Created using Colaboratory

stock_predictor.ipynb

@@ -4,10 +4,7 @@
   "metadata": {
     "colab": {
       "provenance": [],
-      "collapsed_sections": [
-        "Z3N2WMYNV-qX"
-      ],
-      "authorship_tag": "ABX9TyOuk8MIfThoeWnRbBQlPf+h",
+      "authorship_tag": "ABX9TyOc5/oQ0Z2ie5dOI46PpyV0",
       "include_colab_link": true
     },
     "kernelspec": {
@@ -17,7 +14,8 @@
     "language_info": {
       "name": "python"
     },
-    "gpuClass": "standard"
+    "gpuClass": "standard",
+    "accelerator": "GPU"
   },
   "cells": [
     {
@@ -43,15 +41,15 @@
           "base_uri": "https://localhost:8080/"
         },
         "id": "Xr3Qozgfktoc",
-        "outputId": "e80033fb-a41f-438f-fc90-60bc0317d5d3"
+        "outputId": "b4ce9a19-4dc1-43e9-b09e-af91a2b07343"
       },
-      "execution_count": 2,
+      "execution_count": 90,
       "outputs": [
         {
           "output_type": "stream",
           "name": "stdout",
           "text": [
-            "Mounted at /content/drive\n",
+            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n",
             "/content/drive/MyDrive/projects/Stock_Predicter\n"
           ]
         }
@@ -59,7 +57,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 3,
+      "execution_count": 91,
       "metadata": {
         "id": "e8SQqogMQYLh"
       },
@@ -101,7 +99,7 @@
       "metadata": {
         "id": "O6dtJpJwS5Eg"
       },
-      "execution_count": 93,
+      "execution_count": 92,
       "outputs": []
     },
     {
@@ -115,9 +113,9 @@
           "base_uri": "https://localhost:8080/"
         },
         "id": "LwPyk8Uh-Zz_",
-        "outputId": "63953807-ca2e-4e18-c571-a6bcc4f8db5d"
+        "outputId": "2217df50-87e9-48e3-e096-71163331f570"
       },
-      "execution_count": 5,
+      "execution_count": 93,
       "outputs": [
         {
           "output_type": "stream",
@@ -140,136 +138,201 @@
     {
       "cell_type": "code",
       "source": [
-        "def normalize_data(data, relative_to_previous=True, scaler=None):\n",
-        "  def substract_to_values(data, value):\n",
-        "    df_copy = pd.DataFrame.copy(data)\n",
-        "    df_copy[['Open', 'High', 'Low', 'Close', 'Adj Close']] = df_copy[['Open', 'High', 'Low', 'Close', 'Adj Close']] - value\n",
-        "    return df_copy\n",
-        "  if relative_to_previous:\n",
-        "    the_data = pd.DataFrame(substract_to_values(data.iloc[0], data.iloc[0]['Open'])).T\n",
-        "    # the_data = substract_to_values(data.iloc[0], data.iloc[0]['Open']).to_frame().T # This is the same as the previous line\n",
-        "    for i in range(1,len(data)):\n",
-        "      the_data = pd.concat((the_data, substract_to_values(data.iloc[i], data.iloc[i-1]['Close']).to_frame().T))\n",
-        "  else:\n",
-        "    the_data = pd.DataFrame.copy(data)\n",
-        "  \n",
+        "def create_remove_columns(data):\n",
+        "  # create jump column\n",
+        "  data = pd.DataFrame.copy(data)\n",
+        "  data['Jump'] = data['Open'] - data['Close'].shift(1)\n",
+        "  data['Jump'].fillna(0, inplace=True)\n",
+        "  # data = data.reindex(columns=['Open', 'High', 'Low', 'Close', 'Adj Close', 'Jump'])\n",
+        "  data.insert(0,'Jump', data.pop('Jump'))\n",
+        "  return data"
+      ],
+      "metadata": {
+        "id": "Bpym8x-Kxf0p"
+      },
+      "execution_count": 94,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "def normalize_data(data, scaler=None):\n",
+        "  the_data = pd.DataFrame.copy(data)\n",
+        "  # substract the open value to all columns but the first one and the last one which are \"Jump\" and \"Volume\"\n",
+        "  the_data.iloc[:, 1:-1] = the_data.iloc[:,1:-1] - the_data['Open'].values[:, np.newaxis]\n",
+        "  # print('the_data')\n",
+        "  # print(the_data)\n",
+        "\n",
+        "  the_data.pop('Open')\n",
+        "  # todo save an csv with the values for the scaler\n",
         "  if scaler is None:\n",
         "    # Create the scaler\n",
-        "    values = the_data.values\n",
-        "    # print('values')\n",
-        "    # print(values)\n",
+        "    values = np.abs(the_data.values)\n",
         "    max_value = np.max(values[:,:-1])\n",
-        "    # print(max_value)\n",
-        "    min_value = np.min(values[:,:-1])\n",
-        "    # print(min_value)\n",
         "    max_volume = np.max(values[:,-1])\n",
-        "    min_volume = np.min(values[:,-1])\n",
-        "    # print(max_volume, min_volume)\n",
-        "    def scaler(data):\n",
-        "      values = data.values\n",
-        "      # print(values)\n",
-        "      values[:,:-1] = (values[:,:-1] - min_value) / (max_value-min_value) * 2 - 1\n",
-        "      values[:,-1] = (values[:,-1] - min_volume) / (max_volume-min_volume) * 2 - 1\n",
-        "      # print(values)\n",
+        "    def scaler(d):\n",
+        "      data = pd.DataFrame.copy(d)\n",
+        "      print('max_value: ', max_value)\n",
+        "      print('max_volume: ', max_volume)\n",
+        "      data.iloc[:, :-1] = data.iloc[:,:-1].apply(lambda x: x/max_value)\n",
+        "      data.iloc[:, -1] = data.iloc[:,-1].apply(lambda x: x/max_volume)\n",
         "      return data\n",
-        "    def anti_scaler(values):\n",
-        "      decoded_values = (values + 1) * (max_value-min_value) / 2 + min_value  \n",
+        "    def decoder(values):\n",
+        "      decoded_values = values * max_value\n",
         "      return decoded_values\n",
+        "  else:\n",
+        "    decoder = None\n",
         "  \n",
         "  normalized_data = scaler(the_data)\n",
         "\n",
-        "  return normalized_data, scaler, anti_scaler\n",
+        "  return normalized_data, scaler, decoder\n",
         "\n",
         "\n"
       ],
       "metadata": {
         "id": "v9RoqzBvtrOb"
       },
-      "execution_count": 111,
+      "execution_count": 95,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "def create_training_data(norm_data):\n",
