{
 "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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          "2014-09-22T00:00:00",
          "2014-09-23T00:00:00",
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          "2014-11-20T00:00:00",
          "2014-11-21T00:00:00",
          "2014-11-22T00:00:00",
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          "2014-11-25T00:00:00",
          "2014-11-26T00:00:00",
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          "2014-12-22T00:00:00",
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          "2015-01-01T00:00:00",
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          "2015-01-26T00:00:00",
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          "2015-06-08T00:00:00",
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          "2015-06-11T00:00:00",
          "2015-06-12T00:00:00",
          "2015-06-13T00:00:00",
          "2015-06-14T00:00:00",
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          "2015-06-16T00:00:00",
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          "2015-06-22T00:00:00",
          "2015-06-23T00:00:00",
          "2015-06-24T00:00:00",
          "2015-06-25T00:00:00",
          "2015-06-26T00:00:00",
          "2015-06-27T00:00:00",
          "2015-06-28T00:00:00",
          "2015-06-29T00:00:00",
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          "2015-07-02T00:00:00",
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          "2015-07-04T00:00:00",
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          "2015-07-06T00:00:00",
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          "2015-07-08T00:00:00",
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          "2015-07-10T00:00:00",
          "2015-07-11T00:00:00",
          "2015-07-12T00:00:00",
          "2015-07-13T00:00:00",
          "2015-07-14T00:00:00",
          "2015-07-15T00:00:00",
          "2015-07-16T00:00:00",
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          "2015-07-21T00:00:00",
          "2015-07-22T00:00:00",
          "2015-07-23T00:00:00",
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          "2015-07-25T00:00:00",
          "2015-07-26T00:00:00",
          "2015-07-27T00:00:00",
          "2015-07-28T00:00:00",
          "2015-07-29T00:00:00",
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          "2015-08-01T00:00:00",
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          "2015-08-06T00:00:00",
          "2015-08-07T00:00:00",
          "2015-08-08T00:00:00",
          "2015-08-09T00:00:00",
          "2015-08-10T00:00:00",
          "2015-08-11T00:00:00",
          "2015-08-12T00:00:00",
          "2015-08-13T00:00:00",
          "2015-08-14T00:00:00",
          "2015-08-15T00:00:00",
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          "2015-08-17T00:00:00",
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          "2015-08-19T00:00:00",
          "2015-08-20T00:00:00",
          "2015-08-21T00:00:00",
          "2015-08-22T00:00:00",
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          "2015-08-25T00:00:00",
          "2015-08-26T00:00:00",
          "2015-08-27T00:00:00",
          "2015-08-28T00:00:00",
          "2015-08-29T00:00:00",
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          "2015-08-31T00:00:00",
          "2015-09-01T00:00:00",
          "2015-09-02T00:00:00",
          "2015-09-03T00:00:00",
          "2015-09-04T00:00:00",
          "2015-09-05T00:00:00",
          "2015-09-06T00:00:00",
          "2015-09-07T00:00:00",
          "2015-09-08T00:00:00",
          "2015-09-09T00:00:00",
          "2015-09-10T00:00:00",
          "2015-09-11T00:00:00",
          "2015-09-12T00:00:00",
          "2015-09-13T00:00:00",
          "2015-09-14T00:00:00",
          "2015-09-15T00:00:00",
          "2015-09-16T00:00:00",
          "2015-09-17T00:00:00",
          "2015-09-18T00:00:00",
          "2015-09-19T00:00:00",
          "2015-09-20T00:00:00",
          "2015-09-21T00:00:00",
          "2015-09-22T00:00:00",
          "2015-09-23T00:00:00",
          "2015-09-24T00:00:00",
          "2015-09-25T00:00:00",
          "2015-09-26T00:00:00",
          "2015-09-27T00:00:00",
          "2015-09-28T00:00:00",
          "2015-09-29T00:00:00",
          "2015-09-30T00:00:00",
          "2015-10-01T00:00:00",
          "2015-10-02T00:00:00",
          "2015-10-03T00:00:00",
          "2015-10-04T00:00:00",
          "2015-10-05T00:00:00",
          "2015-10-06T00:00:00",
          "2015-10-07T00:00:00",
          "2015-10-08T00:00:00",
          "2015-10-09T00:00:00",
          "2015-10-10T00:00:00",
          "2015-10-11T00:00:00",
          "2015-10-12T00:00:00",
          "2015-10-13T00:00:00",
          "2015-10-14T00:00:00",
          "2015-10-15T00:00:00",
          "2015-10-16T00:00:00",
          "2015-10-17T00:00:00",
          "2015-10-18T00:00:00",
          "2015-10-19T00:00:00",
          "2015-10-20T00:00:00",
          "2015-10-21T00:00:00",
          "2015-10-22T00:00:00",
          "2015-10-23T00:00:00",
          "2015-10-24T00:00:00",
          "2015-10-25T00:00:00",
          "2015-10-26T00:00:00",
          "2015-10-27T00:00:00",
          "2015-10-28T00:00:00",
          "2015-10-29T00:00:00",
          "2015-10-30T00:00:00",
          "2015-10-31T00:00:00",
          "2015-11-01T00:00:00",
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          "2015-11-05T00:00:00",
          "2015-11-06T00:00:00",
          "2015-11-07T00:00:00",
          "2015-11-08T00:00:00",
          "2015-11-09T00:00:00",
          "2015-11-10T00:00:00",
          "2015-11-11T00:00:00",
          "2015-11-12T00:00:00",
          "2015-11-13T00:00:00",
          "2015-11-14T00:00:00",
          "2015-11-15T00:00:00",
          "2015-11-16T00:00:00",
          "2015-11-17T00:00:00",
          "2015-11-18T00:00:00",
          "2015-11-19T00:00:00",
          "2015-11-20T00:00:00",
          "2015-11-21T00:00:00",
          "2015-11-22T00:00:00",
          "2015-11-23T00:00:00",
          "2015-11-24T00:00:00",
          "2015-11-25T00:00:00",
          "2015-11-26T00:00:00",
          "2015-11-27T00:00:00",
          "2015-11-28T00:00:00",
          "2015-11-29T00:00:00",
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          "2015-12-01T00:00:00",
          "2015-12-02T00:00:00",
          "2015-12-03T00:00:00",
          "2015-12-04T00:00:00",
          "2015-12-05T00:00:00",
          "2015-12-06T00:00:00",
          "2015-12-07T00:00:00",
          "2015-12-08T00:00:00",
          "2015-12-09T00:00:00",
          "2015-12-10T00:00:00",
          "2015-12-11T00:00:00",
          "2015-12-12T00:00:00",
          "2015-12-13T00:00:00",
          "2015-12-14T00:00:00",
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          "2015-12-16T00:00:00",
          "2015-12-17T00:00:00",
          "2015-12-18T00:00:00",
          "2015-12-19T00:00:00",
          "2015-12-20T00:00:00",
          "2015-12-21T00:00:00",
          "2015-12-22T00:00:00",
          "2015-12-23T00:00:00",
          "2015-12-24T00:00:00",
          "2015-12-25T00:00:00",
          "2015-12-26T00:00:00",
          "2015-12-27T00:00:00",
          "2015-12-28T00:00:00",
          "2015-12-29T00:00:00",
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          "2015-12-31T00:00:00",
          "2016-01-01T00:00:00",
          "2016-01-02T00:00:00",
          "2016-01-03T00:00:00",
          "2016-01-04T00:00:00",
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          "2016-01-07T00:00:00",
          "2016-01-08T00:00:00",
          "2016-01-09T00:00:00",
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          "2016-01-11T00:00:00",
          "2016-01-12T00:00:00",
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          "2016-01-19T00:00:00",
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          "2016-01-21T00:00:00",
          "2016-01-22T00:00:00",
          "2016-01-23T00:00:00",
          "2016-01-24T00:00:00",
          "2016-01-25T00:00:00",
          "2016-01-26T00:00:00",
          "2016-01-27T00:00:00",
          "2016-01-28T00:00:00",
          "2016-01-29T00:00:00",
          "2016-01-30T00:00:00",
          "2016-01-31T00:00:00",
          "2016-02-01T00:00:00",
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          "2016-02-04T00:00:00",
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          "2016-02-07T00:00:00",
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          "2015-08-25T00:00:00",
          "2015-08-26T00:00:00",
          "2015-08-27T00:00:00",
          "2015-08-28T00:00:00",
          "2015-08-29T00:00:00",
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          "2015-08-31T00:00:00",
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          "2015-09-04T00:00:00",
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          "2015-09-08T00:00:00",
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          "2015-09-12T00:00:00",
          "2015-09-13T00:00:00",
          "2015-09-14T00:00:00",
          "2015-09-15T00:00:00",
          "2015-09-16T00:00:00",
          "2015-09-17T00:00:00",
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          "2015-09-21T00:00:00",
          "2015-09-22T00:00:00",
          "2015-09-23T00:00:00",
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          "2015-09-25T00:00:00",
          "2015-09-26T00:00:00",
          "2015-09-27T00:00:00",
          "2015-09-28T00:00:00",
          "2015-09-29T00:00:00",
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          "2015-10-02T00:00:00",
          "2015-10-03T00:00:00",
          "2015-10-04T00:00:00",
          "2015-10-05T00:00:00",
          "2015-10-06T00:00:00",
          "2015-10-07T00:00:00",
          "2015-10-08T00:00:00",
          "2015-10-09T00:00:00",
          "2015-10-10T00:00:00",
          "2015-10-11T00:00:00",
          "2015-10-12T00:00:00",
          "2015-10-13T00:00:00",
          "2015-10-14T00:00:00",
          "2015-10-15T00:00:00",
          "2015-10-16T00:00:00",
          "2015-10-17T00:00:00",
          "2015-10-18T00:00:00",
          "2015-10-19T00:00:00",
          "2015-10-20T00:00:00",
          "2015-10-21T00:00:00",
          "2015-10-22T00:00:00",
          "2015-10-23T00:00:00",
          "2015-10-24T00:00:00",
          "2015-10-25T00:00:00",
          "2015-10-26T00:00:00",
          "2015-10-27T00:00:00",
          "2015-10-28T00:00:00",
          "2015-10-29T00:00:00",
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          "2015-11-01T00:00:00",
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          "2015-11-06T00:00:00",
          "2015-11-07T00:00:00",
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          "2015-11-09T00:00:00",
          "2015-11-10T00:00:00",
          "2015-11-11T00:00:00",
          "2015-11-12T00:00:00",
          "2015-11-13T00:00:00",
          "2015-11-14T00:00:00",
          "2015-11-15T00:00:00",
          "2015-11-16T00:00:00",
          "2015-11-17T00:00:00",
          "2015-11-18T00:00:00",
          "2015-11-19T00:00:00",
          "2015-11-20T00:00:00",
          "2015-11-21T00:00:00",
          "2015-11-22T00:00:00",
          "2015-11-23T00:00:00",
          "2015-11-24T00:00:00",
          "2015-11-25T00:00:00",
          "2015-11-26T00:00:00",
          "2015-11-27T00:00:00",
          "2015-11-28T00:00:00",
          "2015-11-29T00:00:00",
          "2015-11-30T00:00:00",
          "2015-12-01T00:00:00",
          "2015-12-02T00:00:00",
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          "2015-12-04T00:00:00",
          "2015-12-05T00:00:00",
          "2015-12-06T00:00:00",
          "2015-12-07T00:00:00",
          "2015-12-08T00:00:00",
          "2015-12-09T00:00:00",
          "2015-12-10T00:00:00",
          "2015-12-11T00:00:00",
          "2015-12-12T00:00:00",
          "2015-12-13T00:00:00",
          "2015-12-14T00:00:00",
          "2015-12-15T00:00:00",
          "2015-12-16T00:00:00",
          "2015-12-17T00:00:00",
          "2015-12-18T00:00:00",
          "2015-12-19T00:00:00",
          "2015-12-20T00:00:00",
          "2015-12-21T00:00:00",
          "2015-12-22T00:00:00",
          "2015-12-23T00:00:00",
          "2015-12-24T00:00:00",
          "2015-12-25T00:00:00",
          "2015-12-26T00:00:00",
          "2015-12-27T00:00:00",
          "2015-12-28T00:00:00",
          "2015-12-29T00:00:00",
          "2015-12-30T00:00:00",
          "2015-12-31T00:00:00",
          "2016-01-01T00:00:00",
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          "2016-01-04T00:00:00",
          "2016-01-05T00:00:00",
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          "2016-01-07T00:00:00",
          "2016-01-08T00:00:00",
          "2016-01-09T00:00:00",
          "2016-01-10T00:00:00",
          "2016-01-11T00:00:00",
          "2016-01-12T00:00:00",
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          "2016-01-14T00:00:00",
          "2016-01-15T00:00:00",
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          "2016-01-19T00:00:00",
          "2016-01-20T00:00:00",
          "2016-01-21T00:00:00",
          "2016-01-22T00:00:00",
          "2016-01-23T00:00:00",
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          "2016-01-25T00:00:00",
          "2016-01-26T00:00:00",
          "2016-01-27T00:00:00",
          "2016-01-28T00:00:00",
          "2016-01-29T00:00:00",
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          "2016-02-01T00:00:00",
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          "2016-02-08T00:00:00",
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          "2016-02-19T00:00:00",
          "2016-02-20T00:00:00",
          "2016-02-21T00:00:00",
          "2016-02-22T00:00:00",
          "2016-02-23T00:00:00",
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          "2016-02-25T00:00:00",
          "2016-02-26T00:00:00",
          "2016-02-27T00:00:00",
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          "2016-02-29T00:00:00",
          "2016-03-01T00:00:00",
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          "2016-03-09T00:00:00",
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          "2016-03-27T00:00:00",
          "2016-03-28T00:00:00",
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          "2016-04-07T00:00:00",
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",
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      "              regressor    importance\n",
      "11          REXPDE~TUSD  4.103350e-06\n",
      "10  LorentzianIndicator  2.511120e-06\n",
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      "0                  open  2.117290e-07\n",
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      "5             adj_close  1.869090e-07\n",
      "12                CPIDE  1.220440e-07\n",
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      "7                change  9.281980e-08\n",
      "8        change_percent  7.484040e-08\n",
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",
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     "text": [
      "\n",
      "Future Predictions (next 30 days):\n",
      "           ds          yhat    yhat_lower    yhat_upper\n",
      "0  2024-08-26  18170.678657  18164.253351  18177.057699\n",
      "1  2024-08-27  18170.686485  18163.981210  18177.148656\n",
      "2  2024-08-28  18170.692456  18164.066814  18176.680111\n",
      "3  2024-08-29  18170.694506  18164.437891  18176.927894\n",
      "4  2024-08-30  18170.714034  18164.097301  18177.044645\n",
      "5  2024-08-31  18170.714923  18164.594100  18177.141598\n",
      "6  2024-09-01  18170.715027  18164.183054  18176.739334\n",
      "7  2024-09-02  18170.711409  18164.515832  18176.708640\n",
      "8  2024-09-03  18170.710695  18164.472544  18177.269812\n",
      "9  2024-09-04  18170.708648  18164.598305  18177.691686\n",
      "10 2024-09-05  18170.705081  18164.318580  18177.361395\n",
      "11 2024-09-06  18170.722264  18164.588485  18177.044196\n",
      "12 2024-09-07  18170.724344  18164.534186  18177.140781\n",
      "13 2024-09-08  18170.728960  18164.225945  18176.747868\n",
      "14 2024-09-09  18170.732106  18164.061719  18176.914615\n",
      "15 2024-09-10  18170.738241  18164.428694  18176.610560\n",
      "16 2024-09-11  18170.740427  18164.253876  18176.984414\n",
      "17 2024-09-12  18170.736566  18164.047960  18177.350755\n",
      "18 2024-09-13  18170.748961  18163.923029  18177.476539\n",
      "19 2024-09-14  18170.743701  18164.256702  18177.134155\n",
      "20 2024-09-15  18170.740995  18164.347139  18177.191980\n",
      "21 2024-09-16  18170.738477  18164.081168  18177.039962\n",
      "22 2024-09-17  18170.740686  18164.758016  18177.216748\n",
      "23 2024-09-18  18170.739825  18164.512134  18177.227916\n",
      "24 2024-09-19  18170.733120  18164.055758  18177.467899\n",
      "25 2024-09-20  18170.742851  18164.694513  18177.168788\n",
