2023-06-25 15:09:57 +00:00
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import logging
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2024-03-30 12:36:19 +00:00
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from typing import Any, Dict, List
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2023-06-25 15:09:57 +00:00
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from tabulate import tabulate
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from freqtrade.constants import UNLIMITED_STAKE_AMOUNT, Config
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2023-07-23 17:57:47 +00:00
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from freqtrade.optimize.optimize_reports.optimize_reports import generate_periodic_breakdown_stats
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2023-07-30 08:52:23 +00:00
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from freqtrade.types import BacktestResultType
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2024-01-06 15:02:47 +00:00
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from freqtrade.util import decimals_per_coin, fmt_coin
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2023-06-25 15:09:57 +00:00
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logger = logging.getLogger(__name__)
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def _get_line_floatfmt(stake_currency: str) -> List[str]:
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"""
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Generate floatformat (goes in line with _generate_result_line())
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"""
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2024-05-12 15:20:36 +00:00
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return ["s", "d", ".2f", f".{decimals_per_coin(stake_currency)}f", ".2f", "d", "s", "s"]
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2023-06-25 15:09:57 +00:00
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2024-05-12 15:20:36 +00:00
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def _get_line_header(
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first_column: str, stake_currency: str, direction: str = "Entries"
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) -> List[str]:
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2023-06-25 15:09:57 +00:00
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"""
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Generate header lines (goes in line with _generate_result_line())
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"""
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2024-05-12 15:20:36 +00:00
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return [
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first_column,
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direction,
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"Avg Profit %",
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f"Tot Profit {stake_currency}",
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"Tot Profit %",
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"Avg Duration",
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"Win Draw Loss Win%",
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]
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2023-06-25 15:09:57 +00:00
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2023-07-23 17:57:47 +00:00
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def generate_wins_draws_losses(wins, draws, losses):
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if wins > 0 and losses == 0:
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wl_ratio = "100"
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2023-07-23 17:57:47 +00:00
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elif wins == 0:
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wl_ratio = "0"
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2023-07-23 17:57:47 +00:00
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else:
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wl_ratio = f"{100.0 / (wins + draws + losses) * wins:.1f}" if losses > 0 else "100"
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return f"{wins:>4} {draws:>4} {losses:>4} {wl_ratio:>4}"
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2023-07-23 17:57:47 +00:00
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2023-06-25 15:09:57 +00:00
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def text_table_bt_results(pair_results: List[Dict[str, Any]], stake_currency: str) -> str:
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"""
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Generates and returns a text table for the given backtest data and the results dataframe
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:param pair_results: List of Dictionaries - one entry per pair + final TOTAL row
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:param stake_currency: stake-currency - used to correctly name headers
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:return: pretty printed table with tabulate as string
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"""
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headers = _get_line_header("Pair", stake_currency)
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floatfmt = _get_line_floatfmt(stake_currency)
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output = [
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[
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t["key"],
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t["trades"],
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t["profit_mean_pct"],
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t["profit_total_abs"],
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t["profit_total_pct"],
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t["duration_avg"],
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generate_wins_draws_losses(t["wins"], t["draws"], t["losses"]),
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]
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for t in pair_results
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]
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# Ignore type as floatfmt does allow tuples but mypy does not know that
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return tabulate(output, headers=headers, floatfmt=floatfmt, tablefmt="orgtbl", stralign="right")
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def text_table_tags(tag_type: str, tag_results: List[Dict[str, Any]], stake_currency: str) -> str:
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"""
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Generates and returns a text table for the given backtest data and the results dataframe
