Notebook

Running Pipeline algorithm in research

This is an attempt to document how I tried, and failed to run a pipeline backtest on backtest.

We use the algorithm from https://www.quantopian.com/posts/introducing-the-pipeline-api to demostrate how to run backtest in the environment.

Summary in issues we saw is:

NoEngineRegistered: Attempted to run a pipeline but no pipeline resources were registered.

Looking at https://github.com/quantopian/zipline/blob/master/zipline/pipeline/engine.py#L31 seems like this we need to provide get_pipeline_loader for the algorithm:

algo_obj = TradingAlgorithm(
    initialize=initialize,
    handle_data=handle_data,
    before_trading_start=before_trading_start,
    get_pipeline_loader=???
)

Any way we can provide the pipeline_loader?

In [17]:
# example data input: any way to improve this?
from datetime import datetime
import pytz
start_date = datetime(2015, 10, 25, 9, 0, 0, 0, pytz.utc)
end_date = datetime(2016, 11, 23, 0, 0, 0, 0, pytz.utc)
#start_date = '2015-10-25'
#end_date = '2016-11-23'

Step1 - rename quantopian.algorithm into zipline.api

The original algorithm was from https://www.quantopian.com/posts/introducing-the-pipeline-api - we changed this so this would

And import other necessary api funtions

In [18]:
"""
This example comes from a request in the forums. 
The post can be found here: https://www.quantopian.com/posts/ranking-system-based-on-trading-volume-slash-shares-outstanding

The request was: 

I am stuck trying to build a stock ranking system with two signals:
1. Trading Volume/Shares Outstanding.
2. Price of current day / Price of 60 days ago.
Then rank Russell 2000 stocks every month, long the top 5%, short the bottom 5%.

"""
# STEP1: use from zipline.api instead
#from quantopian.algorithm import attach_pipeline, pipeline_output
from zipline.api import schedule_function, date_rules, time_rules, sid, symbol, symbols, \
        get_datetime, order_target_percent, record, attach_pipeline, set_commission, \
        order_target, pipeline_output, get_open_orders

#from quantopian.algorithm import attach_pipeline, pipeline_output
from quantopian.pipeline import Pipeline, CustomFilter
from quantopian.pipeline.data.builtin import USEquityPricing
from quantopian.pipeline.data import morningstar
from quantopian.pipeline.factors import SimpleMovingAverage, AverageDollarVolume, CustomFactor
import numpy as np
from numpy import nanmin, nanmax, nanmean
from quantopian.pipeline.filters.morningstar import IsPrimaryShare
from quantopian.pipeline.classifiers.morningstar import Sector
from quantopian.pipeline.data import morningstar as mstar
import pandas as pd
from collections import defaultdict
import copy
from copy import deepcopy
import datetime
import pytz
from pandas.tseries.offsets import BDay
import math
import talib


'''
class SidInList(CustomFilter):  #USE IF WANT TO USE SPECIFIC STOCKS
    """
    Filter returns True for any SID included in parameter tuple passed at creation.
    Usage: my_filter = SidInList(sid_list=(23911, 46631))
    """    
    inputs = []
    window_length = 1
    params = ('sid_list',)

    def compute(self, today, assets, out, sid_list):
        out[:] = np.in1d(assets, sid_list)
'''  
class SidInList(CustomFilter): #USE FOR PIPELINE STOCKS
    """
    Filter returns True for any SID included in parameter tuple passed at creation.
    Usage: my_filter = SidInList(sid_list=(23911, 46631))
    """    
    inputs = []
    window_length = 1
    #params = ('sid_list',)

    def compute(self, today, assets, out):
        out[:] = np.in1d(assets)


class WVIXHIST(CustomFactor):    #EXTRA FOR RUNNING HISTORY CALCULATED MANUALLY FOR EACH ADDITIONAL DAY
    inputs = [USEquityPricing.low,USEquityPricing.close]
       
    window_length = 24
    
    def compute(self, today, assets, out, lows, close):
        highest_close = nanmax(close[-23:-1], axis=0)
        WVIXHIST = ((highest_close - lows[22]) / (highest_close)) * 100   
        out[:] = WVIXHIST

        
def initialize(context):
    #set_commission(commission.PerShare(cost=0, min_trade_cost=0))
    #set_slippage(slippage.FixedSlippage(spread=0))
    #set_slippage(slippage.VolumeShareSlippage(volume_limit=100, price_impact=0))
    
    schedule_function(func=close_positions,
                      date_rule=date_rules.every_day(),
                      time_rule=time_rules.market_close(minutes=15)) 
    
    schedule_function(order_positions, date_rule = date_rules.every_day(), time_rule=time_rules.market_open(minutes = 5))
    
    schedule_function(attempt2, date_rule = date_rules.every_day(), time_rule=time_rules.market_open())  
    schedule_function(cleardf, date_rule = date_rules.week_end(), time_rule=time_rules.market_close(minutes = 3))      
    
