def setup_optimize_configuration(args: Dict[str, Any],
method: RunMode) -> Dict[str, Any]:
"""
Prepare the configuration for the Hyperopt module
:param args: Cli args from Arguments()
:param method: Bot running mode
:return: Configuration
"""
config = setup_utils_configuration(args, method)
no_unlimited_runmodes = {
RunMode.BACKTEST: 'backtesting',
RunMode.HYPEROPT: 'hyperoptimization',
}
if method in no_unlimited_runmodes.keys():
if (config['stake_amount'] != constants.UNLIMITED_STAKE_AMOUNT
and config['stake_amount'] > config['dry_run_wallet']):
wallet = round_coin_value(config['dry_run_wallet'],
config['stake_currency'])
stake = round_coin_value(config['stake_amount'],
config['stake_currency'])
raise OperationalException(
f"Starting balance ({wallet}) "
f"is smaller than stake_amount {stake}.")
return config
def text_table_add_metrics(strat_results: Dict) -> str:
if len(strat_results['trades']) > 0:
best_trade = max(strat_results['trades'],
key=lambda x: x['profit_ratio'])
worst_trade = min(strat_results['trades'],
key=lambda x: x['profit_ratio'])
metrics = [
('Backtesting from',
strat_results['backtest_start'].strftime(DATETIME_PRINT_FORMAT)),
('Backtesting to',
strat_results['backtest_end'].strftime(DATETIME_PRINT_FORMAT)),
('Max open trades', strat_results['max_open_trades']),
('', ''), # Empty line to improve readability
('Total trades', strat_results['total_trades']),
('Total Profit %',
f"{round(strat_results['profit_total'] * 100, 2)}%"),
('Trades per day', strat_results['trades_per_day']),
('', ''), # Empty line to improve readability
('Best Pair', f"{strat_results['best_pair']['key']} "
f"{round(strat_results['best_pair']['profit_sum_pct'], 2)}%"),
('Worst Pair', f"{strat_results['worst_pair']['key']} "
f"{round(strat_results['worst_pair']['profit_sum_pct'], 2)}%"),
('Best trade',
f"{best_trade['pair']} {round(best_trade['profit_ratio'] * 100, 2)}%"
),
('Worst trade', f"{worst_trade['pair']} "
f"{round(worst_trade['profit_ratio'] * 100, 2)}%"),
('Best day',
f"{round(strat_results['backtest_best_day'] * 100, 2)}%"),
('Worst day',
f"{round(strat_results['backtest_worst_day'] * 100, 2)}%"),
('Days win/draw/lose', f"{strat_results['winning_days']} / "
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']}"),
('', ''), # Empty line to improve readability
('Abs Profit Min',
round_coin_value(strat_results['csum_min'],
strat_results['stake_currency'])),
('Abs Profit Max',
round_coin_value(strat_results['csum_max'],
strat_results['stake_currency'])),
('Max Drawdown',
f"{round(strat_results['max_drawdown'] * 100, 2)}%"),
('Drawdown Start',
strat_results['drawdown_start'].strftime(DATETIME_PRINT_FORMAT)),
('Drawdown End',
strat_results['drawdown_end'].strftime(DATETIME_PRINT_FORMAT)),
('Market change',
f"{round(strat_results['market_change'] * 100, 2)}%"),
]
return tabulate(metrics,
headers=["Metric", "Value"],
tablefmt="orgtbl")
else:
return ''
def test_round_coin_value():
assert round_coin_value(222.222222, 'USDT') == '222.222 USDT'
assert round_coin_value(222.2, 'USDT') == '222.200 USDT'
assert round_coin_value(222.12745, 'EUR') == '222.127 EUR'
assert round_coin_value(0.1274512123, 'BTC') == '0.12745121 BTC'
assert round_coin_value(0.1274512123, 'ETH') == '0.12745 ETH'
assert round_coin_value(222.222222, 'USDT', False) == '222.222'
assert round_coin_value(222.2, 'USDT', False) == '222.200'
