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creslinux 2018-07-14 23:45:06 +00:00
parent 90e3c38757
commit 07175ebc5a
1 changed files with 21 additions and 16 deletions
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37
freqtrade/optimize/backtesting.py
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@ -22,7 +22,7 @@ from freqtrade.exchange import Exchange
from freqtrade.misc import file_dump_json from freqtrade.misc import file_dump_json
from freqtrade.persistence import Trade from freqtrade.persistence import Trade
from profilehooks import profile from profilehooks import profile
from freqtrade.strategy.resolver import IStrategy, StrategyResolver from collections import OrderedDict
logger = logging.getLogger(__name__) logger = logging.getLogger(__name__)
@ -190,7 +190,7 @@ class Backtesting(object):
return btr return btr
return None return None
@profile
def backtest(self, args: Dict) -> DataFrame: def backtest(self, args: Dict) -> DataFrame:
""" """
Implements backtesting functionality Implements backtesting functionality
@ -218,15 +218,18 @@ class Backtesting(object):
for pair, pair_data in processed.items(): for pair, pair_data in processed.items():
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.loc[:, 'buy'] = ticker_data['buy'].shift(1)
ticker_data.loc[:, 'sell'] = ticker_data['sell'].shift(1)
bslap_pair_results = self.backslap_pair(ticker_data, pair) bslap_pair_results = self.backslap_pair(ticker_data, pair)
last_bslap_results = bslap_results last_bslap_results = bslap_results
bslap_results = last_bslap_results + bslap_pair_results bslap_results = last_bslap_results + bslap_pair_results
bslap_results_df = DataFrame(bslap_results) #bslap_results_df = DataFrame(bslap_results)
print(bslap_results_df.dtypes())
return bslap_results_df res = DataFrame.from_records(bslap_results, columns=BacktestResult._fields)
print(res)
return res
########################### Original BT loop ########################### Original BT loop
# for pair, pair_data in processed.items(): # for pair, pair_data in processed.items():
# pair_data['buy'], pair_data['sell'] = 0, 0 # cleanup from previous run # pair_data['buy'], pair_data['sell'] = 0, 0 # cleanup from previous run
@ -297,14 +300,15 @@ class Backtesting(object):
import numpy as np import numpy as np
import timeit import timeit
import utils_find_1st as utf1st import utils_find_1st as utf1st
from datetime import datetime
stop = self.stop_loss_value stop = self.stop_loss_value
p_stop = (-stop + 1) # What stop really means, e.g 0.01 is 0.99 of price p_stop = (-stop + 1) # What stop really means, e.g 0.01 is 0.99 of price
### backslap debug wrap ### backslap debug wrap
debug_2loops = False # only loop twice, for faster debug debug_2loops = True # 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 = True # print values, to check accuracy
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)
@ -638,24 +642,25 @@ class Backtesting(object):
else: else:
close_index: int = t_exit_ind + 1 close_index: int = t_exit_ind + 1
# Munge the date / delta # Munge the date / delta
start_date: str = bslap.iloc[t_open_ind + 1]['date'] start_date = bslap.iloc[t_open_ind + 1]['date']
end_date: str = bslap.iloc[close_index]['date'] end_date = bslap.iloc[close_index]['date']
def __datetime(date_str): # def __datetime(date_str):
return datetime.strptime(date_str, '%Y-%m-%d %H:%M:%S+00:00') # return datetime.strptime(date_str, '%Y-%m-%d %H:%M:%S+00:00')
trade_start = start_date
trade_end = end_date
trade_mins = (trade_end - trade_start).total_seconds() / 60
# trade_start = __datetime(start_date)
# trade_end = __datetime(end_date)
# trade_mins = (trade_end - trade_start).total_seconds() / 60
# build trade dictionary # build trade dictionary
bslap_result["pair"] = pair bslap_result["pair"] = pair
bslap_result["profit_percent"] = ( np_trade_exit_price - np_trade_enter_price) // np_trade_enter_price * 100 bslap_result["profit_percent"] = ( np_trade_exit_price - np_trade_enter_price)/np_trade_enter_price
bslap_result["profit_abs"] = "" bslap_result["profit_abs"] = ""
bslap_result["open_time"] = start_date bslap_result["open_time"] = start_date
bslap_result["close_time"] = end_date bslap_result["close_time"] = end_date
bslap_result["open_index"] = t_open_ind + 1 bslap_result["open_index"] = t_open_ind + 1
bslap_result["close_index"] = close_index bslap_result["close_index"] = close_index
# bslap_result["trade_duration"] = trade_mins bslap_result["trade_duration"] = trade_mins
bslap_result["open_at_end"] = False bslap_result["open_at_end"] = False
bslap_result["open_rate"] = str.format('{0:.10f}', np_trade_enter_price) bslap_result["open_rate"] = str.format('{0:.10f}', np_trade_enter_price)
bslap_result["close_rate"] = str.format('{0:.10f}', np_trade_exit_price) bslap_result["close_rate"] = str.format('{0:.10f}', np_trade_exit_price)