      "26 2024-09-21  18170.735257  18164.152934  18176.952526\n",
      "27 2024-09-22  18170.730116  18163.863197  18177.812643\n",
      "28 2024-09-23  18170.724064  18164.542031  18177.388678\n",
      "29 2024-09-24  18170.721048  18164.194703  18176.791336\n"
     ]
    }
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   "source": [
    "from prophet import Prophet\n",
    "from sklearn.metrics import mean_squared_error, mean_absolute_error\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import plotly.graph_objects as go\n",
    "from plotly.subplots import make_subplots\n",
    "from datetime import datetime, timedelta\n",
    "\n",
    "# Assuming your dataframe is named combined_df\n",
    "df = combined_df.copy()\n",
    "\n",
    "# Convert 'index' to datetime if it's not already\n",
    "df['index'] = pd.to_datetime(df['index'])\n",
    "\n",
    "# Filter for the last 10 years of data\n",
    "ten_years_ago = datetime.now() - timedelta(days=365*10)\n",
    "df = df[df['index'] >= ten_years_ago]\n",
    "\n",
    "# Remove rows with NaN values\n",
    "df = df.dropna()\n",
    "\n",
    "# Rename columns to match Prophet's requirements\n",
    "df = df.rename(columns={'index': 'ds', 'close': 'y'})\n",
    "\n",
    "# Define potential regressors (all columns except 'ds' and 'y')\n",
    "regressors = [col for col in df.columns if col not in ['ds', 'y']]\n",
    "\n",
    "def train_prophet_model(df, regressors, include_monthly_seasonality=True):\n",
    "    model = Prophet(\n",
    "        yearly_seasonality=True,\n",
    "        daily_seasonality=True,\n",
    "        weekly_seasonality=True,\n",
    "        changepoint_prior_scale=0.05,\n",
    "        seasonality_prior_scale=10.0,\n",
    "        seasonality_mode='additive'\n",
    "    )\n",
    "    \n",
    "    if include_monthly_seasonality:\n",
    "        model.add_seasonality(name='monthly', period=30.5, fourier_order=5)\n",
    "    \n",
    "    for regressor in regressors:\n",
    "        if regressor in df.columns:\n",
    "            model.add_regressor(regressor)\n",
    "    \n",
    "    model.fit(df)\n",
    "    return model\n",
    "\n",
    "# Filter regressors to only include those present in the dataframe\n",
    "regressors = [reg for reg in regressors if reg in df.columns]\n",
    "print(f\"Using regressors: {regressors}\")\n",
    "\n",
    "# Train the model\n",
    "model = train_prophet_model(df, regressors)\n",
    "\n",
    "# Get current date\n",
    "current_date = datetime.now().date()\n",
    "\n",
    "# Make predictions up to the current date\n",
    "future = model.make_future_dataframe(periods=0)  # No future predictions\n",
    "future = future.merge(df[['ds'] + regressors], on='ds', how='left')\n",
    "forecast = model.predict(future)\n",
    "\n",
    "# Evaluate the model\n",
    "y_true = df['y'].values\n",
    "y_pred = forecast['yhat'].values\n",
    "mse = mean_squared_error(y_true, y_pred)\n",
    "rmse = np.sqrt(mse)\n",
    "mae = mean_absolute_error(y_true, y_pred)\n",
    "\n",
    "print(f\"\\nModel Results:\")\n",
    "print(f\"Root Mean Squared Error: {rmse}\")\n",
    "print(f\"Mean Absolute Error: {mae}\")\n",
    "\n",
    "# Plot the results with Plotly\n",
    "fig = go.Figure()\n",
    "fig.add_trace(go.Scatter(x=df['ds'], y=df['y'], mode='lines', name='Actual'))\n",
    "fig.add_trace(go.Scatter(x=forecast['ds'], y=forecast['yhat'], mode='lines', name='Forecast'))\n",
    "fig.add_trace(go.Scatter(x=forecast['ds'], y=forecast['yhat_lower'], mode='lines', line=dict(width=0), showlegend=False))\n",
    "fig.add_trace(go.Scatter(x=forecast['ds'], y=forecast['yhat_upper'], mode='lines', fill='tonexty', line=dict(width=0), name='Confidence Interval'))\n",
    "fig.update_layout(title='Prophet Forecast for DAX (Last 10 Years)', xaxis_title='Date', yaxis_title='Value', hovermode='x unified')\n",
    "fig.update_xaxes(range=[df['ds'].min(), current_date])\n",
    "fig.show()\n",
    "\n",
    "# Plot component-wise effects\n",
    "component_names = ['trend', 'yearly', 'monthly'] + regressors\n",
    "fig = make_subplots(rows=len(component_names), cols=1, subplot_titles=component_names)\n",
    "for i, component in enumerate(component_names, 1):\n",
    "    fig.add_trace(go.Scatter(x=forecast['ds'], y=forecast[component], mode='lines', name=component), row=i, col=1)\n",
    "fig.update_layout(height=300*len(component_names), title_text=\"Prophet Components for DAX\", hovermode='x unified')\n",
    "fig.update_xaxes(range=[df['ds'].min(), current_date])\n",
    "fig.show()\n",
    "\n",
    "# Analyze regressor importance\n",
    "if regressors:\n",
    "    beta_values = model.params['beta'].flatten()\n",
    "    regressor_importance = pd.DataFrame({\n",
    "        'regressor': regressors,\n",
    "        'importance': np.abs(beta_values[:len(regressors)])\n",
    "    })\n",
    "    regressor_importance = regressor_importance.sort_values('importance', ascending=False)\n",
    "    print(\"\\nRegressor Importance:\")\n",
    "    print(regressor_importance)\n",
    "\n",
    "    # Plot regressor importance\n",
    "    fig = go.Figure([go.Bar(x=regressor_importance['regressor'], y=regressor_importance['importance'])])\n",
    "    fig.update_layout(title='Regressor Importance for DAX', xaxis_title='Regressor', yaxis_title='Absolute Importance')\n",
    "    fig.show()\n",
    "else:\n",
    "    print(\"\\nNo regressors were included in the model.\")\n",
    "\n",
    "# Make future predictions (next 30 days)\n",
    "future_dates = pd.date_range(start=current_date + timedelta(days=1), periods=30)\n",
    "future_df = pd.DataFrame({'ds': future_dates})\n",
    "\n",
    "# Estimate future regressor values (using last 30 days average)\n",
    "for regressor in regressors:\n",
    "    future_df[regressor] = df[regressor].tail(30).mean()\n",
    "\n",
    "future_forecast = model.predict(future_df)\n",
    "\n",
    "# Plot future predictions\n",
    "fig = go.Figure()\n",
    "fig.add_trace(go.Scatter(x=df['ds'].tail(365), y=df['y'].tail(365), mode='lines', name='Historical'))\n",
    "fig.add_trace(go.Scatter(x=future_forecast['ds'], y=future_forecast['yhat'], mode='lines', name='Forecast'))\n",
    "fig.add_trace(go.Scatter(x=future_forecast['ds'], y=future_forecast['yhat_lower'], mode='lines', line=dict(width=0), showlegend=False))\n",
    "fig.add_trace(go.Scatter(x=future_forecast['ds'], y=future_forecast['yhat_upper'], mode='lines', fill='tonexty', line=dict(width=0), name='Confidence Interval'))\n",
    "fig.update_layout(title='30-Day Future Forecast for DAX', xaxis_title='Date', yaxis_title='Value', hovermode='x unified')\n",
    "fig.update_xaxes(range=[current_date - timedelta(days=365), future_dates[-1]])\n",
    "fig.show()\n",
    "\n",
    "print(\"\\nFuture Predictions (next 30 days):\")\n",
    "print(future_forecast[['ds', 'yhat', 'yhat_lower', 'yhat_upper']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "a024133a-42c7-4c2f-8747-8bce33536100",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Selected features: ['REXPDE~TUSD', 'LorentzianIndicator', 'CPIDE', 'volume']\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "08:28:23 - cmdstanpy - INFO - Chain [1] start processing\n",
      "08:28:25 - cmdstanpy - INFO - Chain [1] done processing\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Historical Fit Performance Metrics:\n",
      "MSE: 203895.91\n",
      "RMSE: 451.55\n",
      "MAE: 356.90\n",
      "MAPE: nan%\n",
      "\n",
      "Cross-validation parameters:\n",
      "Initial: 1823 days\n",
      "Period: 182 days\n",
      "Horizon: 364 days\n"
     ]
    },
    {
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       "model_id": "b0ae273b88714e76898a14b8b7f92379",
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      "text/plain": [
       "  0%|          | 0/9 [00:00<?, ?it/s]"
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     "output_type": "display_data"
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     "output_type": "stream",
     "text": [
      "08:28:25 - cmdstanpy - INFO - Chain [1] start processing\n",
      "08:28:26 - cmdstanpy - INFO - Chain [1] done processing\n",
      "08:28:26 - cmdstanpy - INFO - Chain [1] start processing\n",
      "08:28:27 - cmdstanpy - INFO - Chain [1] done processing\n",
      "08:28:27 - cmdstanpy - INFO - Chain [1] start processing\n",
      "08:28:27 - cmdstanpy - INFO - Chain [1] done processing\n",
      "08:28:28 - cmdstanpy - INFO - Chain [1] start processing\n",
      "08:28:28 - cmdstanpy - INFO - Chain [1] done processing\n",
      "08:28:29 - cmdstanpy - INFO - Chain [1] start processing\n",
      "08:28:29 - cmdstanpy - INFO - Chain [1] done processing\n",
      "08:28:30 - cmdstanpy - INFO - Chain [1] start processing\n",
      "08:28:30 - cmdstanpy - INFO - Chain [1] done processing\n",
      "08:28:31 - cmdstanpy - INFO - Chain [1] start processing\n",
      "08:28:31 - cmdstanpy - INFO - Chain [1] done processing\n",
      "08:28:32 - cmdstanpy - INFO - Chain [1] start processing\n",
      "08:28:32 - cmdstanpy - INFO - Chain [1] done processing\n",
      "08:28:33 - cmdstanpy - INFO - Chain [1] start processing\n",
      "08:28:33 - cmdstanpy - INFO - Chain [1] done processing\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Cross-Validation Performance Metrics:\n",
      "horizon    200 days 12:00:00\n",
      "mse           3617848.201129\n",
      "rmse             1871.945615\n",
      "mae              1396.358278\n",
      "mape                0.100928\n",
      "dtype: object\n"
     ]
    },
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kl+cCkB4CEIAABCAAAQhAAAIQgAAEIAABCEAgOALIr+DYEhkCEIAABCAAAQhAAAIQgAAEIAABCEDAMwHkl+cCkB4CEIAABCAAAQhAAAIQgAAEIAABCEAgOALIr+DYEhkCEIAABCAAAQhAAAIQgAAEIAABCEDAMwHkl+cCkB4CEIAABCAAAQhAAAIQgAAEIAABCEAgOALIr+DYEhkCEIAABCAAAQhAAAIQgAAEIAABCEDAMwHkl+cCkB4CEIAABCAAAQhAAAIQgAAEIAABCEAgOALIr+DYEhkCEIAABCAAAQhAAAIQgAAEIAABCEDAMwHkl+cCkB4CEIAABCAAAQhAAAIQgAAEIAABCEAgOALIr+DYEhkCEIAABCAAAQhAAAIQgAAEIAABCEDAMwHkl+cCkB4CEIAABCAAAQhAAAIQgAAEIAABCEAgOALIr+DYEhkCEIAABCAAAQhAAAIQgAAEIAABCEDAMwHkl+cCkB4CEIAABCAAAQhAAAIQgAAEIAABCEAgOALIr+DYEhkCEIAABCAAAQhAAAIQgAAEIAABCEDAMwHkl+cCkB4CEIAABCAAAQhAAAIQgAAEIAABCEAgOALIr+DYEhkCEIAABCAAAQhAAAIQgAAEIAABCEDAMwHkl+cCkB4CEIAABCAAAQhAAAIQgAAEIAABCEAgOAL/B/TBJaVNFkXYAAAAAElFTkSuQmCC",
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          "2015-11-21T00:00:00",
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          "2015-11-25T00:00:00",
          "2015-11-26T00:00:00",
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          "2015-12-01T00:00:00",
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          "2015-10-16T00:00:00",
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          "2015-10-21T00:00:00",
          "2015-10-22T00:00:00",
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          "2016-01-22T00:00:00",
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          },
          "ternary": {
           "aaxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           },
           "baxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           },
           "bgcolor": "#E5ECF6",
           "caxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           }
          },
          "title": {
           "x": 0.05
          },
          "xaxis": {
           "automargin": true,
           "gridcolor": "white",
           "linecolor": "white",
           "ticks": "",
           "title": {
            "standoff": 15
           },
           "zerolinecolor": "white",
           "zerolinewidth": 2
          },
          "yaxis": {
           "automargin": true,
           "gridcolor": "white",
           "linecolor": "white",
           "ticks": "",
           "title": {
            "standoff": 15
           },
           "zerolinecolor": "white",
           "zerolinewidth": 2
          }
         }
        },
        "title": {
         "text": "30-Day Future Forecast for DAX"
        },
        "xaxis": {
         "range": [
          "2023-08-26",
          "2024-09-24T00:00:00"
         ],
         "title": {
          "text": "Date"
         },
         "type": "date"
        },
        "yaxis": {
         "autorange": true,
         "range": [
          14455.079647777777,
          19101.689892222224
         ],
         "title": {
          "text": "Value"
         },
         "type": "linear"
        }
       }
      },
      "image/png": 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     "text": [
      "\n",
      "Future Predictions (next 30 days):\n",
      "           ds          yhat    yhat_lower    yhat_upper\n",
      "0  2024-08-26  18072.376217  17475.188480  18623.733289\n",
      "1  2024-08-27  17996.168886  17374.425710  18596.189492\n",
      "2  2024-08-28  18022.329375  17441.360894  18565.615660\n",
      "3  2024-08-29  18019.252979  17457.047738  18578.122090\n",
      "4  2024-08-30  17983.758633  17389.341567  18579.300713\n",
      "5  2024-08-31  18062.263092  17469.446275  18623.687241\n",
      "6  2024-09-01  18058.992385  17501.092589  18673.833705\n",
      "7  2024-09-02  17965.341477  17403.613318  18540.204023\n",
      "8  2024-09-03  17942.468429  17364.090791  18513.160654\n",
      "9  2024-09-04  18082.900198  17507.646990  18616.969704\n",
      "10 2024-09-05  17948.093139  17348.334088  18549.383779\n",
      "11 2024-09-06  18002.905330  17414.096509  18551.813021\n",
      "12 2024-09-07  17948.612229  17423.230610  18495.711602\n",
      "13 2024-09-08  17964.435723  17384.545769  18514.986652\n",
      "14 2024-09-09  17838.286771  17218.118233  18388.806188\n",
      "15 2024-09-10  17937.304527  17365.597414  18520.184775\n",
      "16 2024-09-11  17905.944285  17325.689329  18496.020846\n",
      "17 2024-09-12  17912.662010  17340.650842  18517.685075\n",
      "18 2024-09-13  17807.300252  17220.238420  18414.470989\n",
      "19 2024-09-14  17763.532447  17155.768803  18356.767105\n",
      "20 2024-09-15  17758.922723  17191.342729  18315.392770\n",
      "21 2024-09-16  17709.134749  17146.851291  18323.257002\n",
      "22 2024-09-17  17721.753285  17151.338095  18278.303778\n",
      "23 2024-09-18  17789.296011  17186.730449  18355.512288\n",
      "24 2024-09-19  17715.242053  17135.235293  18288.196100\n",
      "25 2024-09-20  17678.092148  17109.687488  18265.802758\n",
      "26 2024-09-21  17687.038894  17076.330449  18279.698784\n",
      "27 2024-09-22  17634.856792  17093.564506  18182.008145\n",
      "28 2024-09-23  17649.217752  17076.080915  18242.653832\n",
      "29 2024-09-24  17550.765564  16957.329965  18145.729506\n"
     ]
    }
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   "source": [
    "from prophet import Prophet\n",
    "from prophet.diagnostics import cross_validation, performance_metrics\n",
    "from sklearn.metrics import mean_squared_error, mean_absolute_error\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import plotly.graph_objects as go\n",
    "from plotly.subplots import make_subplots\n",
    "from datetime import datetime, timedelta\n",
    "\n",
    "# Assuming your dataframe is named combined_df\n",
    "df = combined_df.copy()\n",
    "\n",
    "# Convert 'index' to datetime if it's not already\n",
    "df['index'] = pd.to_datetime(df['index'])\n",
    "\n",
    "# Filter for the last 10 years of data\n",
    "ten_years_ago = datetime.now() - timedelta(days=365*10)\n",
    "df = df[df['index'] >= ten_years_ago]\n",
    "\n",
    "# Remove rows with NaN values\n",
    "df = df.dropna()\n",
    "\n",
    "# Rename columns to match Prophet's requirements\n",
    "df = df.rename(columns={'index': 'ds', 'close': 'y'})\n",
    "\n",
    "# Use only the specified features\n",
    "selected_features = ['REXPDE~TUSD', 'LorentzianIndicator', 'CPIDE', 'volume']\n",
    "\n",
    "print(f\"Selected features: {selected_features}\")\n",
    "\n",
    "def train_prophet_model(df, regressors):\n",
    "    model = Prophet(\n",
    "        yearly_seasonality=True,\n",
    "        weekly_seasonality=True,\n",
    "        daily_seasonality=False,\n",
    "        changepoint_prior_scale=0.01,\n",
    "        seasonality_prior_scale=1.0,\n",
    "        seasonality_mode='additive'\n",
    "    )\n",
    "    \n",
    "    for regressor in regressors:\n",
    "        model.add_regressor(regressor)\n",
    "    \n",
    "    model.fit(df)\n",
    "    return model\n",
    "\n",
    "# Train the model\n",
    "model = train_prophet_model(df, selected_features)\n",
    "\n",
    "# Get current date\n",
    "current_date = datetime.now().date()\n",
    "\n",
    "# Make predictions up to the current date\n",
    "future = model.make_future_dataframe(periods=0)  # No future predictions\n",
    "future = future.merge(df[['ds'] + selected_features], on='ds', how='left')\n",
    "forecast = model.predict(future)\n",
    "\n",
    "# Calculate performance metrics for historical fit\n",
    "y_true = df['y']\n",
    "y_pred = forecast['yhat']\n",
    "\n",