+        "  prediction_days = 500\n",
+        "  \n",
+        "  x_train_list = []\n",
+        "  y_train_list = []\n",
+        "  \n",
+        "  for i in range(prediction_days, len(norm_data)):\n",
+        "    x_train_list.append(norm_data[i-prediction_days:i])\n",
+        "    y_train_list.append(norm_data.iloc[i].values[0:4])\n",
+        "  \n",
+        "  x_train = np.array(x_train_list)\n",
+        "  y_train = np.array(y_train_list)\n",
+        "  return x_train, y_train"
+      ],
+      "metadata": {
+        "id": "jMXkRAYFomHM"
+      },
+      "execution_count": 96,
       "outputs": []
     },
     {
       "cell_type": "code",
       "source": [
-        "norm_data, the_scaler, the_decoder = normalize_data(data, relative_to_previous=True)\n",
-        "#todo: save the_scaler somehow to use in new runtimes"
+        "#Make all the preprocesing\n",
+        "def preprocessing(data, scaler=None):\n",
+        "  # print(data.head(3))\n",
+        "  data_0 = create_remove_columns(data)\n",
+        "  # print(data_0.head(3))\n",
+        "  #todo: save the_scaler somehow to use in new runtimes\n",
+        "  norm_data, scaler, decoder = normalize_data(data_0, scaler=scaler)\n",
+        "  # print(norm_data.head(3))\n",
+        "  x_train, y_train = create_training_data(norm_data)\n",
+        "  # print(x_train.shape, y_train.shape)\n",
+        "  return x_train, y_train, scaler, decoder"
       ],
       "metadata": {
-        "id": "-kgo__Q3hw1_"
+        "id": "YZWMfusT-I7Z"
       },
-      "execution_count": 112,
+      "execution_count": 97,
       "outputs": []
     },
     {
       "cell_type": "code",
       "source": [
-        "len(norm_data)"
+        "x_train, y_train, scaler, decoder = preprocessing(data)"
       ],
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/"
         },
-        "id": "A1L8giqcsutX",
-        "outputId": "0aaf515b-3835-432c-b882-c2111a221ed4"
+        "id": "PeJjDC0VBG_6",
+        "outputId": "aff2cf0b-e630-4727-bbec-3cf5be4e53a0"
       },
-      "execution_count": 41,
+      "execution_count": 98,
       "outputs": [
         {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "2583"
-            ]
-          },
-          "metadata": {},
-          "execution_count": 41
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "max_value:  10.589996337890625\n",
+            "max_volume:  1460852400.0\n"
+          ]
         }
       ]
     },
     {
       "cell_type": "code",
       "source": [
-        "prediction_days = 100\n",
-        "\n",
-        "x_train_list = []\n",
-        "y_train_list = []\n",
-        "\n",
-        "for i in range(prediction_days, len(norm_data)):\n",
-        "  x_train_list.append(norm_data[i-prediction_days:i])\n",
-        "  y_train_list.append(norm_data.iloc[i].values[0:4])\n",
-        "\n",
-        "x_train = np.array(x_train_list)\n",
-        "y_train = np.array(y_train_list)"
+        "print(x_train.shape)\n",
+        "x_train[1,499,:]"
       ],
       "metadata": {
-        "id": "jMXkRAYFomHM"
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "YkI8vSguuS8A",
+        "outputId": "6e5eeaa2-12de-4dd2-b17c-1d61e415fbd8"
       },
-      "execution_count": 9,
-      "outputs": []
+      "execution_count": 99,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "(2082, 500, 6)\n"
+          ]
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "array([ 0.00212456,  0.05712934, -0.00212456,  0.04461756, -0.22778379,\n",
+              "        0.09233239])"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 99
+        }
+      ]
     },
     {
       "cell_type": "code",
       "source": [
-        "print(x_train.shape)\n",
-        "print(y_train.shape)\n",
-        "print(x_train.shape[1:])"
+        "td = data.iloc[498:501]\n",
+        "# print('td:\\n',td)\n",
+        "td0 = create_remove_columns(td)\n",
+        "print('td0:\\n',td0)\n",
+        "print(decoder(y_train[0]))"
       ],
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/"
         },
-        "id": "G7oMd1fRyOYt",
-        "outputId": "2094c403-096d-4f3a-9b15-bae0fbb7bf11"
+        "id": "QaO34uSds2wJ",
+        "outputId": "af5d9a04-214c-4a2d-c706-5af3a2a1ea5a"
       },
-      "execution_count": 10,
+      "execution_count": 100,
       "outputs": [
         {
           "output_type": "stream",
           "name": "stdout",
           "text": [
-            "(2483, 100, 6)\n",
-            "(2483, 4)\n",
-            "(100, 6)\n"
+            "td0:\n",
+            "                 Jump       Open       High        Low      Close  Adj Close  \\\n",
+            "Date                                                                          \n",
+            "2014-12-23  0.000000  28.307501  28.332500  28.115000  28.135000  25.286961   \n",
+            "2014-12-24  0.010000  28.145000  28.177500  28.002501  28.002501  25.167873   \n",
+            "2014-12-26  0.022499  28.025000  28.629999  28.002501  28.497499  25.612770   \n",
+            "\n",
+            "               Volume  \n",
+            "Date                   \n",
+            "2014-12-23  104113600  \n",
+            "2014-12-24   57918400  \n",
+            "2014-12-26  134884000  \n",
+            "[ 0.02249908  0.60499954 -0.02249908  0.47249985]\n"
           ]
         }
       ]
@@ -298,11 +361,11 @@
         "def create_model():\n",
         "  model = Sequential()\n",
         "  # model.add(LSTM(units=112, return_sequences=True, input_shape=(x_train.shape[1:])))\n",
-        "  model.add(LSTM(units=112, return_sequences=True, input_shape=(None,x_train.shape[-1],)))\n",
+        "  model.add(LSTM(units=1000, return_sequences=True, input_shape=(None,x_train.shape[-1],)))\n",
         "  model.add(Dropout(0.2))\n",
-        "  model.add(LSTM(units=112, return_sequences=True))\n",
+        "  model.add(LSTM(units=1000, return_sequences=True))\n",
