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Ruff format edge

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Matthias 2024-05-12 16:24:43 +02:00
parent 794e30fedb
commit 5eb4ad2208
1 changed files with 164 additions and 131 deletions
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freqtrade/edge/edge_positioning.py
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@ -1,5 +1,6 @@
# pragma pylint: disable=W0603 # pragma pylint: disable=W0603
"""Edge positioning package""" """Edge positioning package"""
import logging import logging
from collections import defaultdict from collections import defaultdict
from copy import deepcopy from copy import deepcopy
@ -46,48 +47,49 @@ class Edge:
_cached_pairs: Dict[str, Any] = {} # Keeps a list of pairs _cached_pairs: Dict[str, Any] = {} # Keeps a list of pairs
def __init__(self, config: Config, exchange, strategy) -> None: def __init__(self, config: Config, exchange, strategy) -> None:
self.config = config self.config = config
self.exchange = exchange self.exchange = exchange
self.strategy: IStrategy = strategy self.strategy: IStrategy = strategy
self.edge_config = self.config.get('edge', {}) self.edge_config = self.config.get("edge", {})
self._cached_pairs: Dict[str, Any] = {} # Keeps a list of pairs self._cached_pairs: Dict[str, Any] = {} # Keeps a list of pairs
self._final_pairs: list = [] self._final_pairs: list = []
# checking max_open_trades. it should be -1 as with Edge # checking max_open_trades. it should be -1 as with Edge
# the number of trades is determined by position size # the number of trades is determined by position size
if self.config['max_open_trades'] != float('inf'): if self.config["max_open_trades"] != float("inf"):
logger.critical('max_open_trades should be -1 in config !') logger.critical("max_open_trades should be -1 in config !")
if self.config['stake_amount'] != UNLIMITED_STAKE_AMOUNT: if self.config["stake_amount"] != UNLIMITED_STAKE_AMOUNT:
raise OperationalException('Edge works only with unlimited stake amount') raise OperationalException("Edge works only with unlimited stake amount")
self._capital_ratio: float = self.config['tradable_balance_ratio'] self._capital_ratio: float = self.config["tradable_balance_ratio"]
self._allowed_risk: float = self.edge_config.get('allowed_risk') self._allowed_risk: float = self.edge_config.get("allowed_risk")
self._since_number_of_days: int = self.edge_config.get('calculate_since_number_of_days', 14) self._since_number_of_days: int = self.edge_config.get("calculate_since_number_of_days", 14)
self._last_updated: int = 0 # Timestamp of pairs last updated time self._last_updated: int = 0 # Timestamp of pairs last updated time
self._refresh_pairs = True self._refresh_pairs = True
self._stoploss_range_min = float(self.edge_config.get('stoploss_range_min', -0.01)) self._stoploss_range_min = float(self.edge_config.get("stoploss_range_min", -0.01))
self._stoploss_range_max = float(self.edge_config.get('stoploss_range_max', -0.05)) self._stoploss_range_max = float(self.edge_config.get("stoploss_range_max", -0.05))
self._stoploss_range_step = float(self.edge_config.get('stoploss_range_step', -0.001)) self._stoploss_range_step = float(self.edge_config.get("stoploss_range_step", -0.001))
# calculating stoploss range # calculating stoploss range
self._stoploss_range = np.arange( self._stoploss_range = np.arange(
self._stoploss_range_min, self._stoploss_range_min, self._stoploss_range_max, self._stoploss_range_step
self._stoploss_range_max,
self._stoploss_range_step
) )
self._timerange: TimeRange = TimeRange.parse_timerange( self._timerange: TimeRange = TimeRange.parse_timerange(
f"{(dt_now() - timedelta(days=self._since_number_of_days)).strftime('%Y%m%d')}-") f"{(dt_now() - timedelta(days=self._since_number_of_days)).strftime('%Y%m%d')}-"
if config.get('fee'): )
self.fee = config['fee'] if config.get("fee"):
self.fee = config["fee"]
else: else:
try: try:
self.fee = self.exchange.get_fee(symbol=expand_pairlist( self.fee = self.exchange.get_fee(
self.config['exchange']['pair_whitelist'], list(self.exchange.markets))[0]) symbol=expand_pairlist(
self.config["exchange"]["pair_whitelist"], list(self.exchange.markets)
)[0]
)
except IndexError: except IndexError:
self.fee = None self.fee = None
@ -95,28 +97,30 @@ class Edge:
if self.fee is None and pairs: if self.fee is None and pairs:
self.fee = self.exchange.get_fee(pairs[0]) self.fee = self.exchange.get_fee(pairs[0])
heartbeat = self.edge_config.get('process_throttle_secs') heartbeat = self.edge_config.get("process_throttle_secs")
if (self._last_updated > 0) and ( if (self._last_updated > 0) and (
self._last_updated + heartbeat > int(dt_now().timestamp())): self._last_updated + heartbeat > int(dt_now().timestamp())
):
return False return False
data: Dict[str, Any] = {} data: Dict[str, Any] = {}
logger.info('Using stake_currency: %s ...', self.config['stake_currency']) logger.info("Using stake_currency: %s ...", self.config["stake_currency"])
logger.info('Using local backtesting data (using whitelist in given config) ...') logger.info("Using local backtesting data (using whitelist in given config) ...")
if self._refresh_pairs: if self._refresh_pairs:
timerange_startup = deepcopy(self._timerange) timerange_startup = deepcopy(self._timerange)
timerange_startup.subtract_start(timeframe_to_seconds( timerange_startup.subtract_start(
self.strategy.timeframe) * self.strategy.startup_candle_count) timeframe_to_seconds(self.strategy.timeframe) * self.strategy.startup_candle_count
)
refresh_data( refresh_data(
datadir=self.config['datadir'], datadir=self.config["datadir"],
pairs=pairs, pairs=pairs,
exchange=self.exchange, exchange=self.exchange,
timeframe=self.strategy.timeframe, timeframe=self.strategy.timeframe,
timerange=timerange_startup, timerange=timerange_startup,
data_format=self.config['dataformat_ohlcv'], data_format=self.config["dataformat_ohlcv"],
candle_type=self.config.get('candle_type_def', CandleType.SPOT), candle_type=self.config.get("candle_type_def", CandleType.SPOT),
) )
# Download informative pairs too # Download informative pairs too
res = defaultdict(list) res = defaultdict(list)
@ -124,26 +128,27 @@ class Edge:
res[timeframe].append(pair) res[timeframe].append(pair)
for timeframe, inf_pairs in res.items(): for timeframe, inf_pairs in res.items():
timerange_startup = deepcopy(self._timerange) timerange_startup = deepcopy(self._timerange)
timerange_startup.subtract_start(timeframe_to_seconds( timerange_startup.subtract_start(
timeframe) * self.strategy.startup_candle_count) timeframe_to_seconds(timeframe) * self.strategy.startup_candle_count
)
refresh_data( refresh_data(
datadir=self.config['datadir'], datadir=self.config["datadir"],
pairs=inf_pairs, pairs=inf_pairs,
exchange=self.exchange, exchange=self.exchange,
timeframe=timeframe, timeframe=timeframe,
timerange=timerange_startup, timerange=timerange_startup,
data_format=self.config['dataformat_ohlcv'], data_format=self.config["dataformat_ohlcv"],
candle_type=self.config.get('candle_type_def', CandleType.SPOT), candle_type=self.config.get("candle_type_def", CandleType.SPOT),
) )
data = load_data( data = load_data(
datadir=self.config['datadir'], datadir=self.config["datadir"],
pairs=pairs, pairs=pairs,
timeframe=self.strategy.timeframe, timeframe=self.strategy.timeframe,
timerange=self._timerange, timerange=self._timerange,
startup_candles=self.strategy.startup_candle_count, startup_candles=self.strategy.startup_candle_count,
data_format=self.config['dataformat_ohlcv'], data_format=self.config["dataformat_ohlcv"],
candle_type=self.config.get('candle_type_def', CandleType.SPOT), candle_type=self.config.get("candle_type_def", CandleType.SPOT),
) )
if not data: if not data:
@ -152,27 +157,29 @@ class Edge:
logger.critical("No data found. Edge is stopped ...") logger.critical("No data found. Edge is stopped ...")
