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.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')