         "  model.add(Dropout(0.2))\n",
-        "  model.add(LSTM(units=50))\n",
+        "  model.add(LSTM(units=1000))\n",
         "  model.add(Dropout(0.2))\n",
         "  model.add(Dense(units=4))\n",
         "  return model\n",
@@ -315,35 +378,35 @@
           "base_uri": "https://localhost:8080/"
         },
         "id": "GXhYAKzXVfku",
-        "outputId": "c54da788-6e82-4679-df1f-d3e89a20d228"
+        "outputId": "bbf96ec2-84fc-4246-9003-32c2f8083bb6"
       },
-      "execution_count": 66,
+      "execution_count": 101,
       "outputs": [
         {
           "output_type": "stream",
           "name": "stdout",
           "text": [
-            "Model: \"sequential_1\"\n",
+            "Model: \"sequential_2\"\n",
             "_________________________________________________________________\n",
             " Layer (type)                Output Shape              Param #   \n",
             "=================================================================\n",
-            " lstm_3 (LSTM)               (None, None, 112)         53312     \n",
+            " lstm_6 (LSTM)               (None, None, 1000)        4028000   \n",
             "                                                                 \n",
-            " dropout_3 (Dropout)         (None, None, 112)         0         \n",
+            " dropout_6 (Dropout)         (None, None, 1000)        0         \n",
             "                                                                 \n",
-            " lstm_4 (LSTM)               (None, None, 112)         100800    \n",
+            " lstm_7 (LSTM)               (None, None, 1000)        8004000   \n",
             "                                                                 \n",
-            " dropout_4 (Dropout)         (None, None, 112)         0         \n",
+            " dropout_7 (Dropout)         (None, None, 1000)        0         \n",
             "                                                                 \n",
-            " lstm_5 (LSTM)               (None, 50)                32600     \n",
+            " lstm_8 (LSTM)               (None, 1000)              8004000   \n",
             "                                                                 \n",
-            " dropout_5 (Dropout)         (None, 50)                0         \n",
+            " dropout_8 (Dropout)         (None, 1000)              0         \n",
             "                                                                 \n",
-            " dense_1 (Dense)             (None, 4)                 204       \n",
+            " dense_2 (Dense)             (None, 4)                 4004      \n",
             "                                                                 \n",
             "=================================================================\n",
-            "Total params: 186,916\n",
-            "Trainable params: 186,916\n",
+            "Total params: 20,040,004\n",
+            "Trainable params: 20,040,004\n",
             "Non-trainable params: 0\n",
             "_________________________________________________________________\n",
             "None\n"
@@ -359,34 +422,7 @@
       "metadata": {
         "id": "ZhoWj_XeXQws"
       },
-      "execution_count": 12,
-      "outputs": []
-    },
-    {
-      "cell_type": "markdown",
-      "source": [
-        "## Create checkpoint callback"
-      ],
-      "metadata": {
-        "id": "XU0vc4n8p92L"
-      }
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "# Directory where the checkpoints will be saved\n",
-        "checkpoint_dir = './training_checkpoints_'+dt.datetime.now().strftime(\"%Y%m%d%H%M%S\")\n",
-        "# Name of the checkpoint files\n",
-        "checkpoint_prefix = os.path.join(checkpoint_dir, \"ckpt_epoch{epoch}_loss{loss}\")\n",
-        "\n",
-        "checkpoint_callback=tf.keras.callbacks.ModelCheckpoint(\n",
-        "    filepath=checkpoint_prefix,\n",
-        "    save_weights_only=True)"
-      ],
-      "metadata": {
-        "id": "M5MBAB1-qCZr"
-      },
-      "execution_count": 35,
+      "execution_count": 102,
       "outputs": []
     },
     {
@@ -409,16 +445,16 @@
           "base_uri": "https://localhost:8080/"
         },
         "id": "HDT9XPXHvqyN",
-        "outputId": "60938333-8afe-4b80-9af3-37bca3d67f83"
+        "outputId": "6bc3a3e9-a7ae-48ff-e64a-fb529c5e1f75"
       },
-      "execution_count": 15,
+      "execution_count": 103,
       "outputs": [
         {
           "output_type": "stream",
           "name": "stdout",
           "text": [
-            "(2483, 100, 6)\n",
-            "(2483, 4)\n"
+            "(2082, 500, 6)\n",
+            "(2082, 4)\n"
           ]
         }
       ]
@@ -426,108 +462,96 @@
     {
       "cell_type": "code",
       "source": [
-        "y_train[-2]"
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "F1wZkJMh3XNH",
-        "outputId": "37a023db-0727-434a-85be-141c3c377907"
-      },
-      "execution_count": 40,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "array([ 0.02002301,  0.0391905 , -0.09898045, -0.05744885])"
-            ]
-          },
-          "metadata": {},
-          "execution_count": 40
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "model.fit(x_train, y_train, epochs=25, batch_size=32, callbacks=[checkpoint_callback])\n"
+        "# Change to False to avoid trainging the model\n",
+        "# if False:\n",
+        "if True:\n",
+        "  # Directory where the checkpoints will be saved\n",
+        "  checkpoint_dir = './training_checkpoints_'+dt.datetime.now().strftime(\"%Y%m%d%H%M%S\")\n",
+        "  # Name of the checkpoint files\n",
+        "  checkpoint_prefix = os.path.join(checkpoint_dir, \"ckpt_epoch{epoch}_loss{loss}\")\n",
+        "  \n",
+        "  checkpoint_callback=tf.keras.callbacks.ModelCheckpoint(\n",
+        "      filepath=checkpoint_prefix,\n",
+        "      save_weights_only=True,\n",
+        "      monitor=\"loss\", mode=\"min\",\n",