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:param pair_results: List of Dictionaries - one entry per pair + final TOTAL row
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:param stake_currency: stake-currency - used to correctly name headers
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:return: pretty printed table with tabulate as string
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"""
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fallback: str = ""
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if tag_type == "enter_tag":
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headers = _get_line_header("TAG", stake_currency)
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else:
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headers = _get_line_header("Exit Reason", stake_currency, "Exits")
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fallback = "exit_reason"
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2024-03-30 12:11:59 +00:00
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2023-06-25 15:09:57 +00:00
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floatfmt = _get_line_floatfmt(stake_currency)
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output = [
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[
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(
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t["key"]
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if t.get("key") is not None and len(str(t["key"])) > 0
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else t.get(fallback, "OTHER")
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),
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t["trades"],
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t["profit_mean_pct"],
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t["profit_total_abs"],
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t["profit_total_pct"],
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t.get("duration_avg"),
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generate_wins_draws_losses(t["wins"], t["draws"], t["losses"]),
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]
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for t in tag_results
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]
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# Ignore type as floatfmt does allow tuples but mypy does not know that
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return tabulate(output, headers=headers, floatfmt=floatfmt, tablefmt="orgtbl", stralign="right")
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2023-06-25 15:09:57 +00:00
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2024-05-12 15:20:36 +00:00
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def text_table_periodic_breakdown(
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days_breakdown_stats: List[Dict[str, Any]], stake_currency: str, period: str
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) -> str:
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2023-06-25 15:09:57 +00:00
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"""
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Generate small table with Backtest results by days
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:param days_breakdown_stats: Days breakdown metrics
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:param stake_currency: Stakecurrency used
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:return: pretty printed table with tabulate as string
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"""
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headers = [
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period.capitalize(),
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f"Tot Profit {stake_currency}",
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"Wins",
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"Draws",
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"Losses",
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]
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output = [
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[
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d["date"],
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fmt_coin(d["profit_abs"], stake_currency, False),
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d["wins"],
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d["draws"],
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d["loses"],
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]
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for d in days_breakdown_stats
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]
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return tabulate(output, headers=headers, tablefmt="orgtbl", stralign="right")
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def text_table_strategy(strategy_results, stake_currency: str) -> str:
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"""
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Generate summary table per strategy
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:param strategy_results: Dict of <Strategyname: DataFrame> containing results for all strategies
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:param stake_currency: stake-currency - used to correctly name headers
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:return: pretty printed table with tabulate as string
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"""
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floatfmt = _get_line_floatfmt(stake_currency)
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headers = _get_line_header("Strategy", stake_currency)
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2023-06-25 15:09:57 +00:00
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# _get_line_header() is also used for per-pair summary. Per-pair drawdown is mostly useless
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# therefore we slip this column in only for strategy summary here.
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headers.append("Drawdown")
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# Align drawdown string on the center two space separator.
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if "max_drawdown_account" in strategy_results[0]:
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drawdown = [f'{t["max_drawdown_account"] * 100:.2f}' for t in strategy_results]
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else:
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# Support for prior backtest results
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drawdown = [f'{t["max_drawdown_per"]:.2f}' for t in strategy_results]
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dd_pad_abs = max([len(t["max_drawdown_abs"]) for t in strategy_results])
