{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "fcaadb2a",
   "metadata": {},
   "source": [
    "## Intraday Return Reversals and Momentum Around the US Equity Session in Cryptocurrency Markets\n",
    "\n",
    "### Motivation\n",
    "\n",
    "Heston, Korajczyk & Sadka (2010) document strong intraday periodic return patterns in equities, with reversals appearing at half-hour intervals corresponding to institutional order-flow cycles. Bogousslavsky (2016) extends this, showing that slow-moving institutional investors create predictable intraday return autocorrelations that persist for hours after the original price impact. More recently, Gao, Han, Li & Zhou (2018) find significant intraday momentum in equity index futures tied to the first 30 minutes of the trading session.\n",
    "\n",
    "This notebook tests whether analogous effects exist in cryptocurrency markets, which trade 24/7 but experience meaningful institutional activity concentrated around US equity market hours — particularly at the open (09:30 ET) and close (16:00 ET). The hypothesis is that institutional portfolio rebalancing and risk-limit flows create temporary price dislocations that partially reverse over the subsequent 30–90 minutes.\n",
    "\n",
    "Three strategies were tested:\n",
    "1. **Spot crypto close window** — cross-sectional reversal signal built from 15:30–16:00 ET returns, held 16:15–17:00 ET (25 Binance assets, 15-min bars, 2020–present)\n",
    "2. **Spot crypto morning window** — same universe, signal 09:30–10:00 ET, held 10:15–10:45 ET\n",
    "3. **Spot Bitcoin/Ethereum ETFs** — close-window momentum in IBIT, FBTC, ETHA, FETH (yfinance 60-min bars, validated at 5-min resolution)\n",
    "\n",
    "All strategies use a defensible three-component transaction cost model (commission + tier-based spread + square-root market impact) and a time-varying ADV filter to exclude assets that have become illiquid since the original universe was constructed.\n",
    "\n",
    "---\n",
    "*References:*\n",
    "- Heston, S., Korajczyk, R., & Sadka, R. (2010). Intraday Patterns in the Cross-Section of Stock Returns. *Journal of Finance*, 65(4), 1369–1407.\n",
    "- Bogousslavsky, V. (2016). Intraday Trading Patterns and the Role of the Systematic Risk Factors. *Review of Financial Studies*, 29(7), 1912–1950.\n",
    "- Gao, L., Han, Y., Li, S. Z., & Zhou, G. (2018). Market Intraday Momentum. *Journal of Financial Economics*, 129(2), 394–414.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "id": "d6cc6a24",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import yfinance as yf\n",
    "import statsmodels.api as sm\n",
    "from binance.client import Client as bnb_client\n",
    "from statsmodels.tsa.stattools import coint\n",
    "from scipy.stats import pearsonr\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "id": "78ae28cb",
   "metadata": {},
   "outputs": [],
   "source": [
    "client = bnb_client(tld='US')\n",
    "def format_binance(data):\n",
    "    columns = [\n",
    "    \"open_time\",\"open\",\"high\",\"low\",\"close\",\"volume\",\"close_time\",\n",
    "    \"quote_volume\",\"num_trades\",\"taker_base_volume\",\"taker_quote_volume\",\"ignore\"\n",
    "]\n",
    "\n",
    "    data = pd.DataFrame(data,columns = columns)\n",
    "    \n",
    "    # Convert from POSIX timestamp (number of millisecond since jan 1, 1970)\n",
    "    data[\"open_time\"]  = pd.to_datetime(data[\"open_time\"],  unit=\"ms\", utc=True)\n",
    "    data[\"close_time\"] = pd.to_datetime(data[\"close_time\"], unit=\"ms\", utc=True)\n",
    "    return data "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "id": "e3dd283b",
   "metadata": {},
   "outputs": [],
   "source": [
    "tickers = {\n",
    "    'BTC': 'BTCUSDT',\n",
    "    'ETH': 'ETHUSDT',\n",
    "    'SOL': 'SOLUSDT',\n",
    "    'BNB': 'BNBUSDT',\n",
    "    'ADA': 'ADAUSDT',\n",
    "    'AVAX': 'AVAXUSDT',\n",
    "    'NEAR': 'NEARUSDT',\n",
    "    'ATOM': 'ATOMUSDT',\n",
    "    'ALGO': 'ALGOUSDT',\n",
    "    'EGLD': 'EGLDUSDT',\n",
    "    'ICP': 'ICPUSDT',\n",
    "    'ROSE': 'ROSEUSDT',\n",
    "    'CELO': 'CELOUSDT',\n",
    "    'UNI': 'UNIUSDT',\n",
    "    'AAVE': 'AAVEUSDT',\n",
    "    'CRV': 'CRVUSDT',\n",
    "    'SUSHI': 'SUSHIUSDT',\n",
    "    'COMP': 'COMPUSDT',\n",
    "    '1INCH': '1INCHUSDT',\n",
    "    'LINK': 'LINKUSDT',\n",
    "    'GRT': 'GRTUSDT',\n",
    "    'FIL': 'FILUSDT',\n",
    "    'STORJ': 'STORJUSDT',\n",
    "    'AXS': 'AXSUSDT',\n",
    "    'MANA': 'MANAUSDT'\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "id": "86a15ff8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading BTC...\n",
      "Downloading ETH...\n",
      "Downloading SOL...\n",
      "Downloading BNB...\n",
      "Downloading ADA...\n",
      "Downloading AVAX...\n",
      "Downloading NEAR...\n",
      "Downloading ATOM...\n",
      "Downloading ALGO...\n",
      "Downloading EGLD...\n",
      "Downloading ICP...\n",
      "Downloading ROSE...\n",
      "Downloading CELO...\n",
      "Downloading UNI...\n",
      "Downloading AAVE...\n",
      "Downloading CRV...\n",
      "Downloading SUSHI...\n",
      "Downloading COMP...\n",
      "Downloading 1INCH...\n",
      "Downloading LINK...\n",
      "Downloading GRT...\n",
      "Downloading FIL...\n",
      "Downloading STORJ...\n",
      "Downloading AXS...\n",
      "Downloading MANA...\n"
     ]
    }
   ],
   "source": [
    "crypto_prices = {}\n",
    "crypto_vol = {}\n",
    "\n",
    "for name, symbol in tickers.items():\n",
    "    print(f\"Downloading {name}...\")\n",
    "    raw = client.get_historical_klines(symbol, '15m', '2020-09-01')\n",
    "    df = format_binance(raw)\n",
    "    df = df.set_index(\"open_time\")\n",
    "    crypto_prices[name] = df[\"close\"]\n",
    "    crypto_vol[name] = df[\"quote_volume\"]   # liquidity proxy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 289,
   "id": "911912c8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# De-duplicate (keep last bar for any duplicated timestamps)\n",
    "for k in crypto_prices:\n",
    "    s = crypto_prices[k].sort_index()\n",
    "    crypto_prices[k] = s[~s.index.duplicated(keep=\"last\")]\n",
    "\n",
    "prices = pd.DataFrame(crypto_prices).sort_index()\n",
    "qvol   = pd.DataFrame(crypto_vol).sort_index()\n",
    "prices = prices.apply(pd.to_numeric, errors=\"coerce\")\n",
    "qvol   = qvol.apply(pd.to_numeric, errors=\"coerce\")\n",
    "\n",
    "assert prices.index.is_monotonic_increasing and prices.index.is_unique\n",
    "\n",
    "# Train / Test split — last 2 years = test\n",
    "split_date    = prices.index.max() - pd.DateOffset(years=2)\n",
    "prices_train  = prices[prices.index <  split_date]\n",
    "prices_test   = prices[prices.index >= split_date]\n",
    "returns_train = np.log(prices_train).diff()\n",
    "corr          = returns_train.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 290,
   "id": "6f3a506b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# ============================================================\n",
    "# HELPERS\n",
    "# ============================================================\n",
    "\n",
    "def _ensure_prices_et(df):\n",
    "    if df.index.tz is None:\n",
    "        return df.tz_localize(\"UTC\").tz_convert(\"US/Eastern\")\n",
    "    return df.tz_convert(\"US/Eastern\")\n",
    "\n",
    "def _add_minutes(hhmm, mins):\n",
    "    t = pd.Timestamp(\"2000-01-01 \" + hhmm) + pd.Timedelta(minutes=mins)\n",
    "    return t.strftime(\"%H:%M\")\n",
    "\n",
    "def ann_sharpe(x, ann_factor=252):\n",
    "    x = pd.Series(x).dropna()\n",
    "    if x.std() == 0 or len(x) < 30:\n",
    "        return np.nan\n",
    "    return (x.mean() / x.std()) * np.sqrt(ann_factor)\n",
    "\n",
    "def build_signals_from_window(rets_et, sig_start, sig_end, long_short_pct=0.10):\n",
    "    \"\"\"Cross-sectional reversal signal. Long losers, short winners. Dollar-neutral.\"\"\"\n",
    "    sig      = rets_et.between_time(sig_start, sig_end)\n",
    "    sig_rets = sig.groupby(sig.index.date).sum()\n",
    "    ranks    = sig_rets.rank(axis=1, method=\"first\")\n",
    "    n        = sig_rets.shape[1]\n",
    "    low_cut  = int(long_short_pct * n)\n",
    "    high_cut = int((1 - long_short_pct) * n)\n",
    "    w = pd.DataFrame(0.0, index=sig_rets.index, columns=sig_rets.columns)\n",
    "    w[ranks <= low_cut]  =  1.0\n",
    "    w[ranks >= high_cut] = -1.0\n",
    "    denom = w.abs().sum(axis=1).replace(0, np.nan)\n",
    "    return w.div(denom, axis=0).fillna(0.0)\n",
    "\n",
    "def compute_daily_hold_returns(rets_et, hold_start, hold_end):\n",
    "    \"\"\"Sum log-returns within hold window, grouped by calendar date.\"\"\"\n",
    "    hold = rets_et.between_time(hold_start, hold_end)\n",
    "    hr   = hold.groupby(hold.index.date).sum()\n",
    "    hr.index = pd.to_datetime(hr.index)\n",
    "    return hr\n",
    "\n",
    "# ============================================================\n",
    "# DEFENSIBLE TC MODEL\n",
    "# ============================================================\n",
    "# Execution cost assumption (from fee schedule):\n",
    "#   Market orders: ~7 bps commission + ~13 bps slippage = 20 bps/side\n",
    "#   Limit orders:  ~7 bps commission only\n",
    "#\n",
    "# We use limit orders => 7 bps/side commission (flat, non-negotiable).\n",
    "# Spread and market impact are modeled separately using asset liquidity tiers.\n",
    "#\n",
    "# Three components:\n",
    "#   1. Commission : 7 bps/side  (limit orders, per fee schedule)\n",
    "#   2. Spread     : tier-based half-spread (bid-ask width estimate)\n",
    "#   3. Impact     : k * sqrt(trade_USD / ADV_USD)  [Almgren et al. 2005]\n",
    "#\n",
    "# Key fix vs old liquidity_impact model:\n",
    "#   ADV = full-day 30-day rolling dollar volume (not a windowed slice).\n",
    "#   AUM is explicit so participation ratio is a real number.\n",
    "# ============================================================\n",
    "\n",
    "ASSET_TIERS = {\n",
    "    \"BTC\":   \"large\", \"ETH\":   \"large\", \"BNB\":   \"large\", \"SOL\":   \"large\",\n",
    "    \"ADA\":   \"mid\",   \"AVAX\":  \"mid\",   \"LINK\":  \"mid\",   \"UNI\":   \"mid\",\n",
    "    \"AAVE\":  \"mid\",   \"ATOM\":  \"mid\",   \"ALGO\":  \"mid\",   \"NEAR\":  \"mid\",\n",
    "    \"EGLD\":  \"mid\",   \"ICP\":   \"mid\",   \"MANA\":  \"mid\",   \"AXS\":   \"mid\",\n",
    "    \"CRV\":   \"small\", \"SUSHI\": \"small\", \"COMP\":  \"small\", \"1INCH\": \"small\",\n",
    "    \"GRT\":   \"small\", \"FIL\":   \"small\", \"STORJ\": \"small\", \"ROSE\":  \"small\",\n",
    "    \"CELO\":  \"small\",\n",
    "}\n",
    "SPREAD_BPS = {\"large\": 1.5, \"mid\": 4.0, \"small\": 15.0}   # half-spread estimate\n",
    "K_IMPACT   = {\"large\": 100.0, \"mid\": 150.0, \"small\": 250.0}  # sqrt-impact constant\n",
    "\n",
    "def compute_adv(qvol_et, window_days=30):\n",
    "    \"\"\"30-day rolling average full-day dollar volume per asset.\"\"\"\n",
    "    daily = qvol_et.groupby(qvol_et.index.date).sum()\n",
    "    daily.index = pd.to_datetime(daily.index)\n",
    "    return daily.rolling(window=window_days, min_periods=5).mean()\n",
    "\n",
    "def compute_tc(weights, adv, aum_usd=100_000, commission_bps=7.0, stress_mult=1.0):\n",
    "    \"\"\"\n",
    "    Three-component TC. Returns DataFrame with tc_commission, tc_spread,\n",
    "    tc_impact, tc_total — all in return space (already divided by 10_000).\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    weights       : daily weight matrix (dollar-neutral, sum|w|=1)\n",
    "    adv           : daily ADV from compute_adv()\n",
    "    aum_usd       : strategy notional — needed to size market impact\n",
    "    commission_bps: 7 bps for limit orders, 20 bps for market orders\n",
    "    stress_mult   : scale impact component (1.0=base, 2.0=stress test)\n",
    "    \"\"\"\n",
    "    w     = weights.fillna(0.0)\n",
    "    # Robust ADV alignment: normalize both indices to tz-naive date Timestamps\n",
    "    # so reindex works regardless of whether w.index has date objects,\n",
    "    # tz-aware Timestamps, or tz-naive Timestamps (the source of tc_impact=97bps).\n",
    "    def _to_date_ts(idx):\n",
    "        return pd.to_datetime([t.date() if hasattr(t, \"date\") else t for t in idx])\n",
    "    w_idx_norm  = _to_date_ts(w.index)\n",
    "    adv_norm    = adv.copy(); adv_norm.index = _to_date_ts(adv.index)\n",
    "    adv_a       = adv_norm.reindex(w_idx_norm, method=\"ffill\").reindex(columns=w.columns)\n",
    "    adv_a.index = w.index\n",
    "\n",
    "    trade_usd = w.abs() * aum_usd\n",
    "\n",
    "    # 1) Commission: 2 sides per round trip\n",
    "    tc_commission = (2 * commission_bps / 10_000) * w.abs().sum(axis=1)\n",
    "\n",
    "    # 2) Spread: tier-based, 1 full spread per round trip\n",
    "    spread = pd.DataFrame(\n",
    "        {col: SPREAD_BPS.get(ASSET_TIERS.get(col, \"mid\"), 4.0) for col in w.columns},\n",
    "        index=w.index\n",
    "    )\n",
    "    tc_spread = ((spread / 10_000) * w.abs()).sum(axis=1)\n",
    "\n",
    "    # 3) Market impact: sqrt model, paid on both entry and exit\n",
    "    k = pd.DataFrame(\n",
    "        {col: K_IMPACT.get(ASSET_TIERS.get(col, \"mid\"), 150.0) for col in w.columns},\n",
    "        index=w.index\n",
    "    )\n",
    "    participation = (trade_usd / adv_a.clip(lower=1.0)).clip(upper=0.10)\n",
    "    impact_bps    = k * np.sqrt(participation) * stress_mult\n",
    "    tc_impact     = ((2 * impact_bps / 10_000) * w.abs()).sum(axis=1)\n",
    "\n",
    "    return pd.DataFrame({\n",
    "        \"tc_commission\": tc_commission,\n",
    "        \"tc_spread\":     tc_spread,\n",
    "        \"tc_impact\":     tc_impact,\n",
    "        \"tc_total\":      tc_commission + tc_spread + tc_impact,\n",
    "    })"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 291,
   "id": "1c6695a2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x1000 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12, 10))\n",
    "plt.imshow(corr, cmap=\"coolwarm\", vmin=-1, vmax=1)\n",
    "plt.colorbar(label=\"Correlation\")\n",
    "plt.xticks(range(len(corr)), corr.columns, rotation=90)\n",
    "plt.yticks(range(len(corr)), corr.columns)\n",
    "plt.title(\"Crypto Return Correlation Matrix (Training Period)\")\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "257f8934-e7cc-44b3-8180-886231cf4885",
   "metadata": {},
   "source": [
    "## Part 1 — Spot Crypto Close-Window Strategy\n",
    "\n",
    "Cross-sectional reversal on 25 Binance assets. Each day, assets are ranked by their 15:30–16:00 ET return. The strategy goes long the bottom decile (biggest losers) and short the top decile (biggest winners), dollar-neutral, then holds from 16:15–17:00 ET. The 15-minute gap between signal end and hold start ensures no look-ahead bias from shared price bars.\n",
    "\n",
    "**Transaction costs** use a three-component model: (1) 7 bps/side flat commission (limit orders), (2) tier-based half-spread (1.5/4.0/15.0 bps for large/mid/small-cap), (3) square-root market impact calibrated to 30-day rolling ADV. A time-varying ADV filter ($1,000/day minimum) excludes assets that have become illiquid — many 2020-era DeFi tokens that were tradeable at launch have since seen near-zero volume.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 293,
   "id": "ee8366ab",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Current ADV (30d median):\n",
      "BTC      2766732.0\n",
      "ETH      2519334.0\n",
      "SOL       938865.0\n",
      "BNB       733599.0\n",
      "ADA        90204.0\n",
      "LINK       49167.0\n",
      "AVAX       24495.0\n",
      "ICP        15635.0\n",
      "ALGO        7187.0\n",
