__feed[instrument].getCloseDataSeries(), 60) # 5分线5均
self.__count = 0
def __init__(self, feed, instrument, fastEMA, slowEMA, rsiPeriod,
overBoughtThreshold, overSoldThreshold):
super(RSI2, self).__init__(feed, 1000)
self.__instrument = instrument
# We'll use adjusted close values, if available, instead of regular close values.
# if feed.barsHaveAdjClose():
# self.setUseAdjustedValues(True)
self.__priceDS = feed.getDataSeries(
self.__instrument).getPriceDataSeries()
self.__fastEMA = ma.EMA(self.__priceDS, fastEMA)
self.__slowEMA = ma.EMA(self.__priceDS, slowEMA)
# self.__rsi = rsi.RSI(self.__priceDS, rsiPeriod)
self.__overBoughtThreshold = overBoughtThreshold
self.__overSoldThreshold = overSoldThreshold
self.__longPos = None
self.__shortPos = None
def __init__(self, feed, instrument, signal_period=50, cash=10000):
super(OBVSignalCrossStrategy, self).__init__(feed, BacktestingBroker(cash, feed))
self.__position = None
self.__instrument = instrument
# TODO OBV signal_period parameter can be removed, but we need to change OBV indicator to implement SequenceDataSeries directly (check MACD implementation)
self.__obv = OBV(feed[instrument], signal_period)
self.__signal = ma.EMA(self.__obv, signal_period)
def __init__(self, feed, instrument, arg_set, ma_short=5, ma_long=10, macd_fast=6, macd_slow=12):
broker_commission = broker.backtesting.TradePercentage(0.0004)
brokers = broker.backtesting.Broker(1000000, feed, broker_commission)
super(Ma_Macd, self).__init__(feed, brokers)
self.__instrument = instrument
self.__feed = feed
# self.setUseAdjustedValues(True)
self.__position = None
self.initState = False # 开头的几个过滤掉
self.code = feed.getKeys()[0]
self.priceDS = feed[instrument].getPriceDataSeries()
self.arg_set = arg_set
self.ma_short = ma.EMA(self.priceDS, ma_short)
self.ma_long = ma.EMA(self.priceDS, ma_long)
self.diff = macd.MACD(self.priceDS, fastEMA=macd_fast, slowEMA=macd_slow, signalEMA=9)
self.dea = self.diff.getSignal()
self.macd = self.diff.getHistogram()
def __init__(self, feed, instrument, bBandsPeriod):
strategy.BacktestingStrategy.__init__(self, feed)
self.setDebugMode(False)
self.__instrument = instrument
self.__feed = feed
self.__position = None
#self.__stochK = stoch.StochasticOscillator(feed[instrument], 5, 3)
self.__mafast = ma.EMA(feed[instrument].getCloseDataSeries(), 5)
self.__maslow = ma.EMA(feed[instrument].getCloseDataSeries(), 30)
self.__atr = atr.ATR(feed[instrument], 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 testMaxRecursion(self):
barFeed = ninjatraderfeed.Feed(bar.Frequency.MINUTE)
barFeed.addBarsFromCSV("any", common.get_data_file_path("nt-spy-minute-2011.csv"))
ema = ma.EMA(barFeed["any"].getPriceDataSeries(), 10)
# Load all the feed.
barFeed.loadAll()
# Check that the max recursion limit bug is not hit when generating the last value first.
self.assertEqual(round(ema[-1], 2), 128.81)
def __init__(self, feed, instrument, smaPeriod):
super(SMACrossOver, self).__init__(feed)
self.__instrument = instrument
self.getBroker().setCash(1000)
