Overall Statistics
Total Trades
180
Average Win
0.52%
Average Loss
-0.44%
Compounding Annual Return
123.566%
Drawdown
5.600%
Expectancy
0.238
Net Profit
9.609%
Sharpe Ratio
4.818
Probabilistic Sharpe Ratio
76.362%
Loss Rate
43%
Win Rate
57%
Profit-Loss Ratio
1.19
Alpha
1.289
Beta
0.009
Annual Standard Deviation
0.266
Annual Variance
0.071
Information Ratio
2.536
Tracking Error
0.862
Treynor Ratio
148.517
Total Fees
$190.61
class CloseOnCloseExecutionModel(ExecutionModel):
    """
    Provides an implementation of IExecutionModel that immediately submits a market order to achieve 
    the desired portfolio targets and an associated market on close order.
    """

    def __init__(self):
        self.targetsCollection = PortfolioTargetCollection()
        self.invested_symbols = []

    def Execute(self, algorithm, targets):
        """
        Immediately submits orders for the specified portfolio targets.
        Input:
         - algorithm
            Algorithm instance running the backtest
         - targets
            The portfolio targets to be ordered
        """
        # for performance we check count value, OrderByMarginImpact and ClearFulfilled are expensive to call
        self.targetsCollection.AddRange(targets)
        if self.targetsCollection.Count > 0:
            for target in self.targetsCollection.OrderByMarginImpact(algorithm):
                # calculate remaining quantity to be ordered
                quantity = OrderSizing.GetUnorderedQuantity(algorithm, target)
                if quantity == 0:
                    continue
                
                algorithm.MarketOrder(target.Symbol, quantity)
                algorithm.MarketOnCloseOrder(target.Symbol, -quantity)
                
            self.targetsCollection.ClearFulfilled(algorithm)
class IntradayMomentumAlphaModel(AlphaModel):
    """
    This class emits insights to take positions for the last `return_bar_count` minutes 
    of the day in the direction of the return for the first `return_bar_count` minutes of the day.
    """
    intraday_momentum_by_symbol = {}
    sign = lambda _, x: int(x and (1, -1)[x < 0])
    
    def __init__(self, algorithm, return_bar_count = 30):
        """
        Input:
         - return_bar_count
            Number of minutes to calculate the morning return over and the number of minutes
            to hold before the close (0 < return_bar_count < 195)
        """
        if return_bar_count <= 0 or return_bar_count >= 195:
            algorithm.Quit(f"Requirement violated:  0 < return_bar_count < 195")
        self.return_bar_count = return_bar_count
    
    
    def Update(self, algorithm, slice):
        """
        Called each time our alpha model receives a new data slice.
        
        Input:
         - algorithm
            Algorithm instance running the backtest
         - data
            A data structure for all of an algorithm's data at a single time step
        
        Returns a list of Insights to the portfolio construction model
        """
        insights = []
        
        for symbol, intraday_momentum in self.intraday_momentum_by_symbol.items():
            if slice.ContainsKey(symbol) and slice[symbol] is not None:
                intraday_momentum.bars_seen_today += 1
        
                # End of the morning return
                if intraday_momentum.bars_seen_today == self.return_bar_count:
                    intraday_momentum.morning_return = (slice[symbol].Close - intraday_momentum.yesterdays_close) / intraday_momentum.yesterdays_close
                
                ## Beginning of the close
                next_close_time = intraday_momentum.exchange.Hours.GetNextMarketClose(slice.Time, False)
                mins_to_close = int((next_close_time - slice.Time).total_seconds() / 60)
                
                if mins_to_close == self.return_bar_count + 1:
                    insight = Insight.Price(intraday_momentum.symbol, 
                                            next_close_time, 
                                            self.sign(intraday_momentum.morning_return))
                    insights.append(insight)
                    continue
                
                # End of the day
                if not intraday_momentum.exchange.DateTimeIsOpen(slice.Time):
                    intraday_momentum.yesterdays_close = slice[symbol].Close
                    intraday_momentum.bars_seen_today = 0
        
        return insights
        
        
    def OnSecuritiesChanged(self, algorithm, changes):
        """
        Called each time our universe has changed.
        
        Input:
         - algorithm
            Algorithm instance running the backtest
         - changes
            The additions and subtractions to the algorithm's security subscriptions
        """
        for security in changes.AddedSecurities:
            self.intraday_momentum_by_symbol[security.Symbol] = IntradayMomentum(security, algorithm)
            
        for security in changes.RemovedSecurities:
            self.intraday_momentum_by_symbol.pop(security.Symbol, None)
            

class IntradayMomentum:
    """
    This class manages the data used for calculating the morning return of a security.
    """
    def __init__(self, security, algorithm):
        """
        Input:
         - security
            The security to trade
         - algorithm
            Algorithm instance running the backtest
        """
        self.symbol = security.Symbol
        self.exchange = security.Exchange
        
        self.bars_seen_today = 0
        self.yesterdays_close = algorithm.History(self.symbol, 1, Resolution.Daily).loc[self.symbol].close[0]
        self.morning_return = 0
# QUANTCONNECT.COM - Democratizing Finance, Empowering Individuals.
# Lean Algorithmic Trading Engine v2.0. Copyright 2014 QuantConnect Corporation.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

from clr import AddReference
AddReference("System")
AddReference("QuantConnect.Algorithm")
AddReference("QuantConnect.Common")

from System import *
from QuantConnect import *
from QuantConnect.Algorithm import *

from IntradayMomentumAlphaModel import IntradayMomentumAlphaModel
from CloseOnCloseExecutionModel import CloseOnCloseExecutionModel

class IntradayETFMomentumAlgorithm(QCAlgorithm):

    def Initialize(self):
        self.SetStartDate(2020, 2, 20)
        self.SetEndDate(2020, 4, 1)
        self.SetCash(50000)
        
        tickers = ['SPY',  # S&P 500
                   'IWM',  # Russell 2000
                   'IYR'   # Real Estate ETF
        ]
        symbols = [ Symbol.Create(ticker, SecurityType.Equity, Market.USA) for ticker in tickers ]
        self.SetUniverseSelection( ManualUniverseSelectionModel(symbols) )
        self.UniverseSettings.Resolution = Resolution.Minute
        
        self.SetAlpha(IntradayMomentumAlphaModel(self))
        
        self.SetPortfolioConstruction(EqualWeightingPortfolioConstructionModel(lambda time: None))
        
        self.SetExecution(CloseOnCloseExecutionModel())