assert round_coin_value(222.12745, 'EUR', False) == '222.127'
assert round_coin_value(0.1274512123, 'BTC', False) == '0.12745121'
assert round_coin_value(0.1274512123, 'ETH', False) == '0.12745'
def text_table_periodic_breakdown(days_breakdown_stats: List[Dict[str, Any]],
stake_currency: str, period: str) -> str:
"""
Generate small table with Backtest results by days
:param days_breakdown_stats: Days breakdown metrics
:param stake_currency: Stakecurrency used
:return: pretty printed table with tabulate as string
"""
headers = [
period.capitalize(),
f'Tot Profit {stake_currency}',
'Wins',
'Draws',
'Losses',
]
output = [[
d['date'],
round_coin_value(d['profit_abs'], stake_currency, False),
d['wins'],
d['draws'],
d['loses'],
] for d in days_breakdown_stats]
return tabulate(output,
headers=headers,
tablefmt="orgtbl",
stralign="right")
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: Exit reason metrics
:param stake_currency: Stakecurrency used
:return: pretty printed table with tabulate as string
"""
headers = [
'Exit Reason',
'Exits',
'Win Draws Loss Win%',
'Avg Profit %',
'Cum Profit %',
f'Tot Profit {stake_currency}',
'Tot Profit %',
]
output = [[
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 exit_reason_stats]
return tabulate(output,
headers=headers,
tablefmt="orgtbl",
stralign="right")
def generate_strategy_comparison(all_results: Dict) -> List[Dict]:
"""
Generate summary per strategy
:param all_results: Dict of <Strategyname: DataFrame> containing results for all strategies
:return: List of Dicts containing the metrics per Strategy
"""
tabular_data = []
for strategy, results in all_results.items():
tabular_data.append(
_generate_result_line(results['results'],
results['config']['dry_run_wallet'],
strategy))
try:
max_drawdown_per, _, _, _, _ = calculate_max_drawdown(
results['results'], value_col='profit_ratio')
max_drawdown_abs, _, _, _, _ = calculate_max_drawdown(
results['results'], value_col='profit_abs')
except ValueError:
max_drawdown_per = 0
max_drawdown_abs = 0
tabular_data[-1]['max_drawdown_per'] = round(max_drawdown_per * 100, 2)
tabular_data[-1]['max_drawdown_abs'] = \
round_coin_value(max_drawdown_abs, results['config']['stake_currency'], False)
return tabular_data
def text_table_sell_reason(sell_reason_stats: List[Dict[str, Any]],
stake_currency: str) -> str:
"""
Generate small table outlining Backtest results
:param sell_reason_stats: Sell reason metrics
:param stake_currency: Stakecurrency used
:return: pretty printed table with tabulate as string
"""
headers = [
'Sell Reason',
'Sells',
'Wins',
'Draws',
'Losses',
'Avg Profit %',
f'Tot Profit {stake_currency}',
'Tot Profit %',
]
output = [[
t['sell_reason'],
t['trades'],
t['wins'],
t['draws'],
t['losses'],
t['profit_mean_pct'],
round_coin_value(t['profit_total_abs'], stake_currency, False),
t['profit_total_pct'],
] for t in sell_reason_stats]
return tabulate(output,
headers=headers,
tablefmt="orgtbl",
stralign="right")
def generate_strategy_comparison(bt_stats: Dict) -> List[Dict]:
"""
Generate summary per strategy
:param bt_stats: Dict of <Strategyname: DataFrame> containing results for all strategies
:return: List of Dicts containing the metrics per Strategy
"""
tabular_data = []
for strategy, result in bt_stats.items():
tabular_data.append(deepcopy(result['results_per_pair'][-1]))
