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SPY_500

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Rename main.py to app.py

d2939e5 verified about 1 year ago

7.44 kB

import datetime as dt
from datetime import timezone
import pandas as pd
import streamlit as st
import yfinance as yf
import plotly.graph_objects as go

st.set_page_config(page_title="SPY Dashboard", layout="wide")

TICKER = "SPY"

# ---------------------- Helpers ---------------------- #
@st.cache_data(ttl=60*60)  # cache 1 hour to be gentle on API
def fetch_prices(timeframe: str) -> pd.DataFrame:
    """Fetch OHLCV data for SPY for the selected timeframe."""
    kwargs = dict(auto_adjust=True, progress=False)

    if timeframe == "1D":
        return yf.download(TICKER, period="1d", interval="1m", **kwargs)
    elif timeframe == "1W":
        return yf.download(TICKER, period="7d", interval="15m", **kwargs)
    elif timeframe == "1M":
        return yf.download(TICKER, period="1mo", interval="1h", **kwargs)
    elif timeframe == "YTD":
        start = dt.datetime(dt.datetime.now().year, 1, 1, tzinfo=timezone.utc)
        return yf.download(TICKER, start=start, interval="1d", **kwargs)
    elif timeframe == "1Y":
        return yf.download(TICKER, period="1y", interval="1d", **kwargs)
    elif timeframe == "5Y":
        return yf.download(TICKER, period="5y", interval="1wk", **kwargs)
    else:  # "ALL"
        # Weekly gives a clean long-term view without too many points
        return yf.download(TICKER, period="max", interval="1wk", **kwargs)

@st.cache_data(ttl=60*10)
def fast_info():
    t = yf.Ticker(TICKER)
    return getattr(t, "fast_info", {})

def is_intraday(df: pd.DataFrame) -> bool:
    if df.empty:
        return False
    last_date = df.index[-1].date()
    return (df.index.date == last_date).sum() > 1

def last_day_stats(df: pd.DataFrame) -> dict:
    """Return open/high/low/close/volume for the latest trading day represented in df."""
    if df.empty:
        return {}

    # pick the latest calendar day present in the data
    last_dt = pd.to_datetime(df.index[-1])
    mask = (pd.to_datetime(df.index).date == last_dt.date())
    day = df.loc[mask]

    if day.empty:
        # fallback to last row
        row = df.iloc[-1]
        return dict(
            open=float(row["Open"]),
            high=float(row["High"]),
            low=float(row["Low"]),
            close=float(row["Close"]),
            volume=int(row.get("Volume", 0)),
        )

    # intraday volume is sum across the day; daily interval has one row already
    vol = int(day["Volume"].sum() if is_intraday(df) else day["Volume"].iloc[-1])

    return dict(
        open=float(day["Open"].iloc[0]),
        high=float(day["High"].max()),
        low=float(day["Low"].min()),
        close=float(day["Close"].iloc[-1]),
        volume=vol,
    )

@st.cache_data(ttl=60*30)
def year_range_52w() -> tuple[float, float]:
    df = yf.download(TICKER, period="1y", interval="1d", auto_adjust=True, progress=False)
    if df.empty:
        return (None, None)
    return (float(df["Low"].min()), float(df["High"].max()))

def human_int(n):
    # 1,234,567 -> 1.23M style
    try:
        n = float(n)
    except Exception:
        return "-"
    for unit in ["", "K", "M", "B", "T"]:
        if abs(n) < 1000:
            return f"{n:.2f}{unit}".rstrip("0").rstrip(".") + unit
        n /= 1000
    return f"{n:.2f}P"

# ---------------------- UI ---------------------- #
st.title("📈 SPY (S&P 500 ETF)")

col_l, col_r = st.columns([3, 1])

with col_l:
    timeframe = st.radio(
        "Timeframe",
        ["1D", "1W", "1M", "YTD", "1Y", "5Y", "ALL"],
        horizontal=True,
        index=3,  # default YTD
    )

with col_r:
    show_ma = st.toggle("Show Moving Averages (20/50)", value=False)
    log_scale = st.toggle("Log Scale", value=False)

