stable/freqtrade/strategy/resolver.py

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# pragma pylint: disable=attribute-defined-outside-init
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"""
This module load custom strategies
"""
import importlib.util
import inspect
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import os
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from collections import OrderedDict
from typing import Optional, Dict, Type
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from pandas import DataFrame
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from freqtrade.constants import Constants
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from freqtrade.logger import Logger
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from freqtrade.strategy.interface import IStrategy
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class StrategyResolver(object):
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"""
This class contains all the logic to load custom strategy class
"""
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def __init__(self, config: Optional[Dict] = None) -> None:
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"""
Load the custom class from config parameter
:param config:
:return:
"""
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config = config or {}
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self.logger = Logger(name=__name__).get_logger()
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# Verify the strategy is in the configuration, otherwise fallback to the default strategy
if 'strategy' in config:
strategy = config['strategy']
else:
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strategy = Constants.DEFAULT_STRATEGY
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# Try to load the strategy
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self._load_strategy(strategy)
# Set attributes
# Check if we need to override configuration
if 'minimal_roi' in config:
self.custom_strategy.minimal_roi = config['minimal_roi']
self.logger.info("Override strategy \'minimal_roi\' with value in config file.")
if 'stoploss' in config:
self.custom_strategy.stoploss = config['stoploss']
self.logger.info(
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"Override strategy \'stoploss\' with value in config file: %s.", config['stoploss']
)
if 'ticker_interval' in config:
self.custom_strategy.ticker_interval = config['ticker_interval']
self.logger.info(
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"Override strategy \'ticker_interval\' with value in config file: %s.",
config['ticker_interval']
)
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# Minimal ROI designed for the strategy
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self.minimal_roi = OrderedDict(sorted(
{int(key): value for (key, value) in self.custom_strategy.minimal_roi.items()}.items(),
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key=lambda t: t[0])) # sort after converting to number
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# Optimal stoploss designed for the strategy
self.stoploss = float(self.custom_strategy.stoploss)
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self.ticker_interval = int(self.custom_strategy.ticker_interval)
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def _load_strategy(self, strategy_name: str) -> None:
"""
Search and loads the specified strategy.
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:param strategy_name: name of the module to import
:return: None
"""
try:
current_path = os.path.dirname(os.path.realpath(__file__))
abs_paths = [
os.path.join(current_path, '..', '..', 'user_data', 'strategies'),
current_path,
]
for path in abs_paths:
self.custom_strategy = self._search_strategy(path, strategy_name)
if self.custom_strategy:
self.logger.info('Using resolved strategy %s from \'%s\'', strategy_name, path)
return None
raise ImportError('not found')
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# Fallback to the default strategy
except (ImportError, TypeError) as error:
self.logger.error(
"Impossible to load Strategy '%s'. This class does not exist"
" or contains Python code errors",
strategy_name
)
self.logger.error(
"The error is:\n%s.",
error
)
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@staticmethod
def _get_valid_strategies(module_path: str, strategy_name: str) -> Optional[Type[IStrategy]]:
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"""
Returns a list of all possible strategies for the given module_path
:param module_path: absolute path to the module
:param strategy_name: Class name of the strategy
:return: Tuple with (name, class) or None
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"""
# Generate spec based on absolute path
spec = importlib.util.spec_from_file_location('user_data.strategies', module_path)
module = importlib.util.module_from_spec(spec)
spec.loader.exec_module(module)
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valid_strategies_gen = (
obj for name, obj in inspect.getmembers(module, inspect.isclass)
if strategy_name == name and IStrategy in obj.__bases__
)
return next(valid_strategies_gen, None)
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def _search_strategy(self, directory: str, strategy_name: str) -> Optional[IStrategy]:
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"""
Search for the strategy_name in the given directory
:param directory: relative or absolute directory path
:return: name of the strategy class
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"""
self.logger.debug('Searching for strategy %s in \'%s\'', strategy_name, directory)
for entry in os.listdir(directory):
# Only consider python files
if not entry.endswith('.py'):
self.logger.debug('Ignoring %s', entry)
continue
strategy = StrategyResolver._get_valid_strategies(
os.path.abspath(os.path.join(directory, entry)), strategy_name
)
if strategy:
return strategy()
return None
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def populate_indicators(self, dataframe: DataFrame) -> 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()
:return: a Dataframe with all mandatory indicators for the strategies
"""
return self.custom_strategy.populate_indicators(dataframe)
def populate_buy_trend(self, dataframe: DataFrame) -> DataFrame:
"""
Based on TA indicators, populates the buy signal for the given dataframe
:param dataframe: DataFrame
:return: DataFrame with buy column
:return:
"""
return self.custom_strategy.populate_buy_trend(dataframe)
def populate_sell_trend(self, dataframe: DataFrame) -> DataFrame:
"""
Based on TA indicators, populates the sell signal for the given dataframe
:param dataframe: DataFrame
:return: DataFrame with buy column
"""
return self.custom_strategy.populate_sell_trend(dataframe)