2018-01-28 05:26:57 +00:00
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"""
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|
IStrategy interface
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This module defines the interface to apply for strategies
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"""
|
2018-07-16 05:11:17 +00:00
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import logging
|
2018-07-22 15:39:35 +00:00
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from abc import ABC, abstractmethod
|
2018-07-16 05:11:17 +00:00
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from datetime import datetime
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from enum import Enum
|
2018-12-11 18:47:48 +00:00
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from typing import Dict, List, NamedTuple, Tuple
|
2018-06-15 16:59:34 +00:00
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import warnings
|
2018-03-17 21:44:47 +00:00
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2018-07-16 05:11:17 +00:00
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import arrow
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2018-01-15 08:35:11 +00:00
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from pandas import DataFrame
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2018-07-16 05:11:17 +00:00
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from freqtrade import constants
|
2018-12-26 13:32:17 +00:00
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from freqtrade.data.dataprovider import DataProvider
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2018-07-16 05:11:17 +00:00
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from freqtrade.persistence import Trade
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2018-12-26 13:32:17 +00:00
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from freqtrade.wallets import Wallets
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2018-07-16 05:11:17 +00:00
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logger = logging.getLogger(__name__)
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class SignalType(Enum):
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"""
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Enum to distinguish between buy and sell signals
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"""
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BUY = "buy"
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SELL = "sell"
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2018-01-15 08:35:11 +00:00
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2018-07-11 17:22:34 +00:00
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class SellType(Enum):
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"""
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Enum to distinguish between sell reasons
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"""
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ROI = "roi"
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STOP_LOSS = "stop_loss"
|
2018-11-26 17:28:13 +00:00
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STOPLOSS_ON_EXCHANGE = "stoploss_on_exchange"
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2018-07-11 17:22:34 +00:00
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TRAILING_STOP_LOSS = "trailing_stop_loss"
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SELL_SIGNAL = "sell_signal"
|
2018-07-11 17:57:01 +00:00
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FORCE_SELL = "force_sell"
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2018-07-11 17:59:30 +00:00
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NONE = ""
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2018-07-11 17:22:34 +00:00
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2018-07-12 20:21:52 +00:00
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class SellCheckTuple(NamedTuple):
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"""
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NamedTuple for Sell type + reason
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"""
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sell_flag: bool
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sell_type: SellType
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2018-01-15 08:35:11 +00:00
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class IStrategy(ABC):
|
2018-01-28 05:26:57 +00:00
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"""
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Interface for freqtrade strategies
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Defines the mandatory structure must follow any custom strategies
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Attributes you can use:
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minimal_roi -> Dict: Minimal ROI designed for the strategy
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stoploss -> float: optimal stoploss designed for the strategy
|
2018-05-31 19:59:22 +00:00
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ticker_interval -> str: value of the ticker interval to use for the strategy
|
2018-01-28 05:26:57 +00:00
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"""
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2018-01-15 08:35:11 +00:00
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2018-07-23 16:38:21 +00:00
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_populate_fun_len: int = 0
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_buy_fun_len: int = 0
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_sell_fun_len: int = 0
|
2018-06-15 03:27:41 +00:00
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# associated minimal roi
|
2018-05-31 19:59:22 +00:00
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minimal_roi: Dict
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2018-06-15 03:27:41 +00:00
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# associated stoploss
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2018-05-31 19:59:22 +00:00
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stoploss: float
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2018-06-15 03:27:41 +00:00
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2019-01-05 06:10:25 +00:00
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# trailing stoploss
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trailing_stop: bool = False
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trailing_stop_positive: float
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trailing_stop_positive_offset: float
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2018-06-15 03:27:41 +00:00
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# associated ticker interval
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2018-05-31 19:59:22 +00:00
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ticker_interval: str
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2018-11-15 05:58:24 +00:00
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# Optional order types
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order_types: Dict = {
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'buy': 'limit',
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'sell': 'limit',
|
2018-11-25 16:22:56 +00:00
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'stoploss': 'limit',
|
2019-01-08 11:39:53 +00:00
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'stoploss_on_exchange': False,
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'stoploss_on_exchange_interval': 60,
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2018-11-15 05:58:24 +00:00
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}
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2018-11-25 21:02:59 +00:00
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# Optional time in force
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order_time_in_force: Dict = {
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'buy': 'gtc',
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'sell': 'gtc',
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}
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2018-08-09 17:24:00 +00:00
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# run "populate_indicators" only for new candle
|
2018-09-01 17:52:40 +00:00
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process_only_new_candles: bool = False
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2018-08-09 17:24:00 +00:00
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|
2018-12-26 13:32:17 +00:00
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# Class level variables (intentional) containing
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# the dataprovider (dp) (access to other candles, historic data, ...)
