V2 与 V3 之间的策略迁移

为支持新市场类型和交易类型（即做空交易/杠杆交易），接口层面必须进行一些调整。 如果您计划使用除现货市场以外的其他市场类型，请将您的策略迁移至新格式。

我们已全力保持与现有策略的兼容性，因此若您仅需在__现货市场__中继续使用 freqtrade，目前无需进行任何更改。

您可将快速摘要作为核对清单使用。完整迁移细节请参阅下方详细章节。

快速摘要 / 迁移核对清单

注意：forcesell、forcebuy、emergencysell 分别更名为 force_exit、force_enter、emergency_exit。

• 策略方法：
  • populate_buy_trend() -> populate_entry_trend()
  • populate_sell_trend() -> populate_exit_trend()
  • custom_sell() -> custom_exit()
  • check_buy_timeout() -> check_entry_timeout()
  • check_sell_timeout() -> check_exit_timeout()
  • 无交易对象回调函数新增 side 参数
    • custom_stake_amount
    • confirm_trade_entry
    • custom_entry_price
  • confirm_trade_exit 中更改参数名称
• 数据框列名：
  • buy -> enter_long
  • sell -> exit_long
  • buy_tag -> enter_tag（同时适用于多头和空头交易）
  • 新增列 enter_short 及对应新列 exit_short
• 交易对象新增以下属性：
  • is_short
  • entry_side
  • exit_side
  • trade_direction
  • 重命名：sell_reason -> exit_reason
• 重命名 trade.nr_of_successful_buys 为 trade.nr_of_successful_entries（主要与 adjust_trade_position() 相关）
• 引入新的 leverage 回调函数
• 信息对现在可以在元组中传递第三个元素，用于定义蜡烛类型
• @informative 装饰器新增可选参数 candle_type
• 辅助方法 stoploss_from_open 和 stoploss_from_absolute 新增 is_short 参数
• INTERFACE_VERSION 应设置为 3
• 策略/配置设置
  • order_time_in_force buy -> entry, sell -> exit
  • order_types buy -> entry, sell -> exit
  • unfilledtimeout buy -> entry, sell -> exit
  • ignore_buying_expired_candle_after -> 移至根级别而非 "ask_strategy/exit_pricing"
• 术语变更
  • 卖出原因更改为反映新的"退出"命名而非卖出。如果策略中使用 exit_reason 检查，请谨慎处理并最终更新策略
    • sell_signal -> exit_signal
    • custom_sell -> custom_exit
    • force_sell -> force_exit
    • emergency_sell -> emergency_exit
  • 订单定价
    • bid_strategy -> entry_pricing
    • ask_strategy -> exit_pricing
    • ask_last_balance -> price_last_balance
    • bid_last_balance -> price_last_balance
  • Webhook 术语从"卖出"改为"退出"，从"买入"改为"入场"
    • webhookbuy -> entry
    • webhookbuyfill -> entry_fill
    • webhookbuycancel -> entry_cancel
    • webhooksell -> exit
    • webhooksellfill -> exit_fill
    • webhooksellcancel -> exit_cancel
  • Telegram 通知设置
    • buy -> entry
    • buy_fill -> entry_fill
    • buy_cancel -> entry_cancel
    • sell -> exit
    • sell_fill -> exit_fill
    • sell_cancel -> exit_cancel
  • 策略/配置设置：
    • use_sell_signal -> use_exit_signal
    • sell_profit_only -> exit_profit_only
    • sell_profit_offset -> exit_profit_offset
    • ignore_roi_if_buy_signal -> ignore_roi_if_entry_signal
    • forcebuy_enable -> force_entry_enable

