def __init__(self, feed, instrument):
strategy.BacktestingStrategy.__init__(self, feed)
self.__rsi = rsi.RSI(feed[instrument].getCloseDataSeries(), 14)
self.__rsi6 = rsi.RSI(feed[instrument].getCloseDataSeries(), 6)
self.__instrument = instrument
self.__position = None
self.getBroker()
def __init__(self, feed, instrument, entrySMA, exitDays, rsiPeriod,
overBoughtThreshold, overSoldThreshold):
super(TestStrat, self).__init__(feed, cash_or_brk=100000)
self.exitDays = exitDays
self.STRATNAME = "RSI_oversold"
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[self.__instrument].getPriceDataSeries()
self.__priceDS = self.getFeed().getDataSeries(
self.__instrument).getPriceDataSeries()
#self.__Ds = self.getFeed().getDataSeries(self.__instrument).getCloseDataSeries()
#self.__closeDS = feed[instrument].
self.__entrySMA = ma.SMA(self.__priceDS, entrySMA)
#self.__exitSMA = ma.SMA(self.__priceDS, exitSMA)
self.__rsi = rsi.RSI(self.__priceDS, rsiPeriod)
#self.__rsi = RSI(self.__Ds, rsiPeriod) # CARL
#self.Cad = talibext.indicator.AD(self.__priceDS,20 ) # Chaikin A/D Line
#self.ADX = talibext.indicator.ADX(self.__priceDS, 20, ) # ADX
self.__overBoughtThreshold = overBoughtThreshold
self.__overSoldThreshold = overSoldThreshold
self.__longPos = None
self.__shortPos = None
self.getBroker().getFillStrategy().setVolumeLimit(None) # hack / CARL
def __init__(self, feed, instrument, arg_set, ma_short=5, ma_long=10, rsi_in=6, rsi_out=12):
broker_commission = broker.backtesting.TradePercentage(0.0004)
brokers = broker.backtesting.Broker(1000000, feed, broker_commission)
super(Ma_Rsi, 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.SMA(self.priceDS, ma_short)
self.ma_long = ma.SMA(self.priceDS, ma_long)
self.rsi_in = rsi.RSI(self.priceDS, rsi_in)
self.rsi_out = rsi.RSI(self.priceDS, rsi_out)
def __init__(self, feed, instrument, cash):
strategy.BacktestingStrategy.__init__(self, feed, cash)
self.__rsi = rsi.RSI(feed[instrument].getCloseDataSeries(), 14)
self.__sma = ma.SMA(self.__rsi, 15)
self.__instrument = instrument
self.__EMA = tf.EMA(feed[instrument].getCloseDataSeries(), 16, .3)
self.__DT = tf.Derivative(feed[instrument])
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,instruments,ind_dt,ts):
strategy.BacktestingStrategy.__init__(self,feed,cash_or_brk=200)
self.__position = []
self.__intrapost = dict()
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.__prices = feed.getDataSeries(self.__instruments)
self.__KDJ = stoch.StochasticOscillator(self.__prices,9)
rsiPeriod = 6
self.__priceDS = feed[self.__instruments].getPriceDataSeries()
self.__rsi = rsi.RSI(self.__priceDS, rsiPeriod)
self.__PK = None
self.__PD = None
self.__fakeShares = 0
self.__prevJ = None
self.__buydays = set()
self.getBroker().setMaxHoldingDays(2)
def __init__(self, feed, instrument):
super(MyStrategy, self).__init__(feed)
self.__rsi = rsi.RSI(feed[instrument].getCloseDataSeries(), 14)
self.__sma_rsi = ma.SMA(self.__rsi, 15)
self.__sma = ma.SMA(feed[instrument].getCloseDataSeries(), 2)
# self.info("__sma: {}, {}.".format(self.__sma, self.__sma[-1]))
self.__instrument = instrument
def __init__(self, feed, instrument):
super(MyStrategy, self).__init__(feed)
self.__rsi = rsi.RSI(feed[instrument].getCloseDataSeries(), 14)
# 5 period sma over closing price
self.__sma5 = ma.SMA(self.__rsi, 5)
# 15 period sma over closing price
self.__sma15 = ma.SMA(self.__rsi, 15)
self.__instrument = instrument
def __init__(self, feed, instrument):
strategy.BacktestingStrategy.__init__(self, feed)
