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Merge pull request #571 from stephendade/userhyper

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Separated out custom hyperopts
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Matthias 2018-11-21 19:14:30 +01:00 committed by GitHub
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2
docs/bot-usage.md
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@ -204,6 +204,8 @@ optional arguments:
number)
--timerange TIMERANGE
specify what timerange of data to use.
--hyperopt PATH specify hyperopt file (default:
freqtrade/optimize/default_hyperopt.py)
-e INT, --epochs INT specify number of epochs (default: 100)
-s {all,buy,roi,stoploss} [{all,buy,roi,stoploss} ...], --spaces {all,buy,roi,stoploss} [{all,buy,roi,stoploss} ...]
Specify which parameters to hyperopt. Space separate

32
docs/hyperopt.md
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@ -19,18 +19,27 @@ and still take a long time.
## Prepare Hyperopting
We recommend you start by taking a look at `hyperopt.py` file located in [freqtrade/optimize](https://github.com/freqtrade/freqtrade/blob/develop/freqtrade/optimize/hyperopt.py)
Before we start digging in Hyperopt, we recommend you to take a look at
an example hyperopt file located into [user_data/hyperopts/](https://github.com/gcarq/freqtrade/blob/develop/user_data/hyperopts/test_hyperopt.py)
### 1. Install a Custom Hyperopt File
This is very simple. Put your hyperopt file into the folder
`user_data/hyperopts`.
Let assume you want a hyperopt file `awesome_hyperopt.py`:
1. Copy the file `user_data/hyperopts/sample_hyperopt.py` into `user_data/hyperopts/awesome_hyperopt.py`
### Configure your Guards and Triggers
### 2. Configure your Guards and Triggers
There are two places you need to change in your hyperopt file to add a
new buy hyperopt for testing:
- Inside [populate_buy_trend()](https://github.com/freqtrade/freqtrade/blob/develop/user_data/hyperopts/test_hyperopt.py#L230-L251).
- Inside [indicator_space()](https://github.com/freqtrade/freqtrade/blob/develop/user_data/hyperopts/test_hyperopt.py#L207-L223).
There are two places you need to change to add a new buy strategy for testing:
- Inside [populate_buy_trend()](https://github.com/freqtrade/freqtrade/blob/develop/freqtrade/optimize/hyperopt.py#L231-L264).
- Inside [hyperopt_space()](https://github.com/freqtrade/freqtrade/blob/develop/freqtrade/optimize/hyperopt.py#L213-L224)
and the associated methods `indicator_space`, `roi_space`, `stoploss_space`.
There you have two different types of indicators: 1. `guards` and 2. `triggers`.
There you have two different type of indicators: 1. `guards` and 2. `triggers`.
1. Guards are conditions like "never buy if ADX < 10", or "never buy if
current price is over EMA10".
1. Guards are conditions like "never buy if ADX < 10", or never buy if
current price is over EMA10.
2. Triggers are ones that actually trigger buy in specific moment, like
"buy when EMA5 crosses over EMA10" or "buy when close price touches lower
bollinger band".
@ -124,9 +133,12 @@ Because hyperopt tries a lot of combinations to find the best parameters it will
We strongly recommend to use `screen` or `tmux` to prevent any connection loss.
```bash
python3 ./freqtrade/main.py -c config.json hyperopt -e 5000
python3 ./freqtrade/main.py -s <strategyname> --hyperopt <hyperoptname> -c config.json hyperopt -e 5000
```
Use `<strategyname>` and `<hyperoptname>` as the names of the custom strategy
(only required for generating sells) and the custom hyperopt used.
The `-e` flag will set how many evaluations hyperopt will do. We recommend
running at least several thousand evaluations.

