Initial commit: Shelly Manager with Textual CLI, Streamlit UI, and comprehensive .gitignore

Shelly device management app with mDNS/subnet discovery, inventory, configuration, and mass operations for Gen1/Gen2+ devices. Includes .gitignore excluding runtime data (device DB, user config), AI conversation history, build artifacts, and common Python/OS patterns.
2026-03-23 21:51:59 +01:00
commit 71803418e5
152 changed files with 23405 additions and 0 deletions
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+[project]
+name = "dashboard-stock-peers-snowflake"
+version = "1.0.0"
+description = "Stock peer analysis dashboard with Snowflake connection"
+requires-python = ">=3.11"
+dependencies = [
+    "altair>=5.5.0",
+    "pandas>=2.2.3",
+    "snowflake-connector-python>=3.3.0",
+    "streamlit[snowflake]>=1.54.0",
+]
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+definition_version: 2
+entities:
+  DASHBOARD_STOCK_PEERS_SNOWFLAKE:
+    type: streamlit
+    identifier:
+      name: DASHBOARD_STOCK_PEERS_SNOWFLAKE
+      database: <FROM_CONNECTION>   # Use: snow connection list
+      schema: <FROM_CONNECTION>
+    query_warehouse: <FROM_CONNECTION>
+    runtime_name: SYSTEM$ST_CONTAINER_RUNTIME_PY3_11
+    # List available: SHOW EXTERNAL ACCESS INTEGRATIONS;
+    # Common: PYPI_ACCESS_INTEGRATION (verify exists)
+    external_access_integrations:
+      - <YOUR_PYPI_INTEGRATION>
+    main_file: streamlit_app.py
+    artifacts:
+      - streamlit_app.py
+      - pyproject.toml
@@ -0,0 +1,384 @@
+"""
+Stock Peer Analysis Dashboard (Snowflake Edition)
+
+A stock comparison dashboard demonstrating:
+- Snowflake connection via st.connection("snowflake")
+- Generating synthetic stock data in Snowflake
+- Normalized price comparison charts
+- Individual stock vs peer average analysis
+
+This template uses synthetic stock data generated in Snowflake.
+Replace the synthetic query with your actual stock data table.
+"""
+
+import streamlit as st
+import pandas as pd
+import altair as alt
+
+st.set_page_config(
+    page_title="Stock peer analysis dashboard",
+    page_icon=":chart_with_upwards_trend:",
+    layout="wide",
+)
+
+"""
+# :material/query_stats: Stock peer analysis
+
+Easily compare stocks against others in their peer group.
+"""
+
+""  # Add some space.
+
+
+# =============================================================================
+# Snowflake Connection
+# =============================================================================
+
+
+def get_snowflake_connection():
+    """Get Snowflake connection via st.connection.
+
+    Displays an error and stops the app if the connection fails.
+    """
+    try:
+        return st.connection("snowflake")
+    except Exception as e:
+        st.error(f"Failed to connect to Snowflake: {e}")
+        st.info(
+            "Make sure you have configured your Snowflake connection in "
+            "`.streamlit/secrets.toml` or via environment variables."
+        )
+        st.stop()
+
+
+# =============================================================================
+# Constants and Configuration
+# =============================================================================
+
+STOCKS = [
+    "AAPL", "ABBV", "ACN", "ADBE", "ADP", "AMD", "AMGN", "AMT", "AMZN", "APD",
+    "AVGO", "AXP", "BA", "BK", "BKNG", "BMY", "BSX", "C", "CAT", "CI",
+    "CL", "CMCSA", "COST", "CRM", "CSCO", "CVX", "DE", "DHR", "DIS", "DUK",
+    "ELV", "EOG", "EQR", "FDX", "GD", "GE", "GILD", "GOOG", "GOOGL", "HD",
+    "HON", "HUM", "IBM", "ICE", "INTC", "ISRG", "JNJ", "JPM", "KO", "LIN",
+    "LLY", "LMT", "LOW", "MA", "MCD", "MDLZ", "META", "MMC", "MO", "MRK",
+    "MSFT", "NEE", "NFLX", "NKE", "NOW", "NVDA", "ORCL", "PEP", "PFE", "PG",
+    "PLD", "PM", "PSA", "REGN", "RTX", "SBUX", "SCHW", "SLB", "SO", "SPGI",
+    "T", "TJX", "TMO", "TSLA", "TXN", "UNH", "UNP", "UPS", "V", "VZ",
+    "WFC", "WM", "WMT", "XOM",
+]
+
+# Base prices for synthetic data (approximate real values for realism)
