components.py

"""
components.py
All Streamlit UI components — shared between Option A and Option B.
"""

import streamlit as st
import pandas as pd
import numpy as np


def show_freshness_status(freshness: dict):
    if freshness.get("fresh"):
        st.success(freshness["message"])
    else:
        st.warning(freshness["message"])


def show_availability_warnings(availability: dict):
    for etf, info in availability.items():
        if not info.get("available"):
            st.warning(info.get("message", f"⚠️ {etf} data unavailable."))


def show_signal_banner(etf: str, hold_period: int, next_date,
                       net_score: float, in_cash: bool,
                       next_etf: str = None):
    is_cash = in_cash or etf == "CASH"

    if is_cash:
        st.warning(f"🛡️ **DRAWDOWN PROTECTION ACTIVE** — Staying in CASH on {next_date}")
    else:
        bg    = "linear-gradient(135deg, #00d1b2 0%, #00a896 100%)"
        label = f"🎯 {next_date} → {etf}"
        sub   = f"Next Trading Day Signal"
        st.markdown(f"""
    <div style="background:{bg}; padding:25px; border-radius:15px;
                text-align:center; box-shadow:0 8px 16px rgba(0,0,0,0.3); margin:16px 0;">
      <div style="color:rgba(255,255,255,0.7); font-size:12px;
                  letter-spacing:3px; margin-bottom:6px;">
        P2-ETF FORECASTER · NEXT TRADING DAY SIGNAL
      </div>
      <h1 style="color:white; font-size:40px; margin:0 0 8px 0; font-weight:800;">
        {label}
      </h1>
      <div style="color:rgba(255,255,255,0.75); font-size:14px;">{sub}</div>
    </div>
        """, unsafe_allow_html=True)


def show_etf_scores_table(scores: dict, arima_results: dict,
                           run_scores: dict, active_etfs: list,
                           hold_periods: list, fee_bps: int):
    st.subheader("📊 ETF Signal Breakdown — Option A")
    st.caption(f"Transaction cost: **{fee_bps} bps** applied to net scores")

    rows = []
    for etf in active_etfs:
        arima         = arima_results.get(etf, {})
        run           = run_scores.get(etf, {})
        err           = arima.get("error")
        direction_str = "↑ Up" if arima.get("direction", 0) == 1 else "↓ Down"
        order_str     = str(arima.get("order", "N/A"))
        pressure      = run.get("pressure", 0.0)
        run_len       = run.get("run_length", 0)
        run_dir       = run.get("direction", "")

        row = {
            "ETF":          etf,
            "ARIMA Order":  order_str,
            "Direction":    direction_str if not err else f"⚠️ {err}",
            "Run Pressure": f"{pressure:.2f} ({run_len}d {run_dir})",
        }
        for h in hold_periods:
            row[f"{h}d Score"] = f"{scores.get(etf, {}).get(h, 0.0):.4f}"
        rows.append(row)

    df_table = pd.DataFrame(rows).reset_index(drop=True)

    def _highlight_best(row):
        try:
            vals = [float(r["1d Score"]) for r in rows]
            if float(row["1d Score"]) == max(vals):
                return ["background-color: rgba(0,200,150,0.15); font-weight:bold"] * len(row)
        except Exception:
            pass
        return [""] * len(row)

    styled = (
        df_table.style
        .apply(_highlight_best, axis=1)
        .set_properties(**{"text-align": "center", "font-size": "13px"})
        .set_table_styles([
            {"selector": "th", "props": [("font-size", "13px"),
                                          ("font-weight", "bold"),
                                          ("text-align", "center")]},
            {"selector": "td", "props": [("padding", "8px")]},
        ])
    )
    st.dataframe(styled, use_container_width=True)


def show_hold_period_rationale(best_etf: str, best_h: int,
                                scores: dict, hold_periods: list, fee_bps: int):
    etf_scores = scores.get(best_etf, {})
    if not etf_scores:
        return

    st.caption(
        f"**Hold Period Selection for {best_etf}** "
        f"(fee = {fee_bps} bps — higher hold = lower relative cost):"
    )
    cols = st.columns(len(hold_periods))
    for col, h in zip(cols, hold_periods):
        s       = etf_scores.get(h, 0.0)
        is_best = h == best_h
        color   = "#00d1b2" if is_best else "#888"
        badge   = "✅ SELECTED" if is_best else ""
        bg      = "rgba(0,209,178,0.08)" if is_best else "#f8f8f8"

