stable/freqtrade/strategy/interface.py
2020-01-21 06:58:48 +01:00

497 lines
21 KiB
Python

"""
IStrategy interface
This module defines the interface to apply for strategies
"""
import logging
from abc import ABC, abstractmethod
from datetime import datetime, timezone
from enum import Enum
from typing import Dict, List, NamedTuple, Optional, Tuple
import warnings
import arrow
from pandas import DataFrame
from freqtrade.data.dataprovider import DataProvider
from freqtrade.exchange import timeframe_to_minutes
from freqtrade.persistence import Trade
from freqtrade.wallets import Wallets
logger = logging.getLogger(__name__)
class SignalType(Enum):
"""
Enum to distinguish between buy and sell signals
"""
BUY = "buy"
SELL = "sell"
class SellType(Enum):
"""
Enum to distinguish between sell reasons
"""
ROI = "roi"
STOP_LOSS = "stop_loss"
STOPLOSS_ON_EXCHANGE = "stoploss_on_exchange"
TRAILING_STOP_LOSS = "trailing_stop_loss"
SELL_SIGNAL = "sell_signal"
FORCE_SELL = "force_sell"
EMERGENCY_SELL = "emergency_sell"
NONE = ""
class SellCheckTuple(NamedTuple):
"""
NamedTuple for Sell type + reason
"""
sell_flag: bool
sell_type: SellType
class IStrategy(ABC):
"""
Interface for freqtrade strategies
Defines the mandatory structure must follow any custom strategies
Attributes you can use:
minimal_roi -> Dict: Minimal ROI designed for the strategy
stoploss -> float: optimal stoploss designed for the strategy
ticker_interval -> str: value of the ticker interval to use for the strategy
"""
# Strategy interface version
# Default to version 2
# Version 1 is the initial interface without metadata dict
# Version 2 populate_* include metadata dict
INTERFACE_VERSION: int = 2
_populate_fun_len: int = 0
_buy_fun_len: int = 0
_sell_fun_len: int = 0
# associated minimal roi
minimal_roi: Dict
# associated stoploss
stoploss: float
# trailing stoploss
trailing_stop: bool = False
trailing_stop_positive: Optional[float] = None
trailing_stop_positive_offset: float = 0.0
trailing_only_offset_is_reached = False
# associated ticker interval
ticker_interval: str
# Optional order types
order_types: Dict = {
'buy': 'limit',
'sell': 'limit',
'stoploss': 'limit',
'stoploss_on_exchange': False,
'stoploss_on_exchange_interval': 60,
}
# Optional time in force
order_time_in_force: Dict = {
'buy': 'gtc',
'sell': 'gtc',
}
# run "populate_indicators" only for new candle
process_only_new_candles: bool = False
# Count of candles the strategy requires before producing valid signals
startup_candle_count: int = 0
# 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
wallets: Optional[Wallets] = None
# Definition of plot_config. See plotting documentation for more details.
plot_config: Dict = {}
def __init__(self, config: dict) -> None:
self.config = config
# Dict to determine if analysis is necessary
self._last_candle_seen_per_pair: Dict[str, datetime] = {}
self._pair_locked_until: Dict[str, datetime] = {}
@abstractmethod
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
"""
Populate indicators that will be used in the Buy and Sell strategy
:param dataframe: Raw data from the exchange and parsed by parse_ticker_dataframe()
:param metadata: Additional information, like the currently traded pair
:return: a Dataframe with all mandatory indicators for the strategies
"""
@abstractmethod
def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
"""
Based on TA indicators, populates the buy signal for the given dataframe
:param dataframe: DataFrame
:param metadata: Additional information, like the currently traded pair
:return: DataFrame with buy column
"""
@abstractmethod
def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
"""
Based on TA indicators, populates the sell signal for the given dataframe
:param dataframe: DataFrame
:param metadata: Additional information, like the currently traded pair
:return: DataFrame with sell column
"""
def informative_pairs(self) -> List[Tuple[str, str]]:
"""
Define additional, informative pair/interval combinations to be cached from the exchange.
These pair/interval combinations are non-tradeable, unless they are part
of the whitelist as well.
For more information, please consult the documentation
:return: List of tuples in the format (pair, interval)
Sample: return [("ETH/USDT", "5m"),
("BTC/USDT", "15m"),
]
"""
return []
def get_strategy_name(self) -> str:
"""
Returns strategy class name
"""
return self.__class__.__name__
def lock_pair(self, pair: str, until: datetime) -> None:
"""
Locks pair until a given timestamp happens.
Locked pairs are not analyzed, and are prevented from opening new trades.
Locks can only count up (allowing users to lock pairs for a longer period of time).
To remove a lock from a pair, use `unlock_pair()`
:param pair: Pair to lock
:param until: datetime in UTC until the pair should be blocked from opening new trades.
Needs to be timezone aware `datetime.now(timezone.utc)`
"""
if pair not in self._pair_locked_until or self._pair_locked_until[pair] < until:
self._pair_locked_until[pair] = until
def unlock_pair(self, pair) -> None:
"""
Unlocks a pair previously locked using lock_pair.
Not used by freqtrade itself, but intended to be used if users lock pairs
manually from within the strategy, to allow an easy way to unlock pairs.
:param pair: Unlock pair to allow trading again
"""
if pair in self._pair_locked_until:
del self._pair_locked_until[pair]
def is_pair_locked(self, pair: str) -> bool:
"""
Checks if a pair is currently locked
"""
if pair not in self._pair_locked_until:
return False
return self._pair_locked_until[pair] >= datetime.now(timezone.utc)
def analyze_ticker(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
"""
Parses the given ticker history and returns a populated DataFrame
add several TA indicators and buy signal to it
:param dataframe: Dataframe containing ticker data
:param metadata: Metadata dictionary with additional data (e.g. 'pair')
:return: DataFrame with ticker data and indicator data
"""
logger.debug("TA Analysis Launched")
dataframe = self.advise_indicators(dataframe, metadata)
dataframe = self.advise_buy(dataframe, metadata)
dataframe = self.advise_sell(dataframe, metadata)
return dataframe
def _analyze_ticker_internal(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
"""
Parses the given ticker history and returns a populated DataFrame
add several TA indicators and buy signal to it
WARNING: Used internally only, may skip analysis if `process_only_new_candles` is set.
:param dataframe: Dataframe containing ticker data
:param metadata: Metadata dictionary with additional data (e.g. 'pair')
:return: DataFrame with ticker data and indicator data
"""
pair = str(metadata.get('pair'))
# Test if seen this pair and last candle before.
# always run if process_only_new_candles is set to false
if (not self.process_only_new_candles or
self._last_candle_seen_per_pair.get(pair, None) != dataframe.iloc[-1]['date']):
# Defs that only make change on new candle data.
dataframe = self.analyze_ticker(dataframe, metadata)
self._last_candle_seen_per_pair[pair] = dataframe.iloc[-1]['date']
else:
logger.debug("Skipping TA Analysis for already analyzed candle")
dataframe['buy'] = 0
dataframe['sell'] = 0
# Other Defs in strategy that want to be called every loop here
# twitter_sell = self.watch_twitter_feed(dataframe, metadata)
logger.debug("Loop Analysis Launched")
return dataframe
def get_signal(self, pair: str, interval: str,
dataframe: DataFrame) -> Tuple[bool, bool]:
"""
Calculates current signal based several technical analysis indicators
:param pair: pair in format ANT/BTC
:param interval: Interval to use (in min)
:param dataframe: Dataframe to analyze
:return: (Buy, Sell) A bool-tuple indicating buy/sell signal
"""
if not isinstance(dataframe, DataFrame) or dataframe.empty:
logger.warning('Empty ticker history for pair %s', pair)
return False, False
try:
dataframe = self._analyze_ticker_internal(dataframe, {'pair': pair})
except ValueError as error:
logger.warning(
'Unable to analyze ticker for pair %s: %s',
pair,
str(error)
)
return False, False
except Exception as error:
logger.exception(
'Unexpected error when analyzing ticker for pair %s: %s',
pair,
str(error)
)
return False, False
if dataframe.empty:
logger.warning('Empty dataframe for pair %s', pair)
return False, False
latest = dataframe.iloc[-1]
# Check if dataframe is out of date
signal_date = arrow.get(latest['date'])
interval_minutes = timeframe_to_minutes(interval)
offset = self.config.get('exchange', {}).get('outdated_offset', 5)
if signal_date < (arrow.utcnow().shift(minutes=-(interval_minutes * 2 + offset))):
logger.warning(
'Outdated history for pair %s. Last tick is %s minutes old',
pair,
(arrow.utcnow() - signal_date).seconds // 60
)
return False, False
(buy, sell) = latest[SignalType.BUY.value] == 1, latest[SignalType.SELL.value] == 1
logger.debug(
'trigger: %s (pair=%s) buy=%s sell=%s',
latest['date'],
pair,
str(buy),
str(sell)
)
return buy, sell
def should_sell(self, trade: Trade, rate: float, date: datetime, buy: bool,
sell: bool, low: float = None, high: float = None,
force_stoploss: float = 0) -> SellCheckTuple:
"""
This function evaluates if one of the conditions required to trigger a sell
has been reached, which can either be a stop-loss, ROI or sell-signal.
:param low: Only used during backtesting to simulate stoploss
:param high: Only used during backtesting, to simulate ROI
:param force_stoploss: Externally provided stoploss
:return: True if trade should be sold, False otherwise
"""
# Set current rate to low for backtesting sell
current_rate = low or rate
current_profit = trade.calc_profit_ratio(current_rate)
trade.adjust_min_max_rates(high or current_rate)
stoplossflag = self.stop_loss_reached(current_rate=current_rate, trade=trade,
current_time=date, current_profit=current_profit,
force_stoploss=force_stoploss, high=high)
if stoplossflag.sell_flag:
logger.debug(f"{trade.pair} - Stoploss hit. sell_flag=True, "
f"sell_type={stoplossflag.sell_type}")
return stoplossflag
# Set current rate to high for backtesting sell
current_rate = high or rate
current_profit = trade.calc_profit_ratio(current_rate)
config_ask_strategy = self.config.get('ask_strategy', {})
if buy and config_ask_strategy.get('ignore_roi_if_buy_signal', False):
# This one is noisy, commented out
# logger.debug(f"{trade.pair} - Buy signal still active. sell_flag=False")
return SellCheckTuple(sell_flag=False, sell_type=SellType.NONE)
# 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(f"{trade.pair} - Required profit reached. sell_flag=True, "
f"sell_type=SellType.ROI")
return SellCheckTuple(sell_flag=True, sell_type=SellType.ROI)
if config_ask_strategy.get('sell_profit_only', False):
# This one is noisy, commented out
# logger.debug(f"{trade.pair} - Checking if trade is profitable...")
if trade.calc_profit(rate=rate) <= 0:
# This one is noisy, commented out
# logger.debug(f"{trade.pair} - Trade is not profitable. sell_flag=False")
return SellCheckTuple(sell_flag=False, sell_type=SellType.NONE)
if sell and not buy and config_ask_strategy.get('use_sell_signal', True):
logger.debug(f"{trade.pair} - Sell signal received. sell_flag=True, "
f"sell_type=SellType.SELL_SIGNAL")
return SellCheckTuple(sell_flag=True, sell_type=SellType.SELL_SIGNAL)
# This one is noisy, commented out...
# logger.debug(f"{trade.pair} - No sell signal. sell_flag=False")
return SellCheckTuple(sell_flag=False, sell_type=SellType.NONE)
def stop_loss_reached(self, current_rate: float, trade: Trade,
current_time: datetime, current_profit: float,
force_stoploss: float, high: float = None) -> SellCheckTuple:
"""
Based on current profit of the trade and configured (trailing) stoploss,
decides to sell or not
:param current_profit: current profit in percent
"""
stop_loss_value = force_stoploss if force_stoploss else self.stoploss
# Initiate stoploss with open_rate. Does nothing if stoploss is already set.
trade.adjust_stop_loss(trade.open_rate, stop_loss_value, initial=True)
if self.trailing_stop:
# trailing stoploss handling
sl_offset = self.trailing_stop_positive_offset
# Make sure current_profit is calculated using high for backtesting.
high_profit = current_profit if not high else trade.calc_profit_ratio(high)
# Don't update stoploss if trailing_only_offset_is_reached is true.
if not (self.trailing_only_offset_is_reached and high_profit < sl_offset):
# Specific handling for trailing_stop_positive
if self.trailing_stop_positive is not None and high_profit > sl_offset:
stop_loss_value = self.trailing_stop_positive
logger.debug(f"{trade.pair} - Using positive stoploss: {stop_loss_value} "
f"offset: {sl_offset:.4g} profit: {current_profit:.4f}%")
trade.adjust_stop_loss(high or current_rate, stop_loss_value)
