About SPY Constituents Mean Reversion

Here's how you can revise your algorithm to add a custom exit rule inside a custom alpha model. This example uses moving averages for entry, and supports exit rules for stop-loss, take-profit, or opposite momentum—plus the logic is clear and commented in both English and Chinese (中文) for '李元骏':

from AlgorithmImports import *
from typing import List

class MovingAverageExitAlphaModel(AlphaModel):
    def __init__(self, algorithm: QCAlgorithm, symbol: Symbol, fast_period: int = 50, slow_period: int = 200):
        """
        自定义Alpha模型 (Custom Alpha Model)
        English: Demonstrates entry signals (long/short) and exit signals (flat) based on moving averages.
        中文: 演示基于移动平均线的多/空进场信号以及平仓信号的示例。
        """
        super().__init__()
        self._algorithm = algorithm
        self._symbol = symbol
        self._fast_period = fast_period
        self._slow_period = slow_period

        self._fast = ExponentialMovingAverage(f"FAST_{symbol.value}", fast_period)
        self._slow = ExponentialMovingAverage(f"SLOW_{symbol.value}", slow_period)
        self._insight_duration = timedelta(days=1)  # 持续时间 (Duration of the insight)

        # 用于在决定退出时检查，我们可以追踪进场价格等 (Track entry price or other info for exit logic)
        self._entry_price = 0.0
        self._stop_loss_percent = 0.03  # 例如，3%止损
        self._take_profit_percent = 0.05  # 例如，5%止盈

        # 注册指标以确保算法会自动更新这些指标 (Register indicators so that the algorithm keeps them updated)
        algorithm.register_indicator(symbol, self._fast, Resolution.DAILY)
        algorithm.register_indicator(symbol, self._slow, Resolution.DAILY)

        self.name = "MovingAverageExitAlphaModel"

    def update(self, algorithm: QCAlgorithm, data: Slice) -> List[Insight]:
        """
        每个数据片更新时调用，会根据MA发出多/空信号，或平仓信号
        (Called on every data slice, emit long/short or flat signals)
        """
        insights: List[Insight] = []

        # 如果均线尚未准备好，则不做任何事 (If MAs not ready, do nothing yet)
        if not (self._fast.is_ready and self._slow.is_ready):
            return insights

        current_price = algorithm.securities[self._symbol].price
        fast_value = self._fast.current.value
        slow_value = self._slow.current.value

        # 如果投资组合中还没有这个标的, 则根据MA决定是否进场 (If not invested, check entry logic)
        if not algorithm.portfolio[self._symbol].invested:
            # 当快MA大于慢MA时，发出看涨做多信号 (English: when fast MA > slow MA, emit Up signal)
            if fast_value > slow_value:
                insights.append(Insight.price(self._symbol, self._insight_duration, InsightDirection.UP))
                self._entry_price = current_price
            # 当快MA小于慢MA时，发出看空做空信号 (English: when fast MA < slow MA, emit Down signal)
            elif fast_value < slow_value:
                insights.append(Insight.price(self._symbol, self._insight_duration, InsightDirection.DOWN))
                self._entry_price = current_price
        else:
            # 如果已经持仓，则检查离场 (平仓) 条件 (If invested, check exit conditions)
            hold = algorithm.portfolio[self._symbol]
            pnl_percent = 0.0
            # 如果进场价格不为0，计算盈亏比 (Only compute PnL% if we have a valid entry price)
            if self._entry_price != 0.0:
                if hold.is_long:
                    pnl_percent = (current_price - self._entry_price) / self._entry_price
                else:
                    pnl_percent = (self._entry_price - current_price) / self._entry_price

            # 止损 (Stop loss)
            if hold.is_long and pnl_percent < -self._stop_loss_percent:
                insights.append(Insight.price(self._symbol, self._insight_duration, InsightDirection.FLAT))
            elif hold.is_short and pnl_percent < -self._stop_loss_percent:
                insights.append(Insight.price(self._symbol, self._insight_duration, InsightDirection.FLAT))

            # 止盈 (Take profit)
            if hold.is_long and pnl_percent > self._take_profit_percent:
                insights.append(Insight.price(self._symbol, self._insight_duration, InsightDirection.FLAT))
            elif hold.is_short and pnl_percent > self._take_profit_percent:
                insights.append(Insight.price(self._symbol, self._insight_duration, InsightDirection.FLAT))

            # 根据MA反向信号退出 (Exit if MAs cross in opposite direction)
            if hold.is_long and fast_value < slow_value:
                insights.append(Insight.price(self._symbol, self._insight_duration, InsightDirection.FLAT))
            elif hold.is_short and fast_value > slow_value:
                insights.append(Insight.price(self._symbol, self._insight_duration, InsightDirection.FLAT))

        return insights

    def on_securities_changed(self, algorithm: QCAlgorithm, changes: SecurityChanges) -> None:
        """
        当证券添加或移除时，会调用这里 (Called when securities are added/removed from the universe)
        """
        pass

class MovingAverageExitAlgorithm(QCAlgorithm):
    def initialize(self) -> None:
        # 基本配置 (Basic algorithm configuration)
        self.set_start_date(2021, 1, 1)
        self.set_end_date(2022, 1, 1)
        self.set_cash(100000)

        self._symbol = self.add_equity("SPY", Resolution.DAILY).symbol

        # 设置投资组合模型 (Set portfolio construction model)
        self.set_portfolio_construction(EqualWeightingPortfolioConstructionModel())

        # Set the Alpha Model
        self.set_alpha(MovingAverageExitAlphaModel(self, self._symbol, fast_period=50, slow_period=200))

        # Execution model
        self.set_execution(ImmediateExecutionModel())

• 入口信号（做多/做空）和出口信号（平仓）都由α模型控制。
• 你可以自定义止损/止盈/均线反转等退出逻辑。
• 入口信号用 InsightDirection.UP/DOWN，退出信号用 InsightDirection.FLAT

修改和集成后，回测时就会按照自定义的退出规则自动触发平仓。如果还有细化需要，欢迎补充！

Sources: Key Concepts - QuantConnect.com, Hybrid Algorithms - QuantConnect.com, Intraday Arbitrage Between Index ETFs, E T F Constituent Universe R S I Alpha Model Algorithm, Mean Reversion Portfolio Algorithm