mirror of
https://github.com/freqtrade/freqtrade.git
synced 2024-09-20 01:21:11 +00:00
Ruff format edge
This commit is contained in:
parent
794e30fedb
commit
5eb4ad2208
|
@ -1,5 +1,6 @@
|
|||
# pragma pylint: disable=W0603
|
||||
""" Edge positioning package """
|
||||
"""Edge positioning package"""
|
||||
|
||||
import logging
|
||||
from collections import defaultdict
|
||||
from copy import deepcopy
|
||||
|
@ -46,48 +47,49 @@ class Edge:
|
|||
_cached_pairs: Dict[str, Any] = {} # Keeps a list of pairs
|
||||
|
||||
def __init__(self, config: Config, exchange, strategy) -> None:
|
||||
|
||||
self.config = config
|
||||
self.exchange = exchange
|
||||
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._final_pairs: list = []
|
||||
|
||||
# checking max_open_trades. it should be -1 as with Edge
|
||||
# the number of trades is determined by position size
|
||||
if self.config['max_open_trades'] != float('inf'):
|
||||
logger.critical('max_open_trades should be -1 in config !')
|
||||
if self.config["max_open_trades"] != float("inf"):
|
||||
logger.critical("max_open_trades should be -1 in config !")
|
||||
|
||||
if self.config['stake_amount'] != UNLIMITED_STAKE_AMOUNT:
|
||||
raise OperationalException('Edge works only with unlimited stake amount')
|
||||
if self.config["stake_amount"] != UNLIMITED_STAKE_AMOUNT:
|
||||
raise OperationalException("Edge works only with unlimited stake amount")
|
||||
|
||||
self._capital_ratio: float = self.config['tradable_balance_ratio']
|
||||
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._capital_ratio: float = self.config["tradable_balance_ratio"]
|
||||
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._last_updated: int = 0 # Timestamp of pairs last updated time
|
||||
self._refresh_pairs = True
|
||||
|
||||
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_step = float(self.edge_config.get('stoploss_range_step', -0.001))
|
||||
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_step = float(self.edge_config.get("stoploss_range_step", -0.001))
|
||||
|
||||
# calculating stoploss range
|
||||
self._stoploss_range = np.arange(
|
||||
self._stoploss_range_min,
|
||||
self._stoploss_range_max,
|
||||
self._stoploss_range_step
|
||||
self._stoploss_range_min, self._stoploss_range_max, self._stoploss_range_step
|
||||
)
|
||||
|
||||
self._timerange: TimeRange = TimeRange.parse_timerange(
|
||||
f"{(dt_now() - timedelta(days=self._since_number_of_days)).strftime('%Y%m%d')}-")
|
||||
if config.get('fee'):
|
||||
self.fee = config['fee']
|
||||
f"{(dt_now() - timedelta(days=self._since_number_of_days)).strftime('%Y%m%d')}-"
|
||||
)
|
||||
if config.get("fee"):
|
||||
self.fee = config["fee"]
|
||||
else:
|
||||
try:
|
||||
self.fee = self.exchange.get_fee(symbol=expand_pairlist(
|
||||
self.config['exchange']['pair_whitelist'], list(self.exchange.markets))[0])
|
||||
self.fee = self.exchange.get_fee(
|
||||
symbol=expand_pairlist(
|
||||
self.config["exchange"]["pair_whitelist"], list(self.exchange.markets)
|
||||
)[0]
|
||||
)
|
||||
except IndexError:
|
||||
self.fee = None
|
||||
|
||||
|
@ -95,28 +97,30 @@ class Edge:
|
|||
if self.fee is None and pairs:
|
||||
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 (
|
||||
self._last_updated + heartbeat > int(dt_now().timestamp())):
|
||||
self._last_updated + heartbeat > int(dt_now().timestamp())
|
||||
):
|
||||
return False
|
||||
|
||||
data: Dict[str, Any] = {}
|
||||
logger.info('Using stake_currency: %s ...', self.config['stake_currency'])
|
||||
logger.info('Using local backtesting data (using whitelist in given config) ...')
|
||||
logger.info("Using stake_currency: %s ...", self.config["stake_currency"])
|
||||
logger.info("Using local backtesting data (using whitelist in given config) ...")
