Update backtesting.py
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creslin
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@ -104,6 +104,8 @@ class Backtesting(object):
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self.backslap_show_trades = False # prints trades in addition to summary report
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self.backslap_save_trades = True # saves trades as a pretty table to backslap.txt
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self.stop_stops: int = 9999 # stop back testing any pair with this many stops, set to 999999 to not hit
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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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@ -389,7 +391,6 @@ class Backtesting(object):
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- Profit
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- trade duration
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- profit abs
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:param bslap_results Dataframe
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:return: bslap_results Dataframe
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"""
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@ -413,45 +414,64 @@ class Backtesting(object):
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if debug:
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from pandas import set_option
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set_option('display.max_rows', 5000)
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set_option('display.max_columns', 10)
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set_option('display.max_columns', 20)
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pd.set_option('display.width', 1000)
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pd.set_option('max_colwidth', 40)
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pd.set_option('precision', 12)
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# # Get before
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# csv = "cryptosher_before_debug"
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# bslap_results_df.to_csv(csv, sep='\t', encoding='utf-8')
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bslap_results_df.to_csv(csv, sep='\t', encoding='utf-8')
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bslap_results_df['trade_duration'] = bslap_results_df['close_time'] - bslap_results_df['open_time']
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# if debug:
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# print(bslap_results_df[['open_time', 'close_time', 'trade_duration']])
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## Spends, Takes, Profit, Absolute Profit
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print(bslap_results_df)
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# Buy Price
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bslap_results_df['buy_sum'] = stake * bslap_results_df['open_rate']
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bslap_results_df['buy_fee'] = bslap_results_df['buy_sum'] * open_fee
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bslap_results_df['buy_spend'] = bslap_results_df['buy_sum'] + bslap_results_df['buy_fee']
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bslap_results_df['buy_vol'] = stake / bslap_results_df['open_rate'] # How many target are we buying
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bslap_results_df['buy_fee'] = stake * open_fee
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bslap_results_df['buy_spend'] = stake + bslap_results_df['buy_fee'] # How much we're spending
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# Sell price
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bslap_results_df['sell_sum'] = stake * bslap_results_df['close_rate']
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bslap_results_df['sell_sum'] = bslap_results_df['buy_vol'] * bslap_results_df['close_rate']
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bslap_results_df['sell_fee'] = bslap_results_df['sell_sum'] * close_fee
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bslap_results_df['sell_take'] = bslap_results_df['sell_sum'] - bslap_results_df['sell_fee']
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# profit_percent
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bslap_results_df['profit_percent'] = bslap_results_df['sell_take'] / bslap_results_df['buy_spend'] - 1
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bslap_results_df['profit_percent'] = (bslap_results_df['sell_take'] - bslap_results_df['buy_spend']) \
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/ bslap_results_df['buy_spend']
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# Absolute profit
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bslap_results_df['profit_abs'] = bslap_results_df['sell_take'] - bslap_results_df['buy_spend']
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# # Get After
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# csv="cryptosher_after_debug"
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# bslap_results_df.to_csv(csv, sep='\t', encoding='utf-8')
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if debug:
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print("\n")
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print(bslap_results_df[
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['buy_sum', 'buy_fee', 'buy_spend', 'sell_sum','sell_fee', 'sell_take', 'profit_percent', 'profit_abs', 'exit_type']])
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['buy_vol', 'buy_fee', 'buy_spend', 'sell_sum','sell_fee', 'sell_take', 'profit_percent', 'profit_abs', 'exit_type']])
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return bslap_results_df
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def np_get_t_open_ind(self, np_buy_arr, t_exit_ind: int, np_buy_arr_len: int):
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def np_get_t_open_ind(self, np_buy_arr, t_exit_ind: int, np_buy_arr_len: int, stop_stops: int, stop_stops_count: int):
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import utils_find_1st as utf1st
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"""
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The purpose of this def is to return the next "buy" = 1
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after t_exit_ind.