+STOCK_BASE_PRICES = {
+    "AAPL": 175, "MSFT": 380, "GOOGL": 140, "AMZN": 180, "NVDA": 500,
+    "META": 350, "TSLA": 250, "JPM": 170, "V": 280, "UNH": 520,
+    "HD": 350, "PG": 160, "MA": 450, "COST": 580, "ABBV": 170,
+    "MRK": 120, "AVGO": 900, "PEP": 180, "KO": 60, "TMO": 550,
+    "ADBE": 550, "CRM": 280, "CSCO": 50, "ACN": 340, "NKE": 100,
+}
+
+DEFAULT_STOCKS = ["AAPL", "MSFT", "GOOGL", "NVDA", "AMZN", "TSLA", "META"]
+
+# Time horizon mapping
+HORIZON_MAP = {
+    "1 Month": 30,
+    "3 Months": 90,
+    "6 Months": 180,
+    "1 Year": 365,
+    "2 Years": 730,
+}
+
+
+def stocks_to_str(stocks):
+    return ",".join(stocks)
+
+
+# =============================================================================
+# Data Loading
+# =============================================================================
+
+
+# -----------------------------------------------------------------------------
+# PRODUCTION PATTERN: Use parameterized queries for real stock data
+# -----------------------------------------------------------------------------
+# For production use with actual stock tables, use parameterized queries:
+#
+#     STOCK_QUERY = """
+#         SELECT trade_date AS date, ticker, close_price
+#         FROM stock_prices
+#         WHERE ticker = ANY(:tickers)
+#           AND trade_date >= DATEADD(day, -:days, CURRENT_DATE())
+#         ORDER BY trade_date, ticker
+#     """
+#
+#     def load_stock_data(tickers: list[str], days: int) -> pd.DataFrame:
+#         conn = get_snowflake_connection()
+#         df = conn.query(
+#             STOCK_QUERY,
+#             params={"tickers": tickers, "days": days}
+#         )
+#         return df
+#
+# The synthetic data generation below uses f-strings for the VALUES clause
+# which cannot be parameterized. This is acceptable for demo/synthetic data
+# but should NOT be used with user input in production.
+# -----------------------------------------------------------------------------
+
+
+def generate_stock_data_query(tickers: list[str], days: int) -> str:
+    """Generate SQL query that creates synthetic stock price data.
+    
+    NOTE: This uses f-strings for VALUES clause construction which is acceptable
+    for synthetic data generation with controlled inputs. For production apps
+    with real tables, always use parameterized queries as shown above.
+    """
+    # Build ticker values and base prices (controlled data, not user input)
+    ticker_values = []
+    for ticker in tickers:
+        base_price = STOCK_BASE_PRICES.get(ticker, 100 + hash(ticker) % 400)
+        growth_rate = 0.0003 + (hash(ticker) % 10) * 0.00005
+        volatility = 0.02 + (hash(ticker) % 5) * 0.005
+        ticker_values.append(f"('{ticker}', {base_price}, {growth_rate}, {volatility})")
+    
+    tickers_cte = ", ".join(ticker_values)
+    
+    return f"""
+    WITH tickers AS (
+        SELECT column1 AS ticker, column2 AS base_price, column3 AS growth_rate, column4 AS volatility
+        FROM VALUES {tickers_cte}
+    ),
+    date_series AS (
+        SELECT DATEADD(day, -seq4(), CURRENT_DATE() - 1) AS trade_date
+        FROM TABLE(GENERATOR(ROWCOUNT => {days}))
+    ),
+    raw_prices AS (
+        SELECT 
+            d.trade_date,
+            t.ticker,
+            t.base_price * POWER(1 + t.growth_rate, DATEDIFF(day, DATEADD(day, -{days}, CURRENT_DATE()), d.trade_date)) 
+                * (1 + (RANDOM() / 10000000000000000000.0 - 0.5) * t.volatility * 2) AS close_price
+        FROM date_series d
+        CROSS JOIN tickers t
+        WHERE DAYOFWEEK(d.trade_date) NOT IN (0, 6)  -- Exclude weekends
+    )
+    SELECT 
+        trade_date AS date,
+        ticker,
+        ROUND(close_price, 2) AS close_price
+    FROM raw_prices
+    ORDER BY trade_date, ticker
+    """
+
+
+@st.cache_data(ttl=3600, show_spinner="Loading stock data from Snowflake...")
+def load_stock_data(tickers: list[str], days: int) -> pd.DataFrame:
+    """Load stock price data from Snowflake."""