8
freqtrade/arguments.py
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@ -104,6 +104,14 @@ class Arguments(object):
type=str,
metavar='PATH',
)
self.parser.add_argument(
'--customhyperopt',
help='specify hyperopt class name (default: %(default)s)',
dest='hyperopt',
default=constants.DEFAULT_HYPEROPT,
type=str,
metavar='NAME',
)
self.parser.add_argument(
'--dynamic-whitelist',
help='dynamically generate and update whitelist'

3
freqtrade/configuration.py
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@ -53,6 +53,9 @@ class Configuration(object):
if self.args.strategy_path:
config.update({'strategy_path': self.args.strategy_path})
# Add the hyperopt file to use
config.update({'hyperopt': self.args.hyperopt})
# Load Common configuration
config = self._load_common_config(config)

1
freqtrade/constants.py
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@ -9,6 +9,7 @@ TICKER_INTERVAL = 5 # min
HYPEROPT_EPOCH = 100 # epochs
RETRY_TIMEOUT = 30 # sec
DEFAULT_STRATEGY = 'DefaultStrategy'
DEFAULT_HYPEROPT = 'DefaultHyperOpts'
DEFAULT_DB_PROD_URL = 'sqlite:///tradesv3.sqlite'
DEFAULT_DB_DRYRUN_URL = 'sqlite://'
UNLIMITED_STAKE_AMOUNT = 'unlimited'

1
freqtrade/optimize/__init__.py
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@ -20,6 +20,7 @@ from pandas import DataFrame
from freqtrade import misc, constants, OperationalException
from freqtrade.exchange import Exchange
from freqtrade.arguments import TimeRange
from freqtrade.optimize.default_hyperopt import DefaultHyperOpts # noqa: F401
logger = logging.getLogger(__name__)

130
freqtrade/optimize/default_hyperopt.py Normal file
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@ -0,0 +1,130 @@
# pragma pylint: disable=missing-docstring, invalid-name, pointless-string-statement
import talib.abstract as ta
from pandas import DataFrame
from typing import Dict, Any, Callable, List
from functools import reduce
from skopt.space import Categorical, Dimension, Integer, Real
import freqtrade.vendor.qtpylib.indicators as qtpylib
from freqtrade.optimize.hyperopt_interface import IHyperOpt
class_name = 'DefaultHyperOpts'
class DefaultHyperOpts(IHyperOpt):
"""
Default hyperopt provided by freqtrade bot.
You can override it with your own hyperopt
"""
@staticmethod
def populate_indicators(dataframe: DataFrame, metadata: dict) -> DataFrame:
dataframe['adx'] = ta.ADX(dataframe)
macd = ta.MACD(dataframe)
dataframe['macd'] = macd['macd']
dataframe['macdsignal'] = macd['macdsignal']
dataframe['mfi'] = ta.MFI(dataframe)
dataframe['rsi'] = ta.RSI(dataframe)
stoch_fast = ta.STOCHF(dataframe)
dataframe['fastd'] = stoch_fast['fastd']
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['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
"""
def populate_buy_trend(dataframe: DataFrame, metadata: dict) -> DataFrame:
"""
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'])
if 'fastd-enabled' in params and params['fastd-enabled']:
conditions.append(dataframe['fastd'] < params['fastd-value'])
if 'adx-enabled' in params and params['adx-enabled']:
conditions.append(dataframe['adx'] > params['adx-value'])
if 'rsi-enabled' in params and params['rsi-enabled']:
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']
))
dataframe.loc[
reduce(lambda x, y: x & y, conditions),
'buy'] = 1
return dataframe
return populate_buy_trend
@staticmethod
def indicator_space() -> List[Dimension]:
"""
Define your Hyperopt space for searching strategy parameters
"""
return [
Integer(10, 25, name='mfi-value'),
Integer(15, 45, name='fastd-value'),
Integer(20, 50, name='adx-value'),
Integer(20, 40, name='rsi-value'),
Categorical([True, False], name='mfi-enabled'),
Categorical([True, False], name='fastd-enabled'),
Categorical([True, False], name='adx-enabled'),
Categorical([True, False], name='rsi-enabled'),
Categorical(['bb_lower', 'macd_cross_signal', 'sar_reversal'], name='trigger')
]
@staticmethod
def generate_roi_table(params: Dict) -> Dict[int, float]:
"""
Generate the ROI table that will be used by Hyperopt
"""
roi_table = {}
roi_table[0] = params['roi_p1'] + params['roi_p2'] + params['roi_p3']
roi_table[params['roi_t3']] = params['roi_p1'] + params['roi_p2']
roi_table[params['roi_t3'] + params['roi_t2']] = params['roi_p1']
roi_table[params['roi_t3'] + params['roi_t2'] + params['roi_t1']] = 0
return roi_table
@staticmethod
def stoploss_space() -> List[Dimension]:
"""
Stoploss Value to search
"""
return [
Real(-0.5, -0.02, name='stoploss'),
]
@staticmethod
def roi_space() -> List[Dimension]:
"""
Values to search for each ROI steps
"""
return [
Integer(10, 120, name='roi_t1'),
Integer(10, 60, name='roi_t2'),
Integer(10, 40, name='roi_t3'),
Real(0.01, 0.04, name='roi_p1'),
Real(0.01, 0.07, name='roi_p2'),
Real(0.01, 0.20, name='roi_p3'),
]

