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Fetcher problems

Hi All

I posted a few issues I can not seem to resolve with fetcher. The most recent problem I encountered was that it does not seem to assign data to one of the 10 tickers in my .csv. I can't understand why it does not assigned only to ticker "XLI". Can anyone see what I am missing?

7 responses

Note log file (and the dates it reports are the dates I decide to trade on but for all dates in the backtest XLI does not have the property):

2002-04-04handle_data:115DEBUGWARNING: rank not found for ticker Security(19657 [XLI])
2002-07-02handle_data:115DEBUGWARNING: rank not found for ticker Security(19657 [XLI])
2002-10-01handle_data:115DEBUGWARNING: rank not found for ticker Security(19657 [XLI])
2002-12-31handle_data:115DEBUGWARNING: rank not found for ticker Security(19657 [XLI])
2003-04-02handle_data:115DEBUGWARNING: rank not found for ticker Security(19657 [XLI])
2003-07-02handle_data:115DEBUGWARNING: rank not found for ticker Security(19657 [XLI])
2003-10-01handle_data:115DEBUGWARNING: rank not found for ticker Security(19657 [XLI])
2003-12-31handle_data:115DEBUGWARNING: rank not found for ticker Security(19657 [XLI])
2004-04-01handle_data:115DEBUGWARNING: rank not found for ticker Security(19657 [XLI])
2004-07-02handle_data:115DEBUGWARNING: rank not found for ticker Security(19657 [XLI])
2004-10-01handle_data:115DEBUGWARNING: rank not found for ticker Security(19657 [XLI])
2004-12-31handle_data:115DEBUGWARNING: rank not found for ticker Security(19657 [XLI])
2005-04-04handle_data:115DEBUGWARNING: rank not found for ticker Security(19657 [XLI])

Clone Algorithm
6
Loading...
Backtest from to with initial capital
Total Returns
--
Alpha
--
Beta
--
Sharpe
--
Sortino
--
Max Drawdown
--
Benchmark Returns
--
Volatility
--
Returns 1 Month 3 Month 6 Month 12 Month
Alpha 1 Month 3 Month 6 Month 12 Month
Beta 1 Month 3 Month 6 Month 12 Month
Sharpe 1 Month 3 Month 6 Month 12 Month
Sortino 1 Month 3 Month 6 Month 12 Month
Volatility 1 Month 3 Month 6 Month 12 Month
Max Drawdown 1 Month 3 Month 6 Month 12 Month
#Use SPY 15 day MA to determine when to go to cash
#When not in cash buy and hold the most undervalued sectors

import datetime
import math
import numpy
import pandas

def clean_col(df):
    df = df.fillna(0)
    df = df[['rank', 'sid']]
#    log.info(' \n %s % df.head()')
    return df

def initialize(context):
    #
    #
    #Variables for later
    context.day=None
    context.pauseUntil=63
    context.timestep=0
    context.margin_req=0 
    context.totalShorts=0
    context.totalLongs=0
    context.cash=0
    context.oldRanks=None
    context.timer=0
    context.holdNmanyDays=63
    context.liquid=1
          
    #
    #
    #Set constraints on borrowing
    context.pct_invested_threshold=1 #Set limit on percent invested (as a decimal)
    context.init_margin=.01 #Set initial margin requirement    
    context.maint_margin=.01 #Set the maintenance margin requirement   


    #
    #
    #Read universe
    context.SPY=sid(8554)
    context.Sectors=[sid(19662), sid(19659), sid(19656), sid(19661), sid(19655), sid(19658), sid(19660), sid(19654), sid(19657)]  
    
    #Read the rankings
    fetch_csv('https://dl.dropboxusercontent.com/s/7shk9xm1xi311at/Ranks_QuantopianTest.csv',
              date_column='Date',
              post_func=clean_col)

    set_commission(commission.PerShare(cost=0.00))
    set_slippage(slippage.FixedSlippage(spread=0.00))
    

def handle_data(context, data):

    update_newFrame(context, data)