+        "      save_best_only=True)\n",
+        "  model.fit(x_train, y_train, epochs=25, batch_size=32, callbacks=[checkpoint_callback])\n"
       ],
       "metadata": {
         "colab": {
-          "base_uri": "https://localhost:8080/"
+          "base_uri": "https://localhost:8080/",
+          "height": 1000
         },
         "id": "9Ccc_Ej2TmYO",
-        "outputId": "235efc3b-616b-4e57-fb87-07efcb377e8e"
+        "outputId": "4e7fe210-6cbb-4a9d-f856-829cfa6bced5"
       },
-      "execution_count": 37,
+      "execution_count": 104,
       "outputs": [
         {
           "output_type": "stream",
           "name": "stdout",
           "text": [
             "Epoch 1/25\n",
-            "78/78 [==============================] - 31s 395ms/step - loss: 0.0117\n",
+            "66/66 [==============================] - 58s 773ms/step - loss: 0.0125\n",
             "Epoch 2/25\n",
-            "78/78 [==============================] - 31s 394ms/step - loss: 0.0111\n",
+            "66/66 [==============================] - 54s 816ms/step - loss: 0.0115\n",
             "Epoch 3/25\n",
-            "78/78 [==============================] - 33s 429ms/step - loss: 0.0109\n",
+            "66/66 [==============================] - 55s 841ms/step - loss: 0.0113\n",
             "Epoch 4/25\n",
-            "78/78 [==============================] - 31s 396ms/step - loss: 0.0109\n",
+            "66/66 [==============================] - 56s 845ms/step - loss: 0.0114\n",
             "Epoch 5/25\n",
-            "78/78 [==============================] - 31s 398ms/step - loss: 0.0108\n",
+            "66/66 [==============================] - 57s 859ms/step - loss: 0.0113\n",
             "Epoch 6/25\n",
-            "78/78 [==============================] - 31s 400ms/step - loss: 0.0108\n",
+            "66/66 [==============================] - 58s 886ms/step - loss: 0.0112\n",
             "Epoch 7/25\n",
-            "78/78 [==============================] - 32s 405ms/step - loss: 0.0108\n",
+            "66/66 [==============================] - 59s 889ms/step - loss: 0.0112\n",
             "Epoch 8/25\n",
-            "78/78 [==============================] - 31s 394ms/step - loss: 0.0108\n",
+            "66/66 [==============================] - 59s 890ms/step - loss: 0.0111\n",
             "Epoch 9/25\n",
-            "78/78 [==============================] - 30s 385ms/step - loss: 0.0108\n",
+            "66/66 [==============================] - 58s 875ms/step - loss: 0.0112\n",
             "Epoch 10/25\n",
-            "78/78 [==============================] - 30s 385ms/step - loss: 0.0108\n",
+            "66/66 [==============================] - 58s 880ms/step - loss: 0.0112\n",
             "Epoch 11/25\n",
-            "78/78 [==============================] - 29s 373ms/step - loss: 0.0108\n",
+            "66/66 [==============================] - 58s 881ms/step - loss: 0.0111\n",
             "Epoch 12/25\n",
-            "78/78 [==============================] - 29s 375ms/step - loss: 0.0107\n",
+            "66/66 [==============================] - 59s 892ms/step - loss: 0.0111\n",
             "Epoch 13/25\n",
-            "78/78 [==============================] - 30s 383ms/step - loss: 0.0107\n",
+            "66/66 [==============================] - 59s 895ms/step - loss: 0.0110\n",
             "Epoch 14/25\n",
-            "78/78 [==============================] - 30s 388ms/step - loss: 0.0107\n",
+            "66/66 [==============================] - 58s 880ms/step - loss: 0.0111\n",
             "Epoch 15/25\n",
-            "78/78 [==============================] - 31s 396ms/step - loss: 0.0108\n",
+            "66/66 [==============================] - 58s 882ms/step - loss: 0.0111\n",
             "Epoch 16/25\n",
-            "78/78 [==============================] - 30s 379ms/step - loss: 0.0107\n",
+            "66/66 [==============================] - 59s 896ms/step - loss: 0.0110\n",
             "Epoch 17/25\n",
-            "78/78 [==============================] - 30s 386ms/step - loss: 0.0107\n",
+            "66/66 [==============================] - 58s 882ms/step - loss: 0.0112\n",
             "Epoch 18/25\n",
-            "78/78 [==============================] - 30s 383ms/step - loss: 0.0108\n",
+            "66/66 [==============================] - 58s 882ms/step - loss: 0.0110\n",
             "Epoch 19/25\n",
-            "78/78 [==============================] - 30s 382ms/step - loss: 0.0107\n",
+            "66/66 [==============================] - 58s 882ms/step - loss: 0.0111\n",
             "Epoch 20/25\n",
-            "78/78 [==============================] - 31s 397ms/step - loss: 0.0107\n",
-            "Epoch 21/25\n",
-            "78/78 [==============================] - 30s 384ms/step - loss: 0.0107\n",
-            "Epoch 22/25\n",
-            "78/78 [==============================] - 30s 381ms/step - loss: 0.0106\n",
-            "Epoch 23/25\n",
-            "78/78 [==============================] - 30s 380ms/step - loss: 0.0106\n",
-            "Epoch 24/25\n",
-            "78/78 [==============================] - 30s 385ms/step - loss: 0.0107\n",
-            "Epoch 25/25\n",
-            "78/78 [==============================] - 30s 383ms/step - loss: 0.0106\n"
+            "24/66 [=========>....................] - ETA: 37s - loss: 0.0099"
           ]
         },
         {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "<keras.callbacks.History at 0x7f5203d70cd0>"
-            ]
-          },
-          "metadata": {},
-          "execution_count": 37
+          "output_type": "error",
+          "ename": "KeyboardInterrupt",
+          "evalue": "ignored",
+          "traceback": [
+            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+            "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
