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app.py

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+import streamlit as st
+import yfinance as yf
+import pandas as pd
+import plotly.graph_objects as go
+from plotly.subplots import make_subplots
+import datetime as dt
+
+# Page config
+st.set_page_config(
+    page_title="Stock Analysis Dashboard",
+    page_icon="📈",
+    layout="wide"
+)
+
+# Sidebar
+st.sidebar.header("Settings")
+ticker = st.sidebar.text_input("Ticker Symbol", "AAPL").upper()
+start = st.sidebar.date_input("Start Date", dt.date.today() - dt.timedelta(days=365))
+end = st.sidebar.date_input("End Date", dt.date.today())
+
+# Cache data
+@st.cache_data
+def fetch_data(ticker, start, end):
+    return yf.download(ticker, start=start, end=end)
+
+# Main
+st.title("📈 Stock Analysis Dashboard")
+st.subheader(f"{ticker} ({start} – {end})")
+
+# Fetch
+data = fetch_data(ticker, start, end)
+
+if data.empty:
+    st.error("No data found. Check ticker or date range.")
+    st.stop()
+
+# KPIs
+latest_close = data["Close"].iloc[-1]
+prev_close = data["Close"].iloc[-2]
+change = latest_close - prev_close
+pct_change = (change / prev_close) * 100
+
+col1, col2, col3, col4 = st.columns(4)
+col1.metric("Close Price", f"${latest_close:,.2f}", f"{pct_change:+.2f}%")
+col2.metric("Volume", f"{data['Volume'].iloc[-1]:,.0f}")
+col3.metric("High", f"${data['High'].max():,.2f}")
+col4.metric("Low", f"${data['Low'].min():,.2f}")
+
+# Chart
+fig = make_subplots(
+    rows=2, cols=1,
+    shared_xaxes=True,
+    vertical_spacing=0.03,
+    subplot_titles=("Price & Volume", "Volume"),
+    row_heights=[0.7, 0.3]
+)
+
+fig.add_trace(
+    go.Candlestick(
+        x=data.index,
+        open=data["Open"],
+        high=data["High"],
+        low=data["Low"],
+        close=data["Close"],
+        name="Candle"
+    ),
+    row=1, col=1
+)
+
+fig.add_trace(
+    go.Bar(
+        x=data.index,
+        y=data["Volume"],
+        name="Volume",
+        marker_color="#636EFA"
+    ),
+    row=2, col=1
+)
+
+fig.update_layout(
+    title=f"{ticker} Price & Volume",
+    xaxis_rangeslider_visible=False,
+    template="plotly_dark",
+    height=600,
+    showlegend=False
+)
+
+st.plotly_chart(fig, use_container_width=True)
+
+# Moving averages
+ma_window = st.sidebar.slider("Moving Average Window", 5, 200, 20)
+data[f"MA{ma_window}"] = data["Close"].rolling(ma_window).mean()
+
+st.subheader("Moving Average")
+ma_fig = go.Figure()
+ma_fig.add_trace(go.Scatter(x=data.index, y=data["Close"], name="Close"))
+ma_fig.add_trace(go.Scatter(x=data.index, y=data[f"MA{ma_window}"], name=f"MA{ma_window}"))
+ma_fig.update_layout(title=f"{ticker} Close vs MA{ma_window}", height=400)
+st.plotly_chart(ma_fig, use_container_width=True)
+
+# Download
+@st.cache_data
+def convert_df(df):
+    return df.to_csv().encode("utf-8")
+
+csv = convert_df(data)
+st.sidebar.download_button(
+    label="Download CSV",
+    data=csv,
+    file_name=f"{ticker}_{start}_{end}.csv",
+    mime="text/csv"
+)