# We want a 15 period SMA over the closing prices.
self.__sma = ma.SMA(feed[instrument].getCloseDataSeries(), 15)
self.__rsi = rsi.RSI(feed[instrument].getCloseDataSeries(), 14)
self.__macd = macd.MACD(feed[instrument].getCloseDataSeries(), 12, 55,
9).getSignal()
self.__instrument = instrument
def __init__(self, feed, brk):
config = ConfigParser.ConfigParser()
config.read('xrpbot.cfg')
strategy.BaseStrategy.__init__(self, feed, brk)
self.__smaPeriod = int(config.get('main', 'sma_period'))
self.__rsiPeriod = int(config.get('main', 'rsi_period'))
self.__instrument = config.get('main', 'instrument')
self.__prices = feed[self.__instrument].getCloseDataSeries()
self.__sma = ma.SMA(self.__prices, self.__smaPeriod)
self.__rsi = rsi.RSI(self.__prices, self.__rsiPeriod)
self.__bid = None
self.__ask = None
self.__spread = None
self.__position = None
self.__posSize = float(config.get('main', 'position_size'))
#self.__posSize = random.randint(45, 93)
self.__strategyUUID = None
self.__engine = config.get('main', 'engine_name')
self.__start_time = time.time()
self.__maxRunTime = int(config.get('main', 'runtime'))
self.__logURL = "https://*****:*****@quantumconfigurations.com/dionysus/index.php?%s"
self.__barsctr = 0
self.__isShuttingDown = False
self.__isExitActive = False
self.__isOrderOpenActive = False
self.__orderbookctr = 0
self.__positionsctr_1 = 0
self.__positionsctr_2 = 0
self.__cmdTimer = time.time()
self.__exitActiveTimer = 0
self.__exitBid = 0
self.__cmdTimerMax = int(config.get('main', 'command_run_interval'))
self.__status = "RUNNING"
self.__pos_type = "N"
self.__last_pos_type = "N"
initCtr = 0
initMax = 5
initDelay = 5
while self.__strategyUUID == None and initCtr < initMax:
self.initDB()
if self.__strategyUUID == None:
self.error("Strategy was not initialized, retry " +
str(initCtr))
initCtr += initCtr
time.sleep(initDelay)
else:
self.info("Strategy " + self.__strategyUUID +
" has been initialized.")
if self.__strategyUUID == None:
self.error("ERROR: Unable to initialize, shutting down")
self.stop()
else:
# Subscribe to order book update events to get bid/ask prices to trade.
feed.getOrderBookUpdateEvent().subscribe(self.__onOrderBookUpdate)
def __init__(self, feed, instrument, brk):
super(MyStrategy, self).__init__(feed, brk)
logger.info("========MyStrategy init=======")
self.__rsi = rsi.RSI(feed[instrument].getCloseDataSeries(), 14)
self.__sma_rsi = ma.SMA(self.__rsi, 15)
self.__sma = ma.SMA(feed[instrument].getCloseDataSeries(), 2)
logger.info("__sma: {}".format(self.__sma))
self.__instrument = instrument
def __init__(self, feed, feed2, instrument, instrument2, smaPeriod,
rsiPeriod):
super(MyStrategy, self).__init__(feed, 1000)