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# and wallets - access to the current balance.
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dp: DataProvider
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wallets: Wallets
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|
2018-07-17 07:47:15 +00:00
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def __init__(self, config: dict) -> None:
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2018-07-16 05:11:17 +00:00
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self.config = config
|
2019-01-21 18:28:53 +00:00
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# Dict to determine if analysis is necessary
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self._last_candle_seen_per_pair: Dict[str, datetime] = {}
|
2018-07-16 05:11:17 +00:00
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|
2018-07-22 15:39:35 +00:00
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@abstractmethod
|
2018-07-29 18:36:03 +00:00
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def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
|
2018-01-15 08:35:11 +00:00
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"""
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Populate indicators that will be used in the Buy and Sell strategy
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:param dataframe: Raw data from the exchange and parsed by parse_ticker_dataframe()
|
2018-07-29 18:36:03 +00:00
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:param metadata: Additional information, like the currently traded pair
|
2018-01-15 08:35:11 +00:00
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:return: a Dataframe with all mandatory indicators for the strategies
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"""
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|
2018-07-22 15:39:35 +00:00
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@abstractmethod
|
2018-07-29 18:36:03 +00:00
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def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
|
2018-01-15 08:35:11 +00:00
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"""
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Based on TA indicators, populates the buy signal for the given dataframe
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:param dataframe: DataFrame
|
2018-07-29 18:36:03 +00:00
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:param metadata: Additional information, like the currently traded pair
|
2018-01-15 08:35:11 +00:00
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:return: DataFrame with buy column
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"""
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|
2018-07-22 15:39:35 +00:00
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@abstractmethod
|
2018-07-29 18:36:03 +00:00
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def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
|
2018-01-15 08:35:11 +00:00
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"""
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Based on TA indicators, populates the sell signal for the given dataframe
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:param dataframe: DataFrame
|
2018-07-29 18:36:03 +00:00
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:param metadata: Additional information, like the currently traded pair
|
2018-03-25 18:24:56 +00:00
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:return: DataFrame with sell column
|
2018-01-15 08:35:11 +00:00
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"""
|
2018-07-12 18:38:14 +00:00
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|
2019-01-26 18:22:45 +00:00
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def informative_pairs(self) -> List[Tuple[str, str]]:
|
2019-01-21 19:22:27 +00:00
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"""
|
2019-01-26 18:22:45 +00:00
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|
|
Define additional, informative pair/interval combinations to be cached from the exchange.
|
2019-01-21 19:22:27 +00:00
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These pair/interval combinations are non-tradeable, unless they are part
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of the whitelist as well.
