jonas
71803418e5
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.
343 lines
7.0 KiB
Python
343 lines
7.0 KiB
Python
import streamlit as st
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import yfinance as yf
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import pandas as pd
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import altair as alt
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st.set_page_config(
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page_title="Stock peer analysis dashboard",
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page_icon=":chart_with_upwards_trend:",
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layout="wide",
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)
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"""
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# :material/query_stats: Stock peer analysis
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Easily compare stocks against others in their peer group.
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"""
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"" # Add some space.
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cols = st.columns([1, 3])
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# Will declare right cell later to avoid showing it when no data.
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STOCKS = [
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"AAPL",
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"ABBV",
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"ACN",
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"ADBE",
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"ADP",
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"AMD",
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"AMGN",
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"AMT",
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"AMZN",
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"APD",
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"AVGO",
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"AXP",
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"BA",
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"BK",
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"BKNG",
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"BMY",
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"BRK.B",
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"BSX",
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"C",
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"CAT",
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"CI",
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"CL",
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"CMCSA",
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"COST",
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"CRM",
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"CSCO",
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"CVX",
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"DE",
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"DHR",
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"DIS",
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"DUK",
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"ELV",
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"EOG",
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"EQR",
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"FDX",
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"GD",
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"GE",
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"GILD",
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"GOOG",
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"GOOGL",
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"HD",
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"HON",
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"HUM",
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"IBM",
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"ICE",
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"INTC",
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"ISRG",
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"JNJ",
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"JPM",
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"KO",
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"LIN",
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"LLY",
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"LMT",
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"LOW",
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"MA",
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"MCD",
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"MDLZ",
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"META",
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"MMC",
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"MO",
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"MRK",
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"MSFT",
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"NEE",
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"NFLX",
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"NKE",
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"NOW",
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"NVDA",
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"ORCL",
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"PEP",
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"PFE",
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"PG",
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"PLD",
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"PM",
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"PSA",
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"REGN",
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"RTX",
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"SBUX",
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"SCHW",
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"SLB",
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"SO",
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"SPGI",
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"T",
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"TJX",
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"TMO",
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"TSLA",
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"TXN",
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"UNH",
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"UNP",
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"UPS",
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"V",
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"VZ",
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"WFC",
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"WM",
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"WMT",
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"XOM",
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]
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DEFAULT_STOCKS = ["AAPL", "MSFT", "GOOGL", "NVDA", "AMZN", "TSLA", "META"]
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def stocks_to_str(stocks):
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return ",".join(stocks)
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if "tickers_input" not in st.session_state:
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st.session_state.tickers_input = st.query_params.get(
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"stocks", stocks_to_str(DEFAULT_STOCKS)
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).split(",")
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# Callback to update query param when input changes
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def update_query_param():
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if st.session_state.tickers_input:
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st.query_params["stocks"] = stocks_to_str(st.session_state.tickers_input)
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else:
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st.query_params.pop("stocks", None)
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top_left_cell = cols[0].container(
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border=True, height="stretch", vertical_alignment="center"
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)
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with top_left_cell:
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# Selectbox for stock tickers
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tickers = st.multiselect(
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"Stock tickers",
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options=sorted(set(STOCKS) | set(st.session_state.tickers_input)),
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default=st.session_state.tickers_input,
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placeholder="Choose stocks to compare. Example: NVDA",
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accept_new_options=True,
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)
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# Time horizon selector
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horizon_map = {
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"1 Month": "1mo",
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"3 Months": "3mo",
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"6 Months": "6mo",
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"1 Year": "1y",
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"5 Years": "5y",
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"10 Years": "10y",
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"20 Years": "20y",
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}
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with top_left_cell:
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# Buttons for picking time horizon
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horizon = st.pills(
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"Time horizon",
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options=list(horizon_map.keys()),
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default="6 Months",
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)
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tickers = [t.upper() for t in tickers]
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# Update query param when text input changes
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if tickers:
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st.query_params["stocks"] = stocks_to_str(tickers)
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else:
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# Clear the param if input is empty
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st.query_params.pop("stocks", None)
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if not tickers:
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top_left_cell.info("Pick some stocks to compare", icon=":material/info:")
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st.stop()
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right_cell = cols[1].container(
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border=True, height="stretch", vertical_alignment="center"
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)
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@st.cache_resource(show_spinner=False, ttl="6h")
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def load_data(tickers, period):
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tickers_obj = yf.Tickers(tickers)
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data = tickers_obj.history(period=period)
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if data is None:
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raise RuntimeError("YFinance returned no data.")
