mohammadian7/freqtrade
1
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity

ruff format: optimize

Browse Source
This commit is contained in:
Matthias
2024-05-12 17:20:36 +02:00
parent 2c60985e2d
commit 801ab4acc9
9 changed files with 1194 additions and 848 deletions
Download Patch File Download Diff File Expand all files Collapse all files
freqtrade/optimize/backtest_caching.py
+11 -7
View File
@@ -17,19 +17,23 @@ def get_strategy_run_id(strategy) -> str:
config = deepcopy(strategy.config) config = deepcopy(strategy.config)
# Options that have no impact on results of individual backtest. # Options that have no impact on results of individual backtest.
not_important_keys = ('strategy_list', 'original_config', 'telegram', 'api_server') not_important_keys = ("strategy_list", "original_config", "telegram", "api_server")
for k in not_important_keys: for k in not_important_keys:
if k in config: if k in config:
del config[k] del config[k]
# Explicitly allow NaN values (e.g. max_open_trades). # Explicitly allow NaN values (e.g. max_open_trades).
# as it does not matter for getting the hash. # as it does not matter for getting the hash.
digest.update(rapidjson.dumps(config, default=str, digest.update(
number_mode=rapidjson.NM_NAN).encode('utf-8')) rapidjson.dumps(config, default=str, number_mode=rapidjson.NM_NAN).encode("utf-8")
)
# Include _ft_params_from_file - so changing parameter files cause cache eviction # Include _ft_params_from_file - so changing parameter files cause cache eviction
digest.update(rapidjson.dumps( digest.update(
strategy._ft_params_from_file, default=str, number_mode=rapidjson.NM_NAN).encode('utf-8')) rapidjson.dumps(
with Path(strategy.__file__).open('rb') as fp: strategy._ft_params_from_file, default=str, number_mode=rapidjson.NM_NAN
).encode("utf-8")
)
with Path(strategy.__file__).open("rb") as fp:
digest.update(fp.read()) digest.update(fp.read())
return digest.hexdigest().lower() return digest.hexdigest().lower()
@@ -37,4 +41,4 @@ def get_strategy_run_id(strategy) -> str:
def get_backtest_metadata_filename(filename: Union[Path, str]) -> Path: def get_backtest_metadata_filename(filename: Union[Path, str]) -> Path:
"""Return metadata filename for specified backtest results file.""" """Return metadata filename for specified backtest results file."""
filename = Path(filename) filename = Path(filename)
return filename.parent / Path(f'{filename.stem}.meta{filename.suffix}') return filename.parent / Path(f"{filename.stem}.meta{filename.suffix}")
freqtrade/optimize/backtesting.py
+457 -279
View File
File diff suppressed because it is too large Load Diff
freqtrade/optimize/base_analysis.py
+3 -5
View File
@@ -25,7 +25,6 @@ class VarHolder:
class BaseAnalysis: class BaseAnalysis:
def __init__(self, config: Dict[str, Any], strategy_obj: Dict): def __init__(self, config: Dict[str, Any], strategy_obj: Dict):
self.failed_bias_check = True self.failed_bias_check = True
self.full_varHolder = VarHolder() self.full_varHolder = VarHolder()
@@ -34,7 +33,7 @@ class BaseAnalysis:
# pull variables the scope of the lookahead_analysis-instance # pull variables the scope of the lookahead_analysis-instance
self.local_config = deepcopy(config) self.local_config = deepcopy(config)
self.local_config['strategy'] = strategy_obj['name'] self.local_config["strategy"] = strategy_obj["name"]
self.strategy_obj = strategy_obj self.strategy_obj = strategy_obj
@staticmethod @staticmethod
@@ -46,7 +45,7 @@ class BaseAnalysis:
self.full_varHolder = VarHolder() self.full_varHolder = VarHolder()
# define datetime in human-readable format # define datetime in human-readable format
parsed_timerange = TimeRange.parse_timerange(self.local_config['timerange']) parsed_timerange = TimeRange.parse_timerange(self.local_config["timerange"])
if parsed_timerange.startdt is None: if parsed_timerange.startdt is None:
self.full_varHolder.from_dt = datetime.fromtimestamp(0, tz=timezone.utc) self.full_varHolder.from_dt = datetime.fromtimestamp(0, tz=timezone.utc)
@@ -58,9 +57,8 @@ class BaseAnalysis:
else: else:
self.full_varHolder.to_dt = parsed_timerange.stopdt self.full_varHolder.to_dt = parsed_timerange.stopdt
self.prepare_data(self.full_varHolder, self.local_config['pairs']) self.prepare_data(self.full_varHolder, self.local_config["pairs"])
def start(self) -> None: def start(self) -> None:
# first make a single backtest # first make a single backtest
self.fill_full_varholder() self.fill_full_varholder()
freqtrade/optimize/bt_progress.py
+3 -2
View File
@@ -25,8 +25,9 @@ class BTProgress:
""" """
Get progress as ratio, capped to be between 0 and 1 (to avoid small calculation errors). Get progress as ratio, capped to be between 0 and 1 (to avoid small calculation errors).
""" """
return max(min(round(self._progress / self._max_steps, 5) return max(
if self._max_steps > 0 else 0, 1), 0) min(round(self._progress / self._max_steps, 5) if self._max_steps > 0 else 0, 1), 0
)
@property @property
def action(self): def action(self):
freqtrade/optimize/edge_cli.py
+8 -6
View File
@@ -3,6 +3,7 @@
""" """
This module contains the edge backtesting interface This module contains the edge backtesting interface
""" """
import logging import logging
from freqtrade import constants from freqtrade import constants
@@ -30,8 +31,8 @@ class EdgeCli:
self.config = config self.config = config
# Ensure using dry-run # Ensure using dry-run
self.config['dry_run'] = True self.config["dry_run"] = True
self.config['stake_amount'] = constants.UNLIMITED_STAKE_AMOUNT self.config["stake_amount"] = constants.UNLIMITED_STAKE_AMOUNT
self.exchange = ExchangeResolver.load_exchange(self.config) self.exchange = ExchangeResolver.load_exchange(self.config)
self.strategy = StrategyResolver.load_strategy(self.config) self.strategy = StrategyResolver.load_strategy(self.config)
self.strategy.dp = DataProvider(config, self.exchange) self.strategy.dp = DataProvider(config, self.exchange)
@@ -42,12 +43,13 @@ class EdgeCli:
# Set refresh_pairs to false for edge-cli (it must be true for edge) # Set refresh_pairs to false for edge-cli (it must be true for edge)
self.edge._refresh_pairs = False self.edge._refresh_pairs = False
self.edge._timerange = TimeRange.parse_timerange(None if self.config.get( self.edge._timerange = TimeRange.parse_timerange(
'timerange') is None else str(self.config.get('timerange'))) None if self.config.get("timerange") is None else str(self.config.get("timerange"))
)
self.strategy.ft_bot_start() self.strategy.ft_bot_start()
def start(self) -> None: def start(self) -> None:
result = self.edge.calculate(self.config['exchange']['pair_whitelist']) result = self.edge.calculate(self.config["exchange"]["pair_whitelist"])
if result: if result:
print('') # blank line for readability print("") # blank line for readability
print(generate_edge_table(self.edge._cached_pairs)) print(generate_edge_table(self.edge._cached_pairs))
freqtrade/optimize/optimize_reports/bt_output.py
+305 -197
View File
@@ -16,28 +16,34 @@ def _get_line_floatfmt(stake_currency: str) -> List[str]:
""" """
Generate floatformat (goes in line with _generate_result_line()) Generate floatformat (goes in line with _generate_result_line())
""" """
return ['s', 'd', '.2f', f'.{decimals_per_coin(stake_currency)}f', return ["s", "d", ".2f", f".{decimals_per_coin(stake_currency)}f", ".2f", "d", "s", "s"]
'.2f', 'd', 's', 's']
def _get_line_header(first_column: str, stake_currency: str, def _get_line_header(
direction: str = 'Entries') -> List[str]: first_column: str, stake_currency: str, direction: str = "Entries"
) -> List[str]:
""" """
Generate header lines (goes in line with _generate_result_line()) Generate header lines (goes in line with _generate_result_line())
""" """
return [first_column, direction, 'Avg Profit %', return [
f'Tot Profit {stake_currency}', 'Tot Profit %', 'Avg Duration', first_column,
'Win Draw Loss Win%'] direction,
"Avg Profit %",
f"Tot Profit {stake_currency}",
"Tot Profit %",
"Avg Duration",
"Win Draw Loss Win%",
]
def generate_wins_draws_losses(wins, draws, losses): def generate_wins_draws_losses(wins, draws, losses):
if wins > 0 and losses == 0: if wins > 0 and losses == 0:
wl_ratio = '100' wl_ratio = "100"
elif wins == 0: elif wins == 0:
wl_ratio = '0' wl_ratio = "0"
else: else:
wl_ratio = f'{100.0 / (wins + draws + losses) * wins:.1f}' if losses > 0 else '100' wl_ratio = f"{100.0 / (wins + draws + losses) * wins:.1f}" if losses > 0 else "100"
return f'{wins:>4} {draws:>4} {losses:>4} {wl_ratio:>4}' return f"{wins:>4} {draws:>4} {losses:>4} {wl_ratio:>4}"
def text_table_bt_results(pair_results: List[Dict[str, Any]], stake_currency: str) -> str: def text_table_bt_results(pair_results: List[Dict[str, Any]], stake_currency: str) -> str:
@@ -48,16 +54,22 @@ def text_table_bt_results(pair_results: List[Dict[str, Any]], stake_currency: st
:return: pretty printed table with tabulate as string :return: pretty printed table with tabulate as string
""" """
headers = _get_line_header('Pair', stake_currency) headers = _get_line_header("Pair", stake_currency)
floatfmt = _get_line_floatfmt(stake_currency) floatfmt = _get_line_floatfmt(stake_currency)
output = [[ output = [
t['key'], t['trades'], t['profit_mean_pct'], t['profit_total_abs'], [
t['profit_total_pct'], t['duration_avg'], t["key"],
generate_wins_draws_losses(t['wins'], t['draws'], t['losses']) t["trades"],
] for t in pair_results] t["profit_mean_pct"],
t["profit_total_abs"],
t["profit_total_pct"],
t["duration_avg"],
generate_wins_draws_losses(t["wins"], t["draws"], t["losses"]),
]
for t in pair_results
]
# Ignore type as floatfmt does allow tuples but mypy does not know that # Ignore type as floatfmt does allow tuples but mypy does not know that
return tabulate(output, headers=headers, return tabulate(output, headers=headers, floatfmt=floatfmt, tablefmt="orgtbl", stralign="right")
floatfmt=floatfmt, tablefmt="orgtbl", stralign="right")