    "mse = mean_squared_error(y_true, y_pred)\n",
    "rmse = np.sqrt(mse)\n",
    "mae = mean_absolute_error(y_true, y_pred)\n",
    "mape = np.mean(np.abs((y_true - y_pred) / y_true)) * 100\n",
    "\n",
    "print(\"\\nHistorical Fit Performance Metrics:\")\n",
    "print(f\"MSE: {mse:.2f}\")\n",
    "print(f\"RMSE: {rmse:.2f}\")\n",
    "print(f\"MAE: {mae:.2f}\")\n",
    "print(f\"MAPE: {mape:.2f}%\")\n",
    "\n",
    "# Perform cross-validation with adjusted parameters\n",
    "total_days = (df['ds'].max() - df['ds'].min()).days\n",
    "initial = f'{total_days // 2} days'  # Use half of the data for initial training\n",
    "period = f'{total_days // 20} days'  # Step size of 1/20th of the data\n",
    "horizon = f'{total_days // 10} days'  # Forecast 1/10th of the data ahead\n",
    "\n",
    "print(f\"\\nCross-validation parameters:\")\n",
    "print(f\"Initial: {initial}\")\n",
    "print(f\"Period: {period}\")\n",
    "print(f\"Horizon: {horizon}\")\n",
    "\n",
    "cv_results = cross_validation(model, initial=initial, period=period, horizon=horizon)\n",
    "cv_metrics = performance_metrics(cv_results)\n",
    "\n",
    "print(\"\\nCross-Validation Performance Metrics:\")\n",
    "print(cv_metrics[['horizon', 'mse', 'rmse', 'mae', 'mape']].mean())\n",
    "\n",
    "# Plot the results with Plotly\n",
    "fig = go.Figure()\n",
    "fig.add_trace(go.Scatter(x=df['ds'], y=df['y'], mode='lines', name='Actual'))\n",
    "fig.add_trace(go.Scatter(x=forecast['ds'], y=forecast['yhat'], mode='lines', name='Forecast'))\n",
    "fig.add_trace(go.Scatter(x=forecast['ds'], y=forecast['yhat_lower'], mode='lines', line=dict(width=0), showlegend=False))\n",
    "fig.add_trace(go.Scatter(x=forecast['ds'], y=forecast['yhat_upper'], mode='lines', fill='tonexty', line=dict(width=0), name='Confidence Interval'))\n",
    "fig.update_layout(title='Prophet Forecast for DAX (Last 10 Years)', xaxis_title='Date', yaxis_title='Value', hovermode='x unified')\n",
    "fig.update_xaxes(range=[df['ds'].min(), current_date])\n",
    "fig.show()\n",
    "\n",
    "# Plot component-wise effects\n",
    "component_names = ['trend', 'yearly'] + selected_features\n",
    "fig = make_subplots(rows=len(component_names), cols=1, subplot_titles=component_names)\n",
    "for i, component in enumerate(component_names, 1):\n",
    "    fig.add_trace(go.Scatter(x=forecast['ds'], y=forecast[component], mode='lines', name=component), row=i, col=1)\n",
    "fig.update_layout(height=300*len(component_names), title_text=\"Prophet Components for DAX\", hovermode='x unified')\n",
    "fig.update_xaxes(range=[df['ds'].min(), current_date])\n",
    "fig.show()\n",
    "\n",
    "# Analyze regressor importance\n",
    "beta_values = model.params['beta'].flatten()\n",
    "if len(beta_values) == len(selected_features):\n",
    "    regressor_importance = pd.DataFrame({\n",
    "        'regressor': selected_features,\n",
    "        'importance': np.abs(beta_values)\n",
    "    })\n",
    "    regressor_importance = regressor_importance.sort_values('importance', ascending=False)\n",
    "    print(\"\\nRegressor Importance:\")\n",
    "    print(regressor_importance)\n",
    "\n",
    "    # Plot regressor importance\n",
    "    fig = go.Figure([go.Bar(x=regressor_importance['regressor'], y=regressor_importance['importance'])])\n",
    "    fig.update_layout(title='Regressor Importance for DAX', xaxis_title='Regressor', yaxis_title='Absolute Importance')\n",
    "    fig.show()\n",
    "else:\n",
    "    print(f\"\\nWarning: Mismatch between number of regressors ({len(selected_features)}) and beta values ({len(beta_values)})\")\n",
    "    print(\"Regressor importance analysis skipped.\")\n",
    "\n",
    "# Make future predictions (next 30 days)\n",
    "future_dates = pd.date_range(start=current_date + timedelta(days=1), periods=30)\n",
    "future_df = pd.DataFrame({'ds': future_dates})\n",
    "\n",
    "# Estimate future regressor values\n",
    "for regressor in selected_features:\n",
    "    if regressor in ['REXPDE~TUSD', 'CPIDE']:\n",
    "        # For economic indicators, use the last known value\n",
    "        future_df[regressor] = df[regressor].iloc[-1]\n",
    "    elif regressor == 'LorentzianIndicator':\n",
    "        # For LorentzianIndicator, use a simple forecast (e.g., last 30-day trend)\n",
    "        last_30_days = df[regressor].tail(30)\n",
    "        trend = np.polyfit(range(30), last_30_days, 1)[0]\n",
    "        future_df[regressor] = df[regressor].iloc[-1] + trend * np.arange(1, 31)\n",
    "    elif regressor == 'volume':\n",
    "        # For volume, use the average of the last 30 days, with some random variation\n",
    "        avg_volume = df[regressor].tail(30).mean()\n",
    "        std_volume = df[regressor].tail(30).std()\n",
    "        future_df[regressor] = np.random.normal(avg_volume, std_volume, 30)\n",
    "\n",
    "future_forecast = model.predict(future_df)\n",
    "\n",
    "# Plot future predictions\n",
    "fig = go.Figure()\n",
    "fig.add_trace(go.Scatter(x=df['ds'].tail(365), y=df['y'].tail(365), mode='lines', name='Historical'))\n",
    "fig.add_trace(go.Scatter(x=future_forecast['ds'], y=future_forecast['yhat'], mode='lines', name='Forecast'))\n",
    "fig.add_trace(go.Scatter(x=future_forecast['ds'], y=future_forecast['yhat_lower'], mode='lines', line=dict(width=0), showlegend=False))\n",
    "fig.add_trace(go.Scatter(x=future_forecast['ds'], y=future_forecast['yhat_upper'], mode='lines', fill='tonexty', line=dict(width=0), name='Confidence Interval'))\n",
    "fig.update_layout(title='30-Day Future Forecast for DAX', xaxis_title='Date', yaxis_title='Value', hovermode='x unified')\n",
    "fig.update_xaxes(range=[current_date - timedelta(days=365), future_dates[-1]])\n",
    "fig.show()\n",
    "\n",
    "print(\"\\nFuture Predictions (next 30 days):\")\n",
    "print(future_forecast[['ds', 'yhat', 'yhat_lower', 'yhat_upper']])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c7fda70a-555e-4503-8aff-2142f32ca046",
   "metadata": {},
   "source": [
    "# Time-series AutoML with H2O"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "id": "401eaf48-91bb-42c3-a9a0-c85e89f3a690",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Checking whether there is an H2O instance running at http://localhost:54321. connected.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "<style>\n",
       "\n",
       "#h2o-table-13.h2o-container {\n",
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       "#h2o-table-13 .h2o-table caption {\n",
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       "  white-space: nowrap; \n",
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       "}\n",
       "\n",
       "</style>      \n",
       "<div id=\"h2o-table-13\" class=\"h2o-container\">\n",
       "  <table class=\"h2o-table\">\n",
       "    <caption></caption>\n",
       "    <thead></thead>\n",
       "    <tbody><tr><td>H2O_cluster_uptime:</td>\n",
       "<td>56 mins 11 secs</td></tr>\n",
       "<tr><td>H2O_cluster_timezone:</td>\n",
       "<td>Etc/UTC</td></tr>\n",
       "<tr><td>H2O_data_parsing_timezone:</td>\n",
       "<td>UTC</td></tr>\n",
       "<tr><td>H2O_cluster_version:</td>\n",
       "<td>3.46.0.4</td></tr>\n",
       "<tr><td>H2O_cluster_version_age:</td>\n",
       "<td>1 month and 15 days</td></tr>\n",
       "<tr><td>H2O_cluster_name:</td>\n",
       "<td>H2O_from_python_marius_tlzho8</td></tr>\n",
       "<tr><td>H2O_cluster_total_nodes:</td>\n",
       "<td>1</td></tr>\n",
       "<tr><td>H2O_cluster_free_memory:</td>\n",
       "<td>1.784 Gb</td></tr>\n",
       "<tr><td>H2O_cluster_total_cores:</td>\n",
       "<td>6</td></tr>\n",
       "<tr><td>H2O_cluster_allowed_cores:</td>\n",
       "<td>6</td></tr>\n",
       "<tr><td>H2O_cluster_status:</td>\n",
       "<td>locked, healthy</td></tr>\n",
       "<tr><td>H2O_connection_url:</td>\n",
       "<td>http://localhost:54321</td></tr>\n",
       "<tr><td>H2O_connection_proxy:</td>\n",
       "<td>{\"http\": null, \"https\": null}</td></tr>\n",
       "<tr><td>H2O_internal_security:</td>\n",
       "<td>False</td></tr>\n",
       "<tr><td>Python_version:</td>\n",
       "<td>3.11.9 final</td></tr></tbody>\n",
       "  </table>\n",
       "</div>\n"
      ],
      "text/plain": [
       "--------------------------  -----------------------------\n",
       "H2O_cluster_uptime:         56 mins 11 secs\n",
       "H2O_cluster_timezone:       Etc/UTC\n",
       "H2O_data_parsing_timezone:  UTC\n",
       "H2O_cluster_version:        3.46.0.4\n",
       "H2O_cluster_version_age:    1 month and 15 days\n",
       "H2O_cluster_name:           H2O_from_python_marius_tlzho8\n",
       "H2O_cluster_total_nodes:    1\n",
       "H2O_cluster_free_memory:    1.784 Gb\n",
       "H2O_cluster_total_cores:    6\n",
       "H2O_cluster_allowed_cores:  6\n",
       "H2O_cluster_status:         locked, healthy\n",
       "H2O_connection_url:         http://localhost:54321\n",
       "H2O_connection_proxy:       {\"http\": null, \"https\": null}\n",
       "H2O_internal_security:      False\n",
       "Python_version:             3.11.9 final\n",
       "--------------------------  -----------------------------"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Features used: ['open', 'high', 'low', 'volume', 'vwap', 'adj_close', 'unadjusted_volume', 'change', 'change_percent', 'KalmanEstimate', 'LorentzianIndicator', 'REXPDE~TUSD', 'CPIDE', 'close_lag_1', 'close_lag_7', 'close_lag_30', 'close_return_1', 'close_return_7', 'close_return_30', 'REXPDE~TUSD_lag_1', 'REXPDE~TUSD_lag_7', 'REXPDE~TUSD_lag_30', 'REXPDE~TUSD_return_1', 'REXPDE~TUSD_return_7', 'REXPDE~TUSD_return_30', 'LorentzianIndicator_lag_1', 'LorentzianIndicator_lag_7', 'LorentzianIndicator_lag_30', 'LorentzianIndicator_return_1', 'LorentzianIndicator_return_7', 'LorentzianIndicator_return_30', 'volume_lag_1', 'volume_lag_7', 'volume_lag_30', 'volume_return_1', 'volume_return_7', 'volume_return_30', 'CPIDE_lag_1', 'CPIDE_lag_7', 'CPIDE_lag_30', 'CPIDE_return_1', 'CPIDE_return_7', 'CPIDE_return_30']\n",
      "\n",
      "DataFrame column types:\n",
      "open                                    float64\n",
      "high                                    float64\n",
      "low                                     float64\n",
      "close                                   float64\n",
      "volume                                  float64\n",
      "vwap                                    float64\n",
      "adj_close                               float64\n",
      "unadjusted_volume                       float64\n",
      "change                                  float64\n",
      "change_percent                          float64\n",
      "KalmanEstimate                          float64\n",
      "LorentzianIndicator                     float64\n",
      "REXPDE~TUSD                             float64\n",
      "CPIDE                                   float64\n",
      "close_lag_1                             float64\n",
      "close_lag_7                             float64\n",
      "close_lag_30                            float64\n",
      "close_return_1                          float64\n",
      "close_return_7                          float64\n",
      "close_return_30                         float64\n",
      "REXPDE~TUSD_lag_1                       float64\n",
      "REXPDE~TUSD_lag_7                       float64\n",
      "REXPDE~TUSD_lag_30                      float64\n",
      "REXPDE~TUSD_return_1                    float64\n",
      "REXPDE~TUSD_return_7                    float64\n",
      "REXPDE~TUSD_return_30                   float64\n",
      "LorentzianIndicator_lag_1               float64\n",
      "LorentzianIndicator_lag_7               float64\n",
      "LorentzianIndicator_lag_30              float64\n",
      "LorentzianIndicator_return_1            float64\n",
      "LorentzianIndicator_return_7            float64\n",
      "LorentzianIndicator_return_30           float64\n",
      "volume_lag_1                            float64\n",
      "volume_lag_7                            float64\n",
      "volume_lag_30                           float64\n",
      "volume_return_1                         float64\n",
      "volume_return_7                         float64\n",
      "volume_return_30                        float64\n",
      "CPIDE_lag_1                             float64\n",
      "CPIDE_lag_7                             float64\n",
      "CPIDE_lag_30                            float64\n",
      "CPIDE_return_1                          float64\n",
      "CPIDE_return_7                          float64\n",
      "CPIDE_return_30                         float64\n",
      "date                             datetime64[ns]\n",
      "dtype: object\n",
      "Parse progress: |████████████████████████████████████████████████████████████████| (done) 100%\n",
      "\n",
      "H2OFrame column types:\n",
      "{'open': 'real', 'high': 'real', 'low': 'real', 'close': 'real', 'volume': 'int', 'vwap': 'real', 'adj_close': 'real', 'unadjusted_volume': 'int', 'change': 'real', 'change_percent': 'real', 'KalmanEstimate': 'real', 'LorentzianIndicator': 'real', 'REXPDE~TUSD': 'int', 'CPIDE': 'real', 'close_lag_1': 'real', 'close_lag_7': 'real', 'close_lag_30': 'real', 'close_return_1': 'real', 'close_return_7': 'real', 'close_return_30': 'real', 'REXPDE~TUSD_lag_1': 'int', 'REXPDE~TUSD_lag_7': 'int', 'REXPDE~TUSD_lag_30': 'int', 'REXPDE~TUSD_return_1': 'real', 'REXPDE~TUSD_return_7': 'real', 'REXPDE~TUSD_return_30': 'real', 'LorentzianIndicator_lag_1': 'real', 'LorentzianIndicator_lag_7': 'real', 'LorentzianIndicator_lag_30': 'real', 'LorentzianIndicator_return_1': 'real', 'LorentzianIndicator_return_7': 'real', 'LorentzianIndicator_return_30': 'real', 'volume_lag_1': 'int', 'volume_lag_7': 'int', 'volume_lag_30': 'int', 'volume_return_1': 'real', 'volume_return_7': 'real', 'volume_return_30': 'real', 'CPIDE_lag_1': 'real', 'CPIDE_lag_7': 'real', 'CPIDE_lag_30': 'real', 'CPIDE_return_1': 'real', 'CPIDE_return_7': 'real', 'CPIDE_return_30': 'real', 'date': 'time'}\n",
      "AutoML progress: |\n",
      "15:20:05.489: Stopping tolerance set by the user is < 70% of the recommended default of 0.01989286700048564, so models may take a long time to converge or may not converge at all.\n",
      "\n",
      "███████████████████████████████████████████████████████████████| (done) 100%\n",
      "\n",
      "Best model: Model Details\n",
      "=============\n",
      "H2OGradientBoostingEstimator : Gradient Boosting Machine\n",
      "Model Key: GBM_grid_1_AutoML_4_20240825_152005_model_10\n",
      "\n",
      "\n",
      "Model Summary: \n",
      "    number_of_trees    number_of_internal_trees    model_size_in_bytes    min_depth    max_depth    mean_depth    min_leaves    max_leaves    mean_leaves\n",
      "--  -----------------  --------------------------  ---------------------  -----------  -----------  ------------  ------------  ------------  -------------\n",
      "    265                265                         180303                 6            6            6             17            64            49.4604\n",
      "\n",
      "ModelMetricsRegression: gbm\n",
      "** Reported on train data. **\n",
      "\n",
      "MSE: 0.21206737272249743\n",
      "RMSE: 0.46050773361855435\n",
      "MAE: 0.34181078724772457\n",
      "RMSLE: 3.671399543919675e-05\n",
      "Mean Residual Deviance: 0.21206737272249743\n",
      "\n",
      "ModelMetricsRegression: gbm\n",
      "** Reported on validation data. **\n",
      "\n",
      "MSE: 39.610729351137486\n",
      "RMSE: 6.293705534193468\n",
      "MAE: 2.792280000891665\n",
      "RMSLE: 0.0004400475320151746\n",
      "Mean Residual Deviance: 39.610729351137486\n",
      "\n",
      "Scoring History: \n",
      "     timestamp            duration    number_of_trees    training_rmse        training_mae         training_deviance    validation_rmse     validation_mae      validation_deviance\n",
      "---  -------------------  ----------  -----------------  -------------------  -------------------  -------------------  ------------------  ------------------  ---------------------\n",
      "     2024-08-25 15:28:22  26.170 sec  0.0                2267.8070877442683   1831.7651450271476   5142948.98722314     2167.37146448559    1727.5369870745326  4697499.06506641\n",
      "     2024-08-25 15:28:22  26.237 sec  5.0                1339.4290444956703   1081.8047211212654   1794070.1652385842   1280.086231604434   1020.2502613844603  1638620.7603432408\n",
      "     2024-08-25 15:28:23  26.288 sec  10.0               791.1037600180482    638.9889297153245    625845.1591146935    756.3318833032002   602.8763082947705   572037.9177009657\n",
      "     2024-08-25 15:28:23  26.384 sec  15.0               467.2540165384569    377.4190457898447    218326.31597132055   446.80941625421246  356.0965450093761   199638.6544534301\n",
      "     2024-08-25 15:28:23  26.468 sec  20.0               275.9894821285658    222.902427996389     76170.19424559394    263.99987481691744  210.28850343236857  69695.93390334808\n",