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dd_pad_per = max([len(dd) for dd in drawdown])
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drawdown = [
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f'{t["max_drawdown_abs"]:>{dd_pad_abs}} {stake_currency} {dd:>{dd_pad_per}}%'
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for t, dd in zip(strategy_results, drawdown)
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]
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output = [
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[
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t["key"],
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t["trades"],
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t["profit_mean_pct"],
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t["profit_total_abs"],
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t["profit_total_pct"],
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t["duration_avg"],
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generate_wins_draws_losses(t["wins"], t["draws"], t["losses"]),
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drawdown,
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]
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for t, drawdown in zip(strategy_results, drawdown)
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]
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# Ignore type as floatfmt does allow tuples but mypy does not know that
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return tabulate(output, headers=headers, floatfmt=floatfmt, tablefmt="orgtbl", stralign="right")
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def text_table_add_metrics(strat_results: Dict) -> str:
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if len(strat_results["trades"]) > 0:
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best_trade = max(strat_results["trades"], key=lambda x: x["profit_ratio"])
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worst_trade = min(strat_results["trades"], key=lambda x: x["profit_ratio"])
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short_metrics = (
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[
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("", ""), # Empty line to improve readability
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(
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"Long / Short",
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f"{strat_results.get('trade_count_long', 'total_trades')} / "
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f"{strat_results.get('trade_count_short', 0)}",
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),
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("Total profit Long %", f"{strat_results['profit_total_long']:.2%}"),
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("Total profit Short %", f"{strat_results['profit_total_short']:.2%}"),
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(
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"Absolute profit Long",
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fmt_coin(
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strat_results["profit_total_long_abs"], strat_results["stake_currency"]
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),
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),
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(
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"Absolute profit Short",
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fmt_coin(
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strat_results["profit_total_short_abs"], strat_results["stake_currency"]
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),
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),
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]
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if strat_results.get("trade_count_short", 0) > 0
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else []
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)
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drawdown_metrics = []
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if "max_relative_drawdown" in strat_results:
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# Compatibility to show old hyperopt results
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drawdown_metrics.append(
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("Max % of account underwater", f"{strat_results['max_relative_drawdown']:.2%}")
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)
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drawdown_metrics.extend(
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[
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(
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("Absolute Drawdown (Account)", f"{strat_results['max_drawdown_account']:.2%}")
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if "max_drawdown_account" in strat_results
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else ("Drawdown", f"{strat_results['max_drawdown']:.2%}")
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),
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(
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"Absolute Drawdown",
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fmt_coin(strat_results["max_drawdown_abs"], strat_results["stake_currency"]),
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),
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(
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"Drawdown high",
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fmt_coin(strat_results["max_drawdown_high"], strat_results["stake_currency"]),
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),
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(
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"Drawdown low",
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fmt_coin(strat_results["max_drawdown_low"], strat_results["stake_currency"]),
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),
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("Drawdown Start", strat_results["drawdown_start"]),
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("Drawdown End", strat_results["drawdown_end"]),
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]
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)
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entry_adjustment_metrics = (
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[
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("Canceled Trade Entries", strat_results.get("canceled_trade_entries", "N/A")),
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("Canceled Entry Orders", strat_results.get("canceled_entry_orders", "N/A")),