def text_table_tags(tag_type: str, tag_results: List[Dict[str, Any]], stake_currency: str) -> str: def text_table_tags(tag_type: str, tag_results: List[Dict[str, Any]], stake_currency: str) -> str:
@@ -67,34 +79,35 @@ 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 :param stake_currency: stake-currency - used to correctly name headers
:return: pretty printed table with tabulate as string :return: pretty printed table with tabulate as string
""" """
fallback: str = '' fallback: str = ""
if (tag_type == "enter_tag"): if tag_type == "enter_tag":
headers = _get_line_header("TAG", stake_currency) headers = _get_line_header("TAG", stake_currency)
else: else:
headers = _get_line_header("Exit Reason", stake_currency, 'Exits') headers = _get_line_header("Exit Reason", stake_currency, "Exits")
fallback = 'exit_reason' fallback = "exit_reason"
floatfmt = _get_line_floatfmt(stake_currency) floatfmt = _get_line_floatfmt(stake_currency)
output = [ output = [
[ [
t['key'] if t.get('key') is not None and len( t["key"]
str(t['key'])) > 0 else t.get(fallback, "OTHER"), if t.get("key") is not None and len(str(t["key"])) > 0
t['trades'], else t.get(fallback, "OTHER"),
t['profit_mean_pct'], t["trades"],
t['profit_total_abs'], t["profit_mean_pct"],
t['profit_total_pct'], t["profit_total_abs"],
t.get('duration_avg'), t["profit_total_pct"],
generate_wins_draws_losses( t.get("duration_avg"),
t['wins'], generate_wins_draws_losses(t["wins"], t["draws"], t["losses"]),
t['draws'], ]
t['losses'])] for t in tag_results] for t in tag_results
]
# Ignore type as floatfmt does allow tuples but mypy does not know that # Ignore type as floatfmt does allow tuples but mypy does not know that
return tabulate(output, headers=headers, return tabulate(output, headers=headers, floatfmt=floatfmt, tablefmt="orgtbl", stralign="right")
floatfmt=floatfmt, tablefmt="orgtbl", stralign="right")
def text_table_periodic_breakdown(days_breakdown_stats: List[Dict[str, Any]], def text_table_periodic_breakdown(
stake_currency: str, period: str) -> str: days_breakdown_stats: List[Dict[str, Any]], stake_currency: str, period: str
) -> str:
""" """
Generate small table with Backtest results by days Generate small table with Backtest results by days
:param days_breakdown_stats: Days breakdown metrics :param days_breakdown_stats: Days breakdown metrics
@@ -103,15 +116,21 @@ def text_table_periodic_breakdown(days_breakdown_stats: List[Dict[str, Any]],
""" """
headers = [ headers = [
period.capitalize(), period.capitalize(),
f'Tot Profit {stake_currency}', f"Tot Profit {stake_currency}",
'Wins', "Wins",
'Draws', "Draws",
'Losses', "Losses",
]
output = [
[
d["date"],
fmt_coin(d["profit_abs"], stake_currency, False),
d["wins"],
d["draws"],
d["loses"],
]
for d in days_breakdown_stats
] ]
output = [[
d['date'], fmt_coin(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") return tabulate(output, headers=headers, tablefmt="orgtbl", stralign="right")
@@ -123,253 +142,340 @@ def text_table_strategy(strategy_results, stake_currency: str) -> str:
:return: pretty printed table with tabulate as string :return: pretty printed table with tabulate as string
""" """
floatfmt = _get_line_floatfmt(stake_currency) floatfmt = _get_line_floatfmt(stake_currency)
headers = _get_line_header('Strategy', stake_currency) headers = _get_line_header("Strategy", stake_currency)
# _get_line_header() is also used for per-pair summary. Per-pair drawdown is mostly useless # _get_line_header() is also used for per-pair summary. Per-pair drawdown is mostly useless
# therefore we slip this column in only for strategy summary here. # therefore we slip this column in only for strategy summary here.
headers.append('Drawdown') headers.append("Drawdown")
# Align drawdown string on the center two space separator. # Align drawdown string on the center two space separator.
if 'max_drawdown_account' in strategy_results[0]: if "max_drawdown_account" in strategy_results[0]:
drawdown = [f'{t["max_drawdown_account"] * 100:.2f}' for t in strategy_results] drawdown = [f'{t["max_drawdown_account"] * 100:.2f}' for t in strategy_results]
else: else:
# Support for prior backtest results # Support for prior backtest results
drawdown = [f'{t["max_drawdown_per"]:.2f}' for t in strategy_results] drawdown = [f'{t["max_drawdown_per"]:.2f}' for t in strategy_results]
dd_pad_abs = max([len(t['max_drawdown_abs']) for t in strategy_results]) dd_pad_abs = max([len(t["max_drawdown_abs"]) for t in strategy_results])
dd_pad_per = max([len(dd) for dd in drawdown]) dd_pad_per = max([len(dd) for dd in drawdown])
drawdown = [f'{t["max_drawdown_abs"]:>{dd_pad_abs}} {stake_currency} {dd:>{dd_pad_per}}%' drawdown = [
for t, dd in zip(strategy_results, drawdown)] f'{t["max_drawdown_abs"]:>{dd_pad_abs}} {stake_currency} {dd:>{dd_pad_per}}%'
for t, dd in zip(strategy_results, drawdown)
]
output = [[ output = [
t['key'], t['trades'], t['profit_mean_pct'], t['profit_total_abs'], [
t['profit_total_pct'], t['duration_avg'], t["key"],
generate_wins_draws_losses(t['wins'], t['draws'], t['losses']), drawdown] t["trades"],
for t, drawdown in zip(strategy_results, drawdown)] t["profit_mean_pct"],
t["profit_total_abs"],
t["profit_total_pct"],
t["duration_avg"],
generate_wins_draws_losses(t["wins"], t["draws"], t["losses"]),
drawdown,
]
for t, drawdown in zip(strategy_results, drawdown)
]
# Ignore type as floatfmt does allow tuples but mypy does not know that # Ignore type as floatfmt does allow tuples but mypy does not know that
return tabulate(output, headers=headers, return tabulate(output, headers=headers, floatfmt=floatfmt, tablefmt="orgtbl", stralign="right")
floatfmt=floatfmt, tablefmt="orgtbl", stralign="right")
def text_table_add_metrics(strat_results: Dict) -> str: def text_table_add_metrics(strat_results: Dict) -> str:
if len(strat_results['trades']) > 0: if len(strat_results["trades"]) > 0:
best_trade = max(strat_results['trades'], key=lambda x: x['profit_ratio']) best_trade = max(strat_results["trades"], key=lambda x: x["profit_ratio"])
worst_trade = min(strat_results['trades'], key=lambda x: x['profit_ratio']) worst_trade = min(strat_results["trades"], key=lambda x: x["profit_ratio"])
short_metrics = [ short_metrics = (
('', ''), # Empty line to improve readability [
('Long / Short', ("", ""), # Empty line to improve readability
(
"Long / Short",
f"{strat_results.get('trade_count_long', 'total_trades')} / " f"{strat_results.get('trade_count_long', 'total_trades')} / "
f"{strat_results.get('trade_count_short', 0)}"), 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%}"), ("Total profit Long %", f"{strat_results['profit_total_long']:.2%}"),
('Absolute profit Long', fmt_coin(strat_results['profit_total_long_abs'], ("Total profit Short %", f"{strat_results['profit_total_short']:.2%}"),
strat_results['stake_currency'])), (
('Absolute profit Short', fmt_coin(strat_results['profit_total_short_abs'], "Absolute profit Long",
strat_results['stake_currency'])), fmt_coin(
] if strat_results.get('trade_count_short', 0) > 0 else [] strat_results["profit_total_long_abs"], strat_results["stake_currency"]
),
),
(
"Absolute profit Short",
fmt_coin(
strat_results["profit_total_short_abs"], strat_results["stake_currency"]
),
),
]
if strat_results.get("trade_count_short", 0) > 0
else []
)
drawdown_metrics = [] drawdown_metrics = []
if 'max_relative_drawdown' in strat_results: if "max_relative_drawdown" in strat_results:
# Compatibility to show old hyperopt results # Compatibility to show old hyperopt results
drawdown_metrics.append( drawdown_metrics.append(
('Max % of account underwater', f"{strat_results['max_relative_drawdown']:.2%}") ("Max % of account underwater", f"{strat_results['max_relative_drawdown']:.2%}")
)
drawdown_metrics.extend(
[
("Absolute Drawdown (Account)", f"{strat_results['max_drawdown_account']:.2%}")
if "max_drawdown_account" in strat_results
else ("Drawdown", f"{strat_results['max_drawdown']:.2%}"),
(
"Absolute Drawdown",
fmt_coin(strat_results["max_drawdown_abs"], strat_results["stake_currency"]),
),
(
"Drawdown high",
fmt_coin(strat_results["max_drawdown_high"], strat_results["stake_currency"]),
),
(
"Drawdown low",
fmt_coin(strat_results["max_drawdown_low"], strat_results["stake_currency"]),
),
("Drawdown Start", strat_results["drawdown_start"]),
("Drawdown End", strat_results["drawdown_end"]),
]
) )
drawdown_metrics.extend([
('Absolute Drawdown (Account)', f"{strat_results['max_drawdown_account']:.2%}")
if 'max_drawdown_account' in strat_results else (
'Drawdown', f"{strat_results['max_drawdown']:.2%}"),
('Absolute Drawdown', fmt_coin(strat_results['max_drawdown_abs'],
strat_results['stake_currency'])),
('Drawdown high', fmt_coin(strat_results['max_drawdown_high'],
strat_results['stake_currency'])),
('Drawdown low', fmt_coin(strat_results['max_drawdown_low'],
strat_results['stake_currency'])),
('Drawdown Start', strat_results['drawdown_start']),
('Drawdown End', strat_results['drawdown_end']),
])
entry_adjustment_metrics = [ entry_adjustment_metrics = (
('Canceled Trade Entries', strat_results.get('canceled_trade_entries', 'N/A')), [
('Canceled Entry Orders', strat_results.get('canceled_entry_orders', 'N/A')), ("Canceled Trade Entries", strat_results.get("canceled_trade_entries", "N/A")),
('Replaced Entry Orders', strat_results.get('replaced_entry_orders', 'N/A')), ("Canceled Entry Orders", strat_results.get("canceled_entry_orders", "N/A")),
] if strat_results.get('canceled_entry_orders', 0) > 0 else [] ("Replaced Entry Orders", strat_results.get("replaced_entry_orders", "N/A")),
]
if strat_results.get("canceled_entry_orders", 0) > 0
else []
)
# Newly added fields should be ignored if they are missing in strat_results. hyperopt-show # 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 # command stores these results and newer version of freqtrade must be able to handle old
