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Update backtesting.py

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creslin 2018-07-26 18:42:20 +00:00 committed by GitHub
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commit 2dc3d6a6b8
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1 changed files with 62 additions and 27 deletions
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freqtrade/optimize/backtesting.py
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@ -104,6 +104,8 @@ class Backtesting(object):
self.backslap_show_trades = False # prints trades in addition to summary report self.backslap_show_trades = False # prints trades in addition to summary report
self.backslap_save_trades = True # saves trades as a pretty table to backslap.txt self.backslap_save_trades = True # saves trades as a pretty table to backslap.txt
self.stop_stops: int = 9999 # stop back testing any pair with this many stops, set to 999999 to not hit
@staticmethod @staticmethod
def get_timeframe(data: Dict[str, DataFrame]) -> Tuple[arrow.Arrow, arrow.Arrow]: def get_timeframe(data: Dict[str, DataFrame]) -> Tuple[arrow.Arrow, arrow.Arrow]:
@ -389,7 +391,6 @@ class Backtesting(object):
- Profit - Profit
- trade duration - trade duration
- profit abs - profit abs
:param bslap_results Dataframe :param bslap_results Dataframe
:return: bslap_results Dataframe :return: bslap_results Dataframe
""" """
@ -413,45 +414,64 @@ class Backtesting(object):
if debug: if debug:
from pandas import set_option from pandas import set_option
set_option('display.max_rows', 5000) set_option('display.max_rows', 5000)
set_option('display.max_columns', 10) set_option('display.max_columns', 20)
pd.set_option('display.width', 1000) pd.set_option('display.width', 1000)
pd.set_option('max_colwidth', 40) pd.set_option('max_colwidth', 40)
pd.set_option('precision', 12) pd.set_option('precision', 12)
# # Get before
# csv = "cryptosher_before_debug"
# bslap_results_df.to_csv(csv, sep='\t', encoding='utf-8')
bslap_results_df.to_csv(csv, sep='\t', encoding='utf-8')
bslap_results_df['trade_duration'] = bslap_results_df['close_time'] - bslap_results_df['open_time'] bslap_results_df['trade_duration'] = bslap_results_df['close_time'] - bslap_results_df['open_time']
# if debug:
# print(bslap_results_df[['open_time', 'close_time', 'trade_duration']])
## Spends, Takes, Profit, Absolute Profit ## Spends, Takes, Profit, Absolute Profit
print(bslap_results_df)
# Buy Price # Buy Price
bslap_results_df['buy_sum'] = stake * bslap_results_df['open_rate'] bslap_results_df['buy_vol'] = stake / bslap_results_df['open_rate'] # How many target are we buying
bslap_results_df['buy_fee'] = bslap_results_df['buy_sum'] * open_fee bslap_results_df['buy_fee'] = stake * open_fee
bslap_results_df['buy_spend'] = bslap_results_df['buy_sum'] + bslap_results_df['buy_fee'] bslap_results_df['buy_spend'] = stake + bslap_results_df['buy_fee'] # How much we're spending
# Sell price # Sell price
bslap_results_df['sell_sum'] = stake * bslap_results_df['close_rate'] bslap_results_df['sell_sum'] = bslap_results_df['buy_vol'] * bslap_results_df['close_rate']
bslap_results_df['sell_fee'] = bslap_results_df['sell_sum'] * close_fee bslap_results_df['sell_fee'] = bslap_results_df['sell_sum'] * close_fee
bslap_results_df['sell_take'] = bslap_results_df['sell_sum'] - bslap_results_df['sell_fee'] bslap_results_df['sell_take'] = bslap_results_df['sell_sum'] - bslap_results_df['sell_fee']
# profit_percent # profit_percent
bslap_results_df['profit_percent'] = bslap_results_df['sell_take'] / bslap_results_df['buy_spend'] - 1 bslap_results_df['profit_percent'] = (bslap_results_df['sell_take'] - bslap_results_df['buy_spend']) \
/ bslap_results_df['buy_spend']
# Absolute profit # Absolute profit
bslap_results_df['profit_abs'] = bslap_results_df['sell_take'] - bslap_results_df['buy_spend'] bslap_results_df['profit_abs'] = bslap_results_df['sell_take'] - bslap_results_df['buy_spend']
# # Get After
# csv="cryptosher_after_debug"
# bslap_results_df.to_csv(csv, sep='\t', encoding='utf-8')
if debug: if debug:
print("\n") print("\n")
print(bslap_results_df[ print(bslap_results_df[
['buy_sum', 'buy_fee', 'buy_spend', 'sell_sum','sell_fee', 'sell_take', 'profit_percent', 'profit_abs', 'exit_type']]) ['buy_vol', 'buy_fee', 'buy_spend', 'sell_sum','sell_fee', 'sell_take', 'profit_percent', 'profit_abs', 'exit_type']])
return bslap_results_df return bslap_results_df
def np_get_t_open_ind(self, np_buy_arr, t_exit_ind: int, np_buy_arr_len: int): 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):
import utils_find_1st as utf1st import utils_find_1st as utf1st
""" """
The purpose of this def is to return the next "buy" = 1 The purpose of this def is to return the next "buy" = 1
after t_exit_ind. after t_exit_ind.
