diff --git a/docs/strategy-advanced.md b/docs/strategy-advanced.md index 4409af6ea..2b9517f3b 100644 --- a/docs/strategy-advanced.md +++ b/docs/strategy-advanced.md @@ -288,6 +288,12 @@ Stoploss values returned from `custom_stoploss()` always specify a percentage re The helper function [`stoploss_from_open()`](strategy-customization.md#stoploss_from_open) can be used to convert from an open price relative stop, to a current price relative stop which can be returned from `custom_stoploss()`. +### Calculating stoploss percentage from absolute price + +Stoploss values returned from `custom_stoploss()` always specify a percentage relative to `current_rate`. In order to set a stoploss at specified absolute price level, we need to use `stop_rate` to calculate what percentage relative to the `current_rate` will give you the same result as if the percentage was specified from the open price. + +The helper function [`stoploss_from_absolute()`](strategy-customization.md#stoploss_from_absolute) can be used to convert from an absolute price, to a current price relative stop which can be returned from `custom_stoploss()`. + #### Stepped stoploss Instead of continuously trailing behind the current price, this example sets fixed stoploss price levels based on the current profit. diff --git a/docs/strategy-customization.md b/docs/strategy-customization.md index cfea60d22..2b22dd274 100644 --- a/docs/strategy-customization.md +++ b/docs/strategy-customization.md @@ -639,6 +639,170 @@ Stoploss values returned from `custom_stoploss` must specify a percentage relati Full examples can be found in the [Custom stoploss](strategy-advanced.md#custom-stoploss) section of the Documentation. +!!! Note + Providing invalid input to `stoploss_from_open()` may produce "CustomStoploss function did not return valid stoploss" warnings. + This may happen if `current_profit` parameter is below specified `open_relative_stop`. Such situations may arise when closing trade + is blocked by `confirm_trade_exit()` method. Warnings can be solved by never blocking stop loss sells by checking `sell_reason` in + `confirm_trade_exit()`, or by using `return stoploss_from_open(...) or 1` idiom, which will request to not change stop loss when + `current_profit < open_relative_stop`. + +### *stoploss_from_absolute()* + +In some situations it may be confusing to deal with stops relative to current rate. Instead, you may define a stoploss level using an absolute price. + +??? Example "Returning a stoploss using absolute price from the custom stoploss function" + + If we want to trail a stop price at 2xATR below current proce we can call `stoploss_from_absolute(current_rate - (candle['atr'] * 2), current_rate)`. + + ``` python + + from datetime import datetime + from freqtrade.persistence import Trade + from freqtrade.strategy import IStrategy, stoploss_from_open + + class AwesomeStrategy(IStrategy): + + use_custom_stoploss = True + + def populate_indicators_1h(self, dataframe: DataFrame, metadata: dict) -> DataFrame: + dataframe['atr'] = ta.ATR(dataframe, timeperiod=14) + return dataframe + + def custom_stoploss(self, pair: str, trade: 'Trade', current_time: datetime, + current_rate: float, current_profit: float, **kwargs) -> float: + dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe) + candle = dataframe.iloc[-1].squeeze() + return stoploss_from_absolute(current_rate - (candle['atr'] * 2), current_rate) + + ``` + +### *@informative()* + +``` python +def informative(timeframe: str, asset: str = '', + fmt: Optional[Union[str, Callable[[KwArg(str)], str]]] = None, + ffill: bool = True) -> Callable[[PopulateIndicators], PopulateIndicators]: + """ + A decorator for populate_indicators_Nn(self, dataframe, metadata), allowing these functions to + define informative indicators. + + Example usage: + + @informative('1h') + def populate_indicators_1h(self, dataframe: DataFrame, metadata: dict) -> DataFrame: + dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14) + return dataframe + + :param timeframe: Informative timeframe. Must always be equal or higher than strategy timeframe. + :param asset: Informative asset, for example BTC, BTC/USDT, ETH/BTC. Do not specify to use + current pair. + :param fmt: Column format (str) or column formatter (callable(name, asset, timeframe)). When not + specified, defaults to: + * {base}_{quote}_{column}_{timeframe} if asset is specified. + * {column}_{timeframe} if asset is not specified. + Format string supports these format variables: + * {asset} - full name of the asset, for example 'BTC/USDT'. + * {base} - base currency in lower case, for example 'eth'. + * {BASE} - same as {base}, except in upper case. + * {quote} - quote currency in lower case, for example 'usdt'. + * {QUOTE} - same as {quote}, except in upper case. + * {column} - name of dataframe column. + * {timeframe} - timeframe of informative dataframe. + :param ffill: ffill dataframe after merging informative pair. + """ +``` + +In most common case it is possible to easily define informative pairs by using a decorator. All decorated `populate_indicators_*` methods run in isolation, +not having access to data from other informative pairs, in the end all informative dataframes are merged and passed to main `populate_indicators()` method. +When hyperopting, use of hyperoptable parameter `.value` attribute is not supported. Please use `.range` attribute. See [optimizing an indicator parameter](hyperopt.md#optimizing-an-indicator-parameter) +for more information. + +??? Example "Fast and easy way to define informative pairs" + + Most of the time we do not need power and flexibility offered by `merge_informative_pair()`, therefore we can use a decorator to quickly define informative pairs. + + ``` python + + from datetime import datetime + from freqtrade.persistence import Trade + from freqtrade.strategy import IStrategy, informative + + class AwesomeStrategy(IStrategy): + + # This method is not required. + # def informative_pairs(self): ... + + # Define informative upper timeframe for each pair. Decorators can be stacked on same + # method. Available in populate_indicators as 'rsi_30m' and 'rsi_1h'. + @informative('30m') + @informative('1h') + def populate_indicators_1h(self, dataframe: DataFrame, metadata: dict) -> DataFrame: + dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14) + return dataframe + + # Define BTC/STAKE informative pair. Available in populate_indicators and other methods as + # 'btc_rsi_1h'. Current stake currency should be specified as {stake} format variable + # instead of hardcoding actual stake currency. Available in populate_indicators and other + # methods as 'btc_usdt_rsi_1h' (when stake currency is USDT). + @informative('1h', 'BTC/{stake}') + def populate_indicators_btc_1h(self, dataframe: DataFrame, metadata: dict) -> DataFrame: + dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14) + return dataframe + + # Define BTC/ETH informative pair. You must specify quote currency if it is different from + # stake currency. Available in populate_indicators and other methods as 'eth_btc_rsi_1h'. + @informative('1h', 'ETH/BTC') + def populate_indicators_eth_btc_1h(self, dataframe: DataFrame, metadata: dict) -> DataFrame: + dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14) + return dataframe + + # Define BTC/STAKE informative pair. A custom formatter may be specified for formatting + # column names. A callable `fmt(**kwargs) -> str` may be specified, to implement custom + # formatting. Available in populate_indicators and other methods as 'rsi_upper'. + @informative('1h', 'BTC/{stake}', '{column}') + def populate_indicators_btc_1h_2(self, dataframe: DataFrame, metadata: dict) -> DataFrame: + dataframe['rsi_upper'] = ta.RSI(dataframe, timeperiod=14) + return dataframe + + def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: + # Strategy timeframe indicators for current pair. + dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14) + # Informative pairs are available in this method. + dataframe['rsi_less'] = dataframe['rsi'] < dataframe['rsi_1h'] + return dataframe + + ``` + +!!! Note + Do not use `@informative` decorator if you need to use data of one informative pair when generating another informative pair. Instead, define informative pairs + manually as described [in the DataProvider section](#complete-data-provider-sample). + +!!! Note + Use string formatting when accessing informative dataframes of other pairs. This will allow easily changing stake currency in config without having to adjust strategy