def __init__(self):
self.dataclose = self.datas[0].close
self.order = None
#self.macd = bt.indicators.MACD(self.datas[0])
#self.sma05 = btind.SMA(self.datas[0],period = self.params.mapperiod,subplot=False)
self.sma20 = btind.SMA(self.datas[0],
period=self.params.mapperiod02,
subplot=False)
self.bbrands = btind.BBands(self.datas[0], subplot=False)
self.max20 = bt.talib.MAX(self.datas[0],
period=self.params.mapperiod02,
plot=False)
self.min20 = bt.talib.MIN(self.datas[0],
period=self.params.mapperiod02,
plot=False)
self.atr = btind.ATR(self.datas[0], plot=False)
self.signaltop = btind.CrossOver(self.dataclose,
self.bbrands.top,
plot=False)
self.signalcrossover = btind.CrossOver(self.dataclose,
self.sma20,
plot=False)
self.signallow = btind.CrossOver(self.datas[0], self.bbrands.bot)
self.signaltop = btind.CrossOver(self.datas[0], self.bbrands.top)
self.buyprice = None
self.buycomm = None
def __init__(self):
super().__init__(verbose=self.p.verbose)
if self.p.verbose:
log.setLevel(logging.DEBUG)
self.hlc3 = (self.data.high + self.data.low + self.data.close)/3
self.baseline = btind.WMA(self.hlc3, period = self.p.wma_period)
# self.exit = btind.HMA(self.hlc3, period = self.p.hma_period)
self.atr = btind.ATR(self.data, period=self.p.atr_period)
# self.stddev = btind.StdDev(self.data.close, period=self.p.stddev_period)
#self.highest = btind.Highest(self.data.high, period=self.p.squeeze_period, plot=False)
#self.lowest = btind.Lowest(self.data.low, period=self.p.squeeze_period, plot=False)
#self.sma = btind.MovAv.SMA(self.data.close, period=self.p.squeeze_period, plot=False)
#self.mean = (self.highest + self.lowest + self.sma) / 3.0
#self.squeeze = btind.MovAv.SMA((self.data.close - self.mean) / self.data.close, period=self.p.squeeze_period, plot=True, subplot=True)
self.squeeze = JackVortex(squeeze_period=self.p.squeeze_period)
# self.macd = btind.MACDHisto(self.data.close, period_me1=self.p.macd_fast_period,
# period_me2=self.p.macd_slow_period, period_signal=self.p.macd_signal_period)
# self.macd_ema = btind.MovAv.EMA(self.data.close, period=self.p.macd_ema_period)
self.stage = self.Start
self.main_order = None
self.stop_order = None
self.limit_order = None
self.exit_order = None
self.main_order_price = None
self.target1_price = None
self.size = None
self.symbol_parameter = SymbolParameter(self.p.symbol, self.broker.get_cash(), self.p.max_loss_percent)
def __init__(self):
self.dataclose = self.datas[0].close
self.order = None
#self.macd = bt.indicators.MACD(self.datas[0])
#self.sma05 = btind.SMA(self.datas[0],period = self.params.mapperiod,subplot=False)
self.sma20 = btind.SMA(self.datas[0],
period=self.params.mapperiod02,
subplot=False)
self.bbrands = btind.BBands(self.datas[0], subplot=False)
self.max55 = bt.talib.MAX(self.datas[0].high,
period=self.params.mapperiod,
plot=False)
self.min55 = bt.talib.MIN(self.datas[0].low,
period=self.params.mapperiod,
plot=False)
self.atr = btind.ATR(self.datas[0])
self.stake = 4000
self.ownprice = 0
## self.signaltop = btind.CrossOver(self.dataclose,self.bbrands.top,plot=False)
self.buyprice = None
self.buycomm = None
def __init__(self):
self.inds = {}
for d in self.datas:
self.inds[d] = {}
self.inds[d]['fast_ema'] = btind.EMA(
period=self.p.trend_filter_fast_period)
self.inds[d]['slow_ema'] = btind.EMA(