# Update "key" to strategy (results_per_pair has it as "Total").
tabular_data[-1]['key'] = strategy
tabular_data[-1]['max_drawdown_account'] = result[
'max_drawdown_account']
tabular_data[-1]['max_drawdown_abs'] = round_coin_value(
result['max_drawdown_abs'], result['stake_currency'], False)
return tabular_data
def get_result_table(config: dict, results: list, total_epochs: int,
highlight_best: bool, print_colorized: bool,
remove_header: int) -> str:
"""
Log result table
"""
if not results:
return ''
tabulate.PRESERVE_WHITESPACE = True
trials = json_normalize(results, max_level=1)
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['is_profit'] = False
trials.loc[trials['is_initial_point'], 'Best'] = '* '
trials.loc[trials['is_best'], 'Best'] = 'Best'
trials.loc[trials['is_initial_point'] & trials['is_best'],
'Best'] = '* Best'
trials.loc[trials['Total profit'] > 0, 'is_profit'] = True
trials['Trades'] = trials['Trades'].astype(str)
perc_multi = 1 if legacy_mode else 100
trials['Epoch'] = trials['Epoch'].apply(lambda x: '{}/{}'.format(
str(x).rjust(len(str(total_epochs)), ' '), total_epochs))
trials['Avg profit'] = trials['Avg profit'].apply(
lambda x: f'{x * perc_multi:,.2f}%'.rjust(7, ' ')
if not isna(x) else "--".rjust(7, ' '))
trials['Avg duration'] = trials['Avg duration'].apply(
lambda x: f'{x:,.1f} m'.rjust(7, ' ') if isinstance(
x, float) else f"{x}" if not isna(x) else "--".rjust(7, ' '))
trials['Objective'] = trials['Objective'].apply(
lambda x: f'{x:,.5f}'.rjust(8, ' ')
if x != 100000 else "N/A".rjust(8, ' '))
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), '({:,.2f}%)'.
format(x['Profit'] * perc_multi).rjust(10, ' ')).rjust(25 + len(
stake_currency)) if x['Total profit'] != 0.0 else '--'.rjust(
25 + len(stake_currency)),
axis=1)
trials = trials.drop(columns=['Total profit'])
if print_colorized:
for i in range(len(trials)):
if trials.loc[i]['is_profit']:
for j in range(len(trials.loc[i]) - 3):
trials.iat[i, j] = "{}{}{}".format(
Fore.GREEN, str(trials.loc[i][j]), Fore.RESET)
if trials.loc[i]['is_best'] and highlight_best:
for j in range(len(trials.loc[i]) - 3):
trials.iat[i, j] = "{}{}{}".format(
Style.BRIGHT, str(trials.loc[i][j]),
Style.RESET_ALL)
trials = trials.drop(
columns=['is_initial_point', 'is_best', 'is_profit'])
if remove_header > 0:
table = tabulate.tabulate(trials.to_dict(orient='list'),
tablefmt='orgtbl',
headers='keys',
stralign="right")
table = table.split("\n", remove_header)[remove_header]
elif remove_header < 0:
table = tabulate.tabulate(trials.to_dict(orient='list'),
tablefmt='psql',
headers='keys',
stralign="right")
table = "\n".join(table.split("\n")[0:remove_header])
else:
table = tabulate.tabulate(trials.to_dict(orient='list'),
tablefmt='psql',
headers='keys',
stralign="right")
return table
def text_table_add_metrics(strat_results: Dict) -> str:
if len(strat_results['trades']) > 0:
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.
metrics = [
('Backtesting from', strat_results['backtest_start']),
('Backtesting to', strat_results['backtest_end']),
('Max open trades', strat_results['max_open_trades']),
('', ''), # 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'],
strat_results['stake_currency'])),
('Absolute profit ',
round_coin_value(strat_results['profit_total_abs'],
strat_results['stake_currency'])),
('Total profit %', f"{strat_results['profit_total']:.2%}"),
('Trades per day', strat_results['trades_per_day']),
('Avg. daily profit %',
f"{(strat_results['profit_total'] / strat_results['backtest_days']):.2%}"
),
('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'],
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%}"),
('Worst Pair', f"{strat_results['worst_pair']['key']} "
f"{strat_results['worst_pair']['profit_sum']:.2%}"),
('Best trade',
f"{best_trade['pair']} {best_trade['profit_ratio']:.2%}"),
('Worst trade', f"{worst_trade['pair']} "
f"{worst_trade['profit_ratio']:.2%}"),
('Best day',
round_coin_value(strat_results['backtest_best_day_abs'],
strat_results['stake_currency'])),
('Worst day',
round_coin_value(strat_results['backtest_worst_day_abs'],
strat_results['stake_currency'])),
('Days win/draw/lose', f"{strat_results['winning_days']} / "
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 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')}"),
('', ''), # Empty line to improve readability
('Min balance',