return False return False
# Fake run-mode to Edge # Fake run-mode to Edge
prior_rm = self.config['runmode'] prior_rm = self.config["runmode"]
self.config['runmode'] = RunMode.EDGE self.config["runmode"] = RunMode.EDGE
preprocessed = self.strategy.advise_all_indicators(data) preprocessed = self.strategy.advise_all_indicators(data)
self.config['runmode'] = prior_rm self.config["runmode"] = prior_rm
# Print timeframe # Print timeframe
min_date, max_date = get_timerange(preprocessed) min_date, max_date = get_timerange(preprocessed)
logger.info(f'Measuring data from {min_date.strftime(DATETIME_PRINT_FORMAT)} ' logger.info(
f'up to {max_date.strftime(DATETIME_PRINT_FORMAT)} ' f"Measuring data from {min_date.strftime(DATETIME_PRINT_FORMAT)} "
f'({(max_date - min_date).days} days)..') f"up to {max_date.strftime(DATETIME_PRINT_FORMAT)} "
f"({(max_date - min_date).days} days).."
)
# TODO: Should edge support shorts? needs to be investigated further # TODO: Should edge support shorts? needs to be investigated further
# * (add enter_short exit_short) # * (add enter_short exit_short)
headers = ['date', 'open', 'high', 'low', 'close', 'enter_long', 'exit_long'] headers = ["date", "open", "high", "low", "close", "enter_long", "exit_long"]
trades: list = [] trades: list = []
for pair, pair_data in preprocessed.items(): for pair, pair_data in preprocessed.items():
# Sorting dataframe by date and reset index # Sorting dataframe by date and reset index
pair_data = pair_data.sort_values(by=['date']) pair_data = pair_data.sort_values(by=["date"])
pair_data = pair_data.reset_index(drop=True) pair_data = pair_data.reset_index(drop=True)
df_analyzed = self.strategy.ft_advise_signals(pair_data, {'pair': pair})[headers].copy() df_analyzed = self.strategy.ft_advise_signals(pair_data, {"pair": pair})[headers].copy()
trades += self._find_trades_for_stoploss_range(df_analyzed, pair, self._stoploss_range) trades += self._find_trades_for_stoploss_range(df_analyzed, pair, self._stoploss_range)
@ -188,8 +195,9 @@ class Edge:
return True return True
def stake_amount(self, pair: str, free_capital: float, def stake_amount(
total_capital: float, capital_in_trade: float) -> float: self, pair: str, free_capital: float, total_capital: float, capital_in_trade: float
) -> float:
stoploss = self.get_stoploss(pair) stoploss = self.get_stoploss(pair)
available_capital = (total_capital + capital_in_trade) * self._capital_ratio available_capital = (total_capital + capital_in_trade) * self._capital_ratio
allowed_capital_at_risk = available_capital * self._allowed_risk allowed_capital_at_risk = available_capital * self._allowed_risk
@ -198,14 +206,18 @@ class Edge:
position_size = min(min(max_position_size, free_capital), available_capital) position_size = min(min(max_position_size, free_capital), available_capital)
if pair in self._cached_pairs: if pair in self._cached_pairs:
logger.info( logger.info(
'winrate: %s, expectancy: %s, position size: %s, pair: %s,' "winrate: %s, expectancy: %s, position size: %s, pair: %s,"
' capital in trade: %s, free capital: %s, total capital: %s,' " capital in trade: %s, free capital: %s, total capital: %s,"
' stoploss: %s, available capital: %s.', " stoploss: %s, available capital: %s.",
self._cached_pairs[pair].winrate, self._cached_pairs[pair].winrate,
self._cached_pairs[pair].expectancy, self._cached_pairs[pair].expectancy,
position_size, pair, position_size,
capital_in_trade, free_capital, total_capital, pair,
stoploss, available_capital capital_in_trade,
free_capital,
total_capital,
stoploss,
available_capital,
) )
return round(position_size, 15) return round(position_size, 15)
@ -213,8 +225,10 @@ class Edge:
if pair in self._cached_pairs: if pair in self._cached_pairs:
return self._cached_pairs[pair].stoploss return self._cached_pairs[pair].stoploss
else: else:
logger.warning(f'Tried to access stoploss of non-existing pair {pair}, ' logger.warning(
'strategy stoploss is returned instead.') f"Tried to access stoploss of non-existing pair {pair}, "
"strategy stoploss is returned instead."