详细说明

populate_buy_trend

在 populate_buy_trend() 中，您需要将分配的列从 'buy' 改为 'enter_long'，同时将方法名从 populate_buy_trend 改为 populate_entry_trend。

def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
    dataframe.loc[
        (
            (qtpylib.crossed_above(dataframe['rsi'], 30)) &  # Signal: RSI crosses above 30
            (dataframe['tema'] <= dataframe['bb_middleband']) &  # Guard
            (dataframe['tema'] > dataframe['tema'].shift(1)) &  # Guard
            (dataframe['volume'] > 0)  # Make sure Volume is not 0
        ),
        ['buy', 'buy_tag']] = (1, 'rsi_cross')

    return dataframe

之后：

def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
    dataframe.loc[
        (
            (qtpylib.crossed_above(dataframe['rsi'], 30)) &  # Signal: RSI crosses above 30
            (dataframe['tema'] <= dataframe['bb_middleband']) &  # Guard
            (dataframe['tema'] > dataframe['tema'].shift(1)) &  # Guard
            (dataframe['volume'] > 0)  # Make sure Volume is not 0
        ),
        ['enter_long', 'enter_tag']] = (1, 'rsi_cross')

    return dataframe

请参考策略文档了解如何进入和退出空头交易。

populate_sell_trend

与 populate_buy_trend 类似，populate_sell_trend() 将被重命名为 populate_exit_trend()。 我们还将列从 'sell' 改为 'exit_long'。

def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
    dataframe.loc[
        (
            (qtpylib.crossed_above(dataframe['rsi'], 70)) &  # Signal: RSI crosses above 70
            (dataframe['tema'] > dataframe['bb_middleband']) &  # Guard
            (dataframe['tema'] < dataframe['tema'].shift(1)) &  # Guard
            (dataframe['volume'] > 0)  # Make sure Volume is not 0
        ),
        ['sell', 'exit_tag']] = (1, 'some_exit_tag')
    return dataframe

之后

def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
    dataframe.loc[
        (
            (qtpylib.crossed_above(dataframe['rsi'], 70)) &  # Signal: RSI crosses above 70
            (dataframe['tema'] > dataframe['bb_middleband']) &  # Guard
            (dataframe['tema'] < dataframe['tema'].shift(1)) &  # Guard
            (dataframe['volume'] > 0)  # Make sure Volume is not 0
        ),
        ['exit_long', 'exit_tag']] = (1, 'some_exit_tag')
    return dataframe

请参考策略文档了解如何进入和退出空头交易。

custom_sell

custom_sell 已重命名为 custom_exit。 现在它会在每次迭代中被调用，与当前利润和 exit_profit_only 设置无关。

class AwesomeStrategy(IStrategy):
    def custom_sell(self, pair: str, trade: 'Trade', current_time: 'datetime', current_rate: float,
                    current_profit: float, **kwargs):
        dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
        last_candle = dataframe.iloc[-1].squeeze()
        # ...

class AwesomeStrategy(IStrategy):
    def custom_exit(self, pair: str, trade: 'Trade', current_time: 'datetime', current_rate: float,
                    current_profit: float, **kwargs):
        dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
        last_candle = dataframe.iloc[-1].squeeze()
        # ...

custom_entry_timeout

check_buy_timeout() 已重命名为 check_entry_timeout()，check_sell_timeout() 已重命名为 check_exit_timeout()。

class AwesomeStrategy(IStrategy):
    def check_buy_timeout(self, pair: str, trade: 'Trade', order: dict, 
                            current_time: datetime, **kwargs) -> bool:
        return False

    def check_sell_timeout(self, pair: str, trade: 'Trade', order: dict, 
                            current_time: datetime, **kwargs) -> bool:
        return False 

class AwesomeStrategy(IStrategy):
    def check_entry_timeout(self, pair: str, trade: 'Trade', order: 'Order', 
                            current_time: datetime, **kwargs) -> bool:
        return False

    def check_exit_timeout(self, pair: str, trade: 'Trade', order: 'Order', 
                            current_time: datetime, **kwargs) -> bool:
        return False 

custom_stake_amount

新增字符串参数 side - 可以是 "long" 或 "short"。

class AwesomeStrategy(IStrategy):
    def custom_stake_amount(self, pair: str, current_time: datetime, current_rate: float,
                            proposed_stake: float, min_stake: Optional[float], max_stake: float,
                            entry_tag: Optional[str], **kwargs) -> float:
        # ... 
        return proposed_stake

class AwesomeStrategy(IStrategy):
    def custom_stake_amount(self, pair: str, current_time: datetime, current_rate: float,
                            proposed_stake: float, min_stake: float | None, max_stake: float,
                            entry_tag: str | None, side: str, **kwargs) -> float:
        # ... 
        return proposed_stake

confirm_trade_entry

新增字符串参数 side - 可以是 "long" 或 "short"。

class AwesomeStrategy(IStrategy):
    def confirm_trade_entry(self, pair: str, order_type: str, amount: float, rate: float,
                            time_in_force: str, current_time: datetime, entry_tag: Optional[str], 
                            **kwargs) -> bool:
      return True

