Overall Statistics
Total Orders
0
Average Win
0%
Average Loss
0%
Compounding Annual Return
0%
Drawdown
0%
Expectancy
0
Start Equity
100000
End Equity
100000
Net Profit
0%
Sharpe Ratio
0
Sortino Ratio
0
Probabilistic Sharpe Ratio
0%
Loss Rate
0%
Win Rate
0%
Profit-Loss Ratio
0
Alpha
0
Beta
0
Annual Standard Deviation
0
Annual Variance
0
Information Ratio
-0.407
Tracking Error
0.095
Treynor Ratio
0
Total Fees
$0.00
Estimated Strategy Capacity
$0
Lowest Capacity Asset
Portfolio Turnover
0%
# region imports
from AlgorithmImports import *
from datetime import timedelta
from System import *
from QuantConnect import *
from QuantConnect.Algorithm import *
from QuantConnect.Data.UniverseSelection import CoarseFundamental
from QuantConnect.Data.Custom import *
from QuantConnect.Data.Fundamental import FineFundamental
from QuantConnect.Data.Market import *
from QuantConnect.Indicators import *
from QuantConnect import Symbol
# endregion

class CalmApricotDolphin(QCAlgorithm):

    def Initialize(self):
        self.SetStartDate(datetime.now() - timedelta(days=150))  # Set your start date here
        self.SetEndDate(datetime.now() - timedelta(-1))  # Set your end date here
        self.SetCash(100000)  # Set your starting cash balance
        self.SetWarmUp(timedelta(days=40))  # Warm up the algorithm with 40 days of data

        # Subscribe to custom data for volume and price data
        self.AddData[Fundamental]("CustomData", Resolution.Daily)

        # Initialize RollingWindows
        self._averagePriceWindow = RollingWindow[IndicatorDataPoint](90) 
        self._averageVolumeWindow = RollingWindow[IndicatorDataPoint](90) 

        self._existingUniverse = set()

        # Schedule the function to run at a specific time each day
        # self.Schedule.On(self.DateRules.EveryDay(), self.TimeRules.At(10, 0), self.SearchAndAddToUniverse)

    def OnData(self, data):
        if isinstance(data, FundamentalData):
            # Update the RollingWindows with new data points
            self._averagePriceWindow.Add(data.Price)
            self._averageVolumeWindow.Add(data.Volume)
            self.SearchAndAddToUniverse()

    def SearchAndAddToUniverse(self):
        for symbol in self.Securities.Keys:
            if symbol not in self._existingUniverse:
                currentPrice = self.Securities[symbol].Price
                yesterdayVolume = self.Securities[symbol].Volume
                conditionA = currentPrice > 0.001
                conditionB = yesterdayVolume > 1
            if conditionA and conditionB:
                self.AddToUniverse(symbol)
    
    def AddToUniverse(self, symbol):
        # Add the symbol to your universe
        if symbol not in self._existingUniverse:
            self.AddEquity(symbol.Value)
            self._existingUniverse.add(symbol)
            self.Log(f"Added {symbol.Value} to the universe")