)
return self.strategy.stoploss return self.strategy.stoploss
def adjust(self, pairs: List[str]) -> list: def adjust(self, pairs: List[str]) -> list:
@ -224,8 +238,8 @@ class Edge:
final = [] final = []
for pair, info in self._cached_pairs.items(): for pair, info in self._cached_pairs.items():
if ( if (
info.expectancy > float(self.edge_config.get('minimum_expectancy', 0.2)) info.expectancy > float(self.edge_config.get("minimum_expectancy", 0.2))
and info.winrate > float(self.edge_config.get('minimum_winrate', 0.60)) and info.winrate > float(self.edge_config.get("minimum_winrate", 0.60))
and pair in pairs and pair in pairs
): ):
final.append(pair) final.append(pair)
@ -234,14 +248,14 @@ class Edge:
self._final_pairs = final self._final_pairs = final
if self._final_pairs: if self._final_pairs:
logger.info( logger.info(
'Minimum expectancy and minimum winrate are met only for %s,' "Minimum expectancy and minimum winrate are met only for %s,"
' so other pairs are filtered out.', " so other pairs are filtered out.",
self._final_pairs self._final_pairs,
) )
else: else:
logger.info( logger.info(
'Edge removed all pairs as no pair with minimum expectancy ' "Edge removed all pairs as no pair with minimum expectancy "
'and minimum winrate was found !' "and minimum winrate was found !"
) )
return self._final_pairs return self._final_pairs
@ -252,14 +266,17 @@ class Edge:
""" """
final = [] final = []
for pair, info in self._cached_pairs.items(): for pair, info in self._cached_pairs.items():
if (info.expectancy > float(self.edge_config.get('minimum_expectancy', 0.2)) and if info.expectancy > float(
info.winrate > float(self.edge_config.get('minimum_winrate', 0.60))): self.edge_config.get("minimum_expectancy", 0.2)
final.append({ ) and info.winrate > float(self.edge_config.get("minimum_winrate", 0.60)):
'Pair': pair, final.append(
'Winrate': info.winrate, {
'Expectancy': info.expectancy, "Pair": pair,
'Stoploss': info.stoploss, "Winrate": info.winrate,
}) "Expectancy": info.expectancy,
"Stoploss": info.stoploss,
}
)
return final return final
def _fill_calculable_fields(self, result: DataFrame) -> DataFrame: def _fill_calculable_fields(self, result: DataFrame) -> DataFrame:
@ -279,28 +296,29 @@ class Edge:
# All returned values are relative, they are defined as ratios. # All returned values are relative, they are defined as ratios.