      "     2024-08-25 15:28:23  26.534 sec  25.0               163.02393409515125   131.66268286876732   26576.803087860215   156.19649878065823  124.21944497011569  24397.346231336167\n",
      "     2024-08-25 15:28:23  26.595 sec  30.0               96.30989622211466    77.77900896876237    9275.596110314496    92.4303735515898    73.35618719650957   8543.37395488643\n",
      "     2024-08-25 15:28:23  26.666 sec  35.0               56.914897549685065   45.96736766360803    3239.3055630911467   54.950025285150154  43.38128926287381   3019.505278838641\n",
      "     2024-08-25 15:28:23  26.747 sec  40.0               33.660746942232926   27.186723619286703   1133.045884709043    32.96765657283606   25.71040371955034   1086.8663799044607\n",
      "     2024-08-25 15:28:23  26.848 sec  45.0               19.948569567649148   16.112147401810446   397.9454277953377    20.23255715365955   15.543300604837833  409.35636897610016\n",
      "---  ---                  ---         ---                ---                  ---                  ---                  ---                 ---                 ---\n",
      "     2024-08-25 15:28:25  29.217 sec  220.0              0.5774730392218062   0.43098327129501385  0.3334751110280697   6.293659693034171   2.8169700225909162  39.61015233172298\n",
      "     2024-08-25 15:28:26  29.281 sec  225.0              0.5605945566221089   0.41884599450929955  0.3142662569143388   6.296492897239999   2.813847540411579   39.64582280499376\n",
      "     2024-08-25 15:28:26  29.346 sec  230.0              0.5470047646209211   0.4076891447368421   0.2992142125179893   6.298017085944119   2.810778540880968   39.66501921484405\n",
      "     2024-08-25 15:28:26  29.420 sec  235.0              0.5334259621145044   0.3975111761723387   0.2845432570577847   6.2948378182961955  2.805359711464768   39.62498315865201\n",
      "     2024-08-25 15:28:26  29.485 sec  240.0              0.5230718751480744   0.38953675306687774  0.2736041865709227   6.2946263141075365  2.8042309477880623  39.62232043425503\n",
      "     2024-08-25 15:28:26  29.534 sec  245.0              0.5137845356151425   0.3816628073060942   0.26397454903726764  6.29695562568025    2.8024185682891063  39.65165015178615\n",
      "     2024-08-25 15:28:26  29.601 sec  250.0              0.5030422046621342   0.3730978865749901   0.2530514596713405   6.296888610241514   2.8008790858133437  39.650806169789305\n",
      "     2024-08-25 15:28:26  29.659 sec  255.0              0.4944384601924157   0.36583414745745946  0.24446939091744704  6.297635582539977   2.7996777626978813  39.66021393047364\n",
      "     2024-08-25 15:28:26  29.714 sec  260.0              0.4808029471585699   0.3563606796596755   0.2311714739963666   6.2955211429398314  2.796881209253175   39.63358646120244\n",
      "     2024-08-25 15:28:26  29.784 sec  265.0              0.46050773361855435  0.34181078724772457  0.21206737272249743  6.293705534193468   2.792280000891665   39.610729351137486\n",
      "[54 rows x 10 columns]\n",
      "\n",
      "\n",
      "Variable Importances: \n",
      "variable               relative_importance    scaled_importance       percentage\n",
      "---------------------  ---------------------  ----------------------  ----------------------\n",
      "adj_close              65597067264.0          1.0                     0.9590094247547796\n",
      "high                   1523658112.0           0.0232275340278237      0.022275424046495275\n",
      "vwap                   1004545152.0           0.015313872919914812    0.0146861484596953\n",
      "low                    256683648.0            0.0039130354253027605   0.0037526378522646743\n",
      "open                   17375846.0             0.0002648875433724756   0.0002540296505943446\n",
      "CPIDE_return_1         586555.0               8.941786949702619e-06   8.575257958914104e-06\n",
      "close_lag_1            551760.6875            8.411362131166633e-06   8.066575558814249e-06\n",
      "KalmanEstimate         144279.390625          2.1994792853213613e-06  2.1093213641760925e-06\n",
      "unadjusted_volume      34905.69140625         5.32122743624644e-07    5.103107262624049e-07\n",
      "close_return_30        32185.701171875        4.906576241029403e-07   4.705452858425076e-07\n",
      "---                    ---                    ---                     ---\n",
      "volume_return_30       789.420166015625       1.2034382007331947e-08  1.154108576613068e-08\n",
      "REXPDE~TUSD_lag_7      738.735107421875       1.1261709375646074e-08  1.080008543009385e-08\n",
      "CPIDE_lag_30           676.3101806640625      1.0310067337952849e-08  9.887451746953205e-09\n",
      "CPIDE_return_30        548.4481811523438      8.360864350003264e-09   8.018147710749374e-09\n",
      "REXPDE~TUSD_return_30  431.3699645996094      6.576055646871082e-09   6.3064993430612565e-09\n",
      "REXPDE~TUSD_lag_30     402.5090637207031      6.1360832200131315e-09  5.88456163907225e-09\n",
      "REXPDE~TUSD            311.0453796386719      4.741757407947034e-09   4.5473900441219995e-09\n",
      "REXPDE~TUSD_return_7   234.057861328125       3.568114720527714e-09   3.421855645592344e-09\n",
      "REXPDE~TUSD_lag_1      129.0416717529297      1.9671866004861533e-09  1.8865504901175363e-09\n",
      "REXPDE~TUSD_return_1   75.47683715820312      1.1506129817426335e-09  1.1034486937363847e-09\n",
      "[43 rows x 4 columns]\n",
      "\n",
      "\n",
      "Model saved to: /home/marius/finance_code/GBM_grid_1_AutoML_4_20240825_152005_model_10\n"
     ]
    }
   ],
   "source": [
    "import h2o\n",
    "from h2o.automl import H2OAutoML\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from datetime import datetime, timedelta\n",
    "\n",
    "# Initialize H2O\n",
    "h2o.init()\n",
    "\n",
    "# Assuming your dataframe is named combined_df\n",
    "df = combined_df.copy()\n",
    "\n",
    "# Convert 'index' to datetime if it's not already\n",
    "df['index'] = pd.to_datetime(df['index'])\n",
    "df.set_index('index', inplace=True)\n",
    "\n",
    "# Filter for the last 10 years of data\n",
    "ten_years_ago = datetime.now() - timedelta(days=365*10)\n",
    "df = df[df.index >= ten_years_ago]\n",
    "\n",
    "# Function to calculate returns for CPIDE\n",
    "def calculate_cpide_returns(series, periods):\n",
    "    returns = series.pct_change(periods)\n",
    "    mask = returns != 0\n",
    "    idx = np.arange(len(returns))\n",
    "    idx_valid = idx[mask]\n",
    "    returns = pd.Series(np.interp(idx, idx_valid, returns[mask]), index=returns.index)\n",
    "    return returns\n",
    "\n",
    "# Feature Engineering\n",
    "for col in ['close', 'REXPDE~TUSD', 'LorentzianIndicator', 'volume']:\n",
    "    df[f'{col}_lag_1'] = df[col].shift(1)\n",
    "    df[f'{col}_lag_7'] = df[col].shift(7)\n",
    "    df[f'{col}_lag_30'] = df[col].shift(30)\n",
    "    df[f'{col}_return_1'] = df[col].pct_change(1)\n",
    "    df[f'{col}_return_7'] = df[col].pct_change(7)\n",
    "    df[f'{col}_return_30'] = df[col].pct_change(30)\n",
    "\n",
    "# Special handling for CPIDE\n",
    "df['CPIDE_lag_1'] = df['CPIDE'].shift(1)\n",
    "df['CPIDE_lag_7'] = df['CPIDE'].shift(7)\n",
    "df['CPIDE_lag_30'] = df['CPIDE'].shift(30)\n",
    "df['CPIDE_return_1'] = calculate_cpide_returns(df['CPIDE'], 1)\n",
    "df['CPIDE_return_7'] = calculate_cpide_returns(df['CPIDE'], 7)\n",
    "df['CPIDE_return_30'] = calculate_cpide_returns(df['CPIDE'], 30)\n",
    "\n",
    "# Remove rows with NaN values\n",
    "df = df.dropna()\n",
    "\n",
    "# Set date as a separate column for H2O\n",
    "df['date'] = df.index\n",
    "\n",
    "# Select all features except 'close' and 'date'\n",
    "features = [col for col in df.columns if col not in ['close', 'date']]\n",
    "target = 'close'\n",
    "\n",
    "print(\"Features used:\", features)\n",
    "print(\"\\nDataFrame column types:\")\n",
    "print(df.dtypes)\n",
    "\n",
    "# Convert to H2OFrame\n",
    "h2o_df = h2o.H2OFrame(df)\n",
    "\n",
    "print(\"\\nH2OFrame column types:\")\n",
    "print(h2o_df.types)\n",
    "\n",
    "# Split the data into train, validation, and test sets\n",
    "train, valid, test = h2o_df.split_frame(ratios=[0.7, 0.15], seed=1234)\n",
    "\n",
    "# Run AutoML\n",
    "aml = H2OAutoML(max_runtime_secs=3600,  # 1 hour\n",
    "                seed=1234,\n",
    "                sort_metric='RMSE',\n",
    "                stopping_rounds=20,\n",
    "                stopping_tolerance=0.01,\n",
    "                stopping_metric='RMSE',\n",
    "                max_models=100,\n",
    "                exclude_algos=['DeepLearning'],\n",
    "                nfolds=0)\n",
    "\n",
    "aml.train(x=features, y=target, training_frame=train, validation_frame=valid)\n",
    "\n",
    "# Get the best model\n",
    "best_model = aml.leader\n",
    "\n",
    "print(\"\\nBest model:\", best_model)\n",
    "\n",
    "# Save the model\n",
    "model_path = h2o.save_model(model=best_model, path=\"./\", force=True)\n",
    "print(f\"\\nModel saved to: {model_path}\")\n",
    "\n",
    "# Don't shutdown H2O here if you plan to use it for predictions immediately after"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "id": "fb91d2e9-c615-4c5b-b4d2-12bf12f04101",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Checking whether there is an H2O instance running at http://localhost:54321. connected.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "<style>\n",
       "\n",
       "#h2o-table-14.h2o-container {\n",
       "  overflow-x: auto;\n",
       "}\n",
       "#h2o-table-14 .h2o-table {\n",
       "  /* width: 100%; */\n",
       "  margin-top: 1em;\n",
       "  margin-bottom: 1em;\n",
       "}\n",
       "#h2o-table-14 .h2o-table caption {\n",
       "  white-space: nowrap;\n",
       "  caption-side: top;\n",
       "  text-align: left;\n",
       "  /* margin-left: 1em; */\n",
       "  margin: 0;\n",
       "  font-size: larger;\n",
       "}\n",
       "#h2o-table-14 .h2o-table thead {\n",
       "  white-space: nowrap; \n",
       "  position: sticky;\n",
       "  top: 0;\n",
       "  box-shadow: 0 -1px inset;\n",
       "}\n",
       "#h2o-table-14 .h2o-table tbody {\n",
       "  overflow: auto;\n",
       "}\n",
       "#h2o-table-14 .h2o-table th,\n",
       "#h2o-table-14 .h2o-table td {\n",
       "  text-align: right;\n",
       "  /* border: 1px solid; */\n",
       "}\n",
       "#h2o-table-14 .h2o-table tr:nth-child(even) {\n",
       "  /* background: #F5F5F5 */\n",
       "}\n",
       "\n",
       "</style>      \n",
       "<div id=\"h2o-table-14\" class=\"h2o-container\">\n",
       "  <table class=\"h2o-table\">\n",
       "    <caption></caption>\n",
       "    <thead></thead>\n",
       "    <tbody><tr><td>H2O_cluster_uptime:</td>\n",
       "<td>1 hour 13 mins</td></tr>\n",
       "<tr><td>H2O_cluster_timezone:</td>\n",
       "<td>Etc/UTC</td></tr>\n",
       "<tr><td>H2O_data_parsing_timezone:</td>\n",
       "<td>UTC</td></tr>\n",
       "<tr><td>H2O_cluster_version:</td>\n",
       "<td>3.46.0.4</td></tr>\n",
       "<tr><td>H2O_cluster_version_age:</td>\n",
       "<td>1 month and 15 days</td></tr>\n",
       "<tr><td>H2O_cluster_name:</td>\n",
       "<td>H2O_from_python_marius_tlzho8</td></tr>\n",
       "<tr><td>H2O_cluster_total_nodes:</td>\n",
       "<td>1</td></tr>\n",
       "<tr><td>H2O_cluster_free_memory:</td>\n",
       "<td>1.427 Gb</td></tr>\n",
       "<tr><td>H2O_cluster_total_cores:</td>\n",
       "<td>6</td></tr>\n",
       "<tr><td>H2O_cluster_allowed_cores:</td>\n",
       "<td>6</td></tr>\n",
       "<tr><td>H2O_cluster_status:</td>\n",
       "<td>locked, healthy</td></tr>\n",
       "<tr><td>H2O_connection_url:</td>\n",
       "<td>http://localhost:54321</td></tr>\n",
       "<tr><td>H2O_connection_proxy:</td>\n",
       "<td>{\"http\": null, \"https\": null}</td></tr>\n",
       "<tr><td>H2O_internal_security:</td>\n",
       "<td>False</td></tr>\n",
       "<tr><td>Python_version:</td>\n",
       "<td>3.11.9 final</td></tr></tbody>\n",
       "  </table>\n",
       "</div>\n"
      ],
      "text/plain": [
       "--------------------------  -----------------------------\n",
       "H2O_cluster_uptime:         1 hour 13 mins\n",
       "H2O_cluster_timezone:       Etc/UTC\n",
       "H2O_data_parsing_timezone:  UTC\n",
       "H2O_cluster_version:        3.46.0.4\n",
       "H2O_cluster_version_age:    1 month and 15 days\n",
       "H2O_cluster_name:           H2O_from_python_marius_tlzho8\n",
       "H2O_cluster_total_nodes:    1\n",
       "H2O_cluster_free_memory:    1.427 Gb\n",
       "H2O_cluster_total_cores:    6\n",
       "H2O_cluster_allowed_cores:  6\n",
       "H2O_cluster_status:         locked, healthy\n",
       "H2O_connection_url:         http://localhost:54321\n",
       "H2O_connection_proxy:       {\"http\": null, \"https\": null}\n",
       "H2O_internal_security:      False\n",
       "Python_version:             3.11.9 final\n",
       "--------------------------  -----------------------------"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Future DataFrame column types:\n",
      "open                                    float64\n",
      "high                                    float64\n",
      "low                                     float64\n",
      "close                                   float64\n",
      "volume                                  float64\n",
      "vwap                                    float64\n",
      "adj_close                               float64\n",
      "unadjusted_volume                       float64\n",
      "change                                  float64\n",
      "change_percent                          float64\n",
      "KalmanEstimate                          float64\n",
      "LorentzianIndicator                     float64\n",
      "REXPDE~TUSD                             float64\n",
      "CPIDE                                   float64\n",
      "close_lag_1                             float64\n",
      "close_lag_7                             float64\n",
      "close_lag_30                            float64\n",
      "close_return_1                          float64\n",
      "close_return_7                          float64\n",
      "close_return_30                         float64\n",
      "REXPDE~TUSD_lag_1                       float64\n",
      "REXPDE~TUSD_lag_7                       float64\n",
      "REXPDE~TUSD_lag_30                      float64\n",
      "REXPDE~TUSD_return_1                    float64\n",
      "REXPDE~TUSD_return_7                    float64\n",
      "REXPDE~TUSD_return_30                   float64\n",
      "LorentzianIndicator_lag_1               float64\n",
      "LorentzianIndicator_lag_7               float64\n",
      "LorentzianIndicator_lag_30              float64\n",
      "LorentzianIndicator_return_1            float64\n",
      "LorentzianIndicator_return_7            float64\n",
      "LorentzianIndicator_return_30           float64\n",
      "volume_lag_1                            float64\n",
      "volume_lag_7                            float64\n",
      "volume_lag_30                           float64\n",
      "volume_return_1                         float64\n",
      "volume_return_7                         float64\n",
      "volume_return_30                        float64\n",
      "CPIDE_lag_1                             float64\n",
      "CPIDE_lag_7                             float64\n",
      "CPIDE_lag_30                            float64\n",
      "CPIDE_return_1                          float64\n",
      "CPIDE_return_7                          float64\n",
      "CPIDE_return_30                         float64\n",
      "date                             datetime64[ns]\n",
      "dtype: object\n",
      "Parse progress: |████████████████████████████████████████████████████████████████| (done) 100%\n",
      "\n",
      "Future H2OFrame column types:\n",
      "{'open': 'real', 'high': 'real', 'low': 'real', 'close': 'real', 'volume': 'real', 'vwap': 'real', 'adj_close': 'real', 'unadjusted_volume': 'real', 'change': 'real', 'change_percent': 'real', 'KalmanEstimate': 'real', 'LorentzianIndicator': 'real', 'REXPDE~TUSD': 'real', 'CPIDE': 'real', 'close_lag_1': 'real', 'close_lag_7': 'real', 'close_lag_30': 'enum', 'close_return_1': 'int', 'close_return_7': 'int', 'close_return_30': 'enum', 'REXPDE~TUSD_lag_1': 'real', 'REXPDE~TUSD_lag_7': 'real', 'REXPDE~TUSD_lag_30': 'enum', 'REXPDE~TUSD_return_1': 'int', 'REXPDE~TUSD_return_7': 'int', 'REXPDE~TUSD_return_30': 'enum', 'LorentzianIndicator_lag_1': 'real', 'LorentzianIndicator_lag_7': 'real', 'LorentzianIndicator_lag_30': 'enum', 'LorentzianIndicator_return_1': 'int', 'LorentzianIndicator_return_7': 'int', 'LorentzianIndicator_return_30': 'enum', 'volume_lag_1': 'real', 'volume_lag_7': 'real', 'volume_lag_30': 'enum', 'volume_return_1': 'int', 'volume_return_7': 'int', 'volume_return_30': 'enum', 'CPIDE_lag_1': 'real', 'CPIDE_lag_7': 'real', 'CPIDE_lag_30': 'enum', 'CPIDE_return_1': 'int', 'CPIDE_return_7': 'int', 'CPIDE_return_30': 'enum', 'date': 'time'}\n",
