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creslinux
2018-07-16 14:16:35 +00:00
parent eed29a6b8a
commit fb0edd71ff
1 changed files with 171 additions and 105 deletions
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216
freqtrade/optimize/backtesting.py
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@@ -73,6 +73,35 @@ class Backtesting(object):
self.stop_loss_value = self.analyze.strategy.stoploss self.stop_loss_value = self.analyze.strategy.stoploss
#### backslap config
'''
Numpy arrays are used for 100x speed up
We requires setting Int values for
buy stop triggers and stop calculated on
# buy 0 - open 1 - close 2 - sell 3 - high 4 - low 5 - stop 6
'''
self.np_buy: int = 0
self.np_open: int = 1
self.np_close: int = 2
self.np_sell: int = 3
self.np_high: int = 4
self.np_low: int = 5
self.np_stop: int = 6
self.np_bto: int = self.np_close # buys_triggered_on - should be close
self.np_bco: int = self.np_open # buys calculated on - open of the next candle.
# self.np_sto: int = self.np_low # stops_triggered_on - Should be low, FT uses close
# self.np_sco: int = self.np_stop # stops_calculated_on - Should be stop, FT uses close
self.np_sto: int = self.np_close # stops_triggered_on - Should be low, FT uses close
self.np_sco: int = self.np_close # stops_calculated_on - Should be stop, FT uses close
self.use_backslap = True # Enable backslap
self.debug = False # Main debug enable, very print heavy, enable 2 loops recommended
self.debug_timing = False # Stages within Backslap
self.debug_2loops = False # Limit each pair to two loops, useful when debugging
self.debug_vector = False # Debug vector calcs
self.debug_timing_main_loop = False # print overall timing per pair
@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]:
""" """
@@ -215,7 +244,7 @@ class Backtesting(object):
realistic: do we try to simulate realistic trades? (default: True) realistic: do we try to simulate realistic trades? (default: True)
:return: DataFrame :return: DataFrame
""" """
debug_timing = False
headers = ['date', 'buy', 'open', 'close', 'sell', 'high', 'low'] headers = ['date', 'buy', 'open', 'close', 'sell', 'high', 'low']
processed = args['processed'] processed = args['processed']
@@ -224,10 +253,11 @@ class Backtesting(object):
trades = [] trades = []
trade_count_lock: Dict = {} trade_count_lock: Dict = {}
########################### Call out BSlap instead of using FT use_backslap = self.use_backslap
debug_timing = self.debug_timing_main_loop
if use_backslap: # Use Back Slap code
########################### Call out BSlap Loop instead of Original BT code
bslap_results: list = [] bslap_results: list = []
for pair, pair_data in processed.items(): for pair, pair_data in processed.items():
if debug_timing: # Start timer if debug_timing: # Start timer
fl = self.s() fl = self.s()
@@ -235,11 +265,10 @@ class Backtesting(object):
ticker_data = self.populate_sell_trend( ticker_data = self.populate_sell_trend(
self.populate_buy_trend(pair_data))[headers].copy() self.populate_buy_trend(pair_data))[headers].copy()
ticker_data.drop(ticker_data.head(1).index, inplace=True)
if debug_timing: # print time taken if debug_timing: # print time taken
flt = self.f(fl) flt = self.f(fl)
print("populate_buy_trend:", pair, round(flt, 10)) #print("populate_buy_trend:", pair, round(flt, 10))
st = self.st() st = self.s()
# #dump same DFs to disk for offline testing in scratch # #dump same DFs to disk for offline testing in scratch
# f_pair:str = pair # f_pair:str = pair
@@ -271,52 +300,67 @@ class Backtesting(object):
return bslap_results_df return bslap_results_df
########################### Original BT loop else: # use Original Back test code
# for pair, pair_data in processed.items(): ########################## Original BT loop
# pair_data['buy'], pair_data['sell'] = 0, 0 # cleanup from previous run
# for pair, pair_data in processed.items():
# ticker_data = self.populate_sell_trend( if debug_timing: # Start timer
# self.populate_buy_trend(pair_data))[headers].copy() fl = self.s()
#
# # to avoid using data from future, we buy/sell with signal from previous candle pair_data['buy'], pair_data['sell'] = 0, 0 # cleanup from previous run
# ticker_data.loc[:, 'buy'] = ticker_data['buy'].shift(1)
# ticker_data.loc[:, 'sell'] = ticker_data['sell'].shift(1) ticker_data = self.populate_sell_trend(
# self.populate_buy_trend(pair_data))[headers].copy()
# ticker_data.drop(ticker_data.head(1).index, inplace=True)
# # to avoid using data from future, we buy/sell with signal from previous candle
# # Convert from Pandas to list for performance reasons ticker_data.loc[:, 'buy'] = ticker_data['buy'].shift(1)
# # (Looping Pandas is slow.) ticker_data.loc[:, 'sell'] = ticker_data['sell'].shift(1)
# ticker = [x for x in ticker_data.itertuples()]
# ticker_data.drop(ticker_data.head(1).index, inplace=True)
# lock_pair_until = None
# for index, row in enumerate(ticker): if debug_timing: # print time taken
# if row.buy == 0 or row.sell == 1: flt = self.f(fl)
# continue # skip rows where no buy signal or that would immediately sell off #print("populate_buy_trend:", pair, round(flt, 10))