period=self.p.trend_filter_slow_period)
self.inds[d]['long_filter'] = self.inds[d]['fast_ema'] > self.inds[
d]['slow_ema']
self.inds[d]['dc'] = DonchianChannels(
period=self.p.donchian_channel_period)
self.inds[d]['atr'] = btind.ATR(
period=self.p.trailing_stop_atr_period)
def __init__(self):
super().__init__()
# self.ema_short = btind.MovAv.EMA(self.data.close, period=self.p.ema_short_period)
self.atr = btind.ATR(self.data, period=self.p.atr_period)
self.stddev = btind.StdDev(self.data.close,
period=self.p.stddev_period)
self.highest = btind.Highest(self.data.high,
period=self.p.squeeze_period,
plot=False)
self.lowest = btind.Lowest(self.data.low,
period=self.p.squeeze_period,
plot=False)
self.sma = btind.MovAv.SMA(self.data.close,
period=self.p.squeeze_period,
plot=False)
self.mean = ((self.highest + self.lowest) / 2.0 + self.sma) / 2.0
self.squeeze = btind.MovAv.SMA(
(self.data.close - self.mean) / self.data.close,
period=self.p.squeeze_period)
# self.macd_fast = btind.MovAv.EMA(self.data.close, period=self.p.macd_fast_period, plot=False)
# self.macd_slow = btind.MovAv.EMA(self.data.close, period=self.p.macd_slow_period, plot=False)
# self.macd = self.macd_fast - self.macd_slow
# self.macd_signal = btind.MovAv.EMA(self.macd, period=self.p.macd_signal_period, plot=False)
# self.macd_histogram = self.macd - self.macd_signal
self.macd = btind.MACDHisto(self.data.close,
period_me1=self.p.macd_fast_period,
period_me2=self.p.macd_slow_period,
period_signal=self.p.macd_signal_period)
self.macd_ema = btind.MovAv.EMA(self.data.close,
period=self.p.macd_ema_period)
self.stage = self.Start
self.main_order = None
self.stop_order = None
self.limit_order = None
self.exit_order = None
self.main_order_price = None
self.target1_price = None
self.size = None
self.symbol_parameter = SymbolParameter(self.p.symbol,
self.broker.get_cash(),
self.p.max_loss_percent)
def __init__(self):
super().__init__()
# indicators
self.atr = btind.ATR(self.data, period=self.p.atr_period)
self.hlc3 = (self.data.high + self.data.low + self.data.close) / 3
self.baseline = btind.WMA(self.hlc3, period=self.p.baseline_period)
self.highest_close = btind.Highest(self.data.close,
period=self.p.breakout_period)
self.lowest_close = btind.Lowest(self.data.close,
period=self.p.breakout_period)
self.stage = self.Start
self.main_order = None
self.stop_order = None
self.limit_order = None
self.exit_order = None
self.main_order_price = None
self.target1_price = None
self.size = None
self.symbol_parameter = SymbolParameter(self.p.symbol,
self.broker.get_cash(),
self.p.max_loss_percent)
def __init__(self):
#Set program start time
start_time=datetime.now().time()
print('Program start at {}'.format(start_time))
print('Program time period: {} to {}'.format( UserInputs.model_params().get('start_date'),
UserInputs.model_params().get('end_date')))
print('Program Parameters: {}'.format(self.params._getitems()))
#initialize counters for prenext/next
self.nextcounter = 0
self.counter = 0
self.prenext_done = False
self.target_short_price = 0
self.target_long_price = 0
self.pos = 0
self.cash_avail = 0
self.data_live = False
#Define dictionaries and lists to be accessed from all timeframes
self.inds = dict()
self.rnghigh_dict = dict()
self.rnglow_dict= dict()
self.stop_dict = defaultdict(list)
