adelorenzo/stable
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity

Merge branch 'develop' into align_userdata

Browse Source
This commit is contained in:
Matthias
2019-08-18 15:00:12 +02:00
parent 2c5a499a8b 9005447590
commit 0a478bc0dc
63 changed files with 1517 additions and 941 deletions
Download Patch File Download Diff File Expand all files Collapse all files

4
freqtrade/optimize/backtesting.py
View File

@@ -12,7 +12,7 @@ from typing import Any, Dict, List, NamedTuple, Optional
from pandas import DataFrame
from freqtrade import OperationalException
from freqtrade.configuration import Arguments
from freqtrade.configuration import TimeRange
from freqtrade.data import history
from freqtrade.data.dataprovider import DataProvider
from freqtrade.exchange import timeframe_to_minutes
@@ -404,7 +404,7 @@ class Backtesting(object):
logger.info('Using stake_currency: %s ...', self.config['stake_currency'])
logger.info('Using stake_amount: %s ...', self.config['stake_amount'])
timerange = Arguments.parse_timerange(None if self.config.get(
timerange = TimeRange.parse_timerange(None if self.config.get(
'timerange') is None else str(self.config.get('timerange')))
data = history.load_data(
datadir=Path(self.config['datadir']) if self.config.get('datadir') else None,

41
freqtrade/optimize/default_hyperopt.py
View File

@@ -14,36 +14,48 @@ from freqtrade.optimize.hyperopt_interface import IHyperOpt
class DefaultHyperOpts(IHyperOpt):
"""
Default hyperopt provided by the Freqtrade bot.
You can override it with your own hyperopt
You can override it with your own Hyperopt
"""
@staticmethod
def populate_indicators(dataframe: DataFrame, metadata: dict) -> DataFrame:
"""
Add several indicators needed for buy and sell strategies defined below.
"""
# ADX
dataframe['adx'] = ta.ADX(dataframe)
# MACD
macd = ta.MACD(dataframe)
dataframe['macd'] = macd['macd']
dataframe['macdsignal'] = macd['macdsignal']
# MFI
dataframe['mfi'] = ta.MFI(dataframe)
# RSI
dataframe['rsi'] = ta.RSI(dataframe)
# Stochastic Fast
stoch_fast = ta.STOCHF(dataframe)
dataframe['fastd'] = stoch_fast['fastd']
# Minus-DI
dataframe['minus_di'] = ta.MINUS_DI(dataframe)
# Bollinger bands
bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2)
dataframe['bb_lowerband'] = bollinger['lower']
dataframe['bb_upperband'] = bollinger['upper']
# SAR
dataframe['sar'] = ta.SAR(dataframe)
return dataframe
@staticmethod
def buy_strategy_generator(params: Dict[str, Any]) -> Callable:
"""
Define the buy strategy parameters to be used by hyperopt
Define the buy strategy parameters to be used by Hyperopt.
"""
def populate_buy_trend(dataframe: DataFrame, metadata: dict) -> DataFrame:
"""
Buy strategy Hyperopt will build and use
Buy strategy Hyperopt will build and use.
"""
conditions = []
# GUARDS AND TRENDS
if 'mfi-enabled' in params and params['mfi-enabled']:
conditions.append(dataframe['mfi'] < params['mfi-value'])
@@ -79,7 +91,7 @@ class DefaultHyperOpts(IHyperOpt):
@staticmethod
def indicator_space() -> List[Dimension]:
"""
Define your Hyperopt space for searching strategy parameters
Define your Hyperopt space for searching buy strategy parameters.
"""
return [
Integer(10, 25, name='mfi-value'),
@@ -96,14 +108,14 @@ class DefaultHyperOpts(IHyperOpt):
@staticmethod
def sell_strategy_generator(params: Dict[str, Any]) -> Callable:
"""
Define the sell strategy parameters to be used by hyperopt
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
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'])
@@ -139,7 +151,7 @@ class DefaultHyperOpts(IHyperOpt):
@staticmethod
def sell_indicator_space() -> List[Dimension]:
"""
Define your Hyperopt space for searching sell strategy parameters
Define your Hyperopt space for searching sell strategy parameters.
"""
return [
Integer(75, 100, name='sell-mfi-value'),
@@ -157,9 +169,9 @@ class DefaultHyperOpts(IHyperOpt):
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
Based on TA indicators. Should be a copy of same method from strategy.
Must align to populate_indicators in this file.
Only used when --spaces does not include buy space.
"""
dataframe.loc[
(
@@ -174,9 +186,9 @@ class DefaultHyperOpts(IHyperOpt):
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
Based on TA indicators. Should be a copy of same method from strategy.
Must align to populate_indicators in this file.
Only used when --spaces does not include sell space.
"""
dataframe.loc[
(
@@ -186,4 +198,5 @@ class DefaultHyperOpts(IHyperOpt):
(dataframe['fastd'] > 54)
),
'sell'] = 1
return dataframe

4
freqtrade/optimize/edge_cli.py
View File

@@ -9,7 +9,7 @@ from tabulate import tabulate
from freqtrade import constants
from freqtrade.edge import Edge
from freqtrade.configuration import Arguments
from freqtrade.configuration import TimeRange
from freqtrade.exchange import Exchange
from freqtrade.resolvers import StrategyResolver
@@ -41,7 +41,7 @@ class EdgeCli(object):
self.edge = Edge(config, self.exchange, self.strategy)
self.edge._refresh_pairs = self.config.get('refresh_pairs', False)
self.timerange = Arguments.parse_timerange(None if self.config.get(
self.timerange = TimeRange.parse_timerange(None if self.config.get(
'timerange') is None else str(self.config.get('timerange')))
self.edge._timerange = self.timerange

