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Merge pull request #232 from gcarq/tweak-hyperopt

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Tweak Hyperopt
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Samuel Husso 2017-12-23 19:25:45 +02:00 committed by GitHub
commit 433bf409f4
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3 changed files with 19 additions and 19 deletions
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8
freqtrade/optimize/backtesting.py
View File

@ -64,7 +64,7 @@ def generate_text_table(
return tabulate(tabular_data, headers=headers) return tabulate(tabular_data, headers=headers)
def backtest(config: Dict, processed: Dict[str, DataFrame], def backtest(stake_amount: float, processed: Dict[str, DataFrame],
max_open_trades: int = 0, realistic: bool = True) -> DataFrame: max_open_trades: int = 0, realistic: bool = True) -> DataFrame:
""" """
Implements backtesting functionality Implements backtesting functionality
@ -98,8 +98,8 @@ def backtest(config: Dict, processed: Dict[str, DataFrame],
trade = Trade( trade = Trade(
open_rate=row.close, open_rate=row.close,
open_date=row.date, open_date=row.date,
stake_amount=config['stake_amount'], stake_amount=stake_amount,
amount=config['stake_amount'] / row.open, amount=stake_amount / row.open,
fee=exchange.get_fee() fee=exchange.get_fee()
) )
@ -170,7 +170,7 @@ def start(args):
# Execute backtest and print results # Execute backtest and print results
results = backtest( results = backtest(
config, preprocess(data), max_open_trades, args.realistic_simulation config['stake_amount'], preprocess(data), max_open_trades, args.realistic_simulation
) )
logger.info( logger.info(
'\n====================== BACKTESTING REPORT ======================================\n%s', '\n====================== BACKTESTING REPORT ======================================\n%s',

26
freqtrade/optimize/hyperopt.py
View File

@ -32,9 +32,13 @@ TARGET_TRADES = 1100
TOTAL_TRIES = None TOTAL_TRIES = None
_CURRENT_TRIES = 0 _CURRENT_TRIES = 0
TOTAL_PROFIT_TO_BEAT = 3 TOTAL_PROFIT_TO_BEAT = 0
AVG_PROFIT_TO_BEAT = 0.2 AVG_PROFIT_TO_BEAT = 0
AVG_DURATION_TO_BEAT = 50 AVG_DURATION_TO_BEAT = 100
# this is expexted avg profit * expected trade count
# for example 3.5%, 1100 trades, EXPECTED_MAX_PROFIT = 3.85
EXPECTED_MAX_PROFIT = 3.85
# Configuration and data used by hyperopt # Configuration and data used by hyperopt
PROCESSED = optimize.preprocess(optimize.load_data()) PROCESSED = optimize.preprocess(optimize.load_data())
@ -101,12 +105,10 @@ def log_results(results):
current_try = results['current_tries'] current_try = results['current_tries']
total_tries = results['total_tries'] total_tries = results['total_tries']
result = results['result'] result = results['result']
profit = results['total_profit'] / 1000 profit = results['total_profit']
outcome = '{:5d}/{}: {}'.format(current_try, total_tries, result)
if profit >= TOTAL_PROFIT_TO_BEAT: if profit >= TOTAL_PROFIT_TO_BEAT:
logger.info(outcome) logger.info('\n{:5d}/{}: {}'.format(current_try, total_tries, result))
else: else:
print('.', end='') print('.', end='')
sys.stdout.flush() sys.stdout.flush()
@ -118,15 +120,15 @@ def optimizer(params):
from freqtrade.optimize import backtesting from freqtrade.optimize import backtesting
backtesting.populate_buy_trend = buy_strategy_generator(params) backtesting.populate_buy_trend = buy_strategy_generator(params)
results = backtest(OPTIMIZE_CONFIG, PROCESSED) results = backtest(OPTIMIZE_CONFIG['stake_amount'], PROCESSED)
result = format_results(results) result = format_results(results)
total_profit = results.profit_percent.sum() * 1000 total_profit = results.profit_percent.sum()
trade_count = len(results.index) trade_count = len(results.index)
trade_loss = 1 - 0.35 * exp(-(trade_count - TARGET_TRADES) ** 2 / 10 ** 5.2) trade_loss = 1 - 0.35 * exp(-(trade_count - TARGET_TRADES) ** 2 / 10 ** 5.2)
profit_loss = max(0, 1 - total_profit / 10000) # max profit 10000 profit_loss = max(0, 1 - total_profit / EXPECTED_MAX_PROFIT)
_CURRENT_TRIES += 1 _CURRENT_TRIES += 1
@ -142,8 +144,6 @@ def optimizer(params):
'result': result, 'result': result,
'results': results 'results': results
} }
# logger.info('{:5d}/{}: {}'.format(_CURRENT_TRIES, TOTAL_TRIES, result))
log_results(result_data) log_results(result_data)
return { return {
@ -157,7 +157,7 @@ def optimizer(params):
def format_results(results: DataFrame): def format_results(results: DataFrame):
return ('Made {:6d} buys. Average profit {: 5.2f}%. ' return ('Made {:6d} buys. Average profit {: 5.2f}%. '
'Total profit was {: 7.3f}. Average duration {:5.1f} mins.').format( 'Total profit was {: 11.8f} BTC. Average 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(),

4
freqtrade/tests/test_optimize_backtesting.py
View File

@ -12,7 +12,7 @@ def test_backtest(default_conf, mocker):
exchange._API = Bittrex({'key': '', 'secret': ''}) exchange._API = Bittrex({'key': '', 'secret': ''})
data = optimize.load_data(ticker_interval=5, pairs=['BTC_ETH']) data = optimize.load_data(ticker_interval=5, pairs=['BTC_ETH'])
results = backtest(default_conf, optimize.preprocess(data), 10, True) results = backtest(default_conf['stake_amount'], optimize.preprocess(data), 10, True)
num_results = len(results) num_results = len(results)
assert num_results > 0 assert num_results > 0
@ -23,7 +23,7 @@ def test_1min_ticker_interval(default_conf, mocker):
# Run a backtesting for an exiting 5min ticker_interval # Run a backtesting for an exiting 5min ticker_interval
data = optimize.load_data(ticker_interval=1, pairs=['BTC_UNITEST']) data = optimize.load_data(ticker_interval=1, pairs=['BTC_UNITEST'])
results = backtest(default_conf, optimize.preprocess(data), 1, True) results = backtest(default_conf['stake_amount'], optimize.preprocess(data), 1, True)
assert len(results) > 0 assert len(results) > 0