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🔀 Merged upstream branches and fixed merge conflicts

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This commit is contained in:
Rik Helsen
2021-06-17 20:24:20 +02:00
parent bd44deea0d 8bb464bd64
commit 90d37f5ec6
119 changed files with 1770 additions and 1399 deletions
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21
freqtrade/optimize/backtesting.py
View File

@@ -17,6 +17,7 @@ from freqtrade.data import history
from freqtrade.data.btanalysis import trade_list_to_dataframe
from freqtrade.data.converter import trim_dataframes
from freqtrade.data.dataprovider import DataProvider
from freqtrade.enums import SellType
from freqtrade.exceptions import DependencyException, OperationalException
from freqtrade.exchange import timeframe_to_minutes, timeframe_to_seconds
from freqtrade.mixins import LoggingMixin
@@ -26,7 +27,7 @@ from freqtrade.persistence import LocalTrade, 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, SellType
from freqtrade.strategy.interface import IStrategy, SellCheckTuple
from freqtrade.strategy.strategy_wrapper import strategy_safe_wrapper
from freqtrade.wallets import Wallets
@@ -224,6 +225,22 @@ class Backtesting:
# 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
and self.strategy.trailing_stop_positive):
if self.strategy.trailing_only_offset_is_reached:
# 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(self.strategy.trailing_stop_positive))
assert stop_rate < sell_row[HIGH_IDX]
return stop_rate
# Set close_rate to stoploss
return trade.stop_loss
elif sell.sell_type == (SellType.ROI):
@@ -519,7 +536,7 @@ class Backtesting:
stats = generate_backtest_stats(data, self.all_results,
min_date=min_date, max_date=max_date)
if self.config.get('export', False):
if self.config.get('export', 'none') == 'trades':
store_backtest_stats(self.config['exportfilename'], stats)
# Show backtest results

12
freqtrade/optimize/hyperopt.py
View File

@@ -12,6 +12,7 @@ from math import ceil
from pathlib import Path
from typing import Any, Dict, List, Optional
import numpy as np
import progressbar
import rapidjson
from colorama import Fore, Style
@@ -162,8 +163,13 @@ class Hyperopt:
While not a valid json object - this allows appending easily.
:param epoch: result dictionary for this epoch.
"""
def default_parser(x):
if isinstance(x, np.integer):
return int(x)
return str(x)
with self.results_file.open('a') as f:
rapidjson.dump(epoch, f, default=str,
rapidjson.dump(epoch, f, default=default_parser,
number_mode=rapidjson.NM_NATIVE | rapidjson.NM_NAN)
f.write("\n")
@@ -463,8 +469,8 @@ class Hyperopt:
f"saved to '{self.results_file}'.")
if self.current_best_epoch:
HyperoptTools.print_epoch_details(self.current_best_epoch, self.total_epochs,
self.print_json)
HyperoptTools.show_epoch_details(self.current_best_epoch, self.total_epochs,
self.print_json)
else:
# This is printed when Ctrl+C is pressed quickly, before first epochs have
# a chance to be evaluated.

18
freqtrade/optimize/hyperopt_loss_onlyprofit.py
View File

@@ -9,23 +9,11 @@ from pandas import DataFrame
from freqtrade.optimize.hyperopt import IHyperOptLoss
# This is assumed to be expected avg profit * expected trade count.
# For example, for 0.35% avg per trade (or 0.0035 as ratio) and 1100 trades,
# expected max profit = 3.85
#
# Note, this is ratio. 3.85 stated above means 385Σ%, 3.0 means 300Σ%.
#
# In this implementation it's only used in calculation of the resulting value
# of the objective function as a normalization coefficient and does not
# represent any limit for profits as in the Freqtrade legacy default loss function.
EXPECTED_MAX_PROFIT = 3.0
class OnlyProfitHyperOptLoss(IHyperOptLoss):
"""
Defines the loss function for hyperopt.
This implementation takes only profit into account.
This implementation takes only absolute profit into account, not looking at any other indicator.
"""
@staticmethod
@@ -34,5 +22,5 @@ class OnlyProfitHyperOptLoss(IHyperOptLoss):
"""
Objective function, returns smaller number for better results.
"""
total_profit = results['profit_ratio'].sum()
return 1 - total_profit / EXPECTED_MAX_PROFIT
total_profit = results['profit_abs'].sum()
return -1 * total_profit

