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