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creslinux
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@ -73,6 +73,35 @@ class Backtesting(object):
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self.stop_loss_value = self.analyze.strategy.stoploss
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#### backslap config
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'''
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Numpy arrays are used for 100x speed up
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We requires setting Int values for
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buy stop triggers and stop calculated on
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# buy 0 - open 1 - close 2 - sell 3 - high 4 - low 5 - stop 6
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'''
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self.np_buy: int = 0
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self.np_open: int = 1
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self.np_close: int = 2
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self.np_sell: int = 3
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self.np_high: int = 4
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self.np_low: int = 5
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self.np_stop: int = 6
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self.np_bto: int = self.np_close # buys_triggered_on - should be close
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self.np_bco: int = self.np_open # buys calculated on - open of the next candle.
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# self.np_sto: int = self.np_low # stops_triggered_on - Should be low, FT uses close
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# self.np_sco: int = self.np_stop # stops_calculated_on - Should be stop, FT uses close
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self.np_sto: int = self.np_close # stops_triggered_on - Should be low, FT uses close
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self.np_sco: int = self.np_close # stops_calculated_on - Should be stop, FT uses close
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self.use_backslap = True # Enable backslap
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self.debug = False # Main debug enable, very print heavy, enable 2 loops recommended
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self.debug_timing = False # Stages within Backslap
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self.debug_2loops = False # Limit each pair to two loops, useful when debugging
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self.debug_vector = False # Debug vector calcs
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self.debug_timing_main_loop = False # print overall timing per pair
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@staticmethod
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def get_timeframe(data: Dict[str, DataFrame]) -> Tuple[arrow.Arrow, arrow.Arrow]:
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"""
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@ -215,7 +244,7 @@ class Backtesting(object):
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realistic: do we try to simulate realistic trades? (default: True)
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:return: DataFrame
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"""
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debug_timing = False
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headers = ['date', 'buy', 'open', 'close', 'sell', 'high', 'low']
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processed = args['processed']
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@ -224,99 +253,114 @@ class Backtesting(object):
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trades = []
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trade_count_lock: Dict = {}
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########################### Call out BSlap instead of using FT
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bslap_results: list = []
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use_backslap = self.use_backslap
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debug_timing = self.debug_timing_main_loop
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if use_backslap: # Use Back Slap code
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########################### Call out BSlap Loop instead of Original BT code
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bslap_results: list = []
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for pair, pair_data in processed.items():
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if debug_timing: # Start timer
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fl = self.s()
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ticker_data = self.populate_sell_trend(
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self.populate_buy_trend(pair_data))[headers].copy()
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if debug_timing: # print time taken
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flt = self.f(fl)
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#print("populate_buy_trend:", pair, round(flt, 10))
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st = self.s()
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# #dump same DFs to disk for offline testing in scratch
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# f_pair:str = pair
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# csv = f_pair.replace("/", "_")
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# csv="/Users/creslin/PycharmProjects/freqtrade_new/frames/" + csv
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# ticker_data.to_csv(csv, sep='\t', encoding='utf-8')
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#call bslap - results are a list of dicts
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bslap_pair_results = self.backslap_pair(ticker_data, pair)
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last_bslap_results = bslap_results
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bslap_results = last_bslap_results + bslap_pair_results
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if debug_timing: # print time taken
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tt = self.f(st)
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print("Time to BackSlap :", pair, round(tt,10))
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print("-----------------------")
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for pair, pair_data in processed.items():
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if debug_timing: # Start timer
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fl = self.s()
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# Switch List of Trade Dicts (bslap_results) to Dataframe
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# Fill missing, calculable columns, profit, duration , abs etc.