code. + + ``` python + def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: + stake = self.config['stake_currency'] + dataframe.loc[ + ( + (dataframe[f'btc_{stake}_rsi_1h'] < 35) + & + (dataframe['volume'] > 0) + ), + ['buy', 'buy_tag']] = (1, 'buy_signal_rsi') + + return dataframe + ``` + + Alternatively column renaming may be used to remove stake currency from column names: `@informative('1h', 'BTC/{stake}', fmt='{base}_{column}_{timeframe}')`. + +!!! Warning "Duplicate method names" + Methods tagged with `@informative()` decorator must always have unique names! Re-using same name (for example when copy-pasting already defined informative method) + will overwrite previously defined method and not produce any errors due to limitations of Python programming language. In such cases you will find that indicators + created in earlier-defined methods are not available in the dataframe. Carefully review method names and make sure they are unique! + +!!! Warning + When using a legacy hyperopt implementation informative pairs defined with a decorator will not be executed. Please update your strategy if necessary. ## Additional data (Wallets) diff --git a/freqtrade/edge/edge_positioning.py b/freqtrade/edge/edge_positioning.py index f12b1b37d..1950f0d08 100644 --- a/freqtrade/edge/edge_positioning.py +++ b/freqtrade/edge/edge_positioning.py @@ -119,7 +119,7 @@ class Edge: ) # Download informative pairs too res = defaultdict(list) - for p, t in self.strategy.informative_pairs(): + for p, t in self.strategy.gather_informative_pairs(): res[t].append(p) for timeframe, inf_pairs in res.items(): timerange_startup = deepcopy(self._timerange) diff --git a/freqtrade/freqtradebot.py b/freqtrade/freqtradebot.py index 7f668273c..1cb8988ff 100644 --- a/freqtrade/freqtradebot.py +++ b/freqtrade/freqtradebot.py @@ -83,10 +83,10 @@ class FreqtradeBot(LoggingMixin): self.dataprovider = DataProvider(self.config, self.exchange, self.pairlists) - # Attach Dataprovider to Strategy baseclass - IStrategy.dp = self.dataprovider - # Attach Wallets to Strategy baseclass - IStrategy.wallets = self.wallets + # Attach Dataprovider to strategy instance + self.strategy.dp = self.dataprovider + # Attach Wallets to strategy instance + self.strategy.wallets = self.wallets # Initializing Edge only if enabled self.edge = Edge(self.config, self.exchange, self.strategy) if \ @@ -160,7 +160,7 @@ class FreqtradeBot(LoggingMixin): # Refreshing candles self.dataprovider.refresh(self.pairlists.create_pair_list(self.active_pair_whitelist), - self.strategy.informative_pairs()) + self.strategy.gather_informative_pairs()) strategy_safe_wrapper(self.strategy.bot_loop_start, supress_error=True)() diff --git a/freqtrade/optimize/backtesting.py b/freqtrade/optimize/backtesting.py index 3e06bfa1b..79c861ee8 100644 --- a/freqtrade/optimize/backtesting.py +++ b/freqtrade/optimize/backtesting.py @@ -154,7 +154,7 @@ class Backtesting: self.strategy: IStrategy = strategy strategy.dp = self.dataprovider # Attach Wallets to Strategy baseclass - IStrategy.wallets = self.wallets + strategy.wallets = self.wallets # Set stoploss_on_exchange to false for backtesting, # since a "perfect" stoploss-sell is assumed anyway # And the regular "stoploss" function would not apply to that case diff --git a/freqtrade/optimize/edge_cli.py b/freqtrade/optimize/edge_cli.py index 417faa685..f211da750 100644 --- a/freqtrade/optimize/edge_cli.py +++ b/freqtrade/optimize/edge_cli.py @@ -8,6 +8,7 @@ from typing import Any, Dict from freqtrade import constants from freqtrade.configuration import TimeRange, validate_config_consistency +from freqtrade.data.dataprovider import DataProvider from freqtrade.edge import Edge from freqtrade.optimize.optimize_reports import generate_edge_table from freqtrade.resolvers import ExchangeResolver, StrategyResolver @@ -33,6 +34,7 @@ class EdgeCli: self.config['stake_amount'] = constants.UNLIMITED_STAKE_AMOUNT self.exchange = ExchangeResolver.load_exchange(self.config['exchange']['name'], self.config) self.strategy = StrategyResolver.load_strategy(self.config) + self.strategy.dp = DataProvider(config, None) validate_config_consistency(self.config) diff --git a/freqtrade/strategy/__init__.py b/freqtrade/strategy/__init__.py index be655fc33..2ea0ad2b4 