# results with missing new fields. # results with missing new fields.
metrics = [ metrics = [
('Backtesting from', strat_results['backtest_start']), ("Backtesting from", strat_results["backtest_start"]),
('Backtesting to', strat_results['backtest_end']), ("Backtesting to", strat_results["backtest_end"]),
('Max open trades', strat_results['max_open_trades']), ("Max open trades", strat_results["max_open_trades"]),
('', ''), # Empty line to improve readability ("", ""), # Empty line to improve readability
('Total/Daily Avg Trades', (
f"{strat_results['total_trades']} / {strat_results['trades_per_day']}"), "Total/Daily Avg Trades",
f"{strat_results['total_trades']} / {strat_results['trades_per_day']}",
('Starting balance', fmt_coin(strat_results['starting_balance'], ),
strat_results['stake_currency'])), (
('Final balance', fmt_coin(strat_results['final_balance'], "Starting balance",
strat_results['stake_currency'])), fmt_coin(strat_results["starting_balance"], strat_results["stake_currency"]),
('Absolute profit ', fmt_coin(strat_results['profit_total_abs'], ),
strat_results['stake_currency'])), (
('Total profit %', f"{strat_results['profit_total']:.2%}"), "Final balance",
('CAGR %', f"{strat_results['cagr']:.2%}" if 'cagr' in strat_results else 'N/A'), fmt_coin(strat_results["final_balance"], strat_results["stake_currency"]),
('Sortino', f"{strat_results['sortino']:.2f}" if 'sortino' in strat_results else 'N/A'), ),
('Sharpe', f"{strat_results['sharpe']:.2f}" if 'sharpe' in strat_results else 'N/A'), (
('Calmar', f"{strat_results['calmar']:.2f}" if 'calmar' in strat_results else 'N/A'), "Absolute profit ",
('Profit factor', f'{strat_results["profit_factor"]:.2f}' if 'profit_factor' fmt_coin(strat_results["profit_total_abs"], strat_results["stake_currency"]),
in strat_results else 'N/A'), ),
('Expectancy (Ratio)', ( ("Total profit %", f"{strat_results['profit_total']:.2%}"),
f"{strat_results['expectancy']:.2f} ({strat_results['expectancy_ratio']:.2f})" if ("CAGR %", f"{strat_results['cagr']:.2%}" if "cagr" in strat_results else "N/A"),
'expectancy_ratio' in strat_results else 'N/A')), ("Sortino", f"{strat_results['sortino']:.2f}" if "sortino" in strat_results else "N/A"),
('Avg. daily profit %', ("Sharpe", f"{strat_results['sharpe']:.2f}" if "sharpe" in strat_results else "N/A"),
f"{(strat_results['profit_total'] / strat_results['backtest_days']):.2%}"), ("Calmar", f"{strat_results['calmar']:.2f}" if "calmar" in strat_results else "N/A"),
('Avg. stake amount', fmt_coin(strat_results['avg_stake_amount'], (
strat_results['stake_currency'])), "Profit factor",
('Total trade volume', fmt_coin(strat_results['total_volume'], f'{strat_results["profit_factor"]:.2f}'
strat_results['stake_currency'])), if "profit_factor" in strat_results
else "N/A",
),
(
"Expectancy (Ratio)",
(
f"{strat_results['expectancy']:.2f} ({strat_results['expectancy_ratio']:.2f})"
if "expectancy_ratio" in strat_results
else "N/A"
),
),
(
"Avg. daily profit %",
f"{(strat_results['profit_total'] / strat_results['backtest_days']):.2%}",
),
(
"Avg. stake amount",
fmt_coin(strat_results["avg_stake_amount"], strat_results["stake_currency"]),
),
(
"Total trade volume",
fmt_coin(strat_results["total_volume"], strat_results["stake_currency"]),
),
*short_metrics, *short_metrics,
('', ''), # Empty line to improve readability ("", ""), # Empty line to improve readability
('Best Pair', f"{strat_results['best_pair']['key']} " (
f"{strat_results['best_pair']['profit_total']:.2%}"), "Best Pair",
('Worst Pair', f"{strat_results['worst_pair']['key']} " f"{strat_results['best_pair']['key']} "
f"{strat_results['worst_pair']['profit_total']:.2%}"), f"{strat_results['best_pair']['profit_total']:.2%}",
('Best trade', f"{best_trade['pair']} {best_trade['profit_ratio']:.2%}"), ),
('Worst trade', f"{worst_trade['pair']} " (
f"{worst_trade['profit_ratio']:.2%}"), "Worst Pair",
f"{strat_results['worst_pair']['key']} "
('Best day', fmt_coin(strat_results['backtest_best_day_abs'], f"{strat_results['worst_pair']['profit_total']:.2%}",
strat_results['stake_currency'])), ),
('Worst day', fmt_coin(strat_results['backtest_worst_day_abs'], ("Best trade", f"{best_trade['pair']} {best_trade['profit_ratio']:.2%}"),
strat_results['stake_currency'])), ("Worst trade", f"{worst_trade['pair']} " f"{worst_trade['profit_ratio']:.2%}"),
('Days win/draw/lose', f"{strat_results['winning_days']} / " (
f"{strat_results['draw_days']} / {strat_results['losing_days']}"), "Best day",
('Avg. Duration Winners', f"{strat_results['winner_holding_avg']}"), fmt_coin(strat_results["backtest_best_day_abs"], strat_results["stake_currency"]),
('Avg. Duration Loser', f"{strat_results['loser_holding_avg']}"), ),
('Max Consecutive Wins / Loss', (
"Worst day",
fmt_coin(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']}"),
(
"Max Consecutive Wins / Loss",
f"{strat_results['max_consecutive_wins']} / {strat_results['max_consecutive_losses']}" f"{strat_results['max_consecutive_wins']} / {strat_results['max_consecutive_losses']}"
if 'max_consecutive_losses' in strat_results else 'N/A'), if "max_consecutive_losses" in strat_results
('Rejected Entry signals', strat_results.get('rejected_signals', 'N/A')), else "N/A",
('Entry/Exit Timeouts', ),
("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_entry_orders', 'N/A')} / "
f"{strat_results.get('timedout_exit_orders', 'N/A')}"), f"{strat_results.get('timedout_exit_orders', 'N/A')}",
),
*entry_adjustment_metrics, *entry_adjustment_metrics,
('', ''), # Empty line to improve readability ("", ""), # Empty line to improve readability
("Min balance", fmt_coin(strat_results["csum_min"], strat_results["stake_currency"])),
('Min balance', fmt_coin(strat_results['csum_min'], strat_results['stake_currency'])), ("Max balance", fmt_coin(strat_results["csum_max"], strat_results["stake_currency"])),
('Max balance', fmt_coin(strat_results['csum_max'], strat_results['stake_currency'])),
*drawdown_metrics, *drawdown_metrics,
('Market change', f"{strat_results['market_change']:.2%}"), ("Market change", f"{strat_results['market_change']:.2%}"),
] ]
return tabulate(metrics, headers=["Metric", "Value"], tablefmt="orgtbl") return tabulate(metrics, headers=["Metric", "Value"], tablefmt="orgtbl")
else: else:
start_balance = fmt_coin(strat_results['starting_balance'], strat_results['stake_currency']) start_balance = fmt_coin(strat_results["starting_balance"], strat_results["stake_currency"])
stake_amount = fmt_coin( stake_amount = (
strat_results['stake_amount'], strat_results['stake_currency'] fmt_coin(strat_results["stake_amount"], strat_results["stake_currency"])
) if strat_results['stake_amount'] != UNLIMITED_STAKE_AMOUNT else 'unlimited' if strat_results["stake_amount"] != UNLIMITED_STAKE_AMOUNT
else "unlimited"
)
message = ("No trades made. " message = (
"No trades made. "
f"Your starting balance was {start_balance}, " f"Your starting balance was {start_balance}, "
f"and your stake was {stake_amount}." f"and your stake was {stake_amount}."