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("Replaced Entry Orders", strat_results.get("replaced_entry_orders", "N/A")),
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]
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if strat_results.get("canceled_entry_orders", 0) > 0
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else []
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)
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2023-06-25 15:09:57 +00:00
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# Newly added fields should be ignored if they are missing in strat_results. hyperopt-show
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# command stores these results and newer version of freqtrade must be able to handle old
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# results with missing new fields.
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metrics = [
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("Backtesting from", strat_results["backtest_start"]),
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("Backtesting to", strat_results["backtest_end"]),
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("Max open trades", strat_results["max_open_trades"]),
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("", ""), # Empty line to improve readability
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(
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"Total/Daily Avg Trades",
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f"{strat_results['total_trades']} / {strat_results['trades_per_day']}",
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),
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(
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"Starting balance",
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fmt_coin(strat_results["starting_balance"], strat_results["stake_currency"]),
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),
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(
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"Final balance",
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fmt_coin(strat_results["final_balance"], strat_results["stake_currency"]),
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),
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(
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"Absolute profit ",
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fmt_coin(strat_results["profit_total_abs"], strat_results["stake_currency"]),
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),
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("Total profit %", f"{strat_results['profit_total']:.2%}"),
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("CAGR %", f"{strat_results['cagr']:.2%}" if "cagr" in strat_results else "N/A"),
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("Sortino", f"{strat_results['sortino']:.2f}" if "sortino" in strat_results else "N/A"),
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("Sharpe", f"{strat_results['sharpe']:.2f}" if "sharpe" in strat_results else "N/A"),
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("Calmar", f"{strat_results['calmar']:.2f}" if "calmar" in strat_results else "N/A"),
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(
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"Profit factor",
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(
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f'{strat_results["profit_factor"]:.2f}'
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if "profit_factor" in strat_results
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else "N/A"
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),
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),
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(
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"Expectancy (Ratio)",
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(
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f"{strat_results['expectancy']:.2f} ({strat_results['expectancy_ratio']:.2f})"
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if "expectancy_ratio" in strat_results
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else "N/A"
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),
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),
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(
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"Avg. daily profit %",
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f"{(strat_results['profit_total'] / strat_results['backtest_days']):.2%}",
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),
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|
(
|
|
|
|
"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"]),
|
|
|
|
),
|
2023-06-25 15:09:57 +00:00
|
|
|
*short_metrics,
|
2024-05-12 15:20:36 +00:00
|
|
|
("", ""), # Empty line to improve readability
|
|
|
|
(
|
|
|
|
"Best Pair",
|
|
|
|
f"{strat_results['best_pair']['key']} "
|
|
|
|
f"{strat_results['best_pair']['profit_total']:.2%}",
|
|
|
|
),
|
|
|
|
(
|
|
|
|
"Worst Pair",
|
|
|
|
f"{strat_results['worst_pair']['key']} "
|
|
|
|
f"{strat_results['worst_pair']['profit_total']:.2%}",
|
|
|
|
),
|
|
|
|
("Best trade", f"{best_trade['pair']} {best_trade['profit_ratio']:.2%}"),
|
2024-05-12 15:51:21 +00:00
|
|
|
("Worst trade", f"{worst_trade['pair']} {worst_trade['profit_ratio']:.2%}"),
|
2024-05-12 15:20:36 +00:00
|
|
|
(
|
|
|
|
"Best day",
|
|
|
|
fmt_coin(strat_results["backtest_best_day_abs"], strat_results["stake_currency"]),
|
|
|
|
),
|
|
|
|
(
|
|
|
|
"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",
|
2024-05-12 16:02:42 +00:00
|
|
|
(
|
2024-05-13 17:49:15 +00:00
|
|
|
(
|
|
|
|
f"{strat_results['max_consecutive_wins']} / "
|
|
|
|
f"{strat_results['max_consecutive_losses']}"
|
|
|
|
)
|
|
|
|
if "max_consecutive_losses" in strat_results
|
|
|
|
else "N/A"
|
|
|
|
),
|
2024-05-12 15:20:36 +00:00
|
|
|
),
|
|
|
|
("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')}",
|
|
|
|
),
|
2023-06-25 15:09:57 +00:00
|
|
|
*entry_adjustment_metrics,
|
2024-05-12 15:20:36 +00:00
|
|
|
("", ""), # Empty line to improve readability
|
|
|
|
("Min balance", fmt_coin(strat_results["csum_min"], strat_results["stake_currency"])),
|
|
|
|
("Max balance", fmt_coin(strat_results["csum_max"], strat_results["stake_currency"])),
|
2023-06-25 15:09:57 +00:00
|
|
|
*drawdown_metrics,
|
2024-05-12 15:20:36 +00:00
|
|
|
("Market change", f"{strat_results['market_change']:.2%}"),
|
2023-06-25 15:09:57 +00:00
|
|
|
]
|
|
|
|
|
|
|
|
return tabulate(metrics, headers=["Metric", "Value"], tablefmt="orgtbl")
|
|
|
|
else:
|
2024-05-12 15:20:36 +00:00
|
|
|
start_balance = fmt_coin(strat_results["starting_balance"], strat_results["stake_currency"])
|
|
|
|
stake_amount = (
|
|
|
|
fmt_coin(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}."