# Data
prices = fetch_prices(timeframe)
fi = fast_info()

if prices.empty:
    st.warning("No data available right now. Market may be closed or the data provider rate-limited. Try a different timeframe.")
    st.stop()

stats = last_day_stats(prices)
prev_close = None
if len(prices) > 1:
    if is_intraday(prices):
        # previous trading day close
        prev_date = pd.to_datetime(prices.index[-1]).date()
        # take rows from prior calendar day
        prior = prices.loc[prices.index.date < prev_date]
        if not prior.empty:
            prev_close = float(prior["Close"].iloc[-1])
    else:
        prev_close = float(prices["Close"].iloc[-2])

# Chart
fig = go.Figure()
fig.add_trace(
    go.Candlestick(
        x=prices.index,
        open=prices["Open"],
        high=prices["High"],
        low=prices["Low"],
        close=prices["Close"],
        name="Price",
        increasing_line_color="#26a69a",
        decreasing_line_color="#ef5350",
    )
)

if show_ma and len(prices) >= 50:
    ma20 = prices["Close"].rolling(20).mean()
    ma50 = prices["Close"].rolling(50).mean()
    fig.add_trace(go.Scatter(x=prices.index, y=ma20, name="MA 20", mode="lines"))
    fig.add_trace(go.Scatter(x=prices.index, y=ma50, name="MA 50", mode="lines"))

fig.update_layout(
    margin=dict(l=10, r=10, t=10, b=10),
    height=520,
    yaxis_type="log" if log_scale else "linear",
    xaxis_rangeslider_visible=False,
)
st.plotly_chart(fig, use_container_width=True)

# Top metrics row
cur = stats.get("close")
chg = (cur - prev_close) if (cur is not None and prev_close) else None
pct = (chg / prev_close * 100) if (chg is not None and prev_close) else None

m1, m2, m3, m4 = st.columns(4)
m1.metric("Last Price", f"${cur:,.2f}" if cur else "-", f"{(chg):+,.2f}" if chg else None)
m2.metric("Change %", f"{(pct):+.2f}%" if pct else "-")

day_range = f"${stats.get('low', 0):,.2f} – ${stats.get('high', 0):,.2f}" if stats else "-"
m3.metric("Day's Range", day_range)
yr_low, yr_high = year_range_52w()
yr_range = f"${yr_low:,.2f} – ${yr_high:,.2f}" if yr_low and yr_high else "-"
m4.metric("52-Week Range", yr_range)

st.write("---")

# Details table (Robinhood-style)
# Pull additional stats from fast_info, with safe fallbacks
open_p = stats.get("open")
high_p = stats.get("high")
low_p = stats.get("low")
vol = stats.get("volume")
avg10d = getattr(fi, "ten_day_average_volume", None) or fi.get("ten_day_average_volume")
avg3m = getattr(fi, "three_month_average_volume", None) or fi.get("three_month_average_volume")
market_cap = getattr(fi, "market_cap", None) or fi.get("market_cap")
prev_close_fi = getattr(fi, "previous_close", None) or fi.get("previous_close")

details = pd.DataFrame(
    {
        "Field": [
            "Previous Close",
            "Open",
            "Day’s High",
            "Day’s Low",
            "Volume (Today)",
            "Avg Volume (10D)",
            "Avg Volume (3M)",
            "Market Cap",
            "52-Week High",
            "52-Week Low",
        ],
        "Value": [
            f"${(prev_close or prev_close_fi):,.2f}" if (prev_close or prev_close_fi) else "-",
            f"${open_p:,.2f}" if open_p else "-",
            f"${high_p:,.2f}" if high_p else "-",
            f"${low_p:,.2f}" if low_p else "-",
            human_int(vol) if vol else "-",
            human_int(avg10d) if avg10d else "-",
            human_int(avg3m) if avg3m else "-",
            human_int(market_cap) if market_cap else "-",
            f"${yr_high:,.2f}" if yr_high else "-",
            f"${yr_low:,.2f}" if yr_low else "-",
        ],
    }
)


st.subheader("Details")
st.dataframe(
    details,
    use_container_width=True,
    hide_index=True,
)
st.caption("Data source: Yahoo Finance via yfinance")

	import datetime as dt
	from datetime import timezone
	import pandas as pd
	import streamlit as st
	import yfinance as yf
	import plotly.graph_objects as go

	st.set_page_config(page_title="SPY Dashboard", layout="wide")