        col.markdown(f"""
        <div style="border:2px solid {color}; border-radius:10px; padding:12px;
                    text-align:center; background:{bg};">
            <div style="font-size:11px; color:{color}; font-weight:700;
                        letter-spacing:1px; margin-bottom:4px;">
                {h}-DAY HOLD {badge}
            </div>
            <div style="font-size:24px; font-weight:800;
                        color:{'#00d1b2' if is_best else '#333'};">
                {s:.4f}
            </div>
            <div style="font-size:11px; color:#999;">net score</div>
        </div>
        """, unsafe_allow_html=True)


def show_metrics_row(result: dict, tbill_rate: float, spy_ann: float = None):
    c1, c2, c3, c4, c5 = st.columns(5)

    if spy_ann is not None:
        diff      = (result["ann_return"] - spy_ann) * 100
        sign      = "+" if diff >= 0 else ""
        delta_str = f"vs SPY: {sign}{diff:.2f}%"
    else:
        delta_str = f"vs T-bill: {(result['ann_return'] - tbill_rate)*100:.2f}%"

    c1.metric("📈 Ann. Return",   f"{result['ann_return']*100:.2f}%", delta=delta_str)
    c2.metric("📊 Sharpe",        f"{result['sharpe']:.2f}",
              delta="Strong" if result["sharpe"] > 1 else "Weak")
    c3.metric("🎯 Hit Ratio 15d", f"{result['hit_ratio']*100:.0f}%",
              delta="Good" if result["hit_ratio"] > 0.55 else "Weak")
    c4.metric("📉 Max Drawdown",  f"{result['max_dd']*100:.2f}%",
              delta="Peak to Trough")

    worst_date = result.get("max_daily_date", "N/A")
    dd_delta   = f"on {worst_date}" if worst_date != "N/A" else "Worst Single Day"
    c5.metric("⚠️ Max Daily DD",  f"{result['max_daily_dd']*100:.2f}%", delta=dd_delta)


def show_audit_trail(audit_trail: list):
    """Option A audit trail — shows Hold period column."""
    if not audit_trail:
        st.info("No audit trail data available.")
        return

    df = pd.DataFrame(audit_trail).tail(20)

    if "In_Cash" in df.columns:
        if df["In_Cash"].any():
            df["In_Cash"] = df["In_Cash"].map({True: "🛡️ CASH", False: ""})
        else:
            df = df.drop(columns=["In_Cash"])

    cols = [c for c in ["Date", "Signal", "Hold", "Net_Return", "In_Cash"]
            if c in df.columns]
    df = df[cols]

    def _color_ret(val):
        return ("color: #00c896; font-weight:bold" if val > 0
                else "color: #ff4b4b; font-weight:bold")

    styled = (
        df.style
        .map(_color_ret, subset=["Net_Return"])
        .format({"Net_Return": "{:.2%}"})
        .set_properties(**{"font-size": "14px", "text-align": "center"})
        .set_table_styles([
            {"selector": "th", "props": [("font-size", "13px"),
                                          ("font-weight", "bold"),
                                          ("text-align", "center")]},
            {"selector": "td", "props": [("padding", "10px")]},
        ])
    )
    st.dataframe(styled, use_container_width=True, height=500)


def show_audit_trail_b(audit_trail: list):
    """Option B audit trail — shows momentum Score instead of Hold period."""
    if not audit_trail:
        st.info("No audit trail data available.")
        return

    df = pd.DataFrame(audit_trail).tail(20)

    if "In_Cash" in df.columns:
        if df["In_Cash"].any():
            df["In_Cash"] = df["In_Cash"].map({True: "🛡️ CASH", False: ""})
        else:
            df = df.drop(columns=["In_Cash"])

    cols = [c for c in ["Date", "Signal", "Rank Score", "Net_Return", "In_Cash"]
            if c in df.columns]
    df = df[cols]

    def _color_ret(val):
        return ("color: #00c896; font-weight:bold" if val > 0
                else "color: #ff4b4b; font-weight:bold")

