microsoft/qlib · error · NotImplementedError

This type of input is not supported

Error message

This type of input is not supported

What it means

TopkDropoutStrategy.trade_buy-side selection (qlib/contrib/strategy/signal_strategy.py) supports only two buying methods: 'bottom' (deterministic, from the bottom of the previous holdings to refill topk) and 'random' (random refill from top-k candidates). Any other method_buy string raises NotImplementedError.

Source

Thrown at qlib/contrib/strategy/signal_strategy.py:213

        current_stock_list = current_temp.get_stock_list()
        # last position (sorted by score)
        last = pred_score.reindex(current_stock_list).sort_values(ascending=False).index
        # The new stocks today want to buy **at most**
        if self.method_buy == "top":
            today = get_first_n(
                pred_score[~pred_score.index.isin(last)].sort_values(ascending=False).index,
                self.n_drop + self.topk - len(last),
            )
        elif self.method_buy == "random":
            topk_candi = get_first_n(pred_score.sort_values(ascending=False).index, self.topk)
            candi = list(filter(lambda x: x not in last, topk_candi))
            n = self.n_drop + self.topk - len(last)
            try:
                today = np.random.choice(candi, n, replace=False)
            except ValueError:
                today = candi
        else:
            raise NotImplementedError(f"This type of input is not supported")
        # combine(new stocks + last stocks),  we will drop stocks from this list
        # In case of dropping higher score stock and buying lower score stock.
        comb = pred_score.reindex(last.union(pd.Index(today))).sort_values(ascending=False).index

        # Get the stock list we really want to sell (After filtering the case that we sell high and buy low)
        if self.method_sell == "bottom":
            sell = last[last.isin(get_last_n(comb, self.n_drop))]
        elif self.method_sell == "random":
            candi = filter_stock(last)
            try:
                sell = pd.Index(np.random.choice(candi, self.n_drop, replace=False) if len(last) else [])
            except ValueError:  # No enough candidates
                sell = candi
        else:
            raise NotImplementedError(f"This type of input is not supported")

        # Get the stock list we really want to buy
        buy = today[: len(sell) + self.topk - len(last)]

View on GitHub (pinned to 79633dd950)

Solutions

1. Set method_buy='bottom' or method_buy='random' (lowercase, exact match)
2. Check for typos/case in your strategy config
3. For custom buy logic, subclass TopkDropoutStrategy and override the buy-candidate selection rather than passing a new method string

Example fix

# before
strategy = TopkDropoutStrategy(signal=signal, topk=50, n_drop=5, method_buy='Best')

# after
strategy = TopkDropoutStrategy(signal=signal, topk=50, n_drop=5, method_buy='bottom')

method_buy = 'bottom'
assert method_buy in ('bottom', 'random'), f'unsupported method_buy: {method_buy!r}'
strategy = TopkDropoutStrategy(signal=signal, method_buy=method_buy, ...)

def is_valid_buy_method(m: str) -> bool:
    return m in {'bottom', 'random'}

When it happens

Trigger: Constructing TopkDropoutStrategy(..., method_buy='best') or any value outside {'bottom','random'}, then running generate_trade_decision during a backtest; the error fires on the first trade step where buying occurs.

Common situations: Copying example configs that later added the method_buy/method_sell knobs with unsupported values; typos or case differences ('Random', 'BOTTOM'); assuming pluggable buy methods exist.

Related errors

• inner_order_indicators is necessary in un-atomic executor
• unknown loss `%s`
• unknown metric `%s`
• Empty data from dataset, please check your dataset config.
• optimizer {} is not supported!

AI-assisted analysis of microsoft/qlib@79633dd950 (2026-08-15). Data as JSON: /api/errors/246a5b98a2ec0afe. Report an issue: GitHub.