    #Set Liquidate to false
    Liquidate = 0
    
    #If its before the pause time
    if context.timestep == 1:
        nshares=context.cash / data[sid(8554)].price
        order(sid(8554), nshares)
        context.liquid=0
        return
    
    elif context.timestep == (context.pauseUntil-1):
        #log.debug ("Liquidating SPY because context.pauseUntil="+str(context.pauseUntil)+" and timestep = "+str(context.timestep))
        Liquidate = 1

    if context.timestep < (context.pauseUntil-1): #context.PauseUntil:
        context.timer=0
        return
    
    if context.timer >= context.holdNmanyDays: #reset the timer to 0 after the specified holding period
        #log.debug ("Liquidating and rotating sectors now because "+str(context.timer)+" indicates we have reached "+str(context.holdNmanyDays))
        Liquidate = 1
        context.timer=0
         

    #
    #
    #Its time to trade!
        
    if context.liquid == 1: #if we are in cash    
              
        ts1=None
        tr1=-100
        ts2=None
        tr2=-100
        ts3=None
        tr3=-100

        for stock in context.Sectors:
            if 'rank' in data[stock]:
                if data[stock]['rank']>tr1:
                    ts3=ts2
                    tr3=tr2
                    ts2=ts1
                    tr2=tr1
                    tr1=data[stock]['rank']
                    ts1=stock
 
                elif data[stock]['rank']>tr2:
                    ts3=ts2
                    tr3=tr2
                    tr2=data[stock]['rank']
                    ts2=stock
                
    #            elif data[stock]['rank']>tr3:
    #                tr3=data[stock]['rank']
    #                ts3=stock
        else:
            log.debug ("WARNING: rank not found for ticker "+str(stock))

                            
        if ts1!=None:
            nshares=context.cash*0.49/data[ts1].price
            #log.debug ("Buying most undervalued stock "+str(ts1)+" with score of "+str(tr1))
            generate_order(ts1, nshares, context, data) 
            context.liquid=0 #we are not in cash

        if ts2!=None:
            nshares=context.cash*0.49/data[ts2].price
            #log.debug ("Buying second most undervalued stock "+str(ts2)+" with score of "+str(tr2))
            generate_order(ts2, nshares, context, data) 
            context.liquid=0 #we are not in cash
        
#        if ts3!=None:
#            nshares=context.cash*0.3333/data[ts3].price
#            generate_order(ts3, nshares, context, data) 
#            context.liquid=0 #we are not in cash
#            log.debug ("Changed context.liquid to "+str(context.liquid))
            
    if Liquidate == 1:
        for i in context.Sectors:
            liquidate_position(i, context)
        liquidate_position(context.SPY, context)
        context.liquid=1
    

    
#
#
#    Supporting Functions
#
#

    

def update_newFrame(context, data):
    #
    context.cash = context.portfolio.cash
    context.portvalue = context.portfolio.positions_value
    context.totalShorts=0
    for sym in data.keys():
        if context.portfolio.positions[sym].amount < 0:
            context.totalShorts += (context.portfolio.positions[sym].amount * data[sym].price)
        else:
            context.totalLongs += (context.portfolio.positions[sym].amount * data[sym].price)

    update_portvals(context)
    
    #Handle assigning the timestep number (1 day is 1 timestep)
    if get_datetime().day <> context.day: #look for a new day
        context.day=get_datetime().day
        context.timestep += 1
        context.timer += 1
        #log.info ( "Cash: "+str(context.cash)+"; Margin Req: "+str(context.margin_req)+" Avail Cash:"+str(context.cash - context.margin_req) )
        if context.timestep>context.pauseUntil:
            if context.cash < context.margin_req: #check for margin calls daily
                generate_marginCall(context, data)
            
    
def update_portvals(context):
    #Update account information when this function is called
    context.total_equity = context.cash + context.portvalue
    context.pct_invested = (context.totalLongs-context.totalShorts) / context.total_equity
    context.pct_cash = context.cash / context.total_equity
    context.margin_req = abs(context.totalShorts * context.maint_margin)
    
        
def generate_order(sym, size, context, data):    
    #log.info("Call to generate_order")
    if size>0: #Opening a long position    