147
freqtrade/optimize/hyperopt.py
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@ -9,22 +9,21 @@ import multiprocessing
import os
import sys
from argparse import Namespace
from functools import reduce
from math import exp
from operator import itemgetter
from typing import Any, Callable, Dict, List
from typing import Any, Dict, List
import talib.abstract as ta
from pandas import DataFrame
from sklearn.externals.joblib import Parallel, delayed, dump, load
from joblib import Parallel, delayed, dump, load, wrap_non_picklable_objects
from skopt import Optimizer
from skopt.space import Categorical, Dimension, Integer, Real
from skopt.space import Dimension
import freqtrade.vendor.qtpylib.indicators as qtpylib
from freqtrade.arguments import Arguments
from freqtrade.configuration import Configuration
from freqtrade.optimize import load_data
from freqtrade.optimize.backtesting import Backtesting
from freqtrade.optimize.hyperopt_resolver import HyperOptResolver
logger = logging.getLogger(__name__)
@ -42,6 +41,9 @@ class Hyperopt(Backtesting):
"""
def __init__(self, config: Dict[str, Any]) -> None:
super().__init__(config)
self.config = config
self.custom_hyperopt = HyperOptResolver(self.config).hyperopt
# set TARGET_TRADES to suit your number concurrent trades so its realistic
# to the number of days
self.target_trades = 600
@ -74,24 +76,6 @@ class Hyperopt(Backtesting):
arg_dict = {dim.name: value for dim, value in zip(dimensions, params)}
return arg_dict
@staticmethod
def populate_indicators(dataframe: DataFrame, metadata: dict) -> DataFrame:
dataframe['adx'] = ta.ADX(dataframe)
macd = ta.MACD(dataframe)
dataframe['macd'] = macd['macd']
dataframe['macdsignal'] = macd['macdsignal']
dataframe['mfi'] = ta.MFI(dataframe)
dataframe['rsi'] = ta.RSI(dataframe)
stoch_fast = ta.STOCHF(dataframe)
dataframe['fastd'] = stoch_fast['fastd']
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['sar'] = ta.SAR(dataframe)
return dataframe
def save_trials(self) -> None:
"""
Save hyperopt trials to file
@ -121,7 +105,8 @@ class Hyperopt(Backtesting):
best_result['params']
)
if 'roi_t1' in best_result['params']:
logger.info('ROI table:\n%s', self.generate_roi_table(best_result['params']))
logger.info('ROI table:\n%s',
self.custom_hyperopt.generate_roi_table(best_result['params']))
def log_results(self, results) -> None:
"""
@ -149,59 +134,6 @@ class Hyperopt(Backtesting):
result = trade_loss + profit_loss + duration_loss
return result
@staticmethod
def generate_roi_table(params: Dict) -> Dict[int, float]:
"""
Generate the ROI table that will be used by Hyperopt
"""
roi_table = {}
roi_table[0] = params['roi_p1'] + params['roi_p2'] + params['roi_p3']
roi_table[params['roi_t3']] = params['roi_p1'] + params['roi_p2']
roi_table[params['roi_t3'] + params['roi_t2']] = params['roi_p1']
roi_table[params['roi_t3'] + params['roi_t2'] + params['roi_t1']] = 0
return roi_table
@staticmethod
def roi_space() -> List[Dimension]:
"""
Values to search for each ROI steps
"""
return [
Integer(10, 120, name='roi_t1'),
Integer(10, 60, name='roi_t2'),
Integer(10, 40, name='roi_t3'),
Real(0.01, 0.04, name='roi_p1'),
Real(0.01, 0.07, name='roi_p2'),
Real(0.01, 0.20, name='roi_p3'),