|
|
|
|
)
|
2023-06-25 15:09:57 +00:00
|
|
|
return message
|
|
|
|
|
|
|
|
|
2024-05-12 15:20:36 +00:00
|
|
|
def show_backtest_result(
|
|
|
|
strategy: str, results: Dict[str, Any], stake_currency: str, backtest_breakdown: List[str]
|
|
|
|
):
|
2023-06-25 15:09:57 +00:00
|
|
|
"""
|
|
|
|
Print results for one strategy
|
|
|
|
"""
|
|
|
|
# Print results
|
|
|
|
print(f"Result for strategy {strategy}")
|
2024-05-12 15:20:36 +00:00
|
|
|
table = text_table_bt_results(results["results_per_pair"], stake_currency=stake_currency)
|
2023-06-25 15:09:57 +00:00
|
|
|
if isinstance(table, str):
|
2024-05-12 15:20:36 +00:00
|
|
|
print(" BACKTESTING REPORT ".center(len(table.splitlines()[0]), "="))
|
2023-06-25 15:09:57 +00:00
|
|
|
print(table)
|
|
|
|
|
2024-05-12 15:20:36 +00:00
|
|
|
table = text_table_bt_results(results["left_open_trades"], stake_currency=stake_currency)
|
2023-06-25 15:09:57 +00:00
|
|
|
if isinstance(table, str) and len(table) > 0:
|
2024-05-12 15:20:36 +00:00
|
|
|
print(" LEFT OPEN TRADES REPORT ".center(len(table.splitlines()[0]), "="))
|
2023-06-25 15:09:57 +00:00
|
|
|
print(table)
|
|
|
|
|
2024-05-12 15:20:36 +00:00
|
|
|
if (enter_tags := results.get("results_per_enter_tag")) is not None:
|
2024-03-30 12:11:59 +00:00
|
|
|
table = text_table_tags("enter_tag", enter_tags, stake_currency)
|
2023-06-25 15:09:57 +00:00
|
|
|
|
|
|
|
if isinstance(table, str) and len(table) > 0:
|
2024-05-12 15:20:36 +00:00
|
|
|
print(" ENTER TAG STATS ".center(len(table.splitlines()[0]), "="))
|
2023-06-25 15:09:57 +00:00
|
|
|
print(table)
|
|
|
|
|
2024-05-12 15:20:36 +00:00
|
|
|
if (exit_reasons := results.get("exit_reason_summary")) is not None:
|
2024-03-30 12:11:59 +00:00
|
|
|
table = text_table_tags("exit_tag", exit_reasons, stake_currency)
|
|
|
|
|
2024-01-04 13:51:21 +00:00
|
|
|
if isinstance(table, str) and len(table) > 0:
|
2024-05-12 15:20:36 +00:00
|
|
|
print(" EXIT REASON STATS ".center(len(table.splitlines()[0]), "="))
|
2024-01-04 13:51:21 +00:00
|
|
|
print(table)
|
2023-06-25 15:09:57 +00:00
|
|
|
|
|
|
|
for period in backtest_breakdown:
|
2024-05-12 15:20:36 +00:00
|
|
|
if period in results.get("periodic_breakdown", {}):
|
|
|
|
days_breakdown_stats = results["periodic_breakdown"][period]
|
2023-06-25 15:09:57 +00:00
|
|
|
else:
|
|
|
|
days_breakdown_stats = generate_periodic_breakdown_stats(
|
2024-05-12 15:20:36 +00:00
|
|
|
trade_list=results["trades"], period=period
|
|
|
|
)
|
|
|
|
table = text_table_periodic_breakdown(
|
|
|
|
days_breakdown_stats=days_breakdown_stats, stake_currency=stake_currency, period=period
|
|
|
|
)
|
2023-06-25 15:09:57 +00:00
|
|
|
if isinstance(table, str) and len(table) > 0:
|
2024-05-12 15:20:36 +00:00
|
|
|
print(f" {period.upper()} BREAKDOWN ".center(len(table.splitlines()[0]), "="))
|
2023-06-25 15:09:57 +00:00
|
|
|
print(table)
|
|
|
|
|
|
|
|
table = text_table_add_metrics(results)
|
|
|
|
if isinstance(table, str) and len(table) > 0:
|
2024-05-12 15:20:36 +00:00
|
|
|
print(" SUMMARY METRICS ".center(len(table.splitlines()[0]), "="))
|
2023-06-25 15:09:57 +00:00
|
|
|
print(table)
|
|
|
|
|
|
|
|
if isinstance(table, str) and len(table) > 0:
|
2024-05-12 15:20:36 +00:00
|
|