100644 --- a/freqtrade/strategy/__init__.py +++ b/freqtrade/strategy/__init__.py @@ -3,5 +3,7 @@ from freqtrade.exchange import (timeframe_to_minutes, timeframe_to_msecs, timefr timeframe_to_prev_date, timeframe_to_seconds) from freqtrade.strategy.hyper import (BooleanParameter, CategoricalParameter, DecimalParameter, IntParameter, RealParameter) +from freqtrade.strategy.informative_decorator import informative from freqtrade.strategy.interface import IStrategy -from freqtrade.strategy.strategy_helper import merge_informative_pair, stoploss_from_open +from freqtrade.strategy.strategy_helper import (merge_informative_pair, stoploss_from_absolute, + stoploss_from_open) diff --git a/freqtrade/strategy/informative_decorator.py b/freqtrade/strategy/informative_decorator.py new file mode 100644 index 000000000..4c5f21108 --- /dev/null +++ b/freqtrade/strategy/informative_decorator.py @@ -0,0 +1,128 @@ +from typing import Any, Callable, NamedTuple, Optional, Union + +from pandas import DataFrame + +from freqtrade.exceptions import OperationalException +from freqtrade.strategy.strategy_helper import merge_informative_pair + + +PopulateIndicators = Callable[[Any, DataFrame, dict], DataFrame] + + +class InformativeData(NamedTuple): + asset: Optional[str] + timeframe: str + fmt: Union[str, Callable[[Any], str], None] + ffill: bool + + +def informative(timeframe: str, asset: str = '', + fmt: Optional[Union[str, Callable[[Any], str]]] = None, + ffill: bool = True) -> Callable[[PopulateIndicators], PopulateIndicators]: + """ + A decorator for populate_indicators_Nn(self, dataframe, metadata), allowing these functions to + define informative indicators. + + Example usage: + + @informative('1h') + def populate_indicators_1h(self, dataframe: DataFrame, metadata: dict) -> DataFrame: + dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14) + return dataframe + + :param timeframe: Informative timeframe. Must always be equal or higher than strategy timeframe. + :param asset: Informative asset, for example BTC, BTC/USDT, ETH/BTC. Do not specify to use + current pair. + :param fmt: Column format (str) or column formatter (callable(name, asset, timeframe)). When not + specified, defaults to: + * {base}_{quote}_{column}_{timeframe} if asset is specified. + * {column}_{timeframe} if asset is not specified. + Format string supports these format variables: + * {asset} - full name of the asset, for example 'BTC/USDT'. + * {base} - base currency in lower case, for example 'eth'. + * {BASE} - same as {base}, except in upper case. + * {quote} - quote currency in lower case, for example 'usdt'. + * {QUOTE} - same as {quote}, except in upper case. + * {column} - name of dataframe column. + * {timeframe} - timeframe of informative dataframe. + :param ffill: ffill dataframe after merging informative pair. + """ + _asset = asset + _timeframe = timeframe + _fmt = fmt + _ffill = ffill + + def decorator(fn: PopulateIndicators): + informative_pairs = getattr(fn, '_ft_informative', []) + informative_pairs.append(InformativeData(_asset, _timeframe, _fmt, _ffill)) + setattr(fn, '_ft_informative', informative_pairs) + return fn + return decorator + + +def _format_pair_name(config, pair: str) -> str: + return pair.format(stake_currency=config['stake_currency'], + stake=config['stake_currency']).upper() + + +def _create_and_merge_informative_pair(strategy, dataframe: DataFrame, metadata: dict, + inf_data: InformativeData, + populate_indicators: PopulateIndicators): + asset = inf_data.asset or '' + timeframe = inf_data.timeframe + fmt = inf_data.fmt + config = strategy.config + + if asset: + # Insert stake currency if needed. + asset = _format_pair_name(config, asset) + else: + # Not specifying an asset will define informative dataframe for current pair. + asset = metadata['pair'] + + if '/' in asset: + base, quote = asset.split('/') + else: + # When futures are supported this may need reevaluation. + # base, quote = asset, '' + raise OperationalException('Not implemented.') + + # Default format. This optimizes for the common case: informative pairs using same stake + # currency. When quote currency matches stake currency, column name will omit base currency. + # This allows easily reconfiguring strategy to use different base