This function will also check is the stop limit for the pair has been reached.
if stop_stops is the limit and stop_stops_count it the number of times the stop has been hit.
t_exit_ind is the index the last trade exited on t_exit_ind is the index the last trade exited on
or 0 if first time around this loop. or 0 if first time around this loop.
stop_stops i
""" """
debug = self.debug
# Timers, to be called if in debug # Timers, to be called if in debug
def s(): def s():
st = timeit.default_timer() st = timeit.default_timer()
@ -478,6 +498,11 @@ class Backtesting(object):
if t_open_ind == np_buy_arr_len -1 : # If buy found on last candle ignore, there is no OPEN in next to use if t_open_ind == np_buy_arr_len -1 : # If buy found on last candle ignore, there is no OPEN in next to use
t_open_ind = -1 # -1 ends the loop t_open_ind = -1 # -1 ends the loop
if stop_stops_count >= stop_stops: # if maximum number of stops allowed in a pair is hit, exit loop
t_open_ind = -1 # -1 ends the loop
if debug:
print("Max stop limit ", stop_stops, "reached. Moving to next pair")
return t_open_ind return t_open_ind
def backslap_pair(self, ticker_data, pair): def backslap_pair(self, ticker_data, pair):
@ -564,6 +589,9 @@ class Backtesting(object):
t_exit_ind = 0 # Start loop from first index t_exit_ind = 0 # Start loop from first index
t_exit_last = 0 # To test for exit t_exit_last = 0 # To test for exit
stop_stops = self.stop_stops # Int of stops within a pair to stop trading a pair at
stop_stops_count = 0 # stop counter per pair
st = s() # Start timer for processing dataframe st = s() # Start timer for processing dataframe
if debug: if debug:
print('Processing:', pair) print('Processing:', pair)
@ -604,11 +632,13 @@ class Backtesting(object):
Requires: np_buy_arr - a 1D array of the 'buy' column. To find next "1" Requires: np_buy_arr - a 1D array of the 'buy' column. To find next "1"
Required: t_exit_ind - Either 0, first loop. Or The index we last exited on Required: t_exit_ind - Either 0, first loop. Or The index we last exited on
Requires: np_buy_arr_len - length of pair array. Requires: np_buy_arr_len - length of pair array.
Requires: stops_stops - number of stops allowed before stop trading a pair
Requires: stop_stop_counts - count of stops hit in the pair
Provides: The next "buy" index after t_exit_ind Provides: The next "buy" index after t_exit_ind
If -1 is returned no buy has been found in remainder of array, skip to exit loop If -1 is returned no buy has been found in remainder of array, skip to exit loop
''' '''
t_open_ind = self.np_get_t_open_ind(np_buy_arr, t_exit_ind, np_buy_arr_len) t_open_ind = self.np_get_t_open_ind(np_buy_arr, t_exit_ind, np_buy_arr_len, stop_stops, stop_stops_count)
if debug: if debug:
print("\n(0) numpy debug \nnp_get_t_open, has returned the next valid buy index as", t_open_ind) print("\n(0) numpy debug \nnp_get_t_open, has returned the next valid buy index as", t_open_ind)
@ -971,6 +1001,9 @@ class Backtesting(object):
# append the dict to the list and print list # append the dict to the list and print list
bslap_pair_results.append(bslap_result) bslap_pair_results.append(bslap_result)
if t_exit_type is "stop":
stop_stops_count = stop_stops_count + 1
if debug: if debug:
print("The trade dict is: \n", bslap_result) print("The trade dict is: \n", bslap_result)
print("Trades dicts in list after append are: \n ", bslap_pair_results) print("Trades dicts in list after append are: \n ", bslap_pair_results)
@ -1008,6 +1041,8 @@ class Backtesting(object):
timerange = Arguments.parse_timerange(None if self.config.get( timerange = Arguments.parse_timerange(None if self.config.get(
'timerange') is None else str(self.config.get('timerange'))) 'timerange') is None else str(self.config.get('timerange')))
ld_files = self.s()
data = optimize.load_data( data = optimize.load_data(
self.config['datadir'], self.config['datadir'],
pairs=pairs, pairs=pairs,
@ -1028,6 +1063,8 @@ class Backtesting(object):
max_open_trades = 0 max_open_trades = 0
preprocessed = self.tickerdata_to_dataframe(data) preprocessed = self.tickerdata_to_dataframe(data)
t_t = self.f(ld_files)
print("Load from json to file to df in mem took", t_t)
# Print timeframe # Print timeframe
min_date, max_date = self.get_timeframe(preprocessed) min_date, max_date = self.get_timeframe(preprocessed)
@ -1092,18 +1129,16 @@ class Backtesting(object):
results results
) )
) )
logger.info(
## TODO. Catch open trades for this report. '\n=============================================== '
# logger.info( 'LEFT OPEN TRADES REPORT'
# '\n=============================================== ' ' ===============================================\n'
# 'LEFT OPEN TRADES REPORT' '%s',
# ' ===============================================\n' self._generate_text_table(
# '%s', data,
# self._generate_text_table( results.loc[results.open_at_end]
# data, )
# results.loc[results.open_at_end] )
# )
# )
def setup_configuration(args: Namespace) -> Dict[str, Any]: def setup_configuration(args: Namespace) -> Dict[str, Any]: