prediction-lstm-sizes.ipynb

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  {
   "cell_type": "markdown",
   "source": "# Libraries",
   "metadata": {
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    "is_collapsed": false,
    "cell_id": "00000-76cf0734-c4e0-45ba-ae2f-c84c8fcca1ef",
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  },
  {
   "cell_type": "markdown",
   "source": "Data manipulation",
   "metadata": {
    "tags": [],
    "is_collapsed": false,
    "cell_id": "00001-3a639b58-67a4-4ee3-98b0-5a8015d03bfe",
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  {
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    "execution_start": 1632709660180,
    "execution_millis": 1,
    "cell_id": "00002-d07ce947-4bce-453c-b945-eb3ec7c031c8",
    "deepnote_cell_type": "code"
   },
   "source": "import numpy as np\nimport pandas as pd\nimport itertools\nimport random as rn",
   "outputs": [],
   "execution_count": null
  },
  {
   "cell_type": "markdown",
   "source": "Data visualization libraries",
   "metadata": {
    "tags": [],
    "is_collapsed": false,
    "cell_id": "00003-54f22f06-3078-4f51-81d4-b459d48e2d58",
    "deepnote_cell_type": "text-cell-p"
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  {
   "cell_type": "code",
   "metadata": {
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    "deepnote_to_be_reexecuted": false,
    "source_hash": "1466c9e2",
    "execution_start": 1632709660181,
    "execution_millis": 1473,
    "cell_id": "00004-4bcf215f-36be-4cbe-8b38-a856bb745899",
    "deepnote_cell_type": "code"
   },
   "source": "from matplotlib import pyplot as plt\nimport seaborn as sns",
   "outputs": [],
   "execution_count": null
  },
  {
   "cell_type": "markdown",
   "source": "Data preparation and models",
   "metadata": {
    "tags": [],
    "is_collapsed": false,
    "cell_id": "00005-3c6061c0-df27-4f54-bb08-9b508c3f6f35",
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    "cell_id": "00006-38c478b6-0ccd-4be0-8c9f-aabba2351aad",
    "deepnote_cell_type": "code"
   },
   "source": "from sklearn.preprocessing import MinMaxScaler\n\nimport tensorflow as tf\nfrom tensorflow import keras\nfrom tensorflow.keras.models import Sequential\nfrom tensorflow.keras.optimizers import Adam\nfrom tensorflow.keras.layers import Dense, LSTM, LeakyReLU, Dropout\n\nnp.random.seed(42)\nrn.seed(42)\ntf.random.set_seed(42)",
   "outputs": [
    {
     "name": "stderr",
     "text": "2021-09-27 02:27:41.945297: W tensorflow/stream_executor/platform/default/dso_loader.cc:60] Could not load dynamic library 'libcudart.so.11.0'; dlerror: libcudart.so.11.0: cannot open shared object file: No such file or directory\n2021-09-27 02:27:41.945366: I tensorflow/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine.\n",
     "output_type": "stream"
    }
   ],
   "execution_count": null
  },
  {
   "cell_type": "markdown",
   "source": "# Data Manipulation",
   "metadata": {
    "tags": [],
    "is_collapsed": false,
    "cell_id": "00007-9f3764fa-07be-48c9-a20c-dc6f1f2f03ba",
    "deepnote_cell_type": "text-cell-h1"
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  },
  {
   "cell_type": "markdown",
   "source": "### Data exploration",
   "metadata": {
    "tags": [],
    "is_collapsed": false,
    "cell_id": "00008-98dbf340-ec4e-4b78-8be7-25f997e02d76",
    "deepnote_cell_type": "text-cell-h3"
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  },
  {
   "cell_type": "markdown",
   "source": "Data source &amp; data collections ...",
   "metadata": {
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    "is_collapsed": false,
    "cell_id": "00009-a1195d99-98c1-4bd5-a142-cf9c8e45a79b",
    "deepnote_cell_type": "text-cell-p"
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  },
  {
   "cell_type": "markdown",
   "source": "Focus on pre-recorded csv files",
   "metadata": {
    "tags": [],
    "is_collapsed": false,
    "cell_id": "00010-bc503c26-a65a-4b39-8be4-277cb5e94ae7",