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bslap_results_df = DataFrame(bslap_results)
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bslap_results_df['open_time'] = to_datetime(bslap_results_df['open_time'])
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bslap_results_df['close_time'] = to_datetime(bslap_results_df['close_time'])
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ticker_data = self.populate_sell_trend(
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### don't use this, itll drop exit type field
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# bslap_results_df = DataFrame(bslap_results, columns=BacktestResult._fields)
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bslap_results_df = self.vector_fill_results_table(bslap_results_df)
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return bslap_results_df
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else: # use Original Back test code
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########################## Original BT loop
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for pair, pair_data in processed.items():
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if debug_timing: # Start timer
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fl = self.s()
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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(
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self.populate_buy_trend(pair_data))[headers].copy()
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ticker_data.drop(ticker_data.head(1).index, inplace=True)
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if debug_timing: # print time taken
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flt = self.f(fl)
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print("populate_buy_trend:", pair, round(flt, 10))
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st = self.st()
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# to avoid using data from future, we buy/sell with signal from previous candle
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ticker_data.loc[:, 'buy'] = ticker_data['buy'].shift(1)
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ticker_data.loc[:, 'sell'] = ticker_data['sell'].shift(1)
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# #dump same DFs to disk for offline testing in scratch
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# f_pair:str = pair
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# csv = f_pair.replace("/", "_")
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# csv="/Users/creslin/PycharmProjects/freqtrade_new/frames/" + csv
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# ticker_data.to_csv(csv, sep='\t', encoding='utf-8')
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ticker_data.drop(ticker_data.head(1).index, inplace=True)
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#call bslap - results are a list of dicts
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bslap_pair_results = self.backslap_pair(ticker_data, pair)
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last_bslap_results = bslap_results
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bslap_results = last_bslap_results + bslap_pair_results
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if debug_timing: # print time taken
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flt = self.f(fl)
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#print("populate_buy_trend:", pair, round(flt, 10))
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st = self.s()
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if debug_timing: # print time taken
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tt = self.f(st)
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print("Time to Back Slap :", pair, round(tt,10))
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print("-----------------------")
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# Convert from Pandas to list for performance reasons
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# (Looping Pandas is slow.)
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ticker = [x for x in ticker_data.itertuples()]
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lock_pair_until = None
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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.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.date, 0) < max_open_trades:
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continue
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trade_count_lock[row.date] = trade_count_lock.get(row.date, 0) + 1
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trade_entry = self._get_sell_trade_entry(pair, row, ticker[index + 1:],
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trade_count_lock, args)
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# Switch List of Trade Dicts (bslap_results) to Dataframe
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# Fill missing, calculable columns, profit, duration , abs etc.
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bslap_results_df = DataFrame(bslap_results)
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bslap_results_df['open_time'] = to_datetime(bslap_results_df['open_time'])
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bslap_results_df['close_time'] = to_datetime(bslap_results_df['close_time'])
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if trade_entry:
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lock_pair_until = trade_entry.close_time
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trades.append(trade_entry)
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else:
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# Set lock_pair_until to end of testing period if trade could not be closed
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# This happens only if the buy-signal was with the last candle
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lock_pair_until = ticker_data.iloc[-1].date
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### don't use this, itll drop exit type field
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# bslap_results_df = DataFrame(bslap_results, columns=BacktestResult._fields)
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if debug_timing: # print time taken
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tt = self.f(st)
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print("Time to BackTest :", pair, round(tt, 10))
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print("-----------------------")
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bslap_results_df = self.vector_fill_results_table(bslap_results_df)
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return bslap_results_df
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########################### Original BT loop
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# for pair, pair_data in processed.items():
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# pair_data['buy'], pair_data['sell'] = 0, 0 # cleanup from previous run
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#
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# ticker_data = self.populate_sell_trend(
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# self.populate_buy_trend(pair_data))[headers].copy()
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#
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# # to avoid using data from future, we buy/sell with signal from previous candle
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# ticker_data.loc[:, 'buy'] = ticker_data['buy'].shift(1)
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# ticker_data.loc[:, 'sell'] = ticker_data['sell'].shift(1)
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#
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# ticker_data.drop(ticker_data.head(1).index, inplace=True)
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#
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# # Convert from Pandas to list for performance reasons
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# # (Looping Pandas is slow.)
