{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "4b866c37-13ff-4f0f-bff4-61b41719ad6a",
   "metadata": {},
   "source": [
    "# The DAX \"Prophet\"\n",
    "\n",
    "* DAX = German index. Not the ETF named \"DAX\" (pitfall)\n",
    "* Data source: FMP (you can use yfinance)\n",
    "\n",
    "\n",
    "* Purpose of this NB: swing trade support (weeks)\n",
    "* Model can detect seasonalities"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4882e088-c9c9-4004-a984-e6e948dd6c96",
   "metadata": {},
   "source": [
    "## Data retrieval ($)\n",
    "\n",
    "We want historical data for our model. This isn't free."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "ec60e3a9-efcc-4619-9fbd-0d09fedcf4f7",
   "metadata": {},
   "outputs": [],
   "source": [
    "from openbb import obb\n",
    "import os\n",
    "from dotenv import load_dotenv\n",
    "\n",
    "def read_api_keys_from_env():\n",
    "    \"\"\"\n",
    "    Read API keys from env\n",
    "    \"\"\"\n",
    "    load_dotenv(\"api_keys\")  # This loads the variables from the file\n",
    "    \n",
    "    obb.user.credentials.nasdaq_api_key = os.getenv('NASDAQ_API_KEY') or print(\"NASDAQ_API_KEY is not set or empty\") or None\n",
    "    obb.user.credentials.fmp_api_key = os.getenv('FMP_API_KEY') or print(\"FMP_API_KEY is not set or empty\") or None\n",
    "    obb.user.credentials.econdb_api_key = os.getenv('ECONDB_API_KEY') or print(\"ECONDB_API_KEY is not set or empty\") or None\n",
    "    obb.user.credentials.fred_api_key = os.getenv('FRED_API_KEY') or print(\"FRED_API_KEY is not set or empty\") or None\n",
    "    obb.user.credentials.intrinio_api_key = os.getenv('INTRINIO_API_KEY') or print(\"INTRINIO_API_KEY is not set or empty\") or None\n",
    "    obb.user.credentials.polygon_api_key = os.getenv('POLYGON_API_KEY') or print(\"POLYGON_API_KEY is not set or empty\") or None\n",
    "    \n",
    "read_api_keys_from_env()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "c0f83ff6-d1b2-424a-9974-4d005110c9e6",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_dax_data(symbol : str):\n",
    "\n",
    "    df_daily = obb.equity.price.historical(symbol = symbol, provider=\"fmp\",start_date=\"2000-01-01\").to_df()\n",
    "    return df_daily\n",
    "\n",
    "# https://site.financialmodelingprep.com/index-summary/%5EGDAXI \n",
    "# https://de.finance.yahoo.com/quote/%5EGDAXI/ \n",
    "df = get_dax_data(\"^GDAXI\") # Not \"DAX\", which is an ETF"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "b4e2d572-5349-4644-a82c-083aff2ffcc2",
   "metadata": {},
   "outputs": [
    {
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       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "      <th>volume</th>\n",
       "      <th>vwap</th>\n",
       "      <th>adj_close</th>\n",
       "      <th>unadjusted_volume</th>\n",
       "      <th>change</th>\n",
       "      <th>change_percent</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2024-08-23</th>\n",
       "      <td>18538.86914</td>\n",
       "      <td>18670.44922</td>\n",
       "      <td>18519.28906</td>\n",
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       "      <td>41122500</td>\n",
       "      <td>18590.43</td>\n",
       "      <td>18633.09961</td>\n",
       "      <td>41122500.0</td>\n",
       "      <td>94.23047</td>\n",
       "      <td>0.005083</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
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      "text/plain": [
       "                   open         high          low        close    volume  \\\n",
       "date                                                                       \n",
       "2024-08-23  18538.86914  18670.44922  18519.28906  18633.09961  41122500   \n",
       "\n",
       "                vwap    adj_close  unadjusted_volume    change  change_percent  \n",
       "date                                                                            \n",
       "2024-08-23  18590.43  18633.09961         41122500.0  94.23047        0.005083  "
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "4801ef6a-4fb4-4091-8b68-74cce0b05c3c",
   "metadata": {},
   "outputs": [
    {
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       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "      <th>volume</th>\n",
       "      <th>vwap</th>\n",
       "      <th>adj_close</th>\n",
       "      <th>unadjusted_volume</th>\n",
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       "      <th>change_percent</th>\n",
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       "      <th>date</th>\n",
