Overall Statistics
Total Trades
5146
Average Win
0.74%
Average Loss
-0.45%
Compounding Annual Return
1.951%
Drawdown
40.100%
Expectancy
0.013
Net Profit
10.154%
Sharpe Ratio
0.051
Sortino Ratio
0.056
Probabilistic Sharpe Ratio
1.249%
Loss Rate
62%
Win Rate
38%
Profit-Loss Ratio
1.63
Alpha
0.008
Beta
0.013
Annual Standard Deviation
0.179
Annual Variance
0.032
Information Ratio
-0.261
Tracking Error
0.25
Treynor Ratio
0.712
Total Fees
$0.00
Estimated Strategy Capacity
$29000000.00
Lowest Capacity Asset
QQQ RIWIV7K5Z9LX
Portfolio Turnover
423.55%
 
# region imports
from AlgorithmImports import *
from QuantConnect.Data import Slice
# endregion

class VwapTrend(QCAlgorithm):
    def Initialize(self):
        self.SetStartDate(2018, 11, 10)
        self.SetEndDate(2023, 11, 11)
        self.SetCash(25000)
        self.Settings.MinimumOrderMarginPortfolioPercentage = 0.01
        self.SetWarmUp(390)
 
        self.asset = self.AddEquity("QQQ", Resolution.Minute)
        self.asset.SetDataNormalizationMode(DataNormalizationMode.Raw)
        self.asset.vwap = self.VWAP(self.asset.Symbol) 
        self.asset.SetFeeModel(ConstantFeeModel(0))

        self.minimum_percent_difference = self.GetParameter("minimum_percent_difference", 0.01)

        # Flat before market close
        self.Schedule.On(self.DateRules.EveryDay(self.asset.Symbol), self.TimeRules.BeforeMarketClose(self.asset.Symbol, 1), self.Liquidate)

    def OnData(self, slice: Slice) -> None:
        if not self.asset.vwap.IsReady or not self.asset.Exchange.ExchangeOpen:
            return

        absolute_difference = self.asset.Close - self.asset.vwap.Current.Value
        filter_difference   = self.asset.Close * self.minimum_percent_difference
 
        if absolute_difference > filter_difference and self.asset.Holdings.Quantity <= 0:
            self.SetHoldings(self.asset.Symbol, 1)

        if absolute_difference < -filter_difference and self.asset.Holdings.Quantity >= 0:
            self.SetHoldings(self.asset.Symbol, -1)