The solution of FadingTheGap at "Accessing-a-Rolling-Window" Occurs Exception " Boot Camp: We couldn't reconcile your trades with what we expected..."

Note that TSLA went though split 5-to-1 in the end of August, and historical prices are split-adjusted. So you need to make a small change in the solution in order to get the required results.

Sorry, split was 5-for-1, not 5-to-1

Hi Shinseitaro,

This is an error in the BootCamp caused by the split event in TSLA. We we fix this ASAP, however, if the error hasn't been fixed yet, please change 

to 

Furthermore, for any future errors in the BootCamp, feel free to move past them as BootCamp lessons aren't locked by order.

Best,
Shile Wen

Hi Shile, Thank you! 

Hi Shile,

I know we can skip this but we need to know how to solve such issues on our own.

The solution you suggested didn't work. I made the above-suggested changes and copied the code to the Algorithm Lab as a new Project and ran it and it gave the same error:

Backtest Handled Error: TLSA 0 not found in portfolio. Request this data when initializing the algorithm.

class FadingTheGap(QCAlgorithm):

def Initialize(self):
self.SetStartDate(2017, 11, 1)
self.SetEndDate(2018, 7, 1)
self.SetCash(100000)
self.AddEquity("TSLA", Resolution.Minute).SetDataNormalizationMode(DataNormalizationMode.Raw)

self.Schedule.On(self.DateRules.EveryDay(), self.TimeRules.BeforeMarketClose("TSLA", 0), self.ClosingBar)
self.Schedule.On(self.DateRules.EveryDay(), self.TimeRules.AfterMarketOpen("TSLA", 1), self.OpeningBar)
self.Schedule.On(self.DateRules.EveryDay(), self.TimeRules.AfterMarketOpen("TSLA", 45), self.ClosePositions)

self.window = RollingWindow[TradeBar](2)

def ClosingBar(self):
if "TSLA" in self.CurrentSlice.Bars:
self.window.Add(self.CurrentSlice["TSLA"])

def OpeningBar(self):
if "TSLA" in self.CurrentSlice.Bars:
self.window.Add(self.CurrentSlice["TSLA"])

#1. If our window is not full use return to wait for tomorrow
if not self.window.IsReady:
return

#2. Calculate the change in overnight price
delta = self.window[0].Open - self.window[1].Close

#3. If delta is less than -$2.5, SetHoldings() to 100% TSLA
if delta < -2.5:
self.SetHoldings("TLSA", 1)

def ClosePositions(self):
self.Liquidate()

Hi Quantitative Learner,

The issue occurs because TSLA is misspelled

To resolve the issue, we recommend using the TSLA Symbol throughout the algorithm instead of the "TSLA" string. See the attached backtest for reference.

Best,
Derek Melchin

Hi, I've tried that too, with no success…

Jaco Van der Walt,

Try this

Thanks so much, Guys, I'll try…

Hi, can you please help?

I'm running the last test of the Fading the Gap Mean Reversion Tutorial and I'm getting an error:

During the algorithm initialization, the following exception has occurred: AttributeError : 'FadingTheGap' object has no attribute 'StandardDeviation' at Initialize self.volatility = self.StandardDeviation("TSLA" in main.py: line 16 AttributeError : 'FadingTheGap' object has no attribute 'StandardDeviation'

Here is the code that I'm entering

class FadingTheGap(QCAlgorithm):

   def Initialize(self):
       self.SetStartDate(2017, 11, 1)
       self.SetEndDate(2018, 7, 1)
       self.SetCash(100000) 
       self.AddEquity("TSLA", Resolution.Minute)
       
       self.Schedule.On(self.DateRules.EveryDay(), self.TimeRules.BeforeMarketClose("TSLA", 0), self.ClosingBar) 
       self.Schedule.On(self.DateRules.EveryDay(), self.TimeRules.AfterMarketOpen("TSLA", 1), self.OpeningBar)
       self.Schedule.On(self.DateRules.EveryDay(), self.TimeRules.AfterMarketOpen("TSLA", 45), self.ClosePositions) 
       
       self.window = RollingWindow[TradeBar](2)
               
       #1. Create a manual Standard Deviation indicator to track recent volatility
       self.volatility = self.StandardDeviation("TSLA",60) # the resolution is not known seeing that we control the data
       # This works out the size of the standard deviation
       
   def OnData(self, data):
       if data["TSLA"] is not None: 
           #2. Update our standard deviation indicator manually with algorithm time and TSLA's close price
           self.volatility.Update(self.Time, data["TSLA"].Close)
   
   def OpeningBar(self):
       if "TSLA" in self.CurrentSlice.Bars:
           self.window.Add(self.CurrentSlice["TSLA"])
       
       #3. Use IsReady to check if both volatility and the window are ready, if not ready 'return'
       if not self.window.IsReady or not self.volatility.IsReady:
           return
       
       delta = self.window[0].Open - self.window[1].Close
       
       #4. Save an approximation of standard deviations to our deviations variable by dividing delta by the current volatility value:
       #   Normally this is delta from the mean, but we'll approximate it with current value for this lesson. 
       deviations = delta / self.volatity.Current.Value
       
       #5. SetHoldings to 100% TSLA if deviations is less than -3 standard deviations from the mean:
       
       if deviations < -3:
           self.SetHoldings ("TSLA", 1)
       
   def ClosePositions(self):
       self.Liquidate()
       
   def ClosingBar(self):
       self.window.Add(self.CurrentSlice["TSLA"])

Hi Jaco

StandardDeviation itself is a class object. It is not inherited from the algorithm class or QCAlgorithm class. You simply need StandardDeviation(…) instead of self.StandardDeviation(…)

Best
Louis

Hello! The problem has come back again, potentially caused by the recent stock split.

Now `SetDataNormalizationMode(DataNormalizationMode.Raw)` cannot fix it… please kindly help…

gowestyang ,

Thank you for your report.
We have fixed this Bootcamp task. Due to Testa's split, the overnight gap needed to be adjusted from $2.5 to $0.9. Ideally, we should express the gap as a rate of change:

delta = (window[0].Open / window[1].Close) - 1

However, the following lesson uses price volatility, not return volatility, and we need to be consistent with the units.

Best regards,
Alex
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