    
    total_minutes = 6*60 + 30     
    for i in range(total_minutes):  
    # Every xx minutes run schedule  
        if i % 30 == 1:  
            schedule_function(  
            stop_loss,  
            date_rule = date_rules.every_day(),  
            time_rule=time_rules.market_open(minutes=i),)  
                

    context.main_df2 = pd.DataFrame() 
    context.main_df2a = pd.DataFrame()
    context.sma_quotientl = pd.DataFrame()
    context.sma_quotients = pd.DataFrame()    
    #context.sma_quotient = pd.DataFrame({'Date': [],'Asset': [], 'sma_quotient': []})
    #context.sma_quotient = pd.DataFrame()
    context.max_lvrg = 0        

    country_ref = mstar.company_reference.country_id.latest.eq('USA')
    have_data = mstar.valuation.market_cap.latest.notnull()
    mkt_cap_thresh = (mstar.valuation.market_cap.latest > 30e6)
    sector_check = Sector().notnull()
    xch = (mstar.company_reference.primary_exchange_id.latest.eq('NAS') or \
           mstar.company_reference.primary_exchange_id.latest.eq('NYS'))
    common_stock = mstar.share_class_reference.security_type.latest.eq('ST00000001')
    not_lp_name = ~mstar.company_reference.standard_name.latest.matches('.* L[\\. ]?P\.?$')
    not_lp_balance_sheet = mstar.balance_sheet.limited_partnership.latest.isnull()
    not_otc = ~mstar.share_class_reference.exchange_id.latest.startswith('OTC')
    not_wi = ~mstar.share_class_reference.symbol.latest.endswith('.WI')
    not_depository = ~mstar.share_class_reference.is_depositary_receipt.latest
    primary_share = IsPrimaryShare()

    sma_1 = SimpleMovingAverage(inputs=[USEquityPricing.close], window_length=5)    
    min_price = (sma_1 > 10)    
    
    # initialize filter/mask ...just leave in in case expand testing set
    tradable_filter = (country_ref & mkt_cap_thresh & sector_check & xch &
                       common_stock & not_lp_name & not_lp_balance_sheet &
                       have_data & not_otc & not_wi & not_depository & primary_share & min_price) 

    # lets screen down to the stocks that have averaged 
    # at least $100M of daily trading dollar volume over the
    # past XX days.
    
    # Create a dollar volume factor.
    dollar_volume = AverageDollarVolume(window_length=5)
    high_dollar_volume = dollar_volume.percentile_between(50, 100)

    #include_filter = SidInList(sid_list = (8554, 19920, 37514, 42667, 42223, 42224, 33217, 37083, 40552))    
    
    #context.include_filter = SidInList(sid_list = (  sid(8554), sid(37514)  )) #USE IF WANT TO USE SPECIFIC STOCKS
                                       
            
    #set_benchmark(symbol('SPY')) 
    
    ''' LIST TO PLAY WITH
          sid(37514): -.15, #SPXL BULL 3x
        sid(37083): -.15, #SPXS Bear 3x       
        sid(37049): -.185, #financial
        sid(37048): -.185,  #financial
        sid(49403): -.15, #pharma
        sid(49402): -.15,  #pharma
        sid(39898): -.15, #natural gas
        sid(49639): -.15, # natural gas Dec 2015
        sid(37513): -.15, #energy
        sid(37044): -.15, #energy   
        sid(40553): -.1, #gold
        sid(40554): -.1,  #gold     
        sid(38294): -.1, #fixed income 20+
        sid(38292): -.1 #fixed income 20+
        
           
    sid(38293) - TYD - Daily 7-10 Year Treasury Bull 3x Shares
    sid(38295) - TYO - Daily 7-10 Year Treasury Bear 3x Shares
    ''' 
    
    sma = SimpleMovingAverage(inputs=[USEquityPricing.close], window_length=10)

    high_cap_high_dv =  high_dollar_volume & tradable_filter #USE FOR PIPELINE STOCKS
    #high_cap_high_dv =  context.include_filter #USE IF WANT TO USE SPECIFIC STOCKS

    

    pipe_screen = (high_cap_high_dv)    

    pipe = Pipeline(screen = pipe_screen)
    
    close = WVIXHIST(mask=high_cap_high_dv)
    

    # Add the desired values to our pipe. 
    pipe.add(close, 'close')
    pipe.add(sma, 'sma')    
    
    #pipe.add(dollar_volume, 'dollar_volume')

    attach_pipeline(pipe, 'example')
    

def handle_data(context, data):  
    '''
    if context.account.leverage > context.max_lvrg:  
        context.max_lvrg = context.account.leverage  
        
    port = len(context.portfolio.positions)
    record(leverage=context.account.leverage, count = port, maxlev = context.max_lvrg)        
    '''
    pass