之后：

class AwesomeStrategy(IStrategy):
    def confirm_trade_entry(self, pair: str, order_type: str, amount: float, rate: float,
                            time_in_force: str, current_time: datetime, entry_tag: str | None, 
                            side: str, **kwargs) -> bool:
      return True

confirm_trade_exit

将参数 sell_reason 更改为 exit_reason。 为保持兼容性，sell_reason 将在有限时间内继续提供。

class AwesomeStrategy(IStrategy):
    def confirm_trade_exit(self, pair: str, trade: Trade, order_type: str, amount: float,
                           rate: float, time_in_force: str, sell_reason: str,
                           current_time: datetime, **kwargs) -> bool:
    return True

变更后：

class AwesomeStrategy(IStrategy):
    def confirm_trade_exit(self, pair: str, trade: Trade, order_type: str, amount: float,
                           rate: float, time_in_force: str, exit_reason: str,
                           current_time: datetime, **kwargs) -> bool:
    return True

custom_entry_price

新增字符串参数 side - 可选值为 "long" 或 "short"。

class AwesomeStrategy(IStrategy):
    def custom_entry_price(self, pair: str, current_time: datetime, proposed_rate: float,
                           entry_tag: Optional[str], **kwargs) -> float:
      return proposed_rate

变更后：

class AwesomeStrategy(IStrategy):
    def custom_entry_price(self, pair: str, trade: Trade | None, current_time: datetime, proposed_rate: float,
                           entry_tag: str | None, side: str, **kwargs) -> float:
      return proposed_rate

调整交易仓位变更

虽然 adjust-trade-position 本身未改变，但您不应再使用 trade.nr_of_successful_buys - 而应使用 trade.nr_of_successful_entries，该参数将同时包含空头开仓。

辅助方法

为 stoploss_from_open 和 stoploss_from_absolute 添加参数 "is_short"。 该参数应赋值为 trade.is_short。

    def custom_stoploss(self, pair: str, trade: 'Trade', current_time: datetime,
                        current_rate: float, current_profit: float, **kwargs) -> float:
        # once the profit has risen above 10%, keep the stoploss at 7% above the open price
        if current_profit > 0.10:
            return stoploss_from_open(0.07, current_profit)

        return stoploss_from_absolute(current_rate - (candle['atr'] * 2), current_rate)

        return 1

变更后：

    def custom_stoploss(self, pair: str, trade: 'Trade', current_time: datetime,
                        current_rate: float, current_profit: float, after_fill: bool, 
                        **kwargs) -> float | None:
        # once the profit has risen above 10%, keep the stoploss at 7% above the open price
        if current_profit > 0.10:
            return stoploss_from_open(0.07, current_profit, is_short=trade.is_short)

        return stoploss_from_absolute(current_rate - (candle['atr'] * 2), current_rate, is_short=trade.is_short, leverage=trade.leverage)

策略/配置设置

order_time_in_force

order_time_in_force 属性从 "buy" 改为 "entry"，"sell" 改为 "exit"。

    order_time_in_force: dict = {
        "buy": "gtc",
        "sell": "gtc",
    }

变更后：

    order_time_in_force: dict = {
        "entry": "GTC",
        "exit": "GTC",
    }

order_types

order_types 已将所有 buy 相关表述改为 entry - sell 相关表述改为 exit。 且两个单词之间用 _ 连接。

    order_types = {
        "buy": "limit",
        "sell": "limit",
        "emergencysell": "market",
        "forcesell": "market",
        "forcebuy": "market",
        "stoploss": "market",
        "stoploss_on_exchange": false,
        "stoploss_on_exchange_interval": 60
    }

变更后：

    order_types = {
        "entry": "limit",
        "exit": "limit",
        "emergency_exit": "market",
        "force_exit": "market",
        "force_entry": "market",
        "stoploss": "market",
        "stoploss_on_exchange": false,
        "stoploss_on_exchange_interval": 60
    }

