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Merge branch 'develop' into pr/italodamato/6563

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This commit is contained in:
Matthias
2022-04-10 09:16:31 +02:00
parent 1559692e47 a99cbe459c
commit 54633e90a7
185 changed files with 34047 additions and 5067 deletions
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568
freqtrade/optimize/backtesting.py
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@@ -9,16 +9,17 @@ from copy import deepcopy
from datetime import datetime, timedelta, timezone
from typing import Any, Dict, List, Optional, Tuple
from numpy import nan
from pandas import DataFrame
from freqtrade import constants
from freqtrade.configuration import TimeRange, validate_config_consistency
from freqtrade.constants import DATETIME_PRINT_FORMAT
from freqtrade.constants import DATETIME_PRINT_FORMAT, LongShort
from freqtrade.data import history
from freqtrade.data.btanalysis import find_existing_backtest_stats, trade_list_to_dataframe
from freqtrade.data.converter import trim_dataframe, trim_dataframes
from freqtrade.data.dataprovider import DataProvider
from freqtrade.enums import BacktestState, SellType
from freqtrade.enums import BacktestState, CandleType, ExitCheckTuple, ExitType, TradingMode
from freqtrade.exceptions import DependencyException, OperationalException
from freqtrade.exchange import timeframe_to_minutes, timeframe_to_seconds
from freqtrade.misc import get_strategy_run_id
@@ -30,7 +31,7 @@ from freqtrade.persistence import LocalTrade, Order, PairLocks, Trade
from freqtrade.plugins.pairlistmanager import PairListManager
from freqtrade.plugins.protectionmanager import ProtectionManager
from freqtrade.resolvers import ExchangeResolver, StrategyResolver
from freqtrade.strategy.interface import IStrategy, SellCheckTuple
from freqtrade.strategy.interface import IStrategy
from freqtrade.strategy.strategy_wrapper import strategy_safe_wrapper
from freqtrade.wallets import Wallets
@@ -39,14 +40,16 @@ logger = logging.getLogger(__name__)
# Indexes for backtest tuples
DATE_IDX = 0
BUY_IDX = 1
OPEN_IDX = 2
CLOSE_IDX = 3
SELL_IDX = 4
LOW_IDX = 5
HIGH_IDX = 6
BUY_TAG_IDX = 7
EXIT_TAG_IDX = 8
OPEN_IDX = 1
HIGH_IDX = 2
LOW_IDX = 3
CLOSE_IDX = 4
LONG_IDX = 5
ELONG_IDX = 6 # Exit long
SHORT_IDX = 7
ESHORT_IDX = 8 # Exit short
ENTER_TAG_IDX = 9
EXIT_TAG_IDX = 10
class Backtesting:
@@ -70,8 +73,8 @@ class Backtesting:
self.run_ids: Dict[str, str] = {}
self.strategylist: List[IStrategy] = []
self.all_results: Dict[str, Dict] = {}
self.exchange = ExchangeResolver.load_exchange(self.config['exchange']['name'], self.config)
self._exchange_name = self.config['exchange']['name']
self.exchange = ExchangeResolver.load_exchange(self._exchange_name, self.config)
self.dataprovider = DataProvider(self.config, self.exchange)
if self.config.get('strategy_list', None):
@@ -123,6 +126,11 @@ class Backtesting:
# Add maximum startup candle count to configuration for informative pairs support
self.config['startup_candle_count'] = self.required_startup
self.exchange.validate_required_startup_candles(self.required_startup, self.timeframe)
self.trading_mode: TradingMode = config.get('trading_mode', TradingMode.SPOT)
# strategies which define "can_short=True" will fail to load in Spot mode.
self._can_short = self.trading_mode != TradingMode.SPOT
self.init_backtest()
def __del__(self):
@@ -146,6 +154,7 @@ class Backtesting:
else:
self.timeframe_detail_min = 0
self.detail_data: Dict[str, DataFrame] = {}
self.futures_data: Dict[str, DataFrame] = {}
def init_backtest(self):
@@ -192,6 +201,7 @@ class Backtesting:
startup_candles=self.required_startup,
fail_without_data=True,
data_format=self.config.get('dataformat_ohlcv', 'json'),
candle_type=self.config.get('candle_type_def', CandleType.SPOT)
)
min_date, max_date = history.get_timerange(data)
@@ -220,9 +230,41 @@ class Backtesting:
startup_candles=0,
fail_without_data=True,
data_format=self.config.get('dataformat_ohlcv', 'json'),
candle_type=self.config.get('candle_type_def', CandleType.SPOT)
)
else:
self.detail_data = {}
if self.trading_mode == TradingMode.FUTURES:
# Load additional futures data.
funding_rates_dict = history.load_data(
datadir=self.config['datadir'],
pairs=self.pairlists.whitelist,
timeframe=self.exchange._ft_has['mark_ohlcv_timeframe'],
timerange=self.timerange,
startup_candles=0,
fail_without_data=True,
data_format=self.config.get('dataformat_ohlcv', 'json'),
candle_type=CandleType.FUNDING_RATE
)
# For simplicity, assign to CandleType.Mark (might contian index candles!)
mark_rates_dict = history.load_data(
datadir=self.config['datadir'],
pairs=self.pairlists.whitelist,
timeframe=self.exchange._ft_has['mark_ohlcv_timeframe'],
timerange=self.timerange,
startup_candles=0,
fail_without_data=True,
data_format=self.config.get('dataformat_ohlcv', 'json'),
candle_type=CandleType.from_string(self.exchange._ft_has["mark_ohlcv_price"])
)
