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
0.557
Tracking Error
0.202
Treynor Ratio
0
Total Fees
$0.00
Estimated Strategy Capacity
$0
Lowest Capacity Asset
 
#region imports
from AlgorithmImports import *
#endregion

from trSum import trSum

class trSumIndicator(QCAlgorithm):

    def Initialize(self):
        self.SetStartDate(2022, 1, 1)  # Set Start Date
        self.SetEndDate(2023, 1, 1)  # Set Start Date
        self.SetCash(100000)  # Set Strategy Cash
        self.resolution = Resolution.Daily
        self.period = 10
        self.sym = self.AddEquity("SPY", self.resolution).Symbol
        self.trSum = trSum("trSum", self.period)
        self.RegisterIndicator(self.sym, self.trSum, self.resolution)
        self.SetWarmUp(20)

    def OnData(self, data):

        if not [self.trSum.IsReady]:
            return
        if self.IsWarmingUp:
            return
        self.Plot("trSum", "Value", self.trSum.Current.Value)
        
#region imports
from AlgorithmImports import *
#endregion
import numpy as np

class trSum(PythonIndicator):
    def __init__(self, name, length):
        self.Name = name
        self.Length = length
        self.highs = RollingWindow[float](length)
        self.lows = RollingWindow[float](length)
        self.closes = RollingWindow[float](length)
        self.true_ranges = RollingWindow[float](length)
        self.Value = 0

    def Update(self, input):
        self.highs.Add(input.High)
        self.lows.Add(input.Low)
        self.closes.Add(input.Close)

        if not (self.highs.IsReady and self.lows.IsReady and self.closes.IsReady):
            return False

        self.true_ranges.Add(self.TrueRange(self.highs[0], self.lows[0], self.closes[1]))

        if not self.true_ranges.IsReady:
            return False
        self.Value = sum(list(self.true_ranges)[0:self.Length])

        return True

    def TrueRange(self, high, low, close):
        return max(high - low, abs(high - self.closes[1]), abs(low - self.closes[1]))