beautified stock analysis app

.gitignore

@@ -0,0 +1,3 @@
+__pycache__/
+app_local.py
+app_localBackup.py

app.py

@@ -5,16 +5,17 @@ import plotly.graph_objs as go
 import numpy as np
 from plotly.subplots import make_subplots
 import os
+from datetime import date, timedelta
 
 from langchain_openai import ChatOpenAI
 
 
-isPswdValid = False # Set to True to temporarily disable password checking
-OPEN_ROUTER_KEY = st.secrets["OPEN_ROUTER_KEY"]
-OPEN_ROUTER_MODEL = "meta-llama/llama-3.1-70b-instruct:free"
+isPswdValid = False # Set to True to temporarily disable password checking. For production set to False.
+OPEN_ROUTER_KEY = st.secrets["OPEN_ROUTER_KEY"] # st.secrets["OPEN_ROUTER_KEY"]
+OPEN_ROUTER_MODEL = "meta-llama/llama-3.3-70b-instruct:free"
 
 try:
-    pswdVal = st.experimental_get_query_params()['pwd'][0]
+    pswdVal = st.query_params()['pwd'][0]
     if pswdVal==st.secrets["PSWD"]:
         isPswdValid = True
 except:
@@ -27,13 +28,92 @@ else:
     llm = ChatOpenAI(model=OPEN_ROUTER_MODEL, temperature=0.1, openai_api_key=OPEN_ROUTER_KEY, openai_api_base="https://openrouter.ai/api/v1")
 
     # Set the Streamlit app title and icon
-    st.set_page_config(page_title="Stock Analysis", page_icon="📈")
+    st.set_page_config(page_title="Stock Analysis", page_icon="📈", layout="wide", initial_sidebar_state="expanded")
+
+    # Global styling for a cleaner, modern look
+    st.markdown(
+        """
+        <style>
+        @import url('https://fonts.googleapis.com/css2?family=Space+Grotesk:wght@400;500;600;700&display=swap');
+        :root {
+            --bg: #0b1220;
+            --card: rgba(255,255,255,0.03);
+            --border: rgba(255,255,255,0.08);
+            --text: #e8edf7;
+            --muted: #a5b4d4;
+            --accent: #6dd6ff;
+            --accent-2: #7cf0c6;
+        }
+        .stApp {
+            background:
+                radial-gradient(circle at 10% 20%, rgba(80, 160, 255, 0.18), transparent 25%),
+                radial-gradient(circle at 85% 10%, rgba(90, 223, 197, 0.15), transparent 22%),
+                radial-gradient(circle at 50% 90%, rgba(255, 255, 255, 0.05), transparent 30%),
+                var(--bg);
+            color: var(--text);
+            font-family: 'Space Grotesk', sans-serif;
+        }
+        div.block-container {
+            padding-top: 2rem;
+            padding-bottom: 2rem;
+            max-width: 1200px;
+        }
+        div[data-testid="stSidebar"] {
+            background: #0f172a;
+            border-right: 1px solid rgba(255,255,255,0.05);
+        }
+        .hero-card {
+            background: linear-gradient(135deg, rgba(71, 120, 210, 0.75), rgba(17, 39, 83, 0.9));
+            border: 1px solid rgba(255,255,255,0.06);
+            border-radius: 16px;
+            padding: 18px 20px;
+            box-shadow: 0 10px 40px rgba(0,0,0,0.35);
+            color: var(--text);
+            margin-bottom: 1rem;
+        }
+        .hero-card h1 { margin-bottom: 0.35rem; font-size: 1.8rem; }
+        .hero-pill {
+            display: inline-block;
+            background: rgba(255,255,255,0.12);
+            padding: 6px 12px;
+            border-radius: 999px;
+            font-size: 0.85rem;
+            letter-spacing: .05em;
+            text-transform: uppercase;
+        }
+        .subdued { color: var(--muted); font-size: 0.95rem; }
+        .metric-card {
+            background: var(--card);
+            border: 1px solid var(--border);
+            padding: 14px 16px;
+            border-radius: 12px;
+            box-shadow: inset 0 1px 0 rgba(255,255,255,0.04);
+        }
+        .metric-card h3 {
+            margin: 0;
+            font-size: .95rem;
+            color: var(--muted);
+            text-transform: uppercase;
+            letter-spacing: .08em;
+        }
+        .metric-value { font-size: 1.4rem; font-weight: 700; color: var(--text); margin-top: 6px; }
+        .delta { color: #7cf0c6; font-weight: 600; font-size: 0.95rem; }
+        .delta.negative { color: #ff9b9b; }
+        .section-caption { color: var(--muted); margin-top: -6px; margin-bottom: 10px; }
+        </style>
+        """,
+        unsafe_allow_html=True,
+    )
 