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For more information, please consult the documentation
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:return: List of tuples in the format (pair, interval)
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Sample: return [("ETH/USDT", "5m"),
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("BTC/USDT", "15m"),
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]
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"""
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return []
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|
2018-07-12 18:38:14 +00:00
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|
|
def get_strategy_name(self) -> str:
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"""
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Returns strategy class name
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|
"""
|
2018-07-19 17:41:42 +00:00
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|
return self.__class__.__name__
|
2018-07-16 05:11:17 +00:00
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|
2018-12-11 18:47:48 +00:00
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def analyze_ticker(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
|
2018-07-16 05:11:17 +00:00
|
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"""
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|
Parses the given ticker history and returns a populated DataFrame
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|
add several TA indicators and buy signal to it
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:return DataFrame with ticker data and indicator data
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"""
|
2018-09-01 17:53:49 +00:00
|
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|
2018-08-09 11:02:41 +00:00
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pair = str(metadata.get('pair'))
|
2018-08-03 07:33:34 +00:00
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|
2018-09-01 17:53:49 +00:00
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# Test if seen this pair and last candle before.
|
2018-12-13 18:43:17 +00:00
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# always run if process_only_new_candles is set to false
|
2018-09-01 17:52:40 +00:00
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|
if (not self.process_only_new_candles or
|
2018-09-01 17:50:45 +00:00
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self._last_candle_seen_per_pair.get(pair, None) != dataframe.iloc[-1]['date']):
|
2018-08-03 07:33:34 +00:00
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# Defs that only make change on new candle data.
|
2019-02-10 18:02:53 +00:00
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logger.debug("TA Analysis Launched")
|
2018-08-03 07:33:34 +00:00
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dataframe = self.advise_indicators(dataframe, metadata)
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dataframe = self.advise_buy(dataframe, metadata)
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dataframe = self.advise_sell(dataframe, metadata)
|
2018-09-01 17:50:45 +00:00
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self._last_candle_seen_per_pair[pair] = dataframe.iloc[-1]['date']
|
2018-08-03 07:33:34 +00:00
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else:
|
2019-02-13 09:42:39 +00:00
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|
logger.debug("Skipping TA Analysis for already analyzed candle")
|
2018-08-09 17:53:47 +00:00
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dataframe['buy'] = 0
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dataframe['sell'] = 0
|
2018-08-03 07:33:34 +00:00
|
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|
# Other Defs in strategy that want to be called every loop here
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|
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# twitter_sell = self.watch_twitter_feed(dataframe, metadata)
|
2019-02-10 18:02:53 +00:00
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logger.debug("Loop Analysis Launched")
|
2018-08-03 07:33:34 +00:00
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|
2018-07-16 05:11:17 +00:00
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return dataframe
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|
2018-08-02 18:11:27 +00:00
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def get_signal(self, pair: str, interval: str,
|
2018-12-11 18:47:48 +00:00
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dataframe: DataFrame) -> Tuple[bool, bool]:
|
2018-07-16 05:11:17 +00:00
|
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|
"""
|
|
|
|
Calculates current signal based several technical analysis indicators
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|
|
:param pair: pair in format ANT/BTC
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:param interval: Interval to use (in min)
|
2018-12-11 18:47:48 +00:00
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:param dataframe: Dataframe to analyze
|
2018-07-16 05:11:17 +00:00
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:return: (Buy, Sell) A bool-tuple indicating buy/sell signal
|
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|
|
"""
|
2018-12-12 18:35:51 +00:00
|
|
|
if not isinstance(dataframe, DataFrame) or dataframe.empty:
|
2018-07-16 05:11:17 +00:00