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This function will also check is the stop limit for the pair has been reached.
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if stop_stops is the limit and stop_stops_count it the number of times the stop has been hit.
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t_exit_ind is the index the last trade exited on
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or 0 if first time around this loop.
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stop_stops i
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"""
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debug = self.debug
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# Timers, to be called if in debug
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def s():
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st = timeit.default_timer()
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@ -478,6 +498,11 @@ class Backtesting(object):
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if t_open_ind == np_buy_arr_len -1 : # If buy found on last candle ignore, there is no OPEN in next to use
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t_open_ind = -1 # -1 ends the loop
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if stop_stops_count >= stop_stops: # if maximum number of stops allowed in a pair is hit, exit loop
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t_open_ind = -1 # -1 ends the loop
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if debug:
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print("Max stop limit ", stop_stops, "reached. Moving to next pair")
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return t_open_ind
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def backslap_pair(self, ticker_data, pair):
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@ -564,6 +589,9 @@ class Backtesting(object):
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t_exit_ind = 0 # Start loop from first index
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t_exit_last = 0 # To test for exit
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stop_stops = self.stop_stops # Int of stops within a pair to stop trading a pair at
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stop_stops_count = 0 # stop counter per pair
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st = s() # Start timer for processing dataframe
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if debug:
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print('Processing:', pair)
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@ -604,11 +632,13 @@ class Backtesting(object):
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Requires: np_buy_arr - a 1D array of the 'buy' column. To find next "1"
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Required: t_exit_ind - Either 0, first loop. Or The index we last exited on
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Requires: np_buy_arr_len - length of pair array.
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Requires: stops_stops - number of stops allowed before stop trading a pair
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Requires: stop_stop_counts - count of stops hit in the pair
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Provides: The next "buy" index after t_exit_ind
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If -1 is returned no buy has been found in remainder of array, skip to exit loop
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'''
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t_open_ind = self.np_get_t_open_ind(np_buy_arr, t_exit_ind, np_buy_arr_len)
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t_open_ind = self.np_get_t_open_ind(np_buy_arr, t_exit_ind, np_buy_arr_len, stop_stops, stop_stops_count)
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if debug:
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print("\n(0) numpy debug \nnp_get_t_open, has returned the next valid buy index as", t_open_ind)
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@ -971,6 +1001,9 @@ class Backtesting(object):
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# append the dict to the list and print list
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bslap_pair_results.append(bslap_result)
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if t_exit_type is "stop":
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stop_stops_count = stop_stops_count + 1
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if debug:
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print("The trade dict is: \n", bslap_result)
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print("Trades dicts in list after append are: \n ", bslap_pair_results)
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@ -1008,6 +1041,8 @@ class Backtesting(object):
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timerange = Arguments.parse_timerange(None if self.config.get(
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'timerange') is None else str(self.config.get('timerange')))
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ld_files = self.s()
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data = optimize.load_data(
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self.config['datadir'],
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pairs=pairs,
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@ -1028,6 +1063,8 @@ class Backtesting(object):
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max_open_trades = 0
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preprocessed = self.tickerdata_to_dataframe(data)
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t_t = self.f(ld_files)
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print("Load from json to file to df in mem took", t_t)
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# Print timeframe
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min_date, max_date = self.get_timeframe(preprocessed)
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@ -1092,18 +1129,16 @@ class Backtesting(object):
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results
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)
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)
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## TODO. Catch open trades for this report.
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# logger.info(
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# '\n=============================================== '
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# 'LEFT OPEN TRADES REPORT'
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# ' ===============================================\n'
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# '%s',
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# self._generate_text_table(
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# data,
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# results.loc[results.open_at_end]
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# )
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# )
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logger.info(
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'\n=============================================== '
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'LEFT OPEN TRADES REPORT'
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' ===============================================\n'
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'%s',
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self._generate_text_table(
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data,
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results.loc[results.open_at_end]
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)
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)
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def setup_configuration(args: Namespace) -> Dict[str, Any]:
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