stake = 0.015 stake = 0.015
result['trade_duration'] = result['close_date'] - result['open_date'] result["trade_duration"] = result["close_date"] - result["open_date"]
result['trade_duration'] = result['trade_duration'].map( result["trade_duration"] = result["trade_duration"].map(
lambda x: int(x.total_seconds() / 60)) lambda x: int(x.total_seconds() / 60)
)
# Spends, Takes, Profit, Absolute Profit # Spends, Takes, Profit, Absolute Profit
# Buy Price # Buy Price
result['buy_vol'] = stake / result['open_rate'] # How many target are we buying result["buy_vol"] = stake / result["open_rate"] # How many target are we buying
result['buy_fee'] = stake * self.fee result["buy_fee"] = stake * self.fee
result['buy_spend'] = stake + result['buy_fee'] # How much we're spending result["buy_spend"] = stake + result["buy_fee"] # How much we're spending
# Sell price # Sell price
result['sell_sum'] = result['buy_vol'] * result['close_rate'] result["sell_sum"] = result["buy_vol"] * result["close_rate"]
result['sell_fee'] = result['sell_sum'] * self.fee result["sell_fee"] = result["sell_sum"] * self.fee
result['sell_take'] = result['sell_sum'] - result['sell_fee'] result["sell_take"] = result["sell_sum"] - result["sell_fee"]
# profit_ratio # profit_ratio
result['profit_ratio'] = (result['sell_take'] - result['buy_spend']) / result['buy_spend'] result["profit_ratio"] = (result["sell_take"] - result["buy_spend"]) / result["buy_spend"]
# Absolute profit # Absolute profit
result['profit_abs'] = result['sell_take'] - result['buy_spend'] result["profit_abs"] = result["sell_take"] - result["buy_spend"]
return result return result
@ -310,8 +328,8 @@ class Edge:
The calculation will be done per pair and per strategy. The calculation will be done per pair and per strategy.
""" """
# Removing pairs having less than min_trades_number # Removing pairs having less than min_trades_number
min_trades_number = self.edge_config.get('min_trade_number', 10) min_trades_number = self.edge_config.get("min_trade_number", 10)
results = results.groupby(['pair', 'stoploss']).filter(lambda x: len(x) > min_trades_number) results = results.groupby(["pair", "stoploss"]).filter(lambda x: len(x) > min_trades_number)
################################### ###################################
# Removing outliers (Only Pumps) from the dataset # Removing outliers (Only Pumps) from the dataset
@ -319,13 +337,15 @@ class Edge:
# Then every value more than (standard deviation + 2*average) is out (pump) # Then every value more than (standard deviation + 2*average) is out (pump)
# #
# Removing Pumps # Removing Pumps
if self.edge_config.get('remove_pumps', False): if self.edge_config.get("remove_pumps", False):
results = results[results['profit_abs'] < 2 * results['profit_abs'].std() results = results[
+ results['profit_abs'].mean()] results["profit_abs"]
< 2 * results["profit_abs"].std() + results["profit_abs"].mean()
]
########################################################################## ##########################################################################
# Removing trades having a duration more than X minutes (set in config) # Removing trades having a duration more than X minutes (set in config)
max_trade_duration = self.edge_config.get('max_trade_duration_minute', 1440) max_trade_duration = self.edge_config.get("max_trade_duration_minute", 1440)
results = results[results.trade_duration < max_trade_duration] results = results[results.trade_duration < max_trade_duration]
####################################################################### #######################################################################
@ -333,44 +353,54 @@ class Edge:
return {} return {}
groupby_aggregator = { groupby_aggregator = {
