| Overall Statistics |
|
Total Trades 248 Average Win 0.45% Average Loss -0.81% Compounding Annual Return 42.677% Drawdown 14.200% Expectancy 0.177 Net Profit 19.385% Sharpe Ratio 1.575 Probabilistic Sharpe Ratio 60.211% Loss Rate 25% Win Rate 75% Profit-Loss Ratio 0.56 Alpha 0.363 Beta 0.076 Annual Standard Deviation 0.244 Annual Variance 0.06 Information Ratio 0.371 Tracking Error 0.269 Treynor Ratio 5.041 Total Fees $262.04 Estimated Strategy Capacity $760000000.00 |
#Imports
from itertools import groupby
from datetime import datetime, timedelta
from pytz import utc
from clr import AddReference
AddReference("System")
AddReference("QuantConnect.Common")
AddReference("QuantConnect.Algorithm")
AddReference("QuantConnect.Algorithm.Framework")
from QuantConnect import Resolution, Extensions
from QuantConnect.Algorithm.Framework.Portfolio import *
from QuantConnect.Algorithm.Framework.Risk import *
#Global variables
Zero = int(0)
class TrailingStop(RiskManagementModel):
def __init__(self):
'''
Initialization variables
'''
# Long Position Variables
self.LongTrail = {}
self.LongTrailingDrawdown = float(0.03)
# Short Position Variables
self.ShortTrail = {}
self.ShortTrailingDrawdown = float(0.03)
def ManageRisk(self, algorithm, targets):
'''
Main risk management handler. Passes algorithm and targets
'''
RiskAdjustedTargets = []
for asset in self.LongTrail:
if not algorithm.Portfolio[asset].Invested:
self.LongTrail[asset] = [algorithm.Securities[asset].Price, 0]
for asset in self.ShortTrail:
if not algorithm.Portfolio[asset].Invested:
self.ShortTrail[asset] = [algorithm.Securities[asset].Price, 0]
invested = [x.Key for x in algorithm.Portfolio if x.Value.Invested]
if invested:
for asset in invested:
if algorithm.Portfolio[asset].IsLong:
if asset not in self.LongTrail or self.LongTrail[asset][1] == 0:
self.LongTrail[asset] = [algorithm.Portfolio[asset].Price, algorithm.Portfolio[asset].Quantity]
elif algorithm.Portfolio[asset].IsShort:
if asset not in self.ShortTrail or self.ShortTrail[asset][1] == 0:
self.ShortTrail[asset] = [algorithm.Portfolio[asset].Price, algorithm.Portfolio[asset].Quantity]
self.TrailingStop(algorithm, asset, RiskAdjustedTargets)
return RiskAdjustedTargets
def TrailingStop(self, algorithm, asset, RiskAdjustedTargets):
'''
Manages trailing stop for both long and short assets respectively
'''
if algorithm.Portfolio[asset].IsLong:
if algorithm.Portfolio[asset].Price > self.LongTrail[asset][0]:
self.LongTrail[asset][0] = algorithm.Portfolio[asset].Price
elif algorithm.Portfolio[asset].Price / self.LongTrail[asset][0] < (1-self.LongTrailingDrawdown):
RiskAdjustedTargets.append(PortfolioTarget(asset, 0))
algorithm.Debug(f'Long trailing Stop Triggered for {asset}. Current Price: {algorithm.Portfolio[asset].Price} | Highest Price: {self.LongTrail[asset][0]} | Loss: {algorithm.Portfolio[asset].Price / self.LongTrail[asset][0]} | Date: {algorithm.Time}')
self.LongTrail.pop(asset)
if algorithm.Portfolio[asset].IsShort:
if algorithm.Portfolio[asset].Price < self.ShortTrail[asset][0]:
self.ShortTrail[asset][0] = algorithm.Portfolio[asset].Price
elif algorithm.Portfolio[asset].Price / self.ShortTrail[asset][0] > 1 / (1-self.ShortTrailingDrawdown):
RiskAdjustedTargets.append(PortfolioTarget(asset, 0))
algorithm.Debug(f'Short trailing Stop Triggered for {asset}. Current Price: {algorithm.Portfolio[asset].Price} | Lowest Price: {self.ShortTrail[asset][0]} | Loss: {algorithm.Portfolio[asset].Price / self.ShortTrail[asset][0]} | Date: {algorithm.Time}')
self.ShortTrail.pop(asset)
return RiskAdjustedTargetsfrom Execution.ImmediateExecutionModel import ImmediateExecutionModel
from Portfolio.EqualWeightingPortfolioConstructionModel import EqualWeightingPortfolioConstructionModel
from risk import TrailingStop
class EnergeticLightBrownCow(QCAlgorithm):
def Initialize(self):
self.SetStartDate(2020, 11, 12) # Set Start Date
self.SetCash(100000) # Set Strategy Cash
self.UniverseSettings.Resolution = Resolution.Daily
tickers = ['SPY', 'TSLA', 'AAPL']
symbols = [Symbol.Create(ticker, SecurityType.Equity, Market.USA) for ticker in tickers]
self.SetUniverseSelection(ManualUniverseSelectionModel(symbols))
# Option 1: Just emitting 1 insight with a long duration -- works
#self.AddAlpha(MyAlpha())
# Option 2: Emitting insights everyday -- doesn't work
self.AddAlpha(ConstantAlphaModel(InsightType.Price, InsightDirection.Up, timedelta(days=1), 0.025, None))
self.SetPortfolioConstruction(EqualWeightingPortfolioConstructionModel(lambda time: None))
self.AddRiskManagement(TrailingStop())
self.SetExecution(ImmediateExecutionModel())
class MyAlpha(AlphaModel):
symbols = []
emitted = False
def Update(self, algorithm, data):
if self.emitted:
return []
for symbol in self.symbols:
if not (data.ContainsKey(symbol) and data[symbol] is not None):
return []
self.emitted = True
return [Insight.Price(symbol, timedelta(days=100), InsightDirection.Down) for symbol in self.symbols]
def OnSecuritiesChanged(self, algorithm, changes):
for security in changes.AddedSecurities:
self.symbols.append(security.Symbol)