currency. In a rare case + # where it is desired to keep quote currency in column name at all times user should specify + # fmt='{base}_{quote}_{column}_{timeframe}' format or similar. + if not fmt: + fmt = '{column}_{timeframe}' # Informatives of current pair + if inf_data.asset: + fmt = '{base}_{quote}_' + fmt # Informatives of other pairs + + inf_metadata = {'pair': asset, 'timeframe': timeframe} + inf_dataframe = strategy.dp.get_pair_dataframe(asset, timeframe) + inf_dataframe = populate_indicators(strategy, inf_dataframe, inf_metadata) + + formatter: Any = None + if callable(fmt): + formatter = fmt # A custom user-specified formatter function. + else: + formatter = fmt.format # A default string formatter. + + fmt_args = { + 'BASE': base.upper(), + 'QUOTE': quote.upper(), + 'base': base.lower(), + 'quote': quote.lower(), + 'asset': asset, + 'timeframe': timeframe, + } + inf_dataframe.rename(columns=lambda column: formatter(column=column, **fmt_args), + inplace=True) + + date_column = formatter(column='date', **fmt_args) + if date_column in dataframe.columns: + raise OperationalException(f'Duplicate column name {date_column} exists in ' + f'dataframe! Ensure column names are unique!') + dataframe = merge_informative_pair(dataframe, inf_dataframe, strategy.timeframe, timeframe, + ffill=inf_data.ffill, append_timeframe=False, + date_column=date_column) + return dataframe diff --git a/freqtrade/strategy/interface.py b/freqtrade/strategy/interface.py index 00ad3faf0..7420bd9fd 100644 --- a/freqtrade/strategy/interface.py +++ b/freqtrade/strategy/interface.py @@ -19,6 +19,9 @@ from freqtrade.exchange import timeframe_to_minutes, timeframe_to_seconds from freqtrade.exchange.exchange import timeframe_to_next_date from freqtrade.persistence import PairLocks, Trade from freqtrade.strategy.hyper import HyperStrategyMixin +from freqtrade.strategy.informative_decorator import (InformativeData, PopulateIndicators, + _create_and_merge_informative_pair, + _format_pair_name) from freqtrade.strategy.strategy_wrapper import strategy_safe_wrapper from freqtrade.wallets import Wallets @@ -118,7 +121,7 @@ class IStrategy(ABC, HyperStrategyMixin): # Class level variables (intentional) containing # the dataprovider (dp) (access to other candles, historic data, ...) # and wallets - access to the current balance. - dp: Optional[DataProvider] = None + dp: Optional[DataProvider] wallets: Optional[Wallets] = None # Filled from configuration stake_currency: str @@ -134,6 +137,24 @@ class IStrategy(ABC, HyperStrategyMixin): self._last_candle_seen_per_pair: Dict[str, datetime] = {} super().__init__(config) + # Gather informative pairs from @informative-decorated methods. + self._ft_informative: List[Tuple[InformativeData, PopulateIndicators]] = [] + for attr_name in dir(self.__class__): + cls_method = getattr(self.__class__, attr_name) + if not callable(cls_method): + continue + informative_data_list = getattr(cls_method, '_ft_informative', None) + if not isinstance(informative_data_list, list): + # Type check is required because mocker would return a mock object that evaluates to + # True, confusing this code. + continue + strategy_timeframe_minutes = timeframe_to_minutes(self.timeframe) + for informative_data in informative_data_list: + if timeframe_to_minutes(informative_data.timeframe) < strategy_timeframe_minutes: + raise OperationalException('Informative timeframe must be equal or higher than ' + 'strategy timeframe!') + self._ft_informative.append((informative_data, cls_method)) + @abstractmethod def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: """ @@ -377,6 +398,23 @@ class IStrategy(ABC, HyperStrategyMixin): # END - Intended to be overridden by strategy ### + def gather_informative_pairs(self) -> ListPairsWithTimeframes: + """ + Internal method which gathers all informative pairs (user or automatically defined). + """ + informative_pairs = self.informative_pairs() + for inf_data, _ in self._ft_informative: + if inf_data.asset: + pair_tf = (_format_pair_name(self.config, inf_data.asset), inf_data.timeframe) + informative_pairs.append(pair_tf) + else: + if not self.dp: + raise OperationalException('@informative decorator with unspecified asset ' + 'requires DataProvider instance.') + for pair in