# Combine data to avoid combining the data per trade.
for pair in self.pairlists.whitelist:
self.futures_data[pair] = funding_rates_dict[pair].merge(
mark_rates_dict[pair], on='date', how="inner", suffixes=["_fund", "_mark"])
else:
self.futures_data = {}
def prepare_backtest(self, enable_protections):
"""
@@ -260,7 +302,8 @@ class Backtesting:
"""
# Every change to this headers list must evaluate further usages of the resulting tuple
# and eventually change the constants for indexes at the top
headers = ['date', 'buy', 'open', 'close', 'sell', 'low', 'high', 'buy_tag', 'exit_tag']
headers = ['date', 'open', 'high', 'low', 'close', 'enter_long', 'exit_long',
'enter_short', 'exit_short', 'enter_tag', 'exit_tag']
data: Dict = {}
self.progress.init_step(BacktestState.CONVERT, len(processed))
@@ -269,19 +312,21 @@ class Backtesting:
pair_data = processed[pair]
self.check_abort()
self.progress.increment()
if not pair_data.empty:
pair_data.loc[:, 'buy'] = 0 # cleanup if buy_signal is exist
pair_data.loc[:, 'sell'] = 0 # cleanup if sell_signal is exist
pair_data.loc[:, 'buy_tag'] = None # cleanup if buy_tag is exist
pair_data.loc[:, 'exit_tag'] = None # cleanup if exit_tag is exist
df_analyzed = self.strategy.advise_sell(
self.strategy.advise_buy(pair_data, {'pair': pair}), {'pair': pair}).copy()
if not pair_data.empty:
# Cleanup from prior runs
pair_data.drop(headers[5:] + ['buy', 'sell'], axis=1, errors='ignore')
df_analyzed = self.strategy.advise_exit(
self.strategy.advise_entry(pair_data, {'pair': pair}),
{'pair': pair}
).copy()
# Trim startup period from analyzed dataframe
df_analyzed = processed[pair] = pair_data = trim_dataframe(
df_analyzed, self.timerange, startup_candles=self.required_startup)
# Update dataprovider cache
self.dataprovider._set_cached_df(pair, self.timeframe, df_analyzed)
self.dataprovider._set_cached_df(
pair, self.timeframe, df_analyzed, self.config['candle_type_def'])
# Create a copy of the dataframe before shifting, that way the buy signal/tag
# remains on the correct candle for callbacks.
@@ -289,112 +334,158 @@ class Backtesting:
# To avoid using data from future, we use buy/sell signals shifted
# from the previous candle
df_analyzed.loc[:, 'buy'] = df_analyzed.loc[:, 'buy'].shift(1)
df_analyzed.loc[:, 'sell'] = df_analyzed.loc[:, 'sell'].shift(1)
df_analyzed.loc[:, 'buy_tag'] = df_analyzed.loc[:, 'buy_tag'].shift(1)
df_analyzed.loc[:, 'exit_tag'] = df_analyzed.loc[:, 'exit_tag'].shift(1)
for col in headers[5:]:
tag_col = col in ('enter_tag', 'exit_tag')
if col in df_analyzed.columns:
df_analyzed.loc[:, col] = df_analyzed.loc[:, col].replace(
[nan], [0 if not tag_col else None]).shift(1)
elif not df_analyzed.empty:
df_analyzed.loc[:, col] = 0 if not tag_col else None
df_analyzed = df_analyzed.drop(df_analyzed.head(1).index)
# Convert from Pandas to list for performance reasons
# (Looping Pandas is slow.)
data[pair] = df_analyzed[headers].values.tolist()
data[pair] = df_analyzed[headers].values.tolist() if not df_analyzed.empty else []
return data
def _get_close_rate(self, sell_row: Tuple, trade: LocalTrade, sell: SellCheckTuple,
def _get_close_rate(self, row: Tuple, trade: LocalTrade, sell: ExitCheckTuple,
trade_dur: int) -> float:
"""
Get close rate for backtesting result
"""
# Special handling if high or low hit STOP_LOSS or ROI
if sell.sell_type in (SellType.STOP_LOSS, SellType.TRAILING_STOP_LOSS):
if trade.stop_loss > sell_row[HIGH_IDX]:
# our stoploss was already higher than candle high,
# possibly due to a cancelled trade exit.
# sell at open price.
return sell_row[OPEN_IDX]
# Special case: trailing triggers within same candle as trade opened. Assume most
# pessimistic price movement, which is moving just enough to arm stoploss and
# immediately going down to stop price.
if sell.sell_type == SellType.TRAILING_STOP_LOSS and trade_dur == 0:
if (
not self.strategy.use_custom_stoploss and self.strategy.trailing_stop
and self.strategy.trailing_only_offset_is_reached
and self.strategy.trailing_stop_positive_offset is not None
and self.strategy.trailing_stop_positive
):
# Worst case: price reaches stop_positive_offset and dives down.
stop_rate = (sell_row[OPEN_IDX] *
(1 + abs(self.strategy.trailing_stop_positive_offset) -
abs(self.strategy.trailing_stop_positive)))
else:
# Worst case: price ticks tiny bit above open and dives down.
stop_rate = sell_row[OPEN_IDX] * (1 - abs(trade.stop_loss_pct))
assert stop_rate < sell_row[HIGH_IDX]
# Limit lower-end to candle low to avoid sells below the low.
# This still remains "worst case" - but "worst realistic case".
return max(sell_row[LOW_IDX], stop_rate)
# Set close_rate to stoploss
return trade.stop_loss
elif sell.sell_type == (SellType.ROI):
roi_entry, roi = self.strategy.min_roi_reached_entry(trade_dur)
if roi is not None and roi_entry is not None:
if roi == -1 and roi_entry % self.timeframe_min == 0:
# When forceselling with ROI=-1, the roi time will always be equal to trade_dur.
# If that entry is a multiple of the timeframe (so on candle open)
# - we'll use open instead of close
return sell_row[OPEN_IDX]
# - (Expected abs profit + open_rate + open_fee) / (fee_close -1)
close_rate = - (trade.open_rate * roi + trade.open_rate *
(1 + trade.fee_open)) / (trade.fee_close - 1)
if (trade_dur > 0 and trade_dur == roi_entry
and roi_entry % self.timeframe_min == 0
and sell_row[OPEN_IDX] > close_rate):
# new ROI entry came into effect.
# use Open rate if open_rate > calculated sell rate
return sell_row[OPEN_IDX]
if (
trade_dur == 0
# Red candle (for longs), TODO: green candle (for shorts)
and sell_row[OPEN_IDX] > sell_row[CLOSE_IDX] # Red candle
and trade.open_rate < sell_row[OPEN_IDX] # trade-open below open_rate
and close_rate > sell_row[CLOSE_IDX]
):
# ROI on opening candles with custom pricing can only
# trigger if the entry was at Open or lower.
# details: https: // github.com/freqtrade/freqtrade/issues/6261
# If open_rate is < open, only allow sells below the close on red candles.
raise ValueError("Opening candle ROI on red candles.")
# Use the maximum between close_rate and low as we
# cannot sell outside of a candle.
# Applies when a new ROI setting comes in place and the whole candle is above that.
return min(max(close_rate, sell_row[LOW_IDX]), sell_row[HIGH_IDX])
else:
# This should not be reached...
return sell_row[OPEN_IDX]
if sell.exit_type in (ExitType.STOP_LOSS, ExitType.TRAILING_STOP_LOSS):
return self._get_close_rate_for_stoploss(row, trade, sell, trade_dur)
elif sell.exit_type == (ExitType.ROI):
return self._get_close_rate_for_roi(row, trade, sell, trade_dur)
else:
return sell_row[OPEN_IDX]
return row[OPEN_IDX]
def _get_close_rate_for_stoploss(self, row: Tuple, trade: LocalTrade, sell: ExitCheckTuple,
trade_dur: int) -> float:
# our stoploss was already lower than candle high,
# possibly due to a cancelled trade exit.
# sell at open price.
is_short = trade.is_short or False
leverage = trade.leverage or 1.0
side_1 = -1 if is_short else 1
if is_short:
if trade.stop_loss < row[LOW_IDX]:
return row[OPEN_IDX]
else:
if trade.stop_loss > row[HIGH_IDX]:
return row[OPEN_IDX]
# Special case: trailing triggers within same candle as trade opened. Assume most
# pessimistic price movement, which is moving just enough to arm stoploss and
# immediately going down to stop price.
if sell.exit_type == ExitType.TRAILING_STOP_LOSS and trade_dur == 0:
if (
not self.strategy.use_custom_stoploss and self.strategy.trailing_stop
and self.strategy.trailing_only_offset_is_reached
and self.strategy.trailing_stop_positive_offset is not None
and self.strategy.trailing_stop_positive
):
# Worst case: price reaches stop_positive_offset and dives down.
stop_rate = (row[OPEN_IDX] *
(1 + side_1 * abs(self.strategy.trailing_stop_positive_offset) -
side_1 * abs(self.strategy.trailing_stop_positive / leverage)))
else:
# Worst case: price ticks tiny bit above open and dives down.
stop_rate = row[OPEN_IDX] * (1 - side_1 * abs(trade.stop_loss_pct / leverage))
if is_short:
assert stop_rate > row[LOW_IDX]
else:
assert stop_rate < row[HIGH_IDX]
# Limit lower-end to candle low to avoid sells below the low.
# This still remains "worst case" - but "worst realistic case".
if is_short:
return min(row[HIGH_IDX], stop_rate)
else:
return max(row[LOW_IDX], stop_rate)
# Set close_rate to stoploss
return trade.stop_loss
def _get_close_rate_for_roi(self, row: Tuple, trade: LocalTrade, sell: ExitCheckTuple,
trade_dur: int) -> float:
is_short = trade.is_short or False
leverage = trade.leverage or 1.0
side_1 = -1 if is_short else 1
roi_entry, roi = self.strategy.min_roi_reached_entry(trade_dur)
if roi is not None and roi_entry is not None:
if roi == -1 and roi_entry % self.timeframe_min == 0:
# When forceselling with ROI=-1, the roi time will always be equal to trade_dur.
# If that entry is a multiple of the timeframe (so on candle open)
# - we'll use open instead of close
return row[OPEN_IDX]
# - (Expected abs profit - open_rate - open_fee) / (fee_close -1)
roi_rate = trade.open_rate * roi / leverage
open_fee_rate = side_1 * trade.open_rate * (1 + side_1 * trade.fee_open)
close_rate = -(roi_rate + open_fee_rate) / (trade.fee_close - side_1 * 1)
if is_short:
is_new_roi = row[OPEN_IDX] < close_rate
else:
is_new_roi = row[OPEN_IDX] > close_rate
if (trade_dur > 0 and trade_dur == roi_entry
and roi_entry % self.timeframe_min == 0
and is_new_roi):
# new ROI entry came into effect.
# use Open rate if open_rate > calculated sell rate
return row[OPEN_IDX]
if (trade_dur == 0 and (
(
is_short
# Red candle (for longs)
and row[OPEN_IDX] < row[CLOSE_IDX] # Red candle
and trade.open_rate > row[OPEN_IDX] # trade-open above open_rate
and close_rate < row[CLOSE_IDX] # closes below close
)
or
(
not is_short
# green candle (for shorts)
and row[OPEN_IDX] > row[CLOSE_IDX] # green candle
and trade.open_rate < row[OPEN_IDX] # trade-open below open_rate
and close_rate > row[CLOSE_IDX] # closes above close
)
)):
# ROI on opening candles with custom pricing can only
# trigger if the entry was at Open or lower wick.
# details: https: // github.com/freqtrade/freqtrade/issues/6261
# If open_rate is < open, only allow sells below the close on red candles.
raise ValueError("Opening candle ROI on red candles.")
# Use the maximum between close_rate and low as we
# cannot sell outside of a candle.
# Applies when a new ROI setting comes in place and the whole candle is above that.
return min(max(close_rate, row[LOW_IDX]), row[HIGH_IDX])
else:
# This should not be reached...
return row[OPEN_IDX]
def _get_adjust_trade_entry_for_candle(self, trade: LocalTrade, row: Tuple
) -> LocalTrade:
current_profit = trade.calc_profit_ratio(row[OPEN_IDX])
min_stake = self.exchange.get_min_pair_stake_amount(trade.pair, row[OPEN_IDX], -0.1)
max_stake = self.wallets.get_available_stake_amount()
max_stake = self.exchange.get_max_pair_stake_amount(trade.pair, row[OPEN_IDX])
stake_available = self.wallets.get_available_stake_amount()
stake_amount = strategy_safe_wrapper(self.strategy.adjust_trade_position,
default_retval=None)(
trade=trade, current_time=row[DATE_IDX].to_pydatetime(), current_rate=row[OPEN_IDX],
current_profit=current_profit, min_stake=min_stake, max_stake=max_stake)
current_profit=current_profit, min_stake=min_stake,
max_stake=min(max_stake, stake_available))
# Check if we should increase our position
if stake_amount is not None and stake_amount > 0.0:
pos_trade = self._enter_trade(trade.pair, row, stake_amount, trade)
pos_trade = self._enter_trade(
trade.pair, row, 'short' if trade.is_short else 'long', stake_amount, trade)
if pos_trade is not None:
self.wallets.update()
return pos_trade
@@ -406,35 +497,38 @@ class Backtesting:
return row[LOW_IDX] <= rate <= row[HIGH_IDX]
def _get_sell_trade_entry_for_candle(self, trade: LocalTrade,
sell_row: Tuple) -> Optional[LocalTrade]:
row: Tuple) -> Optional[LocalTrade]:
# Check if we need to adjust our current positions
if self.strategy.position_adjustment_enable:
check_adjust_buy = True
check_adjust_entry = True
if self.strategy.max_entry_position_adjustment > -1:
count_of_buys = trade.nr_of_successful_buys
check_adjust_buy = (count_of_buys <= self.strategy.max_entry_position_adjustment)
if check_adjust_buy:
trade = self._get_adjust_trade_entry_for_candle(trade, sell_row)
entry_count = trade.nr_of_successful_entries
check_adjust_entry = (entry_count <= self.strategy.max_entry_position_adjustment)
if check_adjust_entry:
trade = self._get_adjust_trade_entry_for_candle(trade, row)
sell_candle_time = sell_row[DATE_IDX].to_pydatetime()
sell = self.strategy.should_sell(trade, sell_row[OPEN_IDX], # type: ignore
sell_candle_time, sell_row[BUY_IDX],
sell_row[SELL_IDX],
low=sell_row[LOW_IDX], high=sell_row[HIGH_IDX])
sell_candle_time: datetime = row[DATE_IDX].to_pydatetime()
enter = row[SHORT_IDX] if trade.is_short else row[LONG_IDX]
exit_ = row[ESHORT_IDX] if trade.is_short else row[ELONG_IDX]
sell = self.strategy.should_exit(
trade, row[OPEN_IDX], sell_candle_time, # type: ignore
enter=enter, exit_=exit_,
low=row[LOW_IDX], high=row[HIGH_IDX]
)
if sell.sell_flag:
if sell.exit_flag:
trade.close_date = sell_candle_time
trade_dur = int((trade.close_date_utc - trade.open_date_utc).total_seconds() // 60)
try:
closerate = self._get_close_rate(sell_row, trade, sell, trade_dur)
closerate = self._get_close_rate(row, trade, sell, trade_dur)
except ValueError:
return None
# call the custom exit price,with default value as previous closerate
current_profit = trade.calc_profit_ratio(closerate)
order_type = self.strategy.order_types['sell']
if sell.sell_type in (SellType.SELL_SIGNAL, SellType.CUSTOM_SELL):
order_type = self.strategy.order_types['exit']
if sell.exit_type in (ExitType.EXIT_SIGNAL, ExitType.CUSTOM_EXIT):
# Custom exit pricing only for sell-signals
if order_type == 'limit':
closerate = strategy_safe_wrapper(self.strategy.custom_exit_price,
@@ -444,28 +538,32 @@ class Backtesting:
proposed_rate=closerate, current_profit=current_profit)
# We can't place orders lower than current low.
# freqtrade does not support this in live, and the order would fill immediately
closerate = max(closerate, sell_row[LOW_IDX])
if trade.is_short:
closerate = min(closerate, row[HIGH_IDX])
else:
closerate = max(closerate, row[LOW_IDX])
# Confirm trade exit:
time_in_force = self.strategy.order_time_in_force['sell']
time_in_force = self.strategy.order_time_in_force['exit']
if not strategy_safe_wrapper(self.strategy.confirm_trade_exit, default_retval=True)(
pair=trade.pair, trade=trade, order_type='limit', amount=trade.amount,
rate=closerate,
time_in_force=time_in_force,
sell_reason=sell.sell_reason,
sell_reason=sell.exit_reason, # deprecated
exit_reason=sell.exit_reason,
current_time=sell_candle_time):
return None
trade.sell_reason = sell.sell_reason
trade.exit_reason = sell.exit_reason
# Checks and adds an exit tag, after checking that the length of the
# sell_row has the length for an exit tag column
# row has the length for an exit tag column
if(
len(sell_row) > EXIT_TAG_IDX
and sell_row[EXIT_TAG_IDX] is not None
and len(sell_row[EXIT_TAG_IDX]) > 0
len(row) > EXIT_TAG_IDX
and row[EXIT_TAG_IDX] is not None
and len(row[EXIT_TAG_IDX]) > 0
):
trade.sell_reason = sell_row[EXIT_TAG_IDX]
trade.exit_reason = row[EXIT_TAG_IDX]
self.order_id_counter += 1
order = Order(
@@ -477,8 +575,8 @@ class Backtesting:
ft_pair=trade.pair,
order_id=str(self.order_id_counter),
symbol=trade.pair,
ft_order_side="sell",
side="sell",
ft_order_side=trade.exit_side,
side=trade.exit_side,
order_type=order_type,
status="open",
price=closerate,
@@ -493,9 +591,19 @@ class Backtesting:
return None
def _get_sell_trade_entry(self, trade: LocalTrade, sell_row: Tuple) -> Optional[LocalTrade]:
def _get_sell_trade_entry(self, trade: LocalTrade, row: Tuple) -> Optional[LocalTrade]:
sell_candle_time: datetime = row[DATE_IDX].to_pydatetime()
if self.trading_mode == TradingMode.FUTURES:
trade.funding_fees = self.exchange.calculate_funding_fees(
self.futures_data[trade.pair],
amount=trade.amount,
is_short=trade.is_short,
open_date=trade.open_date_utc,
close_date=sell_candle_time,
)
if self.timeframe_detail and trade.pair in self.detail_data:
sell_candle_time = sell_row[DATE_IDX].to_pydatetime()
sell_candle_end = sell_candle_time + timedelta(minutes=self.timeframe_min)
detail_data = self.detail_data[trade.pair]
@@ -505,12 +613,15 @@ class Backtesting:
].copy()
if len(detail_data) == 0:
# Fall back to "regular" data if no detail data was found for this candle
return self._get_sell_trade_entry_for_candle(trade, sell_row)
detail_data.loc[:, 'buy'] = sell_row[BUY_IDX]
detail_data.loc[:, 'sell'] = sell_row[SELL_IDX]
detail_data.loc[:, 'buy_tag'] = sell_row[BUY_TAG_IDX]
detail_data.loc[:, 'exit_tag'] = sell_row[EXIT_TAG_IDX]
headers = ['date', 'buy', 'open', 'close', 'sell', 'low', 'high', 'buy_tag', 'exit_tag']
return self._get_sell_trade_entry_for_candle(trade, row)
detail_data.loc[:, 'enter_long'] = row[LONG_IDX]
detail_data.loc[:, 'exit_long'] = row[ELONG_IDX]
detail_data.loc[:, 'enter_short'] = row[SHORT_IDX]
detail_data.loc[:, 'exit_short'] = row[ESHORT_IDX]
detail_data.loc[:, 'enter_tag'] = row[ENTER_TAG_IDX]
detail_data.loc[:, 'exit_tag'] = row[EXIT_TAG_IDX]
headers = ['date', 'open', 'high', 'low', 'close', 'enter_long', 'exit_long',
'enter_short', 'exit_short', 'enter_tag', 'exit_tag']
for det_row in detail_data[headers].values.tolist():
res = self._get_sell_trade_entry_for_candle(trade, det_row)
if res:
@@ -519,60 +630,108 @@ class Backtesting:
return None
else:
return self._get_sell_trade_entry_for_candle(trade, sell_row)
return self._get_sell_trade_entry_for_candle(trade, row)
def _enter_trade(self, pair: str, row: Tuple, stake_amount: Optional[float] = None,
trade: Optional[LocalTrade] = None) -> Optional[LocalTrade]:
def get_valid_price_and_stake(
self, pair: str, row: Tuple, propose_rate: float, stake_amount: Optional[float],
direction: LongShort, current_time: datetime, entry_tag: Optional[str],
trade: Optional[LocalTrade], order_type: str
) -> Tuple[float, float, float, float]:
current_time = row[DATE_IDX].to_pydatetime()
entry_tag = row[BUY_TAG_IDX] if len(row) >= BUY_TAG_IDX + 1 else None
# let's call the custom entry price, using the open price as default price
order_type = self.strategy.order_types['buy']
propose_rate = row[OPEN_IDX]
if order_type == 'limit':
propose_rate = strategy_safe_wrapper(self.strategy.custom_entry_price,
default_retval=row[OPEN_IDX])(
default_retval=propose_rate)(
pair=pair, current_time=current_time,
proposed_rate=propose_rate, entry_tag=entry_tag) # default value is the open rate