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return data["Close"]
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# Load the data
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try:
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data = load_data(tickers, horizon_map[horizon])
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except yf.exceptions.YFRateLimitError as e:
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st.warning("YFinance is rate-limiting us :(\nTry again later.")
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load_data.clear() # Remove the bad cache entry.
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st.stop()
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empty_columns = data.columns[data.isna().all()].tolist()
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if empty_columns:
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st.error(f"Error loading data for the tickers: {', '.join(empty_columns)}.")
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st.stop()
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# Normalize prices (start at 1)
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normalized = data.div(data.iloc[0])
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latest_norm_values = {normalized[ticker].iat[-1]: ticker for ticker in tickers}
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max_norm_value = max(latest_norm_values.items())
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min_norm_value = min(latest_norm_values.items())
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bottom_left_cell = cols[0].container(
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border=True, height="stretch", vertical_alignment="center"
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)
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with bottom_left_cell:
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cols = st.columns(2)
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cols[0].metric(
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"Best stock",
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max_norm_value[1],
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delta=f"{round(max_norm_value[0] * 100)}%",
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width="content",
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)
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cols[1].metric(
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"Worst stock",
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min_norm_value[1],
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delta=f"{round(min_norm_value[0] * 100)}%",
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width="content",
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)
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# Plot normalized prices
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with right_cell:
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st.altair_chart(
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alt.Chart(
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normalized.reset_index().melt(
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id_vars=["Date"], var_name="Stock", value_name="Normalized price"
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)
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)
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.mark_line()
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.encode(
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alt.X("Date:T"),
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alt.Y("Normalized price:Q").scale(zero=False),
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alt.Color("Stock:N"),
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)
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.properties(height=400)
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)
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""
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""
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# Plot individual stock vs peer average
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"""
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## Individual stocks vs peer average
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For the analysis below, the "peer average" when analyzing stock X always
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excludes X itself.
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"""
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if len(tickers) <= 1:
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st.warning("Pick 2 or more tickers to compare them")
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st.stop()
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NUM_COLS = 4
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cols = st.columns(NUM_COLS)
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for i, ticker in enumerate(tickers):
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# Calculate peer average (excluding current stock)
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peers = normalized.drop(columns=[ticker])
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peer_avg = peers.mean(axis=1)
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# Create DataFrame with peer average.
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plot_data = pd.DataFrame(
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{
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"Date": normalized.index,
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ticker: normalized[ticker],
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"Peer average": peer_avg,
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}
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).melt(id_vars=["Date"], var_name="Series", value_name="Price")
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chart = (
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alt.Chart(plot_data)
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.mark_line()
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.encode(
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alt.X("Date:T"),
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alt.Y("Price:Q").scale(zero=False),
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alt.Color(
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"Series:N",
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scale=alt.Scale(domain=[ticker, "Peer average"], range=["red", "gray"]),
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legend=alt.Legend(orient="bottom"),
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),
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alt.Tooltip(["Date", "Series", "Price"]),
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)
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.properties(title=f"{ticker} vs peer average", height=300)
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)
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cell = cols[(i * 2) % NUM_COLS].container(border=True)
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cell.write("")
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cell.altair_chart(chart)
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# Create Delta chart
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plot_data = pd.DataFrame(
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{
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"Date": normalized.index,
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"Delta": normalized[ticker] - peer_avg,
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}
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)
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chart = (
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alt.Chart(plot_data)
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.mark_area()
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.encode(
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alt.X("Date:T"),
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alt.Y("Delta:Q").scale(zero=False),
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)
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.properties(title=f"{ticker} minus peer average", height=300)
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)
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cell = cols[(i * 2 + 1) % NUM_COLS].container(border=True)
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cell.write("")
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cell.altair_chart(chart)
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""
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""
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"""
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## Raw data
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"""
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data
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