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From 'develop' of https://github.com/freqtrade/freqtrade into nullart/maindev

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Nullart
2018-07-10 13:18:09 +08:00
parent 522c1a421d 0bd9674b5c
commit 86ad400c86
93 changed files with 19015 additions and 13 deletions
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32
freqtrade/strategy/__init__.py Executable file
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import logging
from copy import deepcopy
from freqtrade.strategy.interface import IStrategy
logger = logging.getLogger(__name__)
def import_strategy(strategy: IStrategy) -> IStrategy:
"""
Imports given Strategy instance to global scope
of freqtrade.strategy and returns an instance of it
"""
# Copy all attributes from base class and class
attr = deepcopy({**strategy.__class__.__dict__, **strategy.__dict__})
# Adjust module name
attr['__module__'] = 'freqtrade.strategy'
name = strategy.__class__.__name__
clazz = type(name, (IStrategy,), attr)
logger.debug(
'Imported strategy %s.%s as %s.%s',
strategy.__module__, strategy.__class__.__name__,
clazz.__module__, strategy.__class__.__name__,
)
# Modify global scope to declare class
globals()[name] = clazz
return clazz()

240
freqtrade/strategy/default_strategy.py Executable file
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# pragma pylint: disable=missing-docstring, invalid-name, pointless-string-statement
import talib.abstract as ta
from pandas import DataFrame
import freqtrade.vendor.qtpylib.indicators as qtpylib
from freqtrade.indicator_helpers import fishers_inverse
from freqtrade.strategy.interface import IStrategy
class DefaultStrategy(IStrategy):
"""
Default Strategy provided by freqtrade bot.
You can override it with your own strategy
"""
# Minimal ROI designed for the strategy
minimal_roi = {
"40": 0.0,
"30": 0.01,
"20": 0.02,
"0": 0.04
}
# Optimal stoploss designed for the strategy
stoploss = -0.10
# Optimal ticker interval for the strategy
ticker_interval = '5m'
def populate_indicators(self, dataframe: DataFrame) -> DataFrame:
"""
Adds several different TA indicators to the given DataFrame
Performance Note: For the best performance be frugal on the number of indicators
you are using. Let uncomment only the indicator you are using in your strategies
or your hyperopt configuration, otherwise you will waste your memory and CPU usage.
"""
# Momentum Indicator
# ------------------------------------
# ADX
dataframe['adx'] = ta.ADX(dataframe)
# Awesome oscillator
dataframe['ao'] = qtpylib.awesome_oscillator(dataframe)
"""
# Commodity Channel Index: values Oversold:<-100, Overbought:>100
dataframe['cci'] = ta.CCI(dataframe)
"""
# MACD
macd = ta.MACD(dataframe)
dataframe['macd'] = macd['macd']
dataframe['macdsignal'] = macd['macdsignal']
dataframe['macdhist'] = macd['macdhist']
# MFI
dataframe['mfi'] = ta.MFI(dataframe)
# Minus Directional Indicator / Movement
dataframe['minus_dm'] = ta.MINUS_DM(dataframe)
dataframe['minus_di'] = ta.MINUS_DI(dataframe)
# Plus Directional Indicator / Movement
dataframe['plus_dm'] = ta.PLUS_DM(dataframe)
dataframe['plus_di'] = ta.PLUS_DI(dataframe)
dataframe['minus_di'] = ta.MINUS_DI(dataframe)
"""
# ROC
dataframe['roc'] = ta.ROC(dataframe)
"""
# RSI
dataframe['rsi'] = ta.RSI(dataframe)
# Inverse Fisher transform on RSI, values [-1.0, 1.0] (https://goo.gl/2JGGoy)
dataframe['fisher_rsi'] = fishers_inverse(dataframe['rsi'])
# Inverse Fisher transform on RSI normalized, value [0.0, 100.0] (https://goo.gl/2JGGoy)
dataframe['fisher_rsi_norma'] = 50 * (dataframe['fisher_rsi'] + 1)
# Stoch
stoch = ta.STOCH(dataframe)
dataframe['slowd'] = stoch['slowd']
dataframe['slowk'] = stoch['slowk']
# Stoch fast
stoch_fast = ta.STOCHF(dataframe)
dataframe['fastd'] = stoch_fast['fastd']
dataframe['fastk'] = stoch_fast['fastk']
"""
# Stoch RSI
stoch_rsi = ta.STOCHRSI(dataframe)
dataframe['fastd_rsi'] = stoch_rsi['fastd']
dataframe['fastk_rsi'] = stoch_rsi['fastk']
"""
