{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# CTA 1D Label Analysis\n",
    "\n",
    "Explore label distributions and compare different normalization blending strategies.\n",
    "\n",
    "**Purpose**: Understand how different normalization methods affect label distributions and identify optimal blending."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "from qshare.data.pandas.cta_1d.dataset import load_features, load_label\n",
    "from qshare.data.pandas.cta_1d.label import normalize_label_dual, normalize_label\n",
    "from qshare.io.ddb.cta import load_cta_returns\n",
    "\n",
    "import sys\n",
    "sys.path.insert(0, '../')\n",
    "from common.plotting import setup_plot_style\n",
    "from src.labels import BLEND_CONFIGS, get_blend_weights, describe_blend_config\n",
    "\n",
    "setup_plot_style()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Configuration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "CONFIG = {\n",
    "    'dt_range': ['2020-01-01', '2024-12-31'],\n",
    "    'fit_range': ['2020-01-01', '2021-12-31'],  # For zscore normalization\n",
    "    'return_type': 'o2c_twap1min',\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Load Raw Returns\n",
    "\n",
    "Load the raw return series before any normalization."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load returns\n",
    "print(\"Loading raw returns...\")\n",
    "df_returns = load_cta_returns(\n",
    "    since_date=CONFIG['dt_range'][0],\n",
    "    end_date=CONFIG['dt_range'][1],\n",
    ")\n",
    "\n",
    "return_col = CONFIG['return_type']\n",
    "raw_returns = df_returns[return_col].copy()\n",
    "\n",
    "print(f\"\\nRaw {return_col} returns:\")\n",
    "print(raw_returns.describe())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Plot raw return distribution\n",
    "fig, axes = plt.subplots(1, 2, figsize=(14, 4))\n",
    "\n",
    "# Histogram\n",
    "raw_returns.hist(bins=100, ax=axes[0], edgecolor='black')\n",
    "axes[0].set_title(f'Raw {return_col} Distribution')\n",
    "axes[0].axvline(x=0, color='red', linestyle='--')\n",
    "\n",
    "# Time series\n",
    "daily_mean = raw_returns.groupby(level=0).mean()\n",
    "axes[1].plot(daily_mean.index, daily_mean.values)\n",
    "axes[1].set_title('Daily Mean Return')\n",
    "axes[1].axhline(y=0, color='red', linestyle='--')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Compare Normalization Methods\n",
    "\n",
    "Apply each normalization method individually and compare."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load dominant contract mapping for proper label construction\n",
    "from qshare.io.ddb.cta import load_cta_dominant_contracts\n",
    "\n",
    "print(\"Loading dominant contract mapping...\")\n",
    "df_dominant = load_cta_dominant_contracts(\n",
    "    since_date=CONFIG['dt_range'][0],\n",
    "    end_date=CONFIG['dt_range'][1],\n",
    ")\n",
    "\n",
    "# Merge returns with dominant mapping\n",
    "df_merged = df_dominant.join(raw_returns, how='left')\n",
    "\n",
    "# Calculate different normalization methods\n",
    "print(\"\\nApplying normalization methods...\")\n",
    "\n",
    "norm_results = {}\n",
    "\n",
    "# zscore (fit-time)\n",
    "norm_results['zscore'] = normalize_label(\n",
    "    df_merged[return_col],\n",
    "    method='zscore',\n",
    "    fit_range=CONFIG['fit_range']\n",
    ")\n",
    "\n",
    "# cs_zscore (cross-sectional)\n",
    "norm_results['cs_zscore'] = df_merged.groupby(level=0)[return_col].apply(\n",
    "    lambda x: (x - x.mean()) / (x.std() + 1e-8)\n",
    ")\n",
    "\n",
    "# rolling_20\n",
    "norm_results['rolling_20'] = normalize_label(\n",
    "    df_merged[return_col],\n",
    "    method='rolling',\n",
    "    window=20\n",
    ")\n",
    "\n",
