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https://github.com/freqtrade/freqtrade.git
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chore: remove no longer used result formatting methods
This commit is contained in:
Matthias
@@ -5,10 +5,7 @@ from pathlib import Path
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from typing import Any, Dict, Iterator, List, Optional, Tuple
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from typing import Any, Dict, Iterator, List, Optional, Tuple
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import numpy as np
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import numpy as np
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import pandas as pd
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import rapidjson
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import rapidjson
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import tabulate
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from colorama import Fore, Style
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from pandas import isna, json_normalize
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from pandas import isna, json_normalize
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from freqtrade.constants import FTHYPT_FILEVERSION, Config
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from freqtrade.constants import FTHYPT_FILEVERSION, Config
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@@ -16,8 +13,6 @@ from freqtrade.enums import HyperoptState
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from freqtrade.exceptions import OperationalException
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from freqtrade.exceptions import OperationalException
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from freqtrade.misc import deep_merge_dicts, round_dict, safe_value_fallback2
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from freqtrade.misc import deep_merge_dicts, round_dict, safe_value_fallback2
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from freqtrade.optimize.hyperopt_epoch_filters import hyperopt_filter_epochs
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from freqtrade.optimize.hyperopt_epoch_filters import hyperopt_filter_epochs
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from freqtrade.optimize.optimize_reports import generate_wins_draws_losses
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from freqtrade.util import fmt_coin
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logger = logging.getLogger(__name__)
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logger = logging.getLogger(__name__)
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@@ -357,175 +352,6 @@ class HyperoptTools:
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+ f"Objective: {results['loss']:.5f}"
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+ f"Objective: {results['loss']:.5f}"
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)
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)
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@staticmethod
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def prepare_trials_columns(trials: pd.DataFrame) -> pd.DataFrame:
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trials["Best"] = ""
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if "results_metrics.winsdrawslosses" not in trials.columns:
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# Ensure compatibility with older versions of hyperopt results
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trials["results_metrics.winsdrawslosses"] = "N/A"
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has_account_drawdown = "results_metrics.max_drawdown_account" in trials.columns
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if not has_account_drawdown:
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# Ensure compatibility with older versions of hyperopt results
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trials["results_metrics.max_drawdown_account"] = None
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if "is_random" not in trials.columns:
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trials["is_random"] = False
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# New mode, using backtest result for metrics
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trials["results_metrics.winsdrawslosses"] = trials.apply(
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lambda x: generate_wins_draws_losses(
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x["results_metrics.wins"], x["results_metrics.draws"], x["results_metrics.losses"]
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),
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axis=1,
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)
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trials = trials[
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[
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"Best",
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"current_epoch",
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"results_metrics.total_trades",
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"results_metrics.winsdrawslosses",
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"results_metrics.profit_mean",
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"results_metrics.profit_total_abs",
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"results_metrics.profit_total",
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"results_metrics.holding_avg",
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"results_metrics.max_drawdown_account",
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"results_metrics.max_drawdown_abs",
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"loss",
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"is_initial_point",
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"is_random",
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"is_best",
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]
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]
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trials.columns = [
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"Best",
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"Epoch",
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"Trades",
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" Win Draw Loss Win%",
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"Avg profit",
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"Total profit",
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"Profit",
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"Avg duration",
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"max_drawdown_account",
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"max_drawdown_abs",
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"Objective",
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"is_initial_point",
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"is_random",
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"is_best",
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]
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return trials
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@staticmethod
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def get_result_table(
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config: Config,
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results: list,
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total_epochs: int,
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highlight_best: bool,
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print_colorized: bool,
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remove_header: int,
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) -> str:
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"""
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Log result table
