Merge commit '1134c81aad049d4357c8f299ffc801218f3d9574' into feature/objectify
This commit is contained in:
Gerald Lonlas
commit
38510d4b03
@ -172,19 +172,17 @@ class Analyze(object):
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:return True if bot should sell at current rate
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"""
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current_profit = trade.calc_profit_percent(current_rate)
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if self.strategy.stoploss is not None and current_profit < float(self.strategy.stoploss):
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if self.strategy.stoploss is not None and current_profit < self.strategy.stoploss:
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self.logger.debug('Stop loss hit.')
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return True
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# Check if time matches and current rate is above threshold
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time_diff = (current_time.timestamp() - trade.open_date.timestamp()) / 60
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for duration_string, threshold in self.strategy.minimal_roi.items():
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duration = float(duration_string)
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if time_diff > duration and current_profit > threshold:
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return True
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if time_diff < duration:
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for duration, threshold in self.strategy.minimal_roi.items():
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if time_diff <= duration:
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return False
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if current_profit > threshold:
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return True
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return False
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@ -116,7 +116,7 @@ class Configuration(object):
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if 'realistic_simulation' in self.args and self.args.realistic_simulation:
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config.update({'realistic_simulation': True})
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self.logger.info('Parameter --realistic-simulation detected ...')
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self.logger.info('Using max_open_trades: %s ...', config.get('max_open_trades'))
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self.logger.info('Using max_open_trades: %s ...', config.get('max_open_trades'))
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# If --timerange is used we add it to the configuration
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if 'timerange' in self.args and self.args.timerange:
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@ -67,5 +67,5 @@ def file_dump_json(filename, data) -> None:
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:param data: JSON Data to save
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:return:
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"""
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with open(filename, 'w') as file:
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json.dump(data, file)
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with open(filename, 'w') as fp:
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json.dump(data, fp, default=str)
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@ -102,43 +102,35 @@ class Backtesting(object):
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])
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return tabulate(tabular_data, headers=headers, floatfmt=floatfmt)
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def _get_sell_trade_entry(self, pair, row, buy_subset, ticker, trade_count_lock, args):
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def _get_sell_trade_entry(self, pair, buy_row, partial_ticker, trade_count_lock, args):
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stake_amount = args['stake_amount']
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max_open_trades = args.get('max_open_trades', 0)
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trade = Trade(
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open_rate=row.close,
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open_date=row.Index,
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open_rate=buy_row.close,
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open_date=buy_row.date,
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stake_amount=stake_amount,
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amount=stake_amount / row.open,
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amount=stake_amount / buy_row.open,
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fee=exchange.get_fee()
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)
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# calculate win/lose forwards from buy point
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sell_subset = ticker[ticker.index > row.Index][['close', 'sell', 'buy']]
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for row2 in sell_subset.itertuples(index=True):
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for sell_row in partial_ticker:
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if max_open_trades > 0:
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# Increase trade_count_lock for every iteration
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trade_count_lock[row2.Index] = trade_count_lock.get(row2.Index, 0) + 1
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trade_count_lock[sell_row.date] = trade_count_lock.get(sell_row.date, 0) + 1
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buy_signal = row2.buy
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if(
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self.analyze.should_sell(
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trade=trade,
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rate=row2.close,
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date=row2.Index,
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buy=buy_signal,
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sell=row2.sell
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)
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):
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buy_signal = sell_row.buy
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if self.analyze.should_sell(trade, sell_row.close, sell_row.date, buy_signal,
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sell_row.sell):
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return \
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row2, \
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sell_row, \
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(
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pair,
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trade.calc_profit_percent(rate=row2.close),
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trade.calc_profit(rate=row2.close),
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(row2.Index - row.Index).seconds // 60
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),\
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row2.Index
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trade.calc_profit_percent(rate=sell_row.close),