125
freqtrade/optimize/hyperopt_tools.py
View File

@@ -1,8 +1,6 @@
import io
import locale
import logging
from collections import OrderedDict
from pathlib import Path
from typing import Any, Dict, List
@@ -74,8 +72,8 @@ class HyperoptTools():
return epochs
@staticmethod
def print_epoch_details(results, total_epochs: int, print_json: bool,
no_header: bool = False, header_str: str = None) -> None:
def show_epoch_details(results, total_epochs: int, print_json: bool,
no_header: bool = False, header_str: str = None) -> None:
"""
Display details of the hyperopt result
"""
@@ -121,16 +119,9 @@ class HyperoptTools():
if space in ['buy', 'sell']:
result_dict.setdefault('params', {}).update(all_space_params)
elif space == 'roi':
# TODO: get rid of OrderedDict when support for python 3.6 will be
# dropped (dicts keep the order as the language feature)
# Convert keys in min_roi dict to strings because
# rapidjson cannot dump dicts with integer keys...
# OrderedDict is used to keep the numeric order of the items
# in the dict.
result_dict['minimal_roi'] = OrderedDict(
(str(k), v) for k, v in all_space_params.items()
)
result_dict['minimal_roi'] = {str(k): v for k, v in all_space_params.items()}
else: # 'stoploss', 'trailing'
result_dict.update(all_space_params)
@@ -142,13 +133,9 @@ class HyperoptTools():
if space == 'stoploss':
result += f"stoploss = {space_params.get('stoploss')}"
elif space == 'roi':
# TODO: get rid of OrderedDict when support for python 3.6 will be
# dropped (dicts keep the order as the language feature)
minimal_roi_result = rapidjson.dumps(
OrderedDict(
(str(k), v) for k, v in space_params.items()
),
default=str, indent=4, number_mode=rapidjson.NM_NATIVE)
minimal_roi_result = rapidjson.dumps({
str(k): v for k, v in space_params.items()
}, default=str, indent=4, number_mode=rapidjson.NM_NATIVE)
result += f"minimal_roi = {minimal_roi_result}"
elif space == 'trailing':
@@ -204,9 +191,9 @@ class HyperoptTools():
f"Avg profit {results_metrics['profit_mean'] * 100: 6.2f}%. "
f"Median profit {results_metrics['profit_median'] * 100: 6.2f}%. "
f"Total profit {results_metrics['profit_total_abs']: 11.8f} {stake_currency} "
f"({results_metrics['profit_total'] * 100: 7.2f}\N{GREEK CAPITAL LETTER SIGMA}%). "
f"({results_metrics['profit_total'] * 100: 7.2f}%). "
f"Avg duration {results_metrics['holding_avg']} min."
).encode(locale.getpreferredencoding(), 'replace').decode('utf-8')
)
@staticmethod
def _format_explanation_string(results, total_epochs) -> str:
@@ -215,6 +202,47 @@ class HyperoptTools():
f"{results['results_explanation']} " +
f"Objective: {results['loss']:.5f}")
@staticmethod
def prepare_trials_columns(trials, legacy_mode: bool, has_drawdown: bool) -> str:
trials['Best'] = ''
if 'results_metrics.winsdrawslosses' not in trials.columns:
# Ensure compatibility with older versions of hyperopt results
trials['results_metrics.winsdrawslosses'] = 'N/A'
if not has_drawdown:
# Ensure compatibility with older versions of hyperopt results
trials['results_metrics.max_drawdown_abs'] = None
trials['results_metrics.max_drawdown'] = None
if not legacy_mode:
# New mode, using backtest result for metrics
trials['results_metrics.winsdrawslosses'] = trials.apply(
lambda x: f"{x['results_metrics.wins']} {x['results_metrics.draws']:>4} "
f"{x['results_metrics.losses']:>4}", axis=1)
trials = trials[['Best', 'current_epoch', 'results_metrics.total_trades',
'results_metrics.winsdrawslosses',
'results_metrics.profit_mean', 'results_metrics.profit_total_abs',
'results_metrics.profit_total', 'results_metrics.holding_avg',
'results_metrics.max_drawdown', 'results_metrics.max_drawdown_abs',
'loss', 'is_initial_point', 'is_best']]