策略层级设置

• use_sell_signal -> use_exit_signal
• sell_profit_only -> exit_profit_only
• sell_profit_offset -> exit_profit_offset
• ignore_roi_if_buy_signal -> ignore_roi_if_entry_signal

    # These values can be overridden in the config.
    use_sell_signal = True
    sell_profit_only = True
    sell_profit_offset: 0.01
    ignore_roi_if_buy_signal = False

变更后：

    # These values can be overridden in the config.
    use_exit_signal = True
    exit_profit_only = True
    exit_profit_offset: 0.01
    ignore_roi_if_entry_signal = False

unfilledtimeout

unfilledtimeout 已将所有 buy 相关表述改为 entry - sell 相关表述改为 exit。

unfilledtimeout = {
        "buy": 10,
        "sell": 10,
        "exit_timeout_count": 0,
        "unit": "minutes"
    }

变更后：

unfilledtimeout = {
        "entry": 10,
        "exit": 10,
        "exit_timeout_count": 0,
        "unit": "minutes"
    }

order pricing

订单定价在两个方面发生了变化。bid_strategy 更名为 entry_pricing，ask_strategy 更名为 exit_pricing。 属性 ask_last_balance -> price_last_balance 和 bid_last_balance -> price_last_balance 也进行了重命名。 此外，价格侧现在可以定义为 ask、bid、same 或 other。 更多信息请参阅定价文档。

{
    "bid_strategy": {
        "price_side": "bid",
        "use_order_book": true,
        "order_book_top": 1,
        "ask_last_balance": 0.0,
        "check_depth_of_market": {
            "enabled": false,
            "bids_to_ask_delta": 1
        }
    },
    "ask_strategy":{
        "price_side": "ask",
        "use_order_book": true,
        "order_book_top": 1,
        "bid_last_balance": 0.0
        "ignore_buying_expired_candle_after": 120
    }
}

之后：

{
    "entry_pricing": {
        "price_side": "same",
        "use_order_book": true,
        "order_book_top": 1,
        "price_last_balance": 0.0,
        "check_depth_of_market": {
            "enabled": false,
            "bids_to_ask_delta": 1
        }
    },
    "exit_pricing":{
        "price_side": "same",
        "use_order_book": true,
        "order_book_top": 1,
        "price_last_balance": 0.0
    },
    "ignore_buying_expired_candle_after": 120
}

FreqAI 策略

populate_any_indicators() 方法已拆分为 feature_engineering_expand_all()、feature_engineering_expand_basic()、feature_engineering_standard() 和 set_freqai_targets()。

对于每个新函数，交易对（以及必要的时间框架）将自动添加到列中。 因此，使用新逻辑后，特征的定义变得更加简单。

关于每个方法的完整说明，请前往相应的 freqAI 文档页面

def populate_any_indicators(
        self, pair, df, tf, informative=None, set_generalized_indicators=False
    ):

        if informative is None:
            informative = self.dp.get_pair_dataframe(pair, tf)

        # first loop is automatically duplicating indicators for time periods
        for t in self.freqai_info["feature_parameters"]["indicator_periods_candles"]:

            t = int(t)
            informative[f"%-{pair}rsi-period_{t}"] = ta.RSI(informative, timeperiod=t)
            informative[f"%-{pair}mfi-period_{t}"] = ta.MFI(informative, timeperiod=t)
            informative[f"%-{pair}adx-period_{t}"] = ta.ADX(informative, timeperiod=t)
            informative[f"%-{pair}sma-period_{t}"] = ta.SMA(informative, timeperiod=t)
            informative[f"%-{pair}ema-period_{t}"] = ta.EMA(informative, timeperiod=t)

            bollinger = qtpylib.bollinger_bands(
                qtpylib.typical_price(informative), window=t, stds=2.2
            )
            informative[f"{pair}bb_lowerband-period_{t}"] = bollinger["lower"]
            informative[f"{pair}bb_middleband-period_{t}"] = bollinger["mid"]
            informative[f"{pair}bb_upperband-period_{t}"] = bollinger["upper"]