# Overlap Studies
# ------------------------------------
# Previous Bollinger bands
# Because ta.BBANDS implementation is broken with small numbers, it actually
# returns middle band for all the three bands. Switch to qtpylib.bollinger_bands
# and use middle band instead.
dataframe['blower'] = ta.BBANDS(dataframe, nbdevup=2, nbdevdn=2)['lowerband']
# Bollinger bands
bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2)
dataframe['bb_lowerband'] = bollinger['lower']
dataframe['bb_middleband'] = bollinger['mid']
dataframe['bb_upperband'] = bollinger['upper']
# EMA - Exponential Moving Average
dataframe['ema3'] = ta.EMA(dataframe, timeperiod=3)
dataframe['ema5'] = ta.EMA(dataframe, timeperiod=5)
dataframe['ema10'] = ta.EMA(dataframe, timeperiod=10)
dataframe['ema50'] = ta.EMA(dataframe, timeperiod=50)
dataframe['ema100'] = ta.EMA(dataframe, timeperiod=100)
# SAR Parabol
dataframe['sar'] = ta.SAR(dataframe)
# SMA - Simple Moving Average
dataframe['sma'] = ta.SMA(dataframe, timeperiod=40)
# TEMA - Triple Exponential Moving Average
dataframe['tema'] = ta.TEMA(dataframe, timeperiod=9)
# Cycle Indicator
# ------------------------------------
# Hilbert Transform Indicator - SineWave
hilbert = ta.HT_SINE(dataframe)
dataframe['htsine'] = hilbert['sine']
dataframe['htleadsine'] = hilbert['leadsine']
# Pattern Recognition - Bullish candlestick patterns
# ------------------------------------
"""
# Hammer: values [0, 100]
dataframe['CDLHAMMER'] = ta.CDLHAMMER(dataframe)
# Inverted Hammer: values [0, 100]
dataframe['CDLINVERTEDHAMMER'] = ta.CDLINVERTEDHAMMER(dataframe)
# Dragonfly Doji: values [0, 100]
dataframe['CDLDRAGONFLYDOJI'] = ta.CDLDRAGONFLYDOJI(dataframe)
# Piercing Line: values [0, 100]
dataframe['CDLPIERCING'] = ta.CDLPIERCING(dataframe) # values [0, 100]
# Morningstar: values [0, 100]
dataframe['CDLMORNINGSTAR'] = ta.CDLMORNINGSTAR(dataframe) # values [0, 100]
# Three White Soldiers: values [0, 100]
dataframe['CDL3WHITESOLDIERS'] = ta.CDL3WHITESOLDIERS(dataframe) # values [0, 100]
"""
# Pattern Recognition - Bearish candlestick patterns
# ------------------------------------
"""
# Hanging Man: values [0, 100]
dataframe['CDLHANGINGMAN'] = ta.CDLHANGINGMAN(dataframe)
# Shooting Star: values [0, 100]
dataframe['CDLSHOOTINGSTAR'] = ta.CDLSHOOTINGSTAR(dataframe)
# Gravestone Doji: values [0, 100]
dataframe['CDLGRAVESTONEDOJI'] = ta.CDLGRAVESTONEDOJI(dataframe)
# Dark Cloud Cover: values [0, 100]
dataframe['CDLDARKCLOUDCOVER'] = ta.CDLDARKCLOUDCOVER(dataframe)
# Evening Doji Star: values [0, 100]
dataframe['CDLEVENINGDOJISTAR'] = ta.CDLEVENINGDOJISTAR(dataframe)
# Evening Star: values [0, 100]
dataframe['CDLEVENINGSTAR'] = ta.CDLEVENINGSTAR(dataframe)
"""
# Pattern Recognition - Bullish/Bearish candlestick patterns
# ------------------------------------
"""
# Three Line Strike: values [0, -100, 100]
dataframe['CDL3LINESTRIKE'] = ta.CDL3LINESTRIKE(dataframe)
# Spinning Top: values [0, -100, 100]
dataframe['CDLSPINNINGTOP'] = ta.CDLSPINNINGTOP(dataframe) # values [0, -100, 100]
# Engulfing: values [0, -100, 100]
dataframe['CDLENGULFING'] = ta.CDLENGULFING(dataframe) # values [0, -100, 100]
# Harami: values [0, -100, 100]
dataframe['CDLHARAMI'] = ta.CDLHARAMI(dataframe) # values [0, -100, 100]
# Three Outside Up/Down: values [0, -100, 100]
dataframe['CDL3OUTSIDE'] = ta.CDL3OUTSIDE(dataframe) # values [0, -100, 100]
# Three Inside Up/Down: values [0, -100, 100]
dataframe['CDL3INSIDE'] = ta.CDL3INSIDE(dataframe) # values [0, -100, 100]
"""
# Chart type
# ------------------------------------
# Heikinashi stategy
heikinashi = qtpylib.heikinashi(dataframe)
dataframe['ha_open'] = heikinashi['open']
dataframe['ha_close'] = heikinashi['close']
dataframe['ha_high'] = heikinashi['high']
dataframe['ha_low'] = heikinashi['low']
return 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
"""
dataframe.loc[
(
(dataframe['rsi'] < 35) &
(dataframe['fastd'] < 35) &
(dataframe['adx'] > 30) &
(dataframe['plus_di'] > 0.5)
) |
(
(dataframe['adx'] > 65) &
(dataframe['plus_di'] > 0.5)
),
'buy'] = 1
return 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
"""
dataframe.loc[
(
(
(qtpylib.crossed_above(dataframe['rsi'], 70)) |
(qtpylib.crossed_above(dataframe['fastd'], 70))
) &
(dataframe['adx'] > 10) &
(dataframe['minus_di'] > 0)
) |
(
(dataframe['adx'] > 70) &
(dataframe['minus_di'] > 0.5)