+            "\u001b[0;32m<ipython-input-104-78bd1a1c9ef9>\u001b[0m in \u001b[0;36m<cell line: 5>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     12\u001b[0m       \u001b[0mmonitor\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"loss\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmode\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"min\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     13\u001b[0m       save_best_only=True)\n\u001b[0;32m---> 14\u001b[0;31m   \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mepochs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m25\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m32\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcallbacks\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mcheckpoint_callback\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+            "\u001b[0;32m/usr/local/lib/python3.9/dist-packages/keras/utils/traceback_utils.py\u001b[0m in \u001b[0;36merror_handler\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     63\u001b[0m         \u001b[0mfiltered_tb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     64\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 65\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     66\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     67\u001b[0m             \u001b[0mfiltered_tb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_process_traceback_frames\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__traceback__\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/local/lib/python3.9/dist-packages/keras/engine/training.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_batch_size, validation_freq, max_queue_size, workers, use_multiprocessing)\u001b[0m\n\u001b[1;32m   1683\u001b[0m                         ):\n\u001b[1;32m   1684\u001b[0m                             \u001b[0mcallbacks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mon_train_batch_begin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstep\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1685\u001b[0;31m                             \u001b[0mtmp_logs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain_function\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0miterator\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1686\u001b[0m                             \u001b[0;32mif\u001b[0m \u001b[0mdata_handler\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshould_sync\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1687\u001b[0m                                 \u001b[0mcontext\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0masync_wait\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/local/lib/python3.9/dist-packages/tensorflow/python/util/traceback_utils.py\u001b[0m in \u001b[0;36merror_handler\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    148\u001b[0m     \u001b[0mfiltered_tb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    149\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 150\u001b[0;31m       \u001b[0;32mreturn\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    151\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    152\u001b[0m       \u001b[0mfiltered_tb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_process_traceback_frames\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__traceback__\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/local/lib/python3.9/dist-packages/tensorflow/python/eager/polymorphic_function/polymorphic_function.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwds)\u001b[0m\n\u001b[1;32m    892\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    893\u001b[0m       \u001b[0;32mwith\u001b[0m \u001b[0mOptionalXlaContext\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_jit_compile\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 894\u001b[0;31m         \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    895\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    896\u001b[0m       \u001b[0mnew_tracing_count\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexperimental_get_tracing_count\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/local/lib/python3.9/dist-packages/tensorflow/python/eager/polymorphic_function/polymorphic_function.py\u001b[0m in \u001b[0;36m_call\u001b[0;34m(self, *args, **kwds)\u001b[0m\n\u001b[1;32m    924\u001b[0m       \u001b[0;31m# In this case we have created variables on the first call, so we run the\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    925\u001b[0m       \u001b[0;31m# defunned version which is guaranteed to never create variables.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 926\u001b[0;31m       \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_no_variable_creation_fn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# pylint: disable=not-callable\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    927\u001b[0m     \u001b[0;32melif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_variable_creation_fn\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    928\u001b[0m       \u001b[0;31m# Release the lock early so that multiple threads can perform the call\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/local/lib/python3.9/dist-packages/tensorflow/python/eager/polymorphic_function/tracing_compiler.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m    141\u001b[0m       (concrete_function,\n\u001b[1;32m    142\u001b[0m        filtered_flat_args) = self._maybe_define_function(args, kwargs)\n\u001b[0;32m--> 143\u001b[0;31m     return concrete_function._call_flat(\n\u001b[0m\u001b[1;32m    144\u001b[0m         filtered_flat_args, captured_inputs=concrete_function.captured_inputs)  # pylint: disable=protected-access\n\u001b[1;32m    145\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/local/lib/python3.9/dist-packages/tensorflow/python/eager/polymorphic_function/monomorphic_function.py\u001b[0m in \u001b[0;36m_call_flat\u001b[0;34m(self, args, captured_inputs, cancellation_manager)\u001b[0m\n\u001b[1;32m   1755\u001b[0m         and executing_eagerly):\n\u001b[1;32m   1756\u001b[0m       \u001b[0;31m# No tape is watching; skip to running the function.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1757\u001b[0;31m       return self._build_call_outputs(self._inference_function.call(\n\u001b[0m\u001b[1;32m   1758\u001b[0m           ctx, args, cancellation_manager=cancellation_manager))\n\u001b[1;32m   1759\u001b[0m     forward_backward = self._select_forward_and_backward_functions(\n",