proposed_rate=propose_rate, entry_tag=entry_tag,
side=direction,
) # default value is the open rate
# We can't place orders higher than current high (otherwise it'd be a stop limit buy)
# which freqtrade does not support in live.
propose_rate = min(propose_rate, row[HIGH_IDX])
min_stake_amount = self.exchange.get_min_pair_stake_amount(pair, propose_rate, -0.05) or 0
max_stake_amount = self.wallets.get_available_stake_amount()
if direction == "short":
propose_rate = max(propose_rate, row[LOW_IDX])
else:
propose_rate = min(propose_rate, row[HIGH_IDX])
pos_adjust = trade is not None
leverage = trade.leverage if trade else 1.0
if not pos_adjust:
try:
stake_amount = self.wallets.get_trade_stake_amount(pair, None, update=False)
except DependencyException:
return None
return 0, 0, 0, 0
max_leverage = self.exchange.get_max_leverage(pair, stake_amount)
leverage = strategy_safe_wrapper(self.strategy.leverage, default_retval=1.0)(
pair=pair,
current_time=current_time,
current_rate=row[OPEN_IDX],
proposed_leverage=1.0,
max_leverage=max_leverage,
side=direction,
) if self._can_short else 1.0
# Cap leverage between 1.0 and max_leverage.
leverage = min(max(leverage, 1.0), max_leverage)
min_stake_amount = self.exchange.get_min_pair_stake_amount(
pair, propose_rate, -0.05, leverage=leverage) or 0
max_stake_amount = self.exchange.get_max_pair_stake_amount(
pair, propose_rate, leverage=leverage)
stake_available = self.wallets.get_available_stake_amount()
if not pos_adjust:
stake_amount = strategy_safe_wrapper(self.strategy.custom_stake_amount,
default_retval=stake_amount)(
pair=pair, current_time=current_time, current_rate=propose_rate,
proposed_stake=stake_amount, min_stake=min_stake_amount, max_stake=max_stake_amount,
entry_tag=entry_tag)
proposed_stake=stake_amount, min_stake=min_stake_amount,
max_stake=min(stake_available, max_stake_amount),
entry_tag=entry_tag, side=direction)
stake_amount = self.wallets.validate_stake_amount(pair, stake_amount, min_stake_amount)
stake_amount_val = self.wallets.validate_stake_amount(
pair=pair,
stake_amount=stake_amount,
min_stake_amount=min_stake_amount,
max_stake_amount=max_stake_amount,
)
return propose_rate, stake_amount_val, leverage, min_stake_amount
def _enter_trade(self, pair: str, row: Tuple, direction: LongShort,
stake_amount: Optional[float] = None,
trade: Optional[LocalTrade] = None) -> Optional[LocalTrade]:
current_time = row[DATE_IDX].to_pydatetime()
entry_tag = row[ENTER_TAG_IDX] if len(row) >= ENTER_TAG_IDX + 1 else None
# let's call the custom entry price, using the open price as default price
order_type = self.strategy.order_types['entry']
pos_adjust = trade is not None
propose_rate, stake_amount, leverage, min_stake_amount = self.get_valid_price_and_stake(
pair, row, row[OPEN_IDX], stake_amount, direction, current_time, entry_tag, trade,
order_type
)
if not stake_amount:
# In case of pos adjust, still return the original trade
# If not pos adjust, trade is None
return trade
time_in_force = self.strategy.order_time_in_force['entry']
time_in_force = self.strategy.order_time_in_force['buy']
# Confirm trade entry:
if not pos_adjust:
# Confirm trade entry:
if not strategy_safe_wrapper(self.strategy.confirm_trade_entry, default_retval=True)(
pair=pair, order_type=order_type, amount=stake_amount, rate=propose_rate,
time_in_force=time_in_force, current_time=current_time,
entry_tag=entry_tag):
return None
entry_tag=entry_tag, side=direction):
return trade
if stake_amount and (not min_stake_amount or stake_amount > min_stake_amount):
self.order_id_counter += 1
amount = round(stake_amount / propose_rate, 8)
base_currency = self.exchange.get_pair_base_currency(pair)
amount = round((stake_amount / propose_rate) * leverage, 8)
is_short = (direction == 'short')