        #log.info("Buy long "+str(size)+" shares "+str(sym) )
        #log.info("Cash = "+str(context.cash)+"; Current Margin Req.="+str(context.margin_req) )                 

        #Is there enough available cash to buy the position
        if (context.cash-context.margin_req) < size * data[sym].price:
            #log.info("Trade canceled : Insufficient funds.")
            return

        #Deduct price from cash and add to portfolio value
        context.cash -= size * data[sym].price
        context.portvalue += size * data[sym].price
        context.totalLongs += size * data[sym].price
        update_portvals(context)

        #Abort the transaction if the percent invested is greater than the threshold
        #before slippage and commissions
        if context.pct_invested > context.pct_invested_threshold:
            context.cash += size * data[sym].price
            context.portvalue -= size * data[sym].price
            context.totalLongs -= size * data[sym].price
            update_portvals(context)

#            if size>100:
#                log.info("Re-generating order for "+str(size*context.pct_invested_threshold)+" instead of "+str(size))
#                generate_order(sym,size*context.pct_invested_threshold, context,data)
#            return
        
        #Abort the transaction if the investment would generate a margin call
        if context.cash < context.margin_req:
#            log.info("Invest would generate a margin call")
            context.cash += size * data[sym].price
            context.portvalue -= size * data[sym].price
            context.totalLongs -= size * data[sym].price
            update_portvals(context)
            return
    
        order(sym,size)

    else: #Opening a short position
        
        #log.info("Generating a short order for "+str(size)+" shares of "+str(sym)+" and context.cash="+str(context.cash)+" and context.margin_req="+str(context.margin_req) )
        #Is there at least enough available cash to cover the initial maintenance margin
        if (context.cash-context.margin_req) < abs(size * data[sym].price * context.init_margin):
            #log.info("Trade canceled")
            return
        
        #Deduct price from cash and add to portfolio value (note that size is negative)
        context.cash -= size * data[sym].price
        context.portvalue += size * data[sym].price
        context.totalShorts += size * data[sym].price
        update_portvals(context)
        
        #Abort the transaction if the percent invested is greater than the threshold
        #before slippage and commission
        if context.pct_invested > context.pct_invested_threshold:
            context.cash += size * data[sym].price
            context.portvalue -= size * data[sym].price
            context.totalShorts -= size * data[sym].price
            update_portvals(context)
            #log.info("Trade canceled")
            return
            
        #Abort the transaction if the investment would generate a margin call
        if context.cash < context.margin_req:
            context.cash += size * data[sym].price
            context.portvalue -= size * data[sym].price
            context.totalShorts -= size * data[sym].price
            update_portvals(context)
            #log.info("Trade canceled")
            return
        
        order(sym,size)
            
        
def generate_marginCall(context,data):
    #This function should be coded to address margin calls
    #log.info("Margin call")
    return(0)

def liquidate_position(sym,context):
    if context.portfolio.positions[sym].amount is not 0:
        #log.debug("Liquidating "+str(context.portfolio.positions[sym].amount)+" shares of position "+str(sym))
        order(sym, -context.portfolio.positions[sym].amount)
        
This backtest was created using an older version of the backtester. Please re-run this backtest to see results using the latest backtester. Learn more about the recent changes.
There was a runtime error.

There's some forward-fill stuff going on here, but I haven't had time to dig in. I will do so and get back to you.

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Ahh -- then its probably related to some other issues I've been having (https://quantopian.com/posts/help-with-fetcher-and-filling-nan-values - sorry for the double post). Seems like no matter the fill method its always forward filled :-/ although in this case there is no data for XLI so that is bizarre.

Anyway, thanks a bunch for helping me out with this Dan. It is immensely appreciated.