      "gbm prediction progress: | (failed)\n"
     ]
    },
    {
     "ename": "OSError",
     "evalue": "Job with key $03017f00000132d4ffffffff$_8a43f9d4d026c494916fc4baa9e4fc2 failed with an exception: java.lang.IllegalArgumentException: Test/Validation dataset has categorical column 'close_lag_30' which is real-valued in the training data\nstacktrace: \njava.lang.IllegalArgumentException: Test/Validation dataset has categorical column 'close_lag_30' which is real-valued in the training data\n\tat hex.Model.adaptTestForTrain(Model.java:1833)\n\tat hex.Model.adaptTestForTrain(Model.java:1643)\n\tat hex.Model.adaptTestForTrain(Model.java:1639)\n\tat hex.Model.adaptFrameForScore(Model.java:1981)\n\tat hex.Model.score(Model.java:1999)\n\tat water.api.ModelMetricsHandler$1.compute2(ModelMetricsHandler.java:555)\n\tat water.H2O$H2OCountedCompleter.compute(H2O.java:1704)\n\tat jsr166y.CountedCompleter.exec(CountedCompleter.java:468)\n\tat jsr166y.ForkJoinTask.doExec(ForkJoinTask.java:263)\n\tat jsr166y.ForkJoinPool$WorkQueue.runTask(ForkJoinPool.java:976)\n\tat jsr166y.ForkJoinPool.runWorker(ForkJoinPool.java:1479)\n\tat jsr166y.ForkJoinWorkerThread.run(ForkJoinWorkerThread.java:104)\n",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mOSError\u001b[0m                                   Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[120], line 81\u001b[0m\n\u001b[1;32m     78\u001b[0m \u001b[38;5;28mprint\u001b[39m(future_h2o\u001b[38;5;241m.\u001b[39mtypes)\n\u001b[1;32m     80\u001b[0m \u001b[38;5;66;03m# Make predictions\u001b[39;00m\n\u001b[0;32m---> 81\u001b[0m future_predictions \u001b[38;5;241m=\u001b[39m \u001b[43mbest_model\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpredict\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfuture_h2o\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     82\u001b[0m future_pred_df \u001b[38;5;241m=\u001b[39m h2o\u001b[38;5;241m.\u001b[39mas_list(future_predictions, use_pandas\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n\u001b[1;32m     84\u001b[0m \u001b[38;5;66;03m# Plot future predictions\u001b[39;00m\n",
      "File \u001b[0;32m~/miniconda3/envs/openbb/lib/python3.11/site-packages/h2o/model/model_base.py:334\u001b[0m, in \u001b[0;36mModelBase.predict\u001b[0;34m(self, test_data, custom_metric, custom_metric_func)\u001b[0m\n\u001b[1;32m    331\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(test_data, h2o\u001b[38;5;241m.\u001b[39mH2OFrame): \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtest_data must be an instance of H2OFrame\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m    332\u001b[0m j \u001b[38;5;241m=\u001b[39m H2OJob(h2o\u001b[38;5;241m.\u001b[39mapi(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mPOST /4/Predictions/models/\u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m/frames/\u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;241m%\u001b[39m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mmodel_id, test_data\u001b[38;5;241m.\u001b[39mframe_id), data \u001b[38;5;241m=\u001b[39m {\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mcustom_metric_func\u001b[39m\u001b[38;5;124m'\u001b[39m: custom_metric_func}),\n\u001b[1;32m    333\u001b[0m            \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_model_json[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124malgo\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m+\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m prediction\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m--> 334\u001b[0m \u001b[43mj\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpoll\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    335\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m h2o\u001b[38;5;241m.\u001b[39mget_frame(j\u001b[38;5;241m.\u001b[39mdest_key)\n",
      "File \u001b[0;32m~/miniconda3/envs/openbb/lib/python3.11/site-packages/h2o/job.py:88\u001b[0m, in \u001b[0;36mH2OJob.poll\u001b[0;34m(self, poll_updates)\u001b[0m\n\u001b[1;32m     86\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mstatus \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mFAILED\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[1;32m     87\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m (\u001b[38;5;28misinstance\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mjob, \u001b[38;5;28mdict\u001b[39m)) \u001b[38;5;129;01mand\u001b[39;00m (\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mstacktrace\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mlist\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mjob)):\n\u001b[0;32m---> 88\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mEnvironmentError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mJob with key \u001b[39m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[38;5;124m failed with an exception: \u001b[39m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124mstacktrace: \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m     89\u001b[0m                                \u001b[38;5;124m\"\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;241m.\u001b[39mformat(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mjob_key, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mexception, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mjob[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mstacktrace\u001b[39m\u001b[38;5;124m\"\u001b[39m]))\n\u001b[1;32m     90\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m     91\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mEnvironmentError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mJob with key \u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m failed with an exception: \u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;241m%\u001b[39m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mjob_key, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mexception))\n",
      "\u001b[0;31mOSError\u001b[0m: Job with key $03017f00000132d4ffffffff$_8a43f9d4d026c494916fc4baa9e4fc2 failed with an exception: java.lang.IllegalArgumentException: Test/Validation dataset has categorical column 'close_lag_30' which is real-valued in the training data\nstacktrace: \njava.lang.IllegalArgumentException: Test/Validation dataset has categorical column 'close_lag_30' which is real-valued in the training data\n\tat hex.Model.adaptTestForTrain(Model.java:1833)\n\tat hex.Model.adaptTestForTrain(Model.java:1643)\n\tat hex.Model.adaptTestForTrain(Model.java:1639)\n\tat hex.Model.adaptFrameForScore(Model.java:1981)\n\tat hex.Model.score(Model.java:1999)\n\tat water.api.ModelMetricsHandler$1.compute2(ModelMetricsHandler.java:555)\n\tat water.H2O$H2OCountedCompleter.compute(H2O.java:1704)\n\tat jsr166y.CountedCompleter.exec(CountedCompleter.java:468)\n\tat jsr166y.ForkJoinTask.doExec(ForkJoinTask.java:263)\n\tat jsr166y.ForkJoinPool$WorkQueue.runTask(ForkJoinPool.java:976)\n\tat jsr166y.ForkJoinPool.runWorker(ForkJoinPool.java:1479)\n\tat jsr166y.ForkJoinWorkerThread.run(ForkJoinWorkerThread.java:104)\n"
     ]
    }
   ],
   "source": [
    "import h2o\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from datetime import datetime, timedelta\n",
    "from pykalman import KalmanFilter\n",
    "import plotly.graph_objects as go\n",
    "\n",
    "# Initialize H2O (if not already initialized)\n",
    "h2o.init()\n",
    "\n",
    "# Load the saved model\n",
    "best_model = h2o.load_model(\"./GBM_grid_1_AutoML_4_20240825_152005_model_10\")  # Use your actual model path\n",
    "\n",
    "# Assuming df is your original dataframe used for training\n",
    "# If it's not in memory, you'll need to load and preprocess it again\n",
    "\n",
    "def forecast_feature_kalman(series, forecast_horizon):\n",
    "    kf = KalmanFilter(transition_matrices=np.array([[1]]),\n",
    "                      observation_matrices=np.array([[1]]),\n",
    "                      initial_state_mean=np.array([series.iloc[-1]]),\n",
    "                      initial_state_covariance=np.array([[1]]),\n",
    "                      observation_covariance=np.array([[1]]),\n",
    "                      transition_covariance=np.array([[0.01]]))\n",
    "    \n",
    "    state_means, _ = kf.filter(series.values.reshape(-1, 1))\n",
    "    \n",
    "    last_state = state_means[-1]\n",
    "    forecast = np.zeros(forecast_horizon)\n",
    "    for i in range(forecast_horizon):\n",
    "        last_state = np.dot(kf.transition_matrices, last_state)\n",
    "        forecast[i] = last_state.flatten()[0]\n",
    "    \n",
    "    return pd.Series(forecast, index=pd.date_range(start=series.index[-1] + pd.Timedelta(days=1), periods=forecast_horizon))\n",
    "\n",
    "def generate_future_features(df, future_dates, features):\n",
    "    future_df = pd.DataFrame(index=future_dates)\n",
    "    \n",
    "    # Forecast main features\n",
    "    for feature in df.columns:\n",
    "        if feature != 'date':\n",
    "            if feature == 'CPIDE':\n",
    "                future_df[feature] = df[feature].iloc[-1]\n",
    "            else:\n",
    "                future_df[feature] = forecast_feature_kalman(df[feature], len(future_dates))\n",
    "    \n",
    "    # Calculate derived features\n",
    "    for feature in features:\n",
    "        if '_lag_' in feature:\n",
    "            base_feature, lag = feature.split('_lag_')\n",
    "            future_df[feature] = future_df[base_feature].shift(int(lag))\n",
    "        elif '_return_' in feature:\n",
    "            base_feature, period = feature.split('_return_')\n",
    "            future_df[feature] = future_df[base_feature].pct_change(int(period))\n",
    "    \n",
    "    future_df['date'] = future_dates\n",
    "    \n",
    "    # Ensure all dtypes match the original dataframe\n",
    "    for col in future_df.columns:\n",
    "        if col in df.columns:\n",
    "            future_df[col] = future_df[col].astype(df[col].dtype)\n",
    "    \n",
    "    return future_df\n",
    "\n",
    "# Generate future dates\n",
    "last_date = df.index.max()\n",
    "future_dates = pd.date_range(start=last_date + timedelta(days=1), periods=30)\n",
    "\n",
    "# Generate future features\n",
    "future_df = generate_future_features(df, future_dates, df.columns)\n",
    "\n",
    "print(\"Future DataFrame column types:\")\n",
    "print(future_df.dtypes)\n",
    "\n",
    "# Convert future DataFrame to H2OFrame\n",
    "future_h2o = h2o.H2OFrame(future_df)\n",
    "\n",
    "print(\"\\nFuture H2OFrame column types:\")\n",
    "print(future_h2o.types)\n",
    "\n",
    "# Make predictions\n",
    "future_predictions = best_model.predict(future_h2o)\n",
    "future_pred_df = h2o.as_list(future_predictions, use_pandas=True)\n",
    "\n",
    "# Plot future predictions\n",
    "fig = go.Figure()\n",
    "fig.add_trace(go.Scatter(x=df.index[-365:], y=df['close'].tail(365), mode='lines', name='Historical'))\n",
    "fig.add_trace(go.Scatter(x=future_dates, y=future_pred_df['predict'], mode='lines', name='Forecast'))\n",
    "fig.update_layout(title='H2O AutoML: 30-Day Future Forecast for DAX', xaxis_title='Date', yaxis_title='Close Price')\n",
    "fig.show()\n",
    "\n",
    "print(\"\\nFuture Predictions (next 30 days):\")\n",
    "print(pd.concat([future_df['date'], future_pred_df], axis=1))\n",
    "\n",
    "# Shutdown H2O cluster\n",
    "h2o.shutdown()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "d3d6cc09-23ad-4e34-a962-4218b183030b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Future DataFrame Data Types:\n",
      "date                             datetime64[ns]\n",
      "open                                    float64\n",
      "high                                    float64\n",
      "low                                     float64\n",
      "volume                                  float64\n",
      "vwap                                    float64\n",
      "adj_close                               float64\n",
      "unadjusted_volume                       float64\n",
      "change                                  float64\n",
      "change_percent                          float64\n",
      "KalmanEstimate                          float64\n",
      "LorentzianIndicator                     float64\n",
      "REXPDE~TUSD                             float64\n",
      "CPIDE                                   float64\n",
      "close_lag_1                             float64\n",
      "close_lag_7                             float64\n",
      "close_lag_30                            float64\n",
      "close_return_1                          float64\n",
      "close_return_7                          float64\n",
      "close_return_30                         float64\n",
      "REXPDE~TUSD_lag_1                       float64\n",
      "REXPDE~TUSD_lag_7                       float64\n",
      "REXPDE~TUSD_lag_30                      float64\n",
      "REXPDE~TUSD_return_1                    float64\n",
      "REXPDE~TUSD_return_7                    float64\n",
      "REXPDE~TUSD_return_30                   float64\n",
      "LorentzianIndicator_lag_1               float64\n",
      "LorentzianIndicator_lag_7               float64\n",
      "LorentzianIndicator_lag_30              float64\n",
      "LorentzianIndicator_return_1            float64\n",
      "LorentzianIndicator_return_7            float64\n",
      "LorentzianIndicator_return_30           float64\n",
      "CPIDE_lag_1                             float64\n",
      "CPIDE_lag_7                             float64\n",
      "CPIDE_lag_30                            float64\n",
      "CPIDE_return_1                          float64\n",
      "CPIDE_return_7                          float64\n",
      "CPIDE_return_30                         float64\n",
      "volume_lag_1                            float64\n",
      "volume_lag_7                            float64\n",
      "volume_lag_30                           float64\n",
      "volume_return_1                         float64\n",
      "volume_return_7                         float64\n",
      "volume_return_30                        float64\n",
      "dtype: object\n",
      "Parse progress: |████████████████████████████████████████████████████████████████| (done) 100%\n",
      "\n",
      "H2OFrame Column Types After Conversion:\n",
      "{'date': 'time', 'open': 'real', 'high': 'real', 'low': 'real', 'volume': 'real', 'vwap': 'real', 'adj_close': 'real', 'unadjusted_volume': 'real', 'change': 'real', 'change_percent': 'real', 'KalmanEstimate': 'real', 'LorentzianIndicator': 'real', 'REXPDE~TUSD': 'real', 'CPIDE': 'real', 'close_lag_1': 'real', 'close_lag_7': 'real', 'close_lag_30': 'real', 'close_return_1': 'real', 'close_return_7': 'real', 'close_return_30': 'real', 'REXPDE~TUSD_lag_1': 'real', 'REXPDE~TUSD_lag_7': 'real', 'REXPDE~TUSD_lag_30': 'real', 'REXPDE~TUSD_return_1': 'real', 'REXPDE~TUSD_return_7': 'real', 'REXPDE~TUSD_return_30': 'real', 'LorentzianIndicator_lag_1': 'real', 'LorentzianIndicator_lag_7': 'real', 'LorentzianIndicator_lag_30': 'real', 'LorentzianIndicator_return_1': 'real', 'LorentzianIndicator_return_7': 'real', 'LorentzianIndicator_return_30': 'real', 'CPIDE_lag_1': 'real', 'CPIDE_lag_7': 'real', 'CPIDE_lag_30': 'real', 'CPIDE_return_1': 'real', 'CPIDE_return_7': 'real', 'CPIDE_return_30': 'real', 'volume_lag_1': 'real', 'volume_lag_7': 'real', 'volume_lag_30': 'real', 'volume_return_1': 'real', 'volume_return_7': 'real', 'volume_return_30': 'real'}\n",
      "gbm prediction progress: |███████████████████████████████████████████████████████| (done) 100%\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/marius/miniconda3/envs/openbb/lib/python3.11/site-packages/h2o/frame.py:1981: H2ODependencyWarning:\n",
      "\n",
      "Converting H2O frame to pandas dataframe using single-thread.  For faster conversion using multi-thread, install polars and pyarrow and use it as pandas_df = h2o_df.as_data_frame(use_multi_thread=True)\n",
      "\n",
      "\n"
     ]
    },
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",
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      "\n",
      "Future Predictions (next 30 days):\n",
      "         date       predict\n",
      "0  2024-08-24  18226.278084\n",
      "1  2024-08-25  18226.278084\n",
      "2  2024-08-26  18226.278084\n",
      "3  2024-08-27  18226.278084\n",
      "4  2024-08-28  18226.278084\n",
      "5  2024-08-29  18224.811445\n",
      "6  2024-08-30  18223.730802\n",
      "7  2024-08-31  18223.500556\n",
      "8  2024-09-01  18223.500556\n",
      "9  2024-09-02  18223.500556\n",
      "10 2024-09-03  18223.500556\n",
      "11 2024-09-04  18223.500556\n",
      "12 2024-09-05  18223.500556\n",
      "13 2024-09-06  18223.345046\n",
      "14 2024-09-07  18223.345046\n",
      "15 2024-09-08  18223.345046\n",
      "16 2024-09-09  18223.345046\n",
      "17 2024-09-10  18223.345046\n",
      "18 2024-09-11  18223.345046\n",
      "19 2024-09-12  18223.345046\n",
      "20 2024-09-13  18223.345046\n",