]
@staticmethod
def stoploss_space() -> List[Dimension]:
"""
Stoploss search space
"""
return [
Real(-0.5, -0.02, name='stoploss'),
]
@staticmethod
def indicator_space() -> List[Dimension]:
"""
Define your Hyperopt space for searching strategy parameters
"""
return [
Integer(10, 25, name='mfi-value'),
Integer(15, 45, name='fastd-value'),
Integer(20, 50, name='adx-value'),
Integer(20, 40, name='rsi-value'),
Categorical([True, False], name='mfi-enabled'),
Categorical([True, False], name='fastd-enabled'),
Categorical([True, False], name='adx-enabled'),
Categorical([True, False], name='rsi-enabled'),
Categorical(['bb_lower', 'macd_cross_signal', 'sar_reversal'], name='trigger')
]
def has_space(self, space: str) -> bool:
"""
Tell if a space value is contained in the configuration
@ -216,61 +148,20 @@ class Hyperopt(Backtesting):
"""
spaces: List[Dimension] = []
if self.has_space('buy'):
spaces += Hyperopt.indicator_space()
spaces += self.custom_hyperopt.indicator_space()
if self.has_space('roi'):
spaces += Hyperopt.roi_space()
spaces += self.custom_hyperopt.roi_space()
if self.has_space('stoploss'):
spaces += Hyperopt.stoploss_space()
spaces += self.custom_hyperopt.stoploss_space()
return spaces
@staticmethod
def buy_strategy_generator(params: Dict[str, Any]) -> Callable:
"""
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
"""
conditions = []
# GUARDS AND TRENDS
if 'mfi-enabled' in params and params['mfi-enabled']:
conditions.append(dataframe['mfi'] < params['mfi-value'])
if 'fastd-enabled' in params and params['fastd-enabled']:
conditions.append(dataframe['fastd'] < params['fastd-value'])
if 'adx-enabled' in params and params['adx-enabled']:
conditions.append(dataframe['adx'] > params['adx-value'])
if 'rsi-enabled' in params and params['rsi-enabled']:
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']
))
dataframe.loc[
reduce(lambda x, y: x & y, conditions),
'buy'] = 1
return dataframe
return populate_buy_trend
def generate_optimizer(self, _params) -> Dict:
def generate_optimizer(self, _params: Dict) -> Dict:
params = self.get_args(_params)
if self.has_space('roi'):
self.strategy.minimal_roi = self.generate_roi_table(params)
self.strategy.minimal_roi = self.custom_hyperopt.generate_roi_table(params)
if self.has_space('buy'):
self.advise_buy = self.buy_strategy_generator(params)
self.advise_buy = self.custom_hyperopt.buy_strategy_generator(params)
if self.has_space('stoploss'):
self.strategy.stoploss = params['stoploss']
@ -329,7 +220,8 @@ class Hyperopt(Backtesting):
)
def run_optimizer_parallel(self, parallel, asked) -> List:
return parallel(delayed(self.generate_optimizer)(v) for v in asked)
return parallel(delayed(
wrap_non_picklable_objects(self.generate_optimizer))(v) for v in asked)
def load_previous_results(self):
""" read trials file if we have one """
@ -351,7 +243,8 @@ class Hyperopt(Backtesting):
)
if self.has_space('buy'):
self.strategy.advise_indicators = Hyperopt.populate_indicators # type: ignore
self.strategy.advise_indicators = \
self.custom_hyperopt.populate_indicators # type: ignore
dump(self.strategy.tickerdata_to_dataframe(data), TICKERDATA_PICKLE)
self.exchange = None # type: ignore
self.load_previous_results()