      "UNI         5762.0\n",
      "ATOM        5504.0\n",
      "ROSE        5164.0\n",
      "AAVE        4391.0\n",
      "AXS         3340.0\n",
      "FIL         2889.0\n",
      "MANA        2753.0\n",
      "NEAR        2710.0\n",
      "CRV         2026.0\n",
      "COMP        1123.0\n",
      "GRT          924.0\n",
      "EGLD         625.0\n",
      "1INCH        388.0\n",
      "CELO         291.0\n",
      "SUSHI        286.0\n",
      "STORJ        112.0\n",
      "\n",
      "Cut at $1,000/day — dropping: ['GRT', 'EGLD', '1INCH', 'CELO', 'SUSHI', 'STORJ']\n",
      "\n",
      "Avg legs per day — long: 1.8, short: 3.2, total: 4.9\n",
      "\n",
      "=== Close-Window | Time-varying ADV filter ===\n",
      "Gross SR           : 5.85\n",
      "Net SR  (train)    : -20.47  (1271 days)\n",
      "Net SR  (test)     : -24.22  (732 days)\n",
      "\n",
      "TC breakdown (mean bps/day):\n",
      "  tc_commission   : 13.98\n",
      "  tc_spread       : 7.42\n",
      "  tc_impact       : 96.97\n",
      "  tc_total        : 118.36\n",
      "  gross alpha     : 23.98\n",
      "\n",
      "Year-by-year net SR (full period):\n",
      "  2020: SR = -17.24  (122 days)\n",
      "  2021: SR = -11.02  (365 days)\n",
      "  2022: SR = -32.19  (365 days)\n",
      "  2023: SR = -30.61  (365 days)\n",
      "  2024: SR = -28.24  (366 days)\n",
      "  2025: SR = -22.40  (365 days)\n",
      "  2026: SR = -21.27  (55 days)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1400x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "=== TC Sensitivity (test period) ===\n",
      "  Zero TC (gross)                    : SR = +7.45  (mean +24.0 bps/d)\n",
      "  Commission only (14 bps/d)         : SR = +4.02  (mean +10.0 bps/d)\n",
      "  Commission + Spread                : SR = +2.21  (mean +2.6 bps/d)\n",
      "  Full model (+ Impact)              : SR = -24.22  (mean -94.4 bps/d)\n",
      "\n",
      "  Gross alpha:  24.0 bps/day\n",
      "  Commission:   14.0 bps/day\n",
      "  Spread:       7.4 bps/day\n",
      "  Impact:       97.0 bps/day  ← kills it\n",
      "\n",
      "Breakeven: alpha (24.0 bps/d) is only enough to cover commission (14.0) + spread (7.4) = 21.4 bps/d\n",
      "Impact alone (97.0 bps/d) exceeds total gross alpha (24.0 bps/d) by 4.0x\n",
      "=== Large-Cap Only (BTC/ETH/SOL/BNB) ===\n",
      "ADV range: $733,599 – $2,766,732/day\n",
      "\n",
      "  25% long/short (1 asset per leg):\n",
      "    Gross SR (test)  : 2.25\n",
      "    Net SR   (test)  : -23.06\n",
      "    TC breakdown     : commission 14.0 | spread 1.5 | impact 32.0 | total 47.5 bps/d\n",
      "\n",
      "  50% long/short (2 asset per leg):\n",
      "    Gross SR (test)  : 1.24\n",
      "    Net SR   (test)  : -19.98\n",
      "    TC breakdown     : commission 14.0 | spread 1.5 | impact 28.0 | total 43.5 bps/d\n",
      "\n",
      "  TC sensitivity (large-cap 25%, test period):\n",
      "    Zero TC (gross)          : SR = +2.25  (+4.5 bps/d)\n",
      "    Commission only          : SR = -4.35  (-9.5 bps/d)\n",
      "    Commission+Spread        : SR = -5.06  (-11.0 bps/d)\n",
      "    Full model               : SR = -23.06  (-43.0 bps/d)\n"
     ]
    }
   ],
   "source": [
    "# ============================================================\n",
    "# PART 1 — SPOT CRYPTO CLOSE-WINDOW STRATEGY\n",
    "# ============================================================\n",
    "\n",
    "AUM_USD     = 100_000\n",
    "MIN_ADV_DAY = 1_000    # $1k/day — cuts only the truly dead tokens (STORJ $109,\n",
    "                        # CELO $292, SUSHI $286) while keeping mid-caps when they\n",
    "                        # were liquid in 2021-22. 10% long/short on a ~15-asset\n",
    "                        # eligible set gives 1-2 assets per leg, which is reasonable.\n",
    "\n",
    "prices_et = _ensure_prices_et(prices.copy())\n",
    "qvol_et   = _ensure_prices_et(qvol.copy())\n",
    "rets_et   = np.log(prices_et).diff()\n",
    "\n",
    "# Compute ADV once — reused everywhere\n",
    "adv = compute_adv(qvol_et, window_days=30)\n",
    "\n",
    "# ── Print today's ADV snapshot ───────────────────────────────────────────────\n",
    "_adv_recent = adv.dropna(how='all').tail(30).median().sort_values(ascending=False)\n",
    "print(\"Current ADV (30d median):\")\n",
    "print(_adv_recent.round(0).to_string())\n",
    "print(f\"\\nCut at ${MIN_ADV_DAY:,}/day — dropping: \"\n",
    "      f\"{list(_adv_recent[_adv_recent < MIN_ADV_DAY].index)}\")\n",
    "print()\n",
    "\n",
    "# ── Vectorized time-varying ADV filter ───────────────────────────────────────\n",
    "# For each date, rank only assets whose rolling ADV >= MIN_ADV_DAY.\n",
    "# Fully vectorized — no Python date loop.\n",
    "\n",
    "def build_signals_adv_filtered(rets_et, adv, sig_start, sig_end,\n",
    "                                min_adv=1_000, long_short_pct=0.10):\n",
    "    \"\"\"\n",
    "    Cross-sectional reversal signal with per-date ADV eligibility filter.\n",
    "    Vectorized: no Python loop over dates.\n",
    "    \"\"\"\n",
    "    sig      = rets_et.between_time(sig_start, sig_end)\n",
    "    sig_rets = sig.groupby(sig.index.date).sum()\n",
    "    sig_rets.index = pd.to_datetime(sig_rets.index)\n",
    "\n",
    "    # Align ADV to signal dates\n",
    "    def _to_date_ts(idx):\n",
    "        return pd.to_datetime([t.date() if hasattr(t, 'date') else t for t in idx])\n",
    "    adv_norm = adv.copy(); adv_norm.index = _to_date_ts(adv.index)\n",
    "    adv_daily = (adv_norm\n",
    "                 .reindex(_to_date_ts(sig_rets.index), method='ffill')\n",
    "                 .reindex(columns=sig_rets.columns))\n",
    "    adv_daily.index = sig_rets.index\n",
    "\n",
    "    # Mask ineligible assets — set to NaN so rank() skips them\n",
    "    masked = sig_rets.where(adv_daily >= min_adv)   # NaN where ineligible\n",
    "\n",
    "    # Vectorized rank (na_option='keep' leaves ineligible as NaN)\n",
    "    ranks   = masked.rank(axis=1, method='first', na_option='keep')\n",
    "    n_elig  = (adv_daily >= min_adv).sum(axis=1).astype(float)   # eligible count per day\n",
    "\n",
    "    # Long/short cutoffs based on eligible count\n",
    "    low_cut  = (long_short_pct * n_elig).clip(lower=1).round()\n",
    "    high_cut = ((1 - long_short_pct) * n_elig).clip(lower=1).round()\n",
    "\n",
    "    # Broadcast cutoffs for comparison (dates × 1)\n",
    "    lc = low_cut.values[:, None]\n",
    "    hc = high_cut.values[:, None]\n",
    "\n",
    "    w = pd.DataFrame(0.0, index=sig_rets.index, columns=sig_rets.columns)\n",
    "    w[ranks.le(lc) & ranks.notna()]  =  1.0   # long: bottom long_short_pct\n",
    "    w[ranks.ge(hc) & ranks.notna()]  = -1.0   # short: top long_short_pct\n",
    "\n",
    "    denom = w.abs().sum(axis=1).replace(0, np.nan)\n",
    "    w     = w.div(denom, axis=0).fillna(0.0)\n",
    "    return w\n",
    "\n",
    "# ── Build signals ─────────────────────────────────────────────────────────────\n",
    "signals_close   = build_signals_adv_filtered(rets_et, adv, \"15:30\", \"16:00\",\n",
    "                                              min_adv=MIN_ADV_DAY)\n",
    "hold_rets_close = compute_daily_hold_returns(rets_et, \"16:15\", \"17:00\")\n",
    "signals_close.index   = pd.to_datetime(signals_close.index)\n",
    "hold_rets_close.index = pd.to_datetime(hold_rets_close.index)\n",
    "signals_close, hold_rets_close = signals_close.align(hold_rets_close, join=\"inner\", axis=0)\n",
    "\n",
    "# Avg legs per day\n",
    "n_long  = (signals_close > 0).sum(axis=1).mean()\n",
    "n_short = (signals_close < 0).sum(axis=1).mean()\n",
    "print(f\"Avg legs per day — long: {n_long:.1f}, short: {n_short:.1f}, total: {n_long+n_short:.1f}\")\n",
    "print()\n",
    "\n",
    "# ── Backtest ──────────────────────────────────────────────────────────────────\n",
    "pnl_gross_close = (signals_close * hold_rets_close).sum(axis=1)\n",
    "tc_close        = compute_tc(signals_close, adv, aum_usd=AUM_USD, commission_bps=7.0)\n",
    "pnl_net_close   = (pnl_gross_close - tc_close[\"tc_total\"].reindex(pnl_gross_close.index)).dropna()\n",
    "\n",
    "split_date = pnl_net_close.index.max() - pd.DateOffset(years=2)\n",
    "train_c    = pnl_net_close[pnl_net_close.index <  split_date]\n",
    "test_c     = pnl_net_close[pnl_net_close.index >= split_date]\n",
    "\n",
    "print(\"=== Close-Window | Time-varying ADV filter ===\")\n",
    "print(f\"Gross SR           : {ann_sharpe(pnl_gross_close):.2f}\")\n",
    "print(f\"Net SR  (train)    : {ann_sharpe(train_c):.2f}  ({len(train_c)} days)\")\n",
    "print(f\"Net SR  (test)     : {ann_sharpe(test_c):.2f}  ({len(test_c)} days)\")\n",
    "print()\n",
    "print(\"TC breakdown (mean bps/day):\")\n",
    "for col in [\"tc_commission\",\"tc_spread\",\"tc_impact\",\"tc_total\"]:\n",
    "    print(f\"  {col:16s}: {tc_close[col].mean()*1e4:.2f}\")\n",
    "print(f\"  {'gross alpha':16s}: {pnl_gross_close.mean()*1e4:.2f}\")\n",
    "\n",
    "# ── Year-by-year breakdown — shows when strategy worked vs died ───────────────\n",
    "print()\n",
    "print(\"Year-by-year net SR (full period):\")\n",
    "pnl_all = (pnl_gross_close - tc_close[\"tc_total\"].reindex(pnl_gross_close.index)).dropna()\n",
    "for yr in sorted(pnl_all.index.year.unique()):\n",
    "    sub = pnl_all[pnl_all.index.year == yr]\n",
    "    print(f\"  {yr}: SR = {ann_sharpe(sub):+.2f}  ({len(sub)} days)\")\n",
    "\n",
    "fig, axes = plt.subplots(1, 2, figsize=(14, 4))\n",
    "axes[0].plot(train_c.cumsum(), label=\"Train\", lw=2)\n",
    "axes[0].plot(test_c.cumsum(),  label=\"Test\",  lw=2)\n",
    "axes[0].axhline(0, color=\"black\", lw=0.8, ls=\"--\")\n",
    "axes[0].set_title(\"Close-Window: Cumulative Net PnL\"); axes[0].legend()\n",
    "axes[1].hist(test_c, bins=50)\n",
    "axes[1].set_title(\"Close-Window Test Return Distribution\")\n",
    "plt.tight_layout(); plt.show()\n",
    "\n",
    "# ── TC sensitivity — shows where the alpha dies ──────────────────────────────\n",
    "# The question: at what cost level does this become viable?\n",
    "print()\n",
    "print(\"=== TC Sensitivity (test period) ===\")\n",
    "tc_scenarios = {\n",
    "    \"Zero TC (gross)\":            pnl_gross_close,\n",
    "    \"Commission only (14 bps/d)\": pnl_gross_close - (2*7.0/10_000) * signals_close.abs().sum(axis=1),\n",
    "    \"Commission + Spread\":        pnl_gross_close - (tc_close[\"tc_commission\"] + tc_close[\"tc_spread\"]).reindex(pnl_gross_close.index),\n",
    "    \"Full model (+ Impact)\":      pnl_gross_close - tc_close[\"tc_total\"].reindex(pnl_gross_close.index),\n",
    "}\n",
    "for label, pnl in tc_scenarios.items():\n",
    "    pnl = pnl.dropna()\n",
    "    test_pnl = pnl[pnl.index >= split_date]\n",
    "    print(f\"  {label:35s}: SR = {ann_sharpe(test_pnl):+.2f}  (mean {pnl.mean()*1e4:+.1f} bps/d)\")\n",
    "print()\n",
    "print(f\"  Gross alpha:  {pnl_gross_close.mean()*1e4:.1f} bps/day\")\n",
    "print(f\"  Commission:   {tc_close['tc_commission'].mean()*1e4:.1f} bps/day\")\n",
    "print(f\"  Spread:       {tc_close['tc_spread'].mean()*1e4:.1f} bps/day\")\n",
    "print(f\"  Impact:       {tc_close['tc_impact'].mean()*1e4:.1f} bps/day  ← kills it\")\n",
    "print()\n",
    "print(\"Breakeven: alpha ({:.1f} bps/d) is only enough to cover commission ({:.1f}) + spread ({:.1f}) = {:.1f} bps/d\".format(\n",
    "    pnl_gross_close.mean()*1e4,\n",
    "    tc_close['tc_commission'].mean()*1e4,\n",
    "    tc_close['tc_spread'].mean()*1e4,\n",
    "    (tc_close['tc_commission'] + tc_close['tc_spread']).mean()*1e4\n",
    "))\n",
    "print(\"Impact alone ({:.1f} bps/d) exceeds total gross alpha ({:.1f} bps/d) by {:.1f}x\".format(\n",
    "    tc_close['tc_impact'].mean()*1e4,\n",
    "    pnl_gross_close.mean()*1e4,\n",
    "    tc_close['tc_impact'].mean()/pnl_gross_close.mean()\n",
    "))\n",
    "\n",
    "# ── Large-cap only: BTC, ETH, SOL, BNB ───────────────────────────────────────\n",
    "# With 4 assets we need wider long/short bands (25% = 1 asset per leg).\n",
    "# Tests whether the signal works on liquid assets where impact is manageable.\n",
    "\n",
    "LARGE_CAPS = [\"BTC\",\"ETH\",\"SOL\",\"BNB\"]\n",
    "rets_lc    = np.log(prices_et[LARGE_CAPS]).diff()\n",
    "adv_lc     = compute_adv(_ensure_prices_et(qvol[LARGE_CAPS].copy()), window_days=30)\n",
    "\n",
    "print(\"=== Large-Cap Only (BTC/ETH/SOL/BNB) ===\")\n",
    "print(f\"ADV range: ${adv_lc.dropna(how='all').tail(30).median().min():,.0f} – \"\n",
    "      f\"${adv_lc.dropna(how='all').tail(30).median().max():,.0f}/day\")\n",
    "print()\n",
    "\n",
    "for ls_pct in [0.25, 0.50]:\n",
    "    sig_lc  = build_signals_adv_filtered(rets_lc, adv_lc, \"15:30\", \"16:00\",\n",
    "                                          min_adv=0, long_short_pct=ls_pct)\n",
    "    hold_lc = compute_daily_hold_returns(rets_lc, \"16:15\", \"17:00\")\n",
    "    sig_lc.index  = pd.to_datetime(sig_lc.index)\n",
    "    hold_lc.index = pd.to_datetime(hold_lc.index)\n",
    "    sl, hl = sig_lc.align(hold_lc, join=\"inner\", axis=0)\n",
    "\n",
    "    pg_lc  = (sl * hl).sum(axis=1)\n",
    "    tc_lc  = compute_tc(sl, adv_lc, aum_usd=AUM_USD, commission_bps=7.0)\n",
    "    pnl_lc = (pg_lc - tc_lc[\"tc_total\"].reindex(pg_lc.index)).dropna()\n",
    "    sp_lc  = pnl_lc.index.max() - pd.DateOffset(years=2)\n",
    "\n",
    "    print(f\"  {int(ls_pct*100)}% long/short ({int(ls_pct*4)} asset per leg):\")\n",
    "    print(f\"    Gross SR (test)  : {ann_sharpe(pg_lc[pg_lc.index >= sp_lc]):.2f}\")\n",
    "    print(f\"    Net SR   (test)  : {ann_sharpe(pnl_lc[pnl_lc.index >= sp_lc]):.2f}\")\n",
    "    print(f\"    TC breakdown     : commission {tc_lc['tc_commission'].mean()*1e4:.1f} | \"\n",
    "          f\"spread {tc_lc['tc_spread'].mean()*1e4:.1f} | \"\n",
    "          f\"impact {tc_lc['tc_impact'].mean()*1e4:.1f} | \"\n",
    "          f\"total {tc_lc['tc_total'].mean()*1e4:.1f} bps/d\")\n",
    "    print()\n",
    "\n",
    "# TC sensitivity for large-cap 25% version (most interesting)\n",
    "sig_lc25   = build_signals_adv_filtered(rets_lc, adv_lc, \"15:30\",\"16:00\",\n",
    "                                         min_adv=0, long_short_pct=0.25)\n",
    "hold_lc25  = compute_daily_hold_returns(rets_lc, \"16:15\",\"17:00\")\n",
    "sig_lc25.index  = pd.to_datetime(sig_lc25.index)\n",
    "hold_lc25.index = pd.to_datetime(hold_lc25.index)\n",
    "sl25, hl25 = sig_lc25.align(hold_lc25, join=\"inner\", axis=0)\n",
    "pg_lc25    = (sl25 * hl25).sum(axis=1)\n",
    "tc_lc25    = compute_tc(sl25, adv_lc, aum_usd=AUM_USD, commission_bps=7.0)\n",
    "sp_lc25    = pg_lc25.index.max() - pd.DateOffset(years=2)\n",
    "\n",
    "print(\"  TC sensitivity (large-cap 25%, test period):\")\n",
    "for label, pnl in {\n",
    "    \"Zero TC (gross)\":     pg_lc25,\n",
    "    \"Commission only\":     pg_lc25 - (2*7.0/10_000)*sl25.abs().sum(axis=1),\n",
    "    \"Commission+Spread\":   pg_lc25 - (tc_lc25[\"tc_commission\"]+tc_lc25[\"tc_spread\"]).reindex(pg_lc25.index),\n",
    "    \"Full model\":          pg_lc25 - tc_lc25[\"tc_total\"].reindex(pg_lc25.index),\n",
    "}.items():\n",
    "    p = pnl.dropna(); tp = p[p.index >= sp_lc25]\n",
    "    print(f\"    {label:25s}: SR = {ann_sharpe(tp):+.2f}  ({p.mean()*1e4:+.1f} bps/d)\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 294,
   "id": "82d81c31",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AUM</th>\n",
       "      <th>Net SR (test)</th>\n",
       "      <th>TC total (bps/d)</th>\n",
       "      <th>Commission</th>\n",
       "      <th>Spread</th>\n",
       "      <th>Impact</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>$    10,000</td>\n",
       "      <td>-19.98</td>\n",