self.dp.current_whitelist(): + informative_pairs.append((pair, inf_data.timeframe)) + return list(set(informative_pairs)) + def get_strategy_name(self) -> str: """ Returns strategy class name @@ -793,6 +831,12 @@ class IStrategy(ABC, HyperStrategyMixin): :return: a Dataframe with all mandatory indicators for the strategies """ logger.debug(f"Populating indicators for pair {metadata.get('pair')}.") + + # call populate_indicators_Nm() which were tagged with @informative decorator. + for inf_data, populate_fn in self._ft_informative: + dataframe = _create_and_merge_informative_pair( + self, dataframe, metadata, inf_data, populate_fn) + if self._populate_fun_len == 2: warnings.warn("deprecated - check out the Sample strategy to see " "the current function headers!", DeprecationWarning) diff --git a/freqtrade/strategy/strategy_helper.py b/freqtrade/strategy/strategy_helper.py index e089ebf31..175bcaccb 100644 --- a/freqtrade/strategy/strategy_helper.py +++ b/freqtrade/strategy/strategy_helper.py @@ -4,7 +4,9 @@ from freqtrade.exchange import timeframe_to_minutes def merge_informative_pair(dataframe: pd.DataFrame, informative: pd.DataFrame, - timeframe: str, timeframe_inf: str, ffill: bool = True) -> pd.DataFrame: + timeframe: str, timeframe_inf: str, ffill: bool = True, + append_timeframe: bool = True, + date_column: str = 'date') -> pd.DataFrame: """ Correctly merge informative samples to the original dataframe, avoiding lookahead bias. @@ -24,6 +26,8 @@ def merge_informative_pair(dataframe: pd.DataFrame, informative: pd.DataFrame, :param timeframe: Timeframe of the original pair sample. :param timeframe_inf: Timeframe of the informative pair sample. :param ffill: Forwardfill missing values - optional but usually required + :param append_timeframe: Rename columns by appending timeframe. + :param date_column: A custom date column name. :return: Merged dataframe :raise: ValueError if the secondary timeframe is shorter than the dataframe timeframe """ @@ -32,25 +36,29 @@ def merge_informative_pair(dataframe: pd.DataFrame, informative: pd.DataFrame, minutes = timeframe_to_minutes(timeframe) if minutes == minutes_inf: # No need to forwardshift if the timeframes are identical - informative['date_merge'] = informative["date"] + informative['date_merge'] = informative[date_column] elif minutes < minutes_inf: # Subtract "small" timeframe so merging is not delayed by 1 small candle # Detailed explanation in https://github.com/freqtrade/freqtrade/issues/4073 informative['date_merge'] = ( - informative["date"] + pd.to_timedelta(minutes_inf, 'm') - pd.to_timedelta(minutes, 'm') + informative[date_column] + pd.to_timedelta(minutes_inf, 'm') - + pd.to_timedelta(minutes, 'm') ) else: raise ValueError("Tried to merge a faster timeframe to a slower timeframe." "This would create new rows, and can throw off your regular indicators.") # Rename columns to be unique - informative.columns = [f"{col}_{timeframe_inf}" for col in informative.columns] + date_merge = 'date_merge' + if append_timeframe: + date_merge = f'date_merge_{timeframe_inf}' + informative.columns = [f"{col}_{timeframe_inf}" for col in informative.columns] # Combine the 2 dataframes # all indicators on the informative sample MUST be calculated before this point dataframe = pd.merge(dataframe, informative, left_on='date', - right_on=f'date_merge_{timeframe_inf}', how='left') - dataframe = dataframe.drop(f'date_merge_{timeframe_inf}', axis=1) + right_on=date_merge, how='left') + dataframe = dataframe.drop(date_merge, axis=1) if ffill: dataframe = dataframe.ffill() @@ -83,3 +91,28 @@ def stoploss_from_open(open_relative_stop: float, current_profit: float) -> floa # negative stoploss values indicate the requested stop price is higher than the current price return max(stoploss, 0.0) + + +def stoploss_from_absolute(stop_rate: float, current_rate: float) -> float: + """ + Given current price and desired stop price, return a stop loss value that is relative to current + price. + + The requested stop can be positive for a stop above the open price, or negative for + a stop below the open price. The return value is always >= 0. + + Returns 0 if the resulting stop price would be above the current price. + + :param stop_rate: Stop loss price. + :param current_rate: Current