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Matthias 2019-08-12 06:49:41 +02:00
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4
docs/bot-usage.md
View File

@ -56,8 +56,8 @@ freqtrade -c path/far/far/away/config.json
The bot allows you to use multiple configuration files by specifying multiple
`-c/--config` configuration options in the command line. Configuration parameters
defined in the last configuration file override parameters with the same name
defined in the previous configuration file specified in the command line.
defined in latter configuration files override parameters with the same name
defined in the previous configuration files specified in the command line earlier.
For example, you can make a separate configuration file with your key and secrete
for the Exchange you use for trading, specify default configuration file with

10
docs/data-analysis.md
View File

@ -31,6 +31,16 @@ df = load_trades_from_db("sqlite:///tradesv3.sqlite")
df.groupby("pair")["sell_reason"].value_counts()
```
### Load multiple configuration files
This option can be usefull to inspect the results of passing in multiple configs in case of problems
``` python
from freqtrade.configuration import Configuration
config = Configuration.from_files(["config1.json", "config2.json"])
print(config)
```
## Strategy debugging example
Debugging a strategy can be time-consuming. FreqTrade offers helper functions to visualize raw data.

40
freqtrade/configuration/configuration.py
View File

@ -4,7 +4,7 @@ This module contains the configuration class
import logging
import warnings
from argparse import Namespace
from typing import Any, Callable, Dict, Optional
from typing import Any, Callable, Dict, List, Optional
from freqtrade import OperationalException, constants
from freqtrade.configuration.check_exchange import check_exchange
@ -39,43 +39,43 @@ class Configuration(object):
return self.config
def _load_config_files(self) -> Dict[str, Any]:
@staticmethod
def from_files(files: List[str]) -> Dict[str, Any]:
"""
Iterate through the config files passed in the args,
loading all of them and merging their contents.
Iterate through the config files passed in, loading all of them
and merging their contents.
Files are loaded in sequence, parameters in later configuration files
override the same parameter from an earlier file (last definition wins).
:param files: List of file paths
:return: configuration dictionary
"""
# Keep this method as staticmethod, so it can be used from interactive environments
config: Dict[str, Any] = {}
# We expect here a list of config filenames
for path in self.args.config:
logger.info('Using config: %s ...', path)
for path in files:
logger.info(f'Using config: {path} ...')
# Merge config options, overwriting old values
config = deep_merge_dicts(load_config_file(path), config)
return config
def _normalize_config(self, config: Dict[str, Any]) -> None:
"""
Make config more canonical -- i.e. for example add missing parts that we expect
to be normally in it...
"""
# Normalize config
if 'internals' not in config:
config['internals'] = {}
# validate configuration before returning
logger.info('Validating configuration ...')
validate_config_schema(config)
return config
def load_config(self) -> Dict[str, Any]:
"""
Extract information for sys.argv and load the bot configuration
:return: Configuration dictionary
"""
# Load all configs
config: Dict[str, Any] = self._load_config_files()
# Make resulting config more canonical
self._normalize_config(config)
logger.info('Validating configuration ...')
validate_config_schema(config)
config: Dict[str, Any] = Configuration.from_files(self.args.config)
self._validate_config_consistency(config)

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

29
freqtrade/tests/test_configuration.py
View File

@ -133,6 +133,35 @@ def test_load_config_combine_dicts(default_conf, mocker, caplog) -> None:
assert log_has('Validating configuration ...', caplog)
def test_from_config(default_conf, mocker, caplog) -> None:
conf1 = deepcopy(default_conf)
conf2 = deepcopy(default_conf)
del conf1['exchange']['key']
del conf1['exchange']['secret']
del conf2['exchange']['name']
conf2['exchange']['pair_whitelist'] += ['NANO/BTC']
conf2['fiat_display_currency'] = "EUR"
config_files = [conf1, conf2]
configsmock = MagicMock(side_effect=config_files)
mocker.patch(
'freqtrade.configuration.configuration.load_config_file',
configsmock
)
validated_conf = Configuration.from_files(['test_conf.json', 'test2_conf.json'])
exchange_conf = default_conf['exchange']
assert validated_conf['exchange']['name'] == exchange_conf['name']
assert validated_conf['exchange']['key'] == exchange_conf['key']
assert validated_conf['exchange']['secret'] == exchange_conf['secret']
assert validated_conf['exchange']['pair_whitelist'] != conf1['exchange']['pair_whitelist']
assert validated_conf['exchange']['pair_whitelist'] == conf2['exchange']['pair_whitelist']
assert validated_conf['fiat_display_currency'] == "EUR"
assert 'internals' in validated_conf
assert log_has('Validating configuration ...', caplog)
def test_load_config_max_open_trades_minus_one(default_conf, mocker, caplog) -> None:
default_conf['max_open_trades'] = -1
patched_configuration_load_config_file(mocker, default_conf)

44
user_data/hyperopts/sample_hyperopt.py
View File

@ -1,11 +1,10 @@
# pragma pylint: disable=missing-docstring, invalid-name, pointless-string-statement
from functools import reduce
from math import exp
from typing import Any, Callable, Dict, List
from datetime import datetime
import numpy as np# noqa F401
import numpy as np
import talib.abstract as ta
from pandas import DataFrame
from skopt.space import Categorical, Dimension, Integer, Real
@ -16,7 +15,7 @@ from freqtrade.optimize.hyperopt_interface import IHyperOpt
class SampleHyperOpts(IHyperOpt):
"""
This is a sample hyperopt to inspire you.
This is a sample Hyperopt to inspire you.
Feel free to customize it.
More information in https://github.com/freqtrade/freqtrade/blob/develop/docs/hyperopt.md
@ -37,32 +36,44 @@ class SampleHyperOpts(IHyperOpt):
"""
@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'])
@ -98,7 +109,7 @@ class SampleHyperOpts(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'),
@ -115,14 +126,14 @@ class SampleHyperOpts(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'])
@ -158,7 +169,7 @@ class SampleHyperOpts(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'),
@ -176,9 +187,9 @@ class SampleHyperOpts(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[
(
@ -193,9 +204,9 @@ class SampleHyperOpts(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[
(
@ -205,4 +216,5 @@ class SampleHyperOpts(IHyperOpt):
(dataframe['fastd'] > 54)
),
'sell'] = 1
return dataframe