self.target_long_dict = defaultdict(list)
self.target_short_dict = defaultdict(list)
self.size_dict = defaultdict(list)
self.inorder_dict = defaultdict(list)
#Create/Instantiate objects to access user input parameters
self.modelp = UserInputs.model_params()
#Determine interval for timeframe looping
if not self.modelp.get('live_status'):
datalist = UserInputs.datalist('hist')
self.data_feed_count = len(self.datas)
self.ticker_count = len(datalist)
self.number_timeframes = int(self.data_feed_count/self.ticker_count) #Needs to be an integer
elif self.modelp.get('live_status'):
ibdatalist = UserInputs.datalist('ib')
self.data_feed_count = len(self.datas)
self.ticker_count = len(ibdatalist)
self.number_timeframes = int(self.data_feed_count/self.ticker_count) #Needs to be an integer
self.minimum_data = int(self.ticker_count * self.modelp.get('timeframe2')/self.modelp.get('timeframe0')) #since iterating over 5 min periods, and longest timeframe is 1 hour, there are 12 5 min periods in an hour
#Determine # of base timeframe periods within trading day
self.intraday_periods = int(390/self.modelp.get('timeframe0'))
#************************INITITIALIZE INDICATORS*********************************************************
#Initialize dictionary's
for i, d in enumerate(self.datas):
print("Datas in Strategy {}".format(d._name))
#Initialize dictionaries by appending 0 value
self.inds[d._name] = dict() #Dict for all indicators
self.stop_dict[d._name].append(0)
self.target_long_dict[d._name].append(0)
self.target_short_dict[d._name].append(0)
self.size_dict[d._name].append(0) #Need to append twice to reference 2nd to last value at start
self.size_dict[d._name].append(0)
self.inorder_dict[d._name].append(False)
#Get available cash
self.cash_avail = self.broker.getcash()
#Instantiate exact data references (can't loop or will only spit out last value)
if d._name == 'TICK-NYSE0':
self.tick_close = d.close
if d._name == 'SPY0':
self.spy_close = d.close
#Calculate VWAP
self.inds[d._name]['vwap'] = btind.vwap(d,
plot=True)
#Determine on balance volume
self.inds[d._name]['obv'] = btind.obv(d,
period=self.p.obv,
plot=True)
#Determine current ohlcv bars
self.inds[d._name]['ohlc'] = btind.ohlc(d,
period=self.p.ohlc,
plot=False)
#Determine same bar, prior day
self.inds[d._name]['priorday'] = btind.priorday(d,
plot=False)
"""
#Determine opening gap and opening hi/low range (defined by b_time parameter)
self.inds[d._name]['gap'] = btind.gap(d,
period=self.p.breakout_per,
plot=False)
"""
#AVERAGE TRUE RANGE INDICATOR
self.inds[d._name]['atr'] = btind.ATR(d,
period=self.p.atrperiod,
plot=False)
self.inds[d._name]['atr_stop'] = btind.atr_stop(d,self.inds[d._name]['atr'],
atrdist = self.p.atrdist,
dollars_risked = self.p.total_dollars_risked,
dollars_per_trade = self.p.dollars_risked_per_trade,
plot=False)
"""
#Moving Average Indicators - FAST, SLOW, and CROSS
self.inds[d._name]['sma1'] = btind.SMA(d,
period=self.p.sma1,
plot=False)
self.inds[d._name]['sma2'] = btind.SMA(d,
period=self.p.sma2,
plot=False)
self.inds[d._name]['ema1'] = btind.EMA(d,
period=self.p.ema1,
plot=True)
self.inds[d._name]['ema2'] = btind.EMA(d,
period=self.p.ema2,
plot=True)
self.inds[d._name]['cross'] = btind.CrossOver(self.inds[d._name]['ema1'],
self.inds[d._name]['ema2'],