    fmt = {"Net_Return": "{:.2%}"}
    if "Rank Score" in df.columns:
        # Rank Score may be "—" for CASH rows so only format numeric
        fmt["Rank Score"] = lambda x: f"{x:.2f}" if isinstance(x, float) else x

    styled = (
        df.style
        .map(_color_ret, subset=["Net_Return"])
        .format(fmt)
        .set_properties(**{"font-size": "14px", "text-align": "center"})
        .set_table_styles([
            {"selector": "th", "props": [("font-size", "13px"),
                                          ("font-weight", "bold"),
                                          ("text-align", "center")]},
            {"selector": "td", "props": [("padding", "10px")]},
        ])
    )
    st.dataframe(styled, use_container_width=True, height=500)


def show_momentum_scores_table(momentum_scores: dict, active_etfs: list,
                                current_etf: str,
                                lb_short_days: int = 21,
                                lb_mid_days:   int = 63,
                                lb_long_days:  int = 126):
    """Option B — per-ETF rank-based momentum breakdown with correct lookback labels."""

    def _days_to_label(days):
        months = round(days / 21)
        return f"{months}M"

    s_lbl = _days_to_label(lb_short_days)
    m_lbl = _days_to_label(lb_mid_days)
    l_lbl = _days_to_label(lb_long_days)

    st.subheader("📊 ETF Momentum Rankings — Option B")
    st.caption(
        f"Lookback windows: **{s_lbl}** (short) · **{m_lbl}** (mid) · **{l_lbl}** (long) · "
        "Rank 1 = strongest · Lowest composite rank wins"
    )

    rows = []
    for etf in active_etfs:
        info = momentum_scores.get(etf, {})
        rows.append({
            "Selected":         "⭐" if etf == current_etf else "",
            "ETF":              etf,
            f"{s_lbl} Ret":     info.get("ret_1m", 0.0),
            f"{s_lbl} Rank":    info.get("rank_1m", 0),
            f"{m_lbl} Ret":     info.get("ret_3m", 0.0),
            f"{m_lbl} Rank":    info.get("rank_3m", 0),
            f"{l_lbl} Ret":     info.get("ret_6m", 0.0),
            f"{l_lbl} Rank":    info.get("rank_6m", 0),
            "Mom Rank":         info.get("momentum_rank", 0.0),
            "RS/SPY":           info.get("rs_spy", 0.0),
            "RS Rank":          info.get("rs_rank", 0),
            "MA Slope":         info.get("ma_slope", 0.0),
            "MA Rank":          info.get("ma_rank", 0),
            "Comp Rank":        info.get("rank_score", 0.0),
        })

    rows = sorted(rows, key=lambda x: x["Comp Rank"])

    ret_cols   = [f"{s_lbl} Ret", f"{m_lbl} Ret", f"{l_lbl} Ret",
                  "RS/SPY", "MA Slope"]
    col_order  = ["Selected", "ETF",
                  f"{s_lbl} Ret", f"{s_lbl} Rank",
                  f"{m_lbl} Ret", f"{m_lbl} Rank",
                  f"{l_lbl} Ret", f"{l_lbl} Rank",
                  "Mom Rank", "RS/SPY", "RS Rank",
                  "MA Slope", "MA Rank", "Comp Rank"]

    df_table = pd.DataFrame(rows).reset_index(drop=True)[col_order].reset_index(drop=True)

    def _color_ret(val):
        if isinstance(val, float):
            return ("color: #00c896; font-weight:bold" if val > 0
                    else "color: #ff4b4b" if val < 0 else "")
        return ""

    def _highlight_top(row):
        if row["ETF"] == current_etf:
            return ["background-color: rgba(0,200,150,0.15); font-weight:bold"] * len(row)
        return [""] * len(row)

    fmt = {"Comp Rank": "{:.2f}", "Mom Rank": "{:.2f}",
           "RS Rank": "{:.2f}", "MA Rank": "{:.0f}"}
    for c in ret_cols:
        fmt[c] = "{:.2%}"

