{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "beecb70e",
   "metadata": {},
   "source": [
    "# FTDT Quant Lab — Portfolio Construction & Risk Management\n",
    "\n",
    "**Goal:** Combine multiple independent alpha sources into a single risk-managed portfolio targeting Sharpe > 1.5.\n",
    "\n",
    "**Key concepts:**\n",
    "1. **Diversification**: N independent strategies with low correlation → Sharpe scales ~√N\n",
    "2. **Risk Parity**: Allocate capital inversely proportional to strategy volatility\n",
    "3. **Volatility Targeting**: Scale total portfolio to target annualized vol (e.g., 20%)\n",
    "4. **Correlation Penalty**: Reduce allocation to redundant (highly correlated) strategies\n",
    "5. **Regime Adaptation**: Shift strategy weights based on market conditions\n",
    "6. **Drawdown Control**: Kill switch at strategy and portfolio level\n",
    "\n",
    "**Math:**\n",
    "Portfolio Sharpe ≈ √N × avg(individual Sharpe) × √(1 - avg_correlation)\n",
    "\n",
    "If we have 5 strategies with average individual Sharpe 2.0 and average correlation 0.2:\n",
    "Portfolio Sharpe ≈ √5 × 2.0 × √(0.8) ≈ 4.0\n",
    "\n",
    "This is the engine. 5 good strategies + low correlation → Sharpe >> 1.5."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "36f62f63",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Setup\n",
    "import sys; sys.path.insert(0, str(Path.cwd().parent))\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "from strategies.portfolio import PortfolioConstructor, StrategyAllocation\n",
    "from strategies.regime_ensemble import RegimeDetector, RegimeEnsemble, STRATEGY_REGIME_AFFINITY\n",
    "from config.fee_tiers import get_perp_fees, get_strategy_fee_model\n",
    "\n",
    "sns.set_theme(style=\"darkgrid\")\n",
    "plt.rcParams[\"figure.figsize\"] = (14, 6)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d061b751",
   "metadata": {},
   "source": [
    "## 1. Strategy × Regime Affinity Matrix\n",
    "\n",
    "The regime-switching ensemble selects strategies based on their known\n",
    "performance characteristics in each market regime."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bcd7d239",
   "metadata": {},
   "outputs": [],
   "source": [
    "affinity = STRATEGY_REGIME_AFFINITY\n",
    "affinity_df = pd.DataFrame(affinity).T\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(14, 8))\n",
    "sns.heatmap(affinity_df, annot=True, fmt='.1f', cmap='YlOrRd', \n",
    "            vmin=0, vmax=1, ax=ax, cbar_kws={'label': 'Affinity Score'})\n",
    "ax.set_title('Strategy × Regime Affinity Matrix')\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "# Best strategy per regime\n",
    "print(\"Best strategy for each regime:\")\n",
    "for regime in affinity_df.index:\n",
    "    best = affinity_df.loc[regime].idxmax()\n",
    "    score = affinity_df.loc[regime, best]\n",
    "    print(f\"  {regime:20s} → {best:20s} (score: {score:.1f})\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a7c2cdc3",
   "metadata": {},
   "source": [
    "## 2. Portfolio Construction Simulation\n",
    "\n",
    "Simulate the portfolio with 7 strategies, each running independently.\n",
    "Use correlated returns to test the diversification benefits."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6cae17a9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Simulated returns for 7 strategies with some correlation\n",
    "np.random.seed(42)\n",
    "n_bars = 1000\n",
    "\n",
    "strategy_names = [\"pairs\", \"hurst_vpin\", \"cross_sectional\", \"grid_mm\",\n",
    "                  \"spot_perp_basis\", \"momentum\", \"mean_rev\"]\n",
    "\n",
    "# Generate correlated returns\n",
    "base_returns = np.random.randn(n_bars, 3) * 0.005\n",
    "\n",
    "returns = {}\n",
    "returns[\"pairs\"] = base_returns[:, 0] * 0.6 + np.random.randn(n_bars) * 0.003\n",
    "returns[\"hurst_vpin\"] = base_returns[:, 1] * 0.8 + np.random.randn(n_bars) * 0.004\n",
    "returns[\"cross_sectional\"] = base_returns[:, 0] * 0.3 + base_returns[:, 1] * 0.5 + np.random.randn(n_bars) * 0.003\n",
    "returns[\"grid_mm\"] = base_returns[:, 2] * 0.4 + np.random.randn(n_bars) * 0.002\n",
    "returns[\"spot_perp_basis\"] = np.random.randn(n_bars) * 0.003  # uncorrelated\n",
    "returns[\"momentum\"] = base_returns[:, 1] * 0.7 + np.random.randn(n_bars) * 0.004\n",
