| Overall Statistics |
|
Total Trades 327 Average Win 0.23% Average Loss -0.06% Compounding Annual Return 15.697% Drawdown 31.600% Expectancy 2.943 Net Profit 55.282% Sharpe Ratio 0.545 Probabilistic Sharpe Ratio 15.304% Loss Rate 17% Win Rate 83% Profit-Loss Ratio 3.74 Alpha 0.039 Beta 0.995 Annual Standard Deviation 0.27 Annual Variance 0.073 Information Ratio 0.849 Tracking Error 0.045 Treynor Ratio 0.148 Total Fees $327.64 Estimated Strategy Capacity $25000000.00 Lowest Capacity Asset AHA R735QTJ8XC9X |
##########################################
#Kamer Ali Yuksel linkedin.com/in/kyuksel#
##########################################
from AlgorithmImports import *
import numpy as np
syms = ['MSFT', 'AAPL', 'TXN', 'ASML', 'ADBE', 'CRM', 'SBUX', 'MCHP', 'MA', 'ADI', 'SWKS', 'SNPS', 'KLAC']
class MultidimensionalModulatedRegulators(QCAlgorithm):
def Initialize(self):
self.SetStartDate(2020, 1, 1)
self.SetCash(100000)
self.SetExecution(VolumeWeightedAveragePriceExecutionModel())
self.symbols = []
for i in range(len(syms)):
self.symbols.append(Symbol.Create(syms[i], SecurityType.Equity, Market.USA))
self.Debug(syms[i])
self.SetUniverseSelection(ManualUniverseSelectionModel(self.symbols) )
self.UniverseSettings.Resolution = Resolution.Hour
self.AddEquity('VGT', Resolution.Hour)
self.SetBenchmark('VGT')
self.SetBrokerageModel(AlphaStreamsBrokerageModel())
self.constant_weights = np.array([0.28535828, 0.23993727, 0.12635303, 0.05360154, 0.04681825, 0.04518177, 0.04505873, 0.04194283, 0.03757035, 0.03131177, 0.02168459, 0.0151392 , 0.01004238])
self.constant_weights = self.constant_weights / np.sum(np.abs(self.constant_weights))
def OnData(self, data):
rebalance = False
if self.Portfolio.TotalHoldingsValue > 0:
total = 0.0
for i, sym in enumerate(self.symbols):
curr = (self.Securities[sym].Holdings.HoldingsValue/self.Portfolio.TotalPortfolioValue)
diff = self.constant_weights[i] - curr
total += np.abs(diff)
if total > 0.05:
rebalance = True
if rebalance:
for i, sym in enumerate(self.symbols):
curr = (self.Securities[sym].Holdings.HoldingsValue/self.Portfolio.TotalPortfolioValue)
if self.constant_weights[i] < curr:
self.SetHoldings(sym, self.constant_weights[i])
for i, sym in enumerate(self.symbols):
curr = (self.Securities[sym].Holdings.HoldingsValue/self.Portfolio.TotalPortfolioValue)
if self.constant_weights[i] > curr:
self.SetHoldings(sym, self.constant_weights[i])
else:
for i, sym in enumerate(self.symbols):
self.SetHoldings(sym, self.constant_weights[i])