    '''
    # Stop Loss

    if len(context.portfolio.positions) >0:    
        for stock in context.portfolio.positions:
            cost = context.portfolio.positions[stock].cost_basis 
            position = context.portfolio.positions[stock]            

            if position.amount > 0. and cost > 0.:
                if data.current(stock, 'price') < (cost * 0.95):
                    if not get_open_orders(stock):
                        loss = np.around(((data.current(stock, 'price') - cost) / cost * 100.00), decimals=3)
                        log.info("LONG STOP LOSS {stock}->LOSS:{loss} %".format(stock=stock, loss=loss))              
                        order_target_percent(stock, 0)  
                if data.current(stock, 'price') > (cost * 1.075):                        
                    if not get_open_orders(stock):
                        profit = np.around(((data.current(stock, 'price') - cost) / cost * 100.00), decimals=3)
                        log.info("LONG PROFIT {stock}->PROFIT:{profit} %".format(stock=stock, profit=profit))              
                        order_target_percent(stock, 0)  
                        
            if position.amount < 0. and cost > 0.:
                if data.current(stock, 'price') > (cost * 1.05):
                    if not get_open_orders(stock):
                        loss = -np.around(((data.current(stock, 'price') - cost) / cost * 100.00), decimals=3)
                        log.info("SHORT STOP LOSS {stock}->LOSS:{loss} %".format(stock=stock, loss=loss))              
                        order_target_percent(stock, 0)  
                if data.current(stock, 'price') < (cost * 0.925):                        
                    if not get_open_orders(stock):
                        profit = -np.around(((data.current(stock, 'price') - cost) / cost * 100.00), decimals=3)
                        log.info("SHORT PROFIT {stock}->PROFIT:{profit} %".format(stock=stock, profit=profit))              
                        order_target_percent(stock, 0)                          
                            
for stock, position in context.portfolio.positions.iteritems():  #shorthand
            amount = position.amount  
            last_sale_price = position.last_sale_price                              
    '''    
def before_trading_start(context, data):
    context.outputa = pipeline_output('example')
    context.output = context.outputa.close
    
def stop_loss(context, data):
    # Stop Loss

    if len(context.portfolio.positions) >0:    
        for stock in context.portfolio.positions:
            cost = context.portfolio.positions[stock].cost_basis 
            position = context.portfolio.positions[stock]            

            if position.amount > 0. and cost > 0.:
                if data.current(stock, 'price') < (cost * 0.95):
                    if not get_open_orders(stock):
                        loss = np.around(((data.current(stock, 'price') - cost) / cost * 100.00), decimals=3)
                        log.info("LONG STOP LOSS {stock}->LOSS:{loss} %".format(stock=stock, loss=loss))              
                        order_target_percent(stock, 0)  
                if data.current(stock, 'price') > (cost * 1.075):                        
                    if not get_open_orders(stock):
                        profit = np.around(((data.current(stock, 'price') - cost) / cost * 100.00), decimals=3)
                        log.info("LONG PROFIT {stock}->PROFIT:{profit} %".format(stock=stock, profit=profit))              
                        order_target_percent(stock, 0)  
                        
            if position.amount < 0. and cost > 0.:
                if data.current(stock, 'price') > (cost * 1.05):
                    if not get_open_orders(stock):
                        loss = -np.around(((data.current(stock, 'price') - cost) / cost * 100.00), decimals=3)
                        log.info("SHORT STOP LOSS {stock}->LOSS:{loss} %".format(stock=stock, loss=loss))              
                        order_target_percent(stock, 0)  
                if data.current(stock, 'price') < (cost * 0.925):                        
                    if not get_open_orders(stock):
                        profit = -np.around(((data.current(stock, 'price') - cost) / cost * 100.00), decimals=3)
                        log.info("SHORT PROFIT {stock}->PROFIT:{profit} %".format(stock=stock, profit=profit))              
                        order_target_percent(stock, 0)     
  
 
    
def order_positions(context, data):
    """
    Main ordering conditions to always order an equal percentage in each position
    so it does a rolling rebalance by looking at the stocks to order today and the stocks
    we currently hold in our portfolio.
    """
    if context.main_df2.empty:
        context.main_df2 = context.main_df2a.T
        
    if len(context.output.index) != 0:
        #log.info("WVIX = " + "\n" + str(context.main_df2.T[-5:]))
        context.sma_quotientl = pd.DataFrame()
        context.sma_quotients = pd.DataFrame()
        for stock in context.output.index:
            #if context.main_df2[stock][-1:].values == context.main_df2[stock].index[-1:].asobject:
                #context.main_df2 = context.main_df2.ix[context.main_df2[stock == stock]]
                #order_positions(context, data)
            #WVIX_MAX = nanmax(context.main_df2[stock][-22:-1], axis=0)
            vix = context.main_df2[context.main_df2.index == stock].T[-1:] #context.main_df2[stock][-1]
            #WVIX_MAX = nanmax(context.main_df2[context.main_df2.index == stock].T[-22:-1], axis=0)
            