# st = self.s()
# if realistic:
# if lock_pair_until is not None and row.date <= lock_pair_until: # Convert from Pandas to list for performance reasons
# continue # (Looping Pandas is slow.)
# if max_open_trades > 0: ticker = [x for x in ticker_data.itertuples()]
# # 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: lock_pair_until = None
# continue for index, row in enumerate(ticker):
# if row.buy == 0 or row.sell == 1:
# trade_count_lock[row.date] = trade_count_lock.get(row.date, 0) + 1 continue # skip rows where no buy signal or that would immediately sell off
#
# trade_entry = self._get_sell_trade_entry(pair, row, ticker[index + 1:], if realistic:
# trade_count_lock, args) if lock_pair_until is not None and row.date <= lock_pair_until:
# continue
# if max_open_trades > 0:
# if trade_entry: # Check if max_open_trades has already been reached for the given date
# lock_pair_until = trade_entry.close_time if not trade_count_lock.get(row.date, 0) < max_open_trades:
# trades.append(trade_entry) continue
# else:
# # Set lock_pair_until to end of testing period if trade could not be closed trade_count_lock[row.date] = trade_count_lock.get(row.date, 0) + 1
# # This happens only if the buy-signal was with the last candle
# lock_pair_until = ticker_data.iloc[-1].date trade_entry = self._get_sell_trade_entry(pair, row, ticker[index + 1:],
# trade_count_lock, args)
# return DataFrame.from_records(trades, columns=BacktestResult._fields)
######################## Original BT loop end
if trade_entry:
lock_pair_until = trade_entry.close_time
trades.append(trade_entry)
else:
# Set lock_pair_until to end of testing period if trade could not be closed
# This happens only if the buy-signal was with the last candle
lock_pair_until = ticker_data.iloc[-1].date
if debug_timing: # print time taken
tt = self.f(st)
print("Time to BackTest :", pair, round(tt, 10))
print("-----------------------")
return DataFrame.from_records(trades, columns=BacktestResult._fields)
####################### Original BT loop end
def vector_fill_results_table(self, bslap_results_df: DataFrame): def vector_fill_results_table(self, bslap_results_df: DataFrame):
""" """
@@ -333,13 +377,18 @@ class Backtesting(object):
:return: bslap_results Dataframe :return: bslap_results Dataframe
""" """
import pandas as pd import pandas as pd
debug = False debug = self.debug_vector
# stake and fees # stake and fees
stake = 0.015 # stake = 0.015
# 0.05% is 0.00,05 # 0.05% is 0.0005
open_fee = 0.0000 #fee = 0.001
close_fee = 0.0000
stake = self.config.get('stake_amount')
fee = self.fee
open_fee = fee / 2
close_fee = fee / 2
if debug: if debug:
print("Stake is,", stake, "the sum of currency to spend per trade") print("Stake is,", stake, "the sum of currency to spend per trade")
print("The open fee is", open_fee, "The close fee is", close_fee) print("The open fee is", open_fee, "The close fee is", close_fee)
@@ -420,9 +469,12 @@ class Backtesting(object):
from datetime import datetime from datetime import datetime
### backslap debug wrap ### backslap debug wrap
debug_2loops = False # only loop twice, for faster debug # debug_2loops = False # only loop twice, for faster debug
debug_timing = False # print timing for each step # debug_timing = False # print timing for each step
debug = False # print values, to check accuracy # debug = False # print values, to check accuracy
debug_2loops = self.debug_2loops # only loop twice, for faster debug
debug_timing = self.debug_timing # print timing for each step
debug = self.debug # print values, to check accuracy
# Read Stop Loss Values and Stake # Read Stop Loss Values and Stake
stop = self.stop_loss_value stop = self.stop_loss_value
@@ -451,20 +503,34 @@ class Backtesting(object):
buy stop triggers and stop calculated on buy stop triggers and stop calculated on
# buy 0 - open 1 - close 2 - sell 3 - high 4 - low 5 - stop 6 # buy 0 - open 1 - close 2 - sell 3 - high 4 - low 5 - stop 6
''' '''
np_buy: int = 0 # np_buy: int = 0
np_open: int = 1 # np_open: int = 1
np_close: int = 2 # np_close: int = 2
np_sell: int = 3 # np_sell: int = 3
np_high: int = 4 # np_high: int = 4
np_low: int = 5 # np_low: int = 5
np_stop: int = 6 # np_stop: int = 6
np_bto: int = np_close # buys_triggered_on - should be close # np_bto: int = np_close # buys_triggered_on - should be close
np_bco: int = np_open # buys calculated on - open of the next candle. # np_bco: int = np_open # buys calculated on - open of the next candle.
#np_sto: int = np_low # stops_triggered_on - Should be low, FT uses close # #np_sto: int = np_low # stops_triggered_on - Should be low, FT uses close
#np_sco: int = np_stop # stops_calculated_on - Should be stop, FT uses close # #np_sco: int = np_stop # stops_calculated_on - Should be stop, FT uses close
np_sto: int = np_close # stops_triggered_on - Should be low, FT uses close # np_sto: int = np_close # stops_triggered_on - Should be low, FT uses close
np_sco: int = np_close # stops_calculated_on - Should be stop, FT uses close # np_sco: int = np_close # stops_calculated_on - Should be stop, FT uses close
#
#######
# Use vars set at top of backtest
np_buy: int = self.np_buy
np_open: int = self.np_open
np_close: int = self.np_close
np_sell: int = self.np_sell
np_high: int = self.np_high
np_low: int = self.np_low
np_stop: int = self.np_stop
np_bto: int = self.np_bto # buys_triggered_on - should be close
np_bco: int = self.np_bco # buys calculated on - open of the next candle.
np_sto: int = self.np_sto # stops_triggered_on - Should be low, FT uses close
np_sco: int = self.np_sco # stops_calculated_on - Should be stop, FT uses close
### End Config ### End Config
pair: str = pair pair: str = pair