Update app.py

app.py

@@ -7,8 +7,9 @@ import talib
 from pandas import Series
 from numpy import average as npAverage, nan as npNaN, log as npLog, power as npPower, sqrt as npSqrt, zeros_like as npZeroslike
 from pandas_ta.utils import get_offset, verify_series
-from datetime import datetime, timedelta
+from datetime import datetime
 from matplotlib.dates import date2num
+from datetime import datetime
 
 st.set_page_config(layout="wide")
 
@@ -32,14 +33,10 @@ with st.sidebar.expander("How to Use:", expanded=False):
     - **Run Analysis**: Click 'Run' to apply and visualize.
     """)
 
-# Function to fetch data with yfinance adjustments
+# Function to fetch data
 @st.cache_data
 def get_data(ticker, start_date, end_date):
-    data = yf.download(ticker, start=start_date, end=end_date, auto_adjust=False)
-    if isinstance(data.columns, pd.MultiIndex):
-        data.columns = data.columns.get_level_values(0)
-    if data.empty:
-        raise ValueError(f"No data fetched for {ticker} from {start_date} to {end_date}.")
+    data = yf.download(ticker, start=start_date, end=end_date)
     return data
 
 # Function to create the base plot with the stock price
@@ -409,6 +406,8 @@ def calculate_mcginley_dynamic(prices, n):
     return MD
 
 # Function to calculate Anchored Moving Average (AMA)
+from datetime import datetime
+
 def calculate_AMA(prices, anchor_date, data):
     # Ensure the anchor_date is a pandas Timestamp
     anchor_date = pd.to_datetime(anchor_date)
@@ -481,6 +480,8 @@ if 'data' in st.session_state:
     # Moving average method selection
     #st.sidebar.header("Moving Average Methods")
 
+
+
     # SMA with tooltip
     with st.sidebar.expander("Simple Moving Average", expanded=False):
         use_sma = st.checkbox(
@@ -1078,6 +1079,21 @@ if 'data' in st.session_state:
             help="Specify the period (in days) for the FMA."
         )    
 
+
+    # Anchored Moving Average (AMA) with tooltip
+    #use_ama = st.sidebar.checkbox(
+    #    'Anchored Moving Average (AMA)', 
+    #    value=st.session_state.get('use_ama', False),
+    #    help="Select to apply Anchored Moving Average (AMA) to the stock price."
+    #)
+    #ama_anchor_date = st.sidebar.date_input(
+    #    'AMA Anchor Date',
+    #    value=pd.to_datetime(st.session_state.get('ama_anchor_date', '2021-01-01')),
+    #    disabled=not use_ama,
+    #    help="Select the anchor date for the AMA."
+    #)
+
+
     # Grid toggle with tooltip
     show_grid = st.sidebar.checkbox(
         "Show Grid", 
@@ -1160,8 +1176,11 @@ if 'data' in st.session_state:
         st.session_state['use_cma'] = use_cma
         st.session_state['use_mcginley_dynamic'] = use_mcginley_dynamic
         st.session_state['mcginley_dynamic_period'] = mcginley_dynamic_period
+        #st.session_state['use_ama'] = use_ama
+        #st.session_state['ama_anchor_date'] = ama_anchor_date
         st.session_state['use_fma'] = use_fma
         st.session_state['fma_period'] = fma_period
+        # (Save all previous moving average settings here)
         
         # Start with the base price plot
         fig = go.Figure(data=st.session_state['price_plot'].data)
@@ -1186,6 +1205,12 @@ if 'data' in st.session_state:
             st.session_state['McGinley_Dynamic'] = calculate_mcginley_dynamic(data['Close'].tolist(), mcginley_dynamic_period)
             fig.add_trace(go.Scatter(x=data.index, y=st.session_state['McGinley_Dynamic'], mode='lines', name=f'McGinley Dynamic (n={mcginley_dynamic_period})', line=dict(dash='dash', color='orange')))
 
+        # Add AMA if selected
+        #if use_ama:
+        #    st.session_state['AMA'] = calculate_AMA(data['Close'].tolist(), ama_anchor_date, data)
+        #    fig.add_trace(go.Scatter(x=data.index, y=st.session_state['AMA'], mode='lines', name=f'Anchored MA (Anchor Date={ama_anchor_date})', line=dict(dash='dash', color='red')))
+        #    fig.add_shape(type="line", x0=ama_anchor_date, y0=data['Close'].min(), x1=ama_anchor_date, y1=data['Close'].max(), line=dict(color="blue", width=2, dash="dash"))
+
         # Add FMA if selected
         if use_fma:
             st.session_state['FMA'] = filtered_moving_average(data['Close'].values, fma_period)
@@ -1360,6 +1385,7 @@ if 'data' in st.session_state:
             st.session_state['PWMA'] = np.concatenate([np.array([np.nan]*(pwma_period-1)), pwma_values])
             fig.add_trace(go.Scatter(x=data.index, y=st.session_state['PWMA'], mode='lines', name=f'PWMA (n={pwma_period})', line=dict(dash='dash', color='red')))
 
+
         # Update layout with grid toggle
         fig.update_layout(
             title=f'{ticker_symbol} Stock Price with Moving Averages',
@@ -1378,6 +1404,9 @@ if 'data' in st.session_state:
 if 'current_fig' in st.session_state:
     st.plotly_chart(st.session_state['current_fig'], use_container_width=True)
 
+
+
+
 st.markdown(
     """
     <style>
@@ -1390,10 +1419,11 @@ st.markdown(
     unsafe_allow_html=True
 )
 
+
 hide_streamlit_style = """
 <style>
 #MainMenu {visibility: hidden;}
 footer {visibility: hidden;}
 </style>
 """
-st.markdown(hide_streamlit_style, unsafe_allow_html=True)
+st.markdown(hide_streamlit_style, unsafe_allow_html=True)