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
-1.045
Tracking Error
0.185
Treynor Ratio
0
Total Fees
$0.00
Estimated Strategy Capacity
$0
Lowest Capacity Asset
 
# Kalman Filter

from pykalman import KalmanFilter
import numpy as np

# ----------------------------------------
CRYPTO = 'BTCUSD'; PERIOD = 80; LEV = 1.0;
# ----------------------------------------

class EnergeticFluorescentOrangeElephant(QCAlgorithm):

    def Initialize(self):
        self.SetStartDate(2019, 1, 1)
        self.SetEndDate(2021, 10, 31)
        self.crypto = self.AddCrypto(CRYPTO, Resolution.Daily).Symbol
        self.rollingWindow = RollingWindow[TradeBar](PERIOD)
        self.Consolidate(self.crypto, Resolution.Daily, self.CustomBarHandler)
        self.SetWarmUp(5*PERIOD, Resolution.Daily)  
                 

    def OnData(self, data):
        if not self.rollingWindow.IsReady: return
    
        C = np.flipud(np.array([self.rollingWindow[i].Close for i in range(PERIOD)]))
        
        self.kf = KalmanFilter(transition_matrices = [1],
                 observation_matrices = [1],
                 initial_state_mean = 0,
                 initial_state_covariance = 1,
                 observation_covariance=1,
                 transition_covariance=.01)
        
        kf,_ = self.kf.filter(C)
        
        self.Plot(self.crypto, "Price", self.Securities[self.crypto].Price)        
        self.Plot(self.crypto, "Kalman", kf[-1])
        
        
    def CustomBarHandler(self, bar):
        self.rollingWindow.Add(bar)