647 lines
29 KiB
Python
647 lines
29 KiB
Python
# pragma pylint: disable=W0603
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""" Edge positioning package """
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import logging
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from typing import Any, Dict
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import arrow
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from pandas import DataFrame
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import pandas as pd
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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.arguments import Arguments
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from freqtrade.exchange import Exchange
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from freqtrade.strategy.interface import SellType
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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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import numpy as np
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import timeit
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import utils_find_1st as utf1st
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import pdb
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logger = logging.getLogger(__name__)
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class Edge():
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config: Dict = {}
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def __init__(self, config: Dict[str, Any], exchange=None) -> None:
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"""
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constructor
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"""
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self.config = config
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self.strategy: IStrategy = StrategyResolver(self.config).strategy
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self.ticker_interval = self.strategy.ticker_interval
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self.tickerdata_to_dataframe = self.strategy.tickerdata_to_dataframe
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self.get_timeframe = Backtesting.get_timeframe
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self.populate_buy_trend = self.strategy.populate_buy_trend
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self.populate_sell_trend = self.strategy.populate_sell_trend
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self.edge_config = self.config.get('edge', {})
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self._last_updated = None
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self._cached_pairs = []
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self._total_capital = self.edge_config['total_capital_in_stake_currency']
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self._allowed_risk = self.edge_config['allowed_risk']
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###
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#
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###
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if exchange is None:
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self.config['exchange']['secret'] = ''
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self.config['exchange']['password'] = ''
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self.config['exchange']['uid'] = ''
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self.config['dry_run'] = True
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self.exchange = Exchange(self.config)
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else:
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self.exchange = exchange
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self.fee = self.exchange.get_fee()
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self.stop_loss_value = self.strategy.stoploss
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#### backslap config
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'''
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Numpy arrays are used for 100x speed up
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We requires setting Int values for
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buy stop triggers and stop calculated on
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# buy 0 - open 1 - close 2 - sell 3 - high 4 - low 5 - stop 6
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'''
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self.np_buy: int = 0
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self.np_open: int = 1
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self.np_close: int = 2
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self.np_sell: int = 3
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self.np_high: int = 4
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self.np_low: int = 5
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self.np_stop: int = 6
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self.np_bto: int = self.np_close # buys_triggered_on - should be close
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self.np_bco: int = self.np_open # buys calculated on - open of the next candle.
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self.np_sto: int = self.np_low # stops_triggered_on - Should be low, FT uses close
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self.np_sco: int = self.np_stop # stops_calculated_on - Should be stop, FT uses close
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# self.np_sto: int = self.np_close # stops_triggered_on - Should be low, FT uses close
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# self.np_sco: int = self.np_close # stops_calculated_on - Should be stop, FT uses close
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self.debug = False # Main debug enable, very print heavy, enable 2 loops recommended
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self.debug_timing = False # Stages within Backslap
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self.debug_2loops = False # Limit each pair to two loops, useful when debugging
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self.debug_vector = False # Debug vector calcs
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self.debug_timing_main_loop = False # print overall timing per pair - works in Backtest and Backslap
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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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def calculate(self) -> bool:
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pairs = self.config['exchange']['pair_whitelist']
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heartbeat = self.config['edge']['process_throttle_secs']
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if ((self._last_updated is not None) and (self._last_updated + heartbeat > arrow.utcnow().timestamp)):
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return False
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data = {}
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logger.info('Using stake_currency: %s ...', self.config['stake_currency'])
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logger.info('Using stake_amount: %s ...', self.config['stake_amount'])
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logger.info('Using local backtesting data (using whitelist in given config) ...')
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#TODO: add "timerange" to Edge config
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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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data = optimize.load_data(
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self.config['datadir'],
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pairs=pairs,
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ticker_interval=self.ticker_interval,
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refresh_pairs=self.config.get('refresh_pairs', False),
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exchange=self.exchange,
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timerange=timerange
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)
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if not data:
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logger.critical("No data found. Edge is stopped ...")