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|
logger.warning('Empty ticker history for pair %s', pair)
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return False, False
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try:
|
2018-12-11 18:47:48 +00:00
|
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dataframe = self.analyze_ticker(dataframe, {'pair': pair})
|
2018-07-16 05:11:17 +00:00
|
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except ValueError as error:
|
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|
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logger.warning(
|
|
|
|
'Unable to analyze ticker for pair %s: %s',
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|
pair,
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str(error)
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)
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return False, False
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|
|
except Exception as error:
|
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|
|
logger.exception(
|
|
|
|
'Unexpected error when analyzing ticker for pair %s: %s',
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pair,
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str(error)
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)
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return False, False
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|
|
if dataframe.empty:
|
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|
|
logger.warning('Empty dataframe for pair %s', pair)
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|
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return False, False
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|
|
latest = dataframe.iloc[-1]
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|
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# Check if dataframe is out of date
|
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|
|
signal_date = arrow.get(latest['date'])
|
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|
|
interval_minutes = constants.TICKER_INTERVAL_MINUTES[interval]
|
2018-08-20 18:01:57 +00:00
|
|
|
offset = self.config.get('exchange', {}).get('outdated_offset', 5)
|
2018-08-07 07:25:21 +00:00
|
|
|
if signal_date < (arrow.utcnow().shift(minutes=-(interval_minutes * 2 + offset))):
|
2018-07-16 05:11:17 +00:00
|
|
|
logger.warning(
|
|
|
|
'Outdated history for pair %s. Last tick is %s minutes old',
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|
pair,
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(arrow.utcnow() - signal_date).seconds // 60
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)
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|
return False, False
|
|
|
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|
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|
|
(buy, sell) = latest[SignalType.BUY.value] == 1, latest[SignalType.SELL.value] == 1
|
|
|
|
logger.debug(
|
|
|
|
'trigger: %s (pair=%s) buy=%s sell=%s',
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|
|
|
latest['date'],
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|
|
|
pair,
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str(buy),
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|
|
str(sell)
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|
)
|
|
|
|
return buy, sell
|
|
|
|
|
2018-07-11 17:57:01 +00:00
|
|
|
def should_sell(self, trade: Trade, rate: float, date: datetime, buy: bool,
|
2018-11-07 17:15:04 +00:00
|
|
|
sell: bool, low: float = None, high: float = None,
|
|
|
|
force_stoploss: float = 0) -> SellCheckTuple:
|
2018-07-16 05:11:17 +00:00
|
|
|
"""
|
|
|
|
This function evaluate if on the condition required to trigger a sell has been reached
|
|
|
|
if the threshold is reached and updates the trade record.
|
|
|
|
:return: True if trade should be sold, False otherwise
|
|
|
|
"""
|
2018-09-26 14:36:41 +00:00
|
|
|
|
2018-08-16 09:31:41 +00:00
|
|
|
# Set current rate to low for backtesting sell
|
2018-10-30 19:23:31 +00:00
|
|
|
current_rate = low or rate
|
2018-08-16 09:31:41 +00:00
|
|
|
current_profit = trade.calc_profit_percent(current_rate)
|
2018-11-19 19:02:26 +00:00
|
|
|
|
2019-01-16 13:49:47 +00:00
|
|
|
stoplossflag = self.stop_loss_reached(current_rate=current_rate, trade=trade,
|
|
|
|
current_time=date, current_profit=current_profit,
|
|
|
|
force_stoploss=force_stoploss)
|
2018-11-22 15:24:40 +00:00
|
|
|
|
2018-07-12 20:21:52 +00:00
|
|
|
if stoplossflag.sell_flag:
|
|
|
|
return stoplossflag
|
2018-11-22 15:24:40 +00:00
|
|
|
|
2018-08-16 09:31:41 +00:00
|
|
|
# Set current rate to low for backtesting sell
|
2018-10-30 19:23:31 +00:00
|
|
|
current_rate = high or rate
|
2018-08-16 09:31:41 +00:00
|
|
|
current_profit = trade.calc_profit_percent(current_rate)
|
2018-07-16 05:11:17 +00:00
|
|
|
experimental = self.config.get('experimental', {})
|
|
|
|
|
|
|
|
if buy and experimental.get('ignore_roi_if_buy_signal', False):
|
|
|
|
logger.debug('Buy signal still active - not selling.')
|
2018-07-12 20:21:52 +00:00
|
|
|
return SellCheckTuple(sell_flag=False, sell_type=SellType.NONE)
|
2018-07-16 05:11:17 +00:00
|
|
|
|
|
|
|
# Check if minimal roi has been reached and no longer in buy conditions (avoiding a fee)
|
|
|
|
if self.min_roi_reached(trade=trade, current_profit=current_profit, current_time=date):
|
|
|
|
logger.debug('Required profit reached. Selling..')
