Overall Statistics
Total Trades
0
Average Win
0%
Average Loss
0%
Compounding Annual Return
0%
Drawdown
0%
Expectancy
0
Net Profit
0%
Sharpe Ratio
0
Probabilistic Sharpe Ratio
0%
Loss Rate
0%
Win Rate
0%
Profit-Loss Ratio
0
Alpha
0
Beta
0
Annual Standard Deviation
0
Annual Variance
0
Information Ratio
-8.769
Tracking Error
0.22
Treynor Ratio
0
Total Fees
$0.00
from collections import deque
from datetime import datetime, timedelta
from numpy import sum

### Demonstrates how to create a custom indicator and register it for automatic updated
class CustomIndicatorAlgorithm(QCAlgorithm):
    def Initialize(self):
        self.SetStartDate(2013,10,7)
        self.SetEndDate(2013,10,11)
        self.AddEquity("SPY", Resolution.Second)

        # Create a QuantConnect indicator and a python custom indicator for comparison
        self.sma = self.SMA("SPY", 60, Resolution.Minute)
        self.custom = CustomSimpleMovingAverage('custom', 60)
        self.RegisterIndicator("SPY", self.custom, Resolution.Minute)
        
        self.ext = IndicatorExtensions.MAX(self.custom, 1)

    def OnData(self, data):
        if self.Time.second == 0 and self.ext.IsReady:
            self.Plot("SMA", "Value", self.sma.Current.Value)
            self.Plot("Custom SMA", "Value", self.custom.Value)
            self.Plot("Extended", "Value", self.ext.Current.Value)


# Python implementation of SimpleMovingAverage.
# Represents the traditional simple moving average indicator (SMA).
class CustomSimpleMovingAverage(PythonIndicator):
    def __init__(self, name, period):
        self.Name = name
        self.Time = datetime.min
        self.Value = 0
        self.queue = deque(maxlen=period)


    # Update method is mandatory
    def Update(self, input):
        self.queue.appendleft(input.Close)
        count = len(self.queue)
        self.Time = input.EndTime
        self.Value = sum(self.queue) / count
        return count == self.queue.maxlen