     # Create a Streamlit sidebar for user input
     st.sidebar.title("Stock Analysis")
     ticker_symbol = st.sidebar.text_input("Enter Stock Ticker Symbol:", value='AAPL')
-    start_date = st.sidebar.date_input("Start Date", pd.to_datetime('2024-01-01'))
-    end_date = st.sidebar.date_input("End Date", pd.to_datetime('2024-10-01'))
+    default_end = date.today() - timedelta(days=1)
+    default_start = default_end - timedelta(days=365 * 3)
+    start_date = st.sidebar.date_input("Start Date", default_start)
+    end_date = st.sidebar.date_input("End Date", default_end)
+    st.sidebar.markdown("---")
+    st.sidebar.caption("Tip: Choose a wide date range for smoother moving averages and richer AI insights.")
 
     # Fetch stock data from Yahoo Finance
     try:
@@ -42,86 +122,206 @@ else:
         st.error("Error fetching stock data. Please check the ticker symbol and date range.")
     df = stock_data
     df.reset_index(inplace=True)  # Reset index to ensure 'Date' becomes a column
-    
-    # Technical Indicators
-    st.header("Stock Price Chart")
-
-    # Create figure with secondary y-axis
-    fig = make_subplots(specs=[[{"secondary_y": True}]])
-
-    # include candlestick with rangeselector
-    fig.add_trace(go.Candlestick(x=df['Date'], # Except date, query all other data using Symbol
-                    open=df['Open'][ticker_symbol], high=df['High'][ticker_symbol],
-                    low=df['Low'][ticker_symbol], close=df['Close'][ticker_symbol]),
-                secondary_y=True)
-
-    # include a go.Bar trace for volumes
-    fig.add_trace(go.Bar(x=df['Date'], y=df['Volume'][ticker_symbol]),
-                secondary_y=False)
-
-    fig.layout.yaxis2.showgrid=False
-    st.plotly_chart(fig)
-
-    # Technical Indicators
-    st.header("Technical Indicators")
-
-    # Moving Averages
-    st.subheader("Moving Averages")
-    df['SMA_20'] = df['Close'][ticker_symbol].rolling(window=20).mean()
-    df['SMA_50'] = df['Close'][ticker_symbol].rolling(window=50).mean()
-    fig = go.Figure()
-    fig.add_trace(go.Scatter(x=df['Date'], y=df['Close'][ticker_symbol], mode='lines', name='Close Price'))
-    fig.add_trace(go.Scatter(x=df['Date'], y=df['SMA_20'], mode='lines', name='20-Day SMA'))
-    fig.add_trace(go.Scatter(x=df['Date'], y=df['SMA_50'], mode='lines', name='50-Day SMA'))
-    fig.update_layout(title="Moving Averages", xaxis_title="Date", yaxis_title="Price (USD)")
-    st.plotly_chart(fig)
-
-    # RSI (Manual Calculation)
-    st.subheader("Relative Strength Index (RSI)")
-    window_length = 14
-
-    # Calculate the daily price changes
-    delta = df['Close'][ticker_symbol].diff()
-
-    # Separate gains and losses
-    gain = delta.where(delta > 0, 0)
-    loss = -delta.where(delta < 0, 0)
-
-    # Calculate the average gain and average loss
-    avg_gain = gain.rolling(window=window_length, min_periods=1).mean()
-    avg_loss = loss.rolling(window=window_length, min_periods=1).mean()
-
-    # Calculate the RSI
-    rs = avg_gain / avg_loss
-    df['RSI'] = 100 - (100 / (1 + rs))
-
-    fig = go.Figure()
-    fig.add_trace(go.Scatter(x=df['Date'], y=df['RSI'], mode='lines', name='RSI'))
-    fig.add_hline(y=70, line_dash="dash", line_color="red", annotation_text="Overbought")
-    fig.add_hline(y=30, line_dash="dash", line_color="green", annotation_text="Oversold")
-    fig.update_layout(title="RSI Indicator", xaxis_title="Date", yaxis_title="RSI")
-    st.plotly_chart(fig)
-
-    # Volume Analysis
-    st.subheader("Volume Analysis")
-    fig = go.Figure()
-    fig.add_trace(go.Bar(x=df['Date'], y=df['Volume'][ticker_symbol], name='Volume'))
-    fig.update_layout(title="Volume Analysis", xaxis_title="Date", yaxis_title="Volume")
-    st.plotly_chart(fig)
-
-    # Additional Insights
-    st.header("In-depth Analysis")
-    # Prepare text for PaLM
-    chatTextStr = f"""
-    Analyze the following stock data for patterns, trends, and insights.
-    Provide a detailed summary of key market movements.
-    """
-
-    answer = llm.predict(f'''
-    I have yfinance data below on {ticker_symbol} symbol:
-
-    {str(df[['Date', 'Open', 'High', 'Low', 'Close']].tail(30))}
-
-    {chatTextStr}
-    ''')
-    st.write(answer)
+
+    close_series = df['Close'][ticker_symbol]