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trade.calc_profit(rate=sell_row.close),
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(sell_row.date - buy_row.date).seconds // 60
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), \
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sell_row.date
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return None
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def backtest(self, args) -> DataFrame:
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@ -159,6 +151,7 @@ class Backtesting(object):
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stoploss: use stoploss
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:return: DataFrame
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"""
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headers = ['date', 'buy', 'open', 'close', 'sell']
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processed = args['processed']
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max_open_trades = args.get('max_open_trades', 0)
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realistic = args.get('realistic', True)
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@ -167,37 +160,28 @@ class Backtesting(object):
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trades = []
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trade_count_lock = {}
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for pair, pair_data in processed.items():
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pair_data['buy'], pair_data['sell'] = 0, 0
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ticker = self.populate_sell_trend(
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self.populate_buy_trend(pair_data)
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)
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if 'date' in ticker:
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ticker.set_index('date', inplace=True)
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# for each buy point
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pair_data['buy'], pair_data['sell'] = 0, 0 # cleanup from previous run
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ticker_data = self.populate_sell_trend(self.populate_buy_trend(pair_data))[headers]
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ticker = [x for x in ticker_data.itertuples()]
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lock_pair_until = None
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headers = ['buy', 'open', 'close', 'sell']
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buy_subset = ticker[(ticker.buy == 1) & (ticker.sell == 0)][headers]
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for row in buy_subset.itertuples(index=True):
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for index, row in enumerate(ticker):
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if row.buy == 0 or row.sell == 1:
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continue # skip rows where no buy signal or that would immediately sell off
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if realistic:
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if lock_pair_until is not None and row.Index <= lock_pair_until:
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if lock_pair_until is not None and row.date <= lock_pair_until:
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continue
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if max_open_trades > 0:
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# Check if max_open_trades has already been reached for the given date
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if not trade_count_lock.get(row.Index, 0) < max_open_trades:
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if not trade_count_lock.get(row.date, 0) < max_open_trades:
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continue
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if max_open_trades > 0:
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# Increase lock
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trade_count_lock[row.Index] = trade_count_lock.get(row.Index, 0) + 1
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trade_count_lock[row.date] = trade_count_lock.get(row.date, 0) + 1
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ret = self._get_sell_trade_entry(
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pair=pair,
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row=row,
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buy_subset=buy_subset,
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ticker=ticker,
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trade_count_lock=trade_count_lock,
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args=args
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)
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ret = self._get_sell_trade_entry(pair, row, ticker[index + 1:],
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trade_count_lock, args)
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if ret:
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row2, trade_entry, next_date = ret
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@ -208,9 +192,9 @@ class Backtesting(object):
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# record a tuple of pair, current_profit_percent,
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# entry-date, duration
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records.append((pair, trade_entry[1],
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row.Index.strftime('%s'),
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row2.Index.strftime('%s'),
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row.Index, trade_entry[3]))
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row.date.strftime('%s'),
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row2.date.strftime('%s'),
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row.date, trade_entry[3]))
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# For now export inside backtest(), maybe change so that backtest()
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# returns a tuple like: (dataframe, records, logs, etc)
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if record and record.find('trades') >= 0:
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@ -226,6 +210,8 @@ class Backtesting(object):
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"""
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data = {}
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pairs = self.config['exchange']['pair_whitelist']
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self.logger.info('Using stake_currency: %s ...', self.config['stake_currency'])
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self.logger.info('Using stake_amount: %s ...', self.config['stake_amount'])
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if self.config.get('live'):
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self.logger.info('Downloading data for all pairs in whitelist ...')
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@ -233,8 +219,6 @@ class Backtesting(object):
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data[pair] = exchange.get_ticker_history(pair, self.ticker_interval)
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else:
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self.logger.info('Using local backtesting data (using whitelist in given config) ...')
