Self.liquidContract via Futures Boot Camp no longer working ?

Hi all, I've been using the techniques from boot camp to filter for the highest OI. This was working fine until I created a new project this morning and tried backtesting with the same code. It is now giving a run time error saying that .liquidContract does not exist.

Error Message:

[ERROR] FATAL UNHANDLED EXCEPTION:Extensions.SetRuntimeError(): Extensions.SetRuntimeError(): RuntimeError at 11/11/2022 2:30:00 PM UTC. Context: OnData Python.Runtime.PythonException: 'HyperActiveOrangeCat' object has no attribute 'liquidContract', File "/QuantConnect/backtesting/./cache/algorithm/project/main.py", line 96, in OnData, price = self.Securities[self.liquidContract.Symbol].Price, at Python.Runtime.PythonException.ThrowLastAsClrException(), at Python.Runtime.PyObject.Invoke(PyTuple args, PyDict kw), at Python.Runtime.PyObject.TryInvoke(InvokeBinder binder, Object[] args, Object& result), at CallSite.Target(Closure , CallSite , Object , PythonSlice ), at System.Dynamic.UpdateDelegates.UpdateAndExecuteVoid2[T0,T1](CallSite site, T0 arg0, T1 arg1), at QuantConnect.AlgorithmFactory.Python.Wrappers.AlgorithmPythonWrapper.OnData(Slice slice) in /LeanCloud/CI.Builder/bin/Debug/src/QuantConnect/Lean/AlgorithmFactory/Python/Wrappers/AlgorithmPythonWrapper.cs:line 632, at QuantConnect.Lean.Engine.AlgorithmManager.Run(AlgorithmNodePacket job, IAlgorithm algorithm, ISynchronizer synchronizer, ITransactionHandler transactions, IResultHandler results, IRealTimeHandler realtime, ILeanManager leanManager, IAlphaHandler alphas, CancellationToken token) in /LeanCloud/CI.Builder/bin/Debug/src/QuantConnect/Lean/Engine/AlgorithmManager.cs:line 523,ApiConnection.TryRequest(backtest/status/update): Error: The operation has timed out.

Stack Trace:

'HyperActiveOrangeCat' object has no attribute 'liquidContract'
at OnData
price = self.Securities[self.liquidContract.Symbol].Price
at Python.Runtime.PythonException.ThrowLastAsClrException()
at Python.Runtime.PyObject.Invoke(PyTuple args in main.py: line 95

Relevant Code

Initialize:

		self.es = self.AddFuture(Futures.Indices.SP500EMini, Resolution.Tick)
		self.es.SetFilter(5, 100)

OnData(self, slice):

		#  Sort contracts for highest Open Interest and create a variable for the highest OI
		for chain in slice.FutureChains:
			self.popularContracts = [contract for contract in chain.Value if contract.OpenInterest > 1000]
			if len(self.popularContracts) == 0:
				continue
			sortedByOIContracts = sorted(self.popularContracts, key=lambda k : k.OpenInterest, reverse=True)
			self.liquidContract = sortedByOIContracts[0]
		
		# Variable for liquid contract price
		price = self.Securities[self.liquidContract.Symbol].Price

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def Initialize(self):
# instantiate the global variable
self.liquidContract = None
def OnData(self, slice):
for chain in slice.FutureChains:
# ...
self.liquidContract = sortedByOIContracts[0]
# check if any contract fetched and stored
if self.liquidContract is None:
return
price = self.Securities[self.liquidContract.Symbol].Price
# ...

Hi Zach,

The algorithm has many technical and stylistic issues, and since it seemed to be a popular algorithm, we've rewrote it to use the SymbolData pattern, replaced the History calls with RollingWindows, and fixed many stylistic issues. Please view the updated algorithm in the attached backtest.

