Update app.py

app.py

@@ -141,7 +141,7 @@ def run_dtw():
 
         fig1 = go.Figure()
         # Plot the entire stock price data
-        fig1.add_trace(go.Scatter(x=st.session_state.price_data.index, y=st.session_state.price_data, mode='lines', name='Overall stock price', line=dict(color='blue')))
+        fig1.add_trace(go.Scatter(x=st.session_state.price_data.index, y=st.session_state.price_data, mode='lines', name='stock price', line=dict(color='blue')))
         colors = ['red', 'green', 'orange']
         for i, (_, start_index) in enumerate(min_distances[-1]):
             # Plot the pattern period
@@ -163,7 +163,7 @@ def run_dtw():
             past_window = st.session_state.price_data_pct_change[start_index:start_index + n_days + subsequent_days]
             reindexed_past_window = (past_window + 1).cumprod() * 100
             fig2.add_trace(go.Scatter(x=list(range(n_days + subsequent_days)), y=reindexed_past_window,
-                                      mode='lines', name=f"Past window {i + 1} (with subsequent {subsequent_days} days)",
+                                      mode='lines', name=f"Past window {i + 1},
                                       line=dict(color=colors[i % len(colors)], width=3 if i == 0 else 1)))
 
         fig2.add_trace(go.Scatter(x=list(range(n_days)), y=reindexed_current_window, mode='lines',
@@ -186,7 +186,7 @@ def run_corr():
 
         fig1 = go.Figure()
         # Plot the entire stock price data
-        fig1.add_trace(go.Scatter(x=st.session_state.price_data.index, y=st.session_state.price_data, mode='lines', name='Overall stock price', line=dict(color='blue')))
+        fig1.add_trace(go.Scatter(x=st.session_state.price_data.index, y=st.session_state.price_data, mode='lines', name='stock price', line=dict(color='blue')))
         colors = ['red', 'green', 'orange']
         for i, (_, start_index) in enumerate(max_correlations[-1]):
             # Plot the previous period
@@ -208,7 +208,7 @@ def run_corr():
             past_window = st.session_state.price_data_pct_change[start_index:start_index + n_days + subsequent_days]
             reindexed_past_window = (past_window + 1).cumprod() * 100
             fig2.add_trace(go.Scatter(x=list(range(pre_days, pre_days + n_days + subsequent_days)), y=reindexed_past_window,
-                                      mode='lines', name=f"Past window {i + 1} (with subsequent {subsequent_days} days)",
+                                      mode='lines', name=f"Past window {i + 1}",
                                       line=dict(color=colors[i % len(colors)], width=3 if i == 0 else 1)))
 
         fig2.add_trace(go.Scatter(x=list(range(pre_days, pre_days + n_days)), y=reindexed_current_window, mode='lines',
@@ -238,7 +238,7 @@ def run_ta_dtw():
 
         fig1 = go.Figure()
         # Plot the entire stock price data
-        fig1.add_trace(go.Scatter(x=st.session_state.data.index, y=st.session_state.data['Close'], mode='lines', name='Overall stock price', line=dict(color='blue')))
+        fig1.add_trace(go.Scatter(x=st.session_state.data.index, y=st.session_state.data['Close'], mode='lines', name='stock price', line=dict(color='blue')))
         colors = ['red', 'green', 'orange']
         for i, start_index in enumerate(min_distance_indices[-1]):
             # Plot the pattern period
@@ -260,7 +260,7 @@ def run_ta_dtw():
             past_window = st.session_state.price_data_pct_change[start_index:start_index + n_days + subsequent_days]
             reindexed_past_window = (past_window + 1).cumprod() * 100
             fig2.add_trace(go.Scatter(x=list(range(n_days + subsequent_days)), y=reindexed_past_window,
-                                      mode='lines', name=f"Past window {i + 1} (with subsequent {subsequent_days} days)",
+                                      mode='lines', name=f"Past window {i + 1}",
                                       line=dict(color=colors[i % len(colors)], width=3 if i == 0 else 1)))
 
         fig2.add_trace(go.Scatter(x=list(range(n_days)), y=reindexed_current_window, mode='lines',