66
freqtrade/optimize/hyperopt_interface.py Normal file
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@ -0,0 +1,66 @@
"""
IHyperOpt interface
This module defines the interface to apply for hyperopts
"""
from abc import ABC, abstractmethod
from typing import Dict, Any, Callable, List
from pandas import DataFrame
from skopt.space import Dimension
class IHyperOpt(ABC):
"""
Interface for freqtrade hyperopts
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 -> int: value of the ticker interval to use for the strategy
"""
@staticmethod
@abstractmethod
def populate_indicators(dataframe: DataFrame, metadata: dict) -> 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
"""
@staticmethod
@abstractmethod
def buy_strategy_generator(params: Dict[str, Any]) -> Callable:
"""
Create a buy strategy generator
"""
@staticmethod
@abstractmethod
def indicator_space() -> List[Dimension]:
"""
Create an indicator space
"""
@staticmethod
@abstractmethod
def generate_roi_table(params: Dict) -> Dict[int, float]:
"""
Create an roi table
"""
@staticmethod
@abstractmethod
def stoploss_space() -> List[Dimension]:
"""
Create a stoploss space
"""
@staticmethod
@abstractmethod
def roi_space() -> List[Dimension]:
"""
Create a roi space
"""

104
freqtrade/optimize/hyperopt_resolver.py Normal file
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@ -0,0 +1,104 @@
# pragma pylint: disable=attribute-defined-outside-init
"""
This module load custom hyperopts
"""
import importlib.util
import inspect
import logging
import os
from typing import Optional, Dict, Type
from freqtrade.constants import DEFAULT_HYPEROPT
from freqtrade.optimize.hyperopt_interface import IHyperOpt
logger = logging.getLogger(__name__)
class HyperOptResolver(object):
"""
This class contains all the logic to load custom hyperopt class
"""
__slots__ = ['hyperopt']
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 hyperopt is in the configuration, otherwise fallback to the default hyperopt
hyperopt_name = config.get('hyperopt') or DEFAULT_HYPEROPT
self.hyperopt = self._load_hyperopt(hyperopt_name, extra_dir=config.get('hyperopt_path'))
def _load_hyperopt(
self, hyperopt_name: str, extra_dir: Optional[str] = None) -> IHyperOpt:
"""
Search and loads the specified hyperopt.
:param hyperopt_name: name of the module to import
:param extra_dir: additional directory to search for the given hyperopt
:return: HyperOpt instance or None
"""
current_path = os.path.dirname(os.path.realpath(__file__))
abs_paths = [
os.path.join(current_path, '..', '..', 'user_data', 'hyperopts'),
current_path,
]
if extra_dir:
# Add extra hyperopt directory on top of search paths
abs_paths.insert(0, extra_dir)
for path in abs_paths:
hyperopt = self._search_hyperopt(path, hyperopt_name)
if hyperopt:
logger.info('Using resolved hyperopt %s from \'%s\'', hyperopt_name, path)
return hyperopt
raise ImportError(
"Impossible to load Hyperopt '{}'. This class does not exist"
" or contains Python code errors".format(hyperopt_name)
)
@staticmethod
def _get_valid_hyperopts(module_path: str, hyperopt_name: str) -> Optional[Type[IHyperOpt]]:
"""
Returns a list of all possible hyperopts for the given module_path
:param module_path: absolute path to the module
:param hyperopt_name: Class name of the hyperopt
:return: Tuple with (name, class) or None
"""
# Generate spec based on absolute path
spec = importlib.util.spec_from_file_location('user_data.hyperopts', module_path)
module = importlib.util.module_from_spec(spec)
spec.loader.exec_module(module) # type: ignore # importlib does not use typehints
valid_hyperopts_gen = (
obj for name, obj in inspect.getmembers(module, inspect.isclass)
if hyperopt_name == name and IHyperOpt in obj.__bases__
)
return next(valid_hyperopts_gen, None)
@staticmethod
def _search_hyperopt(directory: str, hyperopt_name: str) -> Optional[IHyperOpt]:
"""
Search for the hyperopt_name in the given directory
:param directory: relative or absolute directory path
:return: name of the hyperopt class
"""
logger.debug('Searching for hyperopt %s in \'%s\'', hyperopt_name, directory)
for entry in os.listdir(directory):
# Only consider python files
if not entry.endswith('.py'):
logger.debug('Ignoring %s', entry)
continue
hyperopt = HyperOptResolver._get_valid_hyperopts(
os.path.abspath(os.path.join(directory, entry)), hyperopt_name
)
if hyperopt:
return hyperopt()
return None