    "# rolling_60\n",
    "norm_results['rolling_60'] = normalize_label(\n",
    "    df_merged[return_col],\n",
    "    method='rolling',\n",
    "    window=60\n",
    ")\n",
    "\n",
    "print(\"Done!\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compare distributions\n",
    "fig, axes = plt.subplots(2, 2, figsize=(14, 10))\n",
    "axes = axes.flatten()\n",
    "\n",
    "for i, (method, series) in enumerate(norm_results.items()):\n",
    "    ax = axes[i]\n",
    "    series.dropna().hist(bins=100, ax=ax, edgecolor='black', alpha=0.7)\n",
    "    ax.set_title(f'{method}\\nmean={series.mean():.3f}, std={series.std():.3f}')\n",
    "    ax.axvline(x=0, color='red', linestyle='--')\n",
    "    ax.set_xlim(-5, 5)  # Focus on main distribution\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. Compare Blend Configurations\n",
    "\n",
    "Compare different blending strategies."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Apply each blend configuration\n",
    "blend_results = {}\n",
    "\n",
    "for name in BLEND_CONFIGS.keys():\n",
    "    weights = get_blend_weights(name)\n",
    "    print(f\"\\nProcessing {name}: {weights}\")\n",
    "    \n",
    "    # Calculate blended label\n",
    "    blended = (\n",
    "        weights[0] * norm_results['zscore'] +\n",
    "        weights[1] * norm_results['cs_zscore'] +\n",
    "        weights[2] * norm_results['rolling_20'] +\n",
    "        weights[3] * norm_results['rolling_60']\n",
    "    )\n",
    "    \n",
    "    blend_results[name] = blended\n",
    "    print(f\"  Mean: {blended.mean():.4f}, Std: {blended.std():.4f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Visualize all blend distributions\n",
    "fig, axes = plt.subplots(2, 3, figsize=(15, 10))\n",
    "axes = axes.flatten()\n",
    "\n",
    "for i, (name, series) in enumerate(blend_results.items()):\n",
    "    ax = axes[i]\n",
    "    series.dropna().hist(bins=100, ax=ax, edgecolor='black', alpha=0.7)\n",
    "    weights = get_blend_weights(name)\n",
    "    ax.set_title(f'{name}\\nweights={weights}\\nmean={series.mean():.3f}, std={series.std():.3f}')\n",
    "    ax.axvline(x=0, color='red', linestyle='--')\n",
    "    ax.set_xlim(-5, 5)\n",
    "\n",
    "# Hide last subplot if not used\n",
    "if len(blend_results) < 6:\n",
    "    axes[-1].axis('off')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5. Correlation Analysis\n",
    "\n",
    "Check correlations between different normalization methods."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create comparison DataFrame\n",
    "comparison_df = pd.DataFrame(norm_results)\n",
    "\n",
    "# Add raw returns\n",
    "comparison_df['raw'] = df_merged[return_col]\n",
    "\n",
    "# Calculate correlation matrix\n",
    "corr = comparison_df.corr()\n",
    "\n",
    "# Plot heatmap\n",
    "fig, ax = plt.subplots(figsize=(8, 6))\n",
    "sns.heatmap(corr, annot=True, cmap='RdBu_r', center=0,\n",
    "            vmin=-1, vmax=1, ax=ax)\n",
    "ax.set_title('Correlation: Normalization Methods')\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "source": [
    "# Rolling correlation analysis\n",
    "window = 60\n",
    "\n",
    "# Calculate rolling correlation between zscore and cs_zscore\n",
    "rolling_corr = norm_results['zscore'].rolling(window).corr(norm_results['cs_zscore'])\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(14, 4))\n",
    "ax.plot(rolling_corr.index.get_level_values(0).unique(), rolling_corr.groupby(level=0).mean())\n",
    "ax.set_title(f'Rolling Correlation: zscore vs cs_zscore ({window}d window)')\n",
    "ax.axhline(y=0.5, color='red', linestyle='--', alpha=0.5)\n",
    "ax.set_ylim(-1, 1)\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  }
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