| Overall Statistics |
|
Total Trades 30987 Average Win 0.02% Average Loss -0.01% Compounding Annual Return 10.843% Drawdown 2.100% Expectancy 0.696 Net Profit 246.349% Sharpe Ratio 2.892 Probabilistic Sharpe Ratio 100.000% Loss Rate 36% Win Rate 64% Profit-Loss Ratio 1.67 Alpha 0.096 Beta 0.079 Annual Standard Deviation 0.038 Annual Variance 0.001 Information Ratio -0.348 Tracking Error 0.172 Treynor Ratio 1.386 Total Fees $33257.96 |
import numpy as np
syms = ['ERIE', 'TTEK', 'CLX', 'TMO', 'MO', 'TYL', 'SAFM', 'LLY', 'EXR', 'CBRL', 'MGPI', 'RNR', 'AGNC', 'CCI', 'EXPO', 'MGEE', 'OVBC', 'TDG', 'AAPL', 'HRL', 'GABC',
'CSGP', 'DVA', 'CMS', 'MLAB', 'ORLY', 'ISRG', 'AGM', 'NEE', 'TJX', 'WST', 'DPZ', 'JBSS', 'ROST', 'HIFS', 'AVY', 'BRO', 'USLM', 'NWN', 'ETR', 'FE', 'VAR', 'CAH', 'WNEB',
'ATLO', 'FSP', 'HUBG', 'PAYX', 'ARR', 'J', 'MPB', 'STFC', 'DAKT', 'LMNX', 'SNCR', 'CXW', 'FLIR', 'VSAT', 'OTTR', 'PPL', 'BAX', 'PRGO', 'MKL', 'PICO', 'OFC', 'ARE']
#['CLX', 'ATVI', 'MKTX', 'CMS', 'PRSC', 'WDFC', 'NFLX', 'DPZ']
class MultidimensionalModulatedRegulators(QCAlgorithm):
def Initialize(self):
self.SetStartDate(2009, 1, 1)
self.SetCash(1000000)
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('SPY', Resolution.Hour)
self.SetBenchmark('SPY')
self.SetBrokerageModel(AlphaStreamsBrokerageModel())
self.constant_weights = np.ones(len(syms))
self.constant_weights[-28:] = -1.0
self.constant_weights = self.constant_weights / np.sum(np.abs(self.constant_weights))
self.leverage = 1.0
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] * self.leverage)
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] * self.leverage)
else:
for i, sym in enumerate(self.symbols):
self.SetHoldings(sym, self.constant_weights[i] * self.leverage)