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return
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preprocessed = self.tickerdata_to_dataframe(data)
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# Print timeframe
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min_date, max_date = self.get_timeframe(preprocessed)
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logger.info(
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'Measuring data from %s up to %s (%s days) ...',
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min_date.isoformat(),
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max_date.isoformat(),
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(max_date - min_date).days
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)
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headers = ['date', 'buy', 'open', 'close', 'sell', 'high', 'low']
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stoploss_range_min = float(self.edge_config.get('stoploss_range_min', -0.01))
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stoploss_range_max = float(self.edge_config.get('stoploss_range_max', -0.05))
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stoploss_range_step = float(self.edge_config.get('stoploss_range_step', -0.001))
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stoploss_range = np.arange(stoploss_range_min, stoploss_range_max, stoploss_range_step)
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########################### Call out BSlap Loop instead of Original BT code
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bslap_results: list = []
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for pair, pair_data in preprocessed.items():
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# Sorting dataframe by date and reset index
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pair_data = pair_data.sort_values(by=['date'])
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pair_data = pair_data.reset_index(drop=True)
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ticker_data = self.populate_sell_trend(
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self.populate_buy_trend(pair_data))[headers].copy()
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# call backslap - results are a list of dicts
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for stoploss in stoploss_range:
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bslap_results += self.backslap_pair(ticker_data, pair, round(stoploss, 6))
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# Switch List of Trade Dicts (bslap_results) to Dataframe
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# Fill missing, calculable columns, profit, duration , abs etc.
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bslap_results_df = DataFrame(bslap_results)
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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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else:
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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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self._cached_pairs = self._process_result(data, bslap_results_df, stoploss_range)
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self._last_updated = arrow.utcnow().timestamp
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return True
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def sort_pairs(self, pairs) -> bool:
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if len(self._cached_pairs) == 0:
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self.calculate()
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edge_sorted_pairs = [x[0] for x in self._cached_pairs]
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return [x for _, x in sorted(zip(edge_sorted_pairs,pairs), key=lambda pair: pair[0])]
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def vector_fill_results_table(self, bslap_results_df: DataFrame):
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"""
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The Results frame contains a number of columns that are calculable
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from othe columns. These are left blank till all rows are added,
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to be populated in single vector calls.
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Columns to be populated are:
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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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# stake and fees
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# stake = 0.015
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# 0.05% is 0.0005
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# fee = 0.001
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stake = self.config.get('stake_amount')
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fee = self.fee
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open_fee = fee / 2
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close_fee = fee / 2
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bslap_results_df['trade_duration'] = bslap_results_df['close_time'] - bslap_results_df['open_time']
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bslap_results_df['trade_duration'] = bslap_results_df['trade_duration'].map(lambda x: int(x.total_seconds() / 60))
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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_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'] = 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']) \
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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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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, stop_stops: int,
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stop_stops_count: int):
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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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t_open_ind: int
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"""
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Create a view on our buy index starting after last trade exit
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Search for next buy
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"""
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np_buy_arr_v = np_buy_arr[t_exit_ind:]
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t_open_ind = utf1st.find_1st(np_buy_arr_v, 1, utf1st.cmp_equal)
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'''
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If -1 is returned no buy has been found, preserve the value
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'''
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if t_open_ind != -1: # send back the -1 if no buys found. otherwise update index
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t_open_ind = t_open_ind + t_exit_ind # Align numpy index
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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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return t_open_ind
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def _process_result(self, data: Dict[str, Dict], results: DataFrame, stoploss_range) -> str:
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"""
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This is a temporary version of edge positioning calculation.
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The function will be eventually moved to a plugin called Edge in order to calculate necessary WR, RRR and
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other indictaors related to money management periodically (each X minutes) and keep it in a storage.
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The calulation will be done per pair and per strategy.