'profit_abs': [ "profit_abs": [
('nb_trades', 'count'), # number of all trades ("nb_trades", "count"), # number of all trades
('profit_sum', lambda x: x[x > 0].sum()), # cumulative profit of all winning trades ("profit_sum", lambda x: x[x > 0].sum()), # cumulative profit of all winning trades
('loss_sum', lambda x: abs(x[x < 0].sum())), # cumulative loss of all losing trades ("loss_sum", lambda x: abs(x[x < 0].sum())), # cumulative loss of all losing trades
('nb_win_trades', lambda x: x[x > 0].count()) # number of winning trades ("nb_win_trades", lambda x: x[x > 0].count()), # number of winning trades
], ],
'trade_duration': [('avg_trade_duration', 'mean')] "trade_duration": [("avg_trade_duration", "mean")],
} }
# Group by (pair and stoploss) by applying above aggregator # Group by (pair and stoploss) by applying above aggregator
df = results.groupby(['pair', 'stoploss'])[['profit_abs', 'trade_duration']].agg( df = (
groupby_aggregator).reset_index(col_level=1) results.groupby(["pair", "stoploss"])[["profit_abs", "trade_duration"]]
.agg(groupby_aggregator)
.reset_index(col_level=1)
)
# Dropping level 0 as we don't need it # Dropping level 0 as we don't need it
df.columns = df.columns.droplevel(0) df.columns = df.columns.droplevel(0)
# Calculating number of losing trades, average win and average loss # Calculating number of losing trades, average win and average loss
df['nb_loss_trades'] = df['nb_trades'] - df['nb_win_trades'] df["nb_loss_trades"] = df["nb_trades"] - df["nb_win_trades"]
df['average_win'] = np.where(df['nb_win_trades'] == 0, 0.0, df["average_win"] = np.where(
df['profit_sum'] / df['nb_win_trades']) df["nb_win_trades"] == 0, 0.0, df["profit_sum"] / df["nb_win_trades"]
df['average_loss'] = np.where(df['nb_loss_trades'] == 0, 0.0, )
df['loss_sum'] / df['nb_loss_trades']) df["average_loss"] = np.where(
df["nb_loss_trades"] == 0, 0.0, df["loss_sum"] / df["nb_loss_trades"]
)
# Win rate = number of profitable trades / number of trades # Win rate = number of profitable trades / number of trades
df['winrate'] = df['nb_win_trades'] / df['nb_trades'] df["winrate"] = df["nb_win_trades"] / df["nb_trades"]
# risk_reward_ratio = average win / average loss # risk_reward_ratio = average win / average loss
df['risk_reward_ratio'] = df['average_win'] / df['average_loss'] df["risk_reward_ratio"] = df["average_win"] / df["average_loss"]
# required_risk_reward = (1 / winrate) - 1 # required_risk_reward = (1 / winrate) - 1
df['required_risk_reward'] = (1 / df['winrate']) - 1 df["required_risk_reward"] = (1 / df["winrate"]) - 1
# expectancy = (risk_reward_ratio * winrate) - (lossrate) # expectancy = (risk_reward_ratio * winrate) - (lossrate)
df['expectancy'] = (df['risk_reward_ratio'] * df['winrate']) - (1 - df['winrate']) df["expectancy"] = (df["risk_reward_ratio"] * df["winrate"]) - (1 - df["winrate"])
# sort by expectancy and stoploss # sort by expectancy and stoploss
df = df.sort_values(by=['expectancy', 'stoploss'], ascending=False).groupby( df = (
'pair').first().sort_values(by=['expectancy'], ascending=False).reset_index() df.sort_values(by=["expectancy", "stoploss"], ascending=False)
.groupby("pair")
.first()
.sort_values(by=["expectancy"], ascending=False)
.reset_index()
)
final = {} final = {}
for x in df.itertuples(): for x in df.itertuples():
@ -381,17 +411,17 @@ class Edge:
x.required_risk_reward, x.required_risk_reward,
x.expectancy, x.expectancy,
x.nb_trades, x.nb_trades,
x.avg_trade_duration x.avg_trade_duration,
) )
# Returning a list of pairs in order of "expectancy" # Returning a list of pairs in order of "expectancy"
return final return final
def _find_trades_for_stoploss_range(self, df, pair: str, stoploss_range) -> list: def _find_trades_for_stoploss_range(self, df, pair: str, stoploss_range) -> list:
buy_column = df['enter_long'].values buy_column = df["enter_long"].values
sell_column = df['exit_long'].values sell_column = df["exit_long"].values
date_column = df['date'].values date_column = df["date"].values
ohlc_columns = df[['open', 'high', 'low', 'close']].values ohlc_columns = df[["open", "high", "low", "close"]].values
result: list = [] result: list = []
for stoploss in stoploss_range: for stoploss in stoploss_range:
@ -401,8 +431,9 @@ class Edge:
return result return result
def _detect_next_stop_or_sell_point(self, buy_column, sell_column, date_column, def _detect_next_stop_or_sell_point(
ohlc_columns, stoploss, pair: str): self, buy_column, sell_column, date_column, ohlc_columns, stoploss, pair: str
):
""" """
Iterate through ohlc_columns in order to find the next trade Iterate through ohlc_columns in order to find the next trade
Next trade opens from the first buy signal noticed to Next trade opens from the first buy signal noticed to
@ -429,27 +460,28 @@ class Edge:
open_trade_index += 1 open_trade_index += 1
open_price = ohlc_columns[open_trade_index, 0] open_price = ohlc_columns[open_trade_index, 0]
stop_price = (open_price * (stoploss + 1)) stop_price = open_price * (stoploss + 1)
# Searching for the index where stoploss is hit # Searching for the index where stoploss is hit
stop_index = utf1st.find_1st( stop_index = utf1st.find_1st(
ohlc_columns[open_trade_index:, 2], stop_price, utf1st.cmp_smaller) ohlc_columns[open_trade_index:, 2], stop_price, utf1st.cmp_smaller
)
# If we don't find it then we assume stop_index will be far in future (infinite number) # If we don't find it then we assume stop_index will be far in future (infinite number)
if stop_index == -1: if stop_index == -1:
stop_index = float('inf') stop_index = float("inf")
# Searching for the index where sell is hit # Searching for the index where sell is hit
sell_index = utf1st.find_1st(sell_column[open_trade_index:], 1, utf1st.cmp_equal) sell_index = utf1st.find_1st(sell_column[open_trade_index:], 1, utf1st.cmp_equal)
# If we don't find it then we assume sell_index will be far in future (infinite number) # If we don't find it then we assume sell_index will be far in future (infinite number)
if sell_index == -1: if sell_index == -1:
sell_index = float('inf') sell_index = float("inf")
# Check if we don't find any stop or sell point (in that case trade remains open) # Check if we don't find any stop or sell point (in that case trade remains open)
# It is not interesting for Edge to consider it so we simply ignore the trade # It is not interesting for Edge to consider it so we simply ignore the trade
# And stop iterating there is no more entry # And stop iterating there is no more entry
if stop_index == sell_index == float('inf'): if stop_index == sell_index == float("inf"):
break break
if stop_index <= sell_index: if stop_index <= sell_index:
@ -467,16 +499,17 @@ class Edge:
exit_type = ExitType.EXIT_SIGNAL exit_type = ExitType.EXIT_SIGNAL
exit_price = ohlc_columns[exit_index, 0] exit_price = ohlc_columns[exit_index, 0]
trade = {'pair': pair, trade = {
'stoploss': stoploss, "pair": pair,
'profit_ratio': '', "stoploss": stoploss,
'profit_abs': '', "profit_ratio": "",
'open_date': date_column[open_trade_index], "profit_abs": "",
'close_date': date_column[exit_index], "open_date": date_column[open_trade_index],
'trade_duration': '', "close_date": date_column[exit_index],
'open_rate': round(open_price, 15), "trade_duration": "",
'close_rate': round(exit_price, 15), "open_rate": round(open_price, 15),
'exit_type': exit_type "close_rate": round(exit_price, 15),
"exit_type": exit_type,
} }
result.append(trade) result.append(trade)