    "returns[\"mean_rev\"] = -base_returns[:, 0] * 0.5 + np.random.randn(n_bars) * 0.003\n",
    "\n",
    "# Add positive drift for profitable strategies\n",
    "for name, r in returns.items():\n",
    "    returns[name] = r + 0.0005  # Small positive edge\n",
    "\n",
    "# Compute correlation\n",
    "ret_df = pd.DataFrame(returns)\n",
    "corr = ret_df.corr()\n",
    "sns.heatmap(corr, annot=True, fmt='.2f', cmap='RdBu_r', center=0,\n",
    "            vmin=-1, vmax=1, square=True)\n",
    "plt.title('Strategy Return Correlation Matrix')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2ab6b2b1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Build and simulate portfolio\n",
    "pf = PortfolioConstructor(\n",
    "    capital=100_000,\n",
    "    vol_target=0.20,\n",
    "    max_correlation=0.70,\n",
    "    max_drawdown_stop=0.15,\n",
    "    portfolio_mdd_stop=0.10,\n",
    ")\n",
    "\n",
    "for name in strategy_names:\n",
    "    pf.register_strategy(name)\n",
    "\n",
    "# Feed returns\n",
    "for i in range(n_bars):\n",
    "    for name in strategy_names:\n",
    "        pf.update_returns(name, [returns[name][i]])\n",
    "    pf.update_portfolio_value({\n",
    "        name: returns[name][i] * pf.capital * 0.1\n",
    "        for name in strategy_names\n",
    "    })\n",
    "\n",
    "# Portfolio metrics\n",
    "metrics = pf.summary()\n",
    "print(f\"=== Portfolio Metrics ===\")\n",
    "print(f\"Total Equity:    ${metrics.total_equity:,.2f}\")\n",
    "print(f\"Total PnL:       ${metrics.total_pnl:,.2f} ({metrics.total_pnl_pct*100:.1f}%)\")\n",
    "print(f\"Volatility:      {metrics.vol_20d*100:.1f}%\")\n",
    "print(f\"Sharpe Ratio:    {metrics.sharpe:.2f}\")\n",
    "print(f\"Sortino Ratio:   {metrics.sortino:.2f}\")\n",
    "print(f\"Max Drawdown:    {metrics.max_drawdown_pct*100:.1f}%\")\n",
    "print(f\"Win Rate:        {metrics.win_rate*100:.0f}%\")\n",
    "\n",
    "# Equity curve\n",
    "eq = list(pf.portfolio_equity_history)\n",
    "plt.plot(eq, linewidth=0.5)\n",
    "plt.title('Portfolio Equity Curve')\n",
    "plt.ylabel('Equity ($)')\n",
    "plt.xlabel('Bar')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ff45a282",
   "metadata": {},
   "source": [
    "## 3. Risk Decomposition\n",
    "\n",
    "Where is the risk coming from? Which strategies contribute most to drawdowns?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cad69d26",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Risk attribution per strategy\n",
    "allocations = pf.compute_allocations({\"BTC\": 100000, \"ETH\": 3500, \"SOL\": 200, \"HYPE\": 10})\n",
    "print(\"=== Portfolio Allocation ===\")\n",
    "print(f\"{'Strategy':<20} {'Weight':>8} {'Allocation':>12} {'Vol 20d':>10}\")\n",
    "print(\"-\" * 55)\n",
    "for name in strategy_names:\n",
    "    alloc = allocations.get(name, 0)\n",
    "    st = pf.strategies.get(name)\n",
    "    if st:\n",
    "        print(f\"{name:<20} {st.weight:>7.1%} ${alloc:>10,.0f} {st.vol_20d*100:>8.1f}%\")\n",
    "total_alloc = sum(allocations.values())\n",
    "print(f\"\\n{'Total':<20} {' ':>8} ${total_alloc:>10,.0f}\")\n",
    "print(f\"Reserve: ${pf.capital - total_alloc:>10,.0f}\")\n",
    "\n",
    "# Drawdown per strategy\n",
    "print(f\"\\n=== Drawdown Analysis ===\")\n",
    "for name, st in pf.strategies.items():\n",
    "    if st.peak_equity > 0:\n",
    "        dd = (1.0 - st.equity / st.peak_equity) * 100\n",
    "        print(f\"  {name:<20s}: DD={dd:5.1f}% | Equity=${st.equity:,.0f} | Peak=${st.peak_equity:,.0f}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "04372d8b",
   "metadata": {},
   "source": [
    "## 4. Regime-Adaptive Allocation\n",
    "\n",
    "Test the regime-switching ensemble: how do weights shift across regimes?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7a628f24",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Simulate different regimes\n",
    "ensemble = RegimeEnsemble()\n",
    "\n",
    "# Seed with some signals\n",
    "for name in strategy_names:\n",
    "    ensemble.update_strategy_signal(name, \"BUY\", 0.6 + np.random.random() * 0.2)\n",
    "\n",
    "# Test in different regimes by feeding artificial price patterns\n",
    "np.random.seed(42)\n",
    "\n",
    "print(\"=== Strategy Weights by Regime ===\\n\")\n",
    "\n",
    "# TRENDING: strong upward drift\n",
    "for i in range(200):\n",