+    conn = get_snowflake_connection()
+    query = generate_stock_data_query(tickers, days)
+    df = conn.query(query)
+    df.columns = df.columns.str.lower()
+    
+    # Pivot to get tickers as columns
+    pivoted = df.pivot(index="date", columns="ticker", values="close_price")
+    pivoted.index = pd.to_datetime(pivoted.index)
+    return pivoted
+
+
+# =============================================================================
+# Session State and Query Params
+# =============================================================================
+
+if "tickers_input" not in st.session_state:
+    st.session_state.tickers_input = st.query_params.get(
+        "stocks", stocks_to_str(DEFAULT_STOCKS)
+    ).split(",")
+
+
+# =============================================================================
+# Page Layout
+# =============================================================================
+
+# Check Snowflake connection
+get_snowflake_connection()
+
+cols = st.columns([1, 3])
+
+top_left_cell = cols[0].container(
+    border=True, height="stretch", vertical_alignment="center"
+)
+
+with top_left_cell:
+    # Selectbox for stock tickers
+    tickers = st.multiselect(
+        "Stock tickers",
+        options=sorted(set(STOCKS) | set(st.session_state.tickers_input)),
+        default=st.session_state.tickers_input,
+        placeholder="Choose stocks to compare. Example: NVDA",
+        accept_new_options=True,
+    )
+
+    # Time horizon selector
+    horizon = st.pills(
+        "Time horizon",
+        options=list(HORIZON_MAP.keys()),
+        default="6 Months",
+    )
+
+tickers = [t.upper() for t in tickers]
+
+# Update query param when text input changes
+if tickers:
+    st.query_params["stocks"] = stocks_to_str(tickers)
+else:
+    st.query_params.pop("stocks", None)
+
+if not tickers:
+    top_left_cell.info("Pick some stocks to compare", icon=":material/info:")
+    st.stop()
+
+right_cell = cols[1].container(
+    border=True, height="stretch", vertical_alignment="center"
+)
+
+# Load the data from Snowflake
+try:
+    data = load_stock_data(tickers, HORIZON_MAP[horizon])
+except Exception as e:
+    st.error(f"Error loading stock data: {e}")
+    st.stop()
+
+# Check for missing data
+missing_tickers = [t for t in tickers if t not in data.columns]
+if missing_tickers:
+    st.warning(f"No data available for: {', '.join(missing_tickers)}")
+    # Filter to available tickers
+    tickers = [t for t in tickers if t in data.columns]
+    if not tickers:
+        st.stop()
+
+# Normalize prices (start at 1)
+normalized = data[tickers].div(data[tickers].iloc[0])
+
+latest_norm_values = {normalized[ticker].iat[-1]: ticker for ticker in tickers}
+max_norm_value = max(latest_norm_values.items())
+min_norm_value = min(latest_norm_values.items())
+
+bottom_left_cell = cols[0].container(
+    border=True, height="stretch", vertical_alignment="center"
+)
+
+with bottom_left_cell:
+    metric_cols = st.columns(2)
+    metric_cols[0].metric(
+        "Best stock",
+        max_norm_value[1],
+        delta=f"{round((max_norm_value[0] - 1) * 100)}%",
+        width="content",
+    )
+    metric_cols[1].metric(
+        "Worst stock",
+        min_norm_value[1],
+        delta=f"{round((min_norm_value[0] - 1) * 100)}%",
+        width="content",
+    )
+
+# Plot normalized prices
+with right_cell:
+    st.altair_chart(
+        alt.Chart(
+            normalized.reset_index().melt(
+                id_vars=["date"], var_name="Stock", value_name="Normalized price"
+            )
+        )
+        .mark_line()
+        .encode(
+            alt.X("date:T", title="Date"),
+            alt.Y("Normalized price:Q").scale(zero=False),
+            alt.Color("Stock:N"),
+        )
+        .properties(height=400)
+    )
+
+""
+""
+
+# Plot individual stock vs peer average
+"""
+## Individual stocks vs peer average
+
+For the analysis below, the "peer average" when analyzing stock X always
+excludes X itself.
+"""
+
+if len(tickers) <= 1:
+    st.warning("Pick 2 or more tickers to compare them")
+    st.stop()
+
+NUM_COLS = 4
+chart_cols = st.columns(NUM_COLS)
+
+for i, ticker in enumerate(tickers):
+    # Calculate peer average (excluding current stock)
+    peers = normalized.drop(columns=[ticker])
+    peer_avg = peers.mean(axis=1)
+
+    # Create DataFrame with peer average
+    plot_data = pd.DataFrame(
+        {
+            "Date": normalized.index,
+            ticker: normalized[ticker],
+            "Peer average": peer_avg,
+        }
+    ).melt(id_vars=["Date"], var_name="Series", value_name="Price")
+
+    chart = (
+        alt.Chart(plot_data)
+        .mark_line()
+        .encode(
+            alt.X("Date:T"),
+            alt.Y("Price:Q").scale(zero=False),
+            alt.Color(
+                "Series:N",
+                scale=alt.Scale(domain=[ticker, "Peer average"], range=["red", "gray"]),
+                legend=alt.Legend(orient="bottom"),
+            ),
+            alt.Tooltip(["Date", "Series", "Price"]),
+        )
+        .properties(title=f"{ticker} vs peer average", height=300)
+    )
+
+    cell = chart_cols[(i * 2) % NUM_COLS].container(border=True)
+    cell.write("")
+    cell.altair_chart(chart)
+
+    # Create Delta chart
+    plot_data = pd.DataFrame(
+        {
+            "Date": normalized.index,
+            "Delta": normalized[ticker] - peer_avg,
+        }
+    )
+
+    chart = (
+        alt.Chart(plot_data)
+        .mark_area()
+        .encode(
+            alt.X("Date:T"),
+            alt.Y("Delta:Q").scale(zero=False),
+        )
+        .properties(title=f"{ticker} minus peer average", height=300)
+    )
+
+    cell = chart_cols[(i * 2 + 1) % NUM_COLS].container(border=True)
+    cell.write("")
+    cell.altair_chart(chart)
+
+""
+""
+
+"""
+## Raw data
+"""
+
+st.caption(":material/cloud: Data loaded from Snowflake (synthetic)")
+data[tickers]