78
freqtrade/optimize/hyperopt.py
View File

@@ -7,17 +7,22 @@ This module contains the hyperopt logic
import logging
import sys
from collections import OrderedDict
from operator import itemgetter
from pathlib import Path
from pprint import pprint
from typing import Any, Dict, List, Optional
import rapidjson
from colorama import init as colorama_init
from colorama import Fore, Style
from joblib import Parallel, delayed, dump, load, wrap_non_picklable_objects, cpu_count
from pandas import DataFrame
from skopt import Optimizer
from skopt.space import Dimension
from freqtrade.configuration import Arguments
from freqtrade.configuration import TimeRange
from freqtrade.data.history import load_data, get_timeframe
from freqtrade.optimize.backtesting import Backtesting
# Import IHyperOptLoss to allow users import from this file
@@ -136,30 +141,61 @@ class Hyperopt(Backtesting):
results = sorted(self.trials, key=itemgetter('loss'))
best_result = results[0]
params = best_result['params']
log_str = self.format_results_logstring(best_result)
print(f"\nBest result:\n\n{log_str}\n")
if self.has_space('buy'):
print('Buy hyperspace params:')
pprint({p.name: params.get(p.name) for p in self.hyperopt_space('buy')},
indent=4)
if self.has_space('sell'):
print('Sell hyperspace params:')
pprint({p.name: params.get(p.name) for p in self.hyperopt_space('sell')},
indent=4)
if self.has_space('roi'):
print("ROI table:")
pprint(self.custom_hyperopt.generate_roi_table(params), indent=4)
if self.has_space('stoploss'):
print(f"Stoploss: {params.get('stoploss')}")
if self.config.get('print_json'):
result_dict: Dict = {}
if self.has_space('buy') or self.has_space('sell'):
result_dict['params'] = {}
if self.has_space('buy'):
result_dict['params'].update({p.name: params.get(p.name)
for p in self.hyperopt_space('buy')})
if self.has_space('sell'):
result_dict['params'].update({p.name: params.get(p.name)
for p in self.hyperopt_space('sell')})
if self.has_space('roi'):
# Convert keys in min_roi dict to strings because
# rapidjson cannot dump dicts with integer keys...
# OrderedDict is used to keep the numeric order of the items
# in the dict.
result_dict['minimal_roi'] = OrderedDict(
(str(k), v) for k, v in self.custom_hyperopt.generate_roi_table(params).items()
)
if self.has_space('stoploss'):
result_dict['stoploss'] = params.get('stoploss')
print(rapidjson.dumps(result_dict, default=str, number_mode=rapidjson.NM_NATIVE))
else:
if self.has_space('buy'):
print('Buy hyperspace params:')
pprint({p.name: params.get(p.name) for p in self.hyperopt_space('buy')},
indent=4)
if self.has_space('sell'):
print('Sell hyperspace params:')
pprint({p.name: params.get(p.name) for p in self.hyperopt_space('sell')},
indent=4)
if self.has_space('roi'):
print("ROI table:")
pprint(self.custom_hyperopt.generate_roi_table(params), indent=4)
if self.has_space('stoploss'):
print(f"Stoploss: {params.get('stoploss')}")
def log_results(self, results) -> None:
"""
Log results if it is better than any previous evaluation
"""
print_all = self.config.get('print_all', False)
if print_all or results['loss'] < self.current_best_loss:
is_best_loss = results['loss'] < self.current_best_loss
if print_all or is_best_loss:
if is_best_loss:
self.current_best_loss = results['loss']
log_str = self.format_results_logstring(results)
# Colorize output
if self.config.get('print_colorized', False):
if results['total_profit'] > 0:
log_str = Fore.GREEN + log_str
if print_all and is_best_loss:
log_str = Style.BRIGHT + log_str
if print_all:
print(log_str)
else:
@@ -174,7 +210,6 @@ class Hyperopt(Backtesting):
total = self.total_epochs
res = results['results_explanation']
loss = results['loss']
self.current_best_loss = results['loss']
log_str = f'{current:5d}/{total}: {res} Objective: {loss:.5f}'
log_str = f'*{log_str}' if results['is_initial_point'] else f' {log_str}'
return log_str
@@ -242,6 +277,7 @@ class Hyperopt(Backtesting):
results_explanation = self.format_results(results)
trade_count = len(results.index)
total_profit = results.profit_abs.sum()
# If this evaluation contains too short amount of trades to be
# interesting -- consider it as 'bad' (assigned max. loss value)
@@ -252,6 +288,7 @@ class Hyperopt(Backtesting):
'loss': MAX_LOSS,
'params': params,
'results_explanation': results_explanation,
'total_profit': total_profit,
}
loss = self.calculate_loss(results=results, trade_count=trade_count,
@@ -261,6 +298,7 @@ class Hyperopt(Backtesting):
'loss': loss,
'params': params,
'results_explanation': results_explanation,
'total_profit': total_profit,
}
def format_results(self, results: DataFrame) -> str:
@@ -302,7 +340,7 @@ class Hyperopt(Backtesting):
)
def start(self) -> None:
timerange = Arguments.parse_timerange(None if self.config.get(
timerange = TimeRange.parse_timerange(None if self.config.get(
'timerange') is None else str(self.config.get('timerange')))
data = load_data(
datadir=Path(self.config['datadir']) if self.config.get('datadir') else None,
@@ -344,6 +382,10 @@ class Hyperopt(Backtesting):
logger.info(f'Number of parallel jobs set as: {config_jobs}')
opt = self.get_optimizer(config_jobs)
if self.config.get('print_colorized', False):
colorama_init(autoreset=True)
try:
with Parallel(n_jobs=config_jobs) as parallel:
jobs = parallel._effective_n_jobs()