From 0fd8e5340d3804f4b8bdd1bfaa5cb33e8b926c61 Mon Sep 17 00:00:00 2001 From: =?utf8?q?J=C3=A9r=C3=B4me=20Benoit?= Date: Wed, 2 Apr 2025 22:04:09 +0200 Subject: [PATCH] refactor(qav3): refine typing MIME-Version: 1.0 Content-Type: text/plain; charset=utf8 Content-Transfer-Encoding: 8bit Signed-off-by: Jérôme Benoit --- .../user_data/freqaimodels/QuickAdapterRegressorV3.py | 4 +++- quickadapter/user_data/strategies/QuickAdapterV3.py | 4 ++-- 2 files changed, 5 insertions(+), 3 deletions(-) diff --git a/quickadapter/user_data/freqaimodels/QuickAdapterRegressorV3.py b/quickadapter/user_data/freqaimodels/QuickAdapterRegressorV3.py index f5948e7..fbea95e 100644 --- a/quickadapter/user_data/freqaimodels/QuickAdapterRegressorV3.py +++ b/quickadapter/user_data/freqaimodels/QuickAdapterRegressorV3.py @@ -302,7 +302,9 @@ class QuickAdapterRegressorV3(BaseRegressionModel): prediction_thresholds_smoothing = self.freqai_info.get( "prediction_thresholds_smoothing", "quantile" ) - smoothing_methods: dict[str, Callable] = { + smoothing_methods: dict[ + str, Callable[[pd.DataFrame, int, int], tuple[pd.Series, pd.Series]] + ] = { "quantile": self.quantile_min_max_pred, "mean": QuickAdapterRegressorV3.mean_min_max_pred, "median": QuickAdapterRegressorV3.median_min_max_pred, diff --git a/quickadapter/user_data/strategies/QuickAdapterV3.py b/quickadapter/user_data/strategies/QuickAdapterV3.py index 8cad3a9..9f62adf 100644 --- a/quickadapter/user_data/strategies/QuickAdapterV3.py +++ b/quickadapter/user_data/strategies/QuickAdapterV3.py @@ -7,7 +7,7 @@ from pathlib import Path from statistics import harmonic_mean import talib.abstract as ta from pandas import DataFrame, Series, isna -from typing import Callable, Optional +from typing import Optional from freqtrade.exchange import timeframe_to_minutes, timeframe_to_prev_date from freqtrade.strategy.interface import IStrategy from freqtrade.strategy import stoploss_from_absolute @@ -654,7 +654,7 @@ class QuickAdapterV3(IStrategy): std = derive_gaussian_std_from_window(window) gaussian_window = get_gaussian_window(std, True) odd_window = get_odd_window(window) - smoothing_methods: dict[str, Callable] = { + smoothing_methods: dict[str, Series] = { "gaussian": series.rolling( window=gaussian_window, win_type="gaussian", -- 2.43.0