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"""
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# Removing open trades from dataset
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results = results[results.open_at_end == False]
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###################################
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# Removing pairs having less than min_trades_number
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min_trades_number = self.edge_config.get('min_trade_number', 15)
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results = results.groupby('pair').filter(lambda x: len(x) > min_trades_number)
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###################################
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# Removing outliers (Only Pumps) from the dataset
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# The method to detect outliers is to calculate standard deviation
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# Then every value more than (standard deviation + 2*average) is out (pump)
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#
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# Calculating standard deviation of profits
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std = results[["profit_abs"]].std()
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#
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# Calculating average of profits
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avg = results[["profit_abs"]].mean()
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#
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# Removing Pumps
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if self.edge_config.get('remove_pumps', True):
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results = results[results.profit_abs < float(avg + 2*std)]
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##########################################################################
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# Removing trades having a duration more than X minutes (set in config)
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max_trade_duration = self.edge_config.get('max_trade_duration_minute', 1440)
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results = results[results.trade_duration < max_trade_duration]
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#######################################################################
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# Win Rate is the number of profitable trades
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# Divided by number of trades
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def winrate(x):
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x = x[x > 0].count() / x.count()
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return x
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#############################
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# Risk Reward Ratio
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# 1 / ((loss money / losing trades) / (gained money / winning trades))
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def risk_reward_ratio(x):
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x = abs(1/ ((x[x<0].sum() / x[x < 0].count()) / (x[x > 0].sum() / x[x > 0].count())))
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return x
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##############################
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# Required Risk Reward
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# (1/(winrate - 1)
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def required_risk_reward(x):
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x = (1/(x[x > 0].count()/x.count()) -1)
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return x
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##############################
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def delta(x):
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x = (abs(1/ ((x[x < 0].sum() / x[x < 0].count()) / (x[x > 0].sum() / x[x > 0].count())))) - (1/(x[x > 0].count()/x.count()) -1)
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return x
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# Expectancy
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# Tells you the interest percentage you should hope
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# E.x. if expectancy is 0.35, on $1 trade you should expect a target of $1.35
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def expectancy(x):
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average_win = float(x[x > 0].sum() / x[x > 0].count())
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average_loss = float(abs(x[x < 0].sum() / x[x < 0].count()))
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winrate = float(x[x > 0].count()/x.count())
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x = ((1 + average_win/average_loss) * winrate) - 1
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return x
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##############################
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final = results.groupby(['pair', 'stoploss'])['profit_abs'].\
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agg([winrate, risk_reward_ratio, required_risk_reward, expectancy, delta]).\
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reset_index().sort_values(by=['expectancy', 'stoploss'], ascending=False)\
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.groupby('pair').first().sort_values(by=['expectancy'], ascending=False)
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# Returning an array of pairs in order of "expectancy"
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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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# Read Stop Loss Values and Stake
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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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#### backslap config
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'''
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Numpy arrays are used for 100x speed up
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We requires setting Int values for
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buy stop triggers and stop calculated on
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# buy 0 - open 1 - close 2 - sell 3 - high 4 - low 5 - stop 6
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'''
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#######
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# Use vars set at top of backtest
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np_open: int = self.np_open
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np_sell: int = self.np_sell
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np_bco: int = self.np_bco # buys calculated on - open of the next candle.
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np_sto: int = self.np_sto # stops_triggered_on - Should be low, FT uses close
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np_sco: int = self.np_sco # stops_calculated_on - Should be stop, FT uses close
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# ticker_data: DataFrame = ticker_dfs[t_file]
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bslap: DataFrame = ticker_data
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# Build a single dimension numpy array from "buy" index for faster search
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# (500x faster than pandas)
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np_buy_arr = bslap['buy'].values
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np_buy_arr_len: int = len(np_buy_arr)
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# use numpy array for faster searches in loop, 20x faster than pandas
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# buy 0 - open 1 - close 2 - sell 3 - high 4 - low 5
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np_bslap = np.array(bslap[['buy', 'open', 'close', 'sell', 'high', 'low']])
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# Build a numpy list of date-times.