asset price. + :return: Positive stop loss value relative to current price + """ + + # formula is undefined for current_rate 0, return maximum value + if current_rate == 0: + return 1 + + stoploss = 1 - (stop_rate / current_rate) + + # negative stoploss values indicate the requested stop price is higher than the current price + return max(stoploss, 0.0) diff --git a/tests/rpc/test_rpc_apiserver.py b/tests/rpc/test_rpc_apiserver.py index 2852486ed..43eb70938 100644 --- a/tests/rpc/test_rpc_apiserver.py +++ b/tests/rpc/test_rpc_apiserver.py @@ -1218,6 +1218,7 @@ def test_api_strategies(botclient): assert_response(rc) assert rc.json() == {'strategies': [ 'HyperoptableStrategy', + 'InformativeDecoratorTest', 'StrategyTestV2', 'TestStrategyLegacyV1' ]} diff --git a/tests/strategy/strats/informative_decorator_strategy.py b/tests/strategy/strats/informative_decorator_strategy.py new file mode 100644 index 000000000..a32ad79e8 --- /dev/null +++ b/tests/strategy/strats/informative_decorator_strategy.py @@ -0,0 +1,75 @@ +# pragma pylint: disable=missing-docstring, invalid-name, pointless-string-statement + +from pandas import DataFrame + +from freqtrade.strategy import informative, merge_informative_pair +from freqtrade.strategy.interface import IStrategy + + +class InformativeDecoratorTest(IStrategy): + """ + Strategy used by tests freqtrade bot. + Please do not modify this strategy, it's intended for internal use only. + Please look at the SampleStrategy in the user_data/strategy directory + or strategy repository https://github.com/freqtrade/freqtrade-strategies + for samples and inspiration. + """ + INTERFACE_VERSION = 2 + stoploss = -0.10 + timeframe = '5m' + startup_candle_count: int = 20 + + def informative_pairs(self): + return [('BTC/USDT', '5m')] + + def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: + dataframe['buy'] = 0 + return dataframe + + def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: + dataframe['sell'] = 0 + return dataframe + + # Decorator stacking test. + @informative('30m') + @informative('1h') + def populate_indicators_1h(self, dataframe: DataFrame, metadata: dict) -> DataFrame: + dataframe['rsi'] = 14 + return dataframe + + # Simple informative test. + @informative('1h', 'BTC/{stake}') + def populate_indicators_btc_1h(self, dataframe: DataFrame, metadata: dict) -> DataFrame: + dataframe['rsi'] = 14 + return dataframe + + # Quote currency different from stake currency test. + @informative('1h', 'ETH/BTC') + def populate_indicators_eth_btc_1h(self, dataframe: DataFrame, metadata: dict) -> DataFrame: + dataframe['rsi'] = 14 + return dataframe + + # Formatting test. + @informative('30m', 'BTC/{stake}', '{column}_{BASE}_{QUOTE}_{base}_{quote}_{asset}_{timeframe}') + def populate_indicators_btc_1h_2(self, dataframe: DataFrame, metadata: dict) -> DataFrame: + dataframe['rsi'] = 14 + return dataframe + + # Custom formatter test + @informative('30m', 'ETH/{stake}', fmt=lambda column, **kwargs: column + '_from_callable') + def populate_indicators_eth_30m(self, dataframe: DataFrame, metadata: dict) -> DataFrame: + dataframe['rsi'] = 14 + return dataframe + + def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: + # Strategy timeframe indicators for current pair. + dataframe['rsi'] = 14 + # Informative pairs are available in this method. + dataframe['rsi_less'] = dataframe['rsi'] < dataframe['rsi_1h'] + + # Mixing manual informative pairs with decorators. + informative = self.dp.get_pair_dataframe('BTC/USDT', '5m') + informative['rsi'] = 14 + dataframe = merge_informative_pair(dataframe, informative, self.timeframe, '5m', ffill=True) + + return dataframe diff --git a/tests/strategy/test_interface.py b/tests/strategy/test_interface.py index 250dcf63d..dcb9e3e64 100644 --- a/tests/strategy/test_interface.py +++ b/tests/strategy/test_interface.py @@ -607,7 +607,7 @@ def test_is_informative_pairs_callback(default_conf): strategy = StrategyResolver.load_strategy(default_conf) # Should return empty # Uses fallback to base implementation - assert [] == strategy.informative_pairs() + assert [] == strategy.gather_informative_pairs() @pytest.mark.parametrize('error', [ diff --git a/tests/strategy/test_strategy_helpers.py b/tests/strategy/test_strategy_helpers.py index 