self.__position = None
self.__instrument = instrument
# We'll use adjusted close values instead of regular close values.
self.setUseAdjustedValues(True)
self.__sma = ma.SMA(feed[instrument].getPriceDataSeries(), smaPeriod)
self.__rsi = rsi.RSI(feed[instrument].getPriceDataSeries(), rsiPeriod)
def __init__(self, feed, instrument, bBandsPeriod, overBoughtThreshold,
overSoldThreshold):
super(BBands, self).__init__(feed)
self.__instrument = instrument
self.__bbands = bollinger.BollingerBands(
feed[instrument].getCloseDataSeries(), bBandsPeriod, 2)
self.__rsi = rsi.RSI(feed[instrument].getCloseDataSeries(), 14)
self.overBoughtThreshold = overBoughtThreshold
self.overSoldThreshold = overSoldThreshold
def __init__(self, feed, entrySMA, exitSMA, rsiPeriod, overBoughtThreshold, overSoldThreshold):
strategy.BacktestingStrategy.__init__(self, feed, 2000)
ds = feed["dia"].getCloseDataSeries()
self.__entrySMA = ma.SMA(ds, entrySMA)
self.__exitSMA = ma.SMA(ds, exitSMA)
self.__rsi = rsi.RSI(ds, rsiPeriod)
self.__overBoughtThreshold = overBoughtThreshold
self.__overSoldThreshold = overSoldThreshold
self.__longPos = None
self.__shortPos = None
def __init__(self, feed, entrySMA, exitSMA, rsiPeriod, overBoughtThreshold, overSoldThreshold): initialCash = 2000 strategy.Strategy.__init__(self, feed, initialCash) self.__instrument = feed.getDefaultInstrument() ds = feed.getDataSeries().getCloseDataSeries() self.__entrySMA = ma.SMA(ds, entrySMA) self.__exitSMA = ma.SMA(ds, exitSMA) self.__rsi = rsi.RSI(ds, rsiPeriod) self.__overBoughtThreshold = overBoughtThreshold self.__overSoldThreshold = overSoldThreshold self.__longPos = None self.__shortPos = None
def __init__(self, feed, instrument, capital, selected_features,
predict_n_days, model_form, testing_datetime,
testing_end_datetime):
super(Backtester, self).__init__(feed, capital)
self.capital = capital
self.instrument = instrument
self.mpf_asset = [
'hk_equity_fund', 'growth_fund', 'balanced_fund',
'conservative_fund', 'hkdollar_bond_fund', 'stable_fund'
]
self.positions = {asset: None for asset in self.mpf_asset}
self.current_percentages = None
self.previous_percentages = None
# technical indicators is denfined here
self.hk_equity_fund_macd = macd.MACD(
feed['hk_equity_fund'].getCloseDataSeries(), 12, 26, 9)
self.hk_equity_fund_macd_histogram = self.hk_equity_fund_macd.getHistogram(
)
self.growth_fund_macd = macd.MACD(
feed['growth_fund'].getCloseDataSeries(), 12, 26, 9)
self.growth_fund_macd_histogram = self.growth_fund_macd.getHistogram()
self.balanced_fund_macd = macd.MACD(
feed['balanced_fund'].getCloseDataSeries(), 12, 26, 9)
self.balanced_fund_macd_histogram = self.balanced_fund_macd.getHistogram(
)
self.conservative_fund_macd = macd.MACD(
feed['conservative_fund'].getCloseDataSeries(), 12, 26, 9)
self.conservative_fund_macd_histogram = self.conservative_fund_macd.getHistogram(
)
self.hkdollar_bond_fund_macd = macd.MACD(
feed['hkdollar_bond_fund'].getCloseDataSeries(), 12, 26, 9)
self.hkdollar_bond_fund_macd_histogram = self.hkdollar_bond_fund_macd.getHistogram(
)
self.stable_fund_macd = macd.MACD(
feed['stable_fund'].getCloseDataSeries(), 12, 26, 9)
self.stable_fund_macd_histogram = self.stable_fund_macd.getHistogram()
self.hk_equity_fund_rsi = rsi.RSI(
feed['hk_equity_fund'].getCloseDataSeries(), 14)
self.growth_fund_rsi = rsi.RSI(
feed['growth_fund'].getCloseDataSeries(), 14)
self.balanced_fund_rsi = rsi.RSI(
feed['balanced_fund'].getCloseDataSeries(), 14)
self.conservative_fund_rsi = rsi.RSI(
feed['conservative_fund'].getCloseDataSeries(), 14)
self.hkdollar_bond_fund_rsi = rsi.RSI(
feed['hkdollar_bond_fund'].getCloseDataSeries(), 14)
self.stable_fund_rsi = rsi.RSI(
feed['stable_fund'].getCloseDataSeries(), 14)
# self.testing_datetime = datetime(2014, 8, 2)
# self.testing_end_datetime = datetime(2018, 1, 15)
self.testing_datetime = testing_datetime
self.testing_end_datetime = testing_end_datetime
self.pending_order = None
self.historical_data = list()
self.historical_df = None
self.selected_features = selected_features
self.predict_n_days = predict_n_days
self.model_form = model_form
self.model = None
def __init__(self, feed, instrument, smaPeriod, factor1):
super(MyStrategy, self).__init__(feed, 1000000)