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       "    <tr>\n",
       "      <th>2000-01-03</th>\n",
       "      <td>6961.720215</td>\n",
       "      <td>7159.330078</td>\n",
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       "      <td>43072500</td>\n",
       "      <td>6876.98665</td>\n",
       "      <td>6750.759766</td>\n",
       "      <td>43072500.0</td>\n",
       "      <td>-210.96045</td>\n",
       "      <td>-0.0303</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      "text/plain": [
       "                   open         high          low        close    volume  \\\n",
       "date                                                                       \n",
       "2000-01-03  6961.720215  7159.330078  6720.870117  6750.759766  43072500   \n",
       "\n",
       "                  vwap    adj_close  unadjusted_volume     change  \\\n",
       "date                                                                \n",
       "2000-01-03  6876.98665  6750.759766         43072500.0 -210.96045   \n",
       "\n",
       "            change_percent  \n",
       "date                        \n",
       "2000-01-03         -0.0303  "
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "7d0aca72-041d-461b-8000-dedce6daf3f8",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_csv(\"GDAXI_Jan_2k_Aug24.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f91a23d4-73df-44fd-96b7-3f2a0b0545b8",
   "metadata": {},
   "source": [
    "# Load the data (local file)\n",
    "\n",
    "* Data is present in the CSV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "77d99141-68b3-42ce-871e-b5fc4bbcb087",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "\n",
    "gdax_df = pd.read_csv(\"GDAXI_Jan_2k_Aug24.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "2e82c9db-965c-459e-b2f4-5f805f65e117",
   "metadata": {},
   "outputs": [
    {
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       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "      <th>volume</th>\n",
       "      <th>vwap</th>\n",
       "      <th>adj_close</th>\n",
       "      <th>unadjusted_volume</th>\n",
       "      <th>change</th>\n",
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       "      <th>6260</th>\n",
       "      <td>2024-08-23</td>\n",
       "      <td>18538.86914</td>\n",
       "      <td>18670.44922</td>\n",
       "      <td>18519.28906</td>\n",
       "      <td>18633.09961</td>\n",
       "      <td>41122500</td>\n",
       "      <td>18590.43</td>\n",
       "      <td>18633.09961</td>\n",
       "      <td>41122500.0</td>\n",
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       "            date         open         high          low        close  \\\n",
       "6260  2024-08-23  18538.86914  18670.44922  18519.28906  18633.09961   \n",
       "\n",
       "        volume      vwap    adj_close  unadjusted_volume    change  \\\n",
       "6260  41122500  18590.43  18633.09961         41122500.0  94.23047   \n",
       "\n",
       "      change_percent  \n",
       "6260        0.005083  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gdax_df.tail(1)"
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   "execution_count": 24,
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       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
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       "      <th>vwap</th>\n",
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       "      <th>unadjusted_volume</th>\n",
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       "         date         open         high          low        close    volume  \\\n",
       "0  2000-01-03  6961.720215  7159.330078  6720.870117  6750.759766  43072500   \n",
       "\n",
       "         vwap    adj_close  unadjusted_volume     change  change_percent  \n",
       "0  6876.98665  6750.759766         43072500.0 -210.96045         -0.0303  "
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     "execution_count": 24,
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    "gdax_df.head(1)"
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  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "7903e92f-5636-40e6-8b37-202d5f23eeea",
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   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Last 5 days of complete data:\n",
      "                  close  KalmanEstimate\n",
      "date                                   \n",
      "2024-08-20  18357.51953    18083.979993\n",
      "2024-08-21  18448.94922    18118.697662\n",
      "2024-08-22  18493.39063    18154.340301\n",
      "2024-08-23  18633.09961    18199.882243\n",
      "2024-08-24          NaN    18199.882243\n",
      "\n",
      "Last complete day in dataset:\n",
      "open    NaN\n",
      "high    NaN\n",
      "low     NaN\n",
      "close   NaN\n",
      "Name: 2024-08-24 00:00:00, dtype: float64\n",
      "\n",
      "Next day estimate: 18199.88\n",
      "Estimate uncertainty: 0.32\n",
      "\n",
      "Last month's maximum price: 18670.45\n",
      "\n",
      "Last month's minimum price: 17024.82\n",
      "\n",
      "Data range: from 2000-01-03 to 2024-08-24\n",
      "\n",
      "Current date and time: 2024-08-25 06:54:51.729938\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from pykalman import KalmanFilter\n",
    "import matplotlib.pyplot as plt\n",
    "from datetime import timedelta, datetime\n",
    "\n",
    "# Read the DAX data from the CSV file\n",
    "df = pd.read_csv(\"GDAXI_Jan_2k_Aug24.csv\", parse_dates=['date'], index_col='date')\n",
    "\n",
    "# Remove any rows with NaN values and get the latest complete day\n",
    "df = df.dropna()\n",
    "latest_complete_day = df.index[-1]\n",
    "\n",
    "# Ensure we're only working with complete data up to the latest complete day\n",
    "df = df[:latest_complete_day]\n",
    "\n",
    "# Initialize Kalman Filter\n",
    "kf = KalmanFilter(\n",
    "    initial_state_mean=df['close'].iloc[0],\n",
    "    initial_state_covariance=1,\n",
    "    transition_matrices=[1],\n",
    "    observation_matrices=[1],\n",
    "    observation_covariance=1,\n",
    "    transition_covariance=0.01\n",
    ")\n",
    "\n",
    "# Apply Kalman Filter\n",
    "state_means, state_covariances = kf.filter(df['close'])\n",
    "\n",
    "# Add Kalman Filter estimates to the dataframe\n",
    "df['KalmanEstimate'] = state_means\n",
    "\n",
    "# Get the next day estimate\n",
    "last_mean = state_means[-1]\n",
    "last_covariance = state_covariances[-1]\n",
    "next_mean, next_covariance = kf.filter_update(\n",
    "    filtered_state_mean=last_mean,\n",
    "    filtered_state_covariance=last_covariance\n",
    ")\n",
    "\n",
    "# Add the next day estimate to the dataframe\n",
    "next_day = latest_complete_day + timedelta(days=1)\n",
    "df.loc[next_day, 'KalmanEstimate'] = next_mean[0]\n",
    "\n",
    "# Calculate last month's max and min\n",
    "last_month_start = latest_complete_day - timedelta(days=30)\n",
    "last_month = df.loc[last_month_start:]\n",
    "last_month_max = last_month['high'].max()\n",
    "last_month_min = last_month['low'].min()\n",
    "\n",
    "# Plot the results\n",
    "plt.figure(figsize=(12, 6))\n",
    "plt.plot(df.index, df['close'], label='Closing Price')\n",
    "plt.plot(df.index, df['KalmanEstimate'], label='Kalman Filter Estimate', color='red')\n",
    "plt.scatter(next_day, next_mean, color='green', s=100, label='Next Day Estimate')\n",
    "\n",
    "# Add horizontal lines for last month's max and min\n",
    "plt.axhline(y=last_month_max, color='orange', linestyle='--', label='Last Month Max')\n",
    "plt.axhline(y=last_month_min, color='purple', linestyle='--', label='Last Month Min')\n",
    "\n",
    "plt.title('DAX (German Stock Index) with Kalman Filter Estimate and Additional Data')\n",
    "plt.xlabel('Date')\n",
    "plt.ylabel('Price')\n",
    "plt.legend()\n",
    "plt.show()\n",
    "\n",
    "# Print the last few days of data and the next day estimate\n",
    "print(\"Last 5 days of complete data:\")\n",
    "print(df[['close', 'KalmanEstimate']].tail())\n",
    "\n",
    "print(f\"\\nLast complete day in dataset:\")\n",
    "print(df[['open', 'high', 'low', 'close']].iloc[-1])\n",
    "\n",
    "print(f\"\\nNext day estimate: {next_mean[0]:.2f}\")\n",
    "print(f\"Estimate uncertainty: {np.sqrt(next_covariance[0][0]):.2f}\")\n",
    "\n",
    "print(f\"\\nLast month's maximum price: {last_month_max:.2f}\")\n",
    "print(f\"\\nLast month's minimum price: {last_month_min:.2f}\")\n",
    "\n",
    "# Print the date range of the data\n",
    "print(f\"\\nData range: from {df.index[0].date()} to {df.index[-1].date()}\")\n",
    "\n",
    "print(f\"\\nCurrent date and time: {datetime.now()}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2fd93317-ea7b-4edf-a2d4-b7ebdbe5021e",
   "metadata": {},
   "source": [
    "# Get foundational features ($)\n",
    "\n",
    "We are going to enrich our dataset with\n",
    "\n",
    "* Export stats\n",
    "* Inflation\n",
    "\n",
    "https://www.econdb.com/series/REXPDE/germany-real-exports-of-goods-and-services/?from=2019-08-25&to=2024-08-25 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "53f74d83-3ebf-4cac-b93e-547cb4d9d4e4",
   "metadata": {},
   "outputs": [],
   "source": [
    "from openbb import obb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "de0c2354-e0aa-4034-a4bd-46e25963fd2a",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "# define API_TOKEN\n",
    "\n",
    "export_stats_usd_de_df = pd.read_csv(\n",
    "    'https://www.econdb.com/api/series/REXPDE~TUSD/?token=%s&format=csv' % API_TOKEN,\n",
    "    index_col='Date', parse_dates=['Date'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "0269e523-0a5c-4e09-8805-f6a53016e90a",
   "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>REXPDE~TUSD</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2024-01-01</th>\n",
       "      <td>441754.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            REXPDE~TUSD\n",
       "Date                   \n",
       "2024-01-01     441754.0"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "export_stats_usd_df.tail(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "78ff5b8d-1ab9-4688-86a0-b75e09103cd7",
   "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>REXPDE~TUSD</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1991-01-01</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1991-04-01</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1991-07-01</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1991-10-01</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1992-01-01</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            REXPDE~TUSD\n",
       "Date                   \n",
       "1991-01-01          NaN\n",
       "1991-04-01          NaN\n",
       "1991-07-01          NaN\n",
       "1991-10-01          NaN\n",
       "1992-01-01          NaN"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "export_stats_usd_df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "2396d267-0797-4225-a5fc-f61f32c93fcd",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "API_TOKEN = '964268845594aabc3fdc5fb6e47a4184256d1a35'\n",
    "\n",
    "consumer_price_index_de_df = pd.read_csv(\n",
    "    'https://www.econdb.com/api/series/CPIDE/?token=%s&format=csv' % API_TOKEN,\n",
    "    index_col='Date', parse_dates=['Date'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "196a05a7-3233-416c-b4cc-5de7a1c49a43",
   "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>CPIDE</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2012-03-01</th>\n",
       "      <td>91.7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            CPIDE\n",
       "Date             \n",
       "2012-03-01   91.7"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "consumer_price_index_de_df.head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "94260786-8ec3-48ae-ae1b-382df0460882",
   "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>CPIDE</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2024-07-01</th>\n",
       "      <td>119.8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            CPIDE\n",
       "Date             \n",
       "2024-07-01  119.8"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "consumer_price_index_de_df.tail(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e3d0e740-0414-4698-9c44-15041c2cdbed",
   "metadata": {},
   "source": [
    "# Combine the datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "714e5a58-e423-45d5-960d-faee3d3312eb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "           index         open         high          low        close  \\\n",
      "12271 2024-08-19  18312.50977  18454.93945  18284.85938  18421.68945   \n",
      "12272 2024-08-20  18473.23047  18495.27930  18349.98047  18357.51953   \n",
      "12273 2024-08-21  18358.78906  18476.63086  18352.18945  18448.94922   \n",
      "12274 2024-08-22  18459.27930  18551.39063  18458.60938  18493.39063   \n",
      "12275 2024-08-23  18538.86914  18670.44922  18519.28906  18633.09961   \n",
      "\n",
      "           volume      vwap    adj_close  unadjusted_volume     change  \\\n",
      "12271  43769700.0  18368.50  18421.68945         43769700.0  109.17968   \n",
      "12272  42912000.0  18419.00  18357.51953         42912000.0 -115.71094   \n",
      "12273  36068100.0  18409.14  18448.94922         36068100.0   90.16016   \n",
      "12274  40950900.0  18490.67  18493.39063         40950900.0   34.11133   \n",
      "12275  41122500.0  18590.43  18633.09961         41122500.0   94.23047   \n",
      "\n",
      "       change_percent  REXPDE~TUSD  CPIDE  \n",
      "12271        0.005962     441754.0  119.8  \n",
      "12272       -0.006264     441754.0  119.8  \n",
      "12273        0.004911     441754.0  119.8  \n",
      "12274        0.001848     441754.0  119.8  \n",
      "12275        0.005083     441754.0  119.8  \n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 12276 entries, 0 to 12275\n",
      "Data columns (total 13 columns):\n",
      " #   Column             Non-Null Count  Dtype         \n",
      "---  ------             --------------  -----         \n",
      " 0   index              12276 non-null  datetime64[ns]\n",
      " 1   open               8987 non-null   float64       \n",
      " 2   high               8987 non-null   float64       \n",
      " 3   low                8987 non-null   float64       \n",
      " 4   close              8987 non-null   float64       \n",
      " 5   volume             8987 non-null   float64       \n",
      " 6   vwap               8987 non-null   float64       \n",
      " 7   adj_close          8987 non-null   float64       \n",
      " 8   unadjusted_volume  8987 non-null   float64       \n",
      " 9   change             8987 non-null   float64       \n",
      " 10  change_percent     8987 non-null   float64       \n",
      " 11  REXPDE~TUSD        8989 non-null   float64       \n",
      " 12  CPIDE              4546 non-null   float64       \n",
      "dtypes: datetime64[ns](1), float64(12)\n",
      "memory usage: 1.2 MB\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "\n",
    "# Ensure export_stats_usd_de_df and consumer_price_index_de_df have datetime index\n",
    "export_stats_usd_de_df.index = pd.to_datetime(export_stats_usd_de_df.index)\n",
    "consumer_price_index_de_df.index = pd.to_datetime(consumer_price_index_de_df.index)\n",
    "\n",
    "# Resample export_stats_usd_de_df to daily frequency and forward fill\n",
    "export_stats_daily = export_stats_usd_de_df.resample('D').ffill()\n",
    "\n",
    "# Resample consumer_price_index_de_df to daily frequency and forward fill\n",
    "cpi_daily = consumer_price_index_de_df.resample('D').ffill()\n",
    "\n",
    "# Merge all dataframes\n",
    "combined_df = gdax_df.join([export_stats_daily, cpi_daily], how='outer')\n",
    "\n",
    "# Forward fill any remaining NaN values\n",
    "combined_df = combined_df.ffill()\n",
    "\n",
    "# If you want 'date' as a column instead of index\n",
    "combined_df = combined_df.reset_index()\n",
    "\n",
    "# Display the first few rows of the combined dataframe\n",
    "print(combined_df.tail())\n",
    "\n",
    "# Display info about the combined dataframe\n",
    "print(combined_df.info())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "bd1f6548-d845-4a26-b0be-52951a332ffe",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "           index         open         high          low        close  \\\n",
      "12271 2024-08-19  18312.50977  18454.93945  18284.85938  18421.68945   \n",
      "12272 2024-08-20  18473.23047  18495.27930  18349.98047  18357.51953   \n",
      "12273 2024-08-21  18358.78906  18476.63086  18352.18945  18448.94922   \n",
      "12274 2024-08-22  18459.27930  18551.39063  18458.60938  18493.39063   \n",
      "12275 2024-08-23  18538.86914  18670.44922  18519.28906  18633.09961   \n",
      "\n",
      "           volume      vwap    adj_close  unadjusted_volume     change  \\\n",
      "12271  43769700.0  18368.50  18421.68945         43769700.0  109.17968   \n",
      "12272  42912000.0  18419.00  18357.51953         42912000.0 -115.71094   \n",
      "12273  36068100.0  18409.14  18448.94922         36068100.0   90.16016   \n",
      "12274  40950900.0  18490.67  18493.39063         40950900.0   34.11133   \n",
      "12275  41122500.0  18590.43  18633.09961         41122500.0   94.23047   \n",
      "\n",
      "       change_percent  KalmanEstimate  LorentzianIndicator  REXPDE~TUSD  CPIDE  \n",
      "12271        0.005962    18055.224170             5.594636     441754.0  119.8  \n",
      "12272       -0.006264    18083.979993             5.592532     441754.0  119.8  \n",
      "12273        0.004911    18118.697662             5.505103     441754.0  119.8  \n",
      "12274        0.001848    18154.340301             5.395760     441754.0  119.8  \n",
      "12275        0.005083    18199.882243             5.234629     441754.0  119.8  \n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 12276 entries, 0 to 12275\n",
      "Data columns (total 15 columns):\n",
      " #   Column               Non-Null Count  Dtype         \n",
      "---  ------               --------------  -----         \n",