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self.logger.info('Using stake_currency: %s ...', self.config['stake_currency'])
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self.logger.info('Using stake_amount: %s ...', self.config['stake_amount'])
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timerange = Arguments.parse_timerange(self.config.get('timerange'))
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data = optimize.load_data(
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@ -240,15 +240,15 @@ class Hyperopt(Backtesting):
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return trade_loss + profit_loss + duration_loss
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@staticmethod
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def generate_roi_table(params) -> Dict[str, float]:
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def generate_roi_table(params) -> Dict[int, float]:
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"""
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Generate the ROI table thqt will be used by Hyperopt
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"""
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roi_table = {}
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roi_table["0"] = params['roi_p1'] + params['roi_p2'] + params['roi_p3']
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roi_table[str(params['roi_t3'])] = params['roi_p1'] + params['roi_p2']
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roi_table[str(params['roi_t3'] + params['roi_t2'])] = params['roi_p1']
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roi_table[str(params['roi_t3'] + params['roi_t2'] + params['roi_t1'])] = 0
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roi_table[0] = params['roi_p1'] + params['roi_p2'] + params['roi_p3']
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roi_table[params['roi_t3']] = params['roi_p1'] + params['roi_p2']
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roi_table[params['roi_t3'] + params['roi_t2']] = params['roi_p1']
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roi_table[params['roi_t3'] + params['roi_t2'] + params['roi_t1']] = 0
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return roi_table
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@ -58,11 +58,11 @@ class Strategy(object):
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# Minimal ROI designed for the strategy
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self.minimal_roi = OrderedDict(sorted(
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self.custom_strategy.minimal_roi.items(),
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key=lambda tuple: float(tuple[0]))) # sort after converting to number
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{int(key): value for (key, value) in self.custom_strategy.minimal_roi.items()}.items(),
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key=lambda tuple: tuple[0])) # sort after converting to number
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# Optimal stoploss designed for the strategy
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self.stoploss = self.custom_strategy.stoploss
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self.stoploss = float(self.custom_strategy.stoploss)
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self.ticker_interval = self.custom_strategy.ticker_interval
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@ -285,6 +285,7 @@ def ticker_history_without_bv():
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]
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# FIX: Perhaps change result fixture to use BTC_UNITEST instead?
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@pytest.fixture
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def result():
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with open('freqtrade/tests/testdata/BTC_ETH-1.json') as data_file:
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@ -1,12 +1,14 @@
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# pragma pylint: disable=missing-docstring, W0212, line-too-long, C0103, unused-argument
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import json
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import random
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import math
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from typing import List
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from copy import deepcopy
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from unittest.mock import MagicMock
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from arrow import Arrow
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import pandas as pd
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import numpy as np
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from freqtrade import optimize
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from freqtrade.optimize.backtesting import Backtesting, start, setup_configuration
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from freqtrade.arguments import Arguments
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@ -96,6 +98,70 @@ def mocked_load_data(datadir, pairs=[], ticker_interval=0, refresh_pairs=False,
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return pairdata
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# use for mock freqtrade.exchange.get_ticker_history'
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def _load_pair_as_ticks(pair, tickfreq):
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ticks = optimize.load_data(None, ticker_interval=tickfreq, pairs=[pair])
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ticks = trim_dictlist(ticks, -200)
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return ticks[pair]
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# FIX: fixturize this?