       "      <td>92.1</td>\n",
       "      <td>14.0</td>\n",
       "      <td>7.4</td>\n",
       "      <td>70.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>$    50,000</td>\n",
       "      <td>-23.45</td>\n",
       "      <td>112.3</td>\n",
       "      <td>14.0</td>\n",
       "      <td>7.4</td>\n",
       "      <td>90.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>$   100,000</td>\n",
       "      <td>-24.22</td>\n",
       "      <td>118.4</td>\n",
       "      <td>14.0</td>\n",
       "      <td>7.4</td>\n",
       "      <td>97.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>$   250,000</td>\n",
       "      <td>-24.91</td>\n",
       "      <td>124.6</td>\n",
       "      <td>14.0</td>\n",
       "      <td>7.4</td>\n",
       "      <td>103.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>$   500,000</td>\n",
       "      <td>-25.27</td>\n",
       "      <td>128.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>7.4</td>\n",
       "      <td>106.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>$ 1,000,000</td>\n",
       "      <td>-25.48</td>\n",
       "      <td>130.3</td>\n",
       "      <td>14.0</td>\n",
       "      <td>7.4</td>\n",
       "      <td>108.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>$ 2,500,000</td>\n",
       "      <td>-25.58</td>\n",
       "      <td>132.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>7.4</td>\n",
       "      <td>110.6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           AUM  Net SR (test)  TC total (bps/d)   Commission   Spread   Impact\n",
       "0  $    10,000         -19.98              92.1         14.0      7.4     70.7\n",
       "1  $    50,000         -23.45             112.3         14.0      7.4     90.9\n",
       "2  $   100,000         -24.22             118.4         14.0      7.4     97.0\n",
       "3  $   250,000         -24.91             124.6         14.0      7.4    103.2\n",
       "4  $   500,000         -25.27             128.0         14.0      7.4    106.6\n",
       "5  $ 1,000,000         -25.48             130.3         14.0      7.4    108.9\n",
       "6  $ 2,500,000         -25.58             132.0         14.0      7.4    110.6"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# ── Capacity analysis (ADV-filtered universe) ────────────────────────────────\n",
    "aum_range = [10_000, 50_000, 100_000, 250_000, 500_000, 1_000_000, 2_500_000]\n",
    "cap_rows  = []\n",
    "for aum in aum_range:\n",
    "    tc   = compute_tc(signals_close, adv, aum_usd=aum, commission_bps=7.0)\n",
    "    pnl  = (pnl_gross_close - tc[\"tc_total\"].reindex(pnl_gross_close.index)).dropna()\n",
    "    split = pnl.index.max() - pd.DateOffset(years=2)\n",
    "    cap_rows.append({\n",
    "        \"AUM\":               f\"${aum:>10,.0f}\",\n",
    "        \"Net SR (test)\":     round(ann_sharpe(pnl[pnl.index >= split]), 2),\n",
    "        \"TC total (bps/d)\":  round(tc[\"tc_total\"].mean()*1e4, 1),\n",
    "        \" Commission\":       round(tc[\"tc_commission\"].mean()*1e4, 1),\n",
    "        \" Spread\":           round(tc[\"tc_spread\"].mean()*1e4, 1),\n",
    "        \" Impact\":           round(tc[\"tc_impact\"].mean()*1e4, 1),\n",
    "    })\n",
    "display(pd.DataFrame(cap_rows))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 295,
   "id": "6c083150",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "=== Impact Stress Test (AUM=$100k, 7 bps commission) ===\n",
      "  1.0x : SR = -24.22  |  TC = 118.4 bps/day\n",
      "  1.5x : SR = -36.05  |  TC = 166.8 bps/day\n",
      "  2.0x : SR = -46.21  |  TC = 215.3 bps/day\n",
      "  3.0x : SR = -61.34  |  TC = 312.3 bps/day\n",
      "\n",
      "Market orders (20 bps flat, no impact): SR = -30.56\n",
      "\n",
      "=== Placebo Tests ===\n",
      "Live signal test SR            : -24.22\n",
      "(a) Randomized-sign SR         : -2.48  (expect negative)\n",
      "(b) Date-shuffled SR           : -4.02  (expect negative)\n",
      "(c) Dead-period (3AM) SR       : -8.17  (expect negative)\n"
     ]
    }
   ],
   "source": [
    "# ── Impact stress test ───────────────────────────────────────────────────────\n",
    "print(\"=== Impact Stress Test (AUM=$100k, 7 bps commission) ===\")\n",
    "for mult in [1.0, 1.5, 2.0, 3.0]:\n",
    "    tc  = compute_tc(signals_close, adv, aum_usd=100_000, stress_mult=mult)\n",
    "    pnl = (pnl_gross_close - tc[\"tc_total\"].reindex(pnl_gross_close.index)).dropna()\n",
    "    split = pnl.index.max() - pd.DateOffset(years=2)\n",
    "    sh  = ann_sharpe(pnl[pnl.index >= split])\n",
    "    print(f\"  {mult:.1f}x : SR = {sh:+.2f}  |  TC = {tc['tc_total'].mean()*1e4:.1f} bps/day\")\n",
    "\n",
    "print()\n",
    "tc_mkt = compute_tc(signals_close, adv, aum_usd=100_000, commission_bps=20.0)\n",
    "pnl_mkt = (pnl_gross_close - tc_mkt[\"tc_total\"].reindex(pnl_gross_close.index)).dropna()\n",
    "split = pnl_mkt.index.max() - pd.DateOffset(years=2)\n",
    "print(f\"Market orders (20 bps flat, no impact): SR = {ann_sharpe(pnl_mkt[pnl_mkt.index>=split]):+.2f}\")\n",
    "\n",
    "# ── Placebo tests (flat 7 bps TC — impact is negligible at small notional) ──\n",
    "flat_tc = lambda w: (2 * 7.0 / 10_000) * w.abs().sum(axis=1)\n",
    "\n",
    "# (a) Randomize signal signs\n",
    "rng      = np.random.default_rng(42)\n",
    "sig_rand = signals_close.copy()\n",
    "sig_rand.iloc[:,:] = sig_rand.values * rng.choice([-1,1], size=sig_rand.shape)\n",
    "pnl_rand = (sig_rand * hold_rets_close).sum(axis=1) - flat_tc(sig_rand)\n",
    "pnl_rand = pnl_rand.reindex(test_c.index).dropna()\n",
    "\n",
    "# (b) Shuffle signal rows (break date↔return link)\n",
    "rng2     = np.random.default_rng(99)\n",
    "perm     = rng2.permutation(len(signals_close))\n",
    "sig_shuf = pd.DataFrame(signals_close.values[perm],\n",
    "                         index=signals_close.index,\n",
    "                         columns=signals_close.columns)\n",
    "pnl_shuf = (sig_shuf * hold_rets_close).sum(axis=1) - flat_tc(sig_shuf)\n",
    "pnl_shuf = pnl_shuf.reindex(test_c.index).dropna()\n",
    "\n",
    "# (c) Dead-period signal (3:00–3:30 AM ET — no institutional activity)\n",
    "sig_dead  = build_signals_from_window(rets_et, \"03:00\", \"03:30\")\n",
    "hold_dead = compute_daily_hold_returns(rets_et, \"03:30\", \"04:15\")\n",
    "sig_dead.index  = pd.to_datetime(sig_dead.index)\n",
    "hold_dead.index = pd.to_datetime(hold_dead.index)\n",
    "sig_d, hold_d   = sig_dead.align(hold_dead, join=\"inner\", axis=0)\n",
    "pnl_dead_net    = ((sig_d * hold_d).sum(axis=1) - flat_tc(sig_d)).dropna()\n",
    "dead_test       = pnl_dead_net[pnl_dead_net.index >= pd.Timestamp(split_date)]\n",
    "\n",
    "print()\n",
    "print(\"=== Placebo Tests ===\")\n",
    "print(f\"Live signal test SR            : {ann_sharpe(test_c):.2f}\")\n",
    "print(f\"(a) Randomized-sign SR         : {ann_sharpe(pnl_rand):.2f}  (expect negative)\")\n",
    "print(f\"(b) Date-shuffled SR           : {ann_sharpe(pnl_shuf):.2f}  (expect negative)\")\n",
    "print(f\"(c) Dead-period (3AM) SR       : {ann_sharpe(dead_test):.2f}  (expect negative)\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 296,
   "id": "55c8b7bd-d871-499f-bb5a-f5ee9bdf13e7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Close-window sweep (ADV-filtered, top 20 by test SR):\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>hold_start</th>\n",
       "      <th>hold_end</th>\n",
       "      <th>duration</th>\n",
       "      <th>drop_case</th>\n",
       "      <th>train_SR</th>\n",
       "      <th>test_SR</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>16:15</td>\n",
       "      <td>18:15</td>\n",
       "      <td>120m</td>\n",
       "      <td>none</td>\n",
       "      <td>-15.97</td>\n",
       "      <td>-14.89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>16:15</td>\n",
       "      <td>18:15</td>\n",
       "      <td>120m</td>\n",
       "      <td>drop_large_caps</td>\n",
       "      <td>-18.66</td>\n",
       "      <td>-15.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>16:30</td>\n",
       "      <td>18:00</td>\n",
       "      <td>90m</td>\n",
       "      <td>none</td>\n",
       "      <td>-19.37</td>\n",
       "      <td>-16.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>16:30</td>\n",
       "      <td>18:00</td>\n",
       "      <td>90m</td>\n",
       "      <td>drop_large_caps</td>\n",
       "      <td>-21.81</td>\n",
       "      <td>-16.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>16:30</td>\n",
       "      <td>18:30</td>\n",
       "      <td>120m</td>\n",
       "      <td>none</td>\n",
       "      <td>-16.82</td>\n",
       "      <td>-16.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>16:30</td>\n",
       "      <td>18:30</td>\n",
       "      <td>120m</td>\n",
       "      <td>drop_large_caps</td>\n",
       "      <td>-19.45</td>\n",
       "      <td>-16.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>16:15</td>\n",
       "      <td>17:45</td>\n",
       "      <td>90m</td>\n",
       "      <td>drop_large_caps</td>\n",
       "      <td>-19.48</td>\n",
       "      <td>-17.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>16:15</td>\n",
       "      <td>17:45</td>\n",
       "      <td>90m</td>\n",
       "      <td>none</td>\n",
       "      <td>-16.55</td>\n",
       "      <td>-17.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>16:45</td>\n",
       "      <td>18:45</td>\n",
       "      <td>120m</td>\n",
       "      <td>none</td>\n",
       "      <td>-17.35</td>\n",
       "      <td>-17.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>16:45</td>\n",
       "      <td>18:45</td>\n",
       "      <td>120m</td>\n",
       "      <td>drop_large_caps</td>\n",
       "      <td>-19.85</td>\n",
       "      <td>-18.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>16:00</td>\n",
       "      <td>18:00</td>\n",
       "      <td>120m</td>\n",
       "      <td>drop_large_caps</td>\n",
       "      <td>-24.75</td>\n",
       "      <td>-19.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>16:15</td>\n",
       "      <td>17:15</td>\n",
       "      <td>60m</td>\n",
       "      <td>none</td>\n",
       "      <td>-18.28</td>\n",
       "      <td>-19.66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>16:45</td>\n",
       "      <td>18:15</td>\n",
       "      <td>90m</td>\n",
       "      <td>none</td>\n",
       "      <td>-20.22</td>\n",
       "      <td>-19.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>16:00</td>\n",
       "      <td>18:00</td>\n",
       "      <td>120m</td>\n",
       "      <td>none</td>\n",
       "      <td>-22.35</td>\n",
       "      <td>-19.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>17:00</td>\n",
       "      <td>19:00</td>\n",
       "      <td>120m</td>\n",
       "      <td>none</td>\n",
       "      <td>-18.95</td>\n",
       "      <td>-19.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>16:45</td>\n",
       "      <td>18:15</td>\n",
       "      <td>90m</td>\n",
       "      <td>drop_large_caps</td>\n",
       "      <td>-22.45</td>\n",
       "      <td>-19.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>16:30</td>\n",
       "      <td>17:30</td>\n",
       "      <td>60m</td>\n",
       "      <td>none</td>\n",
       "      <td>-20.25</td>\n",
       "      <td>-20.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>17:00</td>\n",
       "      <td>18:00</td>\n",
       "      <td>60m</td>\n",
       "      <td>drop_large_caps</td>\n",
       "      <td>-27.82</td>\n",
       "      <td>-20.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>17:00</td>\n",
       "      <td>18:00</td>\n",
       "      <td>60m</td>\n",
       "      <td>none</td>\n",
       "      <td>-24.42</td>\n",
       "      <td>-20.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>17:00</td>\n",
       "      <td>19:00</td>\n",
       "      <td>120m</td>\n",
       "      <td>drop_large_caps</td>\n",
       "      <td>-21.50</td>\n",
       "      <td>-20.39</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   hold_start hold_end duration        drop_case  train_SR  test_SR\n",
       "22      16:15    18:15     120m             none    -15.97   -14.89\n",
       "23      16:15    18:15     120m  drop_large_caps    -18.66   -15.33\n",
       "32      16:30    18:00      90m             none    -19.37   -16.68\n",
       "33      16:30    18:00      90m  drop_large_caps    -21.81   -16.82\n",
       "34      16:30    18:30     120m             none    -16.82   -16.85\n",
       "35      16:30    18:30     120m  drop_large_caps    -19.45   -16.93\n",
       "21      16:15    17:45      90m  drop_large_caps    -19.48   -17.16\n",
       "20      16:15    17:45      90m             none    -16.55   -17.52\n",
       "46      16:45    18:45     120m             none    -17.35   -17.93\n",
       "47      16:45    18:45     120m  drop_large_caps    -19.85   -18.03\n",
       "11      16:00    18:00     120m  drop_large_caps    -24.75   -19.58\n",
       "18      16:15    17:15      60m             none    -18.28   -19.66\n",
       "44      16:45    18:15      90m             none    -20.22   -19.67\n",
       "10      16:00    18:00     120m             none    -22.35   -19.75\n",
       "58      17:00    19:00     120m             none    -18.95   -19.82\n",
       "45      16:45    18:15      90m  drop_large_caps    -22.45   -19.96\n",
       "30      16:30    17:30      60m             none    -20.25   -20.04\n",
       "55      17:00    18:00      60m  drop_large_caps    -27.82   -20.16\n",
       "54      17:00    18:00      60m             none    -24.42   -20.20\n",
       "59      17:00    19:00     120m  drop_large_caps    -21.50   -20.39"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "=== Breakeven Analysis — Close Window ===\n",
      "\n",
      "Gross alpha:           24.0 bps/day\n",
      "Commission:            14.0 bps/day\n",
      "Spread:                7.4 bps/day\n",
      "Impact budget left:    2.6 bps/day  (positive — impact affordable)\n",
      "\n",
      "At AUM=$100,000, avg trade size $20,212, avg k=178:\n",
      "  → Each asset needs ADV ≥ $384,866,657/day to break even\n",
      "\n",
      "  With today's universe (median ADV ~$5,000/day):\n",
      "  → Strategy breaks even at AUM ≈ $1\n",
      "\n",
      "AUM sweep — when does net SR become positive? (shows the viable AUM range)\n",
      "           AUM   Net SR (test)  Impact (bps/d)  Total TC (bps/d)\n",
      "  $        100           -1.99            10.9              32.3\n",
      "  $        500           -6.99            24.4              45.8\n",
      "  $      1,000          -10.11            33.7              55.1\n",
      "  $      5,000          -17.38            59.8              81.2\n",
      "  $     10,000          -19.98            70.7              92.1\n",
      "  $     50,000          -23.45            90.9             112.3\n",
      "  $    100,000          -24.22            97.0             118.4\n",
      "  $    500,000          -25.27           106.6             128.0\n"
     ]
    }
   ],
   "source": [
    "# ── Hold-window sweep (Part 1, ADV-filtered) ─────────────────────────────────\n",
    "# ADV precomputed once per drop_case (not per window — that was the bug\n",
    "# causing the previous run to hang).\n",
    "\n",
    "hold_starts_c = [\"16:00\",\"16:15\",\"16:30\",\"16:45\",\"17:00\"]\n",
    "durations_c   = [15, 30, 45, 60, 90, 120]\n",
    "drop_cases_c  = {\n",
    "    \"none\":            set(),\n",
    "    \"drop_large_caps\": {\"BTC\",\"ETH\",\"SOL\",\"BNB\"},\n",
    "}\n",
    "\n",
    "# Precompute per drop_case\n",
    "precomputed = {}\n",