10
freqtrade/tests/optimize/test_hyperopt.py
View File

@ -175,7 +175,7 @@ def test_roi_table_generation(hyperopt) -> None:
'roi_p3': 3,
}
assert hyperopt.generate_roi_table(params) == {0: 6, 15: 3, 25: 1, 30: 0}
assert hyperopt.custom_hyperopt.generate_roi_table(params) == {0: 6, 15: 3, 25: 1, 30: 0}
def test_start_calls_optimizer(mocker, default_conf, caplog) -> None:
@ -243,7 +243,8 @@ def test_populate_indicators(hyperopt) -> None:
tick = load_tickerdata_file(None, 'UNITTEST/BTC', '1m')
tickerlist = {'UNITTEST/BTC': tick}
dataframes = hyperopt.strategy.tickerdata_to_dataframe(tickerlist)
dataframe = hyperopt.populate_indicators(dataframes['UNITTEST/BTC'], {'pair': 'UNITTEST/BTC'})
dataframe = hyperopt.custom_hyperopt.populate_indicators(dataframes['UNITTEST/BTC'],
{'pair': 'UNITTEST/BTC'})
# Check if some indicators are generated. We will not test all of them
assert 'adx' in dataframe
@ -255,9 +256,10 @@ def test_buy_strategy_generator(hyperopt) -> None:
tick = load_tickerdata_file(None, 'UNITTEST/BTC', '1m')
tickerlist = {'UNITTEST/BTC': tick}
dataframes = hyperopt.strategy.tickerdata_to_dataframe(tickerlist)
dataframe = hyperopt.populate_indicators(dataframes['UNITTEST/BTC'], {'pair': 'UNITTEST/BTC'})
dataframe = hyperopt.custom_hyperopt.populate_indicators(dataframes['UNITTEST/BTC'],
{'pair': 'UNITTEST/BTC'})
populate_buy_trend = hyperopt.buy_strategy_generator(
populate_buy_trend = hyperopt.custom_hyperopt.buy_strategy_generator(
{
'adx-value': 20,
'fastd-value': 20,

1
requirements.txt
View File

@ -8,6 +8,7 @@ urllib3==1.24.1
wrapt==1.10.11
pandas==0.23.4
scikit-learn==0.20.0
joblib==0.13.0
scipy==1.1.0
jsonschema==2.6.0
numpy==1.15.4

1
setup.py
View File

@ -31,6 +31,7 @@ setup(name='freqtrade',
'pandas',
'scikit-learn',
'scipy',
'joblib',
'jsonschema',
'TA-Lib',
'tabulate',