            informative[f"%-{pair}bb_width-period_{t}"] = (
                informative[f"{pair}bb_upperband-period_{t}"]
                - informative[f"{pair}bb_lowerband-period_{t}"]
            ) / informative[f"{pair}bb_middleband-period_{t}"]
            informative[f"%-{pair}close-bb_lower-period_{t}"] = (
                informative["close"] / informative[f"{pair}bb_lowerband-period_{t}"]
            )

            informative[f"%-{pair}roc-period_{t}"] = ta.ROC(informative, timeperiod=t)

            informative[f"%-{pair}relative_volume-period_{t}"] = (
                informative["volume"] / informative["volume"].rolling(t).mean()
            ) # (1)

        informative[f"%-{pair}pct-change"] = informative["close"].pct_change()
        informative[f"%-{pair}raw_volume"] = informative["volume"]
        informative[f"%-{pair}raw_price"] = informative["close"]
        # (2)

        indicators = [col for col in informative if col.startswith("%")]
        # This loop duplicates and shifts all indicators to add a sense of recency to data
        for n in range(self.freqai_info["feature_parameters"]["include_shifted_candles"] + 1):
            if n == 0:
                continue
            informative_shift = informative[indicators].shift(n)
            informative_shift = informative_shift.add_suffix("_shift-" + str(n))
            informative = pd.concat((informative, informative_shift), axis=1)

        df = merge_informative_pair(df, informative, self.config["timeframe"], tf, ffill=True)
        skip_columns = [
            (s + "_" + tf) for s in ["date", "open", "high", "low", "close", "volume"]
        ]
        df = df.drop(columns=skip_columns)

        # Add generalized indicators here (because in live, it will call this
        # function to populate indicators during training). Notice how we ensure not to
        # add them multiple times
        if set_generalized_indicators:
            df["%-day_of_week"] = (df["date"].dt.dayofweek + 1) / 7
            df["%-hour_of_day"] = (df["date"].dt.hour + 1) / 25
            # (3)

            # user adds targets here by prepending them with &- (see convention below)
            df["&-s_close"] = (
                df["close"]
                .shift(-self.freqai_info["feature_parameters"]["label_period_candles"])
                .rolling(self.freqai_info["feature_parameters"]["label_period_candles"])
                .mean()
                / df["close"]
                - 1
            )  # (4)

        return df

1. 特征 - 移至 feature_engineering_expand_all
2. 基础特征，不跨 indicator_periods_candles 扩展 - 移至 feature_engineering_expand_basic()。
3. 不应扩展的标准特征 - 移至 feature_engineering_standard()。
4. 目标 - 将此部分移至 set_freqai_targets()。

freqai - 特征工程扩展全部

功能现在会自动扩展。因此，需要移除扩展循环以及 {pair} / {timeframe} 部分。

    def feature_engineering_expand_all(self, dataframe, period, **kwargs) -> DataFrame::
        """
        *Only functional with FreqAI enabled strategies*
        This function will automatically expand the defined features on the config defined
        `indicator_periods_candles`, `include_timeframes`, `include_shifted_candles`, and
        `include_corr_pairs`. In other words, a single feature defined in this function
        will automatically expand to a total of
        `indicator_periods_candles` * `include_timeframes` * `include_shifted_candles` *
        `include_corr_pairs` numbers of features added to the model.

        All features must be prepended with `%` to be recognized by FreqAI internals.

        More details on how these config defined parameters accelerate feature engineering
        in the documentation at:

        https://www.freqtrade.io/en/latest/freqai-parameter-table/#feature-parameters

        https://www.freqtrade.io/en/latest/freqai-feature-engineering/#defining-the-features