round_coin_value(strat_results['csum_min'],
strat_results['stake_currency'])),
('Max balance',
round_coin_value(strat_results['csum_max'],
strat_results['stake_currency'])),
# Compatibility to show old hyperopt results
('Drawdown (Account)',
f"{strat_results['max_drawdown_account']:.2%}")
if 'max_drawdown_account' in strat_results else
('Drawdown', f"{strat_results['max_drawdown']:.2%}"),
('Drawdown',
round_coin_value(strat_results['max_drawdown_abs'],
strat_results['stake_currency'])),
('Drawdown high',
round_coin_value(strat_results['max_drawdown_high'],
strat_results['stake_currency'])),
('Drawdown low',
round_coin_value(strat_results['max_drawdown_low'],
strat_results['stake_currency'])),
('Drawdown Start', strat_results['drawdown_start']),
('Drawdown End', strat_results['drawdown_end']),
('Market change', f"{strat_results['market_change']:.2%}"),
]
return tabulate(metrics,
headers=["Metric", "Value"],
tablefmt="orgtbl")
else:
start_balance = round_coin_value(strat_results['starting_balance'],
strat_results['stake_currency'])
stake_amount = round_coin_value(
strat_results['stake_amount'],
strat_results['stake_currency']) if strat_results[
'stake_amount'] != UNLIMITED_STAKE_AMOUNT else 'unlimited'
message = ("No trades made. "
f"Your starting balance was {start_balance}, "
f"and your stake was {stake_amount}.")
return message
def text_table_add_metrics(strat_results: Dict) -> str:
if len(strat_results['trades']) > 0:
best_trade = max(strat_results['trades'],
key=lambda x: x['profit_ratio'])
worst_trade = min(strat_results['trades'],
key=lambda x: x['profit_ratio'])
metrics = [
('Backtesting from',
strat_results['backtest_start'].strftime(DATETIME_PRINT_FORMAT)),
('Backtesting to',
strat_results['backtest_end'].strftime(DATETIME_PRINT_FORMAT)),
('Max open trades', strat_results['max_open_trades']),
('', ''), # Empty line to improve readability
('Total trades', strat_results['total_trades']),
('Starting balance',
round_coin_value(strat_results['starting_balance'],
strat_results['stake_currency'])),
('Final balance',
round_coin_value(strat_results['final_balance'],
strat_results['stake_currency'])),
('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'],
strat_results['stake_currency'])),
('', ''), # Empty line to improve readability
('Best Pair', f"{strat_results['best_pair']['key']} "
f"{round(strat_results['best_pair']['profit_sum_pct'], 2)}%"),
('Worst Pair', f"{strat_results['worst_pair']['key']} "
f"{round(strat_results['worst_pair']['profit_sum_pct'], 2)}%"),
('Best trade',
f"{best_trade['pair']} {round(best_trade['profit_ratio'] * 100, 2)}%"
),
('Worst trade', f"{worst_trade['pair']} "
f"{round(worst_trade['profit_ratio'] * 100, 2)}%"),
('Best day',
round_coin_value(strat_results['backtest_best_day_abs'],
strat_results['stake_currency'])),
('Worst day',
round_coin_value(strat_results['backtest_worst_day_abs'],
strat_results['stake_currency'])),
('Days win/draw/lose', f"{strat_results['winning_days']} / "
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']}"),
('', ''), # Empty line to improve readability
('Min balance',
round_coin_value(strat_results['csum_min'],
strat_results['stake_currency'])),
('Max balance',
round_coin_value(strat_results['csum_max'],
strat_results['stake_currency'])),
('Drawdown', f"{round(strat_results['max_drawdown'] * 100, 2)}%"),
('Drawdown',
round_coin_value(strat_results['max_drawdown_abs'],
strat_results['stake_currency'])),
('Drawdown high',
round_coin_value(strat_results['max_drawdown_high'],
strat_results['stake_currency'])),
('Drawdown low',
round_coin_value(strat_results['max_drawdown_low'],
strat_results['stake_currency'])),
('Drawdown Start',
strat_results['drawdown_start'].strftime(DATETIME_PRINT_FORMAT)),
('Drawdown End',
strat_results['drawdown_end'].strftime(DATETIME_PRINT_FORMAT)),
('Market change',
f"{round(strat_results['market_change'] * 100, 2)}%"),
]
return tabulate(metrics,
headers=["Metric", "Value"],
tablefmt="orgtbl")
else:
start_balance = round_coin_value(strat_results['starting_balance'],
strat_results['stake_currency'])
stake_amount = round_coin_value(
strat_results['stake_amount'],
strat_results['stake_currency']) if strat_results[
'stake_amount'] != UNLIMITED_STAKE_AMOUNT else 'unlimited'
message = ("No trades made. "
f"Your starting balance was {start_balance}, "
f"and your stake was {stake_amount}.")
