{
 "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",
    "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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      "              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": "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",
       "  overflow-x: auto;\n",
       "}\n",
       "#h2o-table-13 .h2o-table {\n",
       "  /* width: 100%; */\n",
       "  margin-top: 1em;\n",
       "  margin-bottom: 1em;\n",
       "}\n",
       "#h2o-table-13 .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-13 .h2o-table thead {\n",
       "  white-space: nowrap; \n",
       "  position: sticky;\n",
       "  top: 0;\n",
       "  box-shadow: 0 -1px inset;\n",
       "}\n",
       "#h2o-table-13 .h2o-table tbody {\n",
       "  overflow: auto;\n",
       "}\n",
       "#h2o-table-13 .h2o-table th,\n",
       "#h2o-table-13 .h2o-table td {\n",
       "  text-align: right;\n",
       "  /* border: 1px solid; */\n",
       "}\n",
       "#h2o-table-13 .h2o-table tr:nth-child(even) {\n",
       "  /* background: #F5F5F5 */\n",
       "}\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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