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Merge branch 'develop' into tsl_on_exchange

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This commit is contained in:
misagh
2019-01-16 11:52:23 +01:00
parent 29439c05d6 07b4afedf7
commit e31fa8721f
14 changed files with 391 additions and 83 deletions
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2
freqtrade/arguments.py
View File

@@ -272,7 +272,7 @@ class Arguments(object):
'-s', '--spaces',
help='Specify which parameters to hyperopt. Space separate list. \
Default: %(default)s',
choices=['all', 'buy', 'roi', 'stoploss'],
choices=['all', 'buy', 'sell', 'roi', 'stoploss'],
default='all',
nargs='+',
dest='spaces',

116
freqtrade/optimize/default_hyperopt.py
View File

@@ -33,6 +33,7 @@ class DefaultHyperOpts(IHyperOpt):
# Bollinger bands
bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2)
dataframe['bb_lowerband'] = bollinger['lower']
dataframe['bb_upperband'] = bollinger['upper']
dataframe['sar'] = ta.SAR(dataframe)
return dataframe
@@ -57,16 +58,17 @@ class DefaultHyperOpts(IHyperOpt):
conditions.append(dataframe['rsi'] < params['rsi-value'])
# TRIGGERS
if params['trigger'] == 'bb_lower':
conditions.append(dataframe['close'] < dataframe['bb_lowerband'])
if params['trigger'] == 'macd_cross_signal':
conditions.append(qtpylib.crossed_above(
dataframe['macd'], dataframe['macdsignal']
))
if params['trigger'] == 'sar_reversal':
conditions.append(qtpylib.crossed_above(
dataframe['close'], dataframe['sar']
))
if 'trigger' in params:
if params['trigger'] == 'bb_lower':
conditions.append(dataframe['close'] < dataframe['bb_lowerband'])
if params['trigger'] == 'macd_cross_signal':
conditions.append(qtpylib.crossed_above(
dataframe['macd'], dataframe['macdsignal']
))
if params['trigger'] == 'sar_reversal':
conditions.append(qtpylib.crossed_above(
dataframe['close'], dataframe['sar']
))
dataframe.loc[
reduce(lambda x, y: x & y, conditions),
@@ -93,6 +95,67 @@ class DefaultHyperOpts(IHyperOpt):
Categorical(['bb_lower', 'macd_cross_signal', 'sar_reversal'], name='trigger')
]
@staticmethod
def sell_strategy_generator(params: Dict[str, Any]) -> Callable:
"""
Define the sell strategy parameters to be used by hyperopt
"""
def populate_sell_trend(dataframe: DataFrame, metadata: dict) -> DataFrame:
"""
Sell strategy Hyperopt will build and use
"""
# print(params)
conditions = []
# GUARDS AND TRENDS
if 'sell-mfi-enabled' in params and params['sell-mfi-enabled']:
conditions.append(dataframe['mfi'] > params['sell-mfi-value'])
if 'sell-fastd-enabled' in params and params['sell-fastd-enabled']:
conditions.append(dataframe['fastd'] > params['sell-fastd-value'])
if 'sell-adx-enabled' in params and params['sell-adx-enabled']:
conditions.append(dataframe['adx'] < params['sell-adx-value'])
if 'sell-rsi-enabled' in params and params['sell-rsi-enabled']:
conditions.append(dataframe['rsi'] > params['sell-rsi-value'])
# TRIGGERS
if 'sell-trigger' in params:
if params['sell-trigger'] == 'sell-bb_upper':
conditions.append(dataframe['close'] > dataframe['bb_upperband'])
if params['sell-trigger'] == 'sell-macd_cross_signal':
conditions.append(qtpylib.crossed_above(
dataframe['macdsignal'], dataframe['macd']
))
if params['sell-trigger'] == 'sell-sar_reversal':
conditions.append(qtpylib.crossed_above(
dataframe['sar'], dataframe['close']
))
dataframe.loc[
reduce(lambda x, y: x & y, conditions),
'sell'] = 1
return dataframe
return populate_sell_trend
@staticmethod
def sell_indicator_space() -> List[Dimension]:
"""
Define your Hyperopt space for searching sell strategy parameters
"""
return [
Integer(75, 100, name='sell-mfi-value'),
Integer(50, 100, name='sell-fastd-value'),
Integer(50, 100, name='sell-adx-value'),
Integer(60, 100, name='sell-rsi-value'),
Categorical([True, False], name='sell-mfi-enabled'),
Categorical([True, False], name='sell-fastd-enabled'),
Categorical([True, False], name='sell-adx-enabled'),
Categorical([True, False], name='sell-rsi-enabled'),
Categorical(['sell-bb_upper',
'sell-macd_cross_signal',
'sell-sar_reversal'], name='sell-trigger')
]
@staticmethod
def generate_roi_table(params: Dict) -> Dict[int, float]:
"""
@@ -128,3 +191,36 @@ class DefaultHyperOpts(IHyperOpt):
Real(0.01, 0.07, name='roi_p2'),
Real(0.01, 0.20, name='roi_p3'),
]
def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
"""
Based on TA indicators. Should be a copy of from strategy
must align to populate_indicators in this file
Only used when --spaces does not include buy
"""
dataframe.loc[
(
(dataframe['close'] < dataframe['bb_lowerband']) &
(dataframe['mfi'] < 16) &
(dataframe['adx'] > 25) &
(dataframe['rsi'] < 21)
),
'buy'] = 1
return dataframe
def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
"""
Based on TA indicators. Should be a copy of from strategy
must align to populate_indicators in this file
Only used when --spaces does not include sell
"""
dataframe.loc[
(
(qtpylib.crossed_above(
dataframe['macdsignal'], dataframe['macd']
)) &
(dataframe['fastd'] > 54)
),
'sell'] = 1
return dataframe

