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problem with pickling

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This commit is contained in:
Janne Sinivirta 2018-06-19 09:09:54 +03:00
parent b485e6e0ba
commit 0cb1aedf5b
1 changed files with 91 additions and 66 deletions
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157
freqtrade/optimize/hyperopt.py
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@ -10,6 +10,8 @@ import os
import pickle import pickle
import signal import signal
import sys import sys
import multiprocessing
from argparse import Namespace from argparse import Namespace
from functools import reduce from functools import reduce
from math import exp from math import exp
@ -22,6 +24,8 @@ from hyperopt import STATUS_FAIL, STATUS_OK, Trials, fmin, hp, space_eval, tpe
from pandas import DataFrame from pandas import DataFrame
from skopt.space import Real, Integer, Categorical from skopt.space import Real, Integer, Categorical
from skopt import Optimizer
from sklearn.externals.joblib import Parallel, delayed
import freqtrade.vendor.qtpylib.indicators as qtpylib import freqtrade.vendor.qtpylib.indicators as qtpylib
from freqtrade.arguments import Arguments from freqtrade.arguments import Arguments
@ -65,6 +69,21 @@ class Hyperopt(Backtesting):
self.trials_file = os.path.join('user_data', 'hyperopt_trials.pickle') self.trials_file = os.path.join('user_data', 'hyperopt_trials.pickle')
self.trials = Trials() self.trials = Trials()
def get_args(self, params):
dimensions = self.hyperopt_space()
# Ensure the number of dimensions match
# the number of parameters in the list x.
if len(params) != len(dimensions):
msg = "Mismatch in number of search-space dimensions. " \
"len(dimensions)=={} and len(x)=={}"
msg = msg.format(len(dimensions), len(params))
raise ValueError(msg)
# Create a dict where the keys are the names of the dimensions
# and the values are taken from the list of parameters x.
arg_dict = {dim.name: value for dim, value in zip(dimensions, params)}
return arg_dict
@staticmethod @staticmethod
def populate_indicators(dataframe: DataFrame) -> DataFrame: def populate_indicators(dataframe: DataFrame) -> DataFrame:
dataframe['adx'] = ta.ADX(dataframe) dataframe['adx'] = ta.ADX(dataframe)
@ -173,28 +192,17 @@ class Hyperopt(Backtesting):
""" """
Define your Hyperopt space for searching strategy parameters Define your Hyperopt space for searching strategy parameters
""" """
return { return [
'macd_below_zero': hp.choice('macd_below_zero', [ Integer(10, 25, name='mfi-value'),
{'enabled': False}, Integer(15, 45, name='fastd-value'),
{'enabled': True} Integer(20, 50, name='adx-value'),
]), Integer(20, 40, name='rsi-value'),
'mfi': hp.choice('mfi', [ Categorical([True, False], name='mfi-enabled'),
{'enabled': False}, Categorical([True, False], name='fastd-enabled'),
{'enabled': True, 'value': hp.quniform('mfi-value', 10, 25, 5)} Categorical([True, False], name='adx-enabled'),
]), Categorical([True, False], name='rsi-enabled'),
'fastd': hp.choice('fastd', [ ]
{'enabled': False},
{'enabled': True, 'value': hp.quniform('fastd-value', 15, 45, 5)}
]),
'adx': hp.choice('adx', [
{'enabled': False},
{'enabled': True, 'value': hp.quniform('adx-value', 20, 50, 5)}
]),
'rsi': hp.choice('rsi', [
{'enabled': False},
{'enabled': True, 'value': hp.quniform('rsi-value', 20, 40, 5)}
]),
}
def has_space(self, space: str) -> bool: def has_space(self, space: str) -> bool:
""" """
@ -208,14 +216,15 @@ class Hyperopt(Backtesting):
""" """
Return the space to use during Hyperopt Return the space to use during Hyperopt
""" """
spaces: Dict = {} return Hyperopt.indicator_space()
if self.has_space('buy'): # spaces: Dict = {}
spaces = {**spaces, **Hyperopt.indicator_space()} # if self.has_space('buy'):
if self.has_space('roi'): # spaces = {**spaces, **Hyperopt.indicator_space()}
spaces = {**spaces, **Hyperopt.roi_space()} # if self.has_space('roi'):
if self.has_space('stoploss'): # spaces = {**spaces, **Hyperopt.roi_space()}
spaces = {**spaces, **Hyperopt.stoploss_space()} # if self.has_space('stoploss'):
return spaces # spaces = {**spaces, **Hyperopt.stoploss_space()}
# return spaces
@staticmethod @staticmethod
def buy_strategy_generator(params: Dict[str, Any]) -> Callable: def buy_strategy_generator(params: Dict[str, Any]) -> Callable:
@ -228,16 +237,16 @@ class Hyperopt(Backtesting):
""" """
conditions = [] conditions = []
# GUARDS AND TRENDS # GUARDS AND TRENDS
if 'macd_below_zero' in params and params['macd_below_zero']['enabled']: # if 'macd_below_zero' in params and params['macd_below_zero']['enabled']:
conditions.append(dataframe['macd'] < 0) # conditions.append(dataframe['macd'] < 0)
if 'mfi' in params and params['mfi']['enabled']: if 'mfi-enabled' in params and params['mfi-enabled']:
conditions.append(dataframe['mfi'] < params['mfi']['value']) conditions.append(dataframe['mfi'] < params['mfi-value'])
if 'fastd' in params and params['fastd']['enabled']: if 'fastd' in params and params['fastd-enabled']:
conditions.append(dataframe['fastd'] < params['fastd']['value']) conditions.append(dataframe['fastd'] < params['fastd-value'])
if 'adx' in params and params['adx']['enabled']: if 'adx' in params and params['adx-enabled']:
conditions.append(dataframe['adx'] > params['adx']['value']) conditions.append(dataframe['adx'] > params['adx-value'])
if 'rsi' in params and params['rsi']['enabled']: if 'rsi' in params and params['rsi-enabled']:
conditions.append(dataframe['rsi'] < params['rsi']['value']) conditions.append(dataframe['rsi'] < params['rsi-value'])
# TRIGGERS # TRIGGERS
triggers = { triggers = {
@ -254,7 +263,9 @@ class Hyperopt(Backtesting):
return populate_buy_trend return populate_buy_trend
def generate_optimizer(self, params: Dict) -> Dict: def generate_optimizer(self, _params) -> Dict:
params = self.get_args(_params)
if self.has_space('roi'): if self.has_space('roi'):
self.analyze.strategy.minimal_roi = self.generate_roi_table(params) self.analyze.strategy.minimal_roi = self.generate_roi_table(params)
@ -297,12 +308,13 @@ class Hyperopt(Backtesting):
'result': result_explanation, 'result': result_explanation,
} }
) )
return loss
return { # return {
'loss': loss, # 'loss': loss,
'status': STATUS_OK, # 'status': STATUS_OK,
'result': result_explanation, # 'result': result_explanation,
} # }
def format_results(self, results: DataFrame) -> str: def format_results(self, results: DataFrame) -> str:
""" """
@ -347,16 +359,29 @@ class Hyperopt(Backtesting):
) )
try: try:
best_parameters = fmin( # best_parameters = fmin(
fn=self.generate_optimizer, # fn=self.generate_optimizer,
space=self.hyperopt_space(), # space=self.hyperopt_space(),
algo=tpe.suggest, # algo=tpe.suggest,
max_evals=self.total_tries, # max_evals=self.total_tries,
trials=self.trials # trials=self.trials
) # )
# results = sorted(self.trials.results, key=itemgetter('loss'))
# best_result = results[0]['result']
cpus = multiprocessing.cpu_count()
print(f'Found {cpus}. Let\'s make them scream!')
opt = Optimizer(self.hyperopt_space(), "ET", acq_optimizer="sampling")
for i in range(self.total_tries//cpus):
asked = opt.ask(n_points=cpus)
#asked = opt.ask()
#f_val = self.generate_optimizer(asked)
f_val = Parallel(n_jobs=-1)(delayed(self.generate_optimizer)(v) for v in asked)
opt.tell(asked, f_val)
print(f'got value {f_val}')
results = sorted(self.trials.results, key=itemgetter('loss'))
best_result = results[0]['result']
except ValueError: except ValueError:
best_parameters = {} best_parameters = {}
@ -364,20 +389,20 @@ class Hyperopt(Backtesting):
'try with more epochs (param: -e).' 'try with more epochs (param: -e).'
# Improve best parameter logging display # Improve best parameter logging display
if best_parameters: # if best_parameters:
best_parameters = space_eval( # best_parameters = space_eval(
self.hyperopt_space(), # self.hyperopt_space(),
best_parameters # best_parameters
) # )
logger.info('Best parameters:\n%s', json.dumps(best_parameters, indent=4)) # logger.info('Best parameters:\n%s', json.dumps(best_parameters, indent=4))
if 'roi_t1' in best_parameters: # if 'roi_t1' in best_parameters:
logger.info('ROI table:\n%s', self.generate_roi_table(best_parameters)) # logger.info('ROI table:\n%s', self.generate_roi_table(best_parameters))
logger.info('Best Result:\n%s', best_result) # logger.info('Best Result:\n%s', best_result)
# Store trials result to file to resume next time # # Store trials result to file to resume next time
self.save_trials() # self.save_trials()
def signal_handler(self, sig, frame) -> None: def signal_handler(self, sig, frame) -> None:
""" """