self.__position = None
# We'll use adjusted close values instead of regular close values.
self.setUseAdjustedValues(True)
self.__prices = feed[instrument].getPriceDataSeries()
self.__sma = ma.SMA(self.__prices, smaPeriod)
self.__ema20 = ma.EMA(self.__prices, 15)
def __init__(self, feed, instrument, bBandsPeriod):
strategy.BacktestingStrategy.__init__(self, feed)
self.setDebugMode(False)
self.__instrument = instrument
self.__feed = feed
self.__position = None
fastEMA=12
slowEMA=26
signalEMA=9
self.__macd = macd.MACD(feed[instrument].getCloseDataSeries(),
fastEMA,slowEMA,signalEMA, 15)
self.__fastma = ma.EMA(feed[instrument].getCloseDataSeries(),fastEMA)
self.__slowma = ma.EMA(feed[instrument].getCloseDataSeries(),slowEMA)
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, instrument, fastPeriod, slowPeriod, rsiPeriod,
overboughtThreshold, oversoldThreshold):
#this is where we have to define the initial budget
super(RSIMovingAverageStrategy, self).__init__(feed, INITIAL_BUDGET)
#we can track the position: long or short positions
#if it is None we know we can open one
self.longPosition = None
self.shortPosition = None
self.oversoldThreshold = oversoldThreshold
self.overboughtThreshold = overboughtThreshold
#the given stock (for example AAPL)
self.instrument = instrument
#if we want to use adjusted closing prices instead of regular closing prices
self.setUseAdjustedValues(True)
#fast moving average indicator (short-term trend) period is smaller
self.fastMA = ma.EMA(feed[instrument].getPriceDataSeries(), fastPeriod)
#slow moving average indicator (long-term trend) period is larger
self.slowMA = ma.EMA(feed[instrument].getPriceDataSeries(), slowPeriod)
#define the RSI model
self.rsi = rsi.RSI(feed[instrument].getPriceDataSeries(), rsiPeriod)
def __init__(self, feed, instrument, aboveSMA, exitDays):
super(TestStrat, self).__init__(feed,cash_or_brk=100000)
self.exitDays = exitDays
self.STRATNAME = "Above_Ma"
self.__instrument = instrument
# We'll use adjusted close values, if available, instead of regular close values.
if feed.barsHaveAdjClose():
self.setUseAdjustedValues(True)
self.__priceDS = self.getFeed().getDataSeries(self.__instrument).getPriceDataSeries()
self.aboveSMA = ma.EMA(self.__priceDS, aboveSMA)
self.aboveSMAMid = ma.EMA(self.__priceDS, 50)
self.aboveSMALong = ma.EMA(self.__priceDS, 125)
self.__longPos = None
self.__shortPos = None
self.getBroker().getFillStrategy().setVolumeLimit(None) # hack / CARL
def testBoundedFilter(self):
values = [22.2734, 22.1940, 22.0847, 22.1741, 22.1840, 22.1344, 22.2337, 22.4323, 22.2436, 22.2933, 22.1542, 22.3926, 22.3816, 22.6109, 23.3558, 24.0519, 23.7530, 23.8324, 23.9516, 23.6338, 23.8225, 23.8722, 23.6537, 23.1870, 23.0976, 23.3260, 22.6805, 23.0976, 22.4025, 22.1725]
seqDS = dataseries.SequenceDataSeries()
ema = ma.EMA(seqDS, 10, 2)
for value in values:
seqDS.append(value)
self.assertEqual(round(ema[0], 5), 23.08068)
self.assertEqual(round(ema[1], 5), 22.91556)
self.assertEqual(len(ema), 2)
self.assertEqual(len(ema[:]), 2)
self.assertEqual(len(ema.getDateTimes()), 2)
def __init__(self, feed: csvfeed.GenericBarFeed, instrument: str, ma_look: int, ema_look: int, atr_look: int):
super().__init__(feed)
self.long = None
self.short = None
self.instrument = instrument
self.bar = None
self.bar_count = 0
self.market_price = 0
self.current_position = {}
self.was_long = None
self.plot_ma = ma.SMA(feed[self.instrument].getPriceDataSeries(), ma_look)
self.plot_ema = ma.EMA(feed[self.instrument].getPriceDataSeries(), ema_look)
self.longest_look = 4 * max([ma_look, ema_look, atr_look + 1])
def __init__(self, feed, instrument, longEMA, exitDays, multiplier):
super(TestStrat, self).__init__(feed, cash_or_brk=100000)
self.STRATNAME = "Crossing_of_EMA"
self.exitDays = exitDays
self.multiplier = multiplier
self.__instrument = instrument
# We'll use adjusted close values, if available, instead of regular close values.
if feed.barsHaveAdjClose():
self.setUseAdjustedValues(True)
self.__priceDS = self.getFeed().getDataSeries(
self.__instrument).getPriceDataSeries()
self.longEMA = ma.EMA(self.__priceDS, longEMA)
self.shortEMA = ma.EMA(self.__priceDS, int(longEMA * self.multiplier))
self.__longPos = None
self.__shortPos = None
self.getBroker().getFillStrategy().setVolumeLimit(None) # hack / CARL
def __init__(self,
feed,
instruments,
cash,
ema_length,
ema_alpha,
commission_scheme,
printinfo=False):
"""
Params:
commission_scheme: tuple(str, float)
"""
strategy.BacktestingStrategy.__init__(self, feed, cash)
print 'PARAMS: len = %d | alpha = %.2f | Commission: %s' % (
ema_length, ema_alpha, commission_scheme)