I'm going to politely bump this thread. This problem specifically is hard to resolve. I can't fully understand why data are being filled at all in this data frame because there shouldn't be any NAN entries.

In this fetcher problem - https://quantopian.com/posts/help-with-fetcher-and-filling-nan-values - I've got a few ideas for some work-arounds but my feeling is that the issues are related. (Note I use fillna(0) but 'ffill' is used instead, maybe this is a default of post_func and is not getting changed?)

I continue to appreciate the help of all the staff at Quantopian.

Sorry Daniel. Haven't figured this out, but haven't forgotten either. Enlisting help.

Hi Daniel - I started looking into this problem by creating a minimal repro with just a record statement. Surprisingly, the code worked without any issue and plotted as expected, without any log lines. Here's the code for reference:

import datetime  
import math  
import numpy  
import pandas

def clean_col(df):  
    # df = df.fillna(0)  
    # df = df[['rank', 'sid']]  
    log.info(' \n %s' % df[df['sid'] == 19657].head())  
    return df

def initialize(context):

    #  
    #  
    #Read universe  
    context.SPY=sid(8554)  
    context.Sectors=[sid(19662), sid(19659), sid(19656), sid(19661), sid(19655), sid(19658), sid(19660), sid(19654), sid(19657)]  
    # context.Sectors=[sid(19657)]  
    #Read the rankings  
    fetch_csv('https://dl.dropboxusercontent.com/s/7shk9xm1xi311at/Ranks_QuantopianTest.csv',  
              date_column='Date',  
              post_func=clean_col)

def handle_data(context, data):  
    if 'rank' in data[sid(19657)]:  
        record(signal=data[sid(19657)]['rank'])  
    else:  
        log.warn("No rank for XLI")  

Looking at the log line in the code, I realized this is a whitespace problem. The else was outdented by one level, and so was being called at the conclusion of the for loop. Since XLI was the final element in your context.Sectors list, it was always being logged. Attached is a patched version with the desired indentation, and I was able to run a backtest without any of the log warnings or errors.

thanks,
fawce

Clone Algorithm
1
Loading...
Backtest from to with initial capital
Total Returns
--
Alpha
--
Beta
--
Sharpe
--
Sortino
--
Max Drawdown
--
Benchmark Returns
--
Volatility
--
Returns 1 Month 3 Month 6 Month 12 Month
Alpha 1 Month 3 Month 6 Month 12 Month
Beta 1 Month 3 Month 6 Month 12 Month
Sharpe 1 Month 3 Month 6 Month 12 Month
Sortino 1 Month 3 Month 6 Month 12 Month
Volatility 1 Month 3 Month 6 Month 12 Month
Max Drawdown 1 Month 3 Month 6 Month 12 Month
#Use SPY 15 day MA to determine when to go to cash
#When not in cash buy and hold the most undervalued sectors

import datetime
import math
import numpy
import pandas

def clean_col(df):
    df = df.fillna(0)
    df = df[['rank', 'sid']]
#    log.info(' \n %s % df.head()')
    return df

def initialize(context):
    #
    #
    #Variables for later
    context.day=None
    context.pauseUntil=63
    context.timestep=0
    context.margin_req=0 
    context.totalShorts=0
    context.totalLongs=0
    context.cash=0
    context.oldRanks=None
    context.timer=0
    context.holdNmanyDays=63
    context.liquid=1
          
    #
    #
    #Set constraints on borrowing
    context.pct_invested_threshold=1 #Set limit on percent invested (as a decimal)
    context.init_margin=.01 #Set initial margin requirement    
    context.maint_margin=.01 #Set the maintenance margin requirement   


    #
    #
    #Read universe
    context.SPY=sid(8554)
    context.Sectors=[sid(19662), sid(19659), sid(19656), sid(19661), sid(19655), sid(19658), sid(19660), sid(19654), sid(19657)]  
    
