def __init__(self):
btind.SMA()
btind.Stochastic()
btind.RSI()
btind.MACD()
btind.CCI()
TestInd().plotinfo.plot = False
def __init__(self):
self.rsi = btind.RSI(period=self.params.period)
# self.macd = btind.MACD()
# self.macd = btind.EMA(9) - btind.EMA(14)
# self.signal = btind.EMA(14)
self.sma = btind.SimpleMovingAverage(period=21)
self.ema = btind.ExponentialMovingAverage(period=14)
self.accDe = btind.AccDeOsc(period=14)
self.close_over_sma = self.data.close > self.sma
self.close_over_ema = self.data.close > self.ema
self.sma_ema_diff = self.sma - self.ema
self.buy_sig = bt.And(self.close_over_sma, self.close_over_ema,
self.sma_ema_diff > 0)
self.max_pos = 10
self.overSold = self.rsi < 50
self.overBought = self.rsi > 70
self.oscSig = self.accDe > 0
self.buy_price = []
self.sell_price = []
self.buy_price = []
self.sell_price = []
self.ave_buy = 0
self.ave_sell = 0
def set_datalines(self):
self.indicators = []
period = self.dim_time
self.indicators.append(
btind.ExponentialMovingAverage(self.datas[0], period=15))
self.indicators.append(btind.MACDHisto(self.datas[0]))
self.indicators.append(btind.RSI(self.datas[0], period=15))
self.indicators.append(btind.BollingerBandsPct(self.datas[0]))
def __init__(self):
btind.SMA()
btind.Stochastic()
btind.RSI()
btind.MACD()
btind.CCI()
TestInd().plotinfo.plot = False
sma1, sma2 = bt.ind.SMA(period=10), bt.ind.SMA(period=30)
crossover = bt.ind.CrossOver(sma1, sma2)
self.signal_add(bt.SIGNAL_LONG, crossover)
def __init__(self):
sma = btind.SMA(subplot=self.params.smasubplot)
macd = btind.MACD()
# In SMA we passed plot directly as kwarg, here the plotinfo.plot
# attribute is changed - same effect
macd.plotinfo.plot = not self.params.nomacdplot
# Let's put rsi on stochastic/sma or the other way round
stoc = btind.Stochastic()
rsi = btind.RSI()
if self.params.stocrsi:
stoc.plotinfo.plotmaster = rsi
stoc.plotinfo.plotlinelabels = self.p.stocrsilabels
elif self.params.rsioverstoc:
rsi.plotinfo.plotmaster = stoc
elif self.params.rsioversma:
rsi.plotinfo.plotmaster = sma
def __init__(self):
super().__init__()
self.ma = btind.MovingAverageSimple(self.datas[0], period = self.params.ma)
self.ma1 = btind.MovingAverageSimple(self.datas[0], period = self.params.ma1)
self.ma2 = btind.MovingAverageSimple(self.datas[0], period = self.params.ma2)
self.atr = btind.AverageTrueRange(self.datas[0])
self.kst = btind.KnowSureThing(self.datas[0])
self.macd = btind.MACD(self.datas[0])
self.trix = btind.TrixSignal(self.datas[0])
self.ao = btind.AwesomeOscillator(self.datas[0])
self.tsi = btind.TrueStrengthIndicator(self.datas[0])
self.psar = btind.ParabolicSAR(self.datas[0])
self.adx = btind.AverageDirectionalMovementIndex(self.datas[0])
self.dpo = btind.DetrendedPriceOscillator(self.datas[0])
self.rsi = btind.RSI(self.datas[0])
self.accdec = btind.AccelerationDecelerationOscillator(self.datas[0])
self.bbands = btind.BollingerBands(self.datas[0])
self.cci = btind.CommodityChannelIndex(self.datas[0])
self.pgood = btind.PrettyGoodOscillator(self.datas[0])
self.williamsr = btind.WilliamsR(self.datas[0])
self.uo = btind.UltimateOscillator(self.datas[0])