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# We use these when building the trade
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# The rationale is to address a value from a pandas cell is thousands of
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# times more expensive. Processing time went X25 when trying to use any data from pandas
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np_bslap_dates = bslap['date'].values
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loop: int = 0 # how many time around the loop
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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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# Results will be stored in a list of dicts
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bslap_pair_results: list = []
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bslap_result: dict = {}
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while t_exit_ind < np_buy_arr_len:
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loop = loop + 1
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'''
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Dev phases
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Phase 1
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1) Manage buy, sell, stop enter/exit
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a) Find first buy index
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b) Discover first stop and sell hit after buy index
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c) Chose first instance as trade exit
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Phase 2
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2) Manage dynamic Stop and ROI Exit
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a) Create trade slice from 1
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b) search within trade slice for dynamice stop hit
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c) search within trade slice for ROI hit
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'''
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'''
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0 - Find next buy entry
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Finds index for first (buy = 1) flag
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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, stop_stops, stop_stops_count)
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if t_open_ind != -1:
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"""
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1 - Create views to search within for our open trade
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The views are our search space for the next Stop or Sell
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Numpy view is employed as:
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1,000 faster than pandas searches
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Pandas cannot assure it will always return a view, it may make a slow copy.
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The view contains columns:
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buy 0 - open 1 - close 2 - sell 3 - high 4 - low 5
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Requires: np_bslap is our numpy array of the ticker DataFrame
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Requires: t_open_ind is the index row with the buy.
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Provides: np_t_open_v View of array after buy.
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Provides: np_t_open_v_stop View of array after buy +1
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(Stop will search in here to prevent stopping in the past)
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"""
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np_t_open_v = np_bslap[t_open_ind:]
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np_t_open_v_stop = np_bslap[t_open_ind + 1:]
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'''
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2 - Calculate our stop-loss price
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As stop is based on buy price of our trade
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- (BTO)Buys are Triggered On np_bto, typically the CLOSE of candle
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- (BCO)Buys are Calculated On np_bco, default is OPEN of the next candle.
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This is as we only see the CLOSE after it has happened.
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The back test assumption is we have bought at first available price, the OPEN
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Requires: np_bslap - is our numpy array of the ticker DataFrame
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Requires: t_open_ind - is the index row with the first buy.
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Requires: p_stop - is the stop rate, ie. 0.99 is -1%
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Provides: np_t_stop_pri - The value stop-loss will be triggered on
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'''
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np_t_stop_pri = (np_bslap[t_open_ind + 1, np_bco] * p_stop)
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'''
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3 - Find candle STO is under Stop-Loss After Trade opened.
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where [np_sto] (stop tiggered on variable: "close", "low" etc) < np_t_stop_pri
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Requires: np_t_open_v_stop Numpy view of ticker_data after buy row +1 (when trade was opened)
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Requires: np_sto User Var(STO)StopTriggeredOn. Typically set to "low" or "close"
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Requires: np_t_stop_pri The stop-loss price STO must fall under to trigger stop
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Provides: np_t_stop_ind The first candle after trade open where STO is under stop-loss
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'''
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np_t_stop_ind = utf1st.find_1st(np_t_open_v_stop[:, np_sto],
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np_t_stop_pri,
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utf1st.cmp_smaller)
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# plus 1 as np_t_open_v_stop is 1 ahead of view np_t_open_v, used from here on out.
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np_t_stop_ind = np_t_stop_ind + 1
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'''
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4 - Find first sell index after trade open
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First index in the view np_t_open_v where ['sell'] = 1
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Requires: np_t_open_v - view of ticker_data from buy onwards
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Requires: no_sell - integer '3', the buy column in the array
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Provides: np_t_sell_ind index of view where first sell=1 after buy
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'''
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# Use numpy array for faster search for sell
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# Sell uses column 3.
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# buy 0 - open 1 - close 2 - sell 3 - high 4 - low 5
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# Numpy searches 25-35x quicker than pandas on this data
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np_t_sell_ind = utf1st.find_1st(np_t_open_v[:, np_sell],
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1, utf1st.cmp_equal)
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'''
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5 - Determine which was hit first a stop or sell
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To then use as exit index price-field (sell on buy, stop on stop)
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STOP takes priority over SELL as would be 'in candle' from tick data
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Sell would use Open from Next candle.