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def _make_backtest_conf(conf=None, pair='BTC_UNITEST', record=None):
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data = optimize.load_data(None, ticker_interval=8, pairs=[pair])
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data = trim_dictlist(data, -200)
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return {
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'stake_amount': conf['stake_amount'],
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'processed': _BACKTESTING.tickerdata_to_dataframe(data),
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'max_open_trades': 10,
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'realistic': True,
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'record': record
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}
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def _trend(signals, buy_value, sell_value):
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n = len(signals['low'])
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buy = np.zeros(n)
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sell = np.zeros(n)
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for i in range(0, len(signals['buy'])):
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if random.random() > 0.5: # Both buy and sell signals at same timeframe
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buy[i] = buy_value
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sell[i] = sell_value
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signals['buy'] = buy
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signals['sell'] = sell
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return signals
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def _trend_alternate(dataframe=None):
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signals = dataframe
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low = signals['low']
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n = len(low)
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buy = np.zeros(n)
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sell = np.zeros(n)
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for i in range(0, len(buy)):
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if i % 2 == 0:
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buy[i] = 1
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else:
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sell[i] = 1
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signals['buy'] = buy
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signals['sell'] = sell
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return dataframe
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def _run_backtest_1(fun, backtest_conf):
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# strategy is a global (hidden as a singleton), so we
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# emulate strategy being pure, by override/restore here
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# if we dont do this, the override in strategy will carry over
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# to other tests
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old_buy = _BACKTESTING.populate_buy_trend
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old_sell = _BACKTESTING.populate_sell_trend
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_BACKTESTING.populate_buy_trend = fun # Override
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_BACKTESTING.populate_sell_trend = fun # Override
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results = _BACKTESTING.backtest(backtest_conf)
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_BACKTESTING.populate_buy_trend = old_buy # restore override
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_BACKTESTING.populate_sell_trend = old_sell # restore override
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return results
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# Unit tests
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def test_setup_configuration_without_arguments(mocker, default_conf, caplog) -> None:
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"""
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@ -418,3 +484,125 @@ def test_backtest_pricecontours(default_conf) -> None:
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tests = [['raise', 17], ['lower', 0], ['sine', 17]]
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for [contour, numres] in tests:
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simple_backtest(default_conf, contour, numres)
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# Test backtest using offline data (testdata directory)
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def test_backtest_ticks(default_conf):
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ticks = [1, 5]
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fun = _BACKTESTING.populate_buy_trend
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for tick in ticks:
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backtest_conf = _make_backtest_conf(conf=default_conf)
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results = _run_backtest_1(fun, backtest_conf)
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assert not results.empty
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def test_backtest_clash_buy_sell(default_conf):
