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Merge pull request #507 from gcarq/date_indexing_for_backtesting

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Date indexing for backtesting
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Samuel Husso 2018-02-06 12:20:33 +02:00 committed by GitHub
commit c5400b6c37
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3 changed files with 24 additions and 22 deletions
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36
freqtrade/optimize/backtesting.py
View File

@ -33,7 +33,7 @@ def get_timeframe(data: Dict[str, DataFrame]) -> Tuple[arrow.Arrow, arrow.Arrow]
def generate_text_table(
data: Dict[str, Dict], results: DataFrame, stake_currency, ticker_interval) -> str:
data: Dict[str, Dict], results: DataFrame, stake_currency) -> str:
"""
Generates and returns a text table for the given backtest data and the results dataframe
:return: pretty printed table with tabulate as str
@ -49,7 +49,7 @@ def generate_text_table(
len(result.index),
result.profit_percent.mean() * 100.0,
result.profit_BTC.sum(),
result.duration.mean() * ticker_interval,
result.duration.mean(),
len(result[result.profit_BTC > 0]),
len(result[result.profit_BTC < 0])
])
@ -60,7 +60,7 @@ def generate_text_table(
len(results.index),
results.profit_percent.mean() * 100.0,
results.profit_BTC.sum(),
results.duration.mean() * ticker_interval,
results.duration.mean(),
len(results[results.profit_BTC > 0]),
len(results[results.profit_BTC < 0])
])
@ -71,28 +71,28 @@ def get_sell_trade_entry(pair, row, buy_subset, ticker, trade_count_lock, args):
stake_amount = args['stake_amount']
max_open_trades = args.get('max_open_trades', 0)
trade = Trade(open_rate=row.close,
open_date=row.date,
open_date=row.Index,
stake_amount=stake_amount,
amount=stake_amount / row.open,
fee=exchange.get_fee()
)
# calculate win/lose forwards from buy point
sell_subset = ticker[ticker.date > row.date][['close', 'date', 'sell']]
sell_subset = ticker[ticker.index > row.Index][['close', 'sell']]
for row2 in sell_subset.itertuples(index=True):
if max_open_trades > 0:
# Increase trade_count_lock for every iteration
trade_count_lock[row2.date] = trade_count_lock.get(row2.date, 0) + 1
trade_count_lock[row2.Index] = trade_count_lock.get(row2.Index, 0) + 1
# Buy is on is in the buy_subset there is a row that matches the date
# of the sell event
buy_signal = not buy_subset[buy_subset.date == row2.date].empty
if(should_sell(trade, row2.close, row2.date, buy_signal, row2.sell)):
buy_signal = (buy_subset.index == row2.Index).any()
if(should_sell(trade, row2.close, row2.Index, buy_signal, row2.sell)):
return row2, (pair,
trade.calc_profit_percent(rate=row2.close),
trade.calc_profit(rate=row2.close),
row2.Index - row.Index
), row2.date
(row2.Index - row.Index).seconds // 60
), row2.Index
return None
@ -120,22 +120,24 @@ def backtest(args) -> DataFrame:
for pair, pair_data in processed.items():
pair_data['buy'], pair_data['sell'] = 0, 0
ticker = populate_sell_trend(populate_buy_trend(pair_data))
if 'date' in ticker:
ticker.set_index('date', inplace=True)
# for each buy point
lock_pair_until = None
headers = ['buy', 'open', 'close', 'date', 'sell']
headers = ['buy', 'open', 'close', 'sell']
buy_subset = ticker[(ticker.buy == 1) & (ticker.sell == 0)][headers]
for row in buy_subset.itertuples(index=True):
if realistic:
if lock_pair_until is not None and row.date <= lock_pair_until:
if lock_pair_until is not None and row.Index <= lock_pair_until:
continue
if max_open_trades > 0:
# Check if max_open_trades has already been reached for the given date
if not trade_count_lock.get(row.date, 0) < max_open_trades:
if not trade_count_lock.get(row.Index, 0) < max_open_trades:
continue
if max_open_trades > 0:
# Increase lock
trade_count_lock[row.date] = trade_count_lock.get(row.date, 0) + 1
trade_count_lock[row.Index] = trade_count_lock.get(row.Index, 0) + 1
ret = get_sell_trade_entry(pair, row, buy_subset, ticker,
trade_count_lock, args)
@ -148,8 +150,8 @@ def backtest(args) -> DataFrame:
# record a tuple of pair, current_profit_percent,
# entry-date, duration
records.append((pair, trade_entry[1],
row.date.strftime('%s'),
row2.date.strftime('%s'),
row.Index.strftime('%s'),
row2.Index.strftime('%s'),
row.Index, trade_entry[3]))
# For now export inside backtest(), maybe change so that backtest()
# returns a tuple like: (dataframe, records, logs, etc)
@ -231,5 +233,5 @@ def start(args):
})
logger.info(
'\n==================================== BACKTESTING REPORT ====================================\n%s', # noqa
generate_text_table(data, results, config['stake_currency'], strategy.ticker_interval)
generate_text_table(data, results, config['stake_currency'])
)

2
freqtrade/optimize/hyperopt.py
View File

@ -406,7 +406,7 @@ def optimizer(params):
total_profit = results.profit_percent.sum()
trade_count = len(results.index)
trade_duration = results.duration.mean() * 5
trade_duration = results.duration.mean()
if trade_count == 0 or trade_duration > MAX_ACCEPTED_TRADE_DURATION:
print('.', end='')

8
freqtrade/tests/optimize/test_backtesting.py
View File

@ -29,12 +29,12 @@ def test_generate_text_table():
'loss': [0, 0]
}
)
print(generate_text_table({'BTC_ETH': {}}, results, 'BTC', 5))
assert generate_text_table({'BTC_ETH': {}}, results, 'BTC', 5) == (
print(generate_text_table({'BTC_ETH': {}}, results, 'BTC'))
assert generate_text_table({'BTC_ETH': {}}, results, 'BTC') == (
'pair buy count avg profit % total profit BTC avg duration profit loss\n' # noqa
'------- ----------- -------------- ------------------ -------------- -------- ------\n' # noqa
'BTC_ETH 2 15.00 0.60000000 100.0 2 0\n' # noqa
'TOTAL 2 15.00 0.60000000 100.0 2 0') # noqa
'BTC_ETH 2 15.00 0.60000000 20.0 2 0\n' # noqa
'TOTAL 2 15.00 0.60000000 20.0 2 0') # noqa
def test_get_timeframe(default_strategy):