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Merge pull request #393 from gcarq/balancing_hyperopt_2

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Balancing hyperopt objective
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Janne Sinivirta 2018-01-16 18:21:50 +02:00 committed by GitHub
commit fd3568d48f
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1 changed files with 12 additions and 9 deletions
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21
freqtrade/optimize/hyperopt.py
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@ -30,18 +30,19 @@ logging.getLogger('hyperopt.tpe').setLevel(logging.WARNING)
logger = logging.getLogger(__name__) logger = logging.getLogger(__name__)
# set TARGET_TRADES to suit your number concurrent trades so its realistic to 20days of data # set TARGET_TRADES to suit your number concurrent trades so its realistic to 20days of data
TARGET_TRADES = 1100 TARGET_TRADES = 600
TOTAL_TRIES = 0 TOTAL_TRIES = 0
_CURRENT_TRIES = 0 _CURRENT_TRIES = 0
CURRENT_BEST_LOSS = 100 CURRENT_BEST_LOSS = 100
# max average trade duration in minutes # max average trade duration in minutes
# if eval ends with higher value, we consider it a failed eval # if eval ends with higher value, we consider it a failed eval
MAX_ACCEPTED_TRADE_DURATION = 240 MAX_ACCEPTED_TRADE_DURATION = 300
# this is expexted avg profit * expected trade count # this is expexted avg profit * expected trade count
# for example 3.5%, 1100 trades, EXPECTED_MAX_PROFIT = 3.85 # for example 3.5%, 1100 trades, EXPECTED_MAX_PROFIT = 3.85
EXPECTED_MAX_PROFIT = 3.85 # check that the reported Σ% values do not exceed this!
EXPECTED_MAX_PROFIT = 3.0
# Configuration and data used by hyperopt # Configuration and data used by hyperopt
PROCESSED = None # optimize.preprocess(optimize.load_data()) PROCESSED = None # optimize.preprocess(optimize.load_data())
@ -133,10 +134,11 @@ def log_results(results):
if results['loss'] < CURRENT_BEST_LOSS: if results['loss'] < CURRENT_BEST_LOSS:
CURRENT_BEST_LOSS = results['loss'] CURRENT_BEST_LOSS = results['loss']
logger.info('{:5d}/{}: {}'.format( logger.info('{:5d}/{}: {}. Loss {:.5f}'.format(
results['current_tries'], results['current_tries'],
results['total_tries'], results['total_tries'],
results['result'])) results['result'],
results['loss']))
else: else:
print('.', end='') print('.', end='')
sys.stdout.flush() sys.stdout.flush()
@ -144,9 +146,9 @@ def log_results(results):
def calculate_loss(total_profit: float, trade_count: int, trade_duration: float): def calculate_loss(total_profit: float, trade_count: int, trade_duration: float):
""" objective function, returns smaller number for more optimal results """ """ objective function, returns smaller number for more optimal results """
trade_loss = 1 - 0.35 * exp(-(trade_count - TARGET_TRADES) ** 2 / 10 ** 5.2) trade_loss = 1 - 0.25 * exp(-(trade_count - TARGET_TRADES) ** 2 / 10 ** 5.8)
profit_loss = max(0, 1 - total_profit / EXPECTED_MAX_PROFIT) profit_loss = max(0, 1 - total_profit / EXPECTED_MAX_PROFIT)
duration_loss = min(trade_duration / MAX_ACCEPTED_TRADE_DURATION, 1) duration_loss = 0.7 + 0.3 * min(trade_duration / MAX_ACCEPTED_TRADE_DURATION, 1)
return trade_loss + profit_loss + duration_loss return trade_loss + profit_loss + duration_loss
@ -190,12 +192,13 @@ def optimizer(params):
def format_results(results: DataFrame): def format_results(results: DataFrame):
return ('{:6d} trades. Avg profit {: 5.2f}%. ' return ('{:6d} trades. Avg profit {: 5.2f}%. '
'Total profit {: 11.8f} BTC. Avg duration {:5.1f} mins.').format( 'Total profit {: 11.8f} BTC ({:.4f}Σ%). Avg duration {:5.1f} mins.').format(
len(results.index), len(results.index),
results.profit_percent.mean() * 100.0, results.profit_percent.mean() * 100.0,
results.profit_BTC.sum(), results.profit_BTC.sum(),
results.profit_percent.sum(),
results.duration.mean() * 5, results.duration.mean() * 5,
) )
def buy_strategy_generator(params): def buy_strategy_generator(params):