"""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)