|
2018-07-12 20:21:52 +00:00
|
|
|
return SellCheckTuple(sell_flag=True, sell_type=SellType.ROI)
|
2018-07-16 05:11:17 +00:00
|
|
|
|
|
|
|
if experimental.get('sell_profit_only', False):
|
|
|
|
logger.debug('Checking if trade is profitable..')
|
|
|
|
if trade.calc_profit(rate=rate) <= 0:
|
2018-07-12 20:21:52 +00:00
|
|
|
return SellCheckTuple(sell_flag=False, sell_type=SellType.NONE)
|
2018-07-16 05:11:17 +00:00
|
|
|
if sell and not buy and experimental.get('use_sell_signal', False):
|
|
|
|
logger.debug('Sell signal received. Selling..')
|
2018-07-12 20:21:52 +00:00
|
|
|
return SellCheckTuple(sell_flag=True, sell_type=SellType.SELL_SIGNAL)
|
2018-07-16 05:11:17 +00:00
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|
2018-07-12 20:21:52 +00:00
|
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return SellCheckTuple(sell_flag=False, sell_type=SellType.NONE)
|
2018-07-16 05:11:17 +00:00
|
|
|
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|
|
def stop_loss_reached(self, current_rate: float, trade: Trade, current_time: datetime,
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2018-09-21 15:41:31 +00:00
|
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|
current_profit: float, force_stoploss: float) -> SellCheckTuple:
|
2018-07-16 05:11:17 +00:00
|
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|
"""
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|
|
|
Based on current profit of the trade and configured (trailing) stoploss,
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|
|
decides to sell or not
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2018-07-16 19:23:35 +00:00
|
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|
:param current_profit: current profit in percent
|
2018-07-16 05:11:17 +00:00
|
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|
"""
|
|
|
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|
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|
trailing_stop = self.config.get('trailing_stop', False)
|
2018-10-01 15:49:27 +00:00
|
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|
trade.adjust_stop_loss(trade.open_rate, force_stoploss if force_stoploss
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|
|
|
else self.stoploss, initial=True)
|
2018-07-16 05:11:17 +00:00
|
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|
|
2019-01-16 13:49:47 +00:00
|
|
|
# evaluate if the stoploss was hit if stoploss is not on exchange
|
2019-01-18 11:07:51 +00:00
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|
if ((self.stoploss is not None) and
|
|
|
|
(trade.stop_loss >= current_rate) and
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|
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(not self.order_types.get('stoploss_on_exchange'))):
|
2018-07-11 17:57:01 +00:00
|
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|
selltype = SellType.STOP_LOSS
|
2019-01-02 13:44:17 +00:00
|
|
|
# If Trailing stop (and max-rate did move above open rate)
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2019-01-02 12:34:08 +00:00
|
|
|
if trailing_stop and trade.open_rate != trade.max_rate:
|
2018-07-11 17:57:01 +00:00
|
|
|
selltype = SellType.TRAILING_STOP_LOSS
|
2018-07-16 05:11:17 +00:00
|
|
|
logger.debug(
|
|
|
|
f"HIT STOP: current price at {current_rate:.6f}, "
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|
|
|
f"stop loss is {trade.stop_loss:.6f}, "
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|
|
f"initial stop loss was at {trade.initial_stop_loss:.6f}, "
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|
|
f"trade opened at {trade.open_rate:.6f}")
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|
|
|
logger.debug(f"trailing stop saved {trade.stop_loss - trade.initial_stop_loss:.6f}")
|
|
|
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|
|
|
|
logger.debug('Stop loss hit.')