|
print("=" * len(table.splitlines()[0]))
|
2023-06-25 15:09:57 +00:00
|
|
|
|
|
|
|
print()
|
|
|
|
|
|
|
|
|
2023-07-30 08:52:23 +00:00
|
|
|
def show_backtest_results(config: Config, backtest_stats: BacktestResultType):
|
2024-05-12 15:20:36 +00:00
|
|
|
stake_currency = config["stake_currency"]
|
2023-06-25 15:09:57 +00:00
|
|
|
|
2024-05-12 15:20:36 +00:00
|
|
|
for strategy, results in backtest_stats["strategy"].items():
|
2023-06-25 15:09:57 +00:00
|
|
|
show_backtest_result(
|
2024-05-12 15:20:36 +00:00
|
|
|
strategy, results, stake_currency, config.get("backtest_breakdown", [])
|
|
|
|
)
|
2023-06-25 15:09:57 +00:00
|
|
|
|
2024-05-12 15:20:36 +00:00
|
|
|
if len(backtest_stats["strategy"]) > 0:
|
2023-06-25 15:09:57 +00:00
|
|
|
# Print Strategy summary table
|
|
|
|
|
2024-05-12 15:20:36 +00:00
|
|
|
table = text_table_strategy(backtest_stats["strategy_comparison"], stake_currency)
|
|
|
|
print(
|
|
|
|
f"Backtested {results['backtest_start']} -> {results['backtest_end']} |"
|
|
|
|
f" Max open trades : {results['max_open_trades']}"
|
|
|
|
)
|
|
|
|
print(" STRATEGY SUMMARY ".center(len(table.splitlines()[0]), "="))
|
2023-06-25 15:09:57 +00:00
|
|
|
print(table)
|
2024-05-12 15:20:36 +00:00
|
|
|
print("=" * len(table.splitlines()[0]))
|
|
|
|
print("\nFor more details, please look at the detail tables above")
|
2023-06-25 15:09:57 +00:00
|
|
|
|
|
|
|
|
2023-07-30 08:52:23 +00:00
|
|
|
def show_sorted_pairlist(config: Config, backtest_stats: BacktestResultType):
|
2024-05-12 15:20:36 +00:00
|
|
|
if config.get("backtest_show_pair_list", False):
|
|
|
|
for strategy, results in backtest_stats["strategy"].items():
|
2023-06-25 15:09:57 +00:00
|
|
|
print(f"Pairs for Strategy {strategy}: \n[")
|
2024-05-12 15:20:36 +00:00
|
|
|
for result in results["results_per_pair"]:
|
|
|
|
if result["key"] != "TOTAL":
|
2023-06-25 15:09:57 +00:00
|
|
|
print(f'"{result["key"]}", // {result["profit_mean"]:.2%}')
|
|
|
|
print("]")
|
2023-06-25 15:45:01 +00:00
|
|
|
|
|
|
|
|
|
|
|
def generate_edge_table(results: dict) -> str:
|
2024-05-12 15:20:36 +00:00
|
|
|
floatfmt = ("s", ".10g", ".2f", ".2f", ".2f", ".2f", "d", "d", "d")
|
2023-06-25 15:45:01 +00:00
|
|
|
tabular_data = []
|
2024-05-12 15:20:36 +00:00
|
|
|
headers = [
|
|
|
|
"Pair",
|
|
|
|
"Stoploss",
|
|
|
|
"Win Rate",
|
|
|
|
"Risk Reward Ratio",
|
|
|
|
"Required Risk Reward",
|
|
|
|
"Expectancy",
|
|
|
|
"Total Number of Trades",
|
|
|
|
"Average Duration (min)",
|
|
|
|
]
|
2023-06-25 15:45:01 +00:00
|
|
|
|
|
|
|
for result in results.items():
|
|
|
|
if result[1].nb_trades > 0:
|
2024-05-12 15:20:36 +00:00
|
|
|
tabular_data.append(
|
|
|
|
[
|
|
|
|
result[0],
|
|
|
|
result[1].stoploss,
|
|
|
|
result[1].winrate,
|
|
|
|
result[1].risk_reward_ratio,
|
|
|
|
result[1].required_risk_reward,
|
|
|
|
result[1].expectancy,
|
|
|
|
result[1].nb_trades,
|
|
|
|
round(result[1].avg_trade_duration),
|
|
|
|
]
|
|
|
|
)
|
2023-06-25 15:45:01 +00:00
|
|
|
|
|
|
|
# Ignore type as floatfmt does allow tuples but mypy does not know that
|
2024-05-12 15:20:36 +00:00
|
|
|
return tabulate(
|
|
|
|
tabular_data, headers=headers, floatfmt=floatfmt, tablefmt="orgtbl", stralign="right"
|
|
|
|
)
|