|
||||
|
||||
if self._refresh_pairs:
|
||||
timerange_startup = deepcopy(self._timerange)
|
||||
timerange_startup.subtract_start(timeframe_to_seconds(
|
||||
self.strategy.timeframe) * self.strategy.startup_candle_count)
|
||||
timerange_startup.subtract_start(
|
||||
timeframe_to_seconds(self.strategy.timeframe) * self.strategy.startup_candle_count
|
||||
)
|
||||
refresh_data(
|
||||
datadir=self.config['datadir'],
|
||||
datadir=self.config["datadir"],
|
||||
pairs=pairs,
|
||||
exchange=self.exchange,
|
||||
timeframe=self.strategy.timeframe,
|
||||
timerange=timerange_startup,
|
||||
data_format=self.config['dataformat_ohlcv'],
|
||||
candle_type=self.config.get('candle_type_def', CandleType.SPOT),
|
||||
data_format=self.config["dataformat_ohlcv"],
|
||||
candle_type=self.config.get("candle_type_def", CandleType.SPOT),
|
||||
)
|
||||
# Download informative pairs too
|
||||
res = defaultdict(list)
|
||||
|
@ -124,26 +128,27 @@ class Edge:
|
|||
res[timeframe].append(pair)
|
||||
for timeframe, inf_pairs in res.items():
|
||||
timerange_startup = deepcopy(self._timerange)
|
||||
timerange_startup.subtract_start(timeframe_to_seconds(
|
||||
timeframe) * self.strategy.startup_candle_count)
|
||||
timerange_startup.subtract_start(
|
||||
timeframe_to_seconds(timeframe) * self.strategy.startup_candle_count
|
||||
)
|
||||
refresh_data(
|
||||
datadir=self.config['datadir'],
|
||||
datadir=self.config["datadir"],
|
||||
pairs=inf_pairs,
|
||||
exchange=self.exchange,
|
||||
timeframe=timeframe,
|
||||
timerange=timerange_startup,
|
||||
data_format=self.config['dataformat_ohlcv'],
|
||||
candle_type=self.config.get('candle_type_def', CandleType.SPOT),
|
||||
data_format=self.config["dataformat_ohlcv"],
|
||||
candle_type=self.config.get("candle_type_def", CandleType.SPOT),
|
||||
)
|
||||
|
||||
data = load_data(
|
||||
datadir=self.config['datadir'],
|
||||
datadir=self.config["datadir"],
|
||||
pairs=pairs,
|
||||
timeframe=self.strategy.timeframe,
|
||||
timerange=self._timerange,
|
||||
startup_candles=self.strategy.startup_candle_count,
|
||||
data_format=self.config['dataformat_ohlcv'],
|
||||
candle_type=self.config.get('candle_type_def', CandleType.SPOT),
|
||||
data_format=self.config["dataformat_ohlcv"],
|
||||
candle_type=self.config.get("candle_type_def", CandleType.SPOT),
|
||||
)
|
||||
|
||||
if not data:
|
||||
|
@ -152,27 +157,29 @@ class Edge:
|
|||
logger.critical("No data found. Edge is stopped ...")
|
||||
return False
|
||||
# Fake run-mode to Edge
|
||||
prior_rm = self.config['runmode']
|
||||
self.config['runmode'] = RunMode.EDGE
|
||||
prior_rm = self.config["runmode"]
|
||||
self.config["runmode"] = RunMode.EDGE
|
||||
preprocessed = self.strategy.advise_all_indicators(data)
|
||||
self.config['runmode'] = prior_rm
|
||||
self.config["runmode"] = prior_rm
|
||||
|
||||
# Print timeframe
|
||||
min_date, max_date = get_timerange(preprocessed)
|
||||
logger.info(f'Measuring data from {min_date.strftime(DATETIME_PRINT_FORMAT)} '
|
||||
f'up to {max_date.strftime(DATETIME_PRINT_FORMAT)} '
|
||||
f'({(max_date - min_date).days} days)..')