self.__position = None
self.__instrument = instrument
# We'll use adjusted close values instead of regular close values.
self.setUseAdjustedValues(True)
self.__sma = ma.SMA(feed[instrument].getPriceDataSeries(), smaPeriod)
self.__rsi = rsi.RSI(feed[instrument].getPriceDataSeries(), smaPeriod)
self.__volsma = ma.SMA(feed[instrument].getVolumeDataSeries(),
smaPeriod)
self.__vol = feed[instrument].getVolumeDataSeries()
self.__chg = ma.SMA(
feed[instrument].getExtraDataSeries(name="pct_chg"), 1)
def __init__(self, feed, instruments, posMax, entrySma, exitSma, rsiPeriod,
overSoldThreshold):
MyBenchmark.__init__(self, feed, instruments, posMax, exitSma)
self._overSoldThreshold = overSoldThreshold
self._prices = {}
self._entrySmas = {}
self._exitSmas = {}
self._rsis = {}
for instrument in instruments:
priceDS = feed[instrument].getPriceDataSeries()
self._prices[instrument] = priceDS
self._entrySmas[instrument] = ma.SMA(priceDS, entrySma)
self._exitSmas[instrument] = ma.SMA(priceDS, exitSma)
self._rsis[instrument] = rsi.RSI(priceDS, rsiPeriod)
def __init__(self, feed, instrument, entrySMA, exitSMA, rsiPeriod,
overBoughtThreshold, overSoldThreshold):
strategy.BacktestingStrategy.__init__(self, feed)
self.__instrument = instrument
# We'll use adjusted close values instead of regular close values.
self.setUseAdjustedValues(True)
self.__priceDS = feed[instrument].getPriceDataSeries()
self.__entrySMA = ma.SMA(self.__priceDS, entrySMA)
self.__exitSMA = ma.SMA(self.__priceDS, exitSMA)
self.__rsi = rsi.RSI(self.__priceDS, rsiPeriod)
self.__overBoughtThreshold = overBoughtThreshold
self.__overSoldThreshold = overSoldThreshold
self.__longPos = None
self.__shortPos = None
def __init__(self, feed, instrument, entrySMA, exitSMA, rsiPeriod, overBoughtThreshold, overSoldThreshold):
super(RSI2, self).__init__(feed)
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[instrument].getPriceDataSeries()
self.__entrySMA = ma.SMA(self.__priceDS, entrySMA)
self.__exitSMA = ma.SMA(self.__priceDS, exitSMA)
self.__rsi = rsi.RSI(self.__priceDS, rsiPeriod)
self.__overBoughtThreshold = overBoughtThreshold
self.__overSoldThreshold = overSoldThreshold
self.__longPos = None
self.__shortPos = None
def __init__(self, feed, instrument, bBandsPeriod):
super(MyStrategy, self).__init__(feed)
self.__instrument = instrument
# 使用调整后的数据
if feed.barsHaveAdjClose():
self.setUseAdjustedValues(True)
# 持有仓位
self.__holdPosition = 0.0
# 空闲仓位
self.__emptyPosition = 10.0
# 单元持仓金额
self.__unit = self.getBroker().getCash(False) / 10
# 统计收盘价
self.__priceDS = feed[instrument].getPriceDataSeries()
# 计算macd指标
self.__macd = macd.MACD(self.__priceDS, 12, 26, 9)
# 计算KD指标
self.__stoch = stoch.StochasticOscillator(feed[instrument], 9, 3)
# 计算rsi指标
self.__rsi7 = rsi.RSI(self.__priceDS, 7)
self.__rsi14 = rsi.RSI(self.__priceDS, 14)
# 计算布林线
self.__bbands = bollinger.BollingerBands(self.__priceDS, bBandsPeriod,
2)
def __init__(self, feed, instrument, entrySMA, exitSMA, rsiPeriod, overBoughtThreshold, overSoldThreshold):
strategy.BacktestingStrategy.__init__(self, feed)
self.__instrument = instrument
if feed.barsHaveAdjClose():
self.setUseAdjustedValues(True)
self.__typicalPrice = feed[instrument].getAdjCloseDataSeries()
self.__volume = feed[instrument].getVolumeDataSeries()
self.__entrySMA = ma.SMA(self.__priceDS, entrySMA)
self.__exitSMA = ma.SMA(self.__priceDS, exitSMA)
self.__rsi = rsi.RSI(self.__priceDS, rsiPeriod)
self.__overBoughtThreshold = overBoughtThreshold
self.__overSoldThreshold = overSoldThreshold
self.__longPos = None
self.__shortPos = None
self.__MFI = None
def __init__(self, feed, smaPeriod, tickers, trend_indicator):
global LOWERLIMIT
strategy.Strategy.__init__(self, feed, 2000)
self.tickers = tickers
self.period = smaPeriod
self.trend_indi = trend_indicator
self.trend_data = {}
self.positions = []
self.p_tickers = set()