    "    ensemble.feed_price(100000 + i * 50 + np.random.randn() * 200)\n",
    "trending_weights = ensemble.compute_weights()\n",
    "print(\"TRENDING:\")\n",
    "for s, w in sorted(trending_weights.items(), key=lambda x: x[1], reverse=True)[:5]:\n",
    "    print(f\"  {s:20s}: {w:.1%}\")\n",
    "\n",
    "# Reset detector and test MEAN_REVERTING\n",
    "ensemble.detector.prices.clear()\n",
    "for i in range(200):\n",
    "    px = 100000 + np.sin(i * 0.1) * 2000 + np.random.randn() * 500\n",
    "    ensemble.feed_price(px)\n",
    "mr_weights = ensemble.compute_weights()\n",
    "print(\"\\nMEAN_REVERTING:\")\n",
    "for s, w in sorted(mr_weights.items(), key=lambda x: x[1], reverse=True)[:5]:\n",
    "    print(f\"  {s:20s}: {w:.1%}\")\n",
    "\n",
    "# Compare\n",
    "print(f\"\\n=== Weight Shift Analysis ===\")\n",
    "for name in sorted(strategy_names):\n",
    "    tw = trending_weights.get(name, 0)\n",
    "    mw = mr_weights.get(name, 0)\n",
    "    shift = mw - tw\n",
    "    direction = \"▲ MR\" if shift > 0.01 else (\"▼ TREND\" if shift < -0.01 else \"— same\")\n",
    "    print(f\"  {name:20s}: TR={tw:.2%}  MR={mw:.2%}  ({direction})\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e5d9646a",
   "metadata": {},
   "source": [
    "## 5. Sharpe Decomposition\n",
    "\n",
    "Target: Sharpe > 1.5. How many strategies do we need?\n",
    "\n",
    "```\n",
    "Portfolio Sharpe = √N × avg(individual Sharpe) × √(1 - avg_correlation)\n",
    "                 = √N × Sᵢ × √(1 - ρ̄)\n",
    "```\n",
    "\n",
    "**Scenarios:**\n",
    "| N strategies | Avg Sharpe | Avg Corr | Portfolio Sharpe | Target? |\n",
    "|-------------|-----------|---------|-----------------|---------| \n",
    "| 3           | 1.5       | 0.3     | 2.17            | ✅      |\n",
    "| 5           | 1.0       | 0.2     | 2.00            | ✅      |\n",
    "| 5           | 0.8       | 0.5     | 1.26            | ❌      |\n",
    "| 7           | 1.0       | 0.3     | 2.21            | ✅      |\n",
    "| 7           | 0.7       | 0.2     | 1.66            | ✅      |\n",
    "\n",
    "**Conclusion:** With 5-7 strategies averaging 1.0 individual Sharpe and correlation below 0.3, we comfortably exceed Sharpe 1.5. The key is keeping correlation low — redundant strategies destroy the diversification benefit."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "68e1dac1",
   "metadata": {},
   "outputs": [],
   "source": [
    "def portfolio_sharpe(n_strategies, avg_sharpe, avg_correlation):\n",
    "    return np.sqrt(n_strategies) * avg_sharpe * np.sqrt(1 - avg_correlation)\n",
    "\n",
    "# Parameter sweep\n",
    "ns = range(2, 11)\n",
    "sharpes = [0.5, 0.8, 1.0, 1.2, 1.5]\n",
    "corrs = [0.1, 0.2, 0.3, 0.5]\n",
    "\n",
    "print(\"=== Portfolio Sharpe Projections ===\\n\")\n",
    "print(f\"{'N':>3} | \", end=\"\")\n",
    "for s in sharpes:\n",
    "    print(f\"Sᵢ={s:.1f}  \", end=\"\")\n",
    "print(\"| ρ̄=0.2\")\n",
    "\n",
    "for n in ns:\n",
    "    print(f\"{n:3d} | \", end=\"\")\n",
    "    for s in sharpes:\n",
    "        ps = portfolio_sharpe(n, s, 0.2)\n",
    "        marker = \" ✅\" if ps > 1.5 else \"   \"\n",
    "        print(f\"{ps:5.2f}{marker} \", end=\"\")\n",
    "    print()\n",
    "\n",
    "print(f\"\\nTarget line: Sharpe > 1.50\")\n",
    "print(f\"Bold numbers pass the target. Strategy: maximize N × Sᵢ × (1 - ρ̄)\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8f68c80b",
   "metadata": {},
   "source": [
    "## 6. Deployment Pipeline\n",
    "\n",
    "The complete pipeline from idea → deployment:\n",
    "\n",
    "```\n",
    "IDEA → Signal Generation → VBT Backtest → Walk-Forward → \n",
    "→ DSR/PSR/Haircut → QuantVerdict → \n",
    "→ Paper Trading (24h+) → Queue Simulation → \n",
    "→ LIVE (1/10 size, daily PnL stop)\n",
    "```\n",
    "\n",
    "**Operational rules:**\n",
    "- Never deploy more than 2 new strategies simultaneously\n",
    "- Each strategy starts at 1/10 target size for 1 week\n",
    "- Daily PnL stop: halt strategy if -2% in one day\n",
    "- Weekly review: check Sharpe, DD, win rate vs. backtest\n",
    "- Monthly rebalancing: re-run walk-forward to update parameters\n",
    "- Kill switch: any strategy -15% from peak → disabled\n",
    "- Portfolio kill: total equity -10% from peak → all strategies paused"
   ]
  }
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