# Necessary for Margin trading. Disabled until support is enabled.
# interest_rate = self.exchange.get_interest_rate()
if trade is None:
# Enter trade
self.trade_id_counter += 1
@@ -580,6 +739,8 @@ class Backtesting:
id=self.trade_id_counter,
open_order_id=self.order_id_counter,
pair=pair,
base_currency=base_currency,
stake_currency=self.config['stake_currency'],
open_rate=propose_rate,
open_rate_requested=propose_rate,
open_date=current_time,
@@ -589,13 +750,25 @@ class Backtesting:
fee_open=self.fee,
fee_close=self.fee,
is_open=True,
buy_tag=entry_tag,
exchange='backtesting',
orders=[]
enter_tag=entry_tag,
exchange=self._exchange_name,
is_short=is_short,
trading_mode=self.trading_mode,
leverage=leverage,
# interest_rate=interest_rate,
orders=[],
)
trade.adjust_stop_loss(trade.open_rate, self.strategy.stoploss, initial=True)
trade.set_isolated_liq(self.exchange.get_liquidation_price(
pair=pair,
open_rate=propose_rate,
amount=amount,
leverage=leverage,
is_short=is_short,
))
order = Order(
id=self.order_id_counter,
ft_trade_id=trade.id,
@@ -603,8 +776,8 @@ class Backtesting:
ft_pair=trade.pair,
order_id=str(self.order_id_counter),
symbol=trade.pair,
ft_order_side="buy",
side="buy",
ft_order_side=trade.entry_side,
side=trade.entry_side,
order_type=order_type,
status="open",
order_date=current_time,
@@ -635,13 +808,13 @@ class Backtesting:
for pair in open_trades.keys():
if len(open_trades[pair]) > 0:
for trade in open_trades[pair]:
if trade.open_order_id and trade.nr_of_successful_buys == 0:
if trade.open_order_id and trade.nr_of_successful_entries == 0:
# Ignore trade if buy-order did not fill yet
continue
sell_row = data[pair][-1]
trade.close_date = sell_row[DATE_IDX].to_pydatetime()
trade.sell_reason = SellType.FORCE_SELL.value
trade.exit_reason = ExitType.FORCE_EXIT.value
trade.close(sell_row[OPEN_IDX], show_msg=False)
LocalTrade.close_bt_trade(trade)
# Deepcopy object to have wallets update correctly
@@ -658,6 +831,20 @@ class Backtesting:
self.rejected_trades += 1
return False
def check_for_trade_entry(self, row) -> Optional[LongShort]:
enter_long = row[LONG_IDX] == 1
exit_long = row[ELONG_IDX] == 1
enter_short = self._can_short and row[SHORT_IDX] == 1
exit_short = self._can_short and row[ESHORT_IDX] == 1
if enter_long == 1 and not any([exit_long, enter_short]):
# Long
return 'long'
if enter_short == 1 and not any([exit_short, enter_long]):
# Short
return 'short'
return None
def run_protections(self, enable_protections, pair: str, current_time: datetime):
if enable_protections:
self.protections.stop_per_pair(pair, current_time)
@@ -670,19 +857,19 @@ class Backtesting:
"""
for order in [o for o in trade.orders if o.ft_is_open]:
timedout = self.strategy.ft_check_timed_out(order.side, trade, order, current_time)
timedout = self.strategy.ft_check_timed_out(trade, order, current_time)
if timedout:
if order.side == 'buy':
if order.side == trade.entry_side:
self.timedout_entry_orders += 1
if trade.nr_of_successful_buys == 0:
# Remove trade due to buy timeout expiration.
if trade.nr_of_successful_entries == 0:
# Remove trade due to entry timeout expiration.
return True
else:
# Close additional buy order
del trade.orders[trade.orders.index(order)]
if order.side == 'sell':
if order.side == trade.exit_side:
self.timedout_exit_orders += 1
# Close sell order and retry selling on next signal.
# Close exit order and retry exiting on next signal.
del trade.orders[trade.orders.index(order)]
return False
@@ -755,19 +942,27 @@ class Backtesting:
indexes[pair] = row_index
self.dataprovider._set_dataframe_max_index(row_index)
# 1. Process buys.
for t in list(open_trades[pair]):
# 1. Cancel expired buy/sell orders.
if self.check_order_cancel(t, current_time):
# Close trade due to buy timeout expiration.
open_trade_count -= 1
open_trades[pair].remove(t)
self.wallets.update()
# 2. Process buys.
# without positionstacking, we can only have one open trade per pair.
# max_open_trades must be respected
# don't open on the last row
trade_dir = self.check_for_trade_entry(row)
if (
(position_stacking or len(open_trades[pair]) == 0)
and self.trade_slot_available(max_open_trades, open_trade_count_start)
and current_time != end_date
and row[BUY_IDX] == 1
and row[SELL_IDX] != 1
and trade_dir is not None
and not PairLocks.is_pair_locked(pair, row[DATE_IDX])
):
trade = self._enter_trade(pair, row)
trade = self._enter_trade(pair, row, trade_dir)
if trade:
# TODO: hacky workaround to avoid opening > max_open_trades
# This emulates previous behavior - not sure if this is correct
@@ -778,20 +973,20 @@ class Backtesting:
open_trades[pair].append(trade)
for trade in list(open_trades[pair]):
# 2. Process buy orders.
order = trade.select_order('buy', is_open=True)
# 3. Process entry orders.
order = trade.select_order(trade.entry_side, is_open=True)
if order and self._get_order_filled(order.price, row):
order.close_bt_order(current_time)
trade.open_order_id = None
LocalTrade.add_bt_trade(trade)
self.wallets.update()
# 3. Create sell orders (if any)
# 4. Create sell orders (if any)
if not trade.open_order_id:
self._get_sell_trade_entry(trade, row) # Place sell order if necessary
# 4. Process sell orders.
order = trade.select_order('sell', is_open=True)
# 5. Process sell orders.
order = trade.select_order(trade.exit_side, is_open=True)
if order and self._get_order_filled(order.price, row):
trade.open_order_id = None
trade.close_date = current_time
@@ -805,13 +1000,6 @@ class Backtesting:
self.wallets.update()
self.run_protections(enable_protections, pair, current_time)
# 5. Cancel expired buy/sell orders.
if self.check_order_cancel(trade, current_time):
# Close trade due to buy timeout expiration.
open_trade_count -= 1
open_trades[pair].remove(trade)
self.wallets.update()