) )
return message return message
def show_backtest_result(strategy: str, results: Dict[str, Any], stake_currency: str, def show_backtest_result(
backtest_breakdown: List[str]): strategy: str, results: Dict[str, Any], stake_currency: str, backtest_breakdown: List[str]
):
""" """
Print results for one strategy Print results for one strategy
""" """
# Print results # Print results
print(f"Result for strategy {strategy}") print(f"Result for strategy {strategy}")
table = text_table_bt_results(results['results_per_pair'], stake_currency=stake_currency) table = text_table_bt_results(results["results_per_pair"], stake_currency=stake_currency)
if isinstance(table, str): if isinstance(table, str):
print(' BACKTESTING REPORT '.center(len(table.splitlines()[0]), '=')) print(" BACKTESTING REPORT ".center(len(table.splitlines()[0]), "="))
print(table) print(table)
table = text_table_bt_results(results['left_open_trades'], stake_currency=stake_currency) table = text_table_bt_results(results["left_open_trades"], stake_currency=stake_currency)
if isinstance(table, str) and len(table) > 0: if isinstance(table, str) and len(table) > 0:
print(' LEFT OPEN TRADES REPORT '.center(len(table.splitlines()[0]), '=')) print(" LEFT OPEN TRADES REPORT ".center(len(table.splitlines()[0]), "="))
print(table) print(table)
if (enter_tags := results.get('results_per_enter_tag')) is not None: if (enter_tags := results.get("results_per_enter_tag")) is not None:
table = text_table_tags("enter_tag", enter_tags, stake_currency) table = text_table_tags("enter_tag", enter_tags, stake_currency)
if isinstance(table, str) and len(table) > 0: if isinstance(table, str) and len(table) > 0:
print(' ENTER TAG STATS '.center(len(table.splitlines()[0]), '=')) print(" ENTER TAG STATS ".center(len(table.splitlines()[0]), "="))
print(table) print(table)
if (exit_reasons := results.get('exit_reason_summary')) is not None: if (exit_reasons := results.get("exit_reason_summary")) is not None:
table = text_table_tags("exit_tag", exit_reasons, stake_currency) table = text_table_tags("exit_tag", exit_reasons, stake_currency)
if isinstance(table, str) and len(table) > 0: if isinstance(table, str) and len(table) > 0:
print(' EXIT REASON STATS '.center(len(table.splitlines()[0]), '=')) print(" EXIT REASON STATS ".center(len(table.splitlines()[0]), "="))
print(table) print(table)
for period in backtest_breakdown: for period in backtest_breakdown:
if period in results.get('periodic_breakdown', {}): if period in results.get("periodic_breakdown", {}):
days_breakdown_stats = results['periodic_breakdown'][period] days_breakdown_stats = results["periodic_breakdown"][period]
else: else:
days_breakdown_stats = generate_periodic_breakdown_stats( days_breakdown_stats = generate_periodic_breakdown_stats(
trade_list=results['trades'], period=period) trade_list=results["trades"], period=period
table = text_table_periodic_breakdown(days_breakdown_stats=days_breakdown_stats, )
stake_currency=stake_currency, period=period) table = text_table_periodic_breakdown(
days_breakdown_stats=days_breakdown_stats, stake_currency=stake_currency, period=period
)
if isinstance(table, str) and len(table) > 0: if isinstance(table, str) and len(table) > 0:
print(f' {period.upper()} BREAKDOWN '.center(len(table.splitlines()[0]), '=')) print(f" {period.upper()} BREAKDOWN ".center(len(table.splitlines()[0]), "="))
print(table) print(table)
table = text_table_add_metrics(results) table = text_table_add_metrics(results)
if isinstance(table, str) and len(table) > 0: if isinstance(table, str) and len(table) > 0:
print(' SUMMARY METRICS '.center(len(table.splitlines()[0]), '=')) print(" SUMMARY METRICS ".center(len(table.splitlines()[0]), "="))
print(table) print(table)
if isinstance(table, str) and len(table) > 0: if isinstance(table, str) and len(table) > 0:
print('=' * len(table.splitlines()[0])) print("=" * len(table.splitlines()[0]))
print() print()
def show_backtest_results(config: Config, backtest_stats: BacktestResultType): def show_backtest_results(config: Config, backtest_stats: BacktestResultType):
stake_currency = config['stake_currency'] stake_currency = config["stake_currency"]
for strategy, results in backtest_stats['strategy'].items(): for strategy, results in backtest_stats["strategy"].items():
show_backtest_result( show_backtest_result(
strategy, results, stake_currency, strategy, results, stake_currency, config.get("backtest_breakdown", [])
config.get('backtest_breakdown', [])) )
if len(backtest_stats['strategy']) > 0: if len(backtest_stats["strategy"]) > 0:
# Print Strategy summary table # Print Strategy summary table
table = text_table_strategy(backtest_stats['strategy_comparison'], stake_currency) table = text_table_strategy(backtest_stats["strategy_comparison"], stake_currency)
print(f"Backtested {results['backtest_start']} -> {results['backtest_end']} |" print(
f" Max open trades : {results['max_open_trades']}") f"Backtested {results['backtest_start']} -> {results['backtest_end']} |"
print(' STRATEGY SUMMARY '.center(len(table.splitlines()[0]), '=')) f" Max open trades : {results['max_open_trades']}"
)
print(" STRATEGY SUMMARY ".center(len(table.splitlines()[0]), "="))
print(table) print(table)
print('=' * len(table.splitlines()[0])) print("=" * len(table.splitlines()[0]))
print('\nFor more details, please look at the detail tables above') print("\nFor more details, please look at the detail tables above")
def show_sorted_pairlist(config: Config, backtest_stats: BacktestResultType): def show_sorted_pairlist(config: Config, backtest_stats: BacktestResultType):
if config.get('backtest_show_pair_list', False): if config.get("backtest_show_pair_list", False):
for strategy, results in backtest_stats['strategy'].items(): for strategy, results in backtest_stats["strategy"].items():
print(f"Pairs for Strategy {strategy}: \n[") print(f"Pairs for Strategy {strategy}: \n[")
for result in results['results_per_pair']: for result in results["results_per_pair"]:
if result["key"] != 'TOTAL': if result["key"] != "TOTAL":
print(f'"{result["key"]}", // {result["profit_mean"]:.2%}') print(f'"{result["key"]}", // {result["profit_mean"]:.2%}')
print("]") print("]")
def generate_edge_table(results: dict) -> str: def generate_edge_table(results: dict) -> str:
floatfmt = ('s', '.10g', '.2f', '.2f', '.2f', '.2f', 'd', 'd', 'd') floatfmt = ("s", ".10g", ".2f", ".2f", ".2f", ".2f", "d", "d", "d")
tabular_data = [] tabular_data = []
headers = ['Pair', 'Stoploss', 'Win Rate', 'Risk Reward Ratio', headers = [
'Required Risk Reward', 'Expectancy', 'Total Number of Trades', "Pair",
'Average Duration (min)'] "Stoploss",
"Win Rate",
"Risk Reward Ratio",
"Required Risk Reward",
"Expectancy",
"Total Number of Trades",
"Average Duration (min)",
]
for result in results.items(): for result in results.items():
if result[1].nb_trades > 0: if result[1].nb_trades > 0:
tabular_data.append([ tabular_data.append(
[
result[0], result[0],
result[1].stoploss, result[1].stoploss,
result[1].winrate, result[1].winrate,
@@ -377,9 +483,11 @@ def generate_edge_table(results: dict) -> str:
result[1].required_risk_reward, result[1].required_risk_reward,
result[1].expectancy, result[1].expectancy,
result[1].nb_trades, result[1].nb_trades,
round(result[1].avg_trade_duration) round(result[1].avg_trade_duration),
]) ]
)
# Ignore type as floatfmt does allow tuples but mypy does not know that # Ignore type as floatfmt does allow tuples but mypy does not know that
return tabulate(tabular_data, headers=headers, return tabulate(
floatfmt=floatfmt, tablefmt="orgtbl", stralign="right") tabular_data, headers=headers, floatfmt=floatfmt, tablefmt="orgtbl", stralign="right"
)
freqtrade/optimize/optimize_reports/bt_storage.py
+21 -16
View File
@@ -22,17 +22,21 @@ def _generate_filename(recordfilename: Path, appendix: str, suffix: str) -> Path
:return: Generated filename as a Path object :return: Generated filename as a Path object
""" """
if recordfilename.is_dir(): if recordfilename.is_dir():
filename = (recordfilename / f'backtest-result-{appendix}').with_suffix(suffix) filename = (recordfilename / f"backtest-result-{appendix}").with_suffix(suffix)
else: else:
filename = Path.joinpath( filename = Path.joinpath(
recordfilename.parent, f'{recordfilename.stem}-{appendix}' recordfilename.parent, f"{recordfilename.stem}-{appendix}"
).with_suffix(suffix) ).with_suffix(suffix)
return filename return filename
def store_backtest_stats( def store_backtest_stats(
recordfilename: Path, stats: BacktestResultType, dtappendix: str, *, recordfilename: Path,
market_change_data: Optional[DataFrame] = None) -> Path: stats: BacktestResultType,
dtappendix: str,
*,
market_change_data: Optional[DataFrame] = None,
) -> Path:
""" """
Stores backtest results Stores backtest results
:param recordfilename: Path object, which can either be a filename or a directory. :param recordfilename: Path object, which can either be a filename or a directory.