+            "\u001b[0;32m/usr/local/lib/python3.9/dist-packages/tensorflow/python/eager/polymorphic_function/monomorphic_function.py\u001b[0m in \u001b[0;36mcall\u001b[0;34m(self, ctx, args, cancellation_manager)\u001b[0m\n\u001b[1;32m    379\u001b[0m       \u001b[0;32mwith\u001b[0m \u001b[0m_InterpolateFunctionError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    380\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mcancellation_manager\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 381\u001b[0;31m           outputs = execute.execute(\n\u001b[0m\u001b[1;32m    382\u001b[0m               \u001b[0mstr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msignature\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    383\u001b[0m               \u001b[0mnum_outputs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_num_outputs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/local/lib/python3.9/dist-packages/tensorflow/python/eager/execute.py\u001b[0m in \u001b[0;36mquick_execute\u001b[0;34m(op_name, num_outputs, inputs, attrs, ctx, name)\u001b[0m\n\u001b[1;32m     50\u001b[0m   \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     51\u001b[0m     \u001b[0mctx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mensure_initialized\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 52\u001b[0;31m     tensors = pywrap_tfe.TFE_Py_Execute(ctx._handle, device_name, op_name,\n\u001b[0m\u001b[1;32m     53\u001b[0m                                         inputs, attrs, num_outputs)\n\u001b[1;32m     54\u001b[0m   \u001b[0;32mexcept\u001b[0m \u001b[0mcore\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_NotOkStatusException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
+          ]
         }
       ]
     },
@@ -544,25 +568,13 @@
       "cell_type": "code",
       "source": [
         "#print trainings directories to pick one\n",
-        "!ls -d training_checkpoints_*/"
+        "!ls -ld training_checkpoints_*/"
       ],
       "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "59CDDB0i4yTx",
-        "outputId": "497ae253-e3ac-47d0-d066-8e508f55782c"
+        "id": "59CDDB0i4yTx"
       },
-      "execution_count": 49,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "training_checkpoints_20230406041748/\n"
-          ]
-        }
-      ]
+      "execution_count": null,
+      "outputs": []
     },
     {
       "cell_type": "code",
@@ -572,13 +584,19 @@
       "metadata": {
         "id": "tpmru7nG9kbW"
       },
-      "execution_count": 72,
+      "execution_count": 105,
       "outputs": []
     },
     {
       "cell_type": "code",
       "source": [
-        "checkpoint_dir = 'training_checkpoints_20230406041748'\n",
+        "# if checkpoint_dir does not exists, select the one stated in the except block\n",
+        "try:\n",
+        "  checkpoint_dir\n",
+        "except NameError: \n",
+        "  checkpoint_dir = './training_checkpoints_20230406214431'\n",
+        "\n",
+        "print(checkpoint_dir)\n",
         "\n",
         "def load_weights(epoch=None):\n",
         "  if epoch is None:\n",
@@ -600,15 +618,16 @@
           "base_uri": "https://localhost:8080/"
         },
         "id": "wQ0JTXsp4VKF",
-        "outputId": "d4b794c9-7a89-4867-d17c-de1f20b9b607"
+        "outputId": "2b25d414-f188-4c14-ef43-f7af5566a3be"
       },
-      "execution_count": 87,
+      "execution_count": 107,
       "outputs": [
         {
           "output_type": "stream",
           "name": "stdout",
           "text": [
-            "training_checkpoints_20230406041748/ckpt_epoch25_loss0.01064301934093237\n"
+            "./training_checkpoints_20230406230143\n",
+            "./training_checkpoints_20230406230143/ckpt_epoch16_loss0.01097947172820568\n"
           ]
         }
       ]
@@ -616,8 +635,8 @@
     {
       "cell_type": "code",
       "source": [
-        "test_start = dt.date.today() - dt.timedelta(days=200)\n",
-        "test_end = dt.date.today()\n",
+        "test_start = dt.datetime(2013,1,1)\n",
+        "end = dt.datetime(2023,4,5)\n",
         "\n",
         "yfin.pdr_override()\n",
         "test_data = web.data.get_data_yahoo(ticker, test_start, test_end)"
@@ -627,9 +646,9 @@
           "base_uri": "https://localhost:8080/"
         },
         "id": "Mf4q97pfaSCA",
-        "outputId": "4317ef63-be5e-49ca-fdca-1d5760efbba1"
+        "outputId": "7355b53f-c879-4296-d24a-bb8bd739e5d8"
       },
-      "execution_count": 99,
+      "execution_count": 114,
       "outputs": [
         {
           "output_type": "stream",
@@ -643,57 +662,35 @@
     {
       "cell_type": "code",
       "source": [
-        "test_data_norm, _ = normalize_data(test_data, scaler=the_scaler)"
-      ],
-      "metadata": {
-        "id": "xEG2yEdKC8uy"
-      },
-      "execution_count": 100,
-      "outputs": []
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "print(type(test_data_norm))"
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "mhbqRZ6cDhd6",
-        "outputId": "8b40a738-e143-4920-de03-8e8572f4389a"
-      },
-      "execution_count": 102,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "<class 'pandas.core.frame.DataFrame'>\n"
-          ]
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "input_data = np.expand_dims(test_data_norm.values, axis=0)\n",
-        "print(input_data.shape)"
+        "# def close_tester(model, test_data, scaler=None):\n",
+        "model = test_model\n",