      "21 2024-09-14  18223.345046\n",
      "22 2024-09-15  18223.345046\n",
      "23 2024-09-16  18223.345046\n",
      "24 2024-09-17  18223.345046\n",
      "25 2024-09-18  18223.345046\n",
      "26 2024-09-19  18223.345046\n",
      "27 2024-09-20  18223.345046\n",
      "28 2024-09-21  18223.345046\n",
      "29 2024-09-22  18223.345046\n"
     ]
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   ],
   "source": [
    "import h2o\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from datetime import datetime, timedelta\n",
    "import plotly.graph_objects as go\n",
    "\n",
    "# Assuming df and best_model are already defined\n",
    "\n",
    "# Future predictions\n",
    "last_date = df.index.max()\n",
    "future_dates = pd.date_range(start=last_date + timedelta(days=1), periods=30)\n",
    "future_df = pd.DataFrame(index=future_dates)\n",
    "\n",
    "# Create future feature values\n",
    "for feature in features:\n",
    "    if feature in df.columns:\n",
    "        future_df[feature] = np.zeros(30, dtype=np.float64)\n",
    "    \n",
    "    if 'lag' in feature:\n",
    "        lag = int(feature.split('_')[-1])\n",
    "        base_feature = feature.split('_')[0]\n",
    "        future_df[feature] = [float(df[base_feature].iloc[-lag:].values[0])] * 30\n",
    "    elif 'return' in feature:\n",
    "        period = int(feature.split('_')[-1])\n",
    "        base_feature = feature.split('_')[0]\n",
    "        future_df[feature] = [float(df[feature].iloc[-period:].mean())] * 30\n",
    "    elif feature in ['REXPDE~TUSD', 'CPIDE']:\n",
    "        future_df[feature] = [float(df[feature].iloc[-1])] * 30\n",
    "    elif feature in ['LorentzianIndicator', 'KalmanEstimate']:\n",
    "        last_30_days = df[feature].tail(30)\n",
    "        trend = np.polyfit(range(30), last_30_days, 1)[0]\n",
    "        future_df[feature] = df[feature].iloc[-1] + trend * np.arange(1, 31)\n",
    "    else:\n",
    "        # For other features, use the average of the last 30 days\n",
    "        future_df[feature] = [float(df[feature].tail(30).mean())] * 30\n",
    "\n",
    "# Ensure all dtypes are float64\n",
    "for col in future_df.columns:\n",
    "    future_df[col] = future_df[col].astype(np.float64)\n",
    "\n",
    "# Reset index and rename to 'date' for consistency\n",
    "future_df.reset_index(inplace=True)\n",
    "future_df.rename(columns={'index': 'date'}, inplace=True)\n",
    "\n",
    "# Double-check data types\n",
    "print(\"\\nFuture DataFrame Data Types:\")\n",
    "print(future_df.dtypes)\n",
    "\n",
    "# Convert future DataFrame to H2OFrame\n",
    "future_h2o = h2o.H2OFrame(future_df)\n",
    "\n",
    "# Force all columns except 'date' to be numeric\n",
    "for col in future_h2o.columns:\n",
    "    if col != \"date\":\n",
    "        future_h2o[col] = future_h2o[col].asnumeric()\n",
    "\n",
    "# Check column types after conversion\n",
    "print(\"\\nH2OFrame Column Types After Conversion:\")\n",
    "print(future_h2o.types)\n",
    "\n",
    "# Make predictions\n",
    "future_predictions = best_model.predict(future_h2o)\n",
    "future_pred_df = h2o.as_list(future_predictions, use_pandas=True)\n",
    "\n",
    "# Plot future predictions\n",
    "fig = go.Figure()\n",
    "fig.add_trace(go.Scatter(x=df.index[-365:], y=df['close'].tail(365), mode='lines', name='Historical'))\n",
    "fig.add_trace(go.Scatter(x=future_dates, y=future_pred_df['predict'], mode='lines', name='Forecast'))\n",
    "fig.update_layout(title='H2O AutoML: 30-Day Future Forecast for DAX', xaxis_title='Date', yaxis_title='Close Price')\n",
    "fig.show()\n",
    "\n",
    "print(\"\\nFuture Predictions (next 30 days):\")\n",
    "print(pd.concat([future_df['date'], future_pred_df], axis=1))"
   ]
  },
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      "Checking whether there is an H2O instance running at http://localhost:54321. connected.\n"
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       "  <table class=\"h2o-table\">\n",
       "    <caption></caption>\n",
       "    <thead></thead>\n",
       "    <tbody><tr><td>H2O_cluster_uptime:</td>\n",
       "<td>11 mins 07 secs</td></tr>\n",
       "<tr><td>H2O_cluster_timezone:</td>\n",
       "<td>Etc/UTC</td></tr>\n",
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       "<td>UTC</td></tr>\n",
       "<tr><td>H2O_cluster_version:</td>\n",
       "<td>3.46.0.4</td></tr>\n",
       "<tr><td>H2O_cluster_version_age:</td>\n",
       "<td>1 month and 15 days</td></tr>\n",
       "<tr><td>H2O_cluster_name:</td>\n",
       "<td>H2O_from_python_marius_st2i25</td></tr>\n",
       "<tr><td>H2O_cluster_total_nodes:</td>\n",
       "<td>1</td></tr>\n",
       "<tr><td>H2O_cluster_free_memory:</td>\n",
       "<td>2.910 Gb</td></tr>\n",
       "<tr><td>H2O_cluster_total_cores:</td>\n",
       "<td>6</td></tr>\n",
       "<tr><td>H2O_cluster_allowed_cores:</td>\n",
       "<td>6</td></tr>\n",
       "<tr><td>H2O_cluster_status:</td>\n",
       "<td>locked, healthy</td></tr>\n",
       "<tr><td>H2O_connection_url:</td>\n",
       "<td>http://localhost:54321</td></tr>\n",
       "<tr><td>H2O_connection_proxy:</td>\n",
       "<td>{\"http\": null, \"https\": null}</td></tr>\n",
       "<tr><td>H2O_internal_security:</td>\n",
       "<td>False</td></tr>\n",
       "<tr><td>Python_version:</td>\n",
       "<td>3.11.9 final</td></tr></tbody>\n",
       "  </table>\n",
       "</div>\n"
      ],
      "text/plain": [
       "--------------------------  -----------------------------\n",
       "H2O_cluster_uptime:         11 mins 07 secs\n",
       "H2O_cluster_timezone:       Etc/UTC\n",
       "H2O_data_parsing_timezone:  UTC\n",
       "H2O_cluster_version:        3.46.0.4\n",
       "H2O_cluster_version_age:    1 month and 15 days\n",
       "H2O_cluster_name:           H2O_from_python_marius_st2i25\n",
       "H2O_cluster_total_nodes:    1\n",
       "H2O_cluster_free_memory:    2.910 Gb\n",
       "H2O_cluster_total_cores:    6\n",
       "H2O_cluster_allowed_cores:  6\n",
       "H2O_cluster_status:         locked, healthy\n",
       "H2O_connection_url:         http://localhost:54321\n",
       "H2O_connection_proxy:       {\"http\": null, \"https\": null}\n",
       "H2O_internal_security:      False\n",
       "Python_version:             3.11.9 final\n",
       "--------------------------  -----------------------------"
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      "Future DataFrame first few rows:\n",
      "                    open          high           low         close  \\\n",
      "2024-08-24  18180.224652  18274.179719  18092.658184  18205.351114   \n",
      "2024-08-25  18177.261576  18270.905789  18089.133413  18182.966541   \n",
      "2024-08-26  18177.239431  18270.881321  18089.107070  18190.571284   \n",
      "2024-08-27  18177.239281  18270.881156  18089.106892  18186.453103   \n",
      "2024-08-28  18177.239280  18270.881155  18089.106891  18190.222285   \n",
      "\n",
      "                  volume          vwap     adj_close  unadjusted_volume  \\\n",
      "2024-08-24  4.771495e+07  18185.695815  18195.404213       4.771495e+07   \n",
      "2024-08-25  4.776941e+07  18182.351951  18191.788029       4.776941e+07   \n",
      "2024-08-26  4.776982e+07  18182.326960  18191.761002       4.776982e+07   \n",
      "2024-08-27  4.776982e+07  18182.326791  18191.760820       4.776982e+07   \n",
      "2024-08-28  4.776982e+07  18182.326790  18191.760818       4.776982e+07   \n",
      "\n",
      "               change  change_percent  ...  volume_return_1  volume_return_7  \\\n",
      "2024-08-24  15.179561        0.000838  ...              0.0         0.000000   \n",
      "2024-08-25  14.526453        0.000803  ...              0.0        -0.252057   \n",
      "2024-08-26  14.521572        0.000803  ...              0.0         0.091381   \n",
      "2024-08-27  14.521539        0.000803  ...              0.0         0.113204   \n",
      "2024-08-28  14.521538        0.000803  ...              0.0         0.324434   \n",
      "\n",
      "            volume_return_30  CPIDE_lag_1  CPIDE_lag_7  CPIDE_lag_30  \\\n",
      "2024-08-24         -0.192544        119.8        119.8         119.8   \n",
      "2024-08-25         -0.042443        119.8        119.8         119.8   \n",
      "2024-08-26          0.116914        119.8        119.8         119.8   \n",
      "2024-08-27          0.056974        119.8        119.8         119.8   \n",
      "2024-08-28         -0.025336        119.8        119.8         119.8   \n",
      "\n",
      "            CPIDE_return_1  CPIDE_return_7  CPIDE_return_30       date  \n",
      "2024-08-24             0.0             0.0              0.0 2024-08-24  \n",
      "2024-08-25             0.0             0.0              0.0 2024-08-25  \n",
      "2024-08-26             0.0             0.0              0.0 2024-08-26  \n",
      "2024-08-27             0.0             0.0              0.0 2024-08-27  \n",
      "2024-08-28             0.0             0.0              0.0 2024-08-28  \n",
      "\n",
      "[5 rows x 45 columns]\n"
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      "\n",
      "Future Predictions (next 30 days):\n",
      "                   close\n",
      "2024-08-24  18205.351114\n",
      "2024-08-25  18182.966541\n",
      "2024-08-26  18190.571284\n",
      "2024-08-27  18186.453103\n",
      "2024-08-28  18190.222285\n",
      "2024-08-29  18192.257015\n",
      "2024-08-30  18191.466112\n",
      "2024-08-31  18186.231708\n",
      "2024-09-01  18186.494394\n",
      "2024-09-02  18186.132303\n",
      "2024-09-03  18187.041985\n",
      "2024-09-04  18185.973349\n",
      "2024-09-05  18186.919488\n",
      "2024-09-06  18186.009807\n",
      "2024-09-07  18186.622702\n",
      "2024-09-08  18185.994902\n",
      "2024-09-09  18185.967859\n",
      "2024-09-10  18186.274342\n",
      "2024-09-11  18186.459516\n",
      "2024-09-12  18186.305164\n",
      "2024-09-13  18186.244685\n",
      "2024-09-14  18185.393230\n",
      "2024-09-15  18185.864517\n",
      "2024-09-16  18185.551890\n",
      "2024-09-17  18187.316431\n",
      "2024-09-18  18186.252807\n",
      "2024-09-19  18186.252807\n",
      "2024-09-20  18185.715900\n",
      "2024-09-21  18185.800903\n",
      "2024-09-22  18186.538356\n",
      "H2O session _sid_b283 closed.\n"
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     "text": [
      "/tmp/ipykernel_3518115/3684030009.py:100: H2ODeprecationWarning:\n",
      "\n",
      "Deprecated, use ``h2o.cluster().shutdown()``.\n",
      "\n"
     ]
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   "source": [
    "import h2o\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from datetime import datetime, timedelta\n",
    "from pykalman import KalmanFilter\n",
    "import plotly.graph_objects as go\n",
    "\n",
    "# Initialize H2O (if not already initialized)\n",
    "h2o.init()\n",
    "\n",
    "# Load the saved model\n",
    "best_model = h2o.load_model(\"./GBM_grid_1_AutoML_4_20240825_152005_model_10\")  # Use your actual model path\n",
    "\n",
    "def forecast_feature_kalman(series, forecast_horizon=1):\n",
    "    kf = KalmanFilter(transition_matrices=np.array([[1]]),\n",
    "                      observation_matrices=np.array([[1]]),\n",
    "                      initial_state_mean=np.array([series.iloc[-1]]),\n",
    "                      initial_state_covariance=np.array([[1]]),\n",
    "                      observation_covariance=np.array([[1]]),\n",
    "                      transition_covariance=np.array([[0.01]]))\n",
    "    \n",
    "    state_means, _ = kf.filter(series.values.reshape(-1, 1))\n",
    "    \n",
    "    last_state = state_means[-1]\n",
    "    forecast = np.zeros(forecast_horizon)\n",
    "    for i in range(forecast_horizon):\n",
    "        last_state = np.dot(kf.transition_matrices, last_state)\n",
    "        forecast[i] = last_state.flatten()[0]\n",
    "    \n",
    "    return forecast[0]\n",
    "\n",
    "def generate_next_day_features(df, last_date, features):\n",
    "    next_day = pd.DataFrame(index=[last_date + timedelta(days=1)])\n",
    "    \n",
    "    for feature in features:\n",
    "        if feature == 'date':\n",
    "            next_day[feature] = next_day.index[0]\n",
    "        elif '_lag_' in feature:\n",
    "            base_feature, lag = feature.split('_lag_')\n",
    "            lag = int(lag)\n",
    "            next_day[feature] = df[base_feature].iloc[-lag] if len(df) > lag else df[base_feature].iloc[-1]\n",
    "        elif '_return_' in feature:\n",
    "            base_feature, period = feature.split('_return_')\n",
    "            period = int(period)\n",
    "            if len(df) >= period:\n",
    "                current_value = df[base_feature].iloc[-1]\n",
    "                past_value = df[base_feature].iloc[-period]\n",
    "                # Use np.divide with a where condition to avoid division by zero\n",
    "                return_value = np.divide(current_value - past_value, past_value, \n",
    "                                         out=np.zeros_like(current_value, dtype=float), \n",
    "                                         where=past_value!=0)\n",
    "                next_day[feature] = return_value\n",
    "            else:\n",
    "                next_day[feature] = 0  # Use 0 as default return if not enough data\n",
    "        elif feature != 'close':\n",
    "            next_day[feature] = forecast_feature_kalman(df[feature])\n",
    "    \n",
    "    return next_day\n",
    "\n",
    "# Assuming df is your original dataframe with historical data\n",
    "# Make sure df is sorted by date and the index is a DatetimeIndex\n",
    "\n",
    "# Initialize future DataFrame with last 30 days of historical data\n",
    "future_df = df.tail(30).copy()\n",
    "future_dates = pd.date_range(start=df.index[-1] + timedelta(days=1), periods=30)\n",
    "\n",
    "for future_date in future_dates:\n",
    "    next_day = generate_next_day_features(future_df, future_df.index[-1], df.columns)\n",
    "    \n",
    "    # Convert to H2OFrame and make prediction\n",
    "    h2o_next_day = h2o.H2OFrame(next_day)\n",
    "    for col in h2o_next_day.columns:\n",
    "        if col != 'date':\n",
    "            h2o_next_day[col] = h2o_next_day[col].asnumeric()\n",
    "    \n",
    "    prediction = best_model.predict(h2o_next_day)\n",
    "    # Use multi-threaded conversion\n",
    "    pred_df = prediction.as_data_frame(use_pandas=True, use_multi_thread=True)\n",
    "    next_day['close'] = pred_df['predict'].values[0]\n",
    "    \n",
    "    future_df = pd.concat([future_df, next_day])\n",
    "\n",
    "# Keep only the future predictions\n",
    "future_predictions = future_df.iloc[-30:]\n",
    "\n",
    "print(\"Future DataFrame first few rows:\")\n",
    "print(future_predictions.head())\n",
    "\n",
    "# Plot future predictions\n",
    "fig = go.Figure()\n",
    "fig.add_trace(go.Scatter(x=df.index[-365:], y=df['close'].tail(365), mode='lines', name='Historical'))\n",
    "fig.add_trace(go.Scatter(x=future_predictions.index, y=future_predictions['close'], mode='lines', name='Forecast'))\n",
    "fig.update_layout(title='H2O AutoML: 30-Day Future Forecast for DAX', xaxis_title='Date', yaxis_title='Close Price')\n",
    "fig.show()\n",
    "\n",
    "print(\"\\nFuture Predictions (next 30 days):\")\n",
    "print(future_predictions[['close']])\n",
    "\n",
    "# Shutdown H2O cluster\n",
    "h2o.shutdown()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a8750240-2e84-4d6e-8f9a-a4ed82f7ea56",
   "metadata": {},
   "source": [
    "# Kalman estimate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "id": "461af6cf-498b-413c-aa24-9ec87e050549",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "index                  datetime64[ns]\n",
      "open                          float64\n",
      "high                          float64\n",
      "low                           float64\n",
      "close                         float64\n",
      "volume                        float64\n",
      "vwap                          float64\n",
      "adj_close                     float64\n",
      "unadjusted_volume             float64\n",
      "change                        float64\n",
      "change_percent                float64\n",
      "KalmanEstimate                float64\n",
      "LorentzianIndicator           float64\n",
      "REXPDE~TUSD                   float64\n",
      "CPIDE                         float64\n",
      "dtype: object\n"
     ]
    }
   ],
   "source": [
    "df = combined_df.iloc[-365:]\n",
    "print(df.dtypes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "id": "a3240957-f544-46c4-9a9c-374145777e19",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "           index      open      high       low     close      volume  \\\n",
      "11911 2023-08-12  15927.79  15962.45  15803.08  15832.17  60296700.0   \n",
      "\n",
      "           vwap  adj_close  unadjusted_volume  change  change_percent  \\\n",
