Alternative asset during bearish market

Here is a clear example of a skeleton algorithm for dynamic regime switching, matching your specifications. The code demonstrates how to exit equities and allocate to a less-correlated fixed-income ETF (like IEF or SHY) when bearish conditions are detected, and switch back once recovery is signaled.

from AlgorithmImports import *

class DynamicManualUniverseSelectionModel(ManualUniverseSelectionModel):
    """
    ManualUniverseSelectionModel extension that lets us
    dynamically add/remove symbols after initialization.
    """

    def __init__(self, symbols: list[Symbol] | None = None) -> None:
        self._symbols: list[Symbol] = list(symbols or [])
        super().__init__(self._symbols)

    def add_symbol(self, symbol: Symbol) -> None:
        if symbol not in self._symbols:
            self._symbols.append(symbol)

    def clear(self) -> None:
        self._symbols.clear()

    # Return the current universe on each refresh
    def select(self, algorithm: QCAlgorithm, date: datetime) -> list[Symbol]:
        return self._symbols

class RegimeSwitchingAlgorithm(QCAlgorithm):
    """
    Skeleton framework algorithm illustrating regime switching:

    • Bull regime → hold user-defined equity universe
    • Bear regime → exit equities, allocate to a fixed-income ETF
    • Recovery → switch back to equities

    Regime-detection stubs (`_is_bearish_regime`, `_is_bullish_regime`)
    can be replaced with any custom logic.
    """

    def initialize(self) -> None:
        # 1. Backtest parameters
        self.set_start_date(2021, 1, 1)
        self.set_end_date(2022, 1, 1)
        self.set_cash(100_000)

        # 2. User settings
        self._equity_tickers: list[str] = ["SPY"]   # Risk-on assets
        self._bond_ticker: str = "IEF"              # Risk-off asset

        # 3. Framework models
        self._manual_universe = DynamicManualUniverseSelectionModel()
        self.add_universe_selection(self._manual_universe)

        # Place-holder Framework models (all trades are manual via SetHoldings)
        self.add_alpha(ConstantAlphaModel(InsightType.PRICE,
                                          InsightDirection.FLAT,
                                          timedelta(minutes=60)))
        self.set_portfolio_construction(NullPortfolioConstructionModel())
        self.set_execution(ImmediateExecutionModel())

        # 4. Subscribe & register symbols
        self._equity_symbols: list[Symbol] = []
        for ticker in self._equity_tickers:
            symbol = self.add_equity(ticker, Resolution.DAILY).symbol
            self._equity_symbols.append(symbol)
            self._manual_universe.add_symbol(symbol)

        self._bond_symbol: Symbol = self.add_equity(self._bond_ticker,
                                                    Resolution.DAILY).symbol
        self._manual_universe.add_symbol(self._bond_symbol)

        # 5. Regime state
        self._is_bullish: bool = True  # Start assumption

        # 6. Daily regime check (5 min after market open)
        ref_equity = self._equity_tickers[0]
        self.schedule.on(self.date_rules.every_day(ref_equity),
                         self.time_rules.after_market_open(ref_equity, 5),
                         self._check_regime)

        # 7. Initial allocation
        self._allocate_equity_universe()

    # ----------------------------------------------------------------------
    # Regime-detection placeholders
    # ----------------------------------------------------------------------
    def _is_bearish_regime(self) -> bool:
        """Return True when bearish conditions are detected (stub)."""
        return False   # Replace with real logic

    def _is_bullish_regime(self) -> bool:
        """Return True once recovery / bullish conditions return (stub)."""
        return True    # Replace with real logic

    # ----------------------------------------------------------------------
    # Scheduled callback
    # ----------------------------------------------------------------------
    def _check_regime(self) -> None:
        """Evaluate regime daily and rebalance if needed."""
        if self._is_bullish and self._is_bearish_regime():
            # Bull → Bear
            self._is_bullish = False
            self._allocate_bond()
            self.debug(f"{self.time} | Regime shift: BULL → BEAR (holding {self._bond_ticker})")

        elif not self._is_bullish and self._is_bullish_regime():
            # Bear → Bull
            self._is_bullish = True
            self._allocate_equity_universe()
            self.debug(f"{self.time} | Regime shift: BEAR → BULL (holding {self._equity_tickers})")

    # ----------------------------------------------------------------------
    # Allocation helpers
    # ----------------------------------------------------------------------
    def _allocate_bond(self) -> None:
        """Exit equities and move 100 % into the fixed-income ETF."""
        for symbol in self._equity_symbols:
            if self.portfolio[symbol].invested:
                self.liquidate(symbol)

        self.set_holdings(self._bond_symbol, 1.0)

        # Optional: restrict universe to bond only for clarity
        self._manual_universe.clear()
        self._manual_universe.add_symbol(self._bond_symbol)

    def _allocate_equity_universe(self) -> None:
        """Exit bond and invest equally across the equity universe."""
        if self.portfolio[self._bond_symbol].invested:
            self.liquidate(self._bond_symbol)

        weight = 1.0 / len(self._equity_symbols) if self._equity_symbols else 0.0
        for symbol in self._equity_symbols:
            self.set_holdings(symbol, weight)

        # Optional: restore full universe (equities + bond data feed)
        self._manual_universe.clear()
        for symbol in self._equity_symbols + [self._bond_symbol]:
            self._manual_universe.add_symbol(symbol)

Key points for easy customization:

• Replace the logic in _is_bearish_regime and _is_bullish_regime with your preferred signal (volatility, moving average, macro indicator, etc).
• You can set the defensive asset to SHY, IEF, TLT, or something else suited to your findings.
• This modular structure allows dynamic addition/removal in the trading universe and safely rebalances on regime transitions.

Let me know if you want a more detailed example using specific regime signals or alternative defensive assets.

Sources: Key Concepts - QuantConnect.com, Tactical - QuantConnect.com, True Beats - QuantConnect.com, Estimize - QuantConnect.com, Scheduled Universes - QuantConnect.com