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# Override the default buy trend function in our default_strategy
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def fun(dataframe=None):
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buy_value = 1
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sell_value = 1
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return _trend(dataframe, buy_value, sell_value)
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backtest_conf = _make_backtest_conf(conf=default_conf)
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results = _run_backtest_1(fun, backtest_conf)
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assert results.empty
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def test_backtest_only_sell(default_conf):
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# Override the default buy trend function in our default_strategy
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def fun(dataframe=None):
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buy_value = 0
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sell_value = 1
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return _trend(dataframe, buy_value, sell_value)
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backtest_conf = _make_backtest_conf(conf=default_conf)
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results = _run_backtest_1(fun, backtest_conf)
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assert results.empty
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def test_backtest_alternate_buy_sell(default_conf):
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backtest_conf = _make_backtest_conf(conf=default_conf, pair='BTC_UNITEST')
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results = _run_backtest_1(_trend_alternate, backtest_conf)
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assert len(results) == 3
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def test_backtest_record(default_conf, mocker):
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names = []
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records = []
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mocker.patch(
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'freqtrade.optimize.backtesting.file_dump_json',
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new=lambda n, r: (names.append(n), records.append(r))
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)
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backtest_conf = _make_backtest_conf(
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conf=default_conf,
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pair='BTC_UNITEST',
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record="trades"
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)
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results = _run_backtest_1(_trend_alternate, backtest_conf)
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assert len(results) == 3
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# Assert file_dump_json was only called once
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assert names == ['backtest-result.json']
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records = records[0]
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# Ensure records are of correct type
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assert len(records) == 3
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# ('BTC_UNITEST', 0.00331158, '1510684320', '1510691700', 0, 117)
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# Below follows just a typecheck of the schema/type of trade-records
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oix = None
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for (pair, profit, date_buy, date_sell, buy_index, dur) in records:
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assert pair == 'BTC_UNITEST'
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isinstance(profit, float)
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# FIX: buy/sell should be converted to ints
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isinstance(date_buy, str)
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isinstance(date_sell, str)
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isinstance(buy_index, pd._libs.tslib.Timestamp)
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if oix:
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assert buy_index > oix
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oix = buy_index
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assert dur > 0
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def test_backtest_start_live(default_conf, mocker, caplog):
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default_conf['exchange']['pair_whitelist'] = ['BTC_UNITEST']
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mocker.patch('freqtrade.exchange.get_ticker_history',
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new=lambda n, i: _load_pair_as_ticks(n, i))
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mocker.patch('freqtrade.optimize.backtesting.Backtesting.backtest', MagicMock())
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mocker.patch('freqtrade.optimize.backtesting.Backtesting._generate_text_table', MagicMock())
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mocker.patch('freqtrade.configuration.open', mocker.mock_open(
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read_data=json.dumps(default_conf)