	TICKER = "SPY"

	# ---------------------- Helpers ---------------------- #
	@st.cache_data(ttl=60*60) # cache 1 hour to be gentle on API
	def fetch_prices(timeframe: str) -> pd.DataFrame:
	"""Fetch OHLCV data for SPY for the selected timeframe."""
	kwargs = dict(auto_adjust=True, progress=False)

	if timeframe == "1D":
	return yf.download(TICKER, period="1d", interval="1m", **kwargs)
	elif timeframe == "1W":
	return yf.download(TICKER, period="7d", interval="15m", **kwargs)
	elif timeframe == "1M":
	return yf.download(TICKER, period="1mo", interval="1h", **kwargs)
	elif timeframe == "YTD":
	start = dt.datetime(dt.datetime.now().year, 1, 1, tzinfo=timezone.utc)
	return yf.download(TICKER, start=start, interval="1d", **kwargs)
	elif timeframe == "1Y":
	return yf.download(TICKER, period="1y", interval="1d", **kwargs)
	elif timeframe == "5Y":
	return yf.download(TICKER, period="5y", interval="1wk", **kwargs)
	else: # "ALL"
	# Weekly gives a clean long-term view without too many points
	return yf.download(TICKER, period="max", interval="1wk", **kwargs)

	@st.cache_data(ttl=60*10)
	def fast_info():
	t = yf.Ticker(TICKER)
	return getattr(t, "fast_info", {})

	def is_intraday(df: pd.DataFrame) -> bool:
	if df.empty:
	return False
	last_date = df.index[-1].date()
	return (df.index.date == last_date).sum() > 1

	def last_day_stats(df: pd.DataFrame) -> dict:
	"""Return open/high/low/close/volume for the latest trading day represented in df."""
	if df.empty:
	return {}

	# pick the latest calendar day present in the data
	last_dt = pd.to_datetime(df.index[-1])
	mask = (pd.to_datetime(df.index).date == last_dt.date())
	day = df.loc[mask]

	if day.empty:
	# fallback to last row
	row = df.iloc[-1]
	return dict(
	open=float(row["Open"]),
	high=float(row["High"]),
	low=float(row["Low"]),
	close=float(row["Close"]),
	volume=int(row.get("Volume", 0)),
	)

	# intraday volume is sum across the day; daily interval has one row already
	vol = int(day["Volume"].sum() if is_intraday(df) else day["Volume"].iloc[-1])

	return dict(
	open=float(day["Open"].iloc[0]),
	high=float(day["High"].max()),
	low=float(day["Low"].min()),
	close=float(day["Close"].iloc[-1]),
	volume=vol,
	)

	@st.cache_data(ttl=60*30)
	def year_range_52w() -> tuple[float, float]:
	df = yf.download(TICKER, period="1y", interval="1d", auto_adjust=True, progress=False)
	if df.empty:
	return (None, None)
	return (float(df["Low"].min()), float(df["High"].max()))

	def human_int(n):
	# 1,234,567 -> 1.23M style
	try:
	n = float(n)
	except Exception:
	return "-"
	for unit in ["", "K", "M", "B", "T"]:
	if abs(n) < 1000:
	return f"{n:.2f}{unit}".rstrip("0").rstrip(".") + unit
	n /= 1000
	return f"{n:.2f}P"

	# ---------------------- UI ---------------------- #
	st.title("📈 SPY (S&P 500 ETF)")

	col_l, col_r = st.columns([3, 1])

	with col_l:
	timeframe = st.radio(
	"Timeframe",
	["1D", "1W", "1M", "YTD", "1Y", "5Y", "ALL"],
	horizontal=True,
	index=3, # default YTD
	)

	with col_r:
	show_ma = st.toggle("Show Moving Averages (20/50)", value=False)
	log_scale = st.toggle("Log Scale", value=False)