+    high_series = df['High'][ticker_symbol]
+    low_series = df['Low'][ticker_symbol]
+    volume_series = df['Volume'][ticker_symbol]
+
+    latest_close = close_series.iloc[-1] if not close_series.empty else None
+    prev_close = close_series.iloc[-2] if len(close_series) > 1 else None
+    change = (latest_close - prev_close) if latest_close is not None and prev_close is not None else None
+    change_pct = (change / prev_close * 100) if change not in [None, 0] and prev_close not in [None, 0] else None
+    period_high = high_series.max() if not high_series.empty else None
+    period_low = low_series.min() if not low_series.empty else None
+    avg_volume = volume_series.mean() if not volume_series.empty else None
+
+    def fmt_currency(val):
+        return "-" if val is None or pd.isna(val) else f"${val:,.2f}"
+
+    def fmt_delta(delta_val, pct_val):
+        if delta_val is None or pd.isna(delta_val):
+            return "—", ""
+        symbol = "negative" if delta_val < 0 else ""
+        pct_text = f" ({pct_val:+.2f}%)" if pct_val not in [None, np.nan] else ""
+        return f"{delta_val:+.2f}{pct_text}", symbol
+
+    delta_text, delta_class = fmt_delta(change, change_pct)
+
+    st.markdown(
+        f"""
+        <div class="hero-card">
+            <div class="hero-pill">Market Pulse</div>
+            <h1>{ticker_symbol.upper()} | Stock Intelligence</h1>
+            <p class="subdued">Sharper visuals for price action, technicals, and AI commentary across your chosen dates.</p>
+            <div class="subdued">Range: {start_date.strftime('%b %d, %Y')} → {end_date.strftime('%b %d, %Y')}</div>
+        </div>
+        """,
+        unsafe_allow_html=True,
+    )
+
+    mc1, mc2, mc3 = st.columns(3)
+    mc1.markdown(
+        f"""
+        <div class="metric-card">
+            <h3>Last Close</h3>
+            <div class="metric-value">{fmt_currency(latest_close)}</div>
+            <div class="delta {delta_class}">{delta_text}</div>
+        </div>
+        """,
+        unsafe_allow_html=True,
+    )
+    mc2.markdown(
+        f"""
+        <div class="metric-card">
+            <h3>Period Range</h3>
+            <div class="metric-value">{fmt_currency(period_low)} – {fmt_currency(period_high)}</div>
+            <div class="delta">Session High / Low</div>
+        </div>
+        """,
+        unsafe_allow_html=True,
+    )
+    mc3.markdown(
+        f"""
+        <div class="metric-card">
+            <h3>Avg Volume</h3>
+            <div class="metric-value">{'-' if avg_volume is None or pd.isna(avg_volume) else f"{avg_volume:,.0f}"}</div>
+            <div class="delta">Across selected window</div>
+        </div>
+        """,
+        unsafe_allow_html=True,
+    )
+
+    price_tab, indicators_tab, ai_tab = st.tabs(["Price Action", "Technical Indicators", "AI Deep Dive"])
+
+    with price_tab:
+        st.subheader("Stock Price Chart")
+        st.caption("Candlestick price action with volume on a shared timeline for quick at-a-glance context.")
+
+        fig = make_subplots(specs=[[{"secondary_y": True}]])
+        fig.add_trace(
+            go.Candlestick(
+                x=df['Date'],
+                open=df['Open'][ticker_symbol],
+                high=df['High'][ticker_symbol],
+                low=df['Low'][ticker_symbol],
+                close=close_series,
+                name="Price",
+            ),
+            secondary_y=True,
+        )
+        fig.add_trace(
+            go.Bar(
+                x=df['Date'],
+                y=volume_series,
+                name="Volume",
+                marker_color="rgba(109, 214, 255, 0.55)",
+            ),
+            secondary_y=False,
+        )
+        fig.update_layout(
+            template="plotly_dark",
+            plot_bgcolor="rgba(12,19,32,0.7)",
+            paper_bgcolor="rgba(12,19,32,0.7)",
+            legend_orientation="h",
+            legend_yanchor="bottom",
+            legend_y=1.02,
+            legend_x=0,
+            margin=dict(t=50, l=10, r=10, b=20),
+        )
+        fig.update_yaxes(showgrid=False, secondary_y=True)
+        fig.update_yaxes(gridcolor="rgba(255,255,255,0.08)", secondary_y=False, title_text="Volume")
+        fig.update_xaxes(showgrid=False)
