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"""Options flow — put/call ratios, IV smile, open interest by strike."""
import pandas as pd
import plotly.graph_objects as go
import streamlit as st
from services.data_service import get_company_info, get_options_chain
def render_options(ticker: str):
info = get_company_info(ticker)
current_price = info.get("currentPrice") or info.get("regularMarketPrice")
with st.spinner("Loading options data…"):
data = get_options_chain(ticker)
if not data or not data.get("chains"):
st.info("Options data unavailable for this ticker.")
return
expirations = data["expirations"]
chains = data["chains"]
# ── Expiry selector ──────────────────────────────────────────────────────
selected_expiry = st.selectbox(
"Expiration date",
options=expirations,
key=f"options_expiry_{ticker}",
)
chain_data = next((c for c in chains if c["expiry"] == selected_expiry), None)
if chain_data is None:
# Expiry beyond the pre-fetched set — fetch on demand
try:
import yfinance as yf
t = yf.Ticker(ticker.upper())
chain = t.option_chain(selected_expiry)
chain_data = {"expiry": selected_expiry, "calls": chain.calls, "puts": chain.puts}
except Exception:
st.info("Could not load chain for this expiry.")
return
calls: pd.DataFrame = chain_data["calls"].copy()
puts: pd.DataFrame = chain_data["puts"].copy()
# ── Summary metrics ──────────────────────────────────────────────────────
total_call_vol = float(calls["volume"].sum()) if "volume" in calls.columns else 0.0
total_put_vol = float(puts["volume"].sum()) if "volume" in puts.columns else 0.0
total_call_oi = float(calls["openInterest"].sum()) if "openInterest" in calls.columns else 0.0
total_put_oi = float(puts["openInterest"].sum()) if "openInterest" in puts.columns else 0.0
pc_vol = total_put_vol / total_call_vol if total_call_vol > 0 else None
pc_oi = total_put_oi / total_call_oi if total_call_oi > 0 else None
def _pc_delta(val):
if val is None:
return None
if val < 0.7:
return "Bullish"
if val < 1.0:
return "Neutral"
return "Bearish"
m1, m2, m3, m4 = st.columns(4)
m1.metric(
"P/C Ratio (Volume)",
f"{pc_vol:.2f}" if pc_vol is not None else "—",
delta=_pc_delta(pc_vol),
delta_color="inverse" if pc_vol and pc_vol >= 1.0 else "normal",
help="Put/Call volume ratio. >1 = more put activity (bearish bets).",
)
m2.metric(
"P/C Ratio (OI)",
f"{pc_oi:.2f}" if pc_oi is not None else "—",
delta=_pc_delta(pc_oi),
delta_color="inverse" if pc_oi and pc_oi >= 1.0 else "normal",
help="Put/Call open interest ratio.",
)
m3.metric("Total Call Volume", f"{int(total_call_vol):,}" if total_call_vol else "—")
m4.metric("Total Put Volume", f"{int(total_put_vol):,}" if total_put_vol else "—")
st.write("")
# Filter strikes ±30% of current price for cleaner charts
if current_price and not calls.empty:
lo, hi = current_price * 0.70, current_price * 1.30
calls_atm = calls[(calls["strike"] >= lo) & (calls["strike"] <= hi)]
puts_atm = puts[(puts["strike"] >= lo) & (puts["strike"] <= hi)]
else:
calls_atm = calls
puts_atm = puts
if calls_atm.empty and puts_atm.empty:
st.info("No near-the-money options found for this expiry.")
return
chart_col1, chart_col2 = st.columns(2)
# ── IV Smile ─────────────────────────────────────────────────────────────
with chart_col1:
if "impliedVolatility" in calls_atm.columns:
st.markdown("**Implied Volatility Smile**")
fig_iv = go.Figure()
fig_iv.add_trace(go.Scatter(
x=calls_atm["strike"],
y=calls_atm["impliedVolatility"] * 100,
name="Calls IV",
mode="lines+markers",
line=dict(color="#4F8EF7", width=2),
marker=dict(size=4),
))
if not puts_atm.empty and "impliedVolatility" in puts_atm.columns:
fig_iv.add_trace(go.Scatter(
x=puts_atm["strike"],
y=puts_atm["impliedVolatility"] * 100,
name="Puts IV",
mode="lines+markers",
line=dict(color="#F7A24F", width=2),
marker=dict(size=4),
))
if current_price:
fig_iv.add_vline(
x=current_price,
line_dash="dash",
line_color="rgba(255,255,255,0.35)",
annotation_text="ATM",
annotation_position="top",
)
fig_iv.update_layout(
yaxis_title="Implied Volatility (%)",
xaxis_title="Strike",
plot_bgcolor="rgba(0,0,0,0)",
paper_bgcolor="rgba(0,0,0,0)",
margin=dict(l=0, r=0, t=10, b=0),
height=300,
legend=dict(orientation="h", yanchor="bottom", y=1.02, xanchor="right", x=1),
hovermode="x unified",
)
st.plotly_chart(fig_iv, use_container_width=True)
# ── Open Interest by strike ───────────────────────────────────────────────
with chart_col2:
if "openInterest" in calls_atm.columns:
st.markdown("**Open Interest by Strike**")
fig_oi = go.Figure()
fig_oi.add_trace(go.Bar(
x=calls_atm["strike"],
y=calls_atm["openInterest"].fillna(0),
name="Calls OI",
marker_color="rgba(79,142,247,0.75)",
))
if not puts_atm.empty and "openInterest" in puts_atm.columns:
fig_oi.add_trace(go.Bar(
x=puts_atm["strike"],
y=-puts_atm["openInterest"].fillna(0),
name="Puts OI",
marker_color="rgba(247,162,79,0.75)",
))
if current_price:
fig_oi.add_vline(
x=current_price,
line_dash="dash",
line_color="rgba(255,255,255,0.35)",
annotation_text="ATM",
annotation_position="top",
)
fig_oi.update_layout(
barmode="overlay",
yaxis_title="Open Interest (puts mirrored)",
xaxis_title="Strike",
plot_bgcolor="rgba(0,0,0,0)",
paper_bgcolor="rgba(0,0,0,0)",
margin=dict(l=0, r=0, t=10, b=0),
height=300,
legend=dict(orientation="h", yanchor="bottom", y=1.02, xanchor="right", x=1),
)
st.plotly_chart(fig_oi, use_container_width=True)
# ── Raw chain table ───────────────────────────────────────────────────────
with st.expander("Full options chain"):
tab_calls, tab_puts = st.tabs(["Calls", "Puts"])
display_cols = ["strike", "lastPrice", "bid", "ask", "volume", "openInterest", "impliedVolatility"]
with tab_calls:
show_cols = [c for c in display_cols if c in calls.columns]
if show_cols:
df_show = calls[show_cols].copy()
if "impliedVolatility" in df_show.columns:
df_show["impliedVolatility"] = df_show["impliedVolatility"].apply(
lambda v: f"{v*100:.1f}%" if pd.notna(v) else "—"
)
st.dataframe(df_show, use_container_width=True, hide_index=True)
with tab_puts:
show_cols = [c for c in display_cols if c in puts.columns]
if show_cols:
df_show = puts[show_cols].copy()
if "impliedVolatility" in df_show.columns:
df_show["impliedVolatility"] = df_show["impliedVolatility"].apply(
lambda v: f"{v*100:.1f}%" if pd.notna(v) else "—"
)
st.dataframe(df_show, use_container_width=True, hide_index=True)
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