else:
# Legacy mode
trials = trials[['Best', 'current_epoch', 'results_metrics.trade_count',
'results_metrics.winsdrawslosses', 'results_metrics.avg_profit',
'results_metrics.total_profit', 'results_metrics.profit',
'results_metrics.duration', 'results_metrics.max_drawdown',
'results_metrics.max_drawdown_abs', 'loss', 'is_initial_point',
'is_best']]
trials.columns = ['Best', 'Epoch', 'Trades', ' Win Draw Loss', 'Avg profit',
'Total profit', 'Profit', 'Avg duration', 'Max Drawdown',
'max_drawdown_abs', 'Objective', 'is_initial_point', 'is_best']
return trials
@staticmethod
def get_result_table(config: dict, results: list, total_epochs: int, highlight_best: bool,
print_colorized: bool, remove_header: int) -> str:
@@ -225,36 +253,13 @@ class HyperoptTools():
return ''
tabulate.PRESERVE_WHITESPACE = True
trials = json_normalize(results, max_level=1)
trials['Best'] = ''
if 'results_metrics.winsdrawslosses' not in trials.columns:
# Ensure compatibility with older versions of hyperopt results
trials['results_metrics.winsdrawslosses'] = 'N/A'
legacy_mode = True
if 'results_metrics.total_trades' in trials:
legacy_mode = False
# New mode, using backtest result for metrics
trials['results_metrics.winsdrawslosses'] = trials.apply(
lambda x: f"{x['results_metrics.wins']} {x['results_metrics.draws']:>4} "
f"{x['results_metrics.losses']:>4}", axis=1)
trials = trials[['Best', 'current_epoch', 'results_metrics.total_trades',
'results_metrics.winsdrawslosses',
'results_metrics.profit_mean', 'results_metrics.profit_total_abs',
'results_metrics.profit_total', 'results_metrics.holding_avg',
'loss', 'is_initial_point', 'is_best']]
else:
# Legacy mode
trials = trials[['Best', 'current_epoch', 'results_metrics.trade_count',
'results_metrics.winsdrawslosses',
'results_metrics.avg_profit', 'results_metrics.total_profit',
'results_metrics.profit', 'results_metrics.duration',
'loss', 'is_initial_point', 'is_best']]
legacy_mode = 'results_metrics.total_trades' not in trials
has_drawdown = 'results_metrics.max_drawdown_abs' in trials.columns
trials = HyperoptTools.prepare_trials_columns(trials, legacy_mode, has_drawdown)
trials.columns = ['Best', 'Epoch', 'Trades', ' Win Draw Loss', 'Avg profit',
'Total profit', 'Profit', 'Avg duration', 'Objective',
'is_initial_point', 'is_best']
trials['is_profit'] = False
trials.loc[trials['is_initial_point'], 'Best'] = '* '
trials.loc[trials['is_best'], 'Best'] = 'Best'
@@ -277,6 +282,21 @@ class HyperoptTools():
)
stake_currency = config['stake_currency']
if has_drawdown:
trials['Max Drawdown'] = trials.apply(
lambda x: '{} {}'.format(
round_coin_value(x['max_drawdown_abs'], stake_currency),
'({:,.2f}%)'.format(x['Max Drawdown'] * perc_multi).rjust(10, ' ')
).rjust(25 + len(stake_currency))
if x['Max Drawdown'] != 0.0 else '--'.rjust(25 + len(stake_currency)),
axis=1
)
else:
trials = trials.drop(columns=['Max Drawdown'])
trials = trials.drop(columns=['max_drawdown_abs'])
trials['Profit'] = trials.apply(
lambda x: '{} {}'.format(
round_coin_value(x['Total profit'], stake_currency),
@@ -385,10 +405,11 @@ class HyperoptTools():
trials['Avg profit'] = trials['Avg profit'].apply(
lambda x: f'{x * perc_multi:,.2f}%' if not isna(x) else ""
)
trials['Avg duration'] = trials['Avg duration'].apply(
lambda x: f'{x:,.1f} m' if isinstance(
x, float) else f"{x.total_seconds() // 60:,.1f} m" if not isna(x) else ""
)
if perc_multi == 1:
trials['Avg duration'] = trials['Avg duration'].apply(
lambda x: f'{x:,.1f} m' if isinstance(
x, float) else f"{x.total_seconds() // 60:,.1f} m" if not isna(x) else ""
)
trials['Objective'] = trials['Objective'].apply(
lambda x: f'{x:,.5f}' if x != 100000 else ""
)