# Move time one configured time_interval ahead.
self.progress.increment()
current_time += timedelta(minutes=self.timeframe_min)

7
freqtrade/optimize/hyperopt.py
View File

@@ -114,10 +114,8 @@ class Hyperopt:
self.position_stacking = self.config.get('position_stacking', False)
if HyperoptTools.has_space(self.config, 'sell'):
# Make sure use_sell_signal is enabled
if 'ask_strategy' not in self.config:
self.config['ask_strategy'] = {}
self.config['ask_strategy']['use_sell_signal'] = True
# Make sure use_exit_signal is enabled
self.config['use_exit_signal'] = True
self.print_all = self.config.get('print_all', False)
self.hyperopt_table_header = 0
@@ -396,6 +394,7 @@ class Hyperopt:
def prepare_hyperopt_data(self) -> None:
data, timerange = self.backtesting.load_bt_data()
self.backtesting.load_bt_data_detail()
logger.info("Dataload complete. Calculating indicators")
preprocessed = self.backtesting.strategy.advise_all_indicators(data)

94
freqtrade/optimize/optimize_reports.py
View File

@@ -166,7 +166,7 @@ def generate_tag_metrics(tag_type: str,
return []
def generate_sell_reason_stats(max_open_trades: int, results: DataFrame) -> List[Dict]:
def generate_exit_reason_stats(max_open_trades: int, results: DataFrame) -> List[Dict]:
"""
Generate small table outlining Backtest results
:param max_open_trades: Max_open_trades parameter
@@ -175,8 +175,8 @@ def generate_sell_reason_stats(max_open_trades: int, results: DataFrame) -> List
"""
tabular_data = []
for reason, count in results['sell_reason'].value_counts().iteritems():
result = results.loc[results['sell_reason'] == reason]
for reason, count in results['exit_reason'].value_counts().iteritems():
result = results.loc[results['exit_reason'] == reason]
profit_mean = result['profit_ratio'].mean()
profit_sum = result['profit_ratio'].sum()
@@ -184,7 +184,7 @@ def generate_sell_reason_stats(max_open_trades: int, results: DataFrame) -> List
tabular_data.append(
{
'sell_reason': reason,
'exit_reason': reason,
'trades': count,
'wins': len(result[result['profit_abs'] > 0]),
'draws': len(result[result['profit_abs'] == 0]),
@@ -372,20 +372,20 @@ def generate_strategy_stats(pairlist: List[str],
return {}
config = content['config']
max_open_trades = min(config['max_open_trades'], len(pairlist))
starting_balance = config['dry_run_wallet']
start_balance = config['dry_run_wallet']
stake_currency = config['stake_currency']
pair_results = generate_pair_metrics(pairlist, stake_currency=stake_currency,
starting_balance=starting_balance,
starting_balance=start_balance,
results=results, skip_nan=False)
buy_tag_results = generate_tag_metrics("buy_tag", starting_balance=starting_balance,
results=results, skip_nan=False)
enter_tag_results = generate_tag_metrics("enter_tag", starting_balance=start_balance,
results=results, skip_nan=False)
sell_reason_stats = generate_sell_reason_stats(max_open_trades=max_open_trades,
exit_reason_stats = generate_exit_reason_stats(max_open_trades=max_open_trades,
results=results)
left_open_results = generate_pair_metrics(pairlist, stake_currency=stake_currency,
starting_balance=starting_balance,
starting_balance=start_balance,
results=results.loc[results['is_open']],
skip_nan=True)
daily_stats = generate_daily_stats(results)
@@ -405,18 +405,24 @@ def generate_strategy_stats(pairlist: List[str],
'best_pair': best_pair,
'worst_pair': worst_pair,
'results_per_pair': pair_results,
'results_per_buy_tag': buy_tag_results,
'sell_reason_summary': sell_reason_stats,
'results_per_enter_tag': enter_tag_results,
'exit_reason_summary': exit_reason_stats,
'left_open_trades': left_open_results,
# 'days_breakdown_stats': days_breakdown_stats,
'total_trades': len(results),
'trade_count_long': len(results.loc[~results['is_short']]),
'trade_count_short': len(results.loc[results['is_short']]),
'total_volume': float(results['stake_amount'].sum()),
'avg_stake_amount': results['stake_amount'].mean() if len(results) > 0 else 0,
'profit_mean': results['profit_ratio'].mean() if len(results) > 0 else 0,
'profit_median': results['profit_ratio'].median() if len(results) > 0 else 0,
'profit_total': results['profit_abs'].sum() / starting_balance,
'profit_total': results['profit_abs'].sum() / start_balance,
'profit_total_long': results.loc[~results['is_short'], 'profit_abs'].sum() / start_balance,
'profit_total_short': results.loc[results['is_short'], 'profit_abs'].sum() / start_balance,
'profit_total_abs': results['profit_abs'].sum(),
'profit_total_long_abs': results.loc[~results['is_short'], 'profit_abs'].sum(),
'profit_total_short_abs': results.loc[results['is_short'], 'profit_abs'].sum(),
'backtest_start': min_date.strftime(DATETIME_PRINT_FORMAT),
'backtest_start_ts': int(min_date.timestamp() * 1000),
'backtest_end': max_date.strftime(DATETIME_PRINT_FORMAT),
@@ -432,8 +438,8 @@ def generate_strategy_stats(pairlist: List[str],
'stake_amount': config['stake_amount'],
'stake_currency': config['stake_currency'],
'stake_currency_decimals': decimals_per_coin(config['stake_currency']),
'starting_balance': starting_balance,
'dry_run_wallet': starting_balance,
'starting_balance': start_balance,
'dry_run_wallet': start_balance,
'final_balance': content['final_balance'],
'rejected_signals': content['rejected_signals'],
'timedout_entry_orders': content['timedout_entry_orders'],
@@ -454,10 +460,10 @@ def generate_strategy_stats(pairlist: List[str],
'trailing_only_offset_is_reached': config.get('trailing_only_offset_is_reached', False),
'use_custom_stoploss': config.get('use_custom_stoploss', False),
'minimal_roi': config['minimal_roi'],
'use_sell_signal': config['use_sell_signal'],
'sell_profit_only': config['sell_profit_only'],
'sell_profit_offset': config['sell_profit_offset'],
'ignore_roi_if_buy_signal': config['ignore_roi_if_buy_signal'],
'use_exit_signal': config['use_exit_signal'],
'exit_profit_only': config['exit_profit_only'],
'exit_profit_offset': config['exit_profit_offset'],
'ignore_roi_if_entry_signal': config['ignore_roi_if_entry_signal'],
**daily_stats,
**trade_stats
}
@@ -467,7 +473,7 @@ def generate_strategy_stats(pairlist: List[str],