0
user_data/hyperopts/__init__.py Normal file
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139
user_data/hyperopts/sample_hyperopt.py Normal file
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@ -0,0 +1,139 @@
# pragma pylint: disable=missing-docstring, invalid-name, pointless-string-statement
import talib.abstract as ta
from pandas import DataFrame
from typing import Dict, Any, Callable, List
from functools import reduce
import numpy
from skopt.space import Categorical, Dimension, Integer, Real
import freqtrade.vendor.qtpylib.indicators as qtpylib
from freqtrade.optimize.hyperopt_interface import IHyperOpt
class_name = 'SampleHyperOpts'
# This class is a sample. Feel free to customize it.
class SampleHyperOpts(IHyperOpt):
"""
This is a test hyperopt to inspire you.
More information in https://github.com/freqtrade/freqtrade/blob/develop/docs/hyperopt.md
You can:
- Rename the class name (Do not forget to update class_name)
- Add any methods you want to build your hyperopt
- Add any lib you need to build your hyperopt
You must keep:
- the prototype for the methods: populate_indicators, indicator_space, buy_strategy_generator,
roi_space, generate_roi_table, stoploss_space
"""
@staticmethod
def populate_indicators(dataframe: DataFrame, metadata: dict) -> DataFrame:
dataframe['adx'] = ta.ADX(dataframe)
macd = ta.MACD(dataframe)
dataframe['macd'] = macd['macd']
dataframe['macdsignal'] = macd['macdsignal']
dataframe['mfi'] = ta.MFI(dataframe)
dataframe['rsi'] = ta.RSI(dataframe)
stoch_fast = ta.STOCHF(dataframe)
dataframe['fastd'] = stoch_fast['fastd']
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['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
"""
def populate_buy_trend(dataframe: DataFrame, metadata: dict) -> DataFrame:
"""
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'])
if 'fastd-enabled' in params and params['fastd-enabled']:
conditions.append(dataframe['fastd'] < params['fastd-value'])
if 'adx-enabled' in params and params['adx-enabled']:
conditions.append(dataframe['adx'] > params['adx-value'])
if 'rsi-enabled' in params and params['rsi-enabled']:
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']
))
dataframe.loc[
reduce(lambda x, y: x & y, conditions),
'buy'] = 1
return dataframe
return populate_buy_trend
@staticmethod
def indicator_space() -> List[Dimension]:
"""
Define your Hyperopt space for searching strategy parameters
"""
return [
Integer(10, 25, name='mfi-value'),
Integer(15, 45, name='fastd-value'),
Integer(20, 50, name='adx-value'),
Integer(20, 40, name='rsi-value'),
Categorical([True, False], name='mfi-enabled'),
Categorical([True, False], name='fastd-enabled'),
Categorical([True, False], name='adx-enabled'),
Categorical([True, False], name='rsi-enabled'),
Categorical(['bb_lower', 'macd_cross_signal', 'sar_reversal'], name='trigger')
]
@staticmethod
def generate_roi_table(params: Dict) -> Dict[int, float]:
"""
Generate the ROI table that will be used by Hyperopt
"""
roi_table = {}
roi_table[0] = params['roi_p1'] + params['roi_p2'] + params['roi_p3']
roi_table[params['roi_t3']] = params['roi_p1'] + params['roi_p2']
roi_table[params['roi_t3'] + params['roi_t2']] = params['roi_p1']
roi_table[params['roi_t3'] + params['roi_t2'] + params['roi_t1']] = 0
return roi_table
@staticmethod
def stoploss_space() -> List[Dimension]:
"""
Stoploss Value to search
"""
return [
Real(-0.5, -0.02, name='stoploss'),
]
@staticmethod
def roi_space() -> List[Dimension]:
"""
Values to search for each ROI steps
"""
return [
Integer(10, 120, name='roi_t1'),
Integer(10, 60, name='roi_t2'),
Integer(10, 40, name='roi_t3'),
Real(0.01, 0.04, name='roi_p1'),
Real(0.01, 0.07, name='roi_p2'),
Real(0.01, 0.20, name='roi_p3'),
]