Type conversion - Custom Indicator

Hi all,

I tried to implement an indicator, which checks the average percentage price change + the standard deviation over the last X days. However, when the indicator tries to update itself, I receive the following runtime error: Cannot implicitly convert type 'Python.Runtime.PyObject' to 'decimal'

class AverageDay(PythonIndicator):
    def __init__(self, name, period):
        self.Name = name
        self.Time = datetime.min
        self.Value = {}
        self.queue = deque(maxlen=period)

    def __repr__(self):
        return "{0} -> IsReady: {1}. Time: {2}. Value: {3}".format(self.Name, self.IsReady, self.Time, self.Value)

    # Update method is mandatory
    def Update(self, input):
        r = ((input.Close / input.Open) - 1 ) * 100
        self.queue.appendleft(r)
        count = len(self.queue)
        self.Time = input.EndTime
        average = sum(self.queue) / count
        deviation = std(self.queue)
        self.Value["positive"] = average + (2 * deviation)
        self.Value["negative"] = average - (2 * deviation)
        return count == self.queue.maxlen

Maybe the issue is also that I have the resolution set to Resolution.Daily, while the rest of the algorithm runs on an hourly basis. Also, is there a way to send debug messages from the update function of an indicator?

Hey there Simon, welcome to QuantConnect!

You should be able to use the debugger feature to step through the code to find the specific line if you put the class into the same file (main.py). If not please post a slightly longer mock example and I'd be happy to help debug it with you.

My hunch is maybe something about the Value property?

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Hi Jared,

thanks for your response. I used the debugger before and there is no issue with setting the value. The code stops at the time of return.... There is not really any indicator in the debugger on why it fails, so I assume it is something in the backend. I pasted the further part of my logic below. Maybe you find the part where I messed up.

from collections import deque
from datetime import datetime, timedelta
from numpy import sum, std
from QuantConnect.Data.Market import QuoteBar

class PriceOverreactionsForex(QCAlgorithm):

    def Initialize(self):
        self.SetStartDate(2017, 12, 11)  # Set Start Date
        self.SetEndDate(2018, 12, 31) # Set End Date
        self.SetCash(100000)  # Set Strategy Cash
        self.syls = ["USDCAD","AUDUSD","EURJPY", "EURUSD", "USDCAD"] # Define Symbols for Trading
        self.days = 20 # Paramater to calculate reference
        self.open = {}
        self.averageDay = {}
        self.intraDay = {}


        for symbol in self.syls:
            self.AddForex(symbol, Resolution.Hour, Market.Oanda)
            self.averageDay[symbol] = AverageDay(symbol, self.days)
            self.RegisterIndicator(symbol, self.averageDay[symbol], Resolution.Daily)
            self.intraDay[symbol] = 0.0
            self.open[symbol] = 0.0
            
        self.SetWarmup(self.days)
)

    def OnData(self, data):
        
        if self.IsWarmingUp: return
    
        for symbol in self.syls:
                if self.open[symbol] == 0.0:
                    self.open[symbol] = data[symbol].Open
             
  
                self.intraDay[symbol] = UpdateIntraday(symbol, data[symbol].Close)

    

               
    def UpdateIntraday(self, symbol, lastClose):
        r = 0.0
        r = ((lastClose / self.open[symbol])- 1 ) * 100
        return r
        
        
    
class AverageDay(PythonIndicator):
    def __init__(self, name, period):
        self.Name = name
        self.Time = datetime.min
        self.Value = {}
        self.queue = deque(maxlen=period)

    def __repr__(self):
        return "{0} -> IsReady: {1}. Time: {2}. Value: {3}".format(self.Name, self.IsReady, self.Time, self.Value)

    # Update method is mandatory
    def Update(self, input):
        r = ((input.Close / input.Open) - 1 ) * 100
        self.queue.appendleft(r)
        count = len(self.queue)
        self.Time = input.EndTime
        average = sum(self.queue) / count
        deviation = std(self.queue)
        self.Value["positive"] = average + (2 * deviation)
        self.Value["negative"] = average - (2 * deviation)
        return count == self.queue.maxlen

Jared Broad INVESTOR

QuantConnect | April 2020

Upvote

Simon Dallmann INVESTOR

April 2020

Hey Simon,

The value property is a reserved property in the underlying class. Unfortunately, you cannot convert it to a dictionary but I have rejigged it a little in the backtest attached to store the values to two new properties Positive and Negative. Please see the result attached.

Best
Jared

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