plot=False)
#RSI
self.inds[d._name]['rsi']= btind.RSI(d,
safediv=True,
plot=False)
#Bollinger Band
self.inds[d._name]['bollinger'] = btind.BollingerBands(d,
period=self.p.bollinger_period,
devfactor = self.p.bollinger_dist'),
plot=False)
#Stochastics - just prints Slow %d line (not %K also which would be "StochasticFast")
self.inds[d._name]['stochastic'] = btind.StochasticSlow(d,
period=self.p.stoch_per,
period_dfast= self.p.stoch_fast,
safediv=True,
plot=False)
#ADX
self.inds[d._name]['adx'] = btind.ADX(d,
period=self.p.adx,
plot=False)
#Pivots
self.inds[d._name]['pivots'] = btind.pivotpoint.PivotPoint(d,
plot=False)
#Highest and Lowest Values of Period Indicator
self.inds[d._name]['highest'] = btind.Highest(d.high,
period=self.p.breakout_per,
plot=False)
self.inds[d._name]['lowest'] = btind.Lowest(d.low,
period=self.p.breakout_per,
plot=False)
"""
#Slope indicators
self.inds[d._name]['slope']= btind.Slope(d.close,
period=self.p.slope_period,
plot=False)
self.inds[d._name]['slope_obv'] = btind.Slope(self.inds[d._name]['obv'],
period=self.p.slope_period,
plot=False)
"""
self.inds[d._name]['slope_of_slope_obv'] = btind.Slope(self.inds[d._name]['slope_obv'],
period=self.p.slope_period,
plot=False)
self.inds[d._name]['slope_sma1'] = btind.Slope(self.inds[d._name]['sma1'],
period=self.p.slope_period,
plot=False)
self.inds[d._name]['slope_of_slope_sma1'] = btind.Slope(self.inds[d._name]['slope_sma1'],
period=self.p.slope_period,
plot=False)
self.inds[d._name]['slope_of_slope_sma1'] = btind.Slope(self.inds[d._name]['slope_sma1'],
period=self.p.slope_period,
plot=False)
self.inds[d._name]['slope_sma_width'] = btind.Slope(self.inds[d._name]['sma1']-self.inds[d._name]['sma2'],
period=self.p.slope_period,
plot=False)
self.inds[d._name]['slope_adx'] = btind.Slope(self.inds[d._name]['adx'],
period=self.p.slope_period,
plot=False)
self.inds[d._name]['slope_of_slope_adx'] = btind.Slope(self.inds[d._name]['slope_adx'],
period=self.p.slope_period,
plot=False)
self.inds[d._name]['slope_rsi'] = btind.Slope(self.inds[d._name]['rsi'],
period=self.p.slope_period,
plot=False,
plotname = 'Slope_RSI')
self.inds[d._name]['slope_of_slope_rsi'] = btind.Slope(self.inds[d._name]['slope_rsi'],
period=self.p.slope_period,
plot=False,
plotname = 'Slope_of_Slope_RSI')
self.inds[d._name]['slope_ema1'] = btind.Slope(self.inds[d._name]['ema1'],
period=self.p.slope_period,
plot=False,
plotname = 'Slope_EMA1')
self.inds[d._name]['slope_ema2'] = btind.Slope(self.inds[d._name]['ema2'],
period=self.p.slope_period,
plot=False,
plotname = 'Slope_EMA2')
"""
self.inds[d._name]['resistance'] = btind.Resistance(d,
period=self.p.lookback,
min_touches = self.p.min_touches,
tolerance_perc = self.p.tolerance_perc,
bounce_perc = self.p.bounce_perc,
plot=True)
self.inds[d._name]['support'] = btind.Support(d,
period=self.p.lookback,
min_touches = self.p.min_touches,
tolerance_perc = self.p.tolerance_perc,
bounce_perc = self.p.bounce_perc,
plot=True)
#Calculate Hammer Candle Signal
self.inds[d._name]['hammer'] = btind.HammerCandles(d,
plot=False)
#Calculate Engulfing Candle Signal
self.inds[d._name]['engulfing'] = btind.EngulfingCandles(d,
plot=False)
#Calculate Engulfing Candle Signal
self.inds[d._name]['three_line_strike'] = btind.three_line_strike(d,
plot=False)