|
2018-07-12 20:21:52 +00:00
|
|
|
return SellCheckTuple(sell_flag=True, sell_type=selltype)
|
2018-07-16 05:11:17 +00:00
|
|
|
|
|
|
|
# update the stop loss afterwards, after all by definition it's supposed to be hanging
|
|
|
|
if trailing_stop:
|
|
|
|
|
|
|
|
# check if we have a special stop loss for positive condition
|
|
|
|
# and if profit is positive
|
2019-01-16 17:38:20 +00:00
|
|
|
stop_loss_value = force_stoploss if force_stoploss else self.stoploss
|
|
|
|
|
2019-01-13 13:13:41 +00:00
|
|
|
sl_offset = self.config.get('trailing_stop_positive_offset') or 0.0
|
2018-07-16 19:23:35 +00:00
|
|
|
|
|
|
|
if 'trailing_stop_positive' in self.config and current_profit > sl_offset:
|
2018-07-16 05:11:17 +00:00
|
|
|
|
|
|
|
# Ignore mypy error check in configuration that this is a float
|
|
|
|
stop_loss_value = self.config.get('trailing_stop_positive') # type: ignore
|
|
|
|
logger.debug(f"using positive stop loss mode: {stop_loss_value} "
|
2018-07-16 19:23:35 +00:00
|
|
|
f"with offset {sl_offset:.4g} "
|
|
|
|
f"since we have profit {current_profit:.4f}%")
|
2018-07-16 05:11:17 +00:00
|
|
|
|
2019-03-09 19:13:35 +00:00
|
|
|
# if trailing_only_offset_is_reached is true,
|
|
|
|
# we update trailing stoploss only if offset is reached.
|
|
|
|
tsl_only_offset = self.config.get('trailing_only_offset_is_reached', False)
|
2019-03-09 19:30:56 +00:00
|
|
|
if not (tsl_only_offset and current_profit < sl_offset):
|
2019-03-09 19:13:35 +00:00
|
|
|
trade.adjust_stop_loss(current_rate, stop_loss_value)
|
2018-07-16 05:11:17 +00:00
|
|
|
|
2018-07-12 20:21:52 +00:00
|
|
|
return SellCheckTuple(sell_flag=False, sell_type=SellType.NONE)
|
2018-07-16 05:11:17 +00:00
|
|
|
|
|
|
|
def min_roi_reached(self, trade: Trade, current_profit: float, current_time: datetime) -> bool:
|
|
|
|
"""
|
|
|
|
Based an earlier trade and current price and ROI configuration, decides whether bot should
|
2019-01-12 12:44:50 +00:00
|
|
|
sell. Requires current_profit to be in percent!!
|
2018-07-16 05:11:17 +00:00
|
|
|
:return True if bot should sell at current rate
|
|
|
|
"""
|
|
|
|
|
|
|
|
# Check if time matches and current rate is above threshold
|
2019-01-12 12:44:50 +00:00
|
|
|
trade_dur = (current_time.timestamp() - trade.open_date.timestamp()) / 60
|
|
|
|
|
|
|
|
# Get highest entry in ROI dict where key >= trade-duration
|
|
|
|
roi_entry = max(list(filter(lambda x: trade_dur >= x, self.minimal_roi.keys())))
|
|
|
|
threshold = self.minimal_roi[roi_entry]
|
|
|
|
if current_profit > threshold:
|
|
|
|
return True
|
2018-07-16 05:11:17 +00:00
|
|
|
|
|
|
|
return False
|
|
|
|
|
|
|
|
def tickerdata_to_dataframe(self, tickerdata: Dict[str, List]) -> Dict[str, DataFrame]:
|
|
|
|
"""
|
|
|
|
Creates a dataframe and populates indicators for given ticker data
|
|
|
|
"""
|
2018-12-15 13:28:37 +00:00
|
|
|
return {pair: self.advise_indicators(pair_data, {'pair': pair})
|
2018-07-16 05:11:17 +00:00
|
|
|
for pair, pair_data in tickerdata.items()}
|
2018-06-15 03:27:41 +00:00
|
|
|
|
2018-07-29 18:36:03 +00:00
|
|
|
def advise_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
|
2018-06-15 03:27:41 +00:00
|
|
|
"""
|
|
|
|
Populate indicators that will be used in the Buy and Sell strategy
|
2018-07-22 15:39:35 +00:00
|
|
|
This method should not be overridden.