        :param df: strategy dataframe which will receive the features
        :param period: period of the indicator - usage example:
        dataframe["%-ema-period"] = ta.EMA(dataframe, timeperiod=period)
        """

        dataframe["%-rsi-period"] = ta.RSI(dataframe, timeperiod=period)
        dataframe["%-mfi-period"] = ta.MFI(dataframe, timeperiod=period)
        dataframe["%-adx-period"] = ta.ADX(dataframe, timeperiod=period)
        dataframe["%-sma-period"] = ta.SMA(dataframe, timeperiod=period)
        dataframe["%-ema-period"] = ta.EMA(dataframe, timeperiod=period)

        bollinger = qtpylib.bollinger_bands(
            qtpylib.typical_price(dataframe), window=period, stds=2.2
        )
        dataframe["bb_lowerband-period"] = bollinger["lower"]
        dataframe["bb_middleband-period"] = bollinger["mid"]
        dataframe["bb_upperband-period"] = bollinger["upper"]

        dataframe["%-bb_width-period"] = (
            dataframe["bb_upperband-period"]
            - dataframe["bb_lowerband-period"]
        ) / dataframe["bb_middleband-period"]
        dataframe["%-close-bb_lower-period"] = (
            dataframe["close"] / dataframe["bb_lowerband-period"]
        )

        dataframe["%-roc-period"] = ta.ROC(dataframe, timeperiod=period)

        dataframe["%-relative_volume-period"] = (
            dataframe["volume"] / dataframe["volume"].rolling(period).mean()
        )

        return dataframe

Freqai - 特征工程基础

基础特征。请确保从你的特征中移除 {pair} 部分。

    def feature_engineering_expand_basic(self, dataframe: DataFrame, **kwargs) -> DataFrame::
        """
        *Only functional with FreqAI enabled strategies*
        This function will automatically expand the defined features on the config defined
        `include_timeframes`, `include_shifted_candles`, and `include_corr_pairs`.
        In other words, a single feature defined in this function
        will automatically expand to a total of
        `include_timeframes` * `include_shifted_candles` * `include_corr_pairs`
        numbers of features added to the model.

        Features defined here will *not* be automatically duplicated on user defined
        `indicator_periods_candles`

        All features must be prepended with `%` to be recognized by FreqAI internals.

        More details on how these config defined parameters accelerate feature engineering
        in the documentation at:

        https://www.freqtrade.io/en/latest/freqai-parameter-table/#feature-parameters

        https://www.freqtrade.io/en/latest/freqai-feature-engineering/#defining-the-features

        :param df: strategy dataframe which will receive the features
        dataframe["%-pct-change"] = dataframe["close"].pct_change()
        dataframe["%-ema-200"] = ta.EMA(dataframe, timeperiod=200)
        """
        dataframe["%-pct-change"] = dataframe["close"].pct_change()
        dataframe["%-raw_volume"] = dataframe["volume"]
        dataframe["%-raw_price"] = dataframe["close"]
        return dataframe

FreqAI - 特征工程标准

    def feature_engineering_standard(self, dataframe: DataFrame, **kwargs) -> DataFrame:
        """
        *Only functional with FreqAI enabled strategies*
        This optional function will be called once with the dataframe of the base timeframe.
        This is the final function to be called, which means that the dataframe entering this
        function will contain all the features and columns created by all other
        freqai_feature_engineering_* functions.

        This function is a good place to do custom exotic feature extractions (e.g. tsfresh).
        This function is a good place for any feature that should not be auto-expanded upon
        (e.g. day of the week).

        All features must be prepended with `%` to be recognized by FreqAI internals.

        More details about feature engineering available:

        https://www.freqtrade.io/en/latest/freqai-feature-engineering

        :param df: strategy dataframe which will receive the features
        usage example: dataframe["%-day_of_week"] = (dataframe["date"].dt.dayofweek + 1) / 7
        """
        dataframe["%-day_of_week"] = dataframe["date"].dt.dayofweek
        dataframe["%-hour_of_day"] = dataframe["date"].dt.hour
        return dataframe

FreqAI - 设置目标

目标现在拥有其专用的方法。

    def set_freqai_targets(self, dataframe: DataFrame, **kwargs) -> DataFrame:
        """
        *Only functional with FreqAI enabled strategies*
        Required function to set the targets for the model.
        All targets must be prepended with `&` to be recognized by the FreqAI internals.