#Plot ADX and Stochastic on same subplot as stochastic
#self.inds[d._name]['adx'].plotinfo.plotmaster = self.inds[d._name]['stochastic']
print('Start preloading data to meet minimum data requirements')
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):
self.atr_base = btind.ATR(period=self.p.period)
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.dayperiod = 0
self.nextcounter = 0
self.counter = 0
self.prenext_done = False
self.target_short_price = 0
self.target_long_price = 0
self.pos = 0
self.cash_avail = 0
#Define dictionaries and lists to be accessed from all timeframes
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 = defaultdict(list)
self.target_short_dict = defaultdict(list)
self.size_dict = defaultdict(list)
self.inorder_dict = defaultdict(list)
#Create/Instantiate objects to access user input parameters
self.modelp = UserInputs.model_params()
self.indp = UserInputs.ind_params()
datalist = UserInputs.datalist('hist')
ibdatalist = UserInputs.datalist('ib')
self.data_feed_count = len(self.datas)
#Determine interval for timeframe looping
if not self.modelp.get('live_status'):
self.ticker_count = len(datalist)
self.number_timeframes = int(self.data_feed_count/self.ticker_count) #Needs to be an integer
elif self.modelp.get('live_status'):
self.ticker_count = len(ibdatalist)
self.number_timeframes = int(self.data_feed_count/self.ticker_count) #Needs to be an integer
self.minimum_data = int(self.ticker_count * self.modelp.get('timeframe2')/self.modelp.get('timeframe0')) #since iterating over 5 min periods, and longest timeframe is 1 hour, there are 12 5 min periods in an hour
#Determine # of base timeframe periods within trading day
self.intraday_periods = int(390/self.modelp.get('timeframe0'))
#************************INITITIALIZE INDICATORS*********************************************************
#Initialize dictionary's
for i, d in enumerate(self.datas):
self.name_t0 = d._name[:-1]+'0'
self.name_t1 = d._name[:-1]+'1'
self.name_t2 = d._name[:-1]+'2'
print('Datas included in Strategy: {}'.format(d._name))
#Initialize dictionaries by appending 0 value
self.target_long_dict[d._name].append(0)
self.target_short_dict[d._name].append(0)
self.size_dict[d._name].append(0) #Need to append twice to reference 2nd to last value
self.size_dict[d._name].append(0)
self.inorder_dict[d._name].append(False)
#Get available cash
self.cash_avail = round(self.broker.getcash(),2)
#For all indicators
self.inds[d._name] = dict()
#Determine current and prior day bars
self.inds[d._name]['ohlc'] = btind.OHLC(d,
period=self.indp.get('ohlc'),
plot=False)
self.inds[d._name]['prior_ohlc'] = btind.priorday(d,
period=self.indp.get('ohlc'),
plot=False)
#AVERAGE TRUE RANGE INDICATOR
self.inds[d._name]['atr'] = btind.ATR(d,
period=self.indp.get('atrperiod'),
plot=False)
#Moving Average Indicators - FAST, SLOW, and CROSS
self.inds[d._name]['sma1'] = btind.SMA(d,
period=self.indp.get('sma1'),
plot=True)
self.inds[d._name]['sma2'] = btind.SMA(d,
period=self.indp.get('sma2'),
plot=True)
"""
self.inds[d._name]['ema1'] = btind.EMA(d,
period=self.indp.get('ema1'),
plot=False)
self.inds[d._name]['ema2'] = btind.EMA(d,
period=self.indp.get('ema2'),
plot=False)
#This will double pre-next
self.inds[d._name]['cross'] = btind.CrossOver(self.inds[d._name]['ema1'],
