import libraries organized.
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@ -6,11 +6,13 @@ from datetime import datetime, timedelta
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from typing import Any, Dict, List, NamedTuple, Optional, Tuple
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from typing import Any, Dict, List, NamedTuple, Optional, Tuple
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import arrow
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import arrow
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from pandas import DataFrame, to_datetime
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from tabulate import tabulate
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import numpy as np
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from pandas import DataFrame, to_datetime
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import pandas as pd
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from tabulate import tabulate
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import freqtrade.optimize as optimize
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import freqtrade.optimize as optimize
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from freqtrade.optimize.backtesting import BacktestResult
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from freqtrade import DependencyException, constants
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from freqtrade import DependencyException, constants
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from freqtrade.arguments import Arguments
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from freqtrade.arguments import Arguments
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from freqtrade.configuration import Configuration
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from freqtrade.configuration import Configuration
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@ -21,10 +23,12 @@ from freqtrade.strategy.interface import SellType
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from freqtrade.strategy.resolver import IStrategy, StrategyResolver
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from freqtrade.strategy.resolver import IStrategy, StrategyResolver
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from freqtrade.optimize.backtesting import Backtesting
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from freqtrade.optimize.backtesting import Backtesting
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from collections import OrderedDict
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from collections import OrderedDict
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import timeit
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from time import sleep
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import pdb
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import numpy as np
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import timeit
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import utils_find_1st as utf1st
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from time import sleep
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from pandas import set_option
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logger = logging.getLogger(__name__)
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logger = logging.getLogger(__name__)
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@ -178,8 +182,6 @@ class Edge():
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if len(bslap_results_df) > 0: # Only post process a frame if it has a record
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if len(bslap_results_df) > 0: # Only post process a frame if it has a record
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bslap_results_df = self.vector_fill_results_table(bslap_results_df)
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bslap_results_df = self.vector_fill_results_table(bslap_results_df)
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else:
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else:
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from freqtrade.optimize.backtesting import BacktestResult
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bslap_results_df = []
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bslap_results_df = []
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bslap_results_df = DataFrame.from_records(bslap_results_df, columns=BacktestResult._fields)
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bslap_results_df = DataFrame.from_records(bslap_results_df, columns=BacktestResult._fields)
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@ -207,8 +209,6 @@ class Edge():
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:param bslap_results Dataframe
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:param bslap_results Dataframe
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:return: bslap_results Dataframe
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:return: bslap_results Dataframe
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"""
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"""
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import pandas as pd
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import numpy as np
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debug = self.debug_vector
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debug = self.debug_vector
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# stake and fees
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# stake and fees
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@ -247,7 +247,6 @@ class Edge():
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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,
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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,
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stop_stops_count: int):
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stop_stops_count: int):
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import utils_find_1st as utf1st
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"""
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"""
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The purpose of this def is to return the next "buy" = 1
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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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after t_exit_ind.
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@ -373,12 +372,6 @@ class Edge():
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return final.reset_index().values
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return final.reset_index().values
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def backslap_pair(self, ticker_data, pair, stoploss):
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def backslap_pair(self, ticker_data, pair, stoploss):
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import pandas as pd
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import numpy as np
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import timeit
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import utils_find_1st as utf1st
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from datetime import datetime
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### backslap debug wrap
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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_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_timing = False # print timing for each step
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@ -388,7 +381,6 @@ class Edge():
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debug = self.debug # print values, to check accuracy
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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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# Read Stop Loss Values and Stake
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# pdb.set_trace()
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#stop = self.stop_loss_value
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#stop = self.stop_loss_value
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stop = stoploss
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stop = stoploss
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p_stop = (stop + 1) # What stop really means, e.g 0.01 is 0.99 of price
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p_stop = (stop + 1) # What stop really means, e.g 0.01 is 0.99 of price
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@ -398,7 +390,6 @@ class Edge():
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print("p_stop is", p_stop, "value used to multiply to entry price")
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print("p_stop is", p_stop, "value used to multiply to entry price")
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if debug:
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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_rows', 5000)
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set_option('display.max_columns', 8)
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set_option('display.max_columns', 8)
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pd.set_option('display.width', 1000)
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pd.set_option('display.width', 1000)
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