            #vix_avg = nanmean(context.main_df2[context.main_df2.index == stock].T[-10:-1], axis=0)
            vix_avg = pd.ewma(context.main_df2[context.main_df2.index == stock].T[-5:], span = 4, min_periods=3, ignore_na=False)
            #if ((vix_avg[-1] / vix) > 1).item()[-1]: #vix_avg[-1] > vix:
            #sma_quotient = vix_avg[-1] / vix
            sma_quotientl = vix_avg[-1:] / vix
            sma_quotients = vix / vix_avg[-1:]
                #log.info("sma_quotient = " + "\n" + str(sma_quotient[-5:]))
                #if len(context.sma_quotient.T) < len(context.output.index):  ## DOES NOT WORK AFTER FIRST STOCK PASSES
            context.sma_quotientl = pd.concat([context.sma_quotientl, sma_quotientl], axis=1)
            context.sma_quotients = pd.concat([context.sma_quotients, sma_quotients], axis=1)
                #context.sma_quotient = pd.concat([context.sma_quotient.T, sma_quotient.T], axis=1)                            

            
            #!!!context.sma_quotient = context.sma_quotient.append(sma_quotient.T, ignore_index=False)
        todays_list_alll =  context.sma_quotientl.T       
        todays_list_alls =  context.sma_quotients.T   
        
        cutminl = todays_list_alll[todays_list_alll>=1.0].dropna().quantile(0.5)
        cutmaxl = todays_list_alll[todays_list_alll>=1.0].dropna().quantile(0.985)
        
        cutmins = todays_list_alls[todays_list_alls>=1.0].dropna().quantile(0.5)
        cutmaxs = todays_list_alls[todays_list_alls>=1.0].dropna().quantile(0.985)
        

        
        #todays_list_all["longs"] = todays_list_all[todays_list_all>=cutmin][todays_list_all<=cutmax].dropna()#(todays_list_all >=cutoff).dropna()
        #todays_list_all["longs"] = todays_list_all[todays_list_all>=cutmin].dropna()
        
        #todays_list_alll["longs"] = todays_list_alll[todays_list_alll>=cutminl][todays_list_alll<=cutmaxl].dropna()
        todays_list_alll["longs"] = todays_list_alll[todays_list_alll>=cutminl].dropna(how='any')
        todays_list_alll['longs']= todays_list_alll['longs'].replace(np.nan, "False")
        for stock in todays_list_alll[todays_list_alll['longs']<>"False"].index:
            #if data.current(stock, 'price') < talib.SMA(np.array(data.history(stock, 'close', 91, '1d')), timeperiod=90)[-2]:
            if data.current(stock, 'price') < context.outputa.sma[context.outputa.index == stock][-1]:      #USE THIS          
                #todays_list_alll.drop(todays_list_alll.index[[stock]])
                todays_list_alll = todays_list_alll[todays_list_alll.index <> stock] #USE THIS

        #todays_list_alls["shorts"] = todays_list_alls[todays_list_alls>=cutmins][todays_list_alls<=cutmaxs].dropna()
        todays_list_alls["shorts"] = todays_list_alls[todays_list_alls>=cutmins].dropna()        
        todays_list_alls['shorts']= todays_list_alls['shorts'].replace(np.nan, "False")        
    
        for stock in context.portfolio.positions:
            if data.can_trade(stock) and stock not in todays_list_alll[todays_list_alll['longs']<>"False"].index and stock not in todays_list_alls[todays_list_alls['shorts']<>"False"].index:
                order_target_percent(stock, 0)   
            
        for stock in todays_list_alll[todays_list_alll['longs']<>"False"].sort("longs", ascending=False).head(50).index:
            if data.can_trade(stock) and stock not in todays_list_alls[todays_list_alls['shorts']<>"False"].index:
                order_target_percent(stock, min(0.1, 1.0 /  len(todays_list_alll[todays_list_alll['longs']<>"False"].index)))
   
        if len(todays_list_alll[todays_list_alll['longs']<>"False"].index) > 0:
            #print "LONG LIST"
            log.info(str(len(todays_list_alll[todays_list_alll['longs']<>"False"])) + " in LONG LIST")
            #log.info("\n" + str(todays_list_all[todays_list_all['longs']<>"False"]))  
            
        if len(todays_list_alls[todays_list_alls['shorts']<>"False"].index) > 0:
            #print "LONG LIST"
            log.info(str(len(todays_list_alls[todays_list_alls['shorts']<>"False"])) + " in SHORT LIST")            

            
def close_positions(context, data):

    for sec, orders in get_open_orders().iteritems():  
        for oo in orders:  
            log.info("X CANCELED {0:s} with {1:,d} / {2:,d} filled"  
                .format(sec.symbol,  oo.filled, oo.amount))  
            cancel_order(oo)  
            
    for stock in context.portfolio.positions:
        if data.can_trade(stock):
            order_target_percent(stock, 0)    
            
def cleardf(context, data):
    
    #preserve memory - reset df
    context.main_df2 = pd.DataFrame() 

def attempt2(context, data):
    
    #assets_to_test =  [ sid(8554), sid(37514) ] #USE IF WANT TO USE SPECIFIC STOCKS
    assets_to_test = ( context.output.index ) #USE FOR PIPELINE STOCKS

    #Day 1 of kickoff
    if len(context.main_df2.T) < 22:
        context.main_df2a = pd.DataFrame()
        low = data.history(sid(8554), 'low', 24, '1d')
        
        for i in low.index:
            x = abs(22 - len(context.main_df2a))
            low = data.history(assets_to_test, 'low', 23 + x, '1d')
            close = data.history(assets_to_test, 'close', 23 + x + 1, '1d')
            highest_close = nanmax(close[:22], axis=0)
            SynVIX = ((highest_close - low[20:22].shift(1)[-1:]) / (highest_close)) * 100  
            context.main_df2a = context.main_df2a.append(SynVIX[assets_to_test], ignore_index=False).drop_duplicates()

    else:
        #dont use memory recalculating stocks in output that already in df2
        context.main_df2 = pd.concat([context.main_df2, context.output], axis=1)         
        
        #remove from df thats not in output else x days later if in output will not have complete history
        adj1 = context.main_df2.index - context.output.index 
        if len(adj1) > 0:
            for i in range(len(adj1)):
                context.main_df2.drop(context.main_df2.index[context.main_df2.index.get_loc(adj1[i])])
        
        #add to df new in output else and build history
        adj2 = context.output.index - context.main_df2.index #new stock not current in main_df2
        if len(adj2) > 0:
            for i in range(len(adj2)):
                context.main_df2.drop(context.main_df2.index[context.main_df2.index.get_loc(adj2[i])])
            context.main_df2 = context.main_df2.drop(context.main_df2.index[[adj2]]) #remove all from first
            context.main_df2b = pd.DataFrame()
            low = data.history(sid(8554), 'low', 24, '1d')
        
            for i in low.index:
                x = abs(22 - len(context.main_df2b))
                low = data.history(adj2, 'low', 23 + x, '1d')
                close = data.history(adj2, 'close', 23 + x + 1, '1d')
                highest_close = nanmax(close[:22], axis=0)
                SynVIX = ((highest_close - low[20:22].shift(1)[-1:]) / (highest_close)) * 100  
                context.main_df2b = context.main_df2b.append(SynVIX[adj2], ignore_index=False).drop_duplicates()
                context.main_df2 = pd.concat([context.main_df2, context.main_df2b.T], axis=1)
In [19]:
# also mock out the Log object
class log(object):
    @staticmethod
    def info(output):
        print output

Step2 - seperate out and factor out the API creation

Redefining initialize - not that we also limit the number of equities that would be added

In [20]:
from zipline.finance import commission

def create_pipeline(top_size=100):
    pipe = Pipeline()

 
    
    country_ref = mstar.company_reference.country_id.latest.eq('USA')
    have_data = mstar.valuation.market_cap.latest.notnull()
    mkt_cap_thresh = (mstar.valuation.market_cap.latest > 30e6)
    sector_check = Sector().notnull()
    xch = (mstar.company_reference.primary_exchange_id.latest.eq('NAS') or \
           mstar.company_reference.primary_exchange_id.latest.eq('NYS'))
    common_stock = mstar.share_class_reference.security_type.latest.eq('ST00000001')
    not_lp_name = ~mstar.company_reference.standard_name.latest.matches('.* L[\\. ]?P\.?$')
    not_lp_balance_sheet = mstar.balance_sheet.limited_partnership.latest.isnull()
    not_otc = ~mstar.share_class_reference.exchange_id.latest.startswith('OTC')
    not_wi = ~mstar.share_class_reference.symbol.latest.endswith('.WI')
    not_depository = ~mstar.share_class_reference.is_depositary_receipt.latest
    primary_share = IsPrimaryShare()

    sma_1 = SimpleMovingAverage(inputs=[USEquityPricing.close], window_length=5)    
    min_price = (sma_1 > 10)    
    
    # initialize filter/mask ...just leave in in case expand testing set
    tradable_filter = (country_ref & mkt_cap_thresh & sector_check & xch &
                       common_stock & not_lp_name & not_lp_balance_sheet &
                       have_data & not_otc & not_wi & not_depository & primary_share & min_price) 

    # lets screen down to the stocks that have averaged 
    # at least $100M of daily trading dollar volume over the
    # past XX days.
    