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    "cell_id": "00011-60acfd07-e0f6-4b97-b4d3-91bc677a59e8",
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   },
   "source": "df = pd.read_csv('btc_cm_metrics_final.csv')\ncoin_name = \"BTC\"\n#df = pd.read_csv('eth_cm_metrics_final.csv')\n#coin_name = \"ETH\"\n#df = pd.read_csv('ltc_cm_metrics_final.csv')\n#coin_name = \"LTC\"\n#df = pd.read_csv('doge_cm_metrics_final.csv')\n#coin_name = \"DOGE\"\ndf.head()\n\n# slice to a few columns to start\ndf = df[['date', 'PriceUSD', 'TxCnt', 'FeeMeanUSD', 'CapMrktCurUSD']]\n\n# convert date attribute to datetime \ndf.date = pd.to_datetime(df.date)#.dt.strftime('%Y-%m-%d')\n\n# sort the dataset based on dates\ndf = df.sort_values('date')",
   "outputs": [],
   "execution_count": null
  },
  {
   "cell_type": "code",
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    "deepnote_to_be_reexecuted": false,
    "source_hash": "c085b6ba",
    "execution_start": 1632709667248,
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    "cell_id": "00012-d7e4525d-ea07-4e36-bbf5-c48c2c4270c0",
    "deepnote_cell_type": "code"
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   "source": "df.head()",
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      "text/plain": "                       date  PriceUSD  TxCnt  FeeMeanUSD  CapMrktCurUSD\n0 2010-01-01 00:00:00+00:00       NaN    2.0         NaN            NaN\n1 2010-01-02 00:00:00+00:00       NaN    0.0         NaN            NaN\n2 2010-01-03 00:00:00+00:00       NaN    0.0         NaN            NaN\n3 2010-01-04 00:00:00+00:00       NaN    2.0         NaN            NaN\n4 2010-01-05 00:00:00+00:00       NaN    0.0         NaN            NaN",
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      "text/plain": "                          date      PriceUSD     TxCnt  FeeMeanUSD  \\\n4277 2021-09-17 00:00:00+00:00  47213.794129  273808.0    2.587788   \n4278 2021-09-18 00:00:00+00:00  48204.013652  227894.0    2.445872   \n4279 2021-09-19 00:00:00+00:00  47218.473996  199632.0    1.953428   \n4280 2021-09-20 00:00:00+00:00  42855.510345  273371.0    2.219487   \n4281 2021-09-21 00:00:00+00:00  40526.489376  245996.0    2.422268   \n\n      CapMrktCurUSD  \n4277   8.884999e+11  \n4278   9.071812e+11  \n4279   8.886809e+11  \n4280   8.066112e+11  \n4281   7.628063e+11  ",
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      "text/plain": "           PriceUSD          TxCnt   FeeMeanUSD  CapMrktCurUSD\ncount   4084.000000    4282.000000  4084.000000   4.084000e+03\nmean    5709.288371  156825.543204     1.920999   1.034382e+11\nstd    11332.736962  124831.899203     5.343191   2.116611e+11\nmin        0.050541       0.000000     0.000000   1.776705e+05\n25%       92.677576   37143.000000     0.035296   1.047622e+09\n50%      600.739412  153396.500000     0.145361   8.213095e+09\n75%     7236.009425  273103.250000     1.113889   1.280311e+11\nmax    63445.638314  498142.000000    60.949968   1.185279e+12",
      "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>PriceUSD</th>\n      <th>TxCnt</th>\n      <th>FeeMeanUSD</th>\n      <th>CapMrktCurUSD</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>count</th>\n      <td>4084.000000</td>\n      <td>4282.000000</td>\n      <td>4084.000000</td>\n      <td>4.084000e+03</td>\n    </tr>\n    <tr>\n      <th>mean</th>\n      <td>5709.288371</td>\n      <td>156825.543204</td>\n      <td>1.920999</td>\n      <td>1.034382e+11</td>\n    </tr>\n    <tr>\n      <th>std</th>\n      <td>11332.736962</td>\n      <td>124831.899203</td>\n      <td>5.343191</td>\n      <td>2.116611e+11</td>\n    </tr>\n    <tr>\n      <th>min</th>\n      <td>0.050541</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>1.776705e+05</td>\n    </tr>\n    <tr>\n      <th>25%</th>\n      <td>92.677576</td>\n      <td>37143.000000</td>\n      <td>0.035296</td>\n      <td>1.047622e+09</td>\n    </tr>\n    <tr>\n      <th>50%</th>\n      <td>600.739412</td>\n      <td>153396.500000</td>\n      <td>0.145361</td>\n      <td>8.213095e+09</td>\n    </tr>\n    <tr>\n      <th>75%</th>\n      <td>7236.009425</td>\n      <td>273103.250000</td>\n      <td>1.113889</td>\n      <td>1.280311e+11</td>\n    </tr>\n    <tr>\n      <th>max</th>\n      <td>63445.638314</td>\n      <td>498142.000000</td>\n      <td>60.949968</td>\n      <td>1.185279e+12</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {}
