Update app.py

app.py

@@ -13,7 +13,14 @@ st.set_page_config(page_title="Identifying Key Support and Resistance In Price L
 st.title('Key Support and Resistance In Price Levels')
 
 st.markdown("""
-This tool aims to identify key support and resistance price levels in stocks using various algorithmic methods. Select the method from the dropdown menu to view the corresponding analysis.
+This tool aims to identify key support and resistance price levels in stocks using various algorithmic methods. Each method is detailed below:
+1. **Pivot Points**: Short-term trend indicators used to determine potential support and resistance levels based on the high, low, and close prices of previous trading sessions.
+2. **Support and Resistance Levels using Rolling Midpoint Range**: Key price points where the stock's price tends to halt its upward or downward trajectory, identified using a rolling window to calculate dynamic support and resistance levels.
+3. **Swing Highs and Lows**: Local maxima and minima used to identify trends and potential reversal points by pinpointing key inflection points on a stock's chart.
+4. **Fibonacci Retracement Levels**: Horizontal lines indicating potential support and resistance levels based on Fibonacci numbers, helping to identify prospective market reversal points.
+5. **Trendlines**: Straight lines drawn to connect two or more price points, helping identify the market trend direction and potential areas of support and resistance.
+6. **Volume Profile**: A charting tool that shows the amount of volume traded at different price levels over a specified period, helping identify areas of high trading activity which can act as support or resistance.
+7. **KMeans Clustering**: A machine learning algorithm used to partition the dataset into clusters, identifying patterns and grouping similar price movements together to highlight significant price levels.
 """)
 
 # Sidebar for input parameters
@@ -45,8 +52,8 @@ with st.sidebar:
         n_days = st.slider('Lookback Period for Volume Profile and KMeans (Days)', min_value=30, max_value=365, value=60, help="Set the number of days for calculating volume profile and KMeans clustering.")
         num_clusters = st.slider('Number of Clusters for KMeans', min_value=2, max_value=10, value=3, help="Set the number of clusters for KMeans analysis.")
 
-# Select analysis type
-analysis_type = st.selectbox("Select Analysis", ["Pivot Points", "Support and Resistance", "Swing Highs and Lows", "Fibonacci Retracement Levels", "Trendlines", "Volume Profile", "KMeans Clustering"])
+    # Select the page (analysis type)
+    analysis_type = st.radio("Select Analysis", ["Pivot Points", "Support and Resistance", "Swing Highs and Lows", "Fibonacci Retracement Levels", "Trendlines", "Volume Profile", "KMeans Clustering"])
 
 # Function to fetch and cache data
 @st.cache_data
@@ -56,6 +63,54 @@ def get_data(ticker, start_date, end_date):
         st.error("No data found for the given ticker and date range.")
     return data
 
