From 3ca7407b09247009608f54fe03f9bafb7d4840ef Mon Sep 17 00:00:00 2001 From: Matthias Date: Sun, 27 Apr 2025 11:05:51 +0200 Subject: [PATCH] refactor: improve variable naming --- freqtrade/data/btanalysis.py | 16 +++++++++------- 1 file changed, 9 insertions(+), 7 deletions(-) diff --git a/freqtrade/data/btanalysis.py b/freqtrade/data/btanalysis.py index 1d580a7dd..b79c12959 100644 --- a/freqtrade/data/btanalysis.py +++ b/freqtrade/data/btanalysis.py @@ -491,11 +491,12 @@ def load_exit_signal_candles(backtest_dir: Path) -> dict[str, dict[str, pd.DataF return load_backtest_analysis_data(backtest_dir, "exited") -def analyze_trade_parallelism(results: pd.DataFrame, timeframe: str) -> pd.DataFrame: +def analyze_trade_parallelism(trades: pd.DataFrame, timeframe: str) -> pd.DataFrame: """ Find overlapping trades by expanding each trade once per period it was open and then counting overlaps. - :param results: Results Dataframe - can be loaded + :param trades: Trades Dataframe - can be loaded from backtest, or created + via trade_list_to_dataframe :param timeframe: Timeframe used for backtest :return: dataframe with open-counts per time-period in timeframe """ @@ -512,11 +513,11 @@ def analyze_trade_parallelism(results: pd.DataFrame, timeframe: str) -> pd.DataF inclusive="left", ) ) - for row in results[["open_date", "close_date"]].iterrows() + for row in trades[["open_date", "close_date"]].iterrows() ] deltas = [len(x) for x in dates] dates = pd.Series(pd.concat(dates).values, name="date") - df2 = pd.DataFrame(np.repeat(results.values, deltas, axis=0), columns=results.columns) + df2 = pd.DataFrame(np.repeat(trades.values, deltas, axis=0), columns=trades.columns) df2 = pd.concat([dates, df2], axis=1) df2 = df2.set_index("date") @@ -526,17 +527,18 @@ def analyze_trade_parallelism(results: pd.DataFrame, timeframe: str) -> pd.DataF def evaluate_result_multi( - results: pd.DataFrame, timeframe: str, max_open_trades: IntOrInf + trades: pd.DataFrame, timeframe: str, max_open_trades: IntOrInf ) -> pd.DataFrame: """ Find overlapping trades by expanding each trade once per period it was open and then counting overlaps - :param results: Results Dataframe - can be loaded + :param trades: Trades Dataframe - can be loaded from backtest, or created + via trade_list_to_dataframe :param timeframe: Frequency used for the backtest :param max_open_trades: parameter max_open_trades used during backtest run :return: dataframe with open-counts per time-period in freq """ - df_final = analyze_trade_parallelism(results, timeframe) + df_final = analyze_trade_parallelism(trades, timeframe) return df_final[df_final["open_trades"] > max_open_trades]