    # Create a dollar volume factor.
    dollar_volume = AverageDollarVolume(window_length=5)
    high_dollar_volume = dollar_volume.percentile_between(50, 100)
    
    high_cap_high_dv =  high_dollar_volume & tradable_filter #USE FOR PIPELINE STOCKS
    #high_cap_high_dv =  context.include_filter #USE IF WANT TO USE SPECIFIC STOCKS
    
       
    # Add the two factors defined to the pipeline
    sma = SimpleMovingAverage(inputs=[USEquityPricing.close], window_length=10)
    close = WVIXHIST(mask=high_cap_high_dv)    

    

    # Add the desired values to our pipe. 
    pipe.add(close, 'close')
    pipe.add(sma, 'sma')   
    
    pipe.set_screen(high_cap_high_dv)

    return pipe


# NOTE: redefined from above

def initialize_fixed(context):
    pipe = create_pipeline()
    attach_pipeline(pipe, 'example') 
            
    schedule_function(func=close_positions,
                      date_rule=date_rules.every_day(),
                      time_rule=time_rules.market_close(minutes=15)) 
    
    schedule_function(order_positions, date_rule = date_rules.every_day(), time_rule=time_rules.market_open(minutes = 5))
    
    schedule_function(attempt2, date_rule = date_rules.every_day(), time_rule=time_rules.market_open())  
    schedule_function(cleardf, date_rule = date_rules.week_end(), time_rule=time_rules.market_close(minutes = 3))      
    
    
    total_minutes = 6*60 + 30     
    for i in range(total_minutes):  
    # Every xx minutes run schedule  
        if i % 30 == 1:  
            schedule_function(  
            stop_loss,  
            date_rule = date_rules.every_day(),  
            time_rule=time_rules.market_open(minutes=i),)  
                

    context.main_df2 = pd.DataFrame() 
    context.main_df2a = pd.DataFrame()
    context.sma_quotientl = pd.DataFrame()
    context.sma_quotients = pd.DataFrame() 
    #context.sma_quotient = pd.DataFrame({'Date': [],'Asset': [], 'sma_quotient': []})
    #context.sma_quotient = pd.DataFrame()
    context.max_lvrg = 0   
    context.main_df2b = pd.DataFrame()
    
    # see below for fix on pipeline
    context.last_pipeline_index = 0

Step3: validate that pipeline runs properly

Reference: https://www.quantopian.com/posts/pipeline-in-research-build-test-and-visualize-your-factors-and-filters

In [28]:
pipe = create_pipeline(top_size=20)

Help on function create_pipeline in module __main__:

create_pipeline(top_size=100)
In [24]:
from quantopian.research import run_pipeline

pipeline_data = run_pipeline(pipe, 
             start_date=start_date.isoformat(), 
             end_date=end_date.isoformat()
            )

pipeline_data

/usr/local/lib/python2.7/dist-packages/numpy/lib/nanfunctions.py:326: RuntimeWarning: All-NaN slice encountered
  warnings.warn("All-NaN slice encountered", RuntimeWarning)
Out[24]:
close sma
2015-10-26 00:00:00+00:00 Equity(24 [AAPL]) 1.203567 113.034000
Equity(31 [ABAX]) 12.012712 44.470000
Equity(41 [ARCB]) 3.614883 27.726500
Equity(53 [ABMD]) 15.966797 90.074000
Equity(67 [ADSK]) 2.072637 51.974000
Equity(69 [ACAT]) 34.387352 23.204000
Equity(84 [ACET]) 4.295866 29.879200
Equity(110 [ACXM]) 1.187757 21.653500
Equity(114 [ADBE]) 2.379610 86.455000
Equity(122 [ADI]) 1.514673 60.444000
Equity(162 [AEPI]) 2.412798 72.485500
Equity(270 [AKRX]) 48.067501 27.166000
Equity(311 [ALOG]) 2.823313 85.624200
Equity(328 [ALTR]) 0.284199 52.626500
Equity(337 [AMAT]) 0.808143 15.842400
Equity(368 [AMGN]) 3.819512 151.671000
Equity(371 [HWAY]) 14.072229 11.148500
Equity(405 [AMWD]) 4.058053 69.923000
Equity(412 [ANAT]) 0.408918 102.009600
Equity(474 [APOG]) 2.282036 49.853014
Equity(508 [AIRM]) 10.934238 37.363000
Equity(579 [ASTE]) 15.026882 34.042000
Equity(610 [ATNI]) 2.004180 79.911000
Equity(617 [ATRO]) 9.533084 37.749200
Equity(630 [ADP]) 2.196454 87.103000
Equity(659 [AMAG]) 33.259958 37.448000
Equity(739 [BBBY]) 3.391246 58.328000
Equity(799 [BEAV]) 7.873684 44.570000
Equity(932 [BCPC]) 4.353732 66.711000
Equity(977 [BMTC]) 2.535302 30.459000
... ... ... ...
2016-11-23 00:00:00+00:00 Equity(48088 [FGEN]) 2.489177 22.060000
Equity(48091 [VA]) 0.265252 56.082500
Equity(48126 [HABT]) 2.693603 14.682500
Equity(48169 [KLXI]) 2.047523 38.990000
Equity(48304 [BLCM]) 3.157895 21.712000
Equity(48317 [JUNO]) 10.133495 30.827000
Equity(48384 [QRVO]) 4.869960 53.941000
Equity(48547 [ONCE]) 12.828291 57.578500
Equity(48629 [INOV]) 2.243590 15.340000
Equity(48823 [SEDG]) 17.229730 13.290000
Equity(48925 [ADRO]) 9.539474 14.295000
Equity(48934 [ETSY]) 11.595221 12.139500
Equity(48943 [VIRT]) 5.892857 13.730000
Equity(48962 [CSAL]) 20.922570 24.840500
Equity(49000 [BPMC]) 4.338453 35.056000
Equity(49001 [CLCD]) 10.416667 35.950000
Equity(49016 [COLL]) 9.234973 16.661300
Equity(49024 [BOJA]) 2.380952 18.245000
Equity(49126 [WING]) 2.282501 28.890100
Equity(49192 [ALRM]) 9.821125 29.990900
Equity(49236 [NTRA]) 2.481266 11.475000
Equity(49242 [PYPL]) 9.148551 39.953000
Equity(49288 [LITE]) 4.620853 38.987500
Equity(49409 [RGNX]) 12.291667 22.130000
Equity(49465 [ACRS]) 9.092929 24.048000
Equity(49751 [AVXS]) 18.329596 65.302500
Equity(49881 [LSXM_A]) 0.493286 35.558000
Equity(49989 [MSBI]) 2.707276 28.257510
Equity(50138 [PI]) 12.173913 31.124400
Equity(50168 [KNSL]) 2.787706 26.729000