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   ],
   "execution_count": null
  },
  {
   "cell_type": "markdown",
   "source": "Visualization of historical price",
   "metadata": {
    "tags": [],
    "is_collapsed": false,
    "cell_id": "00015-611a95d1-de12-445c-93ba-b6693d8569d6",
    "deepnote_cell_type": "text-cell-p"
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  {
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    "execution_start": 1632709667375,
    "execution_millis": 9,
    "cell_id": "00016-f2203ef8-2d34-4937-9f78-25389b877a11",
    "deepnote_cell_type": "code"
   },
   "source": "# Focus on price column, which is the target variable to predict\nprice = df[['date', 'PriceUSD']]",
   "outputs": [],
   "execution_count": null
  },
  {
   "cell_type": "code",
   "metadata": {
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    "deepnote_to_be_reexecuted": false,
    "source_hash": "517a015c",
    "execution_start": 1632709667403,
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    "cell_id": "00017-cd65dfeb-be83-4ed9-8967-b9295551868c",
    "deepnote_cell_type": "code"
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   "source": "price",
   "outputs": [
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     "output_type": "execute_result",
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         "PriceUSD": 30883.4697462303,
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        {
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        {
         "date": "2021-07-21T00:00:00.000000",
         "PriceUSD": 32119.6572258328,
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        {
         "date": "2021-07-22T00:00:00.000000",
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        {
         "date": "2021-07-23T00:00:00.000000",
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        },
        {
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         "PriceUSD": 39126.5402881356,
         "_deepnote_index_column": 4225
        },
        {
         "date": "2021-07-28T00:00:00.000000",
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        },
        {
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        },
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        {
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         "_deepnote_index_column": 4229
        },
        {
         "date": "2021-08-01T00:00:00.000000",
         "PriceUSD": 39968.9689544126,
         "_deepnote_index_column": 4230
        },
        {
         "date": "2021-08-02T00:00:00.000000",
         "PriceUSD": 39304.1862835769,
         "_deepnote_index_column": 4231
        },
        {
         "date": "2021-08-03T00:00:00.000000",
         "PriceUSD": 38288.8253975453,
         "_deepnote_index_column": 4232
        },
        {
         "date": "2021-08-04T00:00:00.000000",
         "PriceUSD": 39773.9113489188,
         "_deepnote_index_column": 4233
        },
        {
         "date": "2021-08-05T00:00:00.000000",
         "PriceUSD": 40941.650434249,
         "_deepnote_index_column": 4234
        },
        {
         "date": "2021-08-06T00:00:00.000000",
         "PriceUSD": 42790.3573284629,
         "_deepnote_index_column": 4235
        },
        {
         "date": "2021-08-07T00:00:00.000000",
         "PriceUSD": 44484.9005862069,
         "_deepnote_index_column": 4236
        },
        {
         "date": "2021-08-08T00:00:00.000000",
         "PriceUSD": 43972.3010181181,
         "_deepnote_index_column": 4237
        },
        {
         "date": "2021-08-09T00:00:00.000000",
         "PriceUSD": 46265.6424564582,