	# Data
	prices = fetch_prices(timeframe)
	fi = fast_info()

	if prices.empty:
	st.warning("No data available right now. Market may be closed or the data provider rate-limited. Try a different timeframe.")
	st.stop()

	stats = last_day_stats(prices)
	prev_close = None
	if len(prices) > 1:
	if is_intraday(prices):
	# previous trading day close
	prev_date = pd.to_datetime(prices.index[-1]).date()
	# take rows from prior calendar day
	prior = prices.loc[prices.index.date < prev_date]
	if not prior.empty:
	prev_close = float(prior["Close"].iloc[-1])
	else:
	prev_close = float(prices["Close"].iloc[-2])

	# Chart
	fig = go.Figure()
	fig.add_trace(
	go.Candlestick(
	x=prices.index,
	open=prices["Open"],
	high=prices["High"],
	low=prices["Low"],
	close=prices["Close"],
	name="Price",
	increasing_line_color="#26a69a",
	decreasing_line_color="#ef5350",
	)
	)

	if show_ma and len(prices) >= 50:
	ma20 = prices["Close"].rolling(20).mean()
	ma50 = prices["Close"].rolling(50).mean()
	fig.add_trace(go.Scatter(x=prices.index, y=ma20, name="MA 20", mode="lines"))
	fig.add_trace(go.Scatter(x=prices.index, y=ma50, name="MA 50", mode="lines"))

	fig.update_layout(
	margin=dict(l=10, r=10, t=10, b=10),
	height=520,
	yaxis_type="log" if log_scale else "linear",
	xaxis_rangeslider_visible=False,
	)
	st.plotly_chart(fig, use_container_width=True)

	# Top metrics row
	cur = stats.get("close")
	chg = (cur - prev_close) if (cur is not None and prev_close) else None
	pct = (chg / prev_close * 100) if (chg is not None and prev_close) else None

	m1, m2, m3, m4 = st.columns(4)
	m1.metric("Last Price", f"${cur:,.2f}" if cur else "-", f"{(chg):+,.2f}" if chg else None)
	m2.metric("Change %", f"{(pct):+.2f}%" if pct else "-")

	day_range = f"${stats.get('low', 0):,.2f} – ${stats.get('high', 0):,.2f}" if stats else "-"
	m3.metric("Day's Range", day_range)
	yr_low, yr_high = year_range_52w()
	yr_range = f"${yr_low:,.2f} – ${yr_high:,.2f}" if yr_low and yr_high else "-"
	m4.metric("52-Week Range", yr_range)

	st.write("---")

	# Details table (Robinhood-style)
	# Pull additional stats from fast_info, with safe fallbacks
	open_p = stats.get("open")
	high_p = stats.get("high")
	low_p = stats.get("low")
	vol = stats.get("volume")
	avg10d = getattr(fi, "ten_day_average_volume", None) or fi.get("ten_day_average_volume")
	avg3m = getattr(fi, "three_month_average_volume", None) or fi.get("three_month_average_volume")
	market_cap = getattr(fi, "market_cap", None) or fi.get("market_cap")
	prev_close_fi = getattr(fi, "previous_close", None) or fi.get("previous_close")

	details = pd.DataFrame(
	{
	"Field": [
	"Previous Close",
	"Open",
	"Day’s High",
	"Day’s Low",
	"Volume (Today)",
	"Avg Volume (10D)",
	"Avg Volume (3M)",
	"Market Cap",
	"52-Week High",
	"52-Week Low",
	],
	"Value": [
	f"${(prev_close or prev_close_fi):,.2f}" if (prev_close or prev_close_fi) else "-",
	f"${open_p:,.2f}" if open_p else "-",
	f"${high_p:,.2f}" if high_p else "-",
	f"${low_p:,.2f}" if low_p else "-",
	human_int(vol) if vol else "-",
	human_int(avg10d) if avg10d else "-",
	human_int(avg3m) if avg3m else "-",
	human_int(market_cap) if market_cap else "-",
	f"${yr_high:,.2f}" if yr_high else "-",
	f"${yr_low:,.2f}" if yr_low else "-",
	],
	}
	)


	st.subheader("Details")
	st.dataframe(
	details,
	use_container_width=True,
	hide_index=True,
	)
	st.caption("Data source: Yahoo Finance via yfinance")