235636 rows × 2 columns

Step 4: Getting the research pricing

Let's get the data we need to run the backtest

In [ ]:
'''
# we should narrow down to about top 1000 equities
universe = list(set(pipeline_data.index.get_level_values(1)))

assert len(universe) < 5000, "Universe is too big: {}".format(len(universe))
len(universe)
'''
In [ ]:
'''
# ambigious Ambiguous ownership for 1 symbol, multiple assets held the following symbols:
# AGN: intersections: (('2002-01-01 00:00:00', '2015-03-16 00:00:00'),)
 
EXCLUDE_STOCKS = {} #{'AGN'}
universe = [ s for s in universe if s.symbol not in EXCLUDE_STOCKS ]
'''
In [16]:
# https://www.quantopian.com/help#quantopian_research_get_pricing
data = get_pricing(universe,
                   start_date=start_date,
                   end_date=end_date,
                   #frequency='minute',
                   symbol_reference_date=start_date,
                   fields=['price', 'close_price'])

---------------------------------------------------------------------------
NameError                                 Traceback (most recent call last)
<ipython-input-16-8c6f33916ef2> in <module>()
      1 # https://www.quantopian.com/help#quantopian_research_get_pricing
----> 2 data = get_pricing(universe,
      3                    start_date=start_date,
      4                    end_date=end_date,
      5                    #frequency='minute',

NameError: name 'universe' is not defined

Hermmm..... we get an exception on backtest

NoEngineRegistered: Attempted to run a pipeline but no pipeline resources were registered

Step 6: Let's redefined before_trading_start so we use the existing pipeline data

In [ ]:
def before_trading_start_fixed(context, data):
    # Call pipelive_output to get the output
    #context.output = pipeline_output('ranked_2000')
    global pipeline_data
    try:
        t = get_datetime().date() + timedelta(days=1)
        context.outputa = pipeline_data.loc[t]
        context.last_pipeline_index = pipeline_data.index.get_loc(t)
        
   
    
    except KeyError as e:
        # if we cannot find the specific date in pipeline, use last date
        context.outputa = pipeline_data.irow(context.last_pipeline_index).reset_index(level=0, drop=True)
    
    # Narrow down the securities to only the top 200 & update my universe
    context.output = context.outputa.close
    #number_of_stocks = min(len(context.output)/2-1, 100)
    #context.long_list = context.output.sort(['combo_rank'], ascending=False).iloc[:number_of_stocks]
    #context.short_list = context.output.sort(['combo_rank'], ascending=False).iloc[-number_of_stocks:]   
    #print number_of_stocks
In [ ]:
'''
from zipline import TradingAlgorithm
import datetime
import time
from datetime import timedelta
from zipline.api import get_open_orders

#http://www.zipline.io/appendix.html
algo_obj = TradingAlgorithm(
    initialize=initialize_fixed,
    #handle_data=handle_data,
    before_trading_start=before_trading_start_fixed,
    start=start_date,
    data_frequency='daily',
    end=end_date,
    #get_pipeline_loader=run_pipeline
)

# Run algorithms
results = algo_obj.run(
    data#data.transpose(2,1,0),
    #overwrite_sim_params=True
)
'''
In [ ]:
from zipline import TradingAlgorithm
import datetime
import time
from datetime import timedelta
from zipline.api import get_open_orders

#http://www.zipline.io/appendix.html
algo_obj = TradingAlgorithm(
    initialize=initialize,
    #handle_data=handle_data,
    before_trading_start=before_trading_start_fixed,
    start=start_date,
    data_frequency='daily',
    end=end_date,
    #get_pipeline_loader=run_pipeline
)

# Run algorithms
results = algo_obj.run(
    data#data.transpose(2,1,0),
    #overwrite_sim_params=True
)

Success! Let's run some tearsheet to confirm...