@@ -41,32 +45,33 @@ def store_backtest_stats(
:param stats: Dataframe containing the backtesting statistics :param stats: Dataframe containing the backtesting statistics
:param dtappendix: Datetime to use for the filename :param dtappendix: Datetime to use for the filename
""" """
filename = _generate_filename(recordfilename, dtappendix, '.json') filename = _generate_filename(recordfilename, dtappendix, ".json")
# Store metadata separately. # Store metadata separately.
file_dump_json(get_backtest_metadata_filename(filename), stats['metadata']) file_dump_json(get_backtest_metadata_filename(filename), stats["metadata"])
# Don't mutate the original stats dict. # Don't mutate the original stats dict.
stats_copy = { stats_copy = {
'strategy': stats['strategy'], "strategy": stats["strategy"],
'strategy_comparison': stats['strategy_comparison'], "strategy_comparison": stats["strategy_comparison"],
} }
file_dump_json(filename, stats_copy) file_dump_json(filename, stats_copy)
latest_filename = Path.joinpath(filename.parent, LAST_BT_RESULT_FN) latest_filename = Path.joinpath(filename.parent, LAST_BT_RESULT_FN)
file_dump_json(latest_filename, {'latest_backtest': str(filename.name)}) file_dump_json(latest_filename, {"latest_backtest": str(filename.name)})
if market_change_data is not None: if market_change_data is not None:
filename_mc = _generate_filename(recordfilename, f"{dtappendix}_market_change", '.feather') filename_mc = _generate_filename(recordfilename, f"{dtappendix}_market_change", ".feather")
market_change_data.reset_index().to_feather( market_change_data.reset_index().to_feather(
filename_mc, compression_level=9, compression='lz4') filename_mc, compression_level=9, compression="lz4"
)
return filename return filename
def _store_backtest_analysis_data( def _store_backtest_analysis_data(
recordfilename: Path, data: Dict[str, Dict], recordfilename: Path, data: Dict[str, Dict], dtappendix: str, name: str
dtappendix: str, name: str) -> Path: ) -> Path:
""" """
Stores backtest trade candles for analysis Stores backtest trade candles for analysis
:param recordfilename: Path object, which can either be a filename or a directory. :param recordfilename: Path object, which can either be a filename or a directory.
@@ -77,7 +82,7 @@ def _store_backtest_analysis_data(
:param dtappendix: Datetime to use for the filename :param dtappendix: Datetime to use for the filename
:param name: Name to use for the file, e.g. signals, rejected :param name: Name to use for the file, e.g. signals, rejected
""" """
filename = _generate_filename(recordfilename, f"{dtappendix}_{name}", '.pkl') filename = _generate_filename(recordfilename, f"{dtappendix}_{name}", ".pkl")
file_dump_joblib(filename, data) file_dump_joblib(filename, data)
@@ -85,7 +90,7 @@ def _store_backtest_analysis_data(
def store_backtest_analysis_results( def store_backtest_analysis_results(
recordfilename: Path, candles: Dict[str, Dict], trades: Dict[str, Dict], recordfilename: Path, candles: Dict[str, Dict], trades: Dict[str, Dict], dtappendix: str
dtappendix: str) -> None: ) -> None:
_store_backtest_analysis_data(recordfilename, candles, dtappendix, "signals") _store_backtest_analysis_data(recordfilename, candles, dtappendix, "signals")
_store_backtest_analysis_data(recordfilename, trades, dtappendix, "rejected") _store_backtest_analysis_data(recordfilename, trades, dtappendix, "rejected")
freqtrade/optimize/optimize_reports/optimize_reports.py
+319 -277
View File
@@ -24,43 +24,45 @@ from freqtrade.util import decimals_per_coin, fmt_coin
logger = logging.getLogger(__name__) logger = logging.getLogger(__name__)
def generate_trade_signal_candles(preprocessed_df: Dict[str, DataFrame], def generate_trade_signal_candles(
bt_results: Dict[str, Any]) -> DataFrame: preprocessed_df: Dict[str, DataFrame], bt_results: Dict[str, Any]
) -> DataFrame:
signal_candles_only = {} signal_candles_only = {}
for pair in preprocessed_df.keys(): for pair in preprocessed_df.keys():
signal_candles_only_df = DataFrame() signal_candles_only_df = DataFrame()
pairdf = preprocessed_df[pair] pairdf = preprocessed_df[pair]
resdf = bt_results['results'] resdf = bt_results["results"]
pairresults = resdf.loc[(resdf["pair"] == pair)] pairresults = resdf.loc[(resdf["pair"] == pair)]
if pairdf.shape[0] > 0: if pairdf.shape[0] > 0:
for t, v in pairresults.open_date.items(): for t, v in pairresults.open_date.items():
allinds = pairdf.loc[(pairdf['date'] < v)] allinds = pairdf.loc[(pairdf["date"] < v)]
signal_inds = allinds.iloc[[-1]] signal_inds = allinds.iloc[[-1]]
signal_candles_only_df = concat([ signal_candles_only_df = concat(
signal_candles_only_df.infer_objects(), [signal_candles_only_df.infer_objects(), signal_inds.infer_objects()]
signal_inds.infer_objects()]) )
signal_candles_only[pair] = signal_candles_only_df signal_candles_only[pair] = signal_candles_only_df
return signal_candles_only return signal_candles_only
def generate_rejected_signals(preprocessed_df: Dict[str, DataFrame], def generate_rejected_signals(
rejected_dict: Dict[str, DataFrame]) -> Dict[str, DataFrame]: preprocessed_df: Dict[str, DataFrame], rejected_dict: Dict[str, DataFrame]
) -> Dict[str, DataFrame]:
rejected_candles_only = {} rejected_candles_only = {}
for pair, signals in rejected_dict.items(): for pair, signals in rejected_dict.items():
rejected_signals_only_df = DataFrame() rejected_signals_only_df = DataFrame()
pairdf = preprocessed_df[pair] pairdf = preprocessed_df[pair]
for t in signals: for t in signals:
data_df_row = pairdf.loc[(pairdf['date'] == t[0])].copy() data_df_row = pairdf.loc[(pairdf["date"] == t[0])].copy()
data_df_row['pair'] = pair data_df_row["pair"] = pair
data_df_row['enter_tag'] = t[1] data_df_row["enter_tag"] = t[1]
rejected_signals_only_df = concat([ rejected_signals_only_df = concat(
rejected_signals_only_df.infer_objects(), [rejected_signals_only_df.infer_objects(), data_df_row.infer_objects()]
data_df_row.infer_objects()]) )
rejected_candles_only[pair] = rejected_signals_only_df rejected_candles_only[pair] = rejected_signals_only_df
return rejected_candles_only return rejected_candles_only
@@ -70,39 +72,45 @@ def _generate_result_line(result: DataFrame, starting_balance: int, first_column
""" """
Generate one result dict, with "first_column" as key. Generate one result dict, with "first_column" as key.
""" """
profit_sum = result['profit_ratio'].sum() profit_sum = result["profit_ratio"].sum()
# (end-capital - starting capital) / starting capital # (end-capital - starting capital) / starting capital
profit_total = result['profit_abs'].sum() / starting_balance profit_total = result["profit_abs"].sum() / starting_balance
return { return {
'key': first_column, "key": first_column,
'trades': len(result), "trades": len(result),
'profit_mean': result['profit_ratio'].mean() if len(result) > 0 else 0.0, "profit_mean": result["profit_ratio"].mean() if len(result) > 0 else 0.0,
'profit_mean_pct': round(result['profit_ratio'].mean() * 100.0, 2 "profit_mean_pct": round(result["profit_ratio"].mean() * 100.0, 2)
) if len(result) > 0 else 0.0, if len(result) > 0
'profit_sum': profit_sum, else 0.0,
'profit_sum_pct': round(profit_sum * 100.0, 2), "profit_sum": profit_sum,
'profit_total_abs': result['profit_abs'].sum(), "profit_sum_pct": round(profit_sum * 100.0, 2),
'profit_total': profit_total, "profit_total_abs": result["profit_abs"].sum(),
'profit_total_pct': round(profit_total * 100.0, 2), "profit_total": profit_total,
'duration_avg': str(timedelta( "profit_total_pct": round(profit_total * 100.0, 2),
minutes=round(result['trade_duration'].mean())) "duration_avg": str(timedelta(minutes=round(result["trade_duration"].mean())))
) if not result.empty else '0:00', if not result.empty
else "0:00",
# 'duration_max': str(timedelta( # 'duration_max': str(timedelta(
# minutes=round(result['trade_duration'].max())) # minutes=round(result['trade_duration'].max()))
# ) if not result.empty else '0:00', # ) if not result.empty else '0:00',
# 'duration_min': str(timedelta( # 'duration_min': str(timedelta(
# minutes=round(result['trade_duration'].min())) # minutes=round(result['trade_duration'].min()))
# ) if not result.empty else '0:00', # ) if not result.empty else '0:00',
'wins': len(result[result['profit_abs'] > 0]), "wins": len(result[result["profit_abs"] > 0]),
'draws': len(result[result['profit_abs'] == 0]), "draws": len(result[result["profit_abs"] == 0]),
'losses': len(result[result['profit_abs'] < 0]), "losses": len(result[result["profit_abs"] < 0]),
'winrate': len(result[result['profit_abs'] > 0]) / len(result) if len(result) else 0.0, "winrate": len(result[result["profit_abs"] > 0]) / len(result) if len(result) else 0.0,
} }
def generate_pair_metrics(pairlist: List[str], stake_currency: str, starting_balance: int, def generate_pair_metrics(
results: DataFrame, skip_nan: bool = False) -> List[Dict]: pairlist: List[str],
stake_currency: str,
starting_balance: int,
results: DataFrame,
skip_nan: bool = False,
) -> List[Dict]:
""" """
Generates and returns a list for the given backtest data and the results dataframe Generates and returns a list for the given backtest data and the results dataframe
:param pairlist: Pairlist used :param pairlist: Pairlist used
@@ -116,24 +124,23 @@ def generate_pair_metrics(pairlist: List[str], stake_currency: str, starting_bal
tabular_data = [] tabular_data = []
for pair in pairlist: for pair in pairlist:
result = results[results['pair'] == pair] result = results[results["pair"] == pair]
if skip_nan and result['profit_abs'].isnull().all(): if skip_nan and result["profit_abs"].isnull().all():
continue continue
tabular_data.append(_generate_result_line(result, starting_balance, pair)) tabular_data.append(_generate_result_line(result, starting_balance, pair))
# Sort by total profit %: # Sort by total profit %:
tabular_data = sorted(tabular_data, key=lambda k: k['profit_total_abs'], reverse=True) tabular_data = sorted(tabular_data, key=lambda k: k["profit_total_abs"], reverse=True)
# Append Total # Append Total
tabular_data.append(_generate_result_line(results, starting_balance, 'TOTAL')) tabular_data.append(_generate_result_line(results, starting_balance, "TOTAL"))
return tabular_data return tabular_data
def generate_tag_metrics(tag_type: str, def generate_tag_metrics(
starting_balance: int, tag_type: str, starting_balance: int, results: DataFrame, skip_nan: bool = False
results: DataFrame, ) -> List[Dict]:
skip_nan: bool = False) -> List[Dict]:
""" """
Generates and returns a list of metrics for the given tag trades and the results dataframe Generates and returns a list of metrics for the given tag trades and the results dataframe
:param starting_balance: Starting balance :param starting_balance: Starting balance
@@ -147,16 +154,16 @@ def generate_tag_metrics(tag_type: str,
if tag_type in results.columns: if tag_type in results.columns:
for tag, count in results[tag_type].value_counts().items(): for tag, count in results[tag_type].value_counts().items():
result = results[results[tag_type] == tag] result = results[results[tag_type] == tag]
if skip_nan and result['profit_abs'].isnull().all(): if skip_nan and result["profit_abs"].isnull().all():
continue continue
tabular_data.append(_generate_result_line(result, starting_balance, tag)) tabular_data.append(_generate_result_line(result, starting_balance, tag))
# Sort by total profit %: # Sort by total profit %:
tabular_data = sorted(tabular_data, key=lambda k: k['profit_total_abs'], reverse=True) tabular_data = sorted(tabular_data, key=lambda k: k["profit_total_abs"], reverse=True)
# Append Total # Append Total
tabular_data.append(_generate_result_line(results, starting_balance, 'TOTAL')) tabular_data.append(_generate_result_line(results, starting_balance, "TOTAL"))
return tabular_data return tabular_data
else: else:
return [] return []
@@ -171,51 +178,52 @@ def generate_strategy_comparison(bt_stats: Dict) -> List[Dict]:
tabular_data = [] tabular_data = []
for strategy, result in bt_stats.items(): for strategy, result in bt_stats.items():
tabular_data.append(deepcopy(result['results_per_pair'][-1])) tabular_data.append(deepcopy(result["results_per_pair"][-1]))
# Update "key" to strategy (results_per_pair has it as "Total"). # Update "key" to strategy (results_per_pair has it as "Total").