    "for drop_name, drop_cols in drop_cases_c.items():\n",
    "    keep = [c for c in prices_et.columns if c not in drop_cols]\n",
    "    rt   = np.log(prices_et[keep]).diff()\n",
    "    adv_ = compute_adv(_ensure_prices_et(qvol[keep].copy()), window_days=30)\n",
    "    sig  = build_signals_adv_filtered(rt, adv_, \"15:30\", \"16:00\", min_adv=MIN_ADV_DAY)\n",
    "    sig.index = pd.to_datetime(sig.index)\n",
    "    precomputed[drop_name] = (rt, adv_, sig)\n",
    "\n",
    "sweep_rows_c = []\n",
    "for hs in hold_starts_c:\n",
    "    for dur in durations_c:\n",
    "        he = _add_minutes(hs, dur)\n",
    "        if he > \"20:00\": continue\n",
    "        for drop_name in drop_cases_c:\n",
    "            rt, adv_, sig = precomputed[drop_name]\n",
    "            hr = compute_daily_hold_returns(rt, hs, he)\n",
    "            hr.index = pd.to_datetime(hr.index)\n",
    "            sa, ha = sig.align(hr, join=\"inner\", axis=0)\n",
    "            pg     = (sa * ha).sum(axis=1)\n",
    "            tc     = compute_tc(sa, adv_, aum_usd=AUM_USD, commission_bps=7.0)\n",
    "            pnl_n  = (pg - tc[\"tc_total\"].reindex(pg.index)).dropna()\n",
    "            split  = pnl_n.index.max() - pd.DateOffset(years=2)\n",
    "            sweep_rows_c.append({\n",
    "                \"hold_start\": hs, \"hold_end\": he, \"duration\": f\"{dur}m\",\n",
    "                \"drop_case\":  drop_name,\n",
    "                \"train_SR\":   round(ann_sharpe(pnl_n[pnl_n.index <  split]), 2),\n",
    "                \"test_SR\":    round(ann_sharpe(pnl_n[pnl_n.index >= split]), 2),\n",
    "            })\n",
    "\n",
    "sweep_c = pd.DataFrame(sweep_rows_c).sort_values(\"test_SR\", ascending=False)\n",
    "print(\"Close-window sweep (ADV-filtered, top 20 by test SR):\")\n",
    "display(sweep_c.head(20))\n",
    "\n",
    "# ── Breakeven analysis ────────────────────────────────────────────────────────\n",
    "# Question: what minimum asset ADV would make the close strategy net-positive?\n",
    "# At breakeven: gross_alpha = commission + spread + impact\n",
    "# → impact_budget = gross_alpha - commission - spread\n",
    "# → k*sqrt(trade_usd/ADV) = impact_budget/2  (per side, k=weighted avg)\n",
    "# → ADV_needed = trade_usd / (impact_budget / (2*k))^2\n",
    "\n",
    "print(\"=== Breakeven Analysis — Close Window ===\")\n",
    "print()\n",
    "\n",
    "gross_alpha_bps = pnl_gross_close.mean() * 1e4\n",
    "comm_bps        = tc_close[\"tc_commission\"].mean() * 1e4\n",
    "spread_bps      = tc_close[\"tc_spread\"].mean() * 1e4\n",
    "impact_budget   = gross_alpha_bps - comm_bps - spread_bps\n",
    "\n",
    "print(f\"Gross alpha:           {gross_alpha_bps:.1f} bps/day\")\n",
    "print(f\"Commission:            {comm_bps:.1f} bps/day\")\n",
    "print(f\"Spread:                {spread_bps:.1f} bps/day\")\n",
    "print(f\"Impact budget left:    {impact_budget:.1f} bps/day  ({'positive — impact affordable' if impact_budget > 0 else 'NEGATIVE — signal cannot survive even at zero impact'})\")\n",
    "print()\n",
    "\n",
    "if impact_budget > 0:\n",
    "    # Weighted avg k across assets in portfolio (tier-weighted)\n",
    "    k_weighted = np.mean([K_IMPACT.get(ASSET_TIERS.get(c,\"mid\"),150) for c in signals_close.columns])\n",
    "    n_legs     = (signals_close != 0).sum(axis=1).mean()\n",
    "    trade_usd  = AUM_USD / n_legs if n_legs > 0 else AUM_USD\n",
    "\n",
    "    # impact_bps_per_side = k * sqrt(trade/ADV)  ≤  impact_budget/2\n",
    "    # ADV ≥ trade / (impact_budget/(2*k))^2\n",
    "    adv_needed = trade_usd / (impact_budget / (2 * k_weighted)) ** 2\n",
    "    print(f\"At AUM=${AUM_USD:,}, avg trade size ${trade_usd:,.0f}, avg k={k_weighted:.0f}:\")\n",
    "    print(f\"  → Each asset needs ADV ≥ ${adv_needed:,.0f}/day to break even\")\n",
    "    print()\n",
    "\n",
    "    # What AUM makes the current universe break even?\n",
    "    # trade_usd = AUM/n_legs; adv is fixed → lower AUM → smaller trade → lower impact\n",
    "    # adv_needed = (AUM/n_legs) / threshold^2  → solve for AUM\n",
    "    # For ADV = median of current eligible assets:\n",
    "    adv_med_current = 5_000   # rough median of eligible universe today\n",
    "    aum_breakeven   = adv_med_current * (impact_budget / (2 * k_weighted)) ** 2 * n_legs\n",
    "    print(f\"  With today's universe (median ADV ~${adv_med_current:,}/day):\")\n",
    "    print(f\"  → Strategy breaks even at AUM ≈ ${aum_breakeven:,.0f}\")\n",
    "    print()\n",
    "\n",
    "# AUM sweep: at what AUM does net SR turn positive?\n",
    "print(\"AUM sweep — when does net SR become positive? (shows the viable AUM range)\")\n",
    "print(f\"  {'AUM':>12s}  {'Net SR (test)':>14s}  {'Impact (bps/d)':>14s}  {'Total TC (bps/d)':>16s}\")\n",
    "for aum in [100, 500, 1_000, 5_000, 10_000, 50_000, 100_000, 500_000]:\n",
    "    tc_  = compute_tc(signals_close, adv, aum_usd=aum, commission_bps=7.0)\n",
    "    pnl_ = (pnl_gross_close - tc_[\"tc_total\"].reindex(pnl_gross_close.index)).dropna()\n",
    "    sp_  = pnl_.index.max() - pd.DateOffset(years=2)\n",
    "    sr_  = ann_sharpe(pnl_[pnl_.index >= sp_])\n",
    "    print(f\"  ${aum:>11,.0f}  {sr_:>+14.2f}  {tc_['tc_impact'].mean()*1e4:>14.1f}  {tc_['tc_total'].mean()*1e4:>16.1f}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 297,
   "id": "8ba7a7c0-2c31-4aa5-8e2f-bc2e5dacbd52",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "=== Robustness: Close-Window Excluding BTC and ETH ===\n",
      "Universe: ['SOL', 'BNB', 'ADA', 'AVAX', 'NEAR', 'ATOM', 'ALGO', 'EGLD', 'ICP', 'ROSE', 'CELO', 'UNI', 'AAVE', 'CRV', 'SUSHI', 'COMP', '1INCH', 'LINK', 'GRT', 'FIL', 'STORJ', 'AXS', 'MANA']\n",
      "\n",
      "Gross SR (test)    : 7.51  (vs 7.45 with BTC/ETH)\n",
      "Net SR   (test)    : -23.65  (vs -24.22 with BTC/ETH)\n",
      "\n",
      "TC breakdown (ex-BTC/ETH):\n",
      "  tc_commission   : 13.98 bps/d\n",
      "  tc_spread       : 7.76 bps/d\n",
      "  tc_impact       : 101.86 bps/d\n",
      "  tc_total        : 123.61 bps/d\n",
      "  gross alpha     : 24.70 bps/d\n",
      "\n",
      "TC sensitivity (ex-BTC/ETH, test period):\n",
      "  Zero TC (gross)          : SR = +7.51  (+24.7 bps/d)\n",
      "  Commission only          : SR = +4.28  (+10.7 bps/d)\n",
      "  Commission+Spread        : SR = +2.51  (+3.0 bps/d)\n",
      "  Full model               : SR = -23.65  (-98.9 bps/d)\n",
      "\n",
      "Interpretation: if gross SR and TC sensitivity are similar with and without BTC/ETH,\n",
      "the spot crypto signal is independent of ETF-launch microstructure effects.\n"
     ]
    }
   ],
   "source": [
    "# ── Robustness: exclude BTC and ETH from spot crypto backtest ────────────────\n",
    "# Motivation: the test period (last 2 years) coincides with the ETF launch\n",
    "# period. A skeptic could argue that the close-window reversal in test is\n",
    "# partially driven by structural changes in BTC/ETH microstructure associated\n",
    "# with ETF-related institutional flows — i.e. the same effect captured more\n",
    "# cleanly in Part 3. Excluding BTC and ETH from the spot strategy isolates\n",
    "# whether the signal exists independently of those two assets.\n",
    "\n",
    "EXCL = [\"BTC\", \"ETH\"]\n",
    "rets_ex  = np.log(prices_et.drop(columns=EXCL)).diff()\n",
    "adv_ex   = compute_adv(_ensure_prices_et(qvol.drop(columns=EXCL).copy()), window_days=30)\n",
    "\n",
    "sig_ex   = build_signals_adv_filtered(rets_ex, adv_ex, \"15:30\", \"16:00\", min_adv=MIN_ADV_DAY)\n",
    "hold_ex  = compute_daily_hold_returns(rets_ex, \"16:15\", \"17:00\")\n",
    "sig_ex.index  = pd.to_datetime(sig_ex.index)\n",
    "hold_ex.index = pd.to_datetime(hold_ex.index)\n",
    "se, he = sig_ex.align(hold_ex, join=\"inner\", axis=0)\n",
    "\n",
    "pg_ex   = (se * he).sum(axis=1)\n",
    "tc_ex   = compute_tc(se, adv_ex, aum_usd=AUM_USD, commission_bps=7.0)\n",
    "pnl_ex  = (pg_ex - tc_ex[\"tc_total\"].reindex(pg_ex.index)).dropna()\n",
    "split_ex = pnl_ex.index.max() - pd.DateOffset(years=2)\n",
    "\n",
    "print(\"=== Robustness: Close-Window Excluding BTC and ETH ===\")\n",
    "print(f\"Universe: {[c for c in prices_et.columns if c not in EXCL]}\")\n",
    "print()\n",
    "print(f\"Gross SR (test)    : {ann_sharpe(pg_ex[pg_ex.index >= split_ex]):.2f}  \"\n",
    "      f\"(vs {ann_sharpe(pnl_gross_close[pnl_gross_close.index >= split_date]):.2f} with BTC/ETH)\")\n",
    "print(f\"Net SR   (test)    : {ann_sharpe(pnl_ex[pnl_ex.index >= split_ex]):.2f}  \"\n",
    "      f\"(vs {ann_sharpe(pnl_net_close[pnl_net_close.index >= split_date]):.2f} with BTC/ETH)\")\n",
    "print()\n",
    "print(\"TC breakdown (ex-BTC/ETH):\")\n",
    "for col in [\"tc_commission\",\"tc_spread\",\"tc_impact\",\"tc_total\"]:\n",
    "    print(f\"  {col:16s}: {tc_ex[col].mean()*1e4:.2f} bps/d\")\n",
    "print(f\"  {'gross alpha':16s}: {pg_ex.mean()*1e4:.2f} bps/d\")\n",
    "print()\n",
    "\n",
    "# TC sensitivity ex-BTC/ETH\n",
    "print(\"TC sensitivity (ex-BTC/ETH, test period):\")\n",
    "for label, pnl in {\n",
    "    \"Zero TC (gross)\":     pg_ex,\n",
    "    \"Commission only\":     pg_ex - (2*7.0/10_000)*se.abs().sum(axis=1),\n",
    "    \"Commission+Spread\":   pg_ex - (tc_ex[\"tc_commission\"]+tc_ex[\"tc_spread\"]).reindex(pg_ex.index),\n",
    "    \"Full model\":          pg_ex - tc_ex[\"tc_total\"].reindex(pg_ex.index),\n",
    "}.items():\n",
    "    p = pnl.dropna(); tp = p[p.index >= split_ex]\n",
    "    print(f\"  {label:25s}: SR = {ann_sharpe(tp):+.2f}  ({p.mean()*1e4:+.1f} bps/d)\")\n",
    "\n",
    "print()\n",
    "print(\"Interpretation: if gross SR and TC sensitivity are similar with and without BTC/ETH,\")\n",
    "print(\"the spot crypto signal is independent of ETF-launch microstructure effects.\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "47c415df-7788-43d8-ac16-33e6b8ecd3c3",
   "metadata": {},
   "source": [
    "## Part 2 — Spot Crypto Morning Window\n",
    "\n",
    "Same universe and methodology as Part 1, but the signal window is 09:30–10:00 ET (the first 30 minutes of the US equity session) and the hold is 10:15–10:45 ET. No directional assumption is imposed — the signal is built identically to Part 1, and whatever direction produces positive returns is reported honestly.\n",
    "\n",
    "Transaction costs here use the same full three-component model as Part 1, applied to the same ADV-filtered universe. This allows direct apples-to-apples comparison between the two windows.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 299,
   "id": "e173bdb0-11bc-4ec6-a5cf-f1cd4d7aaa74",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "=== Morning Window | Time-varying ADV filter ===\n",
      "Gross SR  (train)  : 3.90\n",
      "Gross SR  (test)   : 7.29  ← REVERSAL\n",
      "Net SR    (train)  : -20.71  (1271 days)\n",
      "Net SR    (test)   : -22.01  (732 days)\n",
      "\n",
      "TC breakdown (mean bps/day):\n",
      "  tc_commission   : 13.98\n",
      "  tc_spread       : 7.63\n",
      "  tc_impact       : 99.21\n",
      "  tc_total        : 120.82\n",
      "  gross alpha     : 22.78\n",
      "\n",
      "Year-by-year net SR:\n",
      "  2020: SR = -18.74  (122 days)\n",
      "  2021: SR = -12.16  (365 days)\n",
      "  2022: SR = -30.40  (365 days)\n",
      "  2023: SR = -27.97  (365 days)\n",
      "  2024: SR = -29.42  (366 days)\n",
      "  2025: SR = -21.37  (365 days)\n",
      "  2026: SR = -10.16  (55 days)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1400x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "=== TC Sensitivity — Morning (test period) ===\n",
      "  Zero TC (gross)                    : SR = +7.29  (mean +22.8 bps/d)\n",
      "  Commission only (14 bps/d)         : SR = +4.19  (mean +8.8 bps/d)\n",
      "  Commission + Spread                : SR = +2.45  (mean +1.2 bps/d)\n",
      "  Full model (+ Impact)              : SR = -22.01  (mean -98.0 bps/d)\n"
     ]
    }
   ],
   "source": [
    "# ============================================================\n",
    "# PART 2 — SPOT CRYPTO MORNING WINDOW\n",
    "# Signal : 09:30–10:00 ET  |  Hold: 10:15–10:45 ET\n",
    "# Universe: same time-varying ADV filter as Part 1\n",
    "# TC: full 3-component model — apples-to-apples with Part 1\n",
    "# ============================================================\n",
    "\n",
    "signals_morning = build_signals_adv_filtered(rets_et, adv, \"09:30\", \"10:00\",\n",
    "                                              min_adv=MIN_ADV_DAY)\n",
    "hold_morning    = compute_daily_hold_returns(rets_et, \"10:15\", \"10:45\")\n",
    "signals_morning.index = pd.to_datetime(signals_morning.index)\n",
    "hold_morning.index    = pd.to_datetime(hold_morning.index)\n",
    "sig_m, hold_m = signals_morning.align(hold_morning, join=\"inner\", axis=0)\n",
    "\n",
    "pnl_gross_morning = (sig_m * hold_m).sum(axis=1)\n",
    "tc_morning        = compute_tc(sig_m, adv, aum_usd=AUM_USD, commission_bps=7.0)\n",
    "pnl_net_morning   = (pnl_gross_morning - tc_morning[\"tc_total\"].reindex(pnl_gross_morning.index)).dropna()\n",
    "\n",
    "split_m    = pnl_net_morning.index.max() - pd.DateOffset(years=2)\n",
    "train_m    = pnl_net_morning[pnl_net_morning.index <  split_m]\n",
    "test_m     = pnl_net_morning[pnl_net_morning.index >= split_m]\n",
    "\n",
    "gross_test_sr = ann_sharpe(pnl_gross_morning[pnl_gross_morning.index >= split_m])\n",
    "direction     = \"REVERSAL\" if gross_test_sr > 0 else \"MOMENTUM\"\n",
    "\n",
    "print(f\"=== Morning Window | Time-varying ADV filter ===\")\n",
    "print(f\"Gross SR  (train)  : {ann_sharpe(pnl_gross_morning[pnl_gross_morning.index < split_m]):.2f}\")\n",
    "print(f\"Gross SR  (test)   : {gross_test_sr:.2f}  ← {direction}\")\n",
    "print(f\"Net SR    (train)  : {ann_sharpe(train_m):.2f}  ({len(train_m)} days)\")\n",
    "print(f\"Net SR    (test)   : {ann_sharpe(test_m):.2f}  ({len(test_m)} days)\")\n",
    "print()\n",
    "print(\"TC breakdown (mean bps/day):\")\n",
    "for col in [\"tc_commission\",\"tc_spread\",\"tc_impact\",\"tc_total\"]:\n",
    "    print(f\"  {col:16s}: {tc_morning[col].mean()*1e4:.2f}\")\n",
    "print(f\"  {'gross alpha':16s}: {pnl_gross_morning.mean()*1e4:.2f}\")\n",
    "\n",
    "# Year-by-year\n",
    "print()\n",
    "print(\"Year-by-year net SR:\")\n",
    "pnl_all_m = (pnl_gross_morning - tc_morning[\"tc_total\"].reindex(pnl_gross_morning.index)).dropna()\n",
    "for yr in sorted(pnl_all_m.index.year.unique()):\n",
    "    sub = pnl_all_m[pnl_all_m.index.year == yr]\n",
    "    print(f\"  {yr}: SR = {ann_sharpe(sub):+.2f}  ({len(sub)} days)\")\n",
    "\n",
    "fig, axes = plt.subplots(1, 2, figsize=(14, 4))\n",
    "axes[0].plot(train_m.cumsum(), label=\"Train\", lw=2)\n",
    "axes[0].plot(test_m.cumsum(),  label=\"Test\",  lw=2)\n",
    "axes[0].axhline(0, color=\"black\", lw=0.8, ls=\"--\")\n",
    "axes[0].set_title(\"Morning Window: Cumulative Net PnL\"); axes[0].legend()\n",
    "axes[1].hist(test_m, bins=50)\n",
    "axes[1].set_title(\"Morning Window Test Return Distribution\")\n",
    "plt.tight_layout(); plt.show()\n",
    "\n",
    "# ── TC sensitivity — morning window ─────────────────────────────────────────\n",
    "print()\n",
    "print(\"=== TC Sensitivity — Morning (test period) ===\")\n",
    "tc_scenarios_m = {\n",
    "    \"Zero TC (gross)\":            pnl_gross_morning,\n",
    "    \"Commission only (14 bps/d)\": pnl_gross_morning - (2*7.0/10_000) * sig_m.abs().sum(axis=1),\n",
    "    \"Commission + Spread\":        pnl_gross_morning - (tc_morning[\"tc_commission\"] + tc_morning[\"tc_spread\"]).reindex(pnl_gross_morning.index),\n",
    "    \"Full model (+ Impact)\":      pnl_gross_morning - tc_morning[\"tc_total\"].reindex(pnl_gross_morning.index),\n",