Check with tearsheet data on the algorithm

Using pyfolio - https://quantopian.github.io/pyfolio/

Examples - https://github.com/quantopian/pyfolio/blob/master/pyfolio/examples/zipline_algo_example.ipynb

In [ ]:
#pipe = create_pipeline(top_size=20)
#pipe.show_graph(format='png')
In [46]:
import pyfolio as pf

returns, positions, transactions, gross_lev = pf.utils.extract_rets_pos_txn_from_zipline(results)
In [47]:
pf.create_full_tear_sheet(returns, positions=positions, 
                          transactions=transactions,
                          gross_lev=gross_lev
                         )

Entire data start date: 2016-01-04
Entire data end date: 2016-09-30


Backtest Months: 9
Performance statistics Backtest
annual_return -0.10
annual_volatility 0.10
sharpe_ratio -1.05
calmar_ratio -0.76
stability_of_timeseries 0.67
max_drawdown -0.13
omega_ratio 0.84
sortino_ratio -1.48
skew 0.76
kurtosis 5.36
tail_ratio 0.95
common_sense_ratio 0.85
information_ratio -0.07
alpha -0.11
beta 0.07
Worst Drawdown Periods net drawdown in % peak date valley date recovery date duration
0 13.24 2016-02-02 2016-08-08 NaT NaN
1 3.28 2016-01-06 2016-01-19 2016-01-28 17
2 0.29 2016-01-28 2016-01-29 2016-02-01 3
3 0.02 2016-01-04 2016-01-05 2016-01-06 3
4 0.00 2016-01-04 2016-01-04 2016-01-04 1 

[-0.013 -0.031]
Stress Events mean min max
New Normal -0.04% -1.98% 3.47%
Top 10 long positions of all time max
sid
Equity(42950 [FB]) 13.05%
Equity(16841 [AMZN]) 12.86%
Equity(47740 [BABA]) 12.85%
Equity(25006 [JPM]) 12.39%
Equity(4151 [JNJ]) 12.32%
Equity(8347 [XOM]) 12.21%
Equity(6653 [T]) 11.87%
Equity(8229 [WMT]) 11.87%
Equity(3149 [GE]) 11.85%
Equity(5923 [PFE]) 11.82%
Top 10 short positions of all time max
sid
Equity(26578 [GOOG_L]) -13.41%
Equity(5061 [MSFT]) -13.20%
Equity(24 [AAPL]) -13.14%
Equity(46631 [GOOG]) -12.94%
Equity(25066 [CHL]) -12.92%
Equity(6653 [T]) -12.90%
Equity(21839 [VZ]) -12.78%
Equity(8347 [XOM]) -12.48%
Equity(25006 [JPM]) -12.28%
Equity(5938 [PG]) -12.25%
Top 10 positions of all time max
sid
Equity(26578 [GOOG_L]) 13.41%
Equity(5061 [MSFT]) 13.20%
Equity(24 [AAPL]) 13.14%
Equity(42950 [FB]) 13.05%
Equity(46631 [GOOG]) 12.94%
Equity(25066 [CHL]) 12.92%
Equity(6653 [T]) 12.90%
Equity(16841 [AMZN]) 12.86%
Equity(47740 [BABA]) 12.85%
Equity(21839 [VZ]) 12.78%
All positions ever held max
sid
Equity(26578 [GOOG_L]) 13.41%
Equity(5061 [MSFT]) 13.20%
Equity(24 [AAPL]) 13.14%
Equity(42950 [FB]) 13.05%
Equity(46631 [GOOG]) 12.94%
Equity(25066 [CHL]) 12.92%
Equity(6653 [T]) 12.90%
Equity(16841 [AMZN]) 12.86%
Equity(47740 [BABA]) 12.85%
Equity(21839 [VZ]) 12.78%
Equity(8347 [XOM]) 12.48%
Equity(25006 [JPM]) 12.39%
Equity(4151 [JNJ]) 12.32%
Equity(5938 [PG]) 12.25%
Equity(8151 [WFC]) 12.11%
Equity(11100 [BRK_B]) 12.07%
Equity(3149 [GE]) 12.04%
Equity(38554 [BUD]) 11.87%
Equity(8229 [WMT]) 11.87%
Equity(4283 [KO]) 11.87%
Equity(5923 [PFE]) 11.82%
Equity(27487 [RDS_A]) 11.36%
Equity(21536 [NVS]) 11.12%
Equity(7543 [TM]) 11.02%