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))
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args = MagicMock()
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args.ticker_interval = 1
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args.level = 10
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args.live = True
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args.datadir = None
|
||||
args.export = None
|
||||
args.strategy = 'default_strategy'
|
||||
args.timerange = '-100' # needed due to MagicMock malleability
|
||||
|
||||
args = [
|
||||
'--config', 'config.json',
|
||||
'--strategy', 'default_strategy',
|
||||
'backtesting',
|
||||
'--ticker-interval', '1',
|
||||
'--live',
|
||||
'--timerange', '-100'
|
||||
]
|
||||
args = get_args(args)
|
||||
start(args)
|
||||
# check the logs, that will contain the backtest result
|
||||
exists = [
|
||||
'Parameter -i/--ticker-interval detected ...',
|
||||
'Using ticker_interval: 1 ...',
|
||||
'Parameter -l/--live detected ...',
|
||||
'Using max_open_trades: 1 ...',
|
||||
'Parameter --timerange detected: -100 ..',
|
||||
'Parameter --datadir detected: freqtrade/tests/testdata ...',
|
||||
'Using stake_currency: BTC ...',
|
||||
'Using stake_amount: 0.001 ...',
|
||||
'Downloading data for all pairs in whitelist ...',
|
||||
'Measuring data from 2017-11-14T19:32:00+00:00 up to 2017-11-14T22:59:00+00:00 (0 days)..'
|
||||
]
|
||||
|
||||
for line in exists:
|
||||
tt.log_has(line, caplog.record_tuples)
|
||||
|
||||
@ -2,6 +2,7 @@
|
||||
import os
|
||||
from copy import deepcopy
|
||||
from unittest.mock import MagicMock
|
||||
import pandas as pd
|
||||
from freqtrade.optimize.hyperopt import Hyperopt
|
||||
import freqtrade.tests.conftest as tt # test tools
|
||||
|
||||
@ -157,7 +158,7 @@ def test_fmin_best_results(mocker, default_conf, caplog) -> None:
|
||||
'"uptrend_long_ema": {\n "enabled": true\n },',
|
||||
'"uptrend_short_ema": {\n "enabled": false\n },',
|
||||
'"uptrend_sma": {\n "enabled": false\n }',
|
||||
'ROI table:\n{\'0\': 6.0, \'3.0\': 3.0, \'5.0\': 1.0, \'6.0\': 0}',
|
||||
'ROI table:\n{0: 6.0, 3.0: 3.0, 5.0: 1.0, 6.0: 0}',
|
||||
'Best Result:\nfoo'
|
||||
]
|
||||
for line in exists:
|
||||
@ -275,7 +276,7 @@ def test_roi_table_generation() -> None:
|
||||
}
|
||||
|
||||
hyperopt = _HYPEROPT
|
||||
assert hyperopt.generate_roi_table(params) == {'0': 6, '15': 3, '25': 1, '30': 0}
|
||||
assert hyperopt.generate_roi_table(params) == {0: 6, 15: 3, 25: 1, 30: 0}
|
||||
|
||||
|
||||
def test_start_calls_fmin(mocker, default_conf) -> None:
|
||||
@ -319,3 +320,36 @@ def test_start_uses_mongotrials(mocker, default_conf) -> None:
|
||||
hyperopt.start()
|
||||
mock_mongotrials.assert_called_once()
|
||||
mock_fmin.assert_called_once()
|
||||
|
||||
|
||||
# test log_trials_result
|
||||
# test buy_strategy_generator def populate_buy_trend
|
||||
# test optimizer if 'ro_t1' in params
|
||||
|
||||
def test_format_results():
|
||||
"""
|
||||
Test Hyperopt.format_results()
|
||||
"""
|
||||
trades = [
|
||||
('BTC_ETH', 2, 2, 123),
|
||||
('BTC_LTC', 1, 1, 123),
|
||||
('BTC_XRP', -1, -2, -246)
|
||||
]
|
||||
labels = ['currency', 'profit_percent', 'profit_BTC', 'duration']
|
||||
df = pd.DataFrame.from_records(trades, columns=labels)
|
||||
x = Hyperopt.format_results(df)
|
||||
assert x.find(' 66.67%')
|
||||
|
||||
|
||||
def test_signal_handler(mocker):
|
||||
"""
|
||||
Test Hyperopt.signal_handler()
|
||||
"""
|
||||
m = MagicMock()
|
||||
mocker.patch('sys.exit', m)
|
||||
mocker.patch('freqtrade.optimize.hyperopt.Hyperopt.save_trials', m)
|
||||
mocker.patch('freqtrade.optimize.hyperopt.Hyperopt.log_trials_result', m)
|
||||
|
||||
hyperopt = _HYPEROPT
|
||||
hyperopt.signal_handler(9, None)
|
||||
assert m.call_count == 3
|
||||
|
||||
@ -55,7 +55,7 @@ def test_strategy(result):
|
||||
strategy = Strategy({'strategy': 'default_strategy'})
|
||||
|
||||
assert hasattr(strategy.custom_strategy, 'minimal_roi')
|
||||
assert strategy.minimal_roi['0'] == 0.04
|
||||
assert strategy.minimal_roi[0] == 0.04
|
||||
|
||||
assert hasattr(strategy.custom_strategy, 'stoploss')
|
||||
assert strategy.stoploss == -0.10
|
||||
@ -83,7 +83,7 @@ def test_strategy_override_minimal_roi(caplog):
|
||||
strategy = Strategy(config)
|
||||
|
||||
assert hasattr(strategy.custom_strategy, 'minimal_roi')
|
||||
assert strategy.minimal_roi['0'] == 0.5
|
||||
assert strategy.minimal_roi[0] == 0.5
|
||||
assert ('freqtrade.strategy.strategy',
|
||||
logging.INFO,
|
||||
'Override strategy \'minimal_roi\' with value in config file.'
|
||||
@ -136,8 +136,8 @@ def test_strategy_singleton():
|
||||
strategy1 = Strategy({'strategy': 'default_strategy'})
|
||||
|
||||
assert hasattr(strategy1.custom_strategy, 'minimal_roi')
|
||||
assert strategy1.minimal_roi['0'] == 0.04
|
||||
assert strategy1.minimal_roi[0] == 0.04
|
||||
|
||||
strategy2 = Strategy()
|
||||
assert hasattr(strategy2.custom_strategy, 'minimal_roi')
|
||||
assert strategy2.minimal_roi['0'] == 0.04
|
||||
assert strategy2.minimal_roi[0] == 0.04
|
||||
|
||||
@ -43,6 +43,11 @@ def test_analyze_object() -> None:
|
||||
assert hasattr(Analyze, 'min_roi_reached')
|
||||
|
||||
|
||||
def test_dataframe_correct_length(result):
|
||||
dataframe = Analyze.parse_ticker_dataframe(result)
|
||||
assert len(result.index) == len(dataframe.index)
|
||||
|
||||
|
||||
def test_dataframe_correct_columns(result):
|
||||
assert result.columns.tolist() == \
|
||||
['close', 'high', 'low', 'open', 'date', 'volume']
|
||||
|
||||
Loading…
Reference in New Issue
Block a user