From 3a6c6c5852a78348100708f42afc4f49a17a57c1 Mon Sep 17 00:00:00 2001
From: "deepsource-autofix[bot]"
 <62050782+deepsource-autofix[bot]@users.noreply.github.com>
Date: Sat, 11 May 2024 13:41:49 +0000
Subject: [PATCH] style: format code with Autopep8, Black, ClangFormat,
 dotnet-format, Go fmt, Gofumpt, Google Java Format, isort, Ktlint, PHP CS
 Fixer, Prettier, RuboCop, Ruff Formatter, Rustfmt, Scalafmt, StandardJS,
 StandardRB, swift-format and Yapf

This commit fixes the style issues introduced in e5ad210 according to the output
from Autopep8, Black, ClangFormat, dotnet-format, Go fmt, Gofumpt, Google Java
Format, isort, Ktlint, PHP CS Fixer, Prettier, RuboCop, Ruff Formatter, Rustfmt,
Scalafmt, StandardJS, StandardRB, swift-format and Yapf.

Details: None
---
 .../market_conditions_analyzer.py                    | 12 +++++++++---
 1 file changed, 9 insertions(+), 3 deletions(-)

diff --git a/market_conditions_analysis/market_conditions_analyzer.py b/market_conditions_analysis/market_conditions_analyzer.py
index 9b7b7b297..645732d63 100644
--- a/market_conditions_analysis/market_conditions_analyzer.py
+++ b/market_conditions_analysis/market_conditions_analyzer.py
@@ -3,13 +3,19 @@ def __init__(self, data_preparation, time_series_analysis):
         self.data_preparation = data_preparation
         self.time_series_analysis = time_series_analysis
 
-    def analyze_market_conditions(self, data_file, target_column, exogenous_variables, num_periods):
+    def analyze_market_conditions(
+        self, data_file, target_column, exogenous_variables, num_periods
+    ):
         """
         Analyzes market conditions using the time series analysis model.
         """
         data = self.data_preparation.prepare_data(data_file)
 
-        model_fit = self.time_series_analysis.fit_model(data, target_column, exogenous_variables)
-        market_trends = self.time_series_analysis.predict_market_trends(data, target_column, exogenous_variables, num_periods)
+        model_fit = self.time_series_analysis.fit_model(
+            data, target_column, exogenous_variables
+        )
+        market_trends = self.time_series_analysis.predict_market_trends(
+            data, target_column, exogenous_variables, num_periods
+        )
 
         return market_trends