    "}\n",
    "for label, pnl in tc_scenarios_m.items():\n",
    "    pnl = pnl.dropna()\n",
    "    test_pnl = pnl[pnl.index >= split_m]\n",
    "    print(f\"  {label:35s}: SR = {ann_sharpe(test_pnl):+.2f}  (mean {pnl.mean()*1e4:+.1f} bps/d)\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 300,
   "id": "c9ae67ec-fe14-4910-bd99-e1636d1721f8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Morning hold sweep (flat 7 bps/side commission, top 20 by best SR):\n"
     ]
    },
    {
     "data": {
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>hold_start</th>\n",
       "      <th>hold_end</th>\n",
       "      <th>duration</th>\n",
       "      <th>test_SR_reversal</th>\n",
       "      <th>test_SR_momentum</th>\n",
       "      <th>best_SR</th>\n",
       "      <th>best_direction</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>10:15</td>\n",
       "      <td>12:15</td>\n",
       "      <td>120m</td>\n",
       "      <td>6.12</td>\n",
       "      <td>-10.74</td>\n",
       "      <td>6.12</td>\n",
       "      <td>reversal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10:15</td>\n",
       "      <td>11:45</td>\n",
       "      <td>90m</td>\n",
       "      <td>5.53</td>\n",
       "      <td>-10.48</td>\n",
       "      <td>5.53</td>\n",
       "      <td>reversal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10:15</td>\n",
       "      <td>11:15</td>\n",
       "      <td>60m</td>\n",
       "      <td>5.25</td>\n",
       "      <td>-10.70</td>\n",
       "      <td>5.25</td>\n",
       "      <td>reversal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10:15</td>\n",
       "      <td>11:00</td>\n",
       "      <td>45m</td>\n",
       "      <td>4.76</td>\n",
       "      <td>-10.45</td>\n",
       "      <td>4.76</td>\n",
       "      <td>reversal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10:15</td>\n",
       "      <td>10:45</td>\n",
       "      <td>30m</td>\n",
       "      <td>4.19</td>\n",
       "      <td>-10.39</td>\n",
       "      <td>4.19</td>\n",
       "      <td>reversal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>10:30</td>\n",
       "      <td>12:30</td>\n",
       "      <td>120m</td>\n",
       "      <td>3.48</td>\n",
       "      <td>-8.23</td>\n",
       "      <td>3.48</td>\n",
       "      <td>reversal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10:15</td>\n",
       "      <td>10:30</td>\n",
       "      <td>15m</td>\n",
       "      <td>3.30</td>\n",
       "      <td>-10.49</td>\n",
       "      <td>3.30</td>\n",
       "      <td>reversal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>10:30</td>\n",
       "      <td>12:00</td>\n",
       "      <td>90m</td>\n",
       "      <td>2.85</td>\n",
       "      <td>-7.95</td>\n",
       "      <td>2.85</td>\n",
       "      <td>reversal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10:30</td>\n",
       "      <td>11:30</td>\n",
       "      <td>60m</td>\n",
       "      <td>2.26</td>\n",
       "      <td>-7.93</td>\n",
       "      <td>2.26</td>\n",
       "      <td>reversal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>10:45</td>\n",
       "      <td>12:45</td>\n",
       "      <td>120m</td>\n",
       "      <td>2.08</td>\n",
       "      <td>-5.43</td>\n",
       "      <td>2.08</td>\n",
       "      <td>reversal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>10:30</td>\n",
       "      <td>11:15</td>\n",
       "      <td>45m</td>\n",
       "      <td>1.95</td>\n",
       "      <td>-8.11</td>\n",
       "      <td>1.95</td>\n",
       "      <td>reversal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>10:45</td>\n",
       "      <td>12:15</td>\n",
       "      <td>90m</td>\n",
       "      <td>1.91</td>\n",
       "      <td>-7.06</td>\n",
       "      <td>1.91</td>\n",
       "      <td>reversal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>11:00</td>\n",
       "      <td>13:00</td>\n",
       "      <td>120m</td>\n",
       "      <td>1.32</td>\n",
       "      <td>-4.64</td>\n",
       "      <td>1.32</td>\n",
       "      <td>reversal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>10:30</td>\n",
       "      <td>11:00</td>\n",
       "      <td>30m</td>\n",
       "      <td>1.23</td>\n",
       "      <td>-7.68</td>\n",
       "      <td>1.23</td>\n",
       "      <td>reversal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>11:00</td>\n",
       "      <td>12:30</td>\n",
       "      <td>90m</td>\n",
       "      <td>1.06</td>\n",
       "      <td>-6.26</td>\n",
       "      <td>1.06</td>\n",
       "      <td>reversal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>10:45</td>\n",
       "      <td>11:45</td>\n",
       "      <td>60m</td>\n",
       "      <td>0.90</td>\n",
       "      <td>-6.67</td>\n",
       "      <td>0.90</td>\n",
       "      <td>reversal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>11:30</td>\n",
       "      <td>13:30</td>\n",
       "      <td>120m</td>\n",
       "      <td>0.63</td>\n",
       "      <td>-3.94</td>\n",
       "      <td>0.63</td>\n",
       "      <td>reversal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>10:45</td>\n",
       "      <td>11:30</td>\n",
       "      <td>45m</td>\n",
       "      <td>0.53</td>\n",
       "      <td>-6.74</td>\n",
       "      <td>0.53</td>\n",
       "      <td>reversal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>11:30</td>\n",
       "      <td>13:00</td>\n",
       "      <td>90m</td>\n",
       "      <td>0.39</td>\n",
       "      <td>-3.91</td>\n",
       "      <td>0.39</td>\n",
       "      <td>reversal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>10:30</td>\n",
       "      <td>10:45</td>\n",
       "      <td>15m</td>\n",
       "      <td>0.22</td>\n",
       "      <td>-7.57</td>\n",
       "      <td>0.22</td>\n",
       "      <td>reversal</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   hold_start hold_end duration  test_SR_reversal  test_SR_momentum  best_SR  \\\n",
       "5       10:15    12:15     120m              6.12            -10.74     6.12   \n",
       "4       10:15    11:45      90m              5.53            -10.48     5.53   \n",
       "3       10:15    11:15      60m              5.25            -10.70     5.25   \n",
       "2       10:15    11:00      45m              4.76            -10.45     4.76   \n",
       "1       10:15    10:45      30m              4.19            -10.39     4.19   \n",
       "11      10:30    12:30     120m              3.48             -8.23     3.48   \n",
       "0       10:15    10:30      15m              3.30            -10.49     3.30   \n",
       "10      10:30    12:00      90m              2.85             -7.95     2.85   \n",
       "9       10:30    11:30      60m              2.26             -7.93     2.26   \n",
       "17      10:45    12:45     120m              2.08             -5.43     2.08   \n",
       "8       10:30    11:15      45m              1.95             -8.11     1.95   \n",
       "16      10:45    12:15      90m              1.91             -7.06     1.91   \n",
       "23      11:00    13:00     120m              1.32             -4.64     1.32   \n",
       "7       10:30    11:00      30m              1.23             -7.68     1.23   \n",
       "22      11:00    12:30      90m              1.06             -6.26     1.06   \n",
       "15      10:45    11:45      60m              0.90             -6.67     0.90   \n",
       "29      11:30    13:30     120m              0.63             -3.94     0.63   \n",
       "14      10:45    11:30      45m              0.53             -6.74     0.53   \n",
       "28      11:30    13:00      90m              0.39             -3.91     0.39   \n",
       "6       10:30    10:45      15m              0.22             -7.57     0.22   \n",
       "\n",
       "   best_direction  \n",
       "5        reversal  \n",
       "4        reversal  \n",
       "3        reversal  \n",
       "2        reversal  \n",
       "1        reversal  \n",
       "11       reversal  \n",
       "0        reversal  \n",
       "10       reversal  \n",
       "9        reversal  \n",
       "17       reversal  \n",
       "8        reversal  \n",
       "16       reversal  \n",
       "23       reversal  \n",
       "7        reversal  \n",
       "22       reversal  \n",
       "15       reversal  \n",
       "29       reversal  \n",
       "14       reversal  \n",
       "28       reversal  \n",
       "6        reversal  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1400x700 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Close  rolling SR: mean=6.49, std=2.53, % positive=95%\n",
      "Morning rolling SR: mean=5.87,  std=2.88, % positive=93%\n"
     ]
    }
   ],
   "source": [
    "# ── Morning hold-window sweep (Part 2) ───────────────────────────────────────\n",
    "# TC: flat 7 bps/side commission — consistent with how the sweep was\n",
    "# originally designed. Full impact at this AUM kills ALL windows identically\n",
    "# (impact >> alpha) so it adds no information to the sweep. Use flat TC to\n",
    "# see which windows have the most gross alpha above commission costs.\n",
    "# (Full-model TC breakdown is already shown in cell 14.)\n",
    "\n",
    "hold_starts_m = [\"10:15\",\"10:30\",\"10:45\",\"11:00\",\"11:30\"]\n",
    "durations_m   = [15, 30, 45, 60, 90, 120]\n",
    "\n",
    "sig_pre_m = build_signals_adv_filtered(rets_et, adv, \"09:30\", \"10:00\",\n",
    "                                        min_adv=MIN_ADV_DAY)\n",
    "sig_pre_m.index = pd.to_datetime(sig_pre_m.index)\n",
    "\n",
    "sweep_rows_m = []\n",
    "for hs in hold_starts_m:\n",
    "    for dur in durations_m:\n",
    "        he = _add_minutes(hs, dur)\n",
    "        if he > \"15:45\": continue\n",
    "        hr = compute_daily_hold_returns(rets_et, hs, he)\n",
    "        hr.index = pd.to_datetime(hr.index)\n",
    "        sa, ha = sig_pre_m.align(hr, join=\"inner\", axis=0)\n",
    "        pg     = (sa * ha).sum(axis=1)\n",
    "        # Flat TC: commission only — same for both directions\n",
    "        flat_tc = (2 * 7.0 / 10_000) * sa.abs().sum(axis=1)\n",
    "        pnl_rev = (pg  - flat_tc.reindex(pg.index)).dropna()\n",
    "        pnl_mom = (-pg - flat_tc.reindex(pg.index)).dropna()   # ← correct: TC subtracted from BOTH\n",
    "        split   = pnl_rev.index.max() - pd.DateOffset(years=2)\n",
    "        net_rev = ann_sharpe(pnl_rev[pnl_rev.index >= split])\n",
    "        net_mom = ann_sharpe(pnl_mom[pnl_mom.index >= split])\n",
    "        sweep_rows_m.append({\n",
    "            \"hold_start\":       hs, \"hold_end\": he, \"duration\": f\"{dur}m\",\n",
    "            \"test_SR_reversal\": round(net_rev, 2),\n",
    "            \"test_SR_momentum\": round(net_mom, 2),\n",
    "            \"best_SR\":          round(max(net_rev, net_mom), 2),\n",
    "            \"best_direction\":   \"reversal\" if net_rev >= net_mom else \"momentum\",\n",
    "        })\n",
    "\n",
    "sweep_m = pd.DataFrame(sweep_rows_m).sort_values(\"best_SR\", ascending=False)\n",
    "print(\"Morning hold sweep (flat 7 bps/side commission, top 20 by best SR):\")\n",
    "display(sweep_m.head(20))\n",
    "\n",
    "# ── Rolling SR — gross signal stability over time ────────────────────────────\n",
    "# Shows whether the effect is stable, fading, or regime-dependent.\n",
    "# Using GROSS PnL so TC doesn't obscure the underlying signal dynamics.\n",
    "# 90-day window (≈ 1 quarter) for both strategies.\n",
    "\n",
    "ROLL = 90\n",
    "\n",
    "# Gross PnL for close window (use full 25-asset universe for signal strength)\n",
    "signals_roll   = build_signals_adv_filtered(rets_et, adv, \"15:30\",\"16:00\", min_adv=MIN_ADV_DAY)\n",
    "hold_roll      = compute_daily_hold_returns(rets_et, \"16:15\",\"17:00\")\n",
    "signals_roll.index = pd.to_datetime(signals_roll.index)\n",
    "hold_roll.index    = pd.to_datetime(hold_roll.index)\n",
    "sr_, hr_       = signals_roll.align(hold_roll, join=\"inner\", axis=0)\n",
    "pg_close_roll  = (sr_ * hr_).sum(axis=1)\n",
    "\n",
    "# Gross PnL for morning\n",
    "signals_mroll  = build_signals_adv_filtered(rets_et, adv, \"09:30\",\"10:00\", min_adv=MIN_ADV_DAY)\n",
    "hold_mroll     = compute_daily_hold_returns(rets_et, \"10:15\",\"10:45\")\n",
    "signals_mroll.index = pd.to_datetime(signals_mroll.index)\n",
    "hold_mroll.index    = pd.to_datetime(hold_mroll.index)\n",
    "sm_, hm_       = signals_mroll.align(hold_mroll, join=\"inner\", axis=0)\n",
    "pg_morn_roll   = (sm_ * hm_).sum(axis=1)\n",
    "\n",
    "def rolling_sr(pnl, window=90):\n",
    "    r = pnl.rolling(window)\n",
    "    return (r.mean() / r.std() * np.sqrt(252)).rename(\"rolling_sr\")\n",
    "\n",
    "rs_close = rolling_sr(pg_close_roll, ROLL)\n",
    "rs_morn  = rolling_sr(pg_morn_roll,  ROLL)\n",
    "\n",
    "fig, axes = plt.subplots(2, 1, figsize=(14, 7), sharex=True)\n",
    "\n",
    "axes[0].plot(rs_close, lw=1.5, color=\"steelblue\")\n",
    "axes[0].axhline(0, color=\"black\", lw=0.8, ls=\"--\")\n",
    "axes[0].axhline(rs_close.mean(), color=\"steelblue\", lw=1, ls=\":\", alpha=0.7,\n",
    "                label=f\"Mean = {rs_close.mean():.2f}\")\n",
    "axes[0].axvline(pd.Timestamp(split_date), color=\"red\", lw=1, ls=\"--\", alpha=0.6,\n",
    "                label=\"Train/Test split\")\n",
    "axes[0].fill_between(rs_close.index, rs_close, 0,\n",
    "                     where=rs_close >= 0, alpha=0.15, color=\"green\")\n",
    "axes[0].fill_between(rs_close.index, rs_close, 0,\n",
    "                     where=rs_close < 0,  alpha=0.15, color=\"red\")\n",
    "axes[0].set_title(f\"Close Window — {ROLL}-Day Rolling Gross SR\")\n",
    "axes[0].set_ylabel(\"Annualised Sharpe\")\n",
    "axes[0].legend(fontsize=9)\n",
    "axes[0].grid(alpha=0.25)\n",
    "\n",
    "axes[1].plot(rs_morn, lw=1.5, color=\"darkorange\")\n",
    "axes[1].axhline(0, color=\"black\", lw=0.8, ls=\"--\")\n",
    "axes[1].axhline(rs_morn.mean(), color=\"darkorange\", lw=1, ls=\":\", alpha=0.7,\n",
    "                label=f\"Mean = {rs_morn.mean():.2f}\")\n",
    "axes[1].axvline(pd.Timestamp(split_date), color=\"red\", lw=1, ls=\"--\", alpha=0.6,\n",
    "                label=\"Train/Test split\")\n",
    "axes[1].fill_between(rs_morn.index, rs_morn, 0,\n",
    "                     where=rs_morn >= 0, alpha=0.15, color=\"green\")\n",
    "axes[1].fill_between(rs_morn.index, rs_morn, 0,\n",
    "                     where=rs_morn < 0,  alpha=0.15, color=\"red\")\n",
    "axes[1].set_title(f\"Morning Window — {ROLL}-Day Rolling Gross SR\")\n",
    "axes[1].set_ylabel(\"Annualised Sharpe\")\n",
    "axes[1].legend(fontsize=9)\n",
    "axes[1].grid(alpha=0.25)\n",
    "\n",
    "plt.suptitle(\"Rolling Gross SR — Signal Strength Over Time\\n(gross = before TC; measures signal stability independent of cost regime)\",\n",
    "             fontsize=11)\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "# Summary stats\n",
    "print(f\"Close  rolling SR: mean={rs_close.mean():.2f}, std={rs_close.std():.2f}, \"\n",
    "      f\"% positive={100*(rs_close>0).mean():.0f}%\")\n",
    "print(f\"Morning rolling SR: mean={rs_morn.mean():.2f},  std={rs_morn.std():.2f}, \"\n",
    "      f\"% positive={100*(rs_morn>0).mean():.0f}%\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 301,
   "id": "aa3d2bc3-2615-4e94-bd38-4bb31baeb518",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Daily PnL correlation (Close vs Morning): 0.121  (p=5.255e-08)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1400x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# ── Strategy comparison: Close vs Morning ───────────────────────────────────\n",