tabular_data[-1]['key'] = strategy tabular_data[-1]["key"] = strategy
tabular_data[-1]['max_drawdown_account'] = result['max_drawdown_account'] tabular_data[-1]["max_drawdown_account"] = result["max_drawdown_account"]
tabular_data[-1]['max_drawdown_abs'] = fmt_coin( tabular_data[-1]["max_drawdown_abs"] = fmt_coin(
result['max_drawdown_abs'], result['stake_currency'], False) result["max_drawdown_abs"], result["stake_currency"], False
)
return tabular_data return tabular_data
def _get_resample_from_period(period: str) -> str: def _get_resample_from_period(period: str) -> str:
if period == 'day': if period == "day":
return '1d' return "1d"
if period == 'week': if period == "week":
# Weekly defaulting to Monday. # Weekly defaulting to Monday.
return '1W-MON' return "1W-MON"
if period == 'month': if period == "month":
return '1ME' return "1ME"
raise ValueError(f"Period {period} is not supported.") raise ValueError(f"Period {period} is not supported.")
def generate_periodic_breakdown_stats( def generate_periodic_breakdown_stats(
trade_list: Union[List, DataFrame], period: str) -> List[Dict[str, Any]]: trade_list: Union[List, DataFrame], period: str
) -> List[Dict[str, Any]]:
results = trade_list if not isinstance(trade_list, list) else DataFrame.from_records(trade_list) results = trade_list if not isinstance(trade_list, list) else DataFrame.from_records(trade_list)
if len(results) == 0: if len(results) == 0:
return [] return []
results['close_date'] = to_datetime(results['close_date'], utc=True) results["close_date"] = to_datetime(results["close_date"], utc=True)
resample_period = _get_resample_from_period(period) resample_period = _get_resample_from_period(period)
resampled = results.resample(resample_period, on='close_date') resampled = results.resample(resample_period, on="close_date")
stats = [] stats = []
for name, day in resampled: for name, day in resampled:
profit_abs = day['profit_abs'].sum().round(10) profit_abs = day["profit_abs"].sum().round(10)
wins = sum(day['profit_abs'] > 0) wins = sum(day["profit_abs"] > 0)
draws = sum(day['profit_abs'] == 0) draws = sum(day["profit_abs"] == 0)
loses = sum(day['profit_abs'] < 0) loses = sum(day["profit_abs"] < 0)
trades = (wins + draws + loses) trades = wins + draws + loses
stats.append( stats.append(
{ {
'date': name.strftime('%d/%m/%Y'), "date": name.strftime("%d/%m/%Y"),
'date_ts': int(name.to_pydatetime().timestamp() * 1000), "date_ts": int(name.to_pydatetime().timestamp() * 1000),
'profit_abs': profit_abs, "profit_abs": profit_abs,
'wins': wins, "wins": wins,
'draws': draws, "draws": draws,
'loses': loses, "loses": loses,
'winrate': wins / trades if trades else 0.0, "winrate": wins / trades if trades else 0.0,
} }
) )
return stats return stats
@@ -235,13 +243,13 @@ def calc_streak(dataframe: DataFrame) -> Tuple[int, int]:
:return: Tuple containing consecutive wins and losses :return: Tuple containing consecutive wins and losses
""" """
df = Series(np.where(dataframe['profit_ratio'] > 0, 'win', 'loss')).to_frame('result') df = Series(np.where(dataframe["profit_ratio"] > 0, "win", "loss")).to_frame("result")
df['streaks'] = df['result'].ne(df['result'].shift()).cumsum().rename('streaks') df["streaks"] = df["result"].ne(df["result"].shift()).cumsum().rename("streaks")
df['counter'] = df['streaks'].groupby(df['streaks']).cumcount() + 1 df["counter"] = df["streaks"].groupby(df["streaks"]).cumcount() + 1
res = df.groupby(df['result']).max() res = df.groupby(df["result"]).max()
# #
cons_wins = int(res.loc['win', 'counter']) if 'win' in res.index else 0 cons_wins = int(res.loc["win", "counter"]) if "win" in res.index else 0
cons_losses = int(res.loc['loss', 'counter']) if 'loss' in res.index else 0 cons_losses = int(res.loc["loss", "counter"]) if "loss" in res.index else 0
return cons_wins, cons_losses return cons_wins, cons_losses
@@ -249,42 +257,51 @@ def generate_trading_stats(results: DataFrame) -> Dict[str, Any]:
"""Generate overall trade statistics""" """Generate overall trade statistics"""
if len(results) == 0: if len(results) == 0:
return { return {
'wins': 0, "wins": 0,
'losses': 0, "losses": 0,
'draws': 0, "draws": 0,
'winrate': 0, "winrate": 0,
'holding_avg': timedelta(), "holding_avg": timedelta(),
'winner_holding_avg': timedelta(), "winner_holding_avg": timedelta(),
'loser_holding_avg': timedelta(), "loser_holding_avg": timedelta(),
'max_consecutive_wins': 0, "max_consecutive_wins": 0,
'max_consecutive_losses': 0, "max_consecutive_losses": 0,
} }
winning_trades = results.loc[results['profit_ratio'] > 0] winning_trades = results.loc[results["profit_ratio"] > 0]
draw_trades = results.loc[results['profit_ratio'] == 0] draw_trades = results.loc[results["profit_ratio"] == 0]
losing_trades = results.loc[results['profit_ratio'] < 0] losing_trades = results.loc[results["profit_ratio"] < 0]
holding_avg = (timedelta(minutes=round(results['trade_duration'].mean())) holding_avg = (
if not results.empty else timedelta()) timedelta(minutes=round(results["trade_duration"].mean()))
winner_holding_avg = (timedelta(minutes=round(winning_trades['trade_duration'].mean())) if not results.empty
if not winning_trades.empty else timedelta()) else timedelta()
loser_holding_avg = (timedelta(minutes=round(losing_trades['trade_duration'].mean())) )
if not losing_trades.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()
)
winstreak, loss_streak = calc_streak(results) winstreak, loss_streak = calc_streak(results)
return { return {
'wins': len(winning_trades), "wins": len(winning_trades),
'losses': len(losing_trades), "losses": len(losing_trades),
'draws': len(draw_trades), "draws": len(draw_trades),
'winrate': len(winning_trades) / len(results) if len(results) else 0.0, "winrate": len(winning_trades) / len(results) if len(results) else 0.0,
'holding_avg': holding_avg, "holding_avg": holding_avg,
'holding_avg_s': holding_avg.total_seconds(), "holding_avg_s": holding_avg.total_seconds(),
'winner_holding_avg': winner_holding_avg, "winner_holding_avg": winner_holding_avg,
'winner_holding_avg_s': winner_holding_avg.total_seconds(), "winner_holding_avg_s": winner_holding_avg.total_seconds(),
'loser_holding_avg': loser_holding_avg, "loser_holding_avg": loser_holding_avg,
'loser_holding_avg_s': loser_holding_avg.total_seconds(), "loser_holding_avg_s": loser_holding_avg.total_seconds(),
'max_consecutive_wins': winstreak, "max_consecutive_wins": winstreak,
'max_consecutive_losses': loss_streak, "max_consecutive_losses": loss_streak,
} }
@@ -292,17 +309,17 @@ def generate_daily_stats(results: DataFrame) -> Dict[str, Any]:
"""Generate daily statistics""" """Generate daily statistics"""
if len(results) == 0: if len(results) == 0:
return { return {
'backtest_best_day': 0, "backtest_best_day": 0,
'backtest_worst_day': 0, "backtest_worst_day": 0,
'backtest_best_day_abs': 0, "backtest_best_day_abs": 0,
'backtest_worst_day_abs': 0, "backtest_worst_day_abs": 0,
'winning_days': 0, "winning_days": 0,
'draw_days': 0, "draw_days": 0,
'losing_days': 0, "losing_days": 0,
'daily_profit_list': [], "daily_profit_list": [],
} }
daily_profit_rel = results.resample('1d', on='close_date')['profit_ratio'].sum() daily_profit_rel = results.resample("1d", on="close_date")["profit_ratio"].sum()
daily_profit = results.resample('1d', on='close_date')['profit_abs'].sum().round(10) daily_profit = results.resample("1d", on="close_date")["profit_abs"].sum().round(10)
worst_rel = min(daily_profit_rel) worst_rel = min(daily_profit_rel)
best_rel = max(daily_profit_rel) best_rel = max(daily_profit_rel)
worst = min(daily_profit) worst = min(daily_profit)
@@ -313,21 +330,23 @@ def generate_daily_stats(results: DataFrame) -> Dict[str, Any]:
daily_profit_list = [(str(idx.date()), val) for idx, val in daily_profit.items()] daily_profit_list = [(str(idx.date()), val) for idx, val in daily_profit.items()]
return { return {
'backtest_best_day': best_rel, "backtest_best_day": best_rel,
'backtest_worst_day': worst_rel, "backtest_worst_day": worst_rel,
'backtest_best_day_abs': best, "backtest_best_day_abs": best,
'backtest_worst_day_abs': worst, "backtest_worst_day_abs": worst,
'winning_days': winning_days, "winning_days": winning_days,
'draw_days': draw_days, "draw_days": draw_days,
'losing_days': losing_days, "losing_days": losing_days,
'daily_profit': daily_profit_list, "daily_profit": daily_profit_list,
} }
def generate_strategy_stats(pairlist: List[str], def generate_strategy_stats(
pairlist: List[str],
strategy: str, strategy: str,
content: Dict[str, Any], content: Dict[str, Any],
min_date: datetime, max_date: datetime, min_date: datetime,
max_date: datetime,
market_change: float, market_change: float,
is_hyperopt: bool = False, is_hyperopt: bool = False,
) -> Dict[str, Any]: ) -> Dict[str, Any]:
@@ -341,174 +360,196 @@ def generate_strategy_stats(pairlist: List[str],
:param market_change: float indicating the market change :param market_change: float indicating the market change
:return: Dictionary containing results per strategy and a strategy summary. :return: Dictionary containing results per strategy and a strategy summary.