),
'sell'] = 1
return dataframe

48
freqtrade/strategy/interface.py Executable file
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"""
IStrategy interface
This module defines the interface to apply for strategies
"""
from abc import ABC, abstractmethod
from typing import Dict
from pandas import DataFrame
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
"""
minimal_roi: Dict
stoploss: float
ticker_interval: str
@abstractmethod
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
"""
@abstractmethod
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
"""
@abstractmethod
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 sell column
"""

134
freqtrade/strategy/resolver.py Executable file
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# pragma pylint: disable=attribute-defined-outside-init
"""
This module load custom strategies
"""
import importlib.util
import inspect
import logging
import os
from collections import OrderedDict
from typing import Dict, Optional, Type
from freqtrade import constants
from freqtrade.strategy import import_strategy
from freqtrade.strategy.interface import IStrategy
logger = logging.getLogger(__name__)
class StrategyResolver(object):
"""
This class contains all the logic to load custom strategy class
"""
__slots__ = ['strategy']
def __init__(self, config: Optional[Dict] = None) -> None:
"""
Load the custom class from config parameter
:param config: configuration dictionary or None
"""
config = config or {}
# Verify the strategy is in the configuration, otherwise fallback to the default strategy
strategy_name = config.get('strategy') or constants.DEFAULT_STRATEGY
self.strategy: IStrategy = self._load_strategy(strategy_name,
extra_dir=config.get('strategy_path'))
# Set attributes
# Check if we need to override configuration
if 'minimal_roi' in config:
self.strategy.minimal_roi = config['minimal_roi']
logger.info("Override strategy \'minimal_roi\' with value in config file.")
if 'stoploss' in config:
self.strategy.stoploss = config['stoploss']
logger.info(
"Override strategy \'stoploss\' with value in config file: %s.", config['stoploss']
)
if 'ticker_interval' in config:
self.strategy.ticker_interval = config['ticker_interval']
logger.info(
"Override strategy \'ticker_interval\' with value in config file: %s.",
config['ticker_interval']
)
# Sort and apply type conversions
self.strategy.minimal_roi = OrderedDict(sorted(
{int(key): value for (key, value) in self.strategy.minimal_roi.items()}.items(),
key=lambda t: t[0]))
self.strategy.stoploss = float(self.strategy.stoploss)
def _load_strategy(
self, strategy_name: str, extra_dir: Optional[str] = None) -> IStrategy:
"""
Search and loads the specified strategy.
:param strategy_name: name of the module to import
:param extra_dir: additional directory to search for the given strategy
:return: Strategy instance or None
"""
current_path = os.path.dirname(os.path.realpath(__file__))
abs_paths = [
os.path.join(os.getcwd(), 'user_data', 'strategies'),
current_path,
]
if extra_dir:
# Add extra strategy directory on top of search paths
abs_paths.insert(0, extra_dir)
for path in abs_paths:
try:
strategy = self._search_strategy(path, strategy_name)
if strategy:
logger.info('Using resolved strategy %s from \'%s\'', strategy_name, path)
return import_strategy(strategy)
except FileNotFoundError:
logger.warning('Path "%s" does not exist', path)
raise ImportError(
"Impossible to load Strategy '{}'. This class does not exist"
" or contains Python code errors".format(strategy_name)
)
@staticmethod
def _get_valid_strategies(module_path: str, strategy_name: str) -> Optional[Type[IStrategy]]:
"""
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
"""
# Generate spec based on absolute path
spec = importlib.util.spec_from_file_location('unknown', module_path)
module = importlib.util.module_from_spec(spec)
spec.loader.exec_module(module) # type: ignore # importlib does not use typehints
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)
@staticmethod
def _search_strategy(directory: str, strategy_name: str) -> Optional[IStrategy]:
"""
Search for the strategy_name in the given directory
:param directory: relative or absolute directory path
:return: name of the strategy class
"""
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'):
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