         "_deepnote_index_column": 4238
        },
        {
         "date": "2021-08-10T00:00:00.000000",
         "PriceUSD": 45488.7912624196,
         "_deepnote_index_column": 4239
        },
        {
         "date": "2021-08-11T00:00:00.000000",
         "PriceUSD": 45676.3263611923,
         "_deepnote_index_column": 4240
        },
        {
         "date": "2021-08-12T00:00:00.000000",
         "PriceUSD": 44405.7483483343,
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        },
        {
         "date": "2021-08-13T00:00:00.000000",
         "PriceUSD": 47717.4853559322,
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        },
        {
         "date": "2021-08-14T00:00:00.000000",
         "PriceUSD": 47124.2050736411,
         "_deepnote_index_column": 4243
        },
        {
         "date": "2021-08-15T00:00:00.000000",
         "PriceUSD": 47074.370710111,
         "_deepnote_index_column": 4244
        },
        {
         "date": "2021-08-16T00:00:00.000000",
         "PriceUSD": 45984.7199070719,
         "_deepnote_index_column": 4245
        },
        {
         "date": "2021-08-17T00:00:00.000000",
         "PriceUSD": 44714.0967982466,
         "_deepnote_index_column": 4246
        },
        {
         "date": "2021-08-18T00:00:00.000000",
         "PriceUSD": 44954.3637469316,
         "_deepnote_index_column": 4247
        },
        {
         "date": "2021-08-19T00:00:00.000000",
         "PriceUSD": 46646.8615049679,
         "_deepnote_index_column": 4248
        },
        {
         "date": "2021-08-20T00:00:00.000000",
         "PriceUSD": 49240.8356049094,
         "_deepnote_index_column": 4249
        },
        {
         "date": "2021-08-21T00:00:00.000000",
         "PriceUSD": 49070.2409701929,
         "_deepnote_index_column": 4250
        },
        {
         "date": "2021-08-22T00:00:00.000000",
         "PriceUSD": 49363.3160952659,
         "_deepnote_index_column": 4251
        },
        {
         "date": "2021-08-23T00:00:00.000000",
         "PriceUSD": 49590.8763676213,
         "_deepnote_index_column": 4252
        },
        {
         "date": "2021-08-24T00:00:00.000000",
         "PriceUSD": 47925.8622127411,
         "_deepnote_index_column": 4253
        },
        {
         "date": "2021-08-25T00:00:00.000000",
         "PriceUSD": 49002.3166049094,
         "_deepnote_index_column": 4254
        },
        {
         "date": "2021-08-26T00:00:00.000000",
         "PriceUSD": 47190.3449929866,
         "_deepnote_index_column": 4255
        },
        {
         "date": "2021-08-27T00:00:00.000000",
         "PriceUSD": 49048.4755891292,
         "_deepnote_index_column": 4256
        },
        {
         "date": "2021-08-28T00:00:00.000000",
         "PriceUSD": 48852.4687995324,
         "_deepnote_index_column": 4257
        },
        {
         "date": "2021-08-29T00:00:00.000000",
         "PriceUSD": 48948.3479579193,
         "_deepnote_index_column": 4258
        },
        {
         "date": "2021-08-30T00:00:00.000000",
         "PriceUSD": 47125.7685189947,
         "_deepnote_index_column": 4259
        },
        {
         "date": "2021-08-31T00:00:00.000000",
         "PriceUSD": 47219.2443424898,
         "_deepnote_index_column": 4260
        },
        {
         "date": "2021-09-01T00:00:00.000000",
         "PriceUSD": 48688.5375572764,
         "_deepnote_index_column": 4261
        },
        {
         "date": "2021-09-02T00:00:00.000000",
         "PriceUSD": 49369.5862746932,
         "_deepnote_index_column": 4262
        },
        {
         "date": "2021-09-03T00:00:00.000000",
         "PriceUSD": 49860.5341635301,
         "_deepnote_index_column": 4263
        },
        {
         "date": "2021-09-04T00:00:00.000000",
         "PriceUSD": 49919.577563647,
         "_deepnote_index_column": 4264
        },
        {
         "date": "2021-09-05T00:00:00.000000",
         "PriceUSD": 51747.6438565167,
         "_deepnote_index_column": 4265
        },
        {
