class Attempt1(Strategy):
    """Strategy based on patent/filing analysis (placeholder)."""

    @staticmethod
    def find_new_stocks(time_step: int) -> Dict[str, int]:
        """Find stocks based on USPTO/SEC filings.

        Args:
            time_step: Current time step.

        Returns:
            Dictionary mapping stock symbols to scores.
        """
        pass

    @staticmethod
    def strategy(portfolio_obj: portfolio, data_obj: DataAPI, time_step: int) -> List[Tuple[str, int, int, float]]:
        """Generate trades based on patent/filing analysis.

        Args:
            portfolio_obj: Current portfolio state.
            data_obj: DataAPI instance for price data.
            time_step: Current time step.

        Returns:
            List of trade tuples (stock, shares, flag, price).
        """
        pass

class Example(Strategy):
    """Simple moving average crossover strategy example."""

    @staticmethod
    def strategy(portfolio_obj: portfolio, data_obj: DataAPI, time_step: int) -> List[Tuple[str, int, int, float]]:
        """Buy when current price exceeds 5-period moving average.

        Args:
            portfolio_obj: Current portfolio state.
            data_obj: DataAPI instance for price data.
            time_step: Current time step.

        Returns:
            List of trade tuples (stock, shares, flag, price).
        """

        #simple moving average window
        cash = portfolio_obj.cash
        output = [] #[(stock, num shares to buy/sell, buy/sell flag, price per share)]

        for stock in portfolio_obj.positions:
            average_price = 0
            if cash < 0:
                break

            window_size = min(5, time_step)
            if window_size == 0:
                continue

            average_price = 0.0
            start_step = time_step - window_size

            for t in range(start_step, time_step):
                average_price += data_obj.get_price(stock, t)

            average_price /= window_size

            curr_price = data_obj.get_price(stock, time_step)

            if average_price < curr_price and curr_price <= cash: 
                output.append((stock, 1, 1, curr_price))
                cash -= curr_price

        print(output)
        return output

class Strategy(ABC):
    """Abstract base class for trading strategies."""

    @staticmethod
    @abstractmethod
    def strategy(portfolio_obj: portfolio, data_obj: DataAPI, time_step: int) -> List[Tuple[str, int, int, float]]:
        """Generate trading signals.

        Args:
            portfolio_obj: Current portfolio state.
            data_obj: DataAPI instance for price data.
            time_step: Current time step.

        Returns:
            List of trade tuples (stock, shares, flag, price).
        """
        pass