    #Read the rankings
    fetch_csv('https://dl.dropboxusercontent.com/s/7shk9xm1xi311at/Ranks_QuantopianTest.csv',
              date_column='Date',
              post_func=clean_col)

    set_commission(commission.PerShare(cost=0.00))
    set_slippage(slippage.FixedSlippage(spread=0.00))
    

def handle_data(context, data):

    update_newFrame(context, data)

    #Set Liquidate to false
    Liquidate = 0
    
    #If its before the pause time
    if context.timestep == 1:
        nshares=context.cash / data[sid(8554)].price
        order(sid(8554), nshares)
        context.liquid=0
        return
    
    elif context.timestep == (context.pauseUntil-1):
        #log.debug ("Liquidating SPY because context.pauseUntil="+str(context.pauseUntil)+" and timestep = "+str(context.timestep))
        Liquidate = 1

    if context.timestep < (context.pauseUntil-1): #context.PauseUntil:
        context.timer=0
        return
    
    if context.timer >= context.holdNmanyDays: #reset the timer to 0 after the specified holding period
        #log.debug ("Liquidating and rotating sectors now because "+str(context.timer)+" indicates we have reached "+str(context.holdNmanyDays))
        Liquidate = 1
        context.timer=0
         

    #
    #
    #Its time to trade!
        
    if context.liquid == 1: #if we are in cash    
              
        ts1=None
        tr1=-100
        ts2=None
        tr2=-100
        ts3=None
        tr3=-100

        for stock in context.Sectors:
            if 'rank' in data[stock]:
                if data[stock]['rank']>tr1:
                    ts3=ts2
                    tr3=tr2
                    ts2=ts1
                    tr2=tr1
                    tr1=data[stock]['rank']
                    ts1=stock
 
                elif data[stock]['rank']>tr2:
                    ts3=ts2
                    tr3=tr2
                    tr2=data[stock]['rank']
                    ts2=stock
                
    #            elif data[stock]['rank']>tr3:
    #                tr3=data[stock]['rank']
    #                ts3=stock
            else:
                log.debug ("WARNING: rank not found for ticker "+str(stock))

                            
        if ts1!=None:
            nshares=context.cash*0.49/data[ts1].price
            #log.debug ("Buying most undervalued stock "+str(ts1)+" with score of "+str(tr1))
            generate_order(ts1, nshares, context, data) 
            context.liquid=0 #we are not in cash

        if ts2!=None:
            nshares=context.cash*0.49/data[ts2].price
            #log.debug ("Buying second most undervalued stock "+str(ts2)+" with score of "+str(tr2))
            generate_order(ts2, nshares, context, data) 
            context.liquid=0 #we are not in cash
        
#        if ts3!=None:
#            nshares=context.cash*0.3333/data[ts3].price
#            generate_order(ts3, nshares, context, data) 
#            context.liquid=0 #we are not in cash
#            log.debug ("Changed context.liquid to "+str(context.liquid))
            
    if Liquidate == 1:
        for i in context.Sectors:
            liquidate_position(i, context)
        liquidate_position(context.SPY, context)
        context.liquid=1
    

    
#
#
#    Supporting Functions
#
#

    

def update_newFrame(context, data):
    #
    context.cash = context.portfolio.cash
    context.portvalue = context.portfolio.positions_value
    context.totalShorts=0
    for sym in data.keys():
        if context.portfolio.positions[sym].amount < 0:
            context.totalShorts += (context.portfolio.positions[sym].amount * data[sym].price)
        else:
            context.totalLongs += (context.portfolio.positions[sym].amount * data[sym].price)

    update_portvals(context)
    
    #Handle assigning the timestep number (1 day is 1 timestep)
    if get_datetime().day <> context.day: #look for a new day
        context.day=get_datetime().day
        context.timestep += 1
        context.timer += 1
        #log.info ( "Cash: "+str(context.cash)+"; Margin Req: "+str(context.margin_req)+" Avail Cash:"+str(context.cash - context.margin_req) )
        if context.timestep>context.pauseUntil:
            if context.cash < context.margin_req: #check for margin calls daily
                generate_marginCall(context, data)
            
    
def update_portvals(context):
    #Update account information when this function is called
    context.total_equity = context.cash + context.portvalue
    context.pct_invested = (context.totalLongs-context.totalShorts) / context.total_equity
    context.pct_cash = context.cash / context.total_equity
    context.margin_req = abs(context.totalShorts * context.maint_margin)
    
        
def generate_order(sym, size, context, data):    
    #log.info("Call to generate_order")
    if size>0: #Opening a long position    