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So in a draw Stop would be hit first on ticker data in live
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|
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Validity of when types of trades may be executed can be summarised as:
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Tick View
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index index Buy Sell open low close high Stop price
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open 2am 94 -1 0 0 ----- ------ ------ ----- -----
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open 3am 95 0 1 0 ----- ------ trg buy ----- -----
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open 4am 96 1 0 1 Enter trgstop trg sel ROI out Stop out
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open 5am 97 2 0 0 Exit ------ ------- ----- -----
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open 6am 98 3 0 0 ----- ------ ------- ----- -----
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-1 means not found till end of view i.e no valid Stop found. Exclude from match.
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Stop tiggering and closing in 96-1, the candle we bought at OPEN in, is valid.
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|
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Buys and sells are triggered at candle close
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Both will open their postions at the open of the next candle. i/e + 1 index
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|
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Stop and buy Indexes are on the view. To map to the ticker dataframe
|
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the t_open_ind index should be summed.
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np_t_stop_ind: Stop Found index in view
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t_exit_ind : Sell found in view
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t_open_ind : Where view was started on ticker_data
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|
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TODO: fix this frig for logic test,, case/switch/dictionary would be better...
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more so when later testing many options, dynamic stop / roi etc
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cludge - Setting np_t_sell_ind as 9999999999 when -1 (not found)
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cludge - Setting np_t_stop_ind as 9999999999 when -1 (not found)
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'''
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# cludge for logic test (-1) means it was not found, set crazy high to lose < test
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np_t_sell_ind = 99999999 if np_t_sell_ind <= 0 else np_t_sell_ind
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np_t_stop_ind = 99999999 if np_t_stop_ind <= 0 else np_t_stop_ind
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# Stoploss trigger found before a sell =1
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if np_t_stop_ind < 99999999 and np_t_stop_ind <= np_t_sell_ind:
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t_exit_ind = t_open_ind + np_t_stop_ind # Set Exit row index
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t_exit_type = SellType.STOP_LOSS # Set Exit type (stop)
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|
np_t_exit_pri = np_sco # The price field our STOP exit will use
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|
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# Buy = 1 found before a stoploss triggered
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|
elif np_t_sell_ind < 99999999 and np_t_sell_ind < np_t_stop_ind:
|
|
# move sell onto next candle, we only look back on sell
|
|
# will use the open price later.
|
|
t_exit_ind = t_open_ind + np_t_sell_ind # Set Exit row index
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|
t_exit_type = SellType.SELL_SIGNAL # Set Exit type (sell)
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|
np_t_exit_pri = np_open # The price field our SELL exit will use
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|
|
# No stop or buy left in view - set t_exit_last -1 to handle gracefully
|
|
else:
|
|
t_exit_last: int = -1 # Signal loop to exit, no buys or sells found.
|
|
t_exit_type = SellType.NONE
|
|
np_t_exit_pri = 999 # field price should be calculated on. 999 a non-existent column