35
freqtrade/optimize/hyperopt.py
View File

@@ -5,17 +5,18 @@ This module contains the hyperopt logic
"""
import logging
from argparse import Namespace
import multiprocessing
import os
import sys
from pathlib import Path
from argparse import Namespace
from math import exp
import multiprocessing
from operator import itemgetter
from pathlib import Path
from pprint import pprint
from typing import Any, Dict, List
from pandas import DataFrame
from joblib import Parallel, delayed, dump, load, wrap_non_picklable_objects
from pandas import DataFrame
from skopt import Optimizer
from skopt.space import Dimension
@@ -26,7 +27,6 @@ from freqtrade.optimize import get_timeframe
from freqtrade.optimize.backtesting import Backtesting
from freqtrade.resolvers import HyperOptResolver
logger = logging.getLogger(__name__)
MAX_LOSS = 100000 # just a big enough number to be bad result in loss optimization
@@ -102,13 +102,13 @@ class Hyperopt(Backtesting):
results = sorted(self.trials, key=itemgetter('loss'))
best_result = results[0]
logger.info(
'Best result:\n%s\nwith values:\n%s',
best_result['result'],
best_result['params']
'Best result:\n%s\nwith values:\n',
best_result['result']
)
pprint(best_result['params'], indent=4)
if 'roi_t1' in best_result['params']:
logger.info('ROI table:\n%s',
self.custom_hyperopt.generate_roi_table(best_result['params']))
logger.info('ROI table:')
pprint(self.custom_hyperopt.generate_roi_table(best_result['params']), indent=4)
def log_results(self, results) -> None:
"""
@@ -151,6 +151,12 @@ class Hyperopt(Backtesting):
spaces: List[Dimension] = []
if self.has_space('buy'):
spaces += self.custom_hyperopt.indicator_space()
if self.has_space('sell'):
spaces += self.custom_hyperopt.sell_indicator_space()
# Make sure experimental is enabled
if 'experimental' not in self.config:
self.config['experimental'] = {}
self.config['experimental']['use_sell_signal'] = True
if self.has_space('roi'):
spaces += self.custom_hyperopt.roi_space()
if self.has_space('stoploss'):
@@ -164,6 +170,13 @@ class Hyperopt(Backtesting):
if self.has_space('buy'):
self.advise_buy = self.custom_hyperopt.buy_strategy_generator(params)
elif hasattr(self.custom_hyperopt, 'populate_buy_trend'):
self.advise_buy = self.custom_hyperopt.populate_buy_trend # type: ignore
if self.has_space('sell'):
self.advise_sell = self.custom_hyperopt.sell_strategy_generator(params)
elif hasattr(self.custom_hyperopt, 'populate_sell_trend'):
self.advise_sell = self.custom_hyperopt.populate_sell_trend # type: ignore
if self.has_space('stoploss'):
self.strategy.stoploss = params['stoploss']
@@ -247,7 +260,7 @@ class Hyperopt(Backtesting):
timerange=timerange
)
if self.has_space('buy'):
if self.has_space('buy') or self.has_space('sell'):
self.strategy.advise_indicators = \
self.custom_hyperopt.populate_indicators # type: ignore
dump(self.strategy.tickerdata_to_dataframe(data), TICKERDATA_PICKLE)