#self.getBroker().setCommission(FixedCommission(9))
self.failed = {}
no_data = set()
self.last_price = {}
if LOWERLIMIT >= len(tickers):
LOWERLIMIT = int(len(tickers) * .5)
max_date = min_date = None
import datetime
now = datetime.datetime.now()
start = now - datetime.timedelta(days=90)
for ticker in tickers:
data = du.get_data(ticker)
if len(data) <= 0:
no_data.add(ticker)
continue
feed.addBarsFromCSV(ticker, mongofeed.MongoRowParser())
if max_date == None:
max_date = data[-1]['date']
elif max_date < data[-1]['date']:
max_date = data[-1]['date']
if min_date == None:
min_date = data[0]['date']
elif min_date > data[0]['date']:
min_date = data[0]['date']
self.trend_data[ticker] = rsi.RSI(
feed.getDataSeries(ticker).getAdjCloseDataSeries(), smaPeriod)
print 'start: %r end: %r' % (min_date, max_date)
print 'No Data: %r' % no_data
metrics.start_date = min_date
metrics.end_date = max_date
metrics.num_transactions = 0
self.start_month = min_date
def __init__(self, feed, instrument, entrySMA, exitSMA, rsiPeriod,
overBoughtThreshold, overSoldThreshold):
super(pyAlgoRSI, self).__init__(feed)
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[instrument].getPriceDataSeries()
self.__entrySMA = ma.SMA(self.__priceDS, entrySMA)
self.__exitSMA = ma.SMA(self.__priceDS, exitSMA)
self.__rsi = rsi.RSI(self.__priceDS, rsiPeriod)
self.__overBoughtThreshold = overBoughtThreshold
self.__overSoldThreshold = overSoldThreshold
self.__longPos = None
self.__shortPos = None
self.__col = [
"buyPrice", "buyTime", "sellPrice", "sellTime", "returns"
]
self.__msdf = pd.DataFrame(columns=self.__col)
def __init__(self, feed, instrument, fastPeriod, slowPeriod, rsiPeriod,
overboughtThreshold, oversoldThreshold):
# define the initial budget
super(RSIMovingAverageStrategy, self).__init__(feed, INITIAL_BUDGET)
# track the position
self.longPosition = None
self.shortPosition = None
self.oversoldThreshold = oversoldThreshold
self.overboughtThreshold = overboughtThreshold
self.instrument = instrument
# adjusted closing prices instead of regular closing prices
self.setUseAdjustedValues(True)
#fast MA indicator
self.fastMA = ma.SMA(feed[instrument].getPriceDataSeries(), fastPeriod)
#slow MA indicator
self.slowMA = ma.SMA(feed[instrument].getPriceDataSeries(), slowPeriod)
# RSI model
self.rsi = rsi.RSI(feed[instrument].getPriceDataSeries(), rsiPeriod)
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, companyID, smaPeriod, rsiPeriod, invest_amount):
strategy.BacktestingStrategy.__init__(self, feed, invest_amount)
self.__sma = ma.SMA(
feed.getDataSeries(companyID).getCloseDataSeries(), smaPeriod)
self.__sma60 = ma.SMA(
feed.getDataSeries(companyID).getCloseDataSeries(), 60)
self.__sma90 = ma.SMA(
feed.getDataSeries(companyID).getCloseDataSeries(), 90)
self.__rsi = rsi.RSI(
feed.getDataSeries(companyID).getCloseDataSeries(), rsiPeriod)
self.__bbands = bollinger.BollingerBands(
feed[companyID].getCloseDataSeries(), bBandsPeriod, 2)
self.__position = None
self.__boxFloor = 1000
self.__boxCeiling = 0
self.__cmpID = companyID
self.__amount = invest_amount
self.__strengthIndex = None
self.__discount = None
self.__fee = 0
self.__lastStatus = False
self.__cross_lower = 0
self.__buyin_price = 0
def __init__(self, feed, instrument, nper):
super(RSIStrategy, self).__init__(feed)
self.position = None
self.instrument = instrument
self.rsi = rsi.RSI(feed[instrument].getPriceDataSeries(), nper)
def testStockChartsRSI(self):
# Test data from http://stockcharts.com/school/doku.php?id=chart_school:technical_indicators:relative_strength_in
common.test_from_csv(self, "rsi-test.csv",
lambda inputDS: rsi.RSI(inputDS, 14), 3)
def __buildRSI(self, values, period, rsiMaxLen=None):
seqDS = dataseries.SequenceDataSeries()
ret = rsi.RSI(seqDS, period, rsiMaxLen)
for value in values:
seqDS.append(value)
return ret
