Update app.py

app.py

@@ -27,7 +27,7 @@ default_tickers = ['BTC-USD', 'ETH-USD', 'BNB-USD', 'JPM', 'BAC', 'WFC', 'C']
 # Function to load adjusted close price data for a given ticker
 def load_ticker_ts_df(ticker, start, end):
     data = yf.download(ticker, start=start, end=end)
-    return data['Adj Close']
+    return data['Close']
     
 # Function to calculate cross-correlation at different lags
 def cross_correlation(series1, series2, lag):
@@ -272,14 +272,11 @@ if page == 'Pairs Trading Analysis':
             **ADF Test:**
             - The Augmented Dickey-Fuller (ADF) test checks whether a time series has a unit root, i.e., whether it is non-stationary.
             - If the p-value is less than 0.05, we reject the null hypothesis that the series has a unit root, indicating that the series is stationary.
-
             **Johansen Cointegration Test:**
             - The Johansen test is used to determine the number of cointegrating relationships among multiple time series.
             - If the test statistic is greater than the critical value, we reject the null hypothesis that there is no cointegration.
-
             **VECM (Vector Error Correction Model):**
             - A VECM is a special form of a VAR (Vector Autoregression) model used for cointegrated series. It corrects for disequilibrium in the short run while keeping the long-term relationship intact.
-
             **Z-Score Trading Strategy:**
             - Z-scores measure how many standard deviations an element is from the mean. In pairs trading, z-scores are used to identify overbought or oversold conditions, triggering buy or sell signals.
             """)