{
 "cells": [
  {
   "cell_type": "code",
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   "id": "a9834d4d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# -*- coding: utf-8 -*-\n",
    "\"\"\"\n",
    "Stationarity Test for BTC/ETH Spread\n",
    "\n",
    "This notebook:\n",
    "1. Loads BTCUSDT and ETHUSDT close prices\n",
    "2. Computes ratio and difference series\n",
    "3. Plots raw series, rolling means, and residuals\n",
    "4. Runs stationarity tests (ADF & KPSS)\n",
    "\"\"\"\n",
    "\n",
    "import pandas as pd\n",
    "import plotly.graph_objects as go\n",
    "from statsmodels.tsa.stattools import adfuller, kpss\n",
    "\n",
    "# ======================================\n",
    "# Load Data\n",
    "# ======================================\n",
    "btc = pd.read_csv(r'C:\\Users\\amirs\\OneDrive\\Desktop\\myAlgoCode\\detector\\BTCUSDT_1m_1000d.csv')\n",
    "eth = pd.read_csv(r'C:\\Users\\amirs\\OneDrive\\Desktop\\myAlgoCode\\detector\\ETHUSDT_1m_1000d.csv')\n",
    "\n",
    "btc_price = btc['close']\n",
    "eth_price = eth['close']\n",
    "\n",
    "# ======================================\n",
    "# Helper: Stationarity Tests\n",
    "# ======================================\n",
    "def run_stationarity_tests(series, name=\"Series\", sample_step=10):\n",
    "    \"\"\"\n",
    "    Run KPSS and ADF tests on a given series.\n",
    "    Subsamples the series for ADF to avoid memory issues.\n",
    "    \"\"\"\n",
    "    series = series.dropna()\n",
    "\n",
    "    print(f\"\\n=== {name} ===\")\n",
    "\n",
    "    # KPSS Test\n",
    "    print(\"\\n--- KPSS Test ---\")\n",
    "    kpss_result = kpss(series, regression='c', nlags=\"auto\")\n",
    "    print(f\"Statistic : {kpss_result[0]:.4f}\")\n",
    "    print(f\"p-value   : {kpss_result[1]:.4f}\")\n",
    "    print(f\"Lags Used : {kpss_result[2]}\")\n",
    "    print(\"Critical Values:\")\n",
    "    for key, value in kpss_result[3].items():\n",
    "        print(f\"   {key} : {value:.4f}\")\n",
    "    print(\"=> Stationary\" if kpss_result[1] > 0.05 else \"=> Non-stationary\")\n",
    "\n",
    "    # ADF Test\n",
    "    print(\"\\n--- Augmented Dickey-Fuller Test ---\")\n",
    "    adf_result = adfuller(series[::sample_step])\n",
    "    print(f\"ADF Statistic : {adf_result[0]:.4f}\")\n",
    "    print(f\"p-value       : {adf_result[1]:.4f}\")\n",
    "    print(f\"Lags Used     : {adf_result[2]}\")\n",
    "    print(f\"Observations  : {adf_result[3]}\")\n",
    "    print(\"Critical Values:\")\n",
    "    for key, value in adf_result[4].items():\n",
    "        print(f\"   {key} : {value:.4f}\")\n",
    "    print(\"=> Stationary\" if adf_result[1] < 0.05 else \"=> Non-stationary\")\n",
    "\n",
    "\n",
    "# ======================================\n",
    "# Ratio Series (BTC/ETH)\n",
    "# ======================================\n",
    "z_ratio = btc_price / eth_price\n",
    "rolling_mean_ratio = z_ratio.rolling(60*24).mean()\n",
    "ratio_resid = z_ratio - rolling_mean_ratio\n",
    "\n",
    "# Plot ratio & rolling mean\n",
    "fig = go.Figure()\n",
    "fig.add_trace(go.Scatter(y=z_ratio, mode=\"lines\", name=\"BTC/ETH Ratio\", line=dict(color=\"green\")))\n",
    "fig.add_trace(go.Scatter(y=rolling_mean_ratio, mode=\"lines\", name=\"Rolling Mean (1d)\", line=dict(color=\"red\")))\n",
    "fig.update_layout(title=\"BTC/ETH Ratio with Rolling Mean\", template=\"plotly\")\n",
    "fig.show()\n",
    "\n",
    "# Plot residuals\n",
    "fig = go.Figure()\n",
    "fig.add_trace(go.Scatter(y=ratio_resid, mode=\"lines\", name=\"Residuals\", line=dict(color=\"blue\")))\n",
    "fig.update_layout(title=\"BTC/ETH Ratio Residuals\", template=\"plotly\")\n",
    "fig.show()\n",
    "\n",
    "# Run tests\n",
    "run_stationarity_tests(ratio_resid, name=\"BTC/ETH Ratio Residuals\")\n",
    "\n",
    "# ======================================\n",
    "# Difference Series (BTC - ETH)\n",
    "# ======================================\n",
    "z_diff = btc_price - eth_price\n",
    "rolling_mean_diff = z_diff.rolling(60*24).mean()\n",
    "diff_resid = z_diff - rolling_mean_diff\n",
    "diff_resid_mean = diff_resid.rolling(60*24*30).mean()  # 30-day rolling mean of residuals\n",
    "\n",
    "# Plot diff residuals\n",
    "fig = go.Figure()\n",
    "fig.add_trace(go.Scatter(y=diff_resid, mode=\"lines\", name=\"Residuals\", line=dict(color=\"green\")))\n",
    "fig.add_trace(go.Scatter(y=diff_resid_mean, mode=\"lines\", name=\"Rolling Mean (30d)\", line=dict(color=\"red\")))\n",
    "fig.update_layout(title=\"BTC-ETH Difference Residuals\", template=\"plotly\")\n",
    "fig.show()\n",
    "\n",
    "# Run tests\n",
    "run_stationarity_tests(diff_resid, name=\"BTC-ETH Difference Residuals\")\n"
   ]
  }
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