      "11911  15881.37   15832.17         60296700.0  -95.62       -0.006003   \n",
      "\n",
      "       KalmanEstimate  LorentzianIndicator  REXPDE~TUSD  CPIDE  \n",
      "11911    16021.801098             5.274866     441556.0  117.5  \n"
     ]
    }
   ],
   "source": [
    "print(df.head(1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "id": "26570013-fa43-48f9-8d8b-1175fa1ae643",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "int64\n"
     ]
    }
   ],
   "source": [
    "print(df.index.dtype)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "id": "49e265a1-1d6b-4f48-bc19-7146403d17cf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Estimate for the next value: 18199.13689976488\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "from pykalman import KalmanFilter\n",
    "\n",
    "# Assuming df is your DataFrame with a 'close' column\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",
    "# Filter the data\n",
    "filtered_state_means, filtered_state_covariances = kf.filter(df['close'])\n",
    "\n",
    "# Get the last filtered state\n",
    "last_state_mean = filtered_state_means[-1]\n",
    "last_state_covariance = filtered_state_covariances[-1]\n",
    "\n",
    "# Predict the next state\n",
    "next_state_mean, next_state_covariance = kf.filter_update(\n",
    "    filtered_state_mean=last_state_mean,\n",
    "    filtered_state_covariance=last_state_covariance\n",
    ")\n",
    "\n",
    "print(f\"Estimate for the next value: {next_state_mean[0]}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "id": "9b952d5e-51cd-4858-b226-26c75c1c7890",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Estimate for the next value: 18271.87958586028\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "from pykalman import KalmanFilter\n",
    "\n",
    "# Assuming df is your DataFrame with a 'close' column\n",
    "\n",
    "# Initialize Kalman Filter\n",
    "kf = KalmanFilter(\n",
    "    initial_state_mean=df['high'].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",
    "# Filter the data\n",
    "filtered_state_means, filtered_state_covariances = kf.filter(df['high'])\n",
    "\n",
    "# Get the last filtered state\n",
    "last_state_mean = filtered_state_means[-1]\n",
    "last_state_covariance = filtered_state_covariances[-1]\n",
    "\n",
    "# Predict the next state\n",
    "next_state_mean, next_state_covariance = kf.filter_update(\n",
    "    filtered_state_mean=last_state_mean,\n",
    "    filtered_state_covariance=last_state_covariance\n",
    ")\n",
    "\n",
    "print(f\"Estimate for the next value: {next_state_mean[0]}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "id": "83038d7b-3aa4-4802-b19e-d19891f63352",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Estimate for the next value: 18199.13689976488\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "from pykalman import KalmanFilter\n",
    "\n",
    "# Assuming df is your DataFrame with a 'close' column\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",
    "# Filter the data\n",
    "filtered_state_means, filtered_state_covariances = kf.filter(df['close'])\n",
    "\n",
    "# Get the last filtered state\n",
    "last_state_mean = filtered_state_means[-1]\n",
    "last_state_covariance = filtered_state_covariances[-1]\n",
    "\n",
    "# Predict the next state\n",
    "next_state_mean, next_state_covariance = kf.filter_update(\n",
    "    filtered_state_mean=last_state_mean,\n",
    "    filtered_state_covariance=last_state_covariance\n",
    ")\n",
    "\n",
    "print(f\"Estimate for the next value: {next_state_mean[0]}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "73da180b-a0a8-488c-b377-fc4591ea0e61",
   "metadata": {},
   "source": [
    "# Buy / sell signals for the Dax"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "id": "69aa7d54-7d05-4a82-9d4a-4269023fb7cd",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import talib as ta"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "id": "d5c705f3-7041-42d6-88c0-1ecdf7882d73",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "open                   float64\n",
      "high                   float64\n",
      "low                    float64\n",
      "close                  float64\n",
      "volume                 float64\n",
      "vwap                   float64\n",
      "adj_close              float64\n",
      "unadjusted_volume      float64\n",
      "change                 float64\n",
      "change_percent         float64\n",
      "KalmanEstimate         float64\n",
      "LorentzianIndicator    float64\n",
      "REXPDE~TUSD            float64\n",
      "CPIDE                  float64\n",
      "dtype: object\n"
     ]
    }
   ],
   "source": [
    "df = combined_df.iloc[-365:].copy()\n",
    "print(df.dtypes)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "id": "47c6f776-ce9f-484c-90b9-8aa2a38b87fe",
   "metadata": {},
   "outputs": [],
   "source": [
    "def ma(ma_type, src, length, volume=None):\n",
    "    if ma_type == 'SMA':\n",
    "        return ta.SMA(src, timeperiod=length)\n",
    "    elif ma_type == 'EMA':\n",
    "        return ta.EMA(src, timeperiod=length)\n",
    "    elif ma_type == 'DEMA':\n",
    "        return ta.DEMA(src, timeperiod=length)\n",
    "    elif ma_type == 'TEMA':\n",
    "        return ta.TEMA(src, timeperiod=length)\n",
    "    elif ma_type == 'WMA':\n",
    "        return ta.WMA(src, timeperiod=length)\n",
    "    elif ma_type == 'VWMA':\n",
    "        if volume is None:\n",
    "            raise ValueError(\"Volume data is required for VWMA\")\n",
    "        return ta.SMA(src * volume, timeperiod=length) / ta.SMA(volume, timeperiod=length)\n",
    "    elif ma_type == 'SMMA':\n",
    "        return ta.SMA(src, timeperiod=length)\n",
    "    elif ma_type == 'HMA':\n",
    "        return ta.WMA(2 * ta.WMA(src, timeperiod=length // 2) - ta.WMA(src, timeperiod=length), timeperiod=int(np.sqrt(length)))\n",
    "    elif ma_type == 'LSMA':\n",
    "        return ta.LINEARREG(src, timeperiod=length)\n",
    "    elif ma_type == 'PEMA':\n",
    "        ema1 = ta.EMA(src, timeperiod=length)\n",
    "        ema2 = ta.EMA(ema1, timeperiod=length)\n",
    "        ema3 = ta.EMA(ema2, timeperiod=length)\n",
    "        ema4 = ta.EMA(ema3, timeperiod=length)\n",
    "        ema5 = ta.EMA(ema4, timeperiod=length)\n",
    "        ema6 = ta.EMA(ema5, timeperiod=length)\n",
    "        ema7 = ta.EMA(ema6, timeperiod=length)\n",
    "        ema8 = ta.EMA(ema7, timeperiod=length)\n",
    "        return 8 * ema1 - 28 * ema2 + 56 * ema3 - 70 * ema4 + 56 * ema5 - 28 * ema6 + 8 * ema7 - ema8\n",
    "    else:\n",
    "        raise ValueError(f\"Unknown MA Type: {ma_type}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "id": "e9289c53-f7b6-44ef-a10b-6f9a5ab4eb53",
   "metadata": {},
   "outputs": [],
   "source": [
    "def kalman_filter(df, sharpness, k_period):\n",
    "    df.loc[:, 'kfilt'] = 0.0\n",
    "    df.loc[:, 'velocity'] = 0.0\n",
    "    for i in range(1, len(df)):\n",
    "        distance = df['close'].iloc[i] - df['kfilt'].iloc[i - 1]\n",
    "        error = df['kfilt'].iloc[i - 1] + distance * np.sqrt(sharpness * k_period / 100)\n",
    "        velocity = df['velocity'].iloc[i - 1] + distance * k_period / 100\n",
    "        df.loc[df.index[i], 'kfilt'] = error + velocity\n",
    "        df.loc[df.index[i], 'velocity'] = velocity\n",
    "    return df['kfilt']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "id": "8bc4ef6b-bc67-4735-8e1e-042f7782cdad",
   "metadata": {},
   "outputs": [],
   "source": [
    "def calculate_roc(df, length):\n",
    "    return ta.ROC(df['kfilt'], timeperiod=length)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "id": "1002d3da-c2c9-4c79-8f73-b9bfecb9df0f",
   "metadata": {},
   "outputs": [],
   "source": [
    "def calculate_stochastic(df, periodK, smoothK, periodD):\n",
    "    k, d = ta.STOCH(df['high'], df['low'], df['close'],\n",
    "                    fastk_period=periodK,\n",
    "                    slowk_period=smoothK, \n",
    "                    slowk_matype=0,\n",
    "                    slowd_period=periodD,\n",
    "                    slowd_matype=0)\n",
    "    return k, d\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "id": "a86a97b2-0327-4717-b0f1-8bcd32d5624d",
   "metadata": {},
   "outputs": [],
   "source": [
    "def calculate_blend(df, ma_type, smoothlen):\n",
    "    roc = calculate_roc(df, length=9)  # Assuming length=9\n",
    "    _, d = calculate_stochastic(df, periodK=14, smoothK=1, periodD=3)  # Default values\n",
    "    blend = (roc + d) / 2\n",
    "    blend = ma(ma_type, blend, smoothlen)\n",
    "    blend = blend.dropna()  # Drop NaN values that could arise from insufficient data\n",
    "    return blend\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "id": "03c913a3-453b-401c-87b8-a0bf41fa66a7",
   "metadata": {},
   "outputs": [],
   "source": [
    "def generate_signals(df, blend):\n",
    "    df.loc[:, 'buy_signal'] = np.where((blend > blend.shift(1)) & (blend.shift(1) <= blend.shift(2)), 1, 0)\n",
    "    df.loc[:, 'sell_signal'] = np.where((blend < blend.shift(1)) & (blend.shift(1) >= blend.shift(2)), 1, 0)\n",
    "    return df\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "id": "95208370-2aec-4a2e-8424-6fe5a80a06c9",
   "metadata": {},
   "outputs": [],
   "source": [
    "def analyze(df, ma_type='TEMA', smoothlen=12, sharpness=25.0, k_period=1.0):\n",
    "    df['kfilt'] = kalman_filter(df, sharpness, k_period)\n",
    "    blend = calculate_blend(df, ma_type, smoothlen)\n",
    "    df['blend'] = blend\n",
    "    df = generate_signals(df, blend)\n",
    "    df = df.dropna(subset=['blend'])  # Drop rows where 'blend' is NaN\n",
    "    return df[['buy_signal', 'sell_signal']]  # Return only the buy and sell signals\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "id": "a35b5788-c54e-419c-9327-fce714d5ca6b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                    open          high           low         close  \\\n",
      "2023-08-19  15597.120000  15612.880000  15468.650000  15574.260000   \n",
      "2023-08-20  15597.120000  15612.880000  15468.650000  15574.260000   \n",
      "2023-08-21  15573.950000  15710.210000  15571.820000  15603.280000   \n",
      "2023-08-22  15684.640000  15799.340000  15677.440000  15705.620000   \n",
      "2023-08-23  15761.250000  15820.950000  15672.430000  15728.410000   \n",
      "...                  ...           ...           ...           ...   \n",
      "2024-08-26  18470.006463  18582.574769  18450.405407  18537.821152   \n",
      "2024-08-27  18470.149396  18582.209304  18451.668874  18537.641506   \n",
      "2024-08-28  18470.316624  18582.192740  18451.403439  18537.936833   \n",
      "2024-08-29  18470.645896  18583.090019  18451.158720  18538.491185   \n",
      "2024-08-30  18471.650998  18582.831214  18451.680800  18538.901426   \n",
      "\n",
      "                  volume          vwap     adj_close  unadjusted_volume  \\\n",
      "2023-08-19  5.990950e+07  15563.230000  15574.260000       5.990950e+07   \n",
      "2023-08-20  5.990950e+07  15563.230000  15574.260000       5.990950e+07   \n",
      "2023-08-21  4.525110e+07  15614.820000  15603.280000       4.525110e+07   \n",
      "2023-08-22  4.280740e+07  15727.470000  15705.620000       4.280740e+07   \n",
      "2023-08-23  5.342540e+07  15745.760000  15728.410000       5.342540e+07   \n",
      "...                  ...           ...           ...                ...   \n",
      "2024-08-26  4.042621e+07  18509.926913  18536.299033       4.042621e+07   \n",
      "2024-08-27  4.042621e+07  18509.730243  18536.621775       4.042621e+07   \n",
      "2024-08-28  4.042621e+07  18509.744616  18537.703402       4.042621e+07   \n",
      "2024-08-29  4.042621e+07  18510.383842  18537.549513       4.042621e+07   \n",
      "2024-08-30  4.042621e+07  18510.479391  18537.659088       4.042621e+07   \n",
      "\n",
      "               change  change_percent  KalmanEstimate  LorentzianIndicator  \\\n",
      "2023-08-19 -22.860000       -0.001466    15906.008411             5.249547   \n",
      "2023-08-20 -22.860000       -0.001466    15906.008411             5.249547   \n",
      "2023-08-21  29.330000        0.001883    15877.211395             5.223702   \n",
      "2023-08-22  20.980000        0.001338    15860.888777             5.188841   \n",
      "2023-08-23 -32.840000       -0.002084    15848.286743             5.163152   \n",
      "...               ...             ...             ...                  ...   \n",
      "2024-08-26  66.122684        0.974946    18160.294818             6.115079   \n",
      "2024-08-27  66.189169        0.942071    18159.784202             6.065491   \n",
      "2024-08-28  65.839109        0.664471    18160.138380             6.988810   \n",
      "2024-08-29  66.436632        1.605050    18160.260280             6.453768   \n",
      "2024-08-30  65.675039        0.881043    18159.978241             5.691005   \n",
      "\n",
      "              REXPDE~TUSD       CPIDE         kfilt    velocity      blend  \\\n",
      "2023-08-19  441556.000000  117.500000      0.000000    0.000000        NaN   \n",
      "2023-08-20  441556.000000  117.500000   7942.872600  155.742600        NaN   \n",
      "2023-08-21  441556.000000  117.500000  12005.422974  232.346674        NaN   \n",
      "2023-08-22  441556.000000  117.500000  14124.870131  269.348644        NaN   \n",
      "2023-08-23  441556.000000  117.500000  15212.024109  285.384043        NaN   \n",
      "...                   ...         ...           ...         ...        ...   \n",
      "2024-08-26  441754.098877  120.530011  18551.078791    6.902298  53.161177   \n",
      "2024-08-27  441753.978259  120.758387  18551.128073    6.767925  51.591696   \n",
      "2024-08-28  441754.154286  121.043271  18551.168466    6.636013  49.939906   \n",
      "2024-08-29  441753.528517  121.267125  18551.339065    6.509240  48.342753   \n",
      "2024-08-30  441754.250399  121.588486  18551.505109    6.384863  46.728891   \n",
      "\n",
      "            buy_signal  sell_signal  \n",
      "2023-08-19           0            0  \n",
      "2023-08-20           0            0  \n",
      "2023-08-21           0            0  \n",
      "2023-08-22           0            0  \n",
      "2023-08-23           0            0  \n",
      "...                ...          ...  \n",
      "2024-08-26           0            0  \n",
      "2024-08-27           0            0  \n",
      "2024-08-28           0            0  \n",
      "2024-08-29           0            0  \n",
      "2024-08-30           0            0  \n",
      "\n",
      "[365 rows x 19 columns]\n"
     ]
    }
   ],
   "source": [
    "# Analyze and print only the buy and sell signals\n",
    "signal_df = analyze(df)\n",
    "print(signal_df)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "id": "1340b1ea-482a-49aa-b259-58449490c862",
   "metadata": {},
   "outputs": [],
   "source": [
    "import plotly.graph_objs as go\n",
    "import plotly.express as px\n",
    "\n",
    "# Example: Assuming 'df' is the DataFrame after analysis and contains the 'buy_signal' and 'sell_signal' columns\n",
    "# and 'close' column to plot the price\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "id": "c3931367-6cb3-43c6-a65f-57a713bbc511",
   "metadata": {},
   "outputs": [
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   "source": [
    "def plot_signals(df):\n",
    "    # Create a candlestick chart\n",
    "    fig = go.Figure(data=[go.Candlestick(x=df.index,\n",
    "                                         open=df['open'],\n",
    "                                         high=df['high'],\n",
    "                                         low=df['low'],\n",
    "                                         close=df['close'])])\n",
    "\n",
    "    # Add Buy signals as green up arrows\n",
    "    buy_signals = df[df['buy_signal'] == 1]\n",
    "    fig.add_trace(go.Scatter(x=buy_signals.index,\n",
    "                             y=buy_signals['close'],\n",
    "                             mode='markers',\n",
    "                             marker=dict(symbol='triangle-up', color='green', size=10),\n",
    "                             name='Buy Signal'))\n",
    "\n",
    "    # Add Sell signals as red down arrows\n",
    "    sell_signals = df[df['sell_signal'] == 1]\n",
    "    fig.add_trace(go.Scatter(x=sell_signals.index,\n",
    "                             y=sell_signals['close'],\n",
    "                             mode='markers',\n",
    "                             marker=dict(symbol='triangle-down', color='red', size=10),\n",
    "                             name='Sell Signal'))\n",
    "\n",
    "    # Update layout for better visuals\n",
    "    fig.update_layout(title='Buy and Sell Signals',\n",
    "                      xaxis_title='Date',\n",
    "                      yaxis_title='Price',\n",
    "                      xaxis_rangeslider_visible=False)\n",