# We wan't to use adjusted close prices instead of close.
self.setUseAdjustedValues(True)
self.printinfo = printinfo
# Commission
self.getBroker().setCommission(
getattr(backtesting, commission_scheme[0])(commission_scheme[1]))
# Initialize indicators for each instrument.
self.__closes = {}
self.__ema = {}
self.__dt = {}
self.__zeroseries = {}
self.__zeroema = {}
for instrument in instruments:
self.__closes[instrument] = feed[instrument].getPriceDataSeries()
self.__ema[instrument] = tf.EMA(self.__closes[instrument],
ema_length, .1)
self.__dt[instrument] = tf.Derivative(self.__ema[instrument])
self.__zeroseries[instrument] = tf.ZeroSeries(
self.__closes[instrument])
self.__zeroema[instrument] = ma.EMA(self.__zeroseries[instrument],
3)
def __init__(self, feed, instrument):
strategy.BacktestingStrategy.__init__(self, feed)
# 自定义日线级别dataseries
self.__instrument = instrument
self.__feed = feed
self.setUseAdjustedValues(False)
self.__position = None
self.__ENE = None
self.__feed_day = self.resampleBarFeed(frequency=bar.Frequency.DAY, callback=self.resample_callback)
self.__MA3 = ma.EMA(self.__feed_day[instrument].getCloseDataSeries(), 3, 5)
self.__MA5 = ma.EMA(self.__feed_day[instrument].getCloseDataSeries(), 5, 5)
self.__MA8 = ma.EMA(self.__feed_day[instrument].getCloseDataSeries(), 8, 5)
self.__MA10 = ma.EMA(self.__feed_day[instrument].getCloseDataSeries(), 10, 5)
self.__MA12 = ma.EMA(self.__feed_day[instrument].getCloseDataSeries(), 12, 5)
self.__MA15 = ma.EMA(self.__feed_day[instrument].getCloseDataSeries(), 15, 5)
self.__MA30 = ma.EMA(self.__feed_day[instrument].getCloseDataSeries(), 30, 5)
self.__MA35 = ma.EMA(self.__feed_day[instrument].getCloseDataSeries(), 35, 5)
self.__MA40 = ma.EMA(self.__feed_day[instrument].getCloseDataSeries(), 40, 5)
self.__MA45 = ma.EMA(self.__feed_day[instrument].getCloseDataSeries(), 45, 5)
self.__MA50 = ma.EMA(self.__feed_day[instrument].getCloseDataSeries(), 50, 5)
self.__MA60 = ma.EMA(self.__feed_day[instrument].getCloseDataSeries(), 60, 5)
self.__EMA12 = ma.EMA(self.__feed_day[instrument].getCloseDataSeries(), 12, 10)
self.__EMA50 = ma.EMA(self.__feed_day[instrument].getCloseDataSeries(), 50, 10)
##############各种信号量########
self.initState = False # 开头的几个过滤掉
self.eneUpMode = False # ENE 日线向下
self.buyType = None # 买入的类型，“UP”是上升趋势中轨买入，‘DOWN’是下降趋势下轨买入
self.buyWaitSignal = False # 买入等待信号
self.sellWaitSignal = False # 卖出等待信号
self.holdDay = datetime.min # 持有的日期
self.sellinfo = ''
self.buyPrice = 0.0
self.min_ma5 = ma.SMA(self.__feed[instrument].getCloseDataSeries(), 5) # 5分线5均
self.min_ma10 = ma.SMA(self.__feed[instrument].getCloseDataSeries(), 10) # 5分线5均
self.min_ma20 = ma.SMA(self.__feed[instrument].getCloseDataSeries(), 20) # 5分线5均
self.min_ma30 = ma.SMA(self.__feed[instrument].getCloseDataSeries(), 30) # 5分线5均
self.min_ma60 = ma.SMA(self.__feed[instrument].getCloseDataSeries(), 60) # 5分线5均
self.min_ma120 = ma.SMA(self.__feed[instrument].getCloseDataSeries(), 120) # 5分线5均
def testStockChartsEMA_Reverse(self):
# Test in reverse order to trigger recursive calls.
# Test data from http://stockcharts.com/school/doku.php?id=chart_school:technical_indicators:moving_averages
common.test_from_csv(self, "sc-ema-10.csv",
lambda inputDS: ma.EMA(inputDS, 10), 3, True)
def testStockChartsEMA(self):
# Test data from http://stockcharts.com/school/doku.php?id=chart_school:technical_indicators:moving_averages
common.test_from_csv(self, "sc-ema-10.csv",
lambda inputDS: ma.EMA(inputDS, 10), 3)
