Overall Statistics
Total Orders
302
Average Win
6.05%
Average Loss
-2.98%
Compounding Annual Return
16.242%
Drawdown
40.300%
Expectancy
0.247
Start Equity
5000
End Equity
10618.78
Net Profit
112.376%
Sharpe Ratio
0.443
Sortino Ratio
0.371
Probabilistic Sharpe Ratio
8.443%
Loss Rate
59%
Win Rate
41%
Profit-Loss Ratio
2.03
Alpha
0.102
Beta
0.379
Annual Standard Deviation
0.305
Annual Variance
0.093
Information Ratio
0.15
Tracking Error
0.317
Treynor Ratio
0.356
Total Fees
$302.00
Estimated Strategy Capacity
$1700000000.00
Lowest Capacity Asset
TSLA UNU3P8Y3WFAD
Portfolio Turnover
13.84%
 
from AlgorithmImports import *

class HSMAlphaStreamModel(QCAlgorithm):
    def Initialize(self):
        self.SetStartDate(2020, 1, 1)
        self.SetEndDate(2024, 12, 31)
        self.SetCash(5000)

        # Add Tesla stock
        self.symbol = self.AddEquity("TSLA", Resolution.Daily).Symbol

        # Use AlphaStreams-compatible brokerage
        self.SetBrokerageModel(BrokerageName.AlphaStreams)

        # Framework modules
        self.SetAlpha(HSMAlphaModel(self.symbol))
        self.SetPortfolioConstruction(EqualWeightingPortfolioConstructionModel())
        self.SetExecution(ImmediateExecutionModel())
        self.SetRiskManagement(TrailingStopRiskManagementModel(0.015))  # Optional: 1.5% trailing stop

class HSMAlphaModel(AlphaModel):
    def __init__(self, symbol):
        self.symbol = symbol
        self.name = "HSMAlphaModel"
        self.lastInsightTime = None
        self.rsi = None
        self.macd = None
        self.adx = None
        self.atr = None

    def Update(self, algorithm, data):
        insights = []

        # Initialize indicators only once
        if self.rsi is None:
            self.rsi = algorithm.RSI(self.symbol, 14, MovingAverageType.Wilders, Resolution.Daily)
            self.macd = algorithm.MACD(self.symbol, 12, 26, 9, MovingAverageType.Exponential, Resolution.Daily)
            self.adx = algorithm.ADX(self.symbol, 14, Resolution.Daily)
            self.atr = algorithm.ATR(self.symbol, 14, MovingAverageType.Wilders, Resolution.Daily)
            return []

        # Wait until indicators are ready
        if not (self.rsi.IsReady and self.macd.IsReady and self.adx.IsReady and self.atr.IsReady):
            return []

        # Prevent emitting multiple insights per day
        if self.lastInsightTime is not None and self.lastInsightTime.date() == algorithm.Time.date():
            return []

        # Signal logic
        if (
            self.rsi.Current.Value > 45
            and self.macd.Current.Value > self.macd.Signal.Current.Value
            and self.adx.Current.Value > 15
        ):
            insight = Insight.Price(
                self.symbol,
                timedelta(days=5),
                InsightDirection.Up,
                magnitude=0.03,         # Higher magnitude for TSLA
                confidence=0.8          # Increased confidence for TSLA's volatility
            )
            insights.append(insight)
            algorithm.Debug(f" TSLA Insight Emitted: {insight}")
            self.lastInsightTime = algorithm.Time

        return insights