+        "scaler = scaler\n",
+        "test_x_train, test_y_train, _, _ = preprocessing(data, scaler=scaler)\n",
+        "print(test_x_train.shape)\n",
+        "print(test_y_train.shape)\n",
+        "results = model.predict(test_x_train)\n",
+        "# the results are tensors of 4 numbers, Jump, High, Low, and Close respectively\n",
+        "\n",
+        "# close_tester(test_model, test_data, scaler=the_scaler)\n"
       ],
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/"
         },
-        "id": "F2bnofchD0xv",
-        "outputId": "0b2261fb-056d-4ec2-a98b-82517d7806f1"
+        "id": "MqCeMf3UoxZm",
+        "outputId": "a0591f06-f804-41e9-b973-627a7693ff89"
       },
-      "execution_count": 104,
+      "execution_count": 115,
       "outputs": [
         {
           "output_type": "stream",
           "name": "stdout",
           "text": [
-            "(1, 138, 6)\n"
+            "max_value:  10.589996337890625\n",
+            "max_volume:  1460852400.0\n",
+            "(2082, 500, 6)\n",
+            "(2082, 4)\n",
+            "66/66 [==============================] - 18s 275ms/step\n"
           ]
         }
       ]
@@ -701,22 +698,46 @@
     {
       "cell_type": "code",
       "source": [
-        "results = test_model.predict(input_data, batch_size=1)"
+        "right_counter = 0\n",
+        "wrong_counter = 0\n",
+        "no_action_counter = 0\n",
+        "# for result, expected in zip(results[:2], test_y_train[:2]):\n",
+        "for result, expected in zip(results[:], test_y_train[:]):\n",
+        "  # print(result)\n",
+        "  # print(expected)\n",
+        "  comparer = result[3] * expected[3]\n",
+        "  if comparer > 0:\n",
+        "    right_counter += 1\n",
+        "  elif comparer == 0:\n",
+        "    no_action_counter\n",
+        "  elif comparer < 0:\n",
+        "    wrong_counter += 1\n",
+        "\n",
+        "  # print('expected: ', decoder(expected))\n",
+        "  # print('result: ', decoder(result))\n",
+        "\n",
+        "print('right_counter :', right_counter)\n",
+        "print('no_action_counter :',no_action_counter)\n",
+        "print('wrong_counter :', wrong_counter)\n",
+        "print('success rate: {}%'.format(right_counter*100/len(results)))"
       ],
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/"
         },
         "id": "AVYFQZnqEqhx",
-        "outputId": "958d1669-c8bc-4eff-bb66-f25eb4dde011"
+        "outputId": "7353f76c-f2e6-4a48-ba31-74ab67bb73ea"
       },
-      "execution_count": 105,
+      "execution_count": 120,
       "outputs": [
         {
           "output_type": "stream",
           "name": "stdout",
           "text": [
-            "1/1 [==============================] - 1s 1s/step\n"
+            "right_counter : 1118\n",
+            "no_action_counter : 0\n",
+            "wrong_counter : 959\n",
+            "success rate: 53.6983669548511%\n"
           ]
         }
       ]
@@ -724,231 +745,32 @@
     {
       "cell_type": "code",
       "source": [
-        "print(results)\n",
-        "print(the_decoder(results))"
+        "test_data.iloc[500,:]"
       ],
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/"
         },
-        "id": "FbdX4ulhExsX",
-        "outputId": "14a763ca-0983-41ec-e88b-da796fa4b51a"
-      },
-      "execution_count": 113,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "[[-0.01962117  0.09634934 -0.10176479 -0.00849891]]\n",
-            "[[-0.06636524  1.3856668  -1.0948591   0.0728941 ]]\n"
-          ]
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "test_data.head()"
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 237
-        },
-        "id": "m0k7toG3E2_9",
-        "outputId": "38ab6e43-1321-4028-9482-8e6687802a7d"
+        "id": "gyhzy_l6sAvi",
+        "outputId": "78f5d2cf-cd21-47b1-b58e-6b3321a802bd"
       },
-      "execution_count": 107,
+      "execution_count": 123,
       "outputs": [
         {
           "output_type": "execute_result",
           "data": {
             "text/plain": [
-              "                  Open        High         Low       Close   Adj Close  \\\n",
-              "Date                                                                     \n",
-              "2022-09-19  149.309998  154.559998  149.100006  154.479996  153.989029   \n",
-              "2022-09-20  153.399994  158.080002  153.080002  156.899994  156.401352   \n",
-              "2022-09-21  157.339996  158.740005  153.600006  153.720001  153.231461   \n",
-              "2022-09-22  152.380005  154.470001  150.910004  152.740005  152.254578   \n",
-              "2022-09-23  151.190002  151.470001  148.559998  150.429993  149.951904   \n",
-              "\n",
-              "               Volume  \n",
-              "Date                   \n",
-              "2022-09-19   81474200  \n",
-              "2022-09-20  107689800  \n",
-              "2022-09-21  101696800  \n",
-              "2022-09-22   86652500  \n",
-              "2022-09-23   96029900  "
-            ],
-            "text/html": [
-              "\n",
-              "  <div id=\"df-51b6b5ba-2841-4317-9ce2-b32b40e2e9fc\">\n",
-              "    <div class=\"colab-df-container\">\n",
-              "      <div>\n",
-              "<style scoped>\n",
-              "    .dataframe tbody tr th:only-of-type {\n",
-              "        vertical-align: middle;\n",
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-              "<table border=\"1\" class=\"dataframe\">\n",
-              "  <thead>\n",
-              "    <tr style=\"text-align: right;\">\n",
-              "      <th></th>\n",
-              "      <th>Open</th>\n",
-              "      <th>High</th>\n",
-              "      <th>Low</th>\n",
-              "      <th>Close</th>\n",
-              "      <th>Adj Close</th>\n",
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-              "    <tr>\n",
-              "      <th>Date</th>\n",