return message
def test_round_coin_value():
assert round_coin_value(222.222222, 'USDT') == '222.222 USDT'
assert round_coin_value(222.2, 'USDT', keep_trailing_zeros=True) == '222.200 USDT'
assert round_coin_value(222.2, 'USDT') == '222.2 USDT'
assert round_coin_value(222.12745, 'EUR') == '222.127 EUR'
assert round_coin_value(0.1274512123, 'BTC') == '0.12745121 BTC'
assert round_coin_value(0.1274512123, 'ETH') == '0.12745 ETH'
assert round_coin_value(222.222222, 'USDT', False) == '222.222'
assert round_coin_value(222.2, 'USDT', False) == '222.2'
assert round_coin_value(222.00, 'USDT', False) == '222'
assert round_coin_value(222.12745, 'EUR', False) == '222.127'
assert round_coin_value(0.1274512123, 'BTC', False) == '0.12745121'
assert round_coin_value(0.1274512123, 'ETH', False) == '0.12745'
assert round_coin_value(222.2, 'USDT', False, True) == '222.200'
def get_result_table(config: dict, results: list, total_epochs: int,
highlight_best: bool, print_colorized: bool,
remove_header: int) -> str:
"""
Log result table
"""
if not results:
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'
]]
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'
trials.loc[trials['is_initial_point'] & trials['is_best'],
'Best'] = '* Best'
trials.loc[trials['Total profit'] > 0, 'is_profit'] = True
trials['Trades'] = trials['Trades'].astype(str)
perc_multi = 1 if legacy_mode else 100
trials['Epoch'] = trials['Epoch'].apply(lambda x: '{}/{}'.format(
str(x).rjust(len(str(total_epochs)), ' '), total_epochs))
trials['Avg profit'] = trials['Avg profit'].apply(
lambda x: f'{x * perc_multi:,.2f}%'.rjust(7, ' ')
if not isna(x) else "--".rjust(7, ' '))
trials['Avg duration'] = trials['Avg duration'].apply(
lambda x: f'{x:,.1f} m'.rjust(7, ' ') if isinstance(
x, float) else f"{x}" if not isna(x) else "--".rjust(7, ' '))
trials['Objective'] = trials['Objective'].apply(
lambda x: f'{x:,.5f}'.rjust(8, ' ')
if x != 100000 else "N/A".rjust(8, ' '))
stake_currency = config['stake_currency']
trials['Profit'] = trials.apply(lambda x: '{} {}'.format(
round_coin_value(x['Total profit'], stake_currency), '({:,.2f}%)'.
