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problem with pickling

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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 signal
import sys
import multiprocessing
from argparse import Namespace
from functools import reduce
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 skopt.space import Real, Integer, Categorical
from skopt import Optimizer
from sklearn.externals.joblib import Parallel, delayed
import freqtrade.vendor.qtpylib.indicators as qtpylib
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 = 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
def populate_indicators(dataframe: DataFrame) -> DataFrame:
dataframe['adx'] = ta.ADX(dataframe)
@ -173,28 +192,17 @@ class Hyperopt(Backtesting):
"""
Define your Hyperopt space for searching strategy parameters
"""
return {
'macd_below_zero': hp.choice('macd_below_zero', [
{'enabled': False},
{'enabled': True}
]),
'mfi': hp.choice('mfi', [
{'enabled': False},
{'enabled': True, 'value': hp.quniform('mfi-value', 10, 25, 5)}
]),
'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)}
]),
}
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'),
]
def has_space(self, space: str) -> bool:
"""
@ -208,14 +216,15 @@ class Hyperopt(Backtesting):
"""
Return the space to use during Hyperopt
"""
spaces: Dict = {}
if self.has_space('buy'):
spaces = {**spaces, **Hyperopt.indicator_space()}
if self.has_space('roi'):
spaces = {**spaces, **Hyperopt.roi_space()}
if self.has_space('stoploss'):
spaces = {**spaces, **Hyperopt.stoploss_space()}
return spaces
return Hyperopt.indicator_space()
# spaces: Dict = {}
# if self.has_space('buy'):
# spaces = {**spaces, **Hyperopt.indicator_space()}
# if self.has_space('roi'):
# spaces = {**spaces, **Hyperopt.roi_space()}
# if self.has_space('stoploss'):
# spaces = {**spaces, **Hyperopt.stoploss_space()}
# return spaces
@staticmethod
def buy_strategy_generator(params: Dict[str, Any]) -> Callable:
@ -228,16 +237,16 @@ class Hyperopt(Backtesting):
"""
conditions = []
# GUARDS AND TRENDS
if 'macd_below_zero' in params and params['macd_below_zero']['enabled']:
conditions.append(dataframe['macd'] < 0)
if 'mfi' in params and params['mfi']['enabled']:
conditions.append(dataframe['mfi'] < params['mfi']['value'])
if 'fastd' in params and params['fastd']['enabled']:
conditions.append(dataframe['fastd'] < params['fastd']['value'])
if 'adx' in params and params['adx']['enabled']:
conditions.append(dataframe['adx'] > params['adx']['value'])
if 'rsi' in params and params['rsi']['enabled']:
conditions.append(dataframe['rsi'] < params['rsi']['value'])
# if 'macd_below_zero' in params and params['macd_below_zero']['enabled']:
# conditions.append(dataframe['macd'] < 0)
if 'mfi-enabled' in params and params['mfi-enabled']:
conditions.append(dataframe['mfi'] < params['mfi-value'])
if 'fastd' in params and params['fastd-enabled']:
conditions.append(dataframe['fastd'] < params['fastd-value'])
if 'adx' in params and params['adx-enabled']:
conditions.append(dataframe['adx'] > params['adx-value'])
if 'rsi' in params and params['rsi-enabled']:
conditions.append(dataframe['rsi'] < params['rsi-value'])
# TRIGGERS
triggers = {
@ -254,7 +263,9 @@ class Hyperopt(Backtesting):
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'):
self.analyze.strategy.minimal_roi = self.generate_roi_table(params)
@ -297,12 +308,13 @@ class Hyperopt(Backtesting):
'result': result_explanation,
}
)
return loss
return {
'loss': loss,
'status': STATUS_OK,
'result': result_explanation,
}
# return {
# 'loss': loss,
# 'status': STATUS_OK,
# 'result': result_explanation,
# }
def format_results(self, results: DataFrame) -> str:
"""
@ -347,16 +359,29 @@ class Hyperopt(Backtesting):
)
try:
best_parameters = fmin(
fn=self.generate_optimizer,
space=self.hyperopt_space(),
algo=tpe.suggest,
max_evals=self.total_tries,
trials=self.trials
)
# best_parameters = fmin(
# fn=self.generate_optimizer,
# space=self.hyperopt_space(),
# algo=tpe.suggest,
# max_evals=self.total_tries,
# 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:
best_parameters = {}
@ -364,20 +389,20 @@ class Hyperopt(Backtesting):
'try with more epochs (param: -e).'
# Improve best parameter logging display
if best_parameters:
best_parameters = space_eval(
self.hyperopt_space(),
best_parameters
)
# if best_parameters:
# best_parameters = space_eval(
# self.hyperopt_space(),
# best_parameters
# )
logger.info('Best parameters:\n%s', json.dumps(best_parameters, indent=4))
if 'roi_t1' in best_parameters:
logger.info('ROI table:\n%s', self.generate_roi_table(best_parameters))
# logger.info('Best parameters:\n%s', json.dumps(best_parameters, indent=4))
# if 'roi_t1' in 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
self.save_trials()
# # Store trials result to file to resume next time
# self.save_trials()
def signal_handler(self, sig, frame) -> None:
"""