    "\n",
    "    # Show the plot\n",
    "    fig.show()\n",
    "\n",
    "# Assuming signal_df contains the 'buy_signal' and 'sell_signal' columns along with price data\n",
    "plot_signals(df)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "id": "80903fc5-738c-4258-8fe4-b3c6ac1925fa",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_signal_list(df):\n",
    "    # Create an empty list to store the signals\n",
    "    signals = []\n",
    "    \n",
    "    # Iterate through the DataFrame and check for buy and sell signals\n",
    "    for index, row in df.iterrows():\n",
    "        if row['buy_signal'] == 1:\n",
    "            signals.append({'Date': index, 'Signal': 'Buy', 'Price': row['close']})\n",
    "        elif row['sell_signal'] == 1:\n",
    "            signals.append({'Date': index, 'Signal': 'Sell', 'Price': row['close']})\n",
    "    \n",
    "    return signals\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "id": "b0ac3c11-8f1d-4a08-bc9f-8d35732abc7e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Date: 2023-10-14 00:00:00, Signal: Sell, Price: 15186.66016\n",
      "Date: 2023-10-24 00:00:00, Signal: Buy, Price: 14879.94043\n",
      "Date: 2023-10-28 00:00:00, Signal: Sell, Price: 14687.41016\n",
      "Date: 2023-10-30 00:00:00, Signal: Buy, Price: 14716.54004\n",
      "Date: 2023-11-11 00:00:00, Signal: Sell, Price: 15234.38965\n",
      "Date: 2023-11-14 00:00:00, Signal: Buy, Price: 15614.42969\n",
      "Date: 2023-11-18 00:00:00, Signal: Sell, Price: 15919.16016\n",
      "Date: 2023-11-19 00:00:00, Signal: Buy, Price: 15919.16016\n",
      "Date: 2023-11-20 00:00:00, Signal: Sell, Price: 15901.33008\n",
      "Date: 2023-11-25 00:00:00, Signal: Buy, Price: 16029.49023\n",
      "Date: 2023-11-26 00:00:00, Signal: Sell, Price: 16029.49023\n",
      "Date: 2023-12-01 00:00:00, Signal: Buy, Price: 16397.51953\n",
      "Date: 2023-12-06 00:00:00, Signal: Sell, Price: 16656.43945\n",
      "Date: 2023-12-09 00:00:00, Signal: Buy, Price: 16759.2207\n",
      "Date: 2023-12-13 00:00:00, Signal: Sell, Price: 16766.05078\n",
      "Date: 2023-12-27 00:00:00, Signal: Buy, Price: 16742.07031\n",
      "Date: 2024-01-03 00:00:00, Signal: Sell, Price: 16538.39062\n",
      "Date: 2024-01-08 00:00:00, Signal: Buy, Price: 16716.4707\n",
      "Date: 2024-01-11 00:00:00, Signal: Sell, Price: 16547.0293\n",
      "Date: 2024-01-12 00:00:00, Signal: Buy, Price: 16704.56055\n",
      "Date: 2024-01-16 00:00:00, Signal: Sell, Price: 16571.67969\n",
      "Date: 2024-01-20 00:00:00, Signal: Buy, Price: 16555.13086\n",
      "Date: 2024-01-31 00:00:00, Signal: Sell, Price: 16903.75977\n",
      "Date: 2024-02-12 00:00:00, Signal: Buy, Price: 17037.34961\n",
      "Date: 2024-02-13 00:00:00, Signal: Sell, Price: 16880.83008\n",
      "Date: 2024-02-14 00:00:00, Signal: Buy, Price: 16945.48047\n",
      "Date: 2024-02-15 00:00:00, Signal: Sell, Price: 17046.68945\n",
      "Date: 2024-02-16 00:00:00, Signal: Buy, Price: 17117.43945\n",
      "Date: 2024-03-01 00:00:00, Signal: Sell, Price: 17735.07031\n",
      "Date: 2024-03-09 00:00:00, Signal: Buy, Price: 17814.50977\n",
      "Date: 2024-03-10 00:00:00, Signal: Sell, Price: 17814.50977\n",
      "Date: 2024-03-13 00:00:00, Signal: Buy, Price: 17961.38086\n",
      "Date: 2024-03-15 00:00:00, Signal: Sell, Price: 17936.65039\n",
      "Date: 2024-03-19 00:00:00, Signal: Buy, Price: 17987.49023\n",
      "Date: 2024-03-31 00:00:00, Signal: Sell, Price: 18492.49023\n",
      "Date: 2024-04-15 00:00:00, Signal: Buy, Price: 18026.58008\n",
      "Date: 2024-04-30 00:00:00, Signal: Sell, Price: 17932.16992\n",
      "Date: 2024-05-06 00:00:00, Signal: Buy, Price: 18175.21094\n",
      "Date: 2024-05-14 00:00:00, Signal: Sell, Price: 18716.41992\n",
      "Date: 2024-05-27 00:00:00, Signal: Buy, Price: 18774.71094\n",
      "Date: 2024-05-29 00:00:00, Signal: Sell, Price: 18473.28906\n",
      "Date: 2024-06-03 00:00:00, Signal: Buy, Price: 18608.16016\n",
      "Date: 2024-06-10 00:00:00, Signal: Sell, Price: 18494.89062\n",
      "Date: 2024-06-12 00:00:00, Signal: Buy, Price: 18630.85938\n",
      "Date: 2024-06-13 00:00:00, Signal: Sell, Price: 18265.67969\n",
      "Date: 2024-06-19 00:00:00, Signal: Buy, Price: 18067.91016\n",
      "Date: 2024-07-07 00:00:00, Signal: Sell, Price: 18472.05078\n",
      "Date: 2024-07-12 00:00:00, Signal: Buy, Price: 18748.17969\n",
      "Date: 2024-07-17 00:00:00, Signal: Sell, Price: 18437.30078\n",
      "Date: 2024-07-24 00:00:00, Signal: Buy, Price: 18387.46094\n",
      "Date: 2024-07-25 00:00:00, Signal: Sell, Price: 18298.7207\n",
      "Date: 2024-07-30 00:00:00, Signal: Buy, Price: 18411.17969\n",
      "Date: 2024-08-01 00:00:00, Signal: Sell, Price: 18083.05078\n",
      "Date: 2024-08-07 00:00:00, Signal: Buy, Price: 17615.15039\n",
      "Date: 2024-08-25 00:00:00, Signal: Sell, Price: 18537.529187856657\n"
     ]
    }
   ],
   "source": [
    "# Assuming df contains the 'buy_signal' and 'sell_signal' columns\n",
    "signal_list = get_signal_list(df)\n",
    "\n",
    "# Display the signal list\n",
    "for signal in signal_list:\n",
    "    print(f\"Date: {signal['Date']}, Signal: {signal['Signal']}, Price: {signal['Price']}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fb0458cb-fc41-4c57-b2d9-b7d870463efd",
   "metadata": {},
   "source": [
    "## Backtest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "id": "f5620cc4-3d06-453d-beb4-d015bb95e0f4",
   "metadata": {},
   "outputs": [],
   "source": [
    "initial_depot = 10000  # Initial depot in Euros\n",
    "depot = initial_depot  # Current depot value\n",
    "position_size_percentage = 0.03   # 3% of the depot for each trade\n",
    "stop_loss_points = 100  # Stop loss of 100 points\n",
    "dax_data = df['close']  # Assuming 'close' is the DAX price column\n",
    "\n",
    "# Generate the signal list as in the previous steps\n",
    "signal_list = get_signal_list(df)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "id": "061a831e-ba43-48c6-bd4f-b5cb51e714e2",
   "metadata": {},
   "outputs": [],
   "source": [
    "from datetime import timedelta\n",
    "\n",
    "# Initialize variables\n",
    "current_position = None  # Track whether in a buy or sell position\n",
    "entry_price = None  # Track the price at which the position was entered\n",
    "entry_date = None  # Track the date at which the position was entered\n",
    "results = []  # Store results of each trade\n",
    "\n",
    "for signal in signal_list:\n",
    "    signal_date = signal['Date']\n",
    "    signal_type = signal['Signal']\n",
    "    signal_price = signal['Price']\n",
    "    \n",
    "    if signal_type == 'Buy':\n",
    "        if current_position is None:  # Enter a new long position\n",
    "            position_size = depot * position_size_percentage\n",
    "            entry_price = signal_price\n",
    "            entry_date = signal_date\n",
    "            current_position = 'long'\n",
    "            results.append({\n",
    "                'Date': signal_date,\n",
    "                'Action': 'Buy',\n",
    "                'Entry Price': entry_price,\n",
    "                'Position Size': position_size,\n",
    "                'Depot': depot\n",
    "            })\n",
    "    \n",
    "    elif signal_type == 'Sell':\n",
    "        if current_position == 'long':  # Exit the long position\n",
    "            profit = (signal_price - entry_price) * (position_size / entry_price)\n",
    "            depot += profit\n",
    "            results.append({\n",
    "                'Date': signal_date,\n",
    "                'Action': 'Sell',\n",
    "                'Exit Price': signal_price,\n",
    "                'Profit': profit,\n",
    "                'Depot': depot\n",
    "            })\n",
    "            current_position = None\n",
    "    \n",
    "    # Implement stop loss logic\n",
    "    if current_position == 'long' and signal_price <= entry_price - stop_loss_points:\n",
    "        # Sell the position if stop loss is hit\n",
    "        loss = (entry_price - (entry_price - stop_loss_points)) * (position_size / entry_price)\n",
    "        depot -= loss\n",
    "        results.append({\n",
    "            'Date': signal_date,\n",
    "            'Action': 'Stop Loss Triggered',\n",
    "            'Exit Price': entry_price - stop_loss_points,\n",
    "            'Loss': loss,\n",
    "            'Depot': depot\n",
    "        })\n",
    "        current_position = None\n",
    "    \n",
    "    # Automatically close the position after 2 days\n",
    "    if current_position == 'long' and (signal_date - entry_date).days >= 2:\n",
    "        profit = (signal_price - entry_price) * (position_size / entry_price)\n",
    "        depot += profit\n",
    "        results.append({\n",
    "            'Date': signal_date,\n",
    "            'Action': 'Auto Close After 2 Days',\n",
    "            'Exit Price': signal_price,\n",
    "            'Profit': profit,\n",
    "            'Depot': depot\n",
    "        })\n",
    "        current_position = None\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "id": "fc54c9b2-c8ad-4698-b3fc-d539ef847901",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Date: 2023-10-24 00:00:00, Action: Buy, Entry Price: 14879.94043, Exit Price: N/A, Profit/Loss: N/A, Depot: 10000.00\n",
      "Date: 2023-10-28 00:00:00, Action: Sell, Entry Price: N/A, Exit Price: 14687.41016, Profit/Loss: -3.88, Depot: 9996.12\n",
      "Date: 2023-10-30 00:00:00, Action: Buy, Entry Price: 14716.54004, Exit Price: N/A, Profit/Loss: N/A, Depot: 9996.12\n",
      "Date: 2023-11-11 00:00:00, Action: Sell, Entry Price: N/A, Exit Price: 15234.38965, Profit/Loss: 10.55, Depot: 10006.67\n",
      "Date: 2023-11-14 00:00:00, Action: Buy, Entry Price: 15614.42969, Exit Price: N/A, Profit/Loss: N/A, Depot: 10006.67\n",
      "Date: 2023-11-18 00:00:00, Action: Sell, Entry Price: N/A, Exit Price: 15919.16016, Profit/Loss: 5.86, Depot: 10012.53\n",
      "Date: 2023-11-19 00:00:00, Action: Buy, Entry Price: 15919.16016, Exit Price: N/A, Profit/Loss: N/A, Depot: 10012.53\n",
      "Date: 2023-11-20 00:00:00, Action: Sell, Entry Price: N/A, Exit Price: 15901.33008, Profit/Loss: -0.34, Depot: 10012.19\n",
      "Date: 2023-11-25 00:00:00, Action: Buy, Entry Price: 16029.49023, Exit Price: N/A, Profit/Loss: N/A, Depot: 10012.19\n",
      "Date: 2023-11-26 00:00:00, Action: Sell, Entry Price: N/A, Exit Price: 16029.49023, Profit/Loss: 0.00, Depot: 10012.19\n",
      "Date: 2023-12-01 00:00:00, Action: Buy, Entry Price: 16397.51953, Exit Price: N/A, Profit/Loss: N/A, Depot: 10012.19\n",
      "Date: 2023-12-06 00:00:00, Action: Sell, Entry Price: N/A, Exit Price: 16656.43945, Profit/Loss: 4.74, Depot: 10016.94\n",
      "Date: 2023-12-09 00:00:00, Action: Buy, Entry Price: 16759.2207, Exit Price: N/A, Profit/Loss: N/A, Depot: 10016.94\n",
      "Date: 2023-12-13 00:00:00, Action: Sell, Entry Price: N/A, Exit Price: 16766.05078, Profit/Loss: 0.12, Depot: 10017.06\n",
      "Date: 2023-12-27 00:00:00, Action: Buy, Entry Price: 16742.07031, Exit Price: N/A, Profit/Loss: N/A, Depot: 10017.06\n",
      "Date: 2024-01-03 00:00:00, Action: Sell, Entry Price: N/A, Exit Price: 16538.39062, Profit/Loss: -3.66, Depot: 10013.40\n",
      "Date: 2024-01-08 00:00:00, Action: Buy, Entry Price: 16716.4707, Exit Price: N/A, Profit/Loss: N/A, Depot: 10013.40\n",
      "Date: 2024-01-11 00:00:00, Action: Sell, Entry Price: N/A, Exit Price: 16547.0293, Profit/Loss: -3.04, Depot: 10010.36\n",
      "Date: 2024-01-12 00:00:00, Action: Buy, Entry Price: 16704.56055, Exit Price: N/A, Profit/Loss: N/A, Depot: 10010.36\n",
      "Date: 2024-01-16 00:00:00, Action: Sell, Entry Price: N/A, Exit Price: 16571.67969, Profit/Loss: -2.39, Depot: 10007.97\n",
      "Date: 2024-01-20 00:00:00, Action: Buy, Entry Price: 16555.13086, Exit Price: N/A, Profit/Loss: N/A, Depot: 10007.97\n",
      "Date: 2024-01-31 00:00:00, Action: Sell, Entry Price: N/A, Exit Price: 16903.75977, Profit/Loss: 6.32, Depot: 10014.29\n",
      "Date: 2024-02-12 00:00:00, Action: Buy, Entry Price: 17037.34961, Exit Price: N/A, Profit/Loss: N/A, Depot: 10014.29\n",
      "Date: 2024-02-13 00:00:00, Action: Sell, Entry Price: N/A, Exit Price: 16880.83008, Profit/Loss: -2.76, Depot: 10011.53\n",
      "Date: 2024-02-14 00:00:00, Action: Buy, Entry Price: 16945.48047, Exit Price: N/A, Profit/Loss: N/A, Depot: 10011.53\n",
      "Date: 2024-02-15 00:00:00, Action: Sell, Entry Price: N/A, Exit Price: 17046.68945, Profit/Loss: 1.79, Depot: 10013.32\n",
      "Date: 2024-02-16 00:00:00, Action: Buy, Entry Price: 17117.43945, Exit Price: N/A, Profit/Loss: N/A, Depot: 10013.32\n",
      "Date: 2024-03-01 00:00:00, Action: Sell, Entry Price: N/A, Exit Price: 17735.07031, Profit/Loss: 10.84, Depot: 10024.16\n",
      "Date: 2024-03-09 00:00:00, Action: Buy, Entry Price: 17814.50977, Exit Price: N/A, Profit/Loss: N/A, Depot: 10024.16\n",
      "Date: 2024-03-10 00:00:00, Action: Sell, Entry Price: N/A, Exit Price: 17814.50977, Profit/Loss: 0.00, Depot: 10024.16\n",
      "Date: 2024-03-13 00:00:00, Action: Buy, Entry Price: 17961.38086, Exit Price: N/A, Profit/Loss: N/A, Depot: 10024.16\n",
      "Date: 2024-03-15 00:00:00, Action: Sell, Entry Price: N/A, Exit Price: 17936.65039, Profit/Loss: -0.41, Depot: 10023.75\n",
      "Date: 2024-03-19 00:00:00, Action: Buy, Entry Price: 17987.49023, Exit Price: N/A, Profit/Loss: N/A, Depot: 10023.75\n",
      "Date: 2024-03-31 00:00:00, Action: Sell, Entry Price: N/A, Exit Price: 18492.49023, Profit/Loss: 8.44, Depot: 10032.19\n",
      "Date: 2024-04-15 00:00:00, Action: Buy, Entry Price: 18026.58008, Exit Price: N/A, Profit/Loss: N/A, Depot: 10032.19\n",
      "Date: 2024-04-30 00:00:00, Action: Sell, Entry Price: N/A, Exit Price: 17932.16992, Profit/Loss: -1.58, Depot: 10030.62\n",
      "Date: 2024-05-06 00:00:00, Action: Buy, Entry Price: 18175.21094, Exit Price: N/A, Profit/Loss: N/A, Depot: 10030.62\n",
      "Date: 2024-05-14 00:00:00, Action: Sell, Entry Price: N/A, Exit Price: 18716.41992, Profit/Loss: 8.96, Depot: 10039.58\n",
      "Date: 2024-05-27 00:00:00, Action: Buy, Entry Price: 18774.71094, Exit Price: N/A, Profit/Loss: N/A, Depot: 10039.58\n",
      "Date: 2024-05-29 00:00:00, Action: Sell, Entry Price: N/A, Exit Price: 18473.28906, Profit/Loss: -4.84, Depot: 10034.74\n",
      "Date: 2024-06-03 00:00:00, Action: Buy, Entry Price: 18608.16016, Exit Price: N/A, Profit/Loss: N/A, Depot: 10034.74\n",
      "Date: 2024-06-10 00:00:00, Action: Sell, Entry Price: N/A, Exit Price: 18494.89062, Profit/Loss: -1.83, Depot: 10032.91\n",
      "Date: 2024-06-12 00:00:00, Action: Buy, Entry Price: 18630.85938, Exit Price: N/A, Profit/Loss: N/A, Depot: 10032.91\n",
      "Date: 2024-06-13 00:00:00, Action: Sell, Entry Price: N/A, Exit Price: 18265.67969, Profit/Loss: -5.90, Depot: 10027.01\n",
      "Date: 2024-06-19 00:00:00, Action: Buy, Entry Price: 18067.91016, Exit Price: N/A, Profit/Loss: N/A, Depot: 10027.01\n",
      "Date: 2024-07-07 00:00:00, Action: Sell, Entry Price: N/A, Exit Price: 18472.05078, Profit/Loss: 6.73, Depot: 10033.74\n",
      "Date: 2024-07-12 00:00:00, Action: Buy, Entry Price: 18748.17969, Exit Price: N/A, Profit/Loss: N/A, Depot: 10033.74\n",
      "Date: 2024-07-17 00:00:00, Action: Sell, Entry Price: N/A, Exit Price: 18437.30078, Profit/Loss: -4.99, Depot: 10028.75\n",
      "Date: 2024-07-24 00:00:00, Action: Buy, Entry Price: 18387.46094, Exit Price: N/A, Profit/Loss: N/A, Depot: 10028.75\n",
      "Date: 2024-07-25 00:00:00, Action: Sell, Entry Price: N/A, Exit Price: 18298.7207, Profit/Loss: -1.45, Depot: 10027.29\n",
      "Date: 2024-07-30 00:00:00, Action: Buy, Entry Price: 18411.17969, Exit Price: N/A, Profit/Loss: N/A, Depot: 10027.29\n",
      "Date: 2024-08-01 00:00:00, Action: Sell, Entry Price: N/A, Exit Price: 18083.05078, Profit/Loss: -5.36, Depot: 10021.93\n",
      "Date: 2024-08-07 00:00:00, Action: Buy, Entry Price: 17615.15039, Exit Price: N/A, Profit/Loss: N/A, Depot: 10021.93\n",
      "Date: 2024-08-25 00:00:00, Action: Sell, Entry Price: N/A, Exit Price: 18537.529187856657, Profit/Loss: 15.74, Depot: 10037.68\n",
      "\n",
      "Final Depot Value: 10037.68 Euros\n"
     ]
    }
   ],
   "source": [
    "# Display the results\n",
    "for trade in results:\n",
    "    profit_loss = trade.get('Profit', trade.get('Loss', 'N/A'))\n",
    "    # Only format profit_loss if it's a number\n",
    "    if isinstance(profit_loss, (int, float)):\n",
    "        profit_loss = f\"{profit_loss:.2f}\"\n",
    "    \n",
    "    print(f\"Date: {trade['Date']}, Action: {trade['Action']}, \"\n",
    "          f\"Entry Price: {trade.get('Entry Price', 'N/A')}, \"\n",
    "          f\"Exit Price: {trade.get('Exit Price', 'N/A')}, \"\n",
    "          f\"Profit/Loss: {profit_loss}, \"\n",
    "          f\"Depot: {trade['Depot']:.2f}\")\n",
    "\n",
    "# Print the final depot value\n",
    "print(f\"\\nFinal Depot Value: {depot:.2f} Euros\")\n"
   ]
  }
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