results, value_col='profit_ratio')
(drawdown_abs, drawdown_start, drawdown_end, high_val, low_val,
max_drawdown) = calculate_max_drawdown(
results, value_col='profit_abs', starting_balance=starting_balance)
results, value_col='profit_abs', starting_balance=start_balance)
strat_stats.update({
'max_drawdown': max_drawdown_legacy, # Deprecated - do not use
'max_drawdown_account': max_drawdown,
@@ -481,7 +487,7 @@ def generate_strategy_stats(pairlist: List[str],
'max_drawdown_high': high_val,
})
csum_min, csum_max = calculate_csum(results, starting_balance)
csum_min, csum_max = calculate_csum(results, start_balance)
strat_stats.update({
'csum_min': csum_min,
'csum_max': csum_max
@@ -566,16 +572,16 @@ def text_table_bt_results(pair_results: List[Dict[str, Any]], stake_currency: st
floatfmt=floatfmt, tablefmt="orgtbl", stralign="right")
def text_table_sell_reason(sell_reason_stats: List[Dict[str, Any]], stake_currency: str) -> str:
def text_table_exit_reason(exit_reason_stats: List[Dict[str, Any]], stake_currency: str) -> str:
"""
Generate small table outlining Backtest results
:param sell_reason_stats: Sell reason metrics
:param sell_reason_stats: Exit reason metrics
:param stake_currency: Stakecurrency used
:return: pretty printed table with tabulate as string
"""
headers = [
'Sell Reason',
'Sells',
'Exit Reason',
'Exits',
'Win Draws Loss Win%',
'Avg Profit %',
'Cum Profit %',
@@ -584,12 +590,12 @@ def text_table_sell_reason(sell_reason_stats: List[Dict[str, Any]], stake_curren
]
output = [[
t['sell_reason'], t['trades'],
t.get('exit_reason', t.get('sell_reason')), t['trades'],
_generate_wins_draws_losses(t['wins'], t['draws'], t['losses']),
t['profit_mean_pct'], t['profit_sum_pct'],
round_coin_value(t['profit_total_abs'], stake_currency, False),
t['profit_total_pct'],
] for t in sell_reason_stats]
] for t in exit_reason_stats]
return tabulate(output, headers=headers, tablefmt="orgtbl", stralign="right")
@@ -600,7 +606,7 @@ def text_table_tags(tag_type: str, tag_results: List[Dict[str, Any]], stake_curr
:param stake_currency: stake-currency - used to correctly name headers
:return: pretty printed table with tabulate as string
"""
if(tag_type == "buy_tag"):
if(tag_type == "enter_tag"):
headers = _get_line_header("TAG", stake_currency)
else:
headers = _get_line_header("TAG", stake_currency, 'Sells')
@@ -686,6 +692,19 @@ def text_table_add_metrics(strat_results: Dict) -> str:
best_trade = max(strat_results['trades'], key=lambda x: x['profit_ratio'])
worst_trade = min(strat_results['trades'], key=lambda x: x['profit_ratio'])
short_metrics = [
('', ''), # Empty line to improve readability
('Long / Short',
f"{strat_results.get('trade_count_long', 'total_trades')} / "
f"{strat_results.get('trade_count_short', 0)}"),
('Total profit Long %', f"{strat_results['profit_total_long']:.2%}"),
('Total profit Short %', f"{strat_results['profit_total_short']:.2%}"),
('Absolute profit Long', round_coin_value(strat_results['profit_total_long_abs'],
strat_results['stake_currency'])),
('Absolute profit Short', round_coin_value(strat_results['profit_total_short_abs'],
strat_results['stake_currency'])),
] if strat_results.get('trade_count_short', 0) > 0 else []
# Newly added fields should be ignored if they are missing in strat_results. hyperopt-show
# command stores these results and newer version of freqtrade must be able to handle old
# results with missing new fields.
@@ -696,6 +715,7 @@ def text_table_add_metrics(strat_results: Dict) -> str:
('', ''), # Empty line to improve readability
('Total/Daily Avg Trades',
f"{strat_results['total_trades']} / {strat_results['trades_per_day']}"),
('Starting balance', round_coin_value(strat_results['starting_balance'],
strat_results['stake_currency'])),
('Final balance', round_coin_value(strat_results['final_balance'],
@@ -710,6 +730,7 @@ def text_table_add_metrics(strat_results: Dict) -> str:
strat_results['stake_currency'])),
('Total trade volume', round_coin_value(strat_results['total_volume'],
strat_results['stake_currency'])),
*short_metrics,
('', ''), # Empty line to improve readability
('Best Pair', f"{strat_results['best_pair']['key']} "
f"{strat_results['best_pair']['profit_sum']:.2%}"),
@@ -727,7 +748,7 @@ def text_table_add_metrics(strat_results: Dict) -> str:
f"{strat_results['draw_days']} / {strat_results['losing_days']}"),
('Avg. Duration Winners', f"{strat_results['winner_holding_avg']}"),
('Avg. Duration Loser', f"{strat_results['loser_holding_avg']}"),
('Rejected Buy signals', strat_results.get('rejected_signals', 'N/A')),
('Rejected Entry signals', strat_results.get('rejected_signals', 'N/A')),
('Entry/Exit Timeouts',
f"{strat_results.get('timedout_entry_orders', 'N/A')} / "
f"{strat_results.get('timedout_exit_orders', 'N/A')}"),
@@ -780,20 +801,23 @@ def show_backtest_result(strategy: str, results: Dict[str, Any], stake_currency:
print(' BACKTESTING REPORT '.center(len(table.splitlines()[0]), '='))
print(table)
if results.get('results_per_buy_tag') is not None:
if (results.get('results_per_enter_tag') is not None
or results.get('results_per_buy_tag') is not None):
# results_per_buy_tag is deprecated and should be removed 2 versions after short golive.
table = text_table_tags(
"buy_tag",
results['results_per_buy_tag'],
"enter_tag",
results.get('results_per_enter_tag', results.get('results_per_buy_tag')),
stake_currency=stake_currency)
if isinstance(table, str) and len(table) > 0:
print(' BUY TAG STATS '.center(len(table.splitlines()[0]), '='))
print(' ENTER TAG STATS '.center(len(table.splitlines()[0]), '='))
print(table)
table = text_table_sell_reason(sell_reason_stats=results['sell_reason_summary'],
exit_reasons = results.get('exit_reason_summary', results.get('sell_reason_summary'))
table = text_table_exit_reason(exit_reason_stats=exit_reasons,
stake_currency=stake_currency)
if isinstance(table, str) and len(table) > 0:
print(' SELL REASON STATS '.center(len(table.splitlines()[0]), '='))
print(' EXIT REASON STATS '.center(len(table.splitlines()[0]), '='))
print(table)
table = text_table_bt_results(results['left_open_trades'], stake_currency=stake_currency)