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# ticker = [x for x in ticker_data.itertuples()]
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#
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# lock_pair_until = None
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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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#
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# if realistic:
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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.date, 0) < max_open_trades:
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# continue
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#
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# trade_count_lock[row.date] = trade_count_lock.get(row.date, 0) + 1
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#
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# trade_entry = self._get_sell_trade_entry(pair, row, ticker[index + 1:],
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# trade_count_lock, args)
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#
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#
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# if trade_entry:
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# lock_pair_until = trade_entry.close_time
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# trades.append(trade_entry)
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# else:
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# # Set lock_pair_until to end of testing period if trade could not be closed
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# # This happens only if the buy-signal was with the last candle
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# lock_pair_until = ticker_data.iloc[-1].date
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#
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# return DataFrame.from_records(trades, columns=BacktestResult._fields)
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######################## Original BT loop end
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return DataFrame.from_records(trades, columns=BacktestResult._fields)
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####################### Original BT loop end
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def vector_fill_results_table(self, bslap_results_df: DataFrame):
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"""
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@ -333,13 +377,18 @@ class Backtesting(object):
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:return: bslap_results Dataframe
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"""
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import pandas as pd
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debug = False
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debug = self.debug_vector
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# stake and fees
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# stake = 0.015
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# 0.05% is 0.0005
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#fee = 0.001
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stake = self.config.get('stake_amount')
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fee = self.fee
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open_fee = fee / 2
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close_fee = fee / 2
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# stake and fees
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stake = 0.015
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# 0.05% is 0.00,05
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open_fee = 0.0000
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close_fee = 0.0000
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if debug:
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print("Stake is,", stake, "the sum of currency to spend per trade")
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print("The open fee is", open_fee, "The close fee is", close_fee)
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@ -420,9 +469,12 @@ class Backtesting(object):
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from datetime import datetime
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### backslap debug wrap
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debug_2loops = False # only loop twice, for faster debug
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debug_timing = False # print timing for each step
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debug = False # print values, to check accuracy
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# debug_2loops = False # only loop twice, for faster debug
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# debug_timing = False # print timing for each step
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# debug = False # print values, to check accuracy
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debug_2loops = self.debug_2loops # only loop twice, for faster debug
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debug_timing = self.debug_timing # print timing for each step
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debug = self.debug # print values, to check accuracy
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# Read Stop Loss Values and Stake
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stop = self.stop_loss_value
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@ -451,20 +503,34 @@ class Backtesting(object):
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buy stop triggers and stop calculated on
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# buy 0 - open 1 - close 2 - sell 3 - high 4 - low 5 - stop 6
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'''
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np_buy: int = 0
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np_open: int = 1
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np_close: int = 2
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np_sell: int = 3
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np_high: int = 4
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np_low: int = 5
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np_stop: int = 6
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np_bto: int = np_close # buys_triggered_on - should be close
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np_bco: int = np_open # buys calculated on - open of the next candle.
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#np_sto: int = np_low # stops_triggered_on - Should be low, FT uses close
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#np_sco: int = np_stop # stops_calculated_on - Should be stop, FT uses close
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np_sto: int = np_close # stops_triggered_on - Should be low, FT uses close
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np_sco: int = np_close # stops_calculated_on - Should be stop, FT uses close
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#
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# np_buy: int = 0
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# np_open: int = 1
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# np_close: int = 2
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# np_sell: int = 3
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# np_high: int = 4
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# np_low: int = 5
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# np_stop: int = 6
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# np_bto: int = np_close # buys_triggered_on - should be close
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# np_bco: int = np_open # buys calculated on - open of the next candle.
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# #np_sto: int = np_low # stops_triggered_on - Should be low, FT uses close
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# #np_sco: int = np_stop # stops_calculated_on - Should be stop, FT uses close
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# np_sto: int = np_close # stops_triggered_on - Should be low, FT uses close
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# np_sco: int = np_close # stops_calculated_on - Should be stop, FT uses close
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#######
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# Use vars set at top of backtest
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np_buy: int = self.np_buy
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np_open: int = self.np_open
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np_close: int = self.np_close
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np_sell: int = self.np_sell
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np_high: int = self.np_high
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np_low: int = self.np_low
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np_stop: int = self.np_stop
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np_bto: int = self.np_bto # buys_triggered_on - should be close
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np_bco: int = self.np_bco # buys calculated on - open of the next candle.
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np_sto: int = self.np_sto # stops_triggered_on - Should be low, FT uses close
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np_sco: int = self.np_sco # stops_calculated_on - Should be stop, FT uses close
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### End Config
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pair: str = pair
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