+        st.plotly_chart(fig, use_container_width=True)
+
+    with indicators_tab:
+        st.subheader("Moving Averages")
+        st.caption("Compare recent closes against short and intermediate trend lines.")
+        df['SMA_20'] = close_series.rolling(window=20).mean()
+        df['SMA_50'] = close_series.rolling(window=50).mean()
+        fig = go.Figure()
+        fig.add_trace(go.Scatter(x=df['Date'], y=close_series, mode='lines', name='Close Price', line=dict(color="#7cf0c6")))
+        fig.add_trace(go.Scatter(x=df['Date'], y=df['SMA_20'], mode='lines', name='20-Day SMA', line=dict(color="#6dd6ff")))
+        fig.add_trace(go.Scatter(x=df['Date'], y=df['SMA_50'], mode='lines', name='50-Day SMA', line=dict(color="#b0b8ff")))
+        fig.update_layout(
+            title="Moving Averages",
+            xaxis_title="Date",
+            yaxis_title="Price (USD)",
+            template="plotly_dark",
+            plot_bgcolor="rgba(12,19,32,0.7)",
+            paper_bgcolor="rgba(12,19,32,0.7)",
+            margin=dict(t=50, l=10, r=10, b=20),
+        )
+        fig.update_xaxes(showgrid=False)
+        fig.update_yaxes(gridcolor="rgba(255,255,255,0.08)")
+        st.plotly_chart(fig, use_container_width=True)
+
+        st.subheader("Relative Strength Index (RSI)")
+        st.caption("Momentum oscillator highlighting overbought/oversold zones.")
+        window_length = 14
+
+        delta = close_series.diff()
+        gain = delta.where(delta > 0, 0)
+        loss = -delta.where(delta < 0, 0)
+
+        avg_gain = gain.rolling(window=window_length, min_periods=1).mean()
+        avg_loss = loss.rolling(window=window_length, min_periods=1).mean()
+
+        rs = avg_gain / avg_loss
+        df['RSI'] = 100 - (100 / (1 + rs))
+
+        fig = go.Figure()
+        fig.add_trace(go.Scatter(x=df['Date'], y=df['RSI'], mode='lines', name='RSI', line=dict(color="#6dd6ff")))
+        fig.add_hline(y=70, line_dash="dash", line_color="#ff9b9b", annotation_text="Overbought")
+        fig.add_hline(y=30, line_dash="dash", line_color="#7cf0c6", annotation_text="Oversold")
+        fig.update_layout(
+            title="RSI Indicator",
+            xaxis_title="Date",
+            yaxis_title="RSI",
+            template="plotly_dark",
+            plot_bgcolor="rgba(12,19,32,0.7)",
+            paper_bgcolor="rgba(12,19,32,0.7)",
+            margin=dict(t=50, l=10, r=10, b=20),
+        )
+        fig.update_xaxes(showgrid=False)
+        fig.update_yaxes(gridcolor="rgba(255,255,255,0.08)")
+        st.plotly_chart(fig, use_container_width=True)
+
+        st.subheader("Volume Analysis")
+        st.caption("Volume bars styled to match the rest of the dashboard.")
+        fig = go.Figure()
+        fig.add_trace(go.Bar(x=df['Date'], y=volume_series, name='Volume', marker_color="rgba(109, 214, 255, 0.55)"))
+        fig.update_layout(
+            title="Volume Analysis",
+            xaxis_title="Date",
+            yaxis_title="Volume",
+            template="plotly_dark",
+            plot_bgcolor="rgba(12,19,32,0.7)",
+            paper_bgcolor="rgba(12,19,32,0.7)",
+            margin=dict(t=50, l=10, r=10, b=20),
+        )
+        fig.update_xaxes(showgrid=False)
+        fig.update_yaxes(gridcolor="rgba(255,255,255,0.08)")
+        st.plotly_chart(fig, use_container_width=True)
+
+    with ai_tab:
+        st.subheader("In-depth Analysis")
+        st.caption("AI-generated commentary stays on a dedicated tab so charts remain uncluttered.")
+        chatTextStr = f"""
+        Analyze the following stock market data to identify notable patterns, trends, and anomalies.
+        Summarize key price movements, volume behavior, and any significant shifts in market sentiment.
+        Provide insights in clear, plain language and do not include any programming code.
+        """
+
+        with st.spinner("Running in-depth AI analysis..."):
+            try:
+                answer = llm.predict(f'''
+        I have yfinance data below on {ticker_symbol} symbol:
+
+        {str(df[['Date', 'Open', 'High', 'Low', 'Close']].tail(30))}
+
+        {chatTextStr}
+        ''')
+                st.write(answer)
+            except Exception as exc:
+                st.error(f"AI analysis failed: {exc}")