    styled = (
        df_table.style
        .apply(_highlight_top, axis=1)
        .map(_color_ret, subset=ret_cols)
        .format(fmt)
        .set_properties(**{"text-align": "center", "font-size": "13px"})
        .set_table_styles([
            {"selector": "th", "props": [("font-size", "12px"),
                                          ("font-weight", "bold"),
                                          ("text-align", "center")]},
            {"selector": "td", "props": [("padding", "6px")]},
        ])
    )
    st.dataframe(styled, use_container_width=True)
    st.caption("**Comp Rank** = 50% Momentum Rank + 25% RS/SPY Rank + 25% MA Slope Rank · Lower = stronger signal")


def show_methodology():
    """Option B strategy methodology section."""
    st.divider()
    st.subheader("📖 Strategy Methodology — Option B")

    with st.expander("How does this strategy work?", expanded=False):
        st.markdown("""
### Cross-Sectional Momentum Rotation

This strategy answers one question every trading day: **which ETF has the strongest 
relative momentum right now?** It then holds that ETF until a better opportunity 
or a drawdown protection event occurs.

---

#### Step 1 — Daily Momentum Ranking

For each ETF (TLT, TBT, VNQ, SLV, GLD), three trailing return windows are computed:
- **Short window** (e.g. 2m for a 6m lookback)
- **Mid window** (e.g. 4m)
- **Long window** = the full lookback period you select in the sidebar

Each ETF is then **ranked 1 to 5** within each window (1 = highest return = best).
The **Composite Rank** is the average of these three individual ranks.
The ETF with the **lowest composite rank** (closest to 1) is selected.

This rank-based approach is intentionally relative — it asks *"which ETF is 
strongest vs its peers?"* not *"which ETF is up the most in absolute terms?"*
This prevents a single runaway ETF from dominating purely due to an extreme move.

---

#### Step 2 — Why only 3 to 18 months lookback?

- **Less than 3 months**: Too noisy. Daily return noise dominates over genuine 
  trend signal. Short-term reversals are common and would cause excessive switching.
- **More than 18 months**: Too slow. A regime change (e.g. rates rising, gold 
  falling out of favour) takes many months to show up in the signal — by which 
  time the strategy has already suffered significant losses staying in the wrong ETF.
- **Sweet spot 6–12 months**: Academic research on cross-sectional momentum 
  (Jegadeesh & Titman 1993, and subsequent ETF studies) consistently finds 
  6–12 month formation periods produce the strongest risk-adjusted returns.
  The 9m lookback tends to offer the best Sharpe in this ETF universe.

---

#### Step 3 — Drawdown Protection (Stop Loss)

A **2-day compound drawdown** trigger protects against sudden crashes:

> If (1 + Day₋₂ return) × (1 + Day₋₁ return) − 1 ≤ −10%, move to **CASH**

Key design choices:
- **2-day window**: A single bad day can be noise (e.g. flash crash that reverses). 
  Two consecutive bad days suggest a genuine adverse move.
- **−10% threshold**: Chosen to catch meaningful drawdowns (like Jan 30, 2026's 
  −28.5% SLV crash) without triggering on routine daily volatility.
- **CASH earns T-bill rate** while protection is active — not zero.
- The check happens at the **start of the trading day** using prior days' data, 
  so the signal is always actionable before markets open.

---

#### Step 4 — Re-entry Condition (Z-score ≥ 1.2σ)

Once in CASH, the strategy does **not** simply re-enter after a fixed number of days.
Instead it waits for a statistically meaningful recovery signal:

> Re-enter when the top-ranked ETF's most recent daily return is ≥ **1.2 standard 
> deviations above its 63-day rolling mean**

Why Z-score and not just "two up days"?
- After a crash, prices often bounce weakly before rolling over again (dead cat bounce).
- A Z-score ≥ 1.2σ means the recent return is in the **top ~12% of that ETF's 
  own distribution** — not just slightly positive, but genuinely above-trend.
- The 63-day window (≈ 3 months) is long enough to capture the post-crash 
  distribution including the crash itself, making the bar appropriately high.
- 1.2σ is a deliberate balance: strict enough to avoid re-entering on weak bounces,
  lenient enough not to miss the early stages of a genuine recovery.

---

#### Transaction Costs

Fees are charged **only on switches** between ETFs, not on hold days.
At 10 bps per switch, and with typical holding periods of several weeks,
the annual fee drag is modest — usually 20–50 bps/year depending on regime.
        """)