3b84fc254..a01b55050 100644 --- a/tests/strategy/test_strategy_helpers.py +++ b/tests/strategy/test_strategy_helpers.py @@ -4,7 +4,9 @@ import numpy as np import pandas as pd import pytest -from freqtrade.strategy import merge_informative_pair, stoploss_from_open, timeframe_to_minutes +from freqtrade.data.dataprovider import DataProvider +from freqtrade.strategy import (merge_informative_pair, stoploss_from_absolute, stoploss_from_open, + timeframe_to_minutes) def generate_test_data(timeframe: str, size: int): @@ -132,3 +134,65 @@ def test_stoploss_from_open(): assert stoploss == 0 else: assert isclose(stop_price, expected_stop_price, rel_tol=0.00001) + + +def test_stoploss_from_absolute(): + assert stoploss_from_absolute(90, 100) == 1 - (90 / 100) + assert stoploss_from_absolute(100, 100) == 0 + assert stoploss_from_absolute(110, 100) == 0 + assert stoploss_from_absolute(100, 0) == 1 + assert stoploss_from_absolute(0, 100) == 1 + + +def test_informative_decorator(mocker, default_conf): + test_data_5m = generate_test_data('5m', 40) + test_data_30m = generate_test_data('30m', 40) + test_data_1h = generate_test_data('1h', 40) + data = { + ('XRP/USDT', '5m'): test_data_5m, + ('XRP/USDT', '30m'): test_data_30m, + ('XRP/USDT', '1h'): test_data_1h, + ('LTC/USDT', '5m'): test_data_5m, + ('LTC/USDT', '30m'): test_data_30m, + ('LTC/USDT', '1h'): test_data_1h, + ('BTC/USDT', '30m'): test_data_30m, + ('BTC/USDT', '5m'): test_data_5m, + ('BTC/USDT', '1h'): test_data_1h, + ('ETH/USDT', '1h'): test_data_1h, + ('ETH/USDT', '30m'): test_data_30m, + ('ETH/BTC', '1h'): test_data_1h, + } + from .strats.informative_decorator_strategy import InformativeDecoratorTest + default_conf['stake_currency'] = 'USDT' + strategy = InformativeDecoratorTest(config=default_conf) + strategy.dp = DataProvider({}, None, None) + mocker.patch.object(strategy.dp, 'current_whitelist', return_value=[ + 'XRP/USDT', 'LTC/USDT', 'BTC/USDT' + ]) + + assert len(strategy._ft_informative) == 6 # Equal to number of decorators used + informative_pairs = [('XRP/USDT', '1h'), ('LTC/USDT', '1h'), ('XRP/USDT', '30m'), + ('LTC/USDT', '30m'), ('BTC/USDT', '1h'), ('BTC/USDT', '30m'), + ('BTC/USDT', '5m'), ('ETH/BTC', '1h'), ('ETH/USDT', '30m')] + for inf_pair in informative_pairs: + assert inf_pair in strategy.gather_informative_pairs() + + def test_historic_ohlcv(pair, timeframe): + return data[(pair, timeframe or strategy.timeframe)].copy() + mocker.patch('freqtrade.data.dataprovider.DataProvider.historic_ohlcv', + side_effect=test_historic_ohlcv) + + analyzed = strategy.advise_all_indicators( + {p: data[(p, strategy.timeframe)] for p in ('XRP/USDT', 'LTC/USDT')}) + expected_columns = [ + 'rsi_1h', 'rsi_30m', # Stacked informative decorators + 'btc_usdt_rsi_1h', # BTC 1h informative + 'rsi_BTC_USDT_btc_usdt_BTC/USDT_30m', # Column formatting + 'rsi_from_callable', # Custom column formatter + 'eth_btc_rsi_1h', # Quote currency not matching stake currency + 'rsi', 'rsi_less', # Non-informative columns + 'rsi_5m', # Manual informative dataframe + ] + for _, dataframe in analyzed.items(): + for col in expected_columns: + assert col in dataframe.columns diff --git a/tests/strategy/test_strategy_loading.py b/tests/strategy/test_strategy_loading.py index 2cbc9d0c6..3a30a824a 100644 --- a/tests/strategy/test_strategy_loading.py +++ b/tests/strategy/test_strategy_loading.py @@ -35,7 +35,7 @@ def test_search_all_strategies_no_failed(): directory = Path(__file__).parent / "strats" strategies = StrategyResolver.search_all_objects(directory, enum_failed=False) assert isinstance(strategies, list) - assert len(strategies) == 3 + assert len(strategies) == 4 assert isinstance(strategies[0], dict) @@ -43,10 +43,10 @@ def test_search_all_strategies_with_failed(): directory = Path(__file__).parent / "strats" strategies = StrategyResolver.search_all_objects(directory, enum_failed=True) assert isinstance(strategies, list) - assert len(strategies) == 4 + assert len(strategies) == 5 # with enum_failed=True search_all_objects() shall find 2 good strategies # and 1 which fails to load - assert len([x for x in strategies if x['class'] is not None]) == 3 + assert len([x for x in strategies if x['class'] is not None]) == 4 assert len([x for x in strategies if x['class'] is None]) == 1