# evaluate if the stoploss was hit if stoploss is not on exchange
# in Dry-Run, this handles stoploss logic as well, as the logic will not be different to
# regular stoploss handling.
if ((self.stoploss is not None) and
(trade.stop_loss >= current_rate) and
(not self.order_types.get('stoploss_on_exchange') or self.config['dry_run'])):
sell_type = SellType.STOP_LOSS
# If initial stoploss is not the same as current one then it is trailing.
if trade.initial_stop_loss != trade.stop_loss:
sell_type = SellType.TRAILING_STOP_LOSS
logger.debug(
f"{trade.pair} - HIT STOP: current price at {current_rate:.6f}, "
f"stoploss is {trade.stop_loss:.6f}, "
f"initial stoploss was at {trade.initial_stop_loss:.6f}, "
f"trade opened at {trade.open_rate:.6f}")
logger.debug(f"{trade.pair} - Trailing stop saved "
f"{trade.stop_loss - trade.initial_stop_loss:.6f}")
return SellCheckTuple(sell_flag=True, sell_type=sell_type)
return SellCheckTuple(sell_flag=False, sell_type=SellType.NONE)
def min_roi_reached_entry(self, trade_dur: int) -> Tuple[Optional[int], Optional[float]]:
"""
Based on trade duration defines the ROI entry that may have been reached.
:param trade_dur: trade duration in minutes
:return: minimal ROI entry value or None if none proper ROI entry was found.
"""
# Get highest entry in ROI dict where key <= trade-duration
roi_list = list(filter(lambda x: x <= trade_dur, self.minimal_roi.keys()))
if not roi_list:
return None, None
roi_entry = max(roi_list)
return roi_entry, self.minimal_roi[roi_entry]
def min_roi_reached(self, trade: Trade, current_profit: float, current_time: datetime) -> bool:
"""
Based on trade duration, current price and ROI configuration, decides whether bot should
sell. Requires current_profit to be in percent!!
:return: True if bot should sell at current rate
"""
# Check if time matches and current rate is above threshold
trade_dur = int((current_time.timestamp() - trade.open_date.timestamp()) // 60)
_, roi = self.min_roi_reached_entry(trade_dur)
if roi is None:
return False
else:
return current_profit > roi
def tickerdata_to_dataframe(self, tickerdata: Dict[str, DataFrame]) -> Dict[str, DataFrame]:
"""
Creates a dataframe and populates indicators for given ticker data
Used by optimize operations only, not during dry / live runs.
"""
return {pair: self.advise_indicators(pair_data, {'pair': pair})
for pair, pair_data in tickerdata.items()}
def advise_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
"""
Populate indicators that will be used in the Buy and Sell strategy
This method should not be overridden.
:param dataframe: Raw data from the exchange and parsed by parse_ticker_dataframe()
:param metadata: Additional information, like the currently traded pair
:return: a Dataframe with all mandatory indicators for the strategies
"""
logger.debug(f"Populating indicators for pair {metadata.get('pair')}.")
if self._populate_fun_len == 2:
warnings.warn("deprecated - check out the Sample strategy to see "
"the current function headers!", DeprecationWarning)
return self.populate_indicators(dataframe) # type: ignore
else:
return self.populate_indicators(dataframe, metadata)
def advise_buy(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
"""
Based on TA indicators, populates the buy signal for the given dataframe
This method should not be overridden.
:param dataframe: DataFrame
:param pair: Additional information, like the currently traded pair
:return: DataFrame with buy column
"""
logger.debug(f"Populating buy signals for pair {metadata.get('pair')}.")
if self._buy_fun_len == 2:
warnings.warn("deprecated - check out the Sample strategy to see "
"the current function headers!", DeprecationWarning)
return self.populate_buy_trend(dataframe) # type: ignore
else:
return self.populate_buy_trend(dataframe, metadata)
def advise_sell(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
"""
Based on TA indicators, populates the sell signal for the given dataframe
This method should not be overridden.
:param dataframe: DataFrame
:param pair: Additional information, like the currently traded pair
:return: DataFrame with sell column
"""
logger.debug(f"Populating sell signals for pair {metadata.get('pair')}.")
if self._sell_fun_len == 2:
warnings.warn("deprecated - check out the Sample strategy to see "
"the current function headers!", DeprecationWarning)
return self.populate_sell_trend(dataframe) # type: ignore
else:
return self.populate_sell_trend(dataframe, metadata)