|
||||
logger.info(
|
||||
f"Measuring data from {min_date.strftime(DATETIME_PRINT_FORMAT)} "
|
||||
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
|
||||
# * (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 = []
|
||||
for pair, pair_data in preprocessed.items():
|
||||
# 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)
|
||||
|
||||
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)
|
||||
|
||||
|
@ -188,8 +195,9 @@ class Edge:
|
|||
|
||||
return True
|
||||
|
||||
def stake_amount(self, pair: str, free_capital: float,
|
||||
total_capital: float, capital_in_trade: float) -> float:
|
||||
def stake_amount(
|
||||
self, pair: str, free_capital: float, total_capital: float, capital_in_trade: float
|
||||
) -> float:
|
||||
stoploss = self.get_stoploss(pair)
|
||||
available_capital = (total_capital + capital_in_trade) * self._capital_ratio
|
||||
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)
|
||||
if pair in self._cached_pairs:
|
||||
logger.info(
|
||||
'winrate: %s, expectancy: %s, position size: %s, pair: %s,'
|
||||
' capital in trade: %s, free capital: %s, total capital: %s,'
|
||||
' stoploss: %s, available capital: %s.',
|
||||
"winrate: %s, expectancy: %s, position size: %s, pair: %s,"
|
||||
" capital in trade: %s, free capital: %s, total capital: %s,"
|
||||
" stoploss: %s, available capital: %s.",
|
||||
self._cached_pairs[pair].winrate,
|
||||
self._cached_pairs[pair].expectancy,
|
||||
position_size, pair,
|
||||
capital_in_trade, free_capital, total_capital,
|
||||
stoploss, available_capital
|
||||
position_size,
|
||||
pair,
|
||||
capital_in_trade,
|
||||
free_capital,
|
||||
total_capital,
|
||||
stoploss,
|
||||
available_capital,
|
||||
)
|
||||
return round(position_size, 15)
|
||||
|
||||
|
@ -213,8 +225,10 @@ class Edge:
|
|||
if pair in self._cached_pairs:
|
||||
return self._cached_pairs[pair].stoploss
|
||||
else:
|
||||
logger.warning(f'Tried to access stoploss of non-existing pair {pair}, '
|
||||
'strategy stoploss is returned instead.')
|
||||
logger.warning(
|
||||
f"Tried to access stoploss of non-existing pair {pair}, "
|
||||
"strategy stoploss is returned instead."
|
||||
)
|
||||
return self.strategy.stoploss
|
||||
|
||||
def adjust(self, pairs: List[str]) -> list:
|
||||
|
@ -224,8 +238,8 @@ class Edge:
|
|||
final = []
|
||||
for pair, info in self._cached_pairs.items():
|
||||
if (
|
||||
info.expectancy > float(self.edge_config.get('minimum_expectancy', 0.2))
|
||||
and info.winrate > float(self.edge_config.get('minimum_winrate', 0.60))
|
||||
info.expectancy > float(self.edge_config.get("minimum_expectancy", 0.2))
|
||||
and info.winrate > float(self.edge_config.get("minimum_winrate", 0.60))
|
||||
and pair in pairs
|
||||
):
|
||||
final.append(pair)
|
||||
|
@ -234,14 +248,14 @@ class Edge:
|
|||
self._final_pairs = final
|
||||
if self._final_pairs:
|
||||
logger.info(
|
||||
'Minimum expectancy and minimum winrate are met only for %s,'
|
||||
' so other pairs are filtered out.',
|
||||
self._final_pairs
|
||||
"Minimum expectancy and minimum winrate are met only for %s,"
|
||||
" so other pairs are filtered out.",
|
||||
self._final_pairs,
|
||||
)
|
||||
else:
|
||||
logger.info(
|
||||
'Edge removed all pairs as no pair with minimum expectancy '
|
||||
'and minimum winrate was found !'
|
||||
"Edge removed all pairs as no pair with minimum expectancy "
|
||||
"and minimum winrate was found !"
|
||||
)
|
||||
|
||||
return self._final_pairs
|
||||
|
@ -252,14 +266,17 @@ class Edge:
|
|||
"""
|
||||
final = []
|
||||
for pair, info in self._cached_pairs.items():
|
||||
if (info.expectancy > float(self.edge_config.get('minimum_expectancy', 0.2)) and
|
||||
info.winrate > float(self.edge_config.get('minimum_winrate', 0.60))):
|
||||
final.append({
|
||||
'Pair': pair,
|
||||
'Winrate': info.winrate,
|
||||
'Expectancy': info.expectancy,
|
||||
'Stoploss': info.stoploss,
|
||||
})
|
||||
if info.expectancy > float(
|
||||
self.edge_config.get("minimum_expectancy", 0.2)
|
||||
) and info.winrate > float(self.edge_config.get("minimum_winrate", 0.60)):
|
||||
final.append(
|
||||
{
|
||||
"Pair": pair,
|
||||
"Winrate": info.winrate,
|
||||
"Expectancy": info.expectancy,
|
||||
"Stoploss": info.stoploss,
|
||||
}
|
||||
)
|
||||
return final
|
||||
|
||||
def _fill_calculable_fields(self, result: DataFrame) -> DataFrame:
|
||||
|
@ -279,28 +296,29 @@ class Edge:
|
|||
# All returned values are relative, they are defined as ratios.
|
||||
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(
|
||||
lambda x: int(x.total_seconds() / 60))
|
||||
result["trade_duration"] = result["trade_duration"].map(
|
||||
lambda x: int(x.total_seconds() / 60)
|
||||
)
|
||||
|
||||
# Spends, Takes, Profit, Absolute Profit
|
||||
|
||||
# Buy Price
|
||||
result['buy_vol'] = stake / result['open_rate'] # How many target are we buying
|
||||
result['buy_fee'] = stake * self.fee
|
||||
result['buy_spend'] = stake + result['buy_fee'] # How much we're spending
|
||||
result["buy_vol"] = stake / result["open_rate"] # How many target are we buying
|
||||
result["buy_fee"] = stake * self.fee
|
||||
result["buy_spend"] = stake + result["buy_fee"] # How much we're spending
|
||||
|
||||
# Sell price
|
||||
result['sell_sum'] = result['buy_vol'] * result['close_rate']
|
||||
result['sell_fee'] = result['sell_sum'] * self.fee
|
||||
result['sell_take'] = result['sell_sum'] - result['sell_fee']
|
||||
result["sell_sum"] = result["buy_vol"] * result["close_rate"]
|
||||
result["sell_fee"] = result["sell_sum"] * self.fee
|
||||
result["sell_take"] = result["sell_sum"] - result["sell_fee"]
|
||||
|
||||
# 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
|
||||
result['profit_abs'] = result['sell_take'] - result['buy_spend']
|
||||
result["profit_abs"] = result["sell_take"] - result["buy_spend"]
|
||||
|
||||
return result
|
||||
|
||||
|
@ -310,8 +328,8 @@ class Edge:
|
|||
The calculation will be done per pair and per strategy.
|
||||
"""
|
||||
# Removing pairs having less than min_trades_number
|
||||
min_trades_number = self.edge_config.get('min_trade_number', 10)
|
||||
results = results.groupby(['pair', 'stoploss']).filter(lambda x: len(x) > min_trades_number)
|
||||
min_trades_number = self.edge_config.get("min_trade_number", 10)
|
||||
results = results.groupby(["pair", "stoploss"]).filter(lambda x: len(x) > min_trades_number)
|
||||
###################################
|
||||
|
||||
# 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)
|
||||
#
|
||||
# Removing Pumps
|
||||
if self.edge_config.get('remove_pumps', False):
|
||||
results = results[results['profit_abs'] < 2 * results['profit_abs'].std()
|
||||
+ results['profit_abs'].mean()]
|
||||
if self.edge_config.get("remove_pumps", False):
|
||||
results = results[
|
||||
results["profit_abs"]
|
||||
< 2 * results["profit_abs"].std() + results["profit_abs"].mean()
|
||||
]
|
||||
##########################################################################
|
||||
|
||||
# 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]
|
||||
#######################################################################
|
||||
|
||||
|
@ -333,44 +353,54 @@ class Edge:
|
|||
return {}
|
||||
|
||||
groupby_aggregator = {
|
||||
'profit_abs': [
|
||||
('nb_trades', 'count'), # number of all 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
|
||||
('nb_win_trades', lambda x: x[x > 0].count()) # number of winning trades
|
||||
"profit_abs": [
|
||||
("nb_trades", "count"), # number of all 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
|
||||
("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
|
||||
df = results.groupby(['pair', 'stoploss'])[['profit_abs', 'trade_duration']].agg(
|
||||
groupby_aggregator).reset_index(col_level=1)
|
||||
df = (
|
||||
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
|
||||
df.columns = df.columns.droplevel(0)
|
||||
|
||||
# Calculating number of losing trades, average win and average loss
|
||||
df['nb_loss_trades'] = df['nb_trades'] - df['nb_win_trades']
|
||||
df['average_win'] = np.where(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["nb_loss_trades"] = df["nb_trades"] - df["nb_win_trades"]
|
||||
df["average_win"] = np.where(
|
||||
