Portfolio strategy: TopkDropoutStrategy

Hello, thanks for the great effort for the qlib project.

My issue

I found some wried behaviors when using TopkDropoutStrategy strategy.
I expected that the number of portfolio instruments in each day be equal to the top k number.
However, due to the tradable check in the current implementation, the portfolio number changed each day.

The reasons may cause that.

1. The tradable check is inconsistent in the get_first_n, get_last_n function and the dealing process. Even we set the only_tradable as False, we also check the instruments can be tradable or not.
2. The buy list should be yielded after we get the true sell list.

• current implementation:

# Get the stock list we really want to buy
buy = today[: len(sell) + self.topk - len(last)]
for code in current_stock_list:
    if not self.trade_exchange.is_stock_tradable(
        stock_id=code, start_time=trade_start_time, end_time=trade_end_time
    ):

• the updated implementation:

buy = today[: len(sell_order_list) + self.topk - len(last)] # note that len(sell) -> len(seller_order_list)
current_stock_list = current_temp.get_stock_list()
value = cash * self.risk_degree / len(buy) if len(buy) > 0 else 0

# open_cost should be considered in the real trading environment, while the backtest in evaluate.py does not
# consider it as the aim of demo is to accomplish same strategy as evaluate.py, so comment out this line
# value = value / (1+self.trade_exchange.open_cost) # set open_cost limit
for code in buy:
    # check is stock suspended
    if not self.trade_exchange.is_stock_tradable(
        stock_id=code, start_time=trade_start_time, end_time=trade_end_time
    ):
        continue

3. Some other problems:

• I find that the behavior of risk_degree may be different from our common sense. Maybe the risk_degree depends on the total amount value rather than the cash value.

Start with TopkDropoutStrategy, especially its get_first_n and get_last_n logic and the dealing process shown in the issue; compare these with the trading behavior in evaluate.py. Done means resolving the inconsistent tradable checks, determining the correct buy-list sizing, and clarifying risk_degree behavior without changing the intended backtest strategy.