|
|
|
|
# TODO: fix having to cludge/uncludge this ..
|
|
# Undo cludge
|
|
np_t_sell_ind = -1 if np_t_sell_ind == 99999999 else np_t_sell_ind
|
|
np_t_stop_ind = -1 if np_t_stop_ind == 99999999 else np_t_stop_ind
|
|
|
|
## use numpy view "np_t_open_v" for speed. Columns are
|
|
# buy 0 - open 1 - close 2 - sell 3 - high 4 - low 5
|
|
# exception is 6 which is use the stop value.
|
|
|
|
# TODO no! this is hard coded bleh fix this open
|
|
np_trade_enter_price = np_bslap[t_open_ind + 1, np_open]
|
|
if t_exit_type == SellType.STOP_LOSS:
|
|
if np_t_exit_pri == 6:
|
|
np_trade_exit_price = np_t_stop_pri
|
|
t_exit_ind = t_exit_ind + 1
|
|
else:
|
|
np_trade_exit_price = np_bslap[t_exit_ind, np_t_exit_pri]
|
|
if t_exit_type == SellType.SELL_SIGNAL:
|
|
np_trade_exit_price = np_bslap[t_exit_ind, np_t_exit_pri]
|
|
|
|
# Catch no exit found
|
|
if t_exit_type == SellType.NONE:
|
|
np_trade_exit_price = 0
|
|
|
|
else: # no buys were found, step 0 returned -1
|
|
# Gracefully exit the loop
|
|
t_exit_last == -1
|
|
|
|
# Loop control - catch no closed trades.
|
|
if t_exit_last >= t_exit_ind or t_exit_last == -1:
|
|
"""
|
|
Break loop and go on to next pair.
|
|
|
|
When last trade exit equals index of last exit, there is no
|
|
opportunity to close any more trades.
|
|
"""
|
|
# TODO :add handing here to record none closed open trades
|
|
break
|
|
else:
|
|
"""
|
|
Add trade to backtest looking results list of dicts
|
|
Loop back to look for more trades.
|
|
"""
|
|
|
|
# We added +1 to t_exit_ind if the exit was a stop-loss, to not exit early in the IF of this ELSE
|
|
# removing the +1 here so prices match.
|
|
if t_exit_type == SellType.STOP_LOSS:
|
|
t_exit_ind = t_exit_ind - 1
|
|
|
|
# Build trade dictionary
|
|
## In general if a field can be calculated later from other fields leave blank here
|
|
## Its X(number of trades faster) to calc all in a single vector than 1 trade at a time
|
|
|
|
# create a new dict
|
|
close_index: int = t_exit_ind
|
|
bslap_result = {} # Must have at start or we end up with a list of multiple same last result
|
|
bslap_result["pair"] = pair
|
|
bslap_result["stoploss"] = stop
|
|
bslap_result["profit_percent"] = "" # To be 1 vector calc across trades when loop complete
|
|
bslap_result["profit_abs"] = "" # To be 1 vector calc across trades when loop complete
|
|
bslap_result["open_time"] = np_bslap_dates[t_open_ind + 1] # use numpy array, pandas 20x slower
|
|
bslap_result["close_time"] = np_bslap_dates[close_index] # use numpy array, pandas 20x slower
|
|
bslap_result["open_index"] = t_open_ind + 1 # +1 as we buy on next.
|
|
bslap_result["close_index"] = close_index
|
|
bslap_result["trade_duration"] = "" # To be 1 vector calc across trades when loop complete
|
|
bslap_result["open_at_end"] = False
|
|
bslap_result["open_rate"] = round(np_trade_enter_price, 15)
|
|
bslap_result["close_rate"] = round(np_trade_exit_price, 15)
|
|
bslap_result["exit_type"] = t_exit_type
|
|
bslap_result["sell_reason"] = t_exit_type #duplicated, but I don't care
|
|
# append the dict to the list and print list
|
|
bslap_pair_results.append(bslap_result)
|
|
|
|
if t_exit_type is SellType.STOP_LOSS:
|
|
stop_stops_count = stop_stops_count + 1
|
|
|
|
"""
|
|
Loop back to start. t_exit_last becomes where loop
|
|
will seek to open new trades from.
|
|
Push index on 1 to not open on close
|
|
"""
|
|
t_exit_last = t_exit_ind + 1
|
|
|
|
# Send back List of trade dicts
|
|
return bslap_pair_results
|
|
|
|
def stake_amount(self, pair: str) -> str:
|
|
info = [x for x in self._cached_pairs if x[0] == pair][0]
|
|
stoploss = info[1]
|
|
allowed_capital_at_risk = round(self._total_capital * self._allowed_risk, 5)
|
|
position_size = abs(round((allowed_capital_at_risk / stoploss), 5))
|
|
return position_size
|
|
|
|
def stoploss(self, pair: str) -> float:
|
|
info = [x for x in self._cached_pairs if x[0] == pair][0]
|
|
return info[1] |