23
freqtrade/optimize/optimize_reports.py
View File

@@ -232,16 +232,23 @@ def generate_trading_stats(results: DataFrame) -> Dict[str, Any]:
zero_duration_trades = len(results.loc[(results['trade_duration'] == 0) &
(results['sell_reason'] == 'trailing_stop_loss')])
holding_avg = (timedelta(minutes=round(results['trade_duration'].mean()))
if not results.empty else timedelta())
winner_holding_avg = (timedelta(minutes=round(winning_trades['trade_duration'].mean()))
if not winning_trades.empty else timedelta())
loser_holding_avg = (timedelta(minutes=round(losing_trades['trade_duration'].mean()))
if not losing_trades.empty else timedelta())
return {
'wins': len(winning_trades),
'losses': len(losing_trades),
'draws': len(draw_trades),
'holding_avg': (timedelta(minutes=round(results['trade_duration'].mean()))
if not results.empty else timedelta()),
'winner_holding_avg': (timedelta(minutes=round(winning_trades['trade_duration'].mean()))
if not winning_trades.empty else timedelta()),
'loser_holding_avg': (timedelta(minutes=round(losing_trades['trade_duration'].mean()))
if not losing_trades.empty else timedelta()),
'holding_avg': holding_avg,
'holding_avg_s': holding_avg.total_seconds(),
'winner_holding_avg': winner_holding_avg,
'winner_holding_avg_s': winner_holding_avg.total_seconds(),
'loser_holding_avg': loser_holding_avg,
'loser_holding_avg_s': loser_holding_avg.total_seconds(),
'zero_duration_trades': zero_duration_trades,
}
@@ -549,7 +556,8 @@ def text_table_add_metrics(strat_results: Dict) -> str:
('Backtesting to', strat_results['backtest_end']),
('Max open trades', strat_results['max_open_trades']),
('', ''), # Empty line to improve readability
('Total trades', strat_results['total_trades']),
('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'],
@@ -557,7 +565,6 @@ def text_table_add_metrics(strat_results: Dict) -> str:
('Absolute profit ', round_coin_value(strat_results['profit_total_abs'],
strat_results['stake_currency'])),
('Total profit %', f"{round(strat_results['profit_total'] * 100, 2):}%"),
('Trades per day', strat_results['trades_per_day']),
('Avg. stake amount', round_coin_value(strat_results['avg_stake_amount'],
strat_results['stake_currency'])),
('Total trade volume', round_coin_value(strat_results['total_volume'],