""" """
results: Dict[str, DataFrame] = content['results'] results: Dict[str, DataFrame] = content["results"]
if not isinstance(results, DataFrame): if not isinstance(results, DataFrame):
return {} return {}
config = content['config'] config = content["config"]
max_open_trades = min(config['max_open_trades'], len(pairlist)) max_open_trades = min(config["max_open_trades"], len(pairlist))
start_balance = config['dry_run_wallet'] start_balance = config["dry_run_wallet"]
stake_currency = config['stake_currency'] stake_currency = config["stake_currency"]
pair_results = generate_pair_metrics(pairlist, stake_currency=stake_currency, pair_results = generate_pair_metrics(
pairlist,
stake_currency=stake_currency,
starting_balance=start_balance, starting_balance=start_balance,
results=results, skip_nan=False) results=results,
skip_nan=False,
)
enter_tag_results = generate_tag_metrics("enter_tag", starting_balance=start_balance, enter_tag_results = generate_tag_metrics(
results=results, skip_nan=False) "enter_tag", starting_balance=start_balance, results=results, skip_nan=False
exit_reason_stats = generate_tag_metrics('exit_reason', starting_balance=start_balance, )
results=results, skip_nan=False) exit_reason_stats = generate_tag_metrics(
"exit_reason", starting_balance=start_balance, results=results, skip_nan=False
)
left_open_results = generate_pair_metrics( left_open_results = generate_pair_metrics(
pairlist, stake_currency=stake_currency, starting_balance=start_balance, pairlist,
results=results.loc[results['exit_reason'] == 'force_exit'], skip_nan=True) stake_currency=stake_currency,
starting_balance=start_balance,
results=results.loc[results["exit_reason"] == "force_exit"],
skip_nan=True,
)
daily_stats = generate_daily_stats(results) daily_stats = generate_daily_stats(results)
trade_stats = generate_trading_stats(results) trade_stats = generate_trading_stats(results)
periodic_breakdown = {} periodic_breakdown = {}
if not is_hyperopt: if not is_hyperopt:
periodic_breakdown = {'periodic_breakdown': generate_all_periodic_breakdown_stats(results)} periodic_breakdown = {"periodic_breakdown": generate_all_periodic_breakdown_stats(results)}
best_pair = max([pair for pair in pair_results if pair['key'] != 'TOTAL'], best_pair = (
key=lambda x: x['profit_sum']) if len(pair_results) > 1 else None max(
worst_pair = min([pair for pair in pair_results if pair['key'] != 'TOTAL'], [pair for pair in pair_results if pair["key"] != "TOTAL"], key=lambda x: x["profit_sum"]
key=lambda x: x['profit_sum']) if len(pair_results) > 1 else None )
winning_profit = results.loc[results['profit_abs'] > 0, 'profit_abs'].sum() if len(pair_results) > 1
losing_profit = results.loc[results['profit_abs'] < 0, 'profit_abs'].sum() else None
)
worst_pair = (
min(
[pair for pair in pair_results if pair["key"] != "TOTAL"], key=lambda x: x["profit_sum"]
)
if len(pair_results) > 1
else None
)
winning_profit = results.loc[results["profit_abs"] > 0, "profit_abs"].sum()
losing_profit = results.loc[results["profit_abs"] < 0, "profit_abs"].sum()
profit_factor = winning_profit / abs(losing_profit) if losing_profit else 0.0 profit_factor = winning_profit / abs(losing_profit) if losing_profit else 0.0
expectancy, expectancy_ratio = calculate_expectancy(results) expectancy, expectancy_ratio = calculate_expectancy(results)
backtest_days = (max_date - min_date).days or 1 backtest_days = (max_date - min_date).days or 1
strat_stats = { strat_stats = {
'trades': results.to_dict(orient='records'), "trades": results.to_dict(orient="records"),
'locks': [lock.to_json() for lock in content['locks']], "locks": [lock.to_json() for lock in content["locks"]],
'best_pair': best_pair, "best_pair": best_pair,
'worst_pair': worst_pair, "worst_pair": worst_pair,
'results_per_pair': pair_results, "results_per_pair": pair_results,
'results_per_enter_tag': enter_tag_results, "results_per_enter_tag": enter_tag_results,
'exit_reason_summary': exit_reason_stats, "exit_reason_summary": exit_reason_stats,
'left_open_trades': left_open_results, "left_open_trades": left_open_results,
"total_trades": len(results),
'total_trades': len(results), "trade_count_long": len(results.loc[~results["is_short"]]),
'trade_count_long': len(results.loc[~results['is_short']]), "trade_count_short": len(results.loc[results["is_short"]]),
'trade_count_short': len(results.loc[results['is_short']]), "total_volume": float(results["stake_amount"].sum()),
'total_volume': float(results['stake_amount'].sum()), "avg_stake_amount": results["stake_amount"].mean() if len(results) > 0 else 0,
'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_mean': results['profit_ratio'].mean() if len(results) > 0 else 0, "profit_median": results["profit_ratio"].median() if len(results) > 0 else 0,
'profit_median': results['profit_ratio'].median() if len(results) > 0 else 0, "profit_total": results["profit_abs"].sum() / start_balance,
'profit_total': results['profit_abs'].sum() / start_balance, "profit_total_long": results.loc[~results["is_short"], "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_short': results.loc[results['is_short'], 'profit_abs'].sum() / start_balance, "profit_total_abs": results["profit_abs"].sum(),
'profit_total_abs': results['profit_abs'].sum(), "profit_total_long_abs": results.loc[~results["is_short"], "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(),
'profit_total_short_abs': results.loc[results['is_short'], 'profit_abs'].sum(), "cagr": calculate_cagr(backtest_days, start_balance, content["final_balance"]),
'cagr': calculate_cagr(backtest_days, start_balance, content['final_balance']), "expectancy": expectancy,
'expectancy': expectancy, "expectancy_ratio": expectancy_ratio,
'expectancy_ratio': expectancy_ratio, "sortino": calculate_sortino(results, min_date, max_date, start_balance),
'sortino': calculate_sortino(results, min_date, max_date, start_balance), "sharpe": calculate_sharpe(results, min_date, max_date, start_balance),
'sharpe': calculate_sharpe(results, min_date, max_date, start_balance), "calmar": calculate_calmar(results, min_date, max_date, start_balance),
'calmar': calculate_calmar(results, min_date, max_date, start_balance), "profit_factor": profit_factor,
'profit_factor': profit_factor, "backtest_start": min_date.strftime(DATETIME_PRINT_FORMAT),
'backtest_start': min_date.strftime(DATETIME_PRINT_FORMAT), "backtest_start_ts": int(min_date.timestamp() * 1000),
'backtest_start_ts': int(min_date.timestamp() * 1000), "backtest_end": max_date.strftime(DATETIME_PRINT_FORMAT),
'backtest_end': max_date.strftime(DATETIME_PRINT_FORMAT), "backtest_end_ts": int(max_date.timestamp() * 1000),
'backtest_end_ts': int(max_date.timestamp() * 1000), "backtest_days": backtest_days,
'backtest_days': backtest_days, "backtest_run_start_ts": content["backtest_start_time"],
"backtest_run_end_ts": content["backtest_end_time"],
'backtest_run_start_ts': content['backtest_start_time'], "trades_per_day": round(len(results) / backtest_days, 2),
'backtest_run_end_ts': content['backtest_end_time'], "market_change": market_change,
"pairlist": pairlist,
'trades_per_day': round(len(results) / backtest_days, 2), "stake_amount": config["stake_amount"],
'market_change': market_change, "stake_currency": config["stake_currency"],
'pairlist': pairlist, "stake_currency_decimals": decimals_per_coin(config["stake_currency"]),
'stake_amount': config['stake_amount'], "starting_balance": start_balance,
'stake_currency': config['stake_currency'], "dry_run_wallet": start_balance,
'stake_currency_decimals': decimals_per_coin(config['stake_currency']), "final_balance": content["final_balance"],
'starting_balance': start_balance, "rejected_signals": content["rejected_signals"],
'dry_run_wallet': start_balance, "timedout_entry_orders": content["timedout_entry_orders"],
'final_balance': content['final_balance'], "timedout_exit_orders": content["timedout_exit_orders"],
'rejected_signals': content['rejected_signals'], "canceled_trade_entries": content["canceled_trade_entries"],
'timedout_entry_orders': content['timedout_entry_orders'], "canceled_entry_orders": content["canceled_entry_orders"],
'timedout_exit_orders': content['timedout_exit_orders'], "replaced_entry_orders": content["replaced_entry_orders"],
'canceled_trade_entries': content['canceled_trade_entries'], "max_open_trades": max_open_trades,