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-              "      <th></th>\n",
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-              "    </tr>\n",
-              "  </thead>\n",
-              "  <tbody>\n",
-              "    <tr>\n",
-              "      <th>2022-09-19</th>\n",
-              "      <td>149.309998</td>\n",
-              "      <td>154.559998</td>\n",
-              "      <td>149.100006</td>\n",
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-              "      <td>81474200</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>2022-09-20</th>\n",
-              "      <td>153.399994</td>\n",
-              "      <td>158.080002</td>\n",
-              "      <td>153.080002</td>\n",
-              "      <td>156.899994</td>\n",
-              "      <td>156.401352</td>\n",
-              "      <td>107689800</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>2022-09-21</th>\n",
-              "      <td>157.339996</td>\n",
-              "      <td>158.740005</td>\n",
-              "      <td>153.600006</td>\n",
-              "      <td>153.720001</td>\n",
-              "      <td>153.231461</td>\n",
-              "      <td>101696800</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>2022-09-22</th>\n",
-              "      <td>152.380005</td>\n",
-              "      <td>154.470001</td>\n",
-              "      <td>150.910004</td>\n",
-              "      <td>152.740005</td>\n",
-              "      <td>152.254578</td>\n",
-              "      <td>86652500</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>2022-09-23</th>\n",
-              "      <td>151.190002</td>\n",
-              "      <td>151.470001</td>\n",
-              "      <td>148.559998</td>\n",
-              "      <td>150.429993</td>\n",
-              "      <td>149.951904</td>\n",
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-              "  </tbody>\n",
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-              "</div>\n",
-              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-51b6b5ba-2841-4317-9ce2-b32b40e2e9fc')\"\n",
-              "              title=\"Convert this dataframe to an interactive table.\"\n",
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-              "  </svg>\n",
-              "      </button>\n",
-              "      \n",
-              "  <style>\n",
-              "    .colab-df-container {\n",
-              "      display:flex;\n",
-              "      flex-wrap:wrap;\n",
-              "      gap: 12px;\n",
-              "    }\n",
-              "\n",
-              "    .colab-df-convert {\n",
-              "      background-color: #E8F0FE;\n",
-              "      border: none;\n",
-              "      border-radius: 50%;\n",
-              "      cursor: pointer;\n",
-              "      display: none;\n",
-              "      fill: #1967D2;\n",
-              "      height: 32px;\n",
-              "      padding: 0 0 0 0;\n",
-              "      width: 32px;\n",
-              "    }\n",
-              "\n",
-              "    .colab-df-convert:hover {\n",
-              "      background-color: #E2EBFA;\n",
-              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
-              "      fill: #174EA6;\n",
-              "    }\n",
-              "\n",
-              "    [theme=dark] .colab-df-convert {\n",
-              "      background-color: #3B4455;\n",
-              "      fill: #D2E3FC;\n",
-              "    }\n",
-              "\n",
-              "    [theme=dark] .colab-df-convert:hover {\n",
-              "      background-color: #434B5C;\n",
-              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
-              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
-              "      fill: #FFFFFF;\n",
-              "    }\n",
-              "  </style>\n",
-              "\n",
-              "      <script>\n",
-              "        const buttonEl =\n",
-              "          document.querySelector('#df-51b6b5ba-2841-4317-9ce2-b32b40e2e9fc button.colab-df-convert');\n",
-              "        buttonEl.style.display =\n",
-              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
-              "\n",
-              "        async function convertToInteractive(key) {\n",
-              "          const element = document.querySelector('#df-51b6b5ba-2841-4317-9ce2-b32b40e2e9fc');\n",
-              "          const dataTable =\n",
-              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
-              "                                                     [key], {});\n",
-              "          if (!dataTable) return;\n",
-              "\n",
-              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
-              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
-              "            + ' to learn more about interactive tables.';\n",
-              "          element.innerHTML = '';\n",
-              "          dataTable['output_type'] = 'display_data';\n",
-              "          await google.colab.output.renderOutput(dataTable, element);\n",
-              "          const docLink = document.createElement('div');\n",
-              "          docLink.innerHTML = docLinkHtml;\n",
-              "          element.appendChild(docLink);\n",
-              "        }\n",
-              "      </script>\n",
-              "    </div>\n",
-              "  </div>\n",
-              "  "
+              "Open         2.802500e+01\n",
+              "High         2.863000e+01\n",
+              "Low          2.800250e+01\n",
+              "Close        2.849750e+01\n",
+              "Adj Close    2.561277e+01\n",
+              "Volume       1.348840e+08\n",
+              "Name: 2014-12-26 00:00:00, dtype: float64"
             ]
           },
           "metadata": {},
-          "execution_count": 107
+          "execution_count": 123
         }
       ]
     }