    "pnl_close_aligned, pnl_morning_aligned = pnl_net_close.align(pnl_net_morning, join=\"inner\")\n",
    "rho, pval = pearsonr(pnl_close_aligned.values, pnl_morning_aligned.values)\n",
    "print(f\"Daily PnL correlation (Close vs Morning): {rho:.3f}  (p={pval:.4g})\")\n",
    "\n",
    "fig, axes = plt.subplots(1, 2, figsize=(14, 5))\n",
    "axes[0].plot(pnl_net_close.cumsum(),   label=\"Close Reversal\", lw=2)\n",
    "axes[0].plot(pnl_net_morning.cumsum(), label=\"Morning Window\",  lw=2)\n",
    "axes[0].set_title(\"Cumulative PnL: Close vs Morning\"); axes[0].legend(); axes[0].grid(alpha=0.3)\n",
    "axes[1].scatter(pnl_close_aligned, pnl_morning_aligned, alpha=0.3, s=10)\n",
    "axes[1].axhline(0, color=\"black\", lw=1); axes[1].axvline(0, color=\"black\", lw=1)\n",
    "axes[1].set_xlabel(\"Close Window Daily PnL\"); axes[1].set_ylabel(\"Morning Window Daily PnL\")\n",
    "axes[1].set_title(\"Daily PnL Scatter\"); axes[1].grid(alpha=0.3)\n",
    "plt.tight_layout(); plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3ec8bdd0-256d-46f1-ae7e-1364c8a4785a",
   "metadata": {},
   "source": [
    "### Parts 1 & 2 — Key Findings\n",
    "\n",
    "**The signal is real and stable.** The close-window reversal produces a gross Sharpe of 5.85 in the test period, with 24.0 bps/day of gross alpha. The morning window is directionally consistent (also reversal) with gross SR 7.29 and 22.8 bps/day. Critically, the two signals are nearly uncorrelated (ρ = 0.12, p < 0.001), suggesting they capture distinct institutional flow dynamics around the open and close rather than the same underlying effect measured twice. Rolling 90-day gross SR is positive 95% of the time for the close window and 93% for the morning window across the full five-year sample, with no evidence of decay or regime change.\n",
    "\n",
    "**The effect is structurally inaccessible at retail scale.** The TC sensitivity analysis reveals a precise cost waterfall. After commission (14.0 bps/day), SR remains at +4.02. After commission plus spread (21.4 bps/day total), SR falls to +2.21. Though positive, there is only 2.6 bps/day of budget remaining for market impact. The actual impact cost is 97.0 bps/day, roughly 37x the remaining budget and 4x the total gross alpha. The root cause is structural: with a time-varying ADV filter and ~5 active assets per day, each position consumes a substantial fraction of the asset's daily volume, driving impact into a regime far beyond any reasonable alpha budget. The AUM sweep confirms this is not a scaling problem; even at $100 AUM the strategy produces negative net SR, because the participation cost of small-cap crypto is prohibitive regardless of notional.\n",
    "\n",
    "**Large-cap assets do not solve the problem.** Restricting to BTC, ETH, SOL, and BNB eliminates the impact problem (total TC drops to 47.5 bps/day) but also largely eliminates the signal as gross alpha falls to just 4.5 bps/day, below commission alone. The reversal effect is concentrated in mid-cap assets where institutional flows create larger temporary dislocations; large caps are too efficiently priced for the same cross-sectional signal to persist.\n",
    "\n",
    "**Interpretation.** The findings are consistent with the Bogousslavsky (2016) slow-moving investor framework: large institutional participants create predictable intraday autocorrelations in less liquid assets, but the same illiquidity that gives rise to the signal also makes it impossible to exploit at meaningful scale without institutional infrastructure of direct market access, algorithmic execution across a wider universe, and a 2021-era liquidity environment when the mid-cap universe was 10–100x more liquid than today.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "73a87f07-8438-40ae-8b9d-cf6110057ad4",
   "metadata": {},
   "source": [
    "## Part 3 — Spot Crypto ETF Strategies (IBIT, FBTC, ETHA, FETH)\n",
    "\n",
    "The launch of spot Bitcoin ETFs (January 2024) and spot Ethereum ETFs (July 2024) provides a natural experiment: if the intraday reversal effect documented above is driven by institutional flows, we should see a *momentum* effect in ETFs where large directional inflows into newly-launched products create persistent intraday trends rather than reversals.\n",
    "\n",
    "ETFs trade on regulated exchanges with a hard market close at 16:00 ET, which imposes session constraints absent in 24/7 spot crypto:\n",
    "- **Signal and hold windows must lie within the same continuous trading session.** A signal ending at 16:00 and a hold starting at 16:00 is valid (different bars). A hold that crosses 16:00 is not — the market close breaks return continuity.\n",
    "- **Valid zones:** pre-market (04:00–09:30), regular session (09:30–16:00), after-hours (16:00–20:00).\n",
    "\n",
    "Data: yfinance 60-min bars, 2-year history. TC: 7 bps/side turnover-based (limit orders; impact negligible given ETF ADV vs. any realistic AUM). After-hours results validated against 5-min bars (~60 days) to confirm they are not coarse-data artifacts.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 304,
   "id": "3ad819e3-924d-47c2-bdf9-fa2529445758",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ETF panel: (8311, 4) | Tickers: ['IBIT', 'FBTC', 'ETHA', 'FETH']\n",
      "Date range: 2024-02-26 → 2026-02-25\n"
     ]
    }
   ],
   "source": [
    "# ============================================================\n",
    "# PART 3 — ETF STRATEGY\n",
    "# Data: yfinance 60m bars, 2y history\n",
    "# TC: 7 bps/side turnover-based (limit orders, no impact model —\n",
    "#     ETF universe is 4 assets, ADV >> any realistic AUM)\n",
    "# ============================================================\n",
    "\n",
    "import yfinance as yf\n",
    "\n",
    "BTC_ETFS    = [\"IBIT\",\"FBTC\"]\n",
    "ETH_ETFS    = [\"ETHA\",\"FETH\"]\n",
    "ETF_TICKERS = BTC_ETFS + ETH_ETFS\n",
    "\n",
    "# ── Session boundary enforcement ─────────────────────────────────────────────\n",
    "SESSION_ZONES = {\n",
    "    \"pre_market\":  (\"04:00\", \"09:30\"),\n",
    "    \"regular\":     (\"09:30\", \"16:00\"),\n",
    "    \"after_hours\": (\"16:00\", \"20:00\"),\n",
    "}\n",
    "\n",
    "def _get_zone(hhmm):\n",
    "    \"\"\"Return which session zone a time falls in, or None if between zones.\"\"\"\n",
    "    for zone, (start, end) in SESSION_ZONES.items():\n",
    "        if start <= hhmm < end:\n",
    "            return zone\n",
    "    return None\n",
    "\n",
    "def _same_zone(sig_start, sig_end, hold_start, hold_end):\n",
    "    \"\"\"\n",
    "    Valid window pair iff:\n",
    "      1. hold_start >= sig_end  (no bar overlap between signal and hold)\n",
    "      2. hold window is entirely within one session zone (does not cross\n",
    "         a market close/open that would break return continuity)\n",
    "    The signal zone is unconstrained — a 15:30-16:00 signal feeding a\n",
    "    16:00-17:00 after-hours hold is valid and not lookahead bias.\n",
    "    \"\"\"\n",
    "    if hold_start < sig_end:\n",
    "        return False\n",
    "    hold_start_zone = _get_zone(hold_start)\n",
    "    hold_end_minus  = _add_minutes(hold_end, -1)   # check just inside end\n",
    "    hold_end_zone   = _get_zone(hold_end_minus)\n",
    "    return (hold_start_zone is not None and\n",
    "            hold_end_zone   is not None and\n",
    "            hold_start_zone == hold_end_zone)\n",
    "\n",
    "# ── yfinance download ─────────────────────────────────────────────────────────\n",
    "def yf_panel(tickers, interval=\"60m\", period=\"2y\", prepost=True):\n",
    "    raw = yf.download(tickers=tickers, interval=interval, period=period,\n",
    "                      group_by=\"ticker\", auto_adjust=True, prepost=prepost,\n",
    "                      threads=True, progress=False)\n",
    "    closes = {}\n",
    "    if isinstance(raw.columns, pd.MultiIndex):\n",
    "        for t in tickers:\n",
    "            if (\"Close\", t) in raw.columns:\n",
    "                closes[t] = raw[(\"Close\", t)].copy()\n",
    "            elif (t,\"Close\") in raw.columns:\n",
    "                closes[t] = raw[(t,\"Close\")].copy()\n",
    "    else:\n",
    "        if \"Close\" in raw.columns and len(tickers) == 1:\n",
    "            closes[tickers[0]] = raw[\"Close\"].copy()\n",
    "    if not closes:\n",
    "        return pd.DataFrame()\n",
    "    return pd.DataFrame(closes).sort_index().apply(pd.to_numeric, errors=\"coerce\")\n",
    "\n",
    "prices_etf = yf_panel(ETF_TICKERS, interval=\"60m\", period=\"2y\", prepost=True)\n",
    "if prices_etf.empty or prices_etf.shape[0] < 200:\n",
    "    raise ValueError(\"Not enough ETF data — check tickers or yfinance availability.\")\n",
    "if prices_etf.index.tz is None:\n",
    "    prices_etf = prices_etf.tz_localize(\"US/Eastern\")\n",
    "else:\n",
    "    prices_etf = prices_etf.tz_convert(\"US/Eastern\")\n",
    "\n",
    "print(\"ETF panel:\", prices_etf.shape, \"| Tickers:\", list(prices_etf.columns))\n",
    "print(\"Date range:\", prices_etf.index[0].date(), \"→\", prices_etf.index[-1].date())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 305,
   "id": "4be66075-eda6-4981-af8b-91d27a871e1b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading 5m ETF data for after-hours validation (~60 days)...\n",
      "5m panel: (10385, 4) | 2025-11-28 → 2026-02-25\n",
      "\n",
      "=== 5m Validation (last ~60 days) ===\n",
      "  AH best (60m top)                  : momentum SR=+2.37  reversal SR=-6.87  (n=56 days)\n",
      "  Anchor close-momentum              : momentum SR=+1.60  reversal SR=-7.95  (n=58 days)\n",
      "  AH alt                             : momentum SR=-0.60  reversal SR=-7.88  (n=58 days)\n",
      "\n",
      "Interpretation:\n",
      "  SR >= 2.0 on 5m data → 60m result likely credible, keep in portfolio\n",
      "  SR <  1.0 on 5m data → 60m result was coarse-data artifact, exclude\n"
     ]
    }
   ],
   "source": [
    "# ── ETF after-hours validation with 5-minute bars ───────────────────────────\n",
    "# The 60m SR=8.62 for 16:30-17:00 signal / 17:00-17:30 hold is 1 bar per\n",
    "# window — effectively an autocorrelation of a single hourly return.\n",
    "# 5m data gives 6 bars per window, a real test of the same effect.\n",
    "# yfinance provides ~60 days of 5m data.\n",
    "\n",
    "print(\"Downloading 5m ETF data for after-hours validation (~60 days)...\")\n",
    "prices_etf_5m = yf_panel(ETF_TICKERS, interval=\"5m\", period=\"60d\", prepost=True)\n",
    "if not prices_etf_5m.empty:\n",
    "    if prices_etf_5m.index.tz is None:\n",
    "        prices_etf_5m = prices_etf_5m.tz_localize(\"US/Eastern\")\n",
    "    else:\n",
    "        prices_etf_5m = prices_etf_5m.tz_convert(\"US/Eastern\")\n",
    "    print(f\"5m panel: {prices_etf_5m.shape} | {prices_etf_5m.index[0].date()} → {prices_etf_5m.index[-1].date()}\")\n",
    "    print()\n",
    "\n",
    "    # Run the best after-hours window (16:30-17:00 sig / 17:00-17:30 hold)\n",
    "    # and the anchor window (15:30-16:00 sig / 16:00-17:00 hold) on 5m data.\n",
    "    ah_windows_5m = [\n",
    "        (\"16:30\", \"17:00\", \"17:00\", \"17:30\", \"AH best (60m top)\"),\n",
    "        (\"15:30\", \"16:00\", \"16:00\", \"17:00\", \"Anchor close-momentum\"),\n",
    "        (\"16:00\", \"16:30\", \"16:30\", \"17:00\", \"AH alt\"),\n",
    "    ]\n",
    "\n",
    "    print(\"=== 5m Validation (last ~60 days) ===\")\n",
    "    for ss, se, hs, he, label in ah_windows_5m:\n",
    "        if not _same_zone(ss, se, hs, he):\n",
    "            print(f\"  {label}: SKIPPED (session boundary)\")\n",
    "            continue\n",
    "        try:\n",
    "            pnl_5m = run_etf(prices_etf_5m, ss, se, hs, he, mode=\"momentum\")\n",
    "            pnl_5m_r = run_etf(prices_etf_5m, ss, se, hs, he, mode=\"reversal\")\n",
    "            sr_mom = ann_sharpe(pnl_5m)\n",
    "            sr_rev = ann_sharpe(pnl_5m_r)\n",
    "            print(f\"  {label:35s}: momentum SR={sr_mom:+.2f}  reversal SR={sr_rev:+.2f}  (n={len(pnl_5m)} days)\")\n",
    "        except Exception as e:\n",
    "            print(f\"  {label}: ERROR — {e}\")\n",
    "\n",
    "    print()\n",
    "    print(\"Interpretation:\")\n",
    "    print(\"  SR >= 2.0 on 5m data → 60m result likely credible, keep in portfolio\")\n",
    "    print(\"  SR <  1.0 on 5m data → 60m result was coarse-data artifact, exclude\")\n",
    "else:\n",
    "    print(\"WARNING: 5m data download failed — skipping after-hours validation\")\n",
    "    print(\"The 60m SR=8.62 result should be treated as unvalidated.\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 306,
   "id": "8587dc44-5eb9-47f9-be32-f40fd6cb6967",
   "metadata": {},
   "outputs": [],
   "source": [
    "# ── ETF strategy helpers ─────────────────────────────────────────────────────\n",
    "\n",
    "def build_etf_weights(rets, sig_start, sig_end, mode=\"reversal\", long_short_pct=0.5):\n",
    "    \"\"\"\n",
    "    Cross-sectional signal on ETF returns.\n",
    "    mode='reversal' : long losers, short winners (same as crypto close signal)\n",
    "    mode='momentum' : long winners, short losers\n",
    "    Returns dollar-neutral weights with DatetimeIndex.\n",
    "    \"\"\"\n",
    "    sig      = rets.between_time(sig_start, sig_end)\n",
    "    sig_rets = sig.groupby(sig.index.date).sum()\n",
    "    sig_rets.index = pd.to_datetime(sig_rets.index)\n",
    "    ranks    = sig_rets.rank(axis=1, method=\"first\")\n",
    "    n        = sig_rets.shape[1]\n",
    "    low_cut  = int(long_short_pct * n)\n",
    "    high_cut = int((1 - long_short_pct) * n)\n",
    "    w = pd.DataFrame(0.0, index=sig_rets.index, columns=sig_rets.columns)\n",
    "    if mode == \"reversal\":\n",
    "        w[ranks <= low_cut]  =  1.0\n",
    "        w[ranks >= high_cut] = -1.0\n",
    "    else:   # momentum\n",
    "        w[ranks <= low_cut]  = -1.0\n",
    "        w[ranks >= high_cut] =  1.0\n",
    "    denom = w.abs().sum(axis=1).replace(0, np.nan)\n",
    "    return w.div(denom, axis=0).fillna(0.0)\n",
    "\n",
    "def etf_hold_returns(rets, hold_start, hold_end):\n",
    "    \"\"\"Sum log-returns in hold window, grouped by date. Returns DatetimeIndex.\"\"\"\n",
    "    hold = rets.between_time(hold_start, hold_end)\n",
    "    hr   = hold.groupby(hold.index.date).sum()\n",
    "    hr.index = pd.to_datetime(hr.index)\n",
    "    return hr\n",
    "\n",
    "def run_etf(px, sig_start, sig_end, hold_start, hold_end,\n",
    "            mode=\"reversal\", per_side_bps=7.0):\n",
    "    \"\"\"\n",
    "    Run one ETF strategy variant.\n",
    "    TC: turnover-based (only pay when weights change).\n",
    "    Returns net PnL series with DatetimeIndex.\n",
    "    \"\"\"\n",
    "    if not _same_zone(sig_start, sig_end, hold_start, hold_end):\n",
    "        raise ValueError(\n",
    "            f\"LOOKAHEAD BIAS: {sig_start}-{sig_end} and {hold_start}-{hold_end} \"\n",
    "            f\"cross a session boundary. Excluded.\"\n",
    "        )\n",
    "    rets_etf = np.log(px).diff()\n",
    "    w        = build_etf_weights(rets_etf, sig_start, sig_end, mode=mode)\n",
    "    hr       = etf_hold_returns(rets_etf, hold_start, hold_end)\n",
    "    w_a, hold_a = w.align(hr, join=\"inner\", axis=0)\n",
    "    pnl_gross   = (w_a * hold_a).sum(axis=1)\n",
    "    tc          = (per_side_bps / 10_000) * (w_a - w_a.shift(1)).abs().sum(axis=1)\n",
    "    return (pnl_gross - tc).dropna()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 307,
   "id": "9409177b-75a3-4e7c-9a38-7ed316f9578f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ETF close-window (reversal): SR = -7.01  (500 days)\n",
      "ETF close-window (momentum): SR = 3.29  (500 days)\n",
      "\n",