self.stoch = btind.Stochastic(self.datas[0])
def __init__(self):
#Set program start time
start_time=datetime.now().time()
print('Program start at {}'.format(start_time))
#print(self.params.sma1, self.p.ema1, self.params.atrperiod) #Proof deep copy worked for params
#initialize counters for prenext/next
self.nextcounter = 0
self.counter = 0
self.counttostop = 0
self.datastatus = 0
self.prenext_done = False
self.bought = 0
self.sold = 0
self.target_long_price = 0
self.target_short_price = 0
self.trade_open_counter = 0
self.trade_close_counter = 0
self.trade_total_counter = 0
self.lost_counter = 0
self.won_counter = 0
#Define dictionaries and lists to be accessed from all timeframes
self.atr_list =[]
self.inds = dict()
self.gap_dict=dict()
self.rnghigh_dict = dict()
self.rnglow_dict= dict()
self.longstop_dict = dict()
self.shortstop_dict = dict()
self.target_long_dict = dict()
self.target_short_dict = dict()
self.size_dict = dict()
self.inorder_dict = dict()
self.sup_dict = dict()
self.res_dict = dict()
self.pos_dict = defaultdict(list)
#Create/Instantiate objects to access user input parameters
modelp = UserInputs.model_params()
indp = UserInputs.ind_params()
datalist = UserInputs.datalist('hist')
ibdatalist = UserInputs.datalist('ib')
#Determine interval for timeframe looping
if not modelp.get('live_status'):
data_feed_count = len(self.datas)
ticker_count = len(datalist)
self.ticker_interval = int(data_feed_count/ticker_count) #Needs to be an integer
elif modelp.get('live_status'):
data_feed_count = len(self.datas)
ticker_count = len(ibdatalist)
self.ticker_interval = int(data_feed_count/ticker_count) #Needs to be an integer
#************************INITITIALIZE INDICATORS*********************************************************
#Initialize dictionary's
for x in range(0, len(self.datas), self.ticker_interval):
d = self.datas[x]
print(d._name)
if not (d._name[:-1]=='VIX' or d._name[:-1]=='TICK-NYSE'):
#Order dictionaries
self.target_long_dict[d._name] = dict()
self.target_short_dict[d._name] = dict()
self.inorder_dict[d._name] = dict()
self.target_long_dict[d._name] = 0
self.target_short_dict[d._name] = 0
self.inorder_dict[d._name] = False
for i, d in enumerate(self.datas):
if not (d._name[:-1]=='VIX' or d._name[:-1]=='TICK-NYSE'):
#Sizing dictionary
self.size_dict[d._name] = dict()
self.size_dict[d._name] = 0
#For support/resistance dictionaries
self.sup_dict[d._name] = dict()
self.res_dict[d._name] = dict()
self.sup_dict[d._name] = 0
self.res_dict[d._name] = 10000
#For all indicators
self.inds[d._name] = dict()
#Moving Average Indicators - FAST, SLOW, and CROSS
self.inds[d._name]['sma1'] = btind.SMA(d,
period=indp.get('sma1'),
plot=False)
self.inds[d._name]['sma2'] = btind.SMA(d,
period=indp.get('sma2'),
plot=True)
self.inds[d._name]['ema1'] = btind.EMA(d,
period=indp.get('ema1'),
plot=False)
self.inds[d._name]['ema2'] = btind.EMA(d,
period=indp.get('ema2'),
plot=False)
self.inds[d._name]['ema3'] = btind.EMA(d,
period=indp.get('ema3'),
plot=False)
#This will double pre-next
self.inds[d._name]['cross'] = btind.CrossOver(self.inds[d._name]['ema2'],
self.inds[d._name]['ema3'],
plot=False)
#RSI