Best,
Shile Wen

def trail_stop(self):
for symbol, sd in self.symbol_data.items():
if not sd.IsReady:
continue
mean_price = np.mean(list(sd.close_data))
# Stop loss percentage is the return over the lookback period
stoploss = abs(sd.weight * self.lookback / 252.0) + 1 # percent change per period
if sd.weight > 0 and sd.stopprice is not None:
if sd.stopprice is not None and sd.stopprice < 0:
sd.stopprice = mean_price / stoploss
else:
sd.stopprice = max(mean_price / stoploss, sd.stopprice)
if mean_price < sd.stopprice:
sd.weight = 0
self.Liquidate(symbol) elif sd.weight < 0 and sd.stopprice is not None:
if sd.stopprice is not None and sd.stopprice < 0:
sd.stopprice = mean_price * stoploss
else:
sd.stopprice = min(mean_price * stoploss, sd.stopprice)
if mean_price > sd.stopprice:
sd.weight = 0
self.Liquidate(symbol) else:
sd.stopprice = None

+ Expand

LoganW INVESTOR

November 2022

Please see this back test of a simpler algorithm with the exact same futures contracts data lines that is working

Upvote

Louis Szeto INVESTOR

QuantConnect | November 2022

Hi LoganW

There could be slices that contains no FutureChains data due to numerous reasons like non-regular market event or no data at that time. If that is the case on the first OnData call, the line “self.liquidContract = sortedByOIContracts[0]” will not be run, causing that error. We recommend adding a check after the for-loop:

def Initialize(self):
    # instantiate the global variable
    self.liquidContract = None
def OnData(self, slice):
    for chain in slice.FutureChains:
        # ...
        self.liquidContract = sortedByOIContracts[0]
	
    # check if any contract fetched and stored
    if self.liquidContract is None:
        return
	
    price = self.Securities[self.liquidContract.Symbol].Price
    # ...

Best
Louis

Shile Wen INVESTOR

UW CFRM | January 2021

3 people upvoted this

Zach Oakes INVESTOR

January 2021

Thanks ! It's very cool. It's like an MR take on Trend -- brilliant interpretation, and MUCH nicer than my translation.

Mohamed Ajmal INVESTOR

June 2021

How can we implement stoploss / reduce drawdown in this algorithm?

Varad Kabade INVESTOR

July 2021

Hi Mohamed Ajmal,
In the last backtest attached by Shile he has implemented the stop-loss which is triggered every day after 10 minutes of market open:

 self.Schedule.On(self.DateRules.EveryDay("SPY"), self.TimeRules.AfterMarketOpen("SPY", 10), self.trail_stop)

Refer to the following code snippet.

def trail_stop(self):
        for symbol, sd in self.symbol_data.items():
            if not sd.IsReady:
                continue
            mean_price = np.mean(list(sd.close_data))
            # Stop loss percentage is the return over the lookback period
            stoploss = abs(sd.weight * self.lookback / 252.0) + 1    # percent change per period
            if sd.weight > 0 and sd.stopprice is not None:
                if sd.stopprice is not None and sd.stopprice < 0:
                    sd.stopprice = mean_price / stoploss
                else:
                    sd.stopprice = max(mean_price / stoploss, sd.stopprice)
                    if mean_price < sd.stopprice:
                        sd.weight = 0
                        self.Liquidate(symbol)            elif sd.weight < 0 and sd.stopprice is not None: 
                if sd.stopprice is not None and sd.stopprice < 0:
                    sd.stopprice = mean_price * stoploss
                else:
                    sd.stopprice = min(mean_price * stoploss, sd.stopprice)
                    if mean_price > sd.stopprice:
                       sd.weight = 0
                       self.Liquidate(symbol)            else:
                sd.stopprice = None
+ Expand
- Collapse

Best,
Varad Kabade

1 person upvoted this

Andres Arizpe INVESTOR

January 2022

This appears to be a very interesting algo.

Is there any documentation I might use to get a basic understanding of it?

Cheers,

Andres

Hi Andres Arizpe,
The above algorithm consists of components like the scheduled events, rolling window, and standard python libraries like numpy and pandas. We recommend going through the following docs[1, 2], and regarding the libraries, please look for their homepage/documentation. Please feel free to ask any specific doubts about the above algorithm.
Best,
Varad Kabade

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