+# Functions for different analyses
+def calculate_pivot_points(df, window):
+    df['Pivot'] = df['Close'].rolling(window=window).mean()
+    df['R1'] = 2 * df['Pivot'] - df['Low'].rolling(window=window).min()
+    df['S1'] = 2 * df['Pivot'] - df['High'].rolling(window=window).max()
+    df['R2'] = df['Pivot'] + (df['High'].rolling(window=window).max() - df['Low'].rolling(window=window).min())
+    df['S2'] = df['Pivot'] - (df['High'].rolling(window=window).max() - df['Low'].rolling(window=window).min())
+    return df
+
+def find_levels(data, window):
+    resistance = data['High'].rolling(window=window).max()
+    support = data['Low'].rolling(window=window).min()
+    return support, resistance
+
+def check_significant_break(data, support, resistance):
+    breaks_above_resistance = (data['Close'] > resistance.shift(1)) & (data['Volume'] > data['Volume'].rolling(window=30).mean())
+    breaks_below_support = (data['Close'] < support.shift(1)) & (data['Volume'] > data['Volume'].rolling(window=30).mean())
+    return breaks_above_resistance, breaks_below_support
+
+def prepare_data_for_trendlines(data, lookback_period):
+    data['Swing_High'] = data['High'][argrelextrema(data['High'].values, np.greater_equal, order=lookback_period)[0]]
+    data['Swing_Low'] = data['Low'][argrelextrema(data['Low'].values, np.less_equal, order=lookback_period)[0]]
+    return data
+
+def calculate_fibonacci_levels(data, lookback_period):
+    high_prices = data["High"].rolling(window=lookback_period).max()
+    low_prices = data["Low"].rolling(window=lookback_period).min()
+    price_diff = high_prices - low_prices
+    levels = np.array([0, 0.236, 0.382, 0.5, 0.618, 0.786, 1])
+    fib_levels = low_prices.values.reshape(-1, 1) + price_diff.values.reshape(-1, 1) * levels
+    return fib_levels, levels
+
+def calculate_volume_profile(data, n_days):
+    filtered_data = data[-n_days:]
+    price_bins = np.linspace(filtered_data['Low'].min(), filtered_data['High'].max(), 100)
+    volume_profile = [filtered_data['Volume'][(filtered_data['Close'] > price_bins[i]) & (filtered_data['Close'] <= price_bins[i+1])].sum() for i in range(len(price_bins)-1)]
+    return price_bins, volume_profile
+
+def calculate_kmeans_clusters(data, n_days, num_clusters):
+    filtered_data = data[-n_days:]
+    X_time = np.linspace(0, 1, len(filtered_data)).reshape(-1, 1)
+    X_price = (filtered_data['Close'].values - np.min(filtered_data['Close'])) / (np.max(filtered_data['Close']) - np.min(filtered_data['Close']))
+    X_cluster = np.column_stack((X_time, X_price))
+    kmeans = KMeans(n_clusters=num_clusters)
+    kmeans.fit(X_cluster)
+    cluster_centers = kmeans.cluster_centers_[:, 1] * (np.max(filtered_data['Close']) - np.min(filtered_data['Close'])) + np.min(filtered_data['Close'])
+    return cluster_centers
+
 # Run analysis only if the user clicks the "Run Analysis" button
 if st.sidebar.button('Run Analysis'):
     data = get_data(ticker, start_date, end_date)
@@ -133,7 +188,7 @@ if st.sidebar.button('Run Analysis'):
             fig3 = go.Figure()
             fig3.add_trace(go.Scatter(x=data_with_trendlines.index, y=data_with_trendlines['Close'], mode='lines', name='Close Price'))
             fig3.add_trace(go.Scatter(x=data_with_trendlines.index, y=data_with_trendlines['Swing_High'], mode='markers', name='Swing Highs', marker=dict(color='red')))
-            fig3.add_trace(go.Scatter(x=data_with_trendlines.index, y=data_with_trendlines['Swing_Low'], mode='markers', name='Swing Lows', marker=dict(color='green')))
+            fig3.add_trace(go.Scatter(x=data_with_trendlines.index, y=data_with_trendlines['Swing_Low'], mode='markers', name='Swing Lows', marker.dict(color='green')))
             fig3.update_layout(
                 title=f'{ticker} with Swing Highs & Lows',
                 xaxis_title='Date',
@@ -157,7 +212,7 @@ if st.sidebar.button('Run Analysis'):
             fig4.add_trace(go.Scatter(x=data.index, y=data['Close'], mode='lines', name='Stock Price'))
             color_list = ['red', 'orange', 'yellow', 'green', 'blue', 'indigo', 'violet']
             for i, (level, color) in enumerate(zip(levels, color_list)):
-                fig4.add_trace(go.Scatter(x=data.index, y=fib_levels[:, i], mode='lines', name=f'Fib {level:.3f}', line=dict(color=color)))
+                fig4.add_trace(go.Scatter(x=data.index, y=fib_levels[:, i], mode='lines', name=f'Fib {level:.3f}', line.dict(color=color)))
             fig4.update_layout(
                 title=f'{ticker} with Fibonacci Retracement Levels',
                 xaxis_title='Date',
@@ -192,8 +247,8 @@ if st.sidebar.button('Run Analysis'):
                 lower_m, lower_b = np.polyfit(swing_lows.index.map(pd.Timestamp.toordinal), swing_lows.values, 1)
                 data_with_trendlines['Upper_Trendline'] = upper_m * data_with_trendlines.index.map(pd.Timestamp.toordinal) + upper_b
                 data_with_trendlines['Lower_Trendline'] = lower_m * data_with_trendlines.index.map(pd.Timestamp.toordinal) + lower_b
-                fig5.add_trace(go.Scatter(x=data_with_trendlines.index, y=data_with_trendlines['Upper_Trendline'], mode='lines', name='Upper Trendline', line=dict(color='orange')))
-                fig5.add_trace(go.Scatter(x=data_with_trendlines.index, y=data_with_trendlines['Lower_Trendline'], mode='lines', name='Lower Trendline', line=dict(color='blue')))
+                fig5.add_trace(go.Scatter(x=data_with_trendlines.index, y=data_with_trendlines['Upper_Trendline'], mode='lines', name='Upper Trendline', line.dict(color='orange')))
+                fig5.add_trace(go.Scatter(x=data_with_trendlines.index, y=data_with_trendlines['Lower_Trendline'], mode='lines', name='Lower Trendline', line.dict(color='blue')))
             
             fig5.update_layout(
                 title=f'{ticker} with Trendlines',
@@ -254,7 +309,7 @@ if st.sidebar.button('Run Analysis'):
                 fig7.add_trace(go.Scatter(x=[data.index[-1]], y=[center], mode='markers+text', name=f'Cluster Center: {center:.2f}', text=[f'{center:.2f}'], textposition='top center'))
                 fig7.add_shape(type="line",
                                x0=data.index[0], x1=data.index[-1], y0=center, y1=center,
-                               line=dict(color='Red', dash="dash"))
+                               line.dict(color='Red', dash="dash"))
             fig7.update_layout(
                 title=f'{ticker} with KMeans Clustering (Last {n_days} Days)',
                 xaxis_title='Date',
@@ -264,6 +319,7 @@ if st.sidebar.button('Run Analysis'):
                 height=600
             )
             st.plotly_chart(fig7, use_container_width=True)
+
     else:
         st.write("No data found for the given ticker and date range.")