self.inds[d._name]['rsi']= btind.RSI(d,
safediv=True,
plot=True)
#AVERAGE TRUE RANGE INDICATOR
self.inds[d._name]['atr'] = btind.ATR(d,
period=indp.get('atrperiod'),
plot=False)
#Bollinger Band
self.inds[d._name]['bollinger'] = btind.BollingerBands(d,
period=indp.get('bollinger_period'),
devfactor = indp.get('bollinger_dist'),
plot=False)
#Stochastics
self.inds[d._name]['stochastic'] = btind.StochasticFast(d,
period=indp.get('stoch_per'),
period_dfast= indp.get('stoch_fast'),
safediv=True,
plot=True)
#ADX
self.inds[d._name]['adx'] = btind.ADX(d,plot=True)
"""
#Pivots
self.inds[d._name]['pivots'] = btind.pivotpoint.PivotPoint(d,
plot=False)
"""
#Average Volume Indicator
self.inds[d._name]['avg_volume'] = btind.Average(d.volume,
period=indp.get('avg_per'),
plot=False)
#Highest and Lowest Values of Period Indicator
self.inds[d._name]['highest'] = btind.Highest(d.high,
period=indp.get('breakout_per'),
plot=False)
self.inds[d._name]['lowest'] = btind.Lowest(d.low,
period=indp.get('breakout_per'),
plot=False)
#Slope indicators
self.inds[d._name]['slope']= btind.Slope(d.close,
period=indp.get('slope_period'),
plot=False)
self.inds[d._name]['slope_sma1'] = btind.Slope(self.inds[d._name]['sma1'],
period=indp.get('slope_period'),
plot=False,
plotname = 'Slope_SMA1')
self.inds[d._name]['slope_of_slope_sma1'] = btind.Slope(self.inds[d._name]['slope_sma1'],
period=indp.get('slope_period'),
plot=False,
plotname = 'Slope_of_Slope_SMA1')
self.inds[d._name]['slope_sma_width'] = btind.Slope(self.inds[d._name]['sma1']-self.inds[d._name]['sma2'],
period=indp.get('slope_period'),
plot=False,
plotname = 'Slope_SMA_WIDTH')
self.inds[d._name]['slope_adx'] = btind.Slope(self.inds[d._name]['adx'],
period=indp.get('slope_period'),
plot=False,
plotname = 'Slope_ADX')
self.inds[d._name]['slope_of_slope_adx'] = btind.Slope(self.inds[d._name]['slope_adx'],
period=indp.get('slope_period'),
plot=False,
plotname = 'Slope_of_Slope_ADX')
self.inds[d._name]['slope_rsi'] = btind.Slope(self.inds[d._name]['rsi'],
period=indp.get('slope_period'),
plot=False,
plotname = 'Slope_RSI')
self.inds[d._name]['slope_of_slope_rsi'] = btind.Slope(self.inds[d._name]['slope_rsi'],
period=indp.get('slope_period'),
plot=False,
plotname = 'Slope_of_Slope_RSI')
self.inds[d._name]['slope_ema1'] = btind.Slope(self.inds[d._name]['ema1'],
period=indp.get('slope_period'),
plot=False,
plotname = 'Slope_EMA1')
self.inds[d._name]['slope_ema2'] = btind.Slope(self.inds[d._name]['ema2'],
period=indp.get('slope_period'),
plot=False,
plotname = 'Slope_EMA2')
self.inds[d._name]['slope_ema3'] = btind.Slope(self.inds[d._name]['ema3'],
period=indp.get('slope_period'),
plot=False,
plotname = 'Slope_EMA3')
#Plot ADX and Slope on same subplot as stochastic
self.inds[d._name]['adx'].plotinfo.plotmaster = self.inds[d._name]['rsi']
def __init__(self):
super().__init__()
self.rsi = btind.RSI(self.datas[0], period = self.p.rsi_period)
def __init__(self):
super().__init__()
self.rsi = btind.RSI(self.datas[0], period = self.params.rsi_period)
self.prank= btind.PercentRank(self.rsi, period = self.params.percent_period) * 100
self.pendingBuy = False
self.pendingSell = False