        More details about feature engineering available:

        https://www.freqtrade.io/en/latest/freqai-feature-engineering

        :param df: strategy dataframe which will receive the targets
        usage example: dataframe["&-target"] = dataframe["close"].shift(-1) / dataframe["close"]
        """
        dataframe["&-s_close"] = (
            dataframe["close"]
            .shift(-self.freqai_info["feature_parameters"]["label_period_candles"])
            .rolling(self.freqai_info["feature_parameters"]["label_period_candles"])
            .mean()
            / dataframe["close"]
            - 1
            )

        return dataframe

FreqAI - 新数据管道

如果你创建了带有自定义 train()/predict() 函数的自定义 IFreqaiModel，并且你仍然依赖 data_cleaning_train/predict()，那么你需要迁移到新的数据管道。如果你的模型不依赖 data_cleaning_train/predict()，那么你无需担心此次迁移。这意味着本迁移指南仅与极少部分的高级用户相关。如果你误入了本指南，欢迎在 Freqtrade 的 Discord 服务器中详细询问你的问题。

转换过程首先需要移除 data_cleaning_train/predict()，并将其替换为添加到你的 IFreqaiModel 类中的 define_data_pipeline() 和 define_label_pipeline() 函数：

class MyCoolFreqaiModel(BaseRegressionModel):
    """
    Some cool custom IFreqaiModel you made before Freqtrade version 2023.6
    """
    def train(
        self, unfiltered_df: DataFrame, pair: str, dk: FreqaiDataKitchen, **kwargs
    ) -> Any:

        # ... your custom stuff

        # Remove these lines
        # data_dictionary = dk.make_train_test_datasets(features_filtered, labels_filtered)
        # self.data_cleaning_train(dk)
        # data_dictionary = dk.normalize_data(data_dictionary)
        # (1)

        # Add these lines. Now we control the pipeline fit/transform ourselves
        dd = dk.make_train_test_datasets(features_filtered, labels_filtered)
        dk.feature_pipeline = self.define_data_pipeline(threads=dk.thread_count)
        dk.label_pipeline = self.define_label_pipeline(threads=dk.thread_count)

        (dd["train_features"],
         dd["train_labels"],
         dd["train_weights"]) = dk.feature_pipeline.fit_transform(dd["train_features"],
                                                                  dd["train_labels"],
                                                                  dd["train_weights"])

        (dd["test_features"],
         dd["test_labels"],
         dd["test_weights"]) = dk.feature_pipeline.transform(dd["test_features"],
                                                             dd["test_labels"],
                                                             dd["test_weights"])

        dd["train_labels"], _, _ = dk.label_pipeline.fit_transform(dd["train_labels"])
        dd["test_labels"], _, _ = dk.label_pipeline.transform(dd["test_labels"])

        # ... your custom code

        return model

    def predict(
        self, unfiltered_df: DataFrame, dk: FreqaiDataKitchen, **kwargs
    ) -> tuple[DataFrame, npt.NDArray[np.int_]]:

        # ... your custom stuff

        # Remove these lines:
        # self.data_cleaning_predict(dk)
        # (2)

        # Add these lines:
        dk.data_dictionary["prediction_features"], outliers, _ = dk.feature_pipeline.transform(
            dk.data_dictionary["prediction_features"], outlier_check=True)

        # Remove this line
        # pred_df = dk.denormalize_labels_from_metadata(pred_df)
        # (3)

        # Replace with these lines
        pred_df, _, _ = dk.label_pipeline.inverse_transform(pred_df)
        if self.freqai_info.get("DI_threshold", 0) > 0:
            dk.DI_values = dk.feature_pipeline["di"].di_values
        else:
            dk.DI_values = np.zeros(outliers.shape[0])
        dk.do_predict = outliers

        # ... your custom code
        return (pred_df, dk.do_predict)

1. 数据归一化与清洗现已通过新的流水线定义实现统一。这由新增的 define_data_pipeline() 和 define_label_pipeline() 函数创建。data_cleaning_train() 与 data_cleaning_predict() 函数不再使用。如需创建自定义流水线，可重写 define_data_pipeline() 函数。
2. 数据归一化与清洗现已通过新的流水线定义实现统一。这由新增的 define_data_pipeline() 和 define_label_pipeline() 函数创建。data_cleaning_train() 与 data_cleaning_predict() 函数不再使用。如需创建自定义流水线，可重写 define_data_pipeline() 函数。
3. 数据反归一化现通过新流水线完成。请用以下代码行替换原有实现。