self.inds[d._name]['ema2'],
plot=False)
#RSI
self.inds[d._name]['rsi']= btind.RSI(d,
safediv=True,
plot=False)
#Bollinger Band
self.inds[d._name]['bollinger'] = btind.BollingerBands(d,
period=self.indp.get('bollinger_period'),
devfactor = self.indp.get('bollinger_dist'),
plot=False)
"""
#Stochastics - just prints Slow %d line (not %K also which would be "StochasticFast")
self.inds[d._name]['stochastic'] = btind.StochasticSlow(d,
period=self.indp.get('stoch_per'),
period_dfast= self.indp.get('stoch_fast'),
safediv=True,
plot=True)
#ADX
self.inds[d._name]['adx'] = btind.ADX(d,
period=self.indp.get('adx'),
plot=True)
"""
#Pivots
self.inds[d._name]['pivots'] = btind.pivotpoint.PivotPoint(d,
plot=False)
"""
#Highest and Lowest Values of Period Indicator
self.inds[d._name]['highest'] = btind.Highest(d.high,
period=self.indp.get('breakout_per'),
plot=False)
self.inds[d._name]['lowest'] = btind.Lowest(d.low,
period=self.indp.get('breakout_per'),
plot=False)
#Slope indicators
self.inds[d._name]['slope']= btind.Slope(d.close,
period=self.indp.get('slope_period'),
plot=False)
self.inds[d._name]['slope_sma1'] = btind.Slope(self.inds[d._name]['sma1'],
period=self.indp.get('slope_period'),
plot=False)
self.inds[d._name]['slope_of_slope_sma1'] = btind.Slope(self.inds[d._name]['slope_sma1'],
period=self.indp.get('slope_period'),
plot=False)
"""
self.inds[d._name]['slope_of_slope_sma1'] = btind.Slope(self.inds[d._name]['slope_sma1'],
period=self.indp.get('slope_period'),
plot=False)
self.inds[d._name]['slope_sma_width'] = btind.Slope(self.inds[d._name]['sma1']-self.inds[d._name]['sma2'],
period=self.indp.get('slope_period'),
plot=False)
self.inds[d._name]['slope_adx'] = btind.Slope(self.inds[d._name]['adx'],
period=self.indp.get('slope_period'),
plot=False)
self.inds[d._name]['slope_of_slope_adx'] = btind.Slope(self.inds[d._name]['slope_adx'],
period=self.indp.get('slope_period'),
plot=False)
self.inds[d._name]['slope_rsi'] = btind.Slope(self.inds[d._name]['rsi'],
period=self.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=self.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=self.indp.get('slope_period'),
plot=False,
plotname = 'Slope_EMA1')
self.inds[d._name]['slope_ema2'] = btind.Slope(self.inds[d._name]['ema2'],
period=self.indp.get('slope_period'),
plot=False,
plotname = 'Slope_EMA2')
"""
self.inds[d._name]['resistance'] = btind.Resistance(d,
period=self.indp.get('lookback'),
plot=True)
self.inds[d._name]['support'] = btind.Support(d,
period=self.indp.get('lookback'),
plot=True)
if d._name == d._name[:-1]+'0' or d._name == d._name[:-1]+'1':
self.inds[d._name]['avgvolume'] = btind.avgvolume(d,
period=self.indp.get('avgvolume'),
plot=False)
if d._name == d._name[:-1]+'0':
self.inds[d._name]['priorday'] = btind.priorday(d,
period=self.indp.get('priorday'),
plot=False)
if d._name == d._name[:-1]+'0' or d._name == d._name[:-1]+'1':
self.inds[d._name]['vwap'] = btind.vwap(d,
period=self.indp.get('vwap_lookback'),
plot=True)
#Plot ADX and Stochastic on same subplot as stochastic
#self.inds[d._name]['adx'].plotinfo.plotmaster = self.inds[d._name]['stochastic']
print('Start preloading data to meet minimum data requirements')
def __init__(self):
ATR = btind.ATR()
self.l.ATR = ATR
self.l.SMMA = btind.SMMA(self.l.ATR, period=self.p.period)