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"]
|
||||
)
|
||||
|
||||
# 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
|
||||
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
|
||||
df['required_risk_reward'] = (1 / df['winrate']) - 1
|
||||
df["required_risk_reward"] = (1 / df["winrate"]) - 1
|
||||
|
||||
# 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
|
||||
df = df.sort_values(by=['expectancy', 'stoploss'], ascending=False).groupby(
|
||||
'pair').first().sort_values(by=['expectancy'], ascending=False).reset_index()
|
||||
df = (
|
||||
df.sort_values(by=["expectancy", "stoploss"], ascending=False)
|
||||
.groupby("pair")
|
||||
.first()
|
||||
.sort_values(by=["expectancy"], ascending=False)
|
||||
.reset_index()
|
||||
)
|
||||
|
||||
final = {}
|
||||
for x in df.itertuples():
|
||||
|
@ -381,17 +411,17 @@ class Edge:
|
|||
x.required_risk_reward,
|
||||
x.expectancy,
|
||||
x.nb_trades,
|
||||
x.avg_trade_duration
|
||||
x.avg_trade_duration,
|
||||
)
|
||||
|
||||
# Returning a list of pairs in order of "expectancy"
|
||||
return final
|
||||
|
||||
def _find_trades_for_stoploss_range(self, df, pair: str, stoploss_range) -> list:
|
||||
buy_column = df['enter_long'].values
|
||||
sell_column = df['exit_long'].values
|
||||
date_column = df['date'].values
|
||||
ohlc_columns = df[['open', 'high', 'low', 'close']].values
|
||||
buy_column = df["enter_long"].values
|
||||
sell_column = df["exit_long"].values
|
||||
date_column = df["date"].values
|
||||
ohlc_columns = df[["open", "high", "low", "close"]].values
|
||||
|
||||
result: list = []
|
||||
for stoploss in stoploss_range:
|
||||
|
@ -401,8 +431,9 @@ class Edge:
|
|||
|
||||
return result
|
||||
|
||||
def _detect_next_stop_or_sell_point(self, buy_column, sell_column, date_column,
|
||||
ohlc_columns, stoploss, pair: str):
|
||||
def _detect_next_stop_or_sell_point(
|
||||
self, buy_column, sell_column, date_column, ohlc_columns, stoploss, pair: str
|
||||
):
|
||||
"""
|
||||
Iterate through ohlc_columns in order to find the next trade
|
||||
Next trade opens from the first buy signal noticed to
|
||||
|
@ -429,27 +460,28 @@ class Edge:
|
|||
open_trade_index += 1
|
||||
|
||||
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
|
||||
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 stop_index == -1:
|
||||
stop_index = float('inf')
|
||||
stop_index = float("inf")
|
||||
|
||||
# Searching for the index where sell is hit
|
||||
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 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)
|
||||
# It is not interesting for Edge to consider it so we simply ignore the trade
|
||||
# And stop iterating there is no more entry
|
||||
if stop_index == sell_index == float('inf'):
|
||||
if stop_index == sell_index == float("inf"):
|
||||
break
|
||||
|
||||
if stop_index <= sell_index:
|
||||
|
@ -467,16 +499,17 @@ class Edge:
|
|||
exit_type = ExitType.EXIT_SIGNAL
|
||||
exit_price = ohlc_columns[exit_index, 0]
|
||||
|
||||
trade = {'pair': pair,
|
||||
'stoploss': stoploss,
|
||||
'profit_ratio': '',
|
||||
'profit_abs': '',
|
||||
'open_date': date_column[open_trade_index],
|
||||
'close_date': date_column[exit_index],
|
||||
'trade_duration': '',
|
||||
'open_rate': round(open_price, 15),
|
||||
'close_rate': round(exit_price, 15),
|
||||
'exit_type': exit_type
|
||||
trade = {
|
||||
"pair": pair,
|
||||
"stoploss": stoploss,
|
||||
"profit_ratio": "",
|
||||
"profit_abs": "",
|
||||
"open_date": date_column[open_trade_index],
|
||||
"close_date": date_column[exit_index],
|
||||
"trade_duration": "",
|
||||
"open_rate": round(open_price, 15),
|
||||
"close_rate": round(exit_price, 15),
|
||||
"exit_type": exit_type,
|
||||
}
|
||||
|
||||
result.append(trade)
|
||||
|
|
Loading…
Reference in New Issue
Block a user