|
2018-06-15 03:27:41 +00:00
|
|
|
:param dataframe: Raw data from the exchange and parsed by parse_ticker_dataframe()
|
2018-07-29 18:36:03 +00:00
|
|
|
:param metadata: Additional information, like the currently traded pair
|
2018-06-15 03:27:41 +00:00
|
|
|
:return: a Dataframe with all mandatory indicators for the strategies
|
|
|
|
"""
|
2018-07-23 16:38:21 +00:00
|
|
|
if self._populate_fun_len == 2:
|
2018-07-22 15:39:35 +00:00
|
|
|
warnings.warn("deprecated - check out the Sample strategy to see "
|
|
|
|
"the current function headers!", DeprecationWarning)
|
|
|
|
return self.populate_indicators(dataframe) # type: ignore
|
|
|
|
else:
|
2018-07-29 18:36:03 +00:00
|
|
|
return self.populate_indicators(dataframe, metadata)
|
2018-06-15 03:27:41 +00:00
|
|
|
|
2018-07-29 18:36:03 +00:00
|
|
|
def advise_buy(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
|
2018-06-15 03:27:41 +00:00
|
|
|
"""
|
|
|
|
Based on TA indicators, populates the buy signal for the given dataframe
|
2018-07-22 15:39:35 +00:00
|
|
|
This method should not be overridden.
|
2018-06-15 03:27:41 +00:00
|
|
|
:param dataframe: DataFrame
|
2018-07-29 18:36:03 +00:00
|
|
|
:param pair: Additional information, like the currently traded pair
|
2018-06-15 03:27:41 +00:00
|
|
|
:return: DataFrame with buy column
|
|
|
|
"""
|
2018-07-23 16:38:21 +00:00
|
|
|
if self._buy_fun_len == 2:
|
2018-07-22 15:39:35 +00:00
|
|
|
warnings.warn("deprecated - check out the Sample strategy to see "
|
|
|
|
"the current function headers!", DeprecationWarning)
|
|
|
|
return self.populate_buy_trend(dataframe) # type: ignore
|
|
|
|
else:
|
2018-07-29 18:36:03 +00:00
|
|
|
return self.populate_buy_trend(dataframe, metadata)
|
2018-06-15 03:27:41 +00:00
|
|
|
|
2018-07-29 18:36:03 +00:00
|
|
|
def advise_sell(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
|
2018-06-15 03:27:41 +00:00
|
|
|
"""
|
|
|
|
Based on TA indicators, populates the sell signal for the given dataframe
|
2018-07-22 15:39:35 +00:00
|
|
|
This method should not be overridden.
|
2018-06-15 03:27:41 +00:00
|
|
|
:param dataframe: DataFrame
|
2018-07-29 18:36:03 +00:00
|
|
|
:param pair: Additional information, like the currently traded pair
|
2018-06-15 03:27:41 +00:00
|
|
|
:return: DataFrame with sell column
|
|
|
|
"""
|
2018-07-23 16:38:21 +00:00
|
|
|
if self._sell_fun_len == 2:
|
2018-07-22 15:39:35 +00:00
|
|
|
warnings.warn("deprecated - check out the Sample strategy to see "
|
|
|
|
"the current function headers!", DeprecationWarning)
|
|
|
|
return self.populate_sell_trend(dataframe) # type: ignore
|
|
|
|
else:
|
2018-07-29 18:36:03 +00:00
|
|
|
return self.populate_sell_trend(dataframe, metadata)
|