diff --git a/freqtrade/optimize/backtesting.py b/freqtrade/optimize/backtesting.py index d6d016aba..bfe8c33eb 100644 --- a/freqtrade/optimize/backtesting.py +++ b/freqtrade/optimize/backtesting.py @@ -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']) ) diff --git a/freqtrade/optimize/hyperopt.py b/freqtrade/optimize/hyperopt.py index 8b89e1985..12c061b4f 100644 --- a/freqtrade/optimize/hyperopt.py +++ b/freqtrade/optimize/hyperopt.py @@ -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='') diff --git a/freqtrade/tests/optimize/test_backtesting.py b/freqtrade/tests/optimize/test_backtesting.py index 0dd4f777a..bf060e374 100644 --- a/freqtrade/tests/optimize/test_backtesting.py +++ b/freqtrade/tests/optimize/test_backtesting.py @@ -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):