        #log.info("Buy long "+str(size)+" shares "+str(sym) )
        #log.info("Cash = "+str(context.cash)+"; Current Margin Req.="+str(context.margin_req) )                 

        #Is there enough available cash to buy the position
        if (context.cash-context.margin_req) < size * data[sym].price:
            #log.info("Trade canceled : Insufficient funds.")
            return

        #Deduct price from cash and add to portfolio value
        context.cash -= size * data[sym].price
        context.portvalue += size * data[sym].price
        context.totalLongs += size * data[sym].price
        update_portvals(context)

        #Abort the transaction if the percent invested is greater than the threshold
        #before slippage and commissions
        if context.pct_invested > context.pct_invested_threshold:
            context.cash += size * data[sym].price
            context.portvalue -= size * data[sym].price
            context.totalLongs -= size * data[sym].price
            update_portvals(context)

#            if size>100:
#                log.info("Re-generating order for "+str(size*context.pct_invested_threshold)+" instead of "+str(size))
#                generate_order(sym,size*context.pct_invested_threshold, context,data)
#            return
        
        #Abort the transaction if the investment would generate a margin call
        if context.cash < context.margin_req:
#            log.info("Invest would generate a margin call")
            context.cash += size * data[sym].price
            context.portvalue -= size * data[sym].price
            context.totalLongs -= size * data[sym].price
            update_portvals(context)
            return
    
        order(sym,size)

    else: #Opening a short position
        
        #log.info("Generating a short order for "+str(size)+" shares of "+str(sym)+" and context.cash="+str(context.cash)+" and context.margin_req="+str(context.margin_req) )
        #Is there at least enough available cash to cover the initial maintenance margin
        if (context.cash-context.margin_req) < abs(size * data[sym].price * context.init_margin):
            #log.info("Trade canceled")
            return
        
        #Deduct price from cash and add to portfolio value (note that size is negative)
        context.cash -= size * data[sym].price
        context.portvalue += size * data[sym].price
        context.totalShorts += size * data[sym].price
        update_portvals(context)
        
        #Abort the transaction if the percent invested is greater than the threshold
        #before slippage and commission
        if context.pct_invested > context.pct_invested_threshold:
            context.cash += size * data[sym].price
            context.portvalue -= size * data[sym].price
            context.totalShorts -= size * data[sym].price
            update_portvals(context)
            #log.info("Trade canceled")
            return
            
        #Abort the transaction if the investment would generate a margin call
        if context.cash < context.margin_req:
            context.cash += size * data[sym].price
            context.portvalue -= size * data[sym].price
            context.totalShorts -= size * data[sym].price
            update_portvals(context)
            #log.info("Trade canceled")
            return
        
        order(sym,size)
            
        
def generate_marginCall(context,data):
    #This function should be coded to address margin calls
    #log.info("Margin call")
    return(0)

def liquidate_position(sym,context):
    if context.portfolio.positions[sym].amount is not 0:
        #log.debug("Liquidating "+str(context.portfolio.positions[sym].amount)+" shares of position "+str(sym))
        order(sym, -context.portfolio.positions[sym].amount)
        
This backtest was created using an older version of the backtester. Please re-run this backtest to see results using the latest backtester. Learn more about the recent changes.
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@John - Duh, well I feel dumb for making that mistake. Thank you, sir. I was certain that the problems I was having here were related to the other issues I was having with filling nan values - https://quantopian.com/posts/help-with-fetcher-and-filling-nan-values - but I guess not. Do you think perhaps you can look at the other issues? I'd be much obliged.