format(x['Profit'] * perc_multi).rjust(10, ' ')).rjust(25 + len(
stake_currency)) if x['Total profit'] != 0.0 else '--'.rjust(
25 + len(stake_currency)),
axis=1)
trials = trials.drop(columns=['Total profit'])
if print_colorized:
for i in range(len(trials)):
if trials.loc[i]['is_profit']:
for j in range(len(trials.loc[i]) - 3):
trials.iat[i, j] = "{}{}{}".format(
Fore.GREEN, str(trials.loc[i][j]), Fore.RESET)
if trials.loc[i]['is_best'] and highlight_best:
for j in range(len(trials.loc[i]) - 3):
trials.iat[i, j] = "{}{}{}".format(
Style.BRIGHT, str(trials.loc[i][j]),
Style.RESET_ALL)
trials = trials.drop(
columns=['is_initial_point', 'is_best', 'is_profit'])
if remove_header > 0:
table = tabulate.tabulate(trials.to_dict(orient='list'),
tablefmt='orgtbl',
headers='keys',
stralign="right")
table = table.split("\n", remove_header)[remove_header]
elif remove_header < 0:
table = tabulate.tabulate(trials.to_dict(orient='list'),
tablefmt='psql',
headers='keys',
stralign="right")
table = "\n".join(table.split("\n")[0:remove_header])
else:
table = tabulate.tabulate(trials.to_dict(orient='list'),
tablefmt='psql',
headers='keys',
stralign="right")
return table
def text_table_add_metrics(strat_results: Dict) -> str:
if len(strat_results['trades']) > 0:
best_trade = max(strat_results['trades'],
key=lambda x: x['profit_ratio'])
worst_trade = min(strat_results['trades'],
key=lambda x: x['profit_ratio'])
# 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.
zero_duration_trades = '--'
if 'zero_duration_trades' in strat_results:
zero_duration_trades_per = \
100.0 / strat_results['total_trades'] * strat_results['zero_duration_trades']
zero_duration_trades = f'{zero_duration_trades_per:.2f}% ' \
f'({strat_results["zero_duration_trades"]})'
metrics = [
('Backtesting from', strat_results['backtest_start']),
('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']),
('Starting balance',
round_coin_value(strat_results['starting_balance'],
strat_results['stake_currency'])),
('Final balance',
round_coin_value(strat_results['final_balance'],
strat_results['stake_currency'])),
('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'],
strat_results['stake_currency'])),
('', ''), # Empty line to improve readability
('Best Pair', f"{strat_results['best_pair']['key']} "
f"{round(strat_results['best_pair']['profit_sum_pct'], 2)}%"),
('Worst Pair', f"{strat_results['worst_pair']['key']} "
f"{round(strat_results['worst_pair']['profit_sum_pct'], 2)}%"),
('Best trade',
f"{best_trade['pair']} {round(best_trade['profit_ratio'] * 100, 2)}%"
),
('Worst trade', f"{worst_trade['pair']} "
f"{round(worst_trade['profit_ratio'] * 100, 2)}%"),
('Best day',
round_coin_value(strat_results['backtest_best_day_abs'],
strat_results['stake_currency'])),
('Worst day',
round_coin_value(strat_results['backtest_worst_day_abs'],
strat_results['stake_currency'])),
('Days win/draw/lose', f"{strat_results['winning_days']} / "
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']}"),
('Zero Duration Trades', zero_duration_trades),
('Rejected Buy signals',
strat_results.get('rejected_signals', 'N/A')),
('', ''), # Empty line to improve readability
('Min balance',
round_coin_value(strat_results['csum_min'],
strat_results['stake_currency'])),
('Max balance',
round_coin_value(strat_results['csum_max'],
strat_results['stake_currency'])),
('Drawdown', f"{round(strat_results['max_drawdown'] * 100, 2)}%"),
('Drawdown',
round_coin_value(strat_results['max_drawdown_abs'],
strat_results['stake_currency'])),
('Drawdown high',
round_coin_value(strat_results['max_drawdown_high'],
strat_results['stake_currency'])),
('Drawdown low',
round_coin_value(strat_results['max_drawdown_low'],
strat_results['stake_currency'])),
('Drawdown Start', strat_results['drawdown_start']),
('Drawdown End', strat_results['drawdown_end']),
('Market change',
f"{round(strat_results['market_change'] * 100, 2)}%"),
]
return tabulate(metrics,
headers=["Metric", "Value"],
tablefmt="orgtbl")
else:
start_balance = round_coin_value(strat_results['starting_balance'],
strat_results['stake_currency'])
stake_amount = round_coin_value(
strat_results['stake_amount'],
strat_results['stake_currency']) if strat_results[
'stake_amount'] != UNLIMITED_STAKE_AMOUNT else 'unlimited'
message = ("No trades made. "
f"Your starting balance was {start_balance}, "
f"and your stake was {stake_amount}.")
return message