'canceled_entry_orders': content['canceled_entry_orders'], "max_open_trades_setting": (
'replaced_entry_orders': content['replaced_entry_orders'], config["max_open_trades"] if config["max_open_trades"] != float("inf") else -1
'max_open_trades': max_open_trades, ),
'max_open_trades_setting': (config['max_open_trades'] "timeframe": config["timeframe"],
if config['max_open_trades'] != float('inf') else -1), "timeframe_detail": config.get("timeframe_detail", ""),
'timeframe': config['timeframe'], "timerange": config.get("timerange", ""),
'timeframe_detail': config.get('timeframe_detail', ''), "enable_protections": config.get("enable_protections", False),
'timerange': config.get('timerange', ''), "strategy_name": strategy,
'enable_protections': config.get('enable_protections', False),
'strategy_name': strategy,
# Parameters relevant for backtesting # Parameters relevant for backtesting
'stoploss': config['stoploss'], "stoploss": config["stoploss"],
'trailing_stop': config.get('trailing_stop', False), "trailing_stop": config.get("trailing_stop", False),
'trailing_stop_positive': config.get('trailing_stop_positive'), "trailing_stop_positive": config.get("trailing_stop_positive"),
'trailing_stop_positive_offset': config.get('trailing_stop_positive_offset', 0.0), "trailing_stop_positive_offset": config.get("trailing_stop_positive_offset", 0.0),
'trailing_only_offset_is_reached': config.get('trailing_only_offset_is_reached', False), "trailing_only_offset_is_reached": config.get("trailing_only_offset_is_reached", False),
'use_custom_stoploss': config.get('use_custom_stoploss', False), "use_custom_stoploss": config.get("use_custom_stoploss", False),
'minimal_roi': config['minimal_roi'], "minimal_roi": config["minimal_roi"],
'use_exit_signal': config['use_exit_signal'], "use_exit_signal": config["use_exit_signal"],
'exit_profit_only': config['exit_profit_only'], "exit_profit_only": config["exit_profit_only"],
'exit_profit_offset': config['exit_profit_offset'], "exit_profit_offset": config["exit_profit_offset"],
'ignore_roi_if_entry_signal': config['ignore_roi_if_entry_signal'], "ignore_roi_if_entry_signal": config["ignore_roi_if_entry_signal"],
**periodic_breakdown, **periodic_breakdown,
**daily_stats, **daily_stats,
**trade_stats **trade_stats,
} }
try: try:
max_drawdown_legacy, _, _, _, _, _ = calculate_max_drawdown( max_drawdown_legacy, _, _, _, _, _ = calculate_max_drawdown(
results, value_col='profit_ratio') results, value_col="profit_ratio"
(drawdown_abs, drawdown_start, drawdown_end, high_val, low_val, )
max_drawdown) = calculate_max_drawdown( (drawdown_abs, drawdown_start, drawdown_end, high_val, low_val, max_drawdown) = (
results, value_col='profit_abs', starting_balance=start_balance) calculate_max_drawdown(results, value_col="profit_abs", starting_balance=start_balance)
)
# max_relative_drawdown = Underwater # max_relative_drawdown = Underwater
(_, _, _, _, _, max_relative_drawdown) = calculate_max_drawdown( (_, _, _, _, _, max_relative_drawdown) = calculate_max_drawdown(
results, value_col='profit_abs', starting_balance=start_balance, relative=True) results, value_col="profit_abs", starting_balance=start_balance, relative=True
)
strat_stats.update({ strat_stats.update(
'max_drawdown': max_drawdown_legacy, # Deprecated - do not use {
'max_drawdown_account': max_drawdown, "max_drawdown": max_drawdown_legacy, # Deprecated - do not use
'max_relative_drawdown': max_relative_drawdown, "max_drawdown_account": max_drawdown,
'max_drawdown_abs': drawdown_abs, "max_relative_drawdown": max_relative_drawdown,
'drawdown_start': drawdown_start.strftime(DATETIME_PRINT_FORMAT), "max_drawdown_abs": drawdown_abs,
'drawdown_start_ts': drawdown_start.timestamp() * 1000, "drawdown_start": drawdown_start.strftime(DATETIME_PRINT_FORMAT),
'drawdown_end': drawdown_end.strftime(DATETIME_PRINT_FORMAT), "drawdown_start_ts": drawdown_start.timestamp() * 1000,
'drawdown_end_ts': drawdown_end.timestamp() * 1000, "drawdown_end": drawdown_end.strftime(DATETIME_PRINT_FORMAT),
"drawdown_end_ts": drawdown_end.timestamp() * 1000,
'max_drawdown_low': low_val, "max_drawdown_low": low_val,
'max_drawdown_high': high_val, "max_drawdown_high": high_val,
}) }
)
csum_min, csum_max = calculate_csum(results, start_balance) csum_min, csum_max = calculate_csum(results, start_balance)
strat_stats.update({ strat_stats.update({"csum_min": csum_min, "csum_max": csum_max})
'csum_min': csum_min,
'csum_max': csum_max
})
except ValueError: except ValueError:
strat_stats.update({ strat_stats.update(
'max_drawdown': 0.0, {
'max_drawdown_account': 0.0, "max_drawdown": 0.0,
'max_relative_drawdown': 0.0, "max_drawdown_account": 0.0,
'max_drawdown_abs': 0.0, "max_relative_drawdown": 0.0,
'max_drawdown_low': 0.0, "max_drawdown_abs": 0.0,
'max_drawdown_high': 0.0, "max_drawdown_low": 0.0,
'drawdown_start': datetime(1970, 1, 1, tzinfo=timezone.utc), "max_drawdown_high": 0.0,
'drawdown_start_ts': 0, "drawdown_start": datetime(1970, 1, 1, tzinfo=timezone.utc),
'drawdown_end': datetime(1970, 1, 1, tzinfo=timezone.utc), "drawdown_start_ts": 0,
'drawdown_end_ts': 0, "drawdown_end": datetime(1970, 1, 1, tzinfo=timezone.utc),
'csum_min': 0, "drawdown_end_ts": 0,
'csum_max': 0 "csum_min": 0,
}) "csum_max": 0,
}
)
return strat_stats return strat_stats
def generate_backtest_stats(btdata: Dict[str, DataFrame], def generate_backtest_stats(
btdata: Dict[str, DataFrame],
all_results: Dict[str, Dict[str, Union[DataFrame, Dict]]], all_results: Dict[str, Dict[str, Union[DataFrame, Dict]]],
min_date: datetime, max_date: datetime min_date: datetime,
max_date: datetime,
) -> BacktestResultType: ) -> BacktestResultType:
""" """
:param btdata: Backtest data :param btdata: Backtest data
@@ -519,29 +560,30 @@ def generate_backtest_stats(btdata: Dict[str, DataFrame],
:return: Dictionary containing results per strategy and a strategy summary. :return: Dictionary containing results per strategy and a strategy summary.
""" """
result: BacktestResultType = { result: BacktestResultType = {
'metadata': {}, "metadata": {},
'strategy': {}, "strategy": {},
'strategy_comparison': [], "strategy_comparison": [],
} }
market_change = calculate_market_change(btdata, 'close') market_change = calculate_market_change(btdata, "close")
metadata = {} metadata = {}
pairlist = list(btdata.keys()) pairlist = list(btdata.keys())
for strategy, content in all_results.items(): for strategy, content in all_results.items():
strat_stats = generate_strategy_stats(pairlist, strategy, content, strat_stats = generate_strategy_stats(
min_date, max_date, market_change=market_change) pairlist, strategy, content, min_date, max_date, market_change=market_change
)
metadata[strategy] = { metadata[strategy] = {
'run_id': content['run_id'], "run_id": content["run_id"],
'backtest_start_time': content['backtest_start_time'], "backtest_start_time": content["backtest_start_time"],
'timeframe': content['config']['timeframe'], "timeframe": content["config"]["timeframe"],
'timeframe_detail': content['config'].get('timeframe_detail', None), "timeframe_detail": content["config"].get("timeframe_detail", None),
'backtest_start_ts': int(min_date.timestamp()), "backtest_start_ts": int(min_date.timestamp()),
'backtest_end_ts': int(max_date.timestamp()), "backtest_end_ts": int(max_date.timestamp()),
} }
result['strategy'][strategy] = strat_stats result["strategy"][strategy] = strat_stats
strategy_results = generate_strategy_comparison(bt_stats=result['strategy']) strategy_results = generate_strategy_comparison(bt_stats=result["strategy"])
result['metadata'] = metadata result["metadata"] = metadata
result['strategy_comparison'] = strategy_results result["strategy_comparison"] = strategy_results
return result return result
freqtrade/optimize/space/decimalspace.py
+11 -3
View File
@@ -3,9 +3,17 @@ from skopt.space import Integer
class SKDecimal(Integer): class SKDecimal(Integer):
def __init__(
def __init__(self, low, high, decimals=3, prior="uniform", base=10, transform=None, self,
name=None, dtype=np.int64): low,
high,
decimals=3,
prior="uniform",
base=10,
transform=None,
name=None,
dtype=np.int64,
):
self.decimals = decimals self.decimals = decimals
self.pow_dot_one = pow(0.1, self.decimals) self.pow_dot_one = pow(0.1, self.decimals)