      " 0   index                12276 non-null  datetime64[ns]\n",
      " 1   open                 8987 non-null   float64       \n",
      " 2   high                 8987 non-null   float64       \n",
      " 3   low                  8987 non-null   float64       \n",
      " 4   close                8987 non-null   float64       \n",
      " 5   volume               8987 non-null   float64       \n",
      " 6   vwap                 8987 non-null   float64       \n",
      " 7   adj_close            8987 non-null   float64       \n",
      " 8   unadjusted_volume    8987 non-null   float64       \n",
      " 9   change               8987 non-null   float64       \n",
      " 10  change_percent       8987 non-null   float64       \n",
      " 11  KalmanEstimate       8987 non-null   float64       \n",
      " 12  LorentzianIndicator  8970 non-null   float64       \n",
      " 13  REXPDE~TUSD          8989 non-null   float64       \n",
      " 14  CPIDE                4546 non-null   float64       \n",
      "dtypes: datetime64[ns](1), float64(14)\n",
      "memory usage: 1.4 MB\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from pykalman import KalmanFilter\n",
    "import talib\n",
    "\n",
    "# Function to calculate Lorentzian indicator\n",
    "def calculate_lorentzian(data, period=14):\n",
    "    high_low = data['high'] - data['low']\n",
    "    high_close = np.abs(data['high'] - data['close'].shift())\n",
    "    low_close = np.abs(data['low'] - data['close'].shift())\n",
    "    ranges = pd.concat([high_low, high_close, low_close], axis=1)\n",
    "    true_range = np.max(ranges, axis=1)\n",
    "    atr = talib.SMA(true_range, timeperiod=period)\n",
    "    lorentzian = np.log(1 + atr)\n",
    "    return lorentzian\n",
    "\n",
    "# Assuming gdax_df is your DAX data\n",
    "# Ensure gdax_df has 'date' as index and contains 'open', 'high', 'low', 'close' columns\n",
    "\n",
    "# Calculate Kalman Filter estimates\n",
    "kf = KalmanFilter(\n",
    "    initial_state_mean=gdax_df['close'].iloc[0],\n",
    "    initial_state_covariance=1,\n",
    "    transition_matrices=[1],\n",
    "    observation_matrices=[1],\n",
    "    observation_covariance=1,\n",
    "    transition_covariance=0.01\n",
    ")\n",
    "\n",
    "state_means, _ = kf.filter(gdax_df['close'])\n",
    "gdax_df['KalmanEstimate'] = state_means\n",
    "\n",
    "# Calculate Lorentzian indicator\n",
    "gdax_df['LorentzianIndicator'] = calculate_lorentzian(gdax_df)\n",
    "\n",
    "# Ensure export_stats_usd_de_df and consumer_price_index_de_df have datetime index\n",
    "export_stats_usd_de_df.index = pd.to_datetime(export_stats_usd_de_df.index)\n",
    "consumer_price_index_de_df.index = pd.to_datetime(consumer_price_index_de_df.index)\n",
    "\n",
    "# Resample export_stats_usd_de_df to daily frequency and forward fill\n",
    "export_stats_daily = export_stats_usd_de_df.resample('D').ffill()\n",
    "\n",
    "# Resample consumer_price_index_de_df to daily frequency and forward fill\n",
    "cpi_daily = consumer_price_index_de_df.resample('D').ffill()\n",
    "\n",
    "# Merge all dataframes\n",
    "combined_df = gdax_df.join([export_stats_daily, cpi_daily], how='outer')\n",
    "\n",
    "# Forward fill any remaining NaN values\n",
    "combined_df = combined_df.ffill()\n",
    "\n",
    "# If you want 'date' as a column instead of index\n",
    "combined_df = combined_df.reset_index()\n",
    "\n",
    "# Display the first few rows of the combined dataframe\n",
    "print(combined_df.tail())\n",
    "\n",
    "# Display info about the combined dataframe\n",
    "print(combined_df.info())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ab1388dd-a79a-4c1b-be1f-3865e5ab6d86",
   "metadata": {},
   "source": [
    "# Now we fit Prophet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "73e18e29-154b-43ed-947b-c0ac49e54f8f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using regressors: ['open', 'high', 'low', 'volume', 'vwap', 'adj_close', 'unadjusted_volume', 'change', 'change_percent', 'KalmanEstimate', 'LorentzianIndicator', 'REXPDE~TUSD', 'CPIDE']\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "07:41:25 - cmdstanpy - INFO - Chain [1] start processing\n",
      "07:41:32 - cmdstanpy - INFO - Chain [1] done processing\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Model Results:\n",
      "Root Mean Squared Error: 0.24804108650748838\n",
      "Mean Absolute Error: 0.2048658067697176\n"
     ]
    },
    {
     "data": {
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