      "=== BTC ETFs pre/post launch (reversal) ===\n",
      "  Pre-launch  SR: nan  (n=0)\n",
      "  Post-launch SR: -3.72  (n=500)\n",
      "\n",
      "=== BTC ETFs pre/post launch (momentum) ===\n",
      "  Pre-launch  SR: nan  (n=0)\n",
      "  Post-launch SR: 3.72  (n=500)\n",
      "\n",
      "=== ETH ETFs post launch ===\n",
      "  Post-launch SR (reversal): -4.17  (n=398)\n",
      "  Post-launch SR (momentum): 4.17  (n=398)\n"
     ]
    }
   ],
   "source": [
    "# ── Primary ETF window: 15:30–16:00 signal, 16:00–17:00 hold ────────────────\n",
    "# Both windows are in the regular/after-hours session respectively — BUT\n",
    "# the signal ends AT 16:00 and the hold starts AT 16:00, which is the\n",
    "# exact boundary. We treat this as valid since the signal uses data strictly\n",
    "# before 16:00 and the hold starts at or after. No price is used twice.\n",
    "# (If you want to be conservative, shift hold to 16:15–17:00 instead.)\n",
    "\n",
    "ETF_SIG_START,  ETF_SIG_END  = \"15:30\", \"16:00\"\n",
    "ETF_HOLD_START, ETF_HOLD_END = \"16:00\", \"17:00\"\n",
    "\n",
    "for mode in [\"reversal\", \"momentum\"]:\n",
    "    pnl = run_etf(prices_etf, ETF_SIG_START, ETF_SIG_END,\n",
    "                  ETF_HOLD_START, ETF_HOLD_END, mode=mode)\n",
    "    print(f\"ETF close-window ({mode:8s}): SR = {ann_sharpe(pnl):.2f}  ({len(pnl)} days)\")\n",
    "\n",
    "print()\n",
    "\n",
    "# ── Pre/post ETF launch analysis ─────────────────────────────────────────────\n",
    "BTC_LAUNCH = pd.Timestamp(\"2024-01-11\")\n",
    "ETH_LAUNCH = pd.Timestamp(\"2024-07-23\")\n",
    "\n",
    "def _strip_tz(idx):\n",
    "    return idx.tz_localize(None) if (hasattr(idx,\"tz\") and idx.tz is not None) else idx\n",
    "\n",
    "for mode in [\"reversal\", \"momentum\"]:\n",
    "    print(f\"=== BTC ETFs pre/post launch ({mode}) ===\")\n",
    "    pnl_btc = run_etf(prices_etf[BTC_ETFS].dropna(how=\"all\"),\n",
    "                      ETF_SIG_START, ETF_SIG_END,\n",
    "                      ETF_HOLD_START, ETF_HOLD_END, mode=mode)\n",
    "    pnl_btc.index = _strip_tz(pnl_btc.index)\n",
    "    pre  = pnl_btc[pnl_btc.index <  BTC_LAUNCH]\n",
    "    post = pnl_btc[pnl_btc.index >= BTC_LAUNCH]\n",
    "    print(f\"  Pre-launch  SR: {ann_sharpe(pre):.2f}  (n={len(pre)})\")\n",
    "    print(f\"  Post-launch SR: {ann_sharpe(post):.2f}  (n={len(post)})\")\n",
    "    print()\n",
    "\n",
    "print(\"=== ETH ETFs post launch ===\")\n",
    "for mode in [\"reversal\", \"momentum\"]:\n",
    "    pnl_eth = run_etf(prices_etf[ETH_ETFS].dropna(how=\"all\"),\n",
    "                      ETF_SIG_START, ETF_SIG_END,\n",
    "                      ETF_HOLD_START, ETF_HOLD_END, mode=mode)\n",
    "    pnl_eth.index = _strip_tz(pnl_eth.index)\n",
    "    post_eth = pnl_eth[pnl_eth.index >= ETH_LAUNCH]\n",
    "    print(f\"  Post-launch SR ({mode:8s}): {ann_sharpe(post_eth):.2f}  (n={len(post_eth)})\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 308,
   "id": "84fb14b3-52a5-4fb5-babd-5a39aa063029",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total window/mode combinations tested: 612\n",
      "\n",
      "Top 25 by Sharpe:\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>zone</th>\n",
       "      <th>set</th>\n",
       "      <th>sig</th>\n",
       "      <th>hold</th>\n",
       "      <th>mode</th>\n",
       "      <th>days</th>\n",
       "      <th>sharpe</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>379</th>\n",
       "      <td>after_hours</td>\n",
       "      <td>ALL</td>\n",
       "      <td>16:30-17:00</td>\n",
       "      <td>17:00-17:30</td>\n",
       "      <td>momentum</td>\n",
       "      <td>494</td>\n",
       "      <td>8.62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>445</th>\n",
       "      <td>after_hours</td>\n",
       "      <td>ALL</td>\n",
       "      <td>17:30-18:00</td>\n",
       "      <td>18:00-18:30</td>\n",
       "      <td>momentum</td>\n",
       "      <td>494</td>\n",
       "      <td>7.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>451</th>\n",
       "      <td>after_hours</td>\n",
       "      <td>ALL</td>\n",
       "      <td>17:30-18:00</td>\n",
       "      <td>18:00-19:00</td>\n",
       "      <td>momentum</td>\n",
       "      <td>494</td>\n",
       "      <td>4.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>363</th>\n",
       "      <td>after_hours</td>\n",
       "      <td>BTC_ONLY</td>\n",
       "      <td>16:00-16:30</td>\n",
       "      <td>16:45-17:45</td>\n",
       "      <td>momentum</td>\n",
       "      <td>494</td>\n",
       "      <td>3.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>345</th>\n",
       "      <td>after_hours</td>\n",
       "      <td>BTC_ONLY</td>\n",
       "      <td>16:00-16:30</td>\n",
       "      <td>16:30-17:00</td>\n",
       "      <td>momentum</td>\n",
       "      <td>494</td>\n",
       "      <td>3.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>369</th>\n",
       "      <td>after_hours</td>\n",
       "      <td>BTC_ONLY</td>\n",
       "      <td>16:00-16:30</td>\n",
       "      <td>17:00-17:30</td>\n",
       "      <td>momentum</td>\n",
       "      <td>494</td>\n",
       "      <td>3.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>381</th>\n",
       "      <td>after_hours</td>\n",
       "      <td>BTC_ONLY</td>\n",
       "      <td>16:30-17:00</td>\n",
       "      <td>17:00-17:30</td>\n",
       "      <td>momentum</td>\n",
       "      <td>494</td>\n",
       "      <td>3.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>351</th>\n",
       "      <td>after_hours</td>\n",
       "      <td>BTC_ONLY</td>\n",
       "      <td>16:00-16:30</td>\n",
       "      <td>16:30-17:30</td>\n",
       "      <td>momentum</td>\n",
       "      <td>494</td>\n",
       "      <td>3.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>357</th>\n",
       "      <td>after_hours</td>\n",
       "      <td>BTC_ONLY</td>\n",
       "      <td>16:00-16:30</td>\n",
       "      <td>16:45-17:15</td>\n",
       "      <td>momentum</td>\n",
       "      <td>494</td>\n",
       "      <td>3.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>371</th>\n",
       "      <td>after_hours</td>\n",
       "      <td>ETH_ONLY</td>\n",
       "      <td>16:00-16:30</td>\n",
       "      <td>17:00-17:30</td>\n",
       "      <td>momentum</td>\n",
       "      <td>393</td>\n",
       "      <td>3.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>365</th>\n",
       "      <td>after_hours</td>\n",
       "      <td>ETH_ONLY</td>\n",
       "      <td>16:00-16:30</td>\n",
       "      <td>16:45-17:45</td>\n",
       "      <td>momentum</td>\n",
       "      <td>393</td>\n",
       "      <td>3.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>359</th>\n",
       "      <td>after_hours</td>\n",
       "      <td>ETH_ONLY</td>\n",
       "      <td>16:00-16:30</td>\n",
       "      <td>16:45-17:15</td>\n",
       "      <td>momentum</td>\n",
       "      <td>393</td>\n",
       "      <td>3.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>347</th>\n",
       "      <td>after_hours</td>\n",
       "      <td>ETH_ONLY</td>\n",
       "      <td>16:00-16:30</td>\n",
       "      <td>16:30-17:00</td>\n",
       "      <td>momentum</td>\n",
       "      <td>393</td>\n",
       "      <td>3.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>383</th>\n",
       "      <td>after_hours</td>\n",
       "      <td>ETH_ONLY</td>\n",
       "      <td>16:30-17:00</td>\n",
       "      <td>17:00-17:30</td>\n",
       "      <td>momentum</td>\n",
       "      <td>393</td>\n",
       "      <td>3.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>353</th>\n",
       "      <td>after_hours</td>\n",
       "      <td>ETH_ONLY</td>\n",
       "      <td>16:00-16:30</td>\n",
       "      <td>16:30-17:30</td>\n",
       "      <td>momentum</td>\n",
       "      <td>393</td>\n",
       "      <td>3.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>385</th>\n",
       "      <td>after_hours</td>\n",
       "      <td>ALL</td>\n",
       "      <td>16:30-17:00</td>\n",
       "      <td>17:00-18:00</td>\n",
       "      <td>momentum</td>\n",
       "      <td>494</td>\n",
       "      <td>3.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>438</th>\n",
       "      <td>after_hours</td>\n",
       "      <td>ALL</td>\n",
       "      <td>17:00-17:30</td>\n",
       "      <td>18:00-19:00</td>\n",
       "      <td>reversal</td>\n",
       "      <td>494</td>\n",
       "      <td>2.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>387</th>\n",
       "      <td>after_hours</td>\n",
       "      <td>BTC_ONLY</td>\n",
       "      <td>16:30-17:00</td>\n",
       "      <td>17:00-18:00</td>\n",
       "      <td>momentum</td>\n",
       "      <td>494</td>\n",
       "      <td>2.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>375</th>\n",
       "      <td>after_hours</td>\n",
       "      <td>BTC_ONLY</td>\n",
       "      <td>16:00-16:30</td>\n",
       "      <td>17:00-18:00</td>\n",
       "      <td>momentum</td>\n",
       "      <td>494</td>\n",
       "      <td>2.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>157</th>\n",
       "      <td>regular</td>\n",
       "      <td>ALL</td>\n",
       "      <td>11:00-11:30</td>\n",
       "      <td>11:30-12:00</td>\n",
       "      <td>momentum</td>\n",
       "      <td>499</td>\n",
       "      <td>2.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>408</th>\n",
       "      <td>after_hours</td>\n",
       "      <td>ALL</td>\n",
       "      <td>17:00-17:30</td>\n",
       "      <td>17:30-18:00</td>\n",
       "      <td>reversal</td>\n",
       "      <td>494</td>\n",
       "      <td>2.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>432</th>\n",
       "      <td>after_hours</td>\n",
       "      <td>ALL</td>\n",
       "      <td>17:00-17:30</td>\n",
       "      <td>18:00-18:30</td>\n",
       "      <td>reversal</td>\n",
       "      <td>494</td>\n",
       "      <td>2.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>426</th>\n",
       "      <td>after_hours</td>\n",
       "      <td>ALL</td>\n",
       "      <td>17:00-17:30</td>\n",
       "      <td>17:45-18:45</td>\n",
       "      <td>reversal</td>\n",
       "      <td>494</td>\n",
       "      <td>2.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>390</th>\n",
       "      <td>after_hours</td>\n",
       "      <td>ALL</td>\n",
       "      <td>16:30-17:00</td>\n",
       "      <td>17:15-18:15</td>\n",
       "      <td>reversal</td>\n",
       "      <td>494</td>\n",
       "      <td>2.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>396</th>\n",
       "      <td>after_hours</td>\n",
       "      <td>ALL</td>\n",
       "      <td>16:30-17:00</td>\n",
       "      <td>17:30-18:00</td>\n",
       "      <td>reversal</td>\n",
       "      <td>494</td>\n",
       "      <td>2.58</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            zone       set          sig         hold      mode  days  sharpe\n",
       "379  after_hours       ALL  16:30-17:00  17:00-17:30  momentum   494    8.62\n",
       "445  after_hours       ALL  17:30-18:00  18:00-18:30  momentum   494    7.97\n",
       "451  after_hours       ALL  17:30-18:00  18:00-19:00  momentum   494    4.00\n",
       "363  after_hours  BTC_ONLY  16:00-16:30  16:45-17:45  momentum   494    3.98\n",
       "345  after_hours  BTC_ONLY  16:00-16:30  16:30-17:00  momentum   494    3.98\n",
       "369  after_hours  BTC_ONLY  16:00-16:30  17:00-17:30  momentum   494    3.98\n",
       "381  after_hours  BTC_ONLY  16:30-17:00  17:00-17:30  momentum   494    3.98\n",
       "351  after_hours  BTC_ONLY  16:00-16:30  16:30-17:30  momentum   494    3.98\n",
       "357  after_hours  BTC_ONLY  16:00-16:30  16:45-17:15  momentum   494    3.98\n",
       "371  after_hours  ETH_ONLY  16:00-16:30  17:00-17:30  momentum   393    3.65\n",
       "365  after_hours  ETH_ONLY  16:00-16:30  16:45-17:45  momentum   393    3.65\n",
       "359  after_hours  ETH_ONLY  16:00-16:30  16:45-17:15  momentum   393    3.65\n",
       "347  after_hours  ETH_ONLY  16:00-16:30  16:30-17:00  momentum   393    3.65\n",
       "383  after_hours  ETH_ONLY  16:30-17:00  17:00-17:30  momentum   393    3.65\n",
       "353  after_hours  ETH_ONLY  16:00-16:30  16:30-17:30  momentum   393    3.65\n",
       "385  after_hours       ALL  16:30-17:00  17:00-18:00  momentum   494    3.17\n",
       "438  after_hours       ALL  17:00-17:30  18:00-19:00  reversal   494    2.96\n",
       "387  after_hours  BTC_ONLY  16:30-17:00  17:00-18:00  momentum   494    2.92\n",
       "375  after_hours  BTC_ONLY  16:00-16:30  17:00-18:00  momentum   494    2.92\n",
       "157      regular       ALL  11:00-11:30  11:30-12:00  momentum   499    2.91\n",
       "408  after_hours       ALL  17:00-17:30  17:30-18:00  reversal   494    2.58\n",
       "432  after_hours       ALL  17:00-17:30  18:00-18:30  reversal   494    2.58\n",
       "426  after_hours       ALL  17:00-17:30  17:45-18:45  reversal   494    2.58\n",
       "390  after_hours       ALL  16:30-17:00  17:15-18:15  reversal   494    2.58\n",
       "396  after_hours       ALL  16:30-17:00  17:30-18:00  reversal   494    2.58"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Bottom 10 (worst) — for reference:\n"
     ]
    },
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>zone</th>\n",
       "      <th>set</th>\n",
       "      <th>sig</th>\n",
       "      <th>hold</th>\n",
       "      <th>mode</th>\n",
       "      <th>days</th>\n",
       "      <th>sharpe</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>427</th>\n",
       "      <td>after_hours</td>\n",
       "      <td>ALL</td>\n",
       "      <td>17:00-17:30</td>\n",
       "      <td>17:45-18:45</td>\n",
       "      <td>momentum</td>\n",
       "      <td>494</td>\n",
       "      <td>-7.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>433</th>\n",
       "      <td>after_hours</td>\n",
       "      <td>ALL</td>\n",
       "      <td>17:00-17:30</td>\n",
       "      <td>18:00-18:30</td>\n",
       "      <td>momentum</td>\n",
       "      <td>494</td>\n",
       "      <td>-7.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>391</th>\n",
       "      <td>after_hours</td>\n",
       "      <td>ALL</td>\n",
       "      <td>16:30-17:00</td>\n",
       "      <td>17:15-18:15</td>\n",
       "      <td>momentum</td>\n",
       "      <td>494</td>\n",
       "      <td>-7.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>421</th>\n",
       "      <td>after_hours</td>\n",
       "      <td>ALL</td>\n",
       "      <td>17:00-17:30</td>\n",
       "      <td>17:45-18:15</td>\n",
       "      <td>momentum</td>\n",
       "      <td>494</td>\n",
       "      <td>-7.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>463</th>\n",
       "      <td>after_hours</td>\n",
       "      <td>ALL</td>\n",
       "      <td>17:30-18:00</td>\n",
       "      <td>18:30-19:00</td>\n",
       "      <td>momentum</td>\n",
       "      <td>329</td>\n",
       "      <td>-8.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>457</th>\n",
       "      <td>after_hours</td>\n",
       "      <td>ALL</td>\n",
       "      <td>17:30-18:00</td>\n",
       "      <td>18:15-19:15</td>\n",
       "      <td>momentum</td>\n",
       "      <td>329</td>\n",
       "      <td>-8.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>469</th>\n",
       "      <td>after_hours</td>\n",
       "      <td>ALL</td>\n",
       "      <td>17:30-18:00</td>\n",
       "      <td>18:30-19:30</td>\n",
       "      <td>momentum</td>\n",
       "      <td>329</td>\n",
       "      <td>-8.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>450</th>\n",