         "date": "2021-09-06T00:00:00.000000",
         "PriceUSD": 52640.5883740503,
         "_deepnote_index_column": 4266
        },
        {
         "date": "2021-09-07T00:00:00.000000",
         "PriceUSD": 46854.4006028638,
         "_deepnote_index_column": 4267
        },
        {
         "date": "2021-09-08T00:00:00.000000",
         "PriceUSD": 46187.9757306254,
         "_deepnote_index_column": 4268
        },
        {
         "date": "2021-09-09T00:00:00.000000",
         "PriceUSD": 46448.7748290473,
         "_deepnote_index_column": 4269
        },
        {
         "date": "2021-09-10T00:00:00.000000",
         "PriceUSD": 44765.2755762712,
         "_deepnote_index_column": 4270
        },
        {
         "date": "2021-09-11T00:00:00.000000",
         "PriceUSD": 45098.5916447691,
         "_deepnote_index_column": 4271
        },
        {
         "date": "2021-09-12T00:00:00.000000",
         "PriceUSD": 46180.6124563998,
         "_deepnote_index_column": 4272
        },
        {
         "date": "2021-09-13T00:00:00.000000",
         "PriceUSD": 44991.5221277031,
         "_deepnote_index_column": 4273
        },
        {
         "date": "2021-09-14T00:00:00.000000",
         "PriceUSD": 47022.6177597896,
         "_deepnote_index_column": 4274
        },
        {
         "date": "2021-09-15T00:00:00.000000",
         "PriceUSD": 48167.1893676213,
         "_deepnote_index_column": 4275
        },
        {
         "date": "2021-09-16T00:00:00.000000",
         "PriceUSD": 47797.3283237873,
         "_deepnote_index_column": 4276
        },
        {
         "date": "2021-09-17T00:00:00.000000",
         "PriceUSD": 47213.7941294565,
         "_deepnote_index_column": 4277
        },
        {
         "date": "2021-09-18T00:00:00.000000",
         "PriceUSD": 48204.0136519579,
         "_deepnote_index_column": 4278
        },
        {
         "date": "2021-09-19T00:00:00.000000",
         "PriceUSD": 47218.4739964933,
         "_deepnote_index_column": 4279
        },
        {
         "date": "2021-09-20T00:00:00.000000",
         "PriceUSD": 42855.5103448276,
         "_deepnote_index_column": 4280
        },
        {
         "date": "2021-09-21T00:00:00.000000",
         "PriceUSD": 40526.4893760958,
         "_deepnote_index_column": 4281
        }
       ]
      },
      "text/plain": "                          date      PriceUSD\n0    2010-01-01 00:00:00+00:00           NaN\n1    2010-01-02 00:00:00+00:00           NaN\n2    2010-01-03 00:00:00+00:00           NaN\n3    2010-01-04 00:00:00+00:00           NaN\n4    2010-01-05 00:00:00+00:00           NaN\n...                        ...           ...\n4277 2021-09-17 00:00:00+00:00  47213.794129\n4278 2021-09-18 00:00:00+00:00  48204.013652\n4279 2021-09-19 00:00:00+00:00  47218.473996\n4280 2021-09-20 00:00:00+00:00  42855.510345\n4281 2021-09-21 00:00:00+00:00  40526.489376\n\n[4282 rows x 2 columns]",
      "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>date</th>\n      <th>PriceUSD</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>2010-01-01 00:00:00+00:00</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2010-01-02 00:00:00+00:00</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>2010-01-03 00:00:00+00:00</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>2010-01-04 00:00:00+00:00</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>2010-01-05 00:00:00+00:00</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>4277</th>\n      <td>2021-09-17 00:00:00+00:00</td>\n      <td>47213.794129</td>\n    </tr>\n    <tr>\n      <th>4278</th>\n      <td>2021-09-18 00:00:00+00:00</td>\n      <td>48204.013652</td>\n    </tr>\n    <tr>\n      <th>4279</th>\n      <td>2021-09-19 00:00:00+00:00</td>\n      <td>47218.473996</td>\n    </tr>\n    <tr>\n      <th>4280</th>\n      <td>2021-09-20 00:00:00+00:00</td>\n      <td>42855.510345</td>\n    </tr>\n    <tr>\n      <th>4281</th>\n      <td>2021-09-21 00:00:00+00:00</td>\n      <td>40526.489376</td>\n    </tr>\n