14
freqtrade/optimize/hyperopt_interface.py
View File

@@ -37,6 +37,13 @@ class IHyperOpt(ABC):
Create a buy strategy generator
"""
@staticmethod
@abstractmethod
def sell_strategy_generator(params: Dict[str, Any]) -> Callable:
"""
Create a sell strategy generator
"""
@staticmethod
@abstractmethod
def indicator_space() -> List[Dimension]:
@@ -44,6 +51,13 @@ class IHyperOpt(ABC):
Create an indicator space
"""
@staticmethod
@abstractmethod
def sell_indicator_space() -> List[Dimension]:
"""
Create a sell indicator space
"""
@staticmethod
@abstractmethod
def generate_roi_table(params: Dict) -> Dict[int, float]:

7
freqtrade/resolvers/hyperopt_resolver.py
View File

@@ -32,6 +32,13 @@ class HyperOptResolver(IResolver):
hyperopt_name = config.get('hyperopt') or DEFAULT_HYPEROPT
self.hyperopt = self._load_hyperopt(hyperopt_name, extra_dir=config.get('hyperopt_path'))
if not hasattr(self.hyperopt, 'populate_buy_trend'):
logger.warning("Custom Hyperopt does not provide populate_buy_trend. "
"Using populate_buy_trend from DefaultStrategy.")
if not hasattr(self.hyperopt, 'populate_sell_trend'):
logger.warning("Custom Hyperopt does not provide populate_sell_trend. "
"Using populate_sell_trend from DefaultStrategy.")
def _load_hyperopt(
self, hyperopt_name: str, extra_dir: Optional[str] = None) -> IHyperOpt:
"""

36
freqtrade/tests/optimize/test_hyperopt.py
View File

@@ -9,7 +9,8 @@ import pytest
from freqtrade.data.converter import parse_ticker_dataframe
from freqtrade.data.history import load_tickerdata_file
from freqtrade.optimize.hyperopt import Hyperopt, start
from freqtrade.resolvers import StrategyResolver
from freqtrade.optimize.default_hyperopt import DefaultHyperOpts
from freqtrade.resolvers import StrategyResolver, HyperOptResolver
from freqtrade.tests.conftest import log_has, patch_exchange
from freqtrade.tests.optimize.test_backtesting import get_args
@@ -38,6 +39,28 @@ def create_trials(mocker, hyperopt) -> None:
return [{'loss': 1, 'result': 'foo', 'params': {}}]
def test_hyperoptresolver(mocker, default_conf, caplog) -> None:
mocker.patch(
'freqtrade.configuration.Configuration._load_config_file',
lambda *args, **kwargs: default_conf
)
hyperopts = DefaultHyperOpts
delattr(hyperopts, 'populate_buy_trend')
delattr(hyperopts, 'populate_sell_trend')
mocker.patch(
'freqtrade.resolvers.hyperopt_resolver.HyperOptResolver._load_hyperopt',
MagicMock(return_value=hyperopts)
)
x = HyperOptResolver(default_conf, ).hyperopt
assert not hasattr(x, 'populate_buy_trend')
assert not hasattr(x, 'populate_sell_trend')
assert log_has("Custom Hyperopt does not provide populate_sell_trend. "
"Using populate_sell_trend from DefaultStrategy.", caplog.record_tuples)
assert log_has("Custom Hyperopt does not provide populate_buy_trend. "
"Using populate_buy_trend from DefaultStrategy.", caplog.record_tuples)
def test_start(mocker, default_conf, caplog) -> None:
start_mock = MagicMock()
mocker.patch(
@@ -201,7 +224,7 @@ def test_start_calls_optimizer(mocker, default_conf, caplog) -> None:
hyperopt.start()
parallel.assert_called_once()
assert 'Best result:\nfoo result\nwith values:\n{}' in caplog.text
assert 'Best result:\nfoo result\nwith values:\n\n' in caplog.text
assert dumper.called
@@ -312,6 +335,15 @@ def test_generate_optimizer(mocker, default_conf) -> None:
'mfi-enabled': False,
'rsi-enabled': False,
'trigger': 'macd_cross_signal',
'sell-adx-value': 0,
'sell-fastd-value': 75,
'sell-mfi-value': 0,
'sell-rsi-value': 0,
'sell-adx-enabled': False,
'sell-fastd-enabled': True,
'sell-mfi-enabled': False,
'sell-rsi-enabled': False,
'sell-trigger': 'macd_cross_signal',
'roi_t1': 60.0,
'roi_t2': 30.0,
'roi_t3': 20.0,