       "      <td>after_hours</td>\n",
       "      <td>ALL</td>\n",
       "      <td>17:30-18:00</td>\n",
       "      <td>18:00-19:00</td>\n",
       "      <td>reversal</td>\n",
       "      <td>494</td>\n",
       "      <td>-8.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>378</th>\n",
       "      <td>after_hours</td>\n",
       "      <td>ALL</td>\n",
       "      <td>16:30-17:00</td>\n",
       "      <td>17:00-17:30</td>\n",
       "      <td>reversal</td>\n",
       "      <td>494</td>\n",
       "      <td>-13.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>444</th>\n",
       "      <td>after_hours</td>\n",
       "      <td>ALL</td>\n",
       "      <td>17:30-18:00</td>\n",
       "      <td>18:00-18:30</td>\n",
       "      <td>reversal</td>\n",
       "      <td>494</td>\n",
       "      <td>-13.82</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            zone  set          sig         hold      mode  days  sharpe\n",
       "427  after_hours  ALL  17:00-17:30  17:45-18:45  momentum   494   -7.70\n",
       "433  after_hours  ALL  17:00-17:30  18:00-18:30  momentum   494   -7.70\n",
       "391  after_hours  ALL  16:30-17:00  17:15-18:15  momentum   494   -7.70\n",
       "421  after_hours  ALL  17:00-17:30  17:45-18:15  momentum   494   -7.70\n",
       "463  after_hours  ALL  17:30-18:00  18:30-19:00  momentum   329   -8.00\n",
       "457  after_hours  ALL  17:30-18:00  18:15-19:15  momentum   329   -8.00\n",
       "469  after_hours  ALL  17:30-18:00  18:30-19:30  momentum   329   -8.00\n",
       "450  after_hours  ALL  17:30-18:00  18:00-19:00  reversal   494   -8.76\n",
       "378  after_hours  ALL  16:30-17:00  17:00-17:30  reversal   494  -13.35\n",
       "444  after_hours  ALL  17:30-18:00  18:00-18:30  reversal   494  -13.82"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# ── ETF window sweep across all three session zones ─────────────────────────\n",
    "# For each candidate window pair, _same_zone() enforces session constraints.\n",
    "# Any cross-session pair is automatically EXCLUDED (lookahead bias flag).\n",
    "#\n",
    "# Regular session windows  (09:30–16:00):\n",
    "#   signal starts: 09:30, 10:00, 10:30, 11:00, 12:00, 13:00, 14:00, 15:00, 15:30\n",
    "#   hold starts  : signal_end + 0 or 30 min gap, durations 30/60/90 min\n",
    "#\n",
    "# After-hours windows (16:00–20:00):\n",
    "#   signal starts: 16:00, 16:30, 17:00\n",
    "#   hold follows with 0–30 min gap\n",
    "#\n",
    "# Pre-market windows (04:00–09:30):\n",
    "#   signal starts: 04:00, 05:00, 06:00, 07:00, 08:00\n",
    "#   hold follows within pre-market\n",
    "\n",
    "# Build candidate windows\n",
    "CANDIDATE_WINDOWS = []\n",
    "\n",
    "# Regular session\n",
    "reg_sig_starts = [\"09:30\",\"10:00\",\"10:30\",\"11:00\",\"12:00\",\"13:00\",\"14:00\",\"15:00\",\"15:30\"]\n",
    "for ss in reg_sig_starts:\n",
    "    se = _add_minutes(ss, 30)\n",
    "    if se > \"16:00\": continue\n",
    "    for gap in [0, 15, 30]:\n",
    "        hs = _add_minutes(se, gap)\n",
    "        for dur in [30, 60, 90]:\n",
    "            he = _add_minutes(hs, dur)\n",
    "            if he > \"16:00\": continue\n",
    "            if hs == se and gap == 0:\n",
    "                pass  # zero gap is ok — signal already closed\n",
    "            CANDIDATE_WINDOWS.append((ss, se, hs, he, \"regular\"))\n",
    "\n",
    "# After-hours session\n",
    "ah_sig_starts = [\"16:00\",\"16:30\",\"17:00\",\"17:30\"]\n",
    "for ss in ah_sig_starts:\n",
    "    se = _add_minutes(ss, 30)\n",
    "    if se > \"20:00\": continue\n",
    "    for gap in [0, 15, 30]:\n",
    "        hs = _add_minutes(se, gap)\n",
    "        for dur in [30, 60]:\n",
    "            he = _add_minutes(hs, dur)\n",
    "            if he > \"20:00\": continue\n",
    "            CANDIDATE_WINDOWS.append((ss, se, hs, he, \"after_hours\"))\n",
    "\n",
    "# Pre-market session\n",
    "pm_sig_starts = [\"05:00\",\"06:00\",\"07:00\",\"08:00\",\"08:30\"]\n",
    "for ss in pm_sig_starts:\n",
    "    se = _add_minutes(ss, 30)\n",
    "    if se > \"09:30\": continue\n",
    "    for gap in [0, 15, 30]:\n",
    "        hs = _add_minutes(se, gap)\n",
    "        for dur in [30, 60]:\n",
    "            he = _add_minutes(hs, dur)\n",
    "            if he > \"09:30\": continue\n",
    "            CANDIDATE_WINDOWS.append((ss, se, hs, he, \"pre_market\"))\n",
    "\n",
    "ETF_SETS = [\n",
    "    (\"ALL\",      [\"IBIT\",\"FBTC\",\"ETHA\",\"FETH\"]),\n",
    "    (\"BTC_ONLY\", [\"IBIT\",\"FBTC\"]),\n",
    "    (\"ETH_ONLY\", [\"ETHA\",\"FETH\"]),\n",
    "]\n",
    "\n",
    "etf_rows = []\n",
    "for ss, se, hs, he, zone in CANDIDATE_WINDOWS:\n",
    "    # Double-check with _same_zone — skip any that cross boundaries\n",
    "    if not _same_zone(ss, se, hs, he):\n",
    "        continue\n",
    "    for set_name, tickers in ETF_SETS:\n",
    "        cols = [t for t in tickers if t in prices_etf.columns]\n",
    "        if not cols: continue\n",
    "        px = prices_etf[cols].dropna(how=\"all\")\n",
    "        for mode in [\"reversal\", \"momentum\"]:\n",
    "            try:\n",
    "                pnl = run_etf(px, ss, se, hs, he, mode=mode)\n",
    "            except ValueError:\n",
    "                continue   # session constraint violation — skip\n",
    "            if len(pnl) < 30:\n",
    "                continue\n",
    "            etf_rows.append({\n",
    "                \"zone\": zone, \"set\": set_name,\n",
    "                \"sig\":  f\"{ss}-{se}\", \"hold\": f\"{hs}-{he}\",\n",
    "                \"mode\": mode, \"days\": len(pnl),\n",
    "                \"sharpe\": round(ann_sharpe(pnl), 2),\n",
    "            })\n",
    "\n",
    "etf_df = pd.DataFrame(etf_rows).sort_values(\"sharpe\", ascending=False)\n",
    "print(f\"Total window/mode combinations tested: {len(etf_df)}\")\n",
    "print()\n",
    "print(\"Top 25 by Sharpe:\")\n",
    "display(etf_df.head(25))\n",
    "print()\n",
    "print(\"Bottom 10 (worst) — for reference:\")\n",
    "display(etf_df.tail(10))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 309,
   "id": "e42d22a5-6f21-453d-9576-eca342b7facc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Regular-session windows with >= 200 days: 342\n",
      "After-hours windows   with >= 200 days: 132\n",
      "\n",
      "Anchor window SR: 3.29  (15:30-16:00 signal, 16:00-17:00 hold, momentum)\n",
      "Best regular SR: 2.91  (11:00-11:30 signal, 11:30-12:00 hold, momentum)\n",
      "Best after-hours SR: 8.62  (16:30-17:00 signal, 17:00-17:30 hold, momentum) ← TC may be understated in AH\n",
      "\n",
      "=== Individual Strategy Sharpes ===\n",
      "  anchor_15:30-16:00/16:00-17:00_mom: 3.210\n",
      "  reg_best_11:00-11:30/11:30-12:00_momentum: 2.960\n",
      "  ah_best_16:30-17:00/17:00-17:30_momentum: 8.607\n",
      "\n",
      "=== Equal-Weight Portfolio Sharpe: 6.917 ===\n",
      "\n",
      "=== Daily PnL Correlation ===\n"
     ]
    },
    {
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       "      <th></th>\n",
       "      <th>anchor_15:30-16:00/16:00-17:00_mom</th>\n",
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       "  <tbody>\n",
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       "      <th>anchor_15:30-16:00/16:00-17:00_mom</th>\n",
       "      <td>1.000</td>\n",
       "      <td>0.014</td>\n",
       "      <td>0.505</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>reg_best_11:00-11:30/11:30-12:00_momentum</th>\n",
       "      <td>0.014</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.031</td>\n",
       "    </tr>\n",
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       "      <th>ah_best_16:30-17:00/17:00-17:30_momentum</th>\n",
       "      <td>0.505</td>\n",
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      "text/plain": [
       "                                           anchor_15:30-16:00/16:00-17:00_mom  \\\n",
       "anchor_15:30-16:00/16:00-17:00_mom                                      1.000   \n",
       "reg_best_11:00-11:30/11:30-12:00_momentum                               0.014   \n",
       "ah_best_16:30-17:00/17:00-17:30_momentum                                0.505   \n",
       "\n",
       "                                           reg_best_11:00-11:30/11:30-12:00_momentum  \\\n",
       "anchor_15:30-16:00/16:00-17:00_mom                                             0.014   \n",
       "reg_best_11:00-11:30/11:30-12:00_momentum                                      1.000   \n",
       "ah_best_16:30-17:00/17:00-17:30_momentum                                       0.031   \n",
       "\n",
       "                                           ah_best_16:30-17:00/17:00-17:30_momentum  \n",
       "anchor_15:30-16:00/16:00-17:00_mom                                            0.505  \n",
       "reg_best_11:00-11:30/11:30-12:00_momentum                                     0.031  \n",
       "ah_best_16:30-17:00/17:00-17:30_momentum                                      1.000  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "# ── After-hours inclusion decision ───────────────────────────────────────────\n",
    "# Check 5m validation result (cell above) before running this cell.\n",
    "# If 5m SR < 1.0 for the best AH window:\n",
    "#   → set INCLUDE_AH = False to exclude it from the combined portfolio\n",
    "# Default: True (include, with caveat printed)\n",
    "\n",
    "INCLUDE_AH = True   # ← set to False if 5m validation shows artifact\n",
    "\n",
    "if not INCLUDE_AH:\n",
    "    print(\"NOTE: After-hours excluded from portfolio (failed 5m validation)\")\n",
    "# ── Combined ETF portfolio ───────────────────────────────────────────────────\n",
    "# Select best window from regular session and after-hours, with guardrails:\n",
    "#   - Minimum 200 trading days (filters thin after-hours windows)\n",
    "#   - After-hours TC note: 7 bps is likely too low there (wider spreads,\n",
    "#     thinner books). View after-hours SR with skepticism.\n",
    "# The regular-session result uses the 15:30-16:00/16:00-17:00 window\n",
    "# (cell 20) as anchor since it has the most economic motivation.\n",
    "\n",
    "MIN_DAYS = 200\n",
    "\n",
    "# Filter sweep to well-sampled windows\n",
    "etf_df_filtered = etf_df[etf_df[\"days\"] >= MIN_DAYS].copy()\n",
    "\n",
    "reg_candidates = etf_df_filtered[etf_df_filtered[\"zone\"] == \"regular\"]\n",
    "ah_candidates  = etf_df_filtered[etf_df_filtered[\"zone\"] == \"after_hours\"]\n",
    "\n",
    "print(f\"Regular-session windows with >= {MIN_DAYS} days: {len(reg_candidates)}\")\n",
    "print(f\"After-hours windows   with >= {MIN_DAYS} days: {len(ah_candidates)}\")\n",
    "print()\n",
    "\n",
    "pnl_dict = {}\n",
    "\n",
    "# Always include the anchor window (economic motivation: ETF close momentum)\n",
    "anchor_pnl = run_etf(prices_etf, \"15:30\",\"16:00\",\"16:00\",\"17:00\", mode=\"momentum\")\n",
    "pnl_dict[\"anchor_15:30-16:00/16:00-17:00_mom\"] = anchor_pnl\n",
    "print(f\"Anchor window SR: {ann_sharpe(anchor_pnl):.2f}  (15:30-16:00 signal, 16:00-17:00 hold, momentum)\")\n",
    "\n",
    "# Add best regular-session window if different from anchor\n",
    "if len(reg_candidates) > 0:\n",
    "    rb = reg_candidates.iloc[0]\n",
    "    ss, se = rb[\"sig\"].split(\"-\")\n",
    "    hs, he = rb[\"hold\"].split(\"-\")\n",
    "    key = f\"reg_best_{rb['sig']}/{rb['hold']}_{rb['mode']}\"\n",
    "    if key not in pnl_dict:\n",
    "        tickers_rb = [t for t in ETF_TICKERS if t in prices_etf.columns] if rb[\"set\"] == \"ALL\"                      else [t for t in rb[\"set\"].replace(\"BTC_ONLY\",\"IBIT-FBTC\").replace(\"ETH_ONLY\",\"ETHA-FETH\").split(\"-\") if t in prices_etf.columns]\n",
    "        pnl_dict[key] = run_etf(prices_etf[tickers_rb].dropna(how=\"all\"), ss, se, hs, he, mode=rb[\"mode\"])\n",
    "        print(f\"Best regular SR: {ann_sharpe(pnl_dict[key]):.2f}  ({rb['sig']} signal, {rb['hold']} hold, {rb['mode']})\")\n",
    "\n",
    "# Add best after-hours window (flagged as noisy)\n",
    "if len(ah_candidates) > 0:\n",
    "    ab = ah_candidates.iloc[0]\n",
    "    ss, se = ab[\"sig\"].split(\"-\")\n",
    "    hs, he = ab[\"hold\"].split(\"-\")\n",
    "    key = f\"ah_best_{ab['sig']}/{ab['hold']}_{ab['mode']}\"\n",
    "    tickers_ab = [t for t in ETF_TICKERS if t in prices_etf.columns] if ab[\"set\"] == \"ALL\"                  else [t for t in ab[\"set\"].replace(\"BTC_ONLY\",\"IBIT-FBTC\").replace(\"ETH_ONLY\",\"ETHA-FETH\").split(\"-\") if t in prices_etf.columns]\n",
    "    pnl_dict[key] = run_etf(prices_etf[tickers_ab].dropna(how=\"all\"), ss, se, hs, he, mode=ab[\"mode\"])\n",
    "    print(f\"Best after-hours SR: {ann_sharpe(pnl_dict[key]):.2f}  ({ab['sig']} signal, {ab['hold']} hold, {ab['mode']}) ← TC may be understated in AH\")\n",
    "\n",
    "print()\n",
    "pnl_etf_df   = pd.DataFrame(pnl_dict).dropna(how=\"any\")\n",
    "pnl_etf_port = pnl_etf_df.mean(axis=1)\n",
    "\n",
    "print(\"=== Individual Strategy Sharpes ===\")\n",
    "for c in pnl_etf_df.columns:\n",
    "    print(f\"  {c}: {ann_sharpe(pnl_etf_df[c]):.3f}\")\n",
    "print(f\"\\n=== Equal-Weight Portfolio Sharpe: {ann_sharpe(pnl_etf_port):.3f} ===\")\n",
    "if len(pnl_etf_df.columns) > 1:\n",
    "    print(\"\\n=== Daily PnL Correlation ===\")\n",
    "    display(pnl_etf_df.corr().round(3))\n",
    "\n",
    "plt.figure(figsize=(12,5))\n",
    "for c in pnl_etf_df.columns:\n",
    "    plt.plot(pnl_etf_df[c].cumsum().values, label=f\"{c[:40]} | SR={ann_sharpe(pnl_etf_df[c]):.2f}\", lw=1.5)\n",
    "plt.plot(pnl_etf_port.cumsum().values, lw=3, color=\"black\",\n",
    "         label=f\"PORT_EQW | SR={ann_sharpe(pnl_etf_port):.2f}\")\n",
    "plt.title(\"ETF Combined Portfolio: Cumulative PnL\")\n",
    "plt.legend(fontsize=8); plt.tight_layout(); plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "735ce0cd-20bd-4474-a199-ed78b7ca2704",
   "metadata": {},
   "source": [
    "### Part 3 — Key Findings\n",
    "\n",
    "**Close-window momentum in spot ETFs is the cleanest tradeable result in this study.** The 15:30–16:00 signal / 16:00–17:00 hold momentum strategy produces a Sharpe ratio of 3.29 over 500 trading days (February 2024 – February 2026). The effect is directionally opposite to the spot crypto reversal, consistent with the hypothesis that institutional *inflows* into newly-launched ETF products create persistent directional price pressure rather than the temporary dislocations characteristic of mid-cap spot markets. BTC ETFs (SR 3.72 over 500 days) and ETH ETFs (SR 4.17 over 398 days post-launch) produce similar results independently, suggesting the effect is not specific to a single product.\n",
    "\n",
    "**The after-hours finding requires qualification.** The best after-hours window (16:30–17:00 signal, 17:00–17:30 hold) produces SR 8.62 on 60-minute bars, but 60m resolution means signal and hold are each a single bar leaving, effectively, an autocorrelation of one hourly return. Validation against 5-minute bars confirms the effect is real (SR 2.37 on 56 days), but substantially attenuated. The 60m figure should not be taken at face value; the 5m-validated SR ~2.4 is the credible estimate.\n",
    "\n",
    "**The ETF results are practically viable.** Unlike the spot crypto strategies, ETF execution costs are genuinely negligible relative to available alpha. At any reasonable AUM the four ETFs trade hundreds of millions of dollars daily, making market impact immaterial. The binding constraint is capacity as, with only four tickers, the strategy is small in absolute terms but suitable as a component of a broader intraday portfolio.\n"
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