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"""
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if not results:
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return ""
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tabulate.PRESERVE_WHITESPACE = True
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trials = json_normalize(results, max_level=1)
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trials = HyperoptTools.prepare_trials_columns(trials)
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trials["is_profit"] = False
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trials.loc[trials["is_initial_point"] | trials["is_random"], "Best"] = "* "
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trials.loc[trials["is_best"], "Best"] = "Best"
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trials.loc[
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(trials["is_initial_point"] | trials["is_random"]) & trials["is_best"], "Best"
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] = "* Best"
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trials.loc[trials["Total profit"] > 0, "is_profit"] = True
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trials["Trades"] = trials["Trades"].astype(str)
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# perc_multi = 1 if legacy_mode else 100
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trials["Epoch"] = trials["Epoch"].apply(
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lambda x: "{}/{}".format(str(x).rjust(len(str(total_epochs)), " "), total_epochs)
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)
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trials["Avg profit"] = trials["Avg profit"].apply(
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lambda x: f"{x:,.2%}".rjust(7, " ") if not isna(x) else "--".rjust(7, " ")
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)
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trials["Avg duration"] = trials["Avg duration"].apply(
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lambda x: (
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f"{x:,.1f} m".rjust(7, " ")
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if isinstance(x, float)
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else f"{x}"
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if not isna(x)
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else "--".rjust(7, " ")
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)
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)
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trials["Objective"] = trials["Objective"].apply(
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lambda x: f"{x:,.5f}".rjust(8, " ") if x != 100000 else "N/A".rjust(8, " ")
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)
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stake_currency = config["stake_currency"]
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trials["Max Drawdown (Acct)"] = trials.apply(
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lambda x: (
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"{} {}".format(
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fmt_coin(x["max_drawdown_abs"], stake_currency, keep_trailing_zeros=True),
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(f"({x['max_drawdown_account']:,.2%})").rjust(10, " "),
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).rjust(25 + len(stake_currency))
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if x["max_drawdown_account"] != 0.0
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else "--".rjust(25 + len(stake_currency))
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),
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axis=1,
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)
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trials = trials.drop(columns=["max_drawdown_abs", "max_drawdown_account"])
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trials["Profit"] = trials.apply(
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lambda x: (
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"{} {}".format(
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fmt_coin(x["Total profit"], stake_currency, keep_trailing_zeros=True),
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f"({x['Profit']:,.2%})".rjust(10, " "),
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).rjust(25 + len(stake_currency))
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if x["Total profit"] != 0.0
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else "--".rjust(25 + len(stake_currency))
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),
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axis=1,
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)
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trials = trials.drop(columns=["Total profit"])
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if print_colorized:
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trials2 = trials.astype(str)
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for i in range(len(trials)):
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if trials.loc[i]["is_profit"]:
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for j in range(len(trials.loc[i]) - 3):
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trials2.iat[i, j] = f"{Fore.GREEN}{str(trials.iloc[i, j])}{Fore.RESET}"
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if trials.loc[i]["is_best"] and highlight_best:
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for j in range(len(trials.loc[i]) - 3):
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trials2.iat[i, j] = (
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f"{Style.BRIGHT}{str(trials.iloc[i, j])}{Style.RESET_ALL}"
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)
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trials = trials2
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del trials2
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trials = trials.drop(columns=["is_initial_point", "is_best", "is_profit", "is_random"])
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if remove_header > 0:
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table = tabulate.tabulate(
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trials.to_dict(orient="list"), tablefmt="orgtbl", headers="keys", stralign="right"
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)
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table = table.split("\n", remove_header)[remove_header]
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elif remove_header < 0:
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table = tabulate.tabulate(
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trials.to_dict(orient="list"), tablefmt="psql", headers="keys", stralign="right"
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)
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table = "\n".join(table.split("\n")[0:remove_header])
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else:
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table = tabulate.tabulate(
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trials.to_dict(orient="list"), tablefmt="psql", headers="keys", stralign="right"
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)
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return table
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@staticmethod
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@staticmethod
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def export_csv_file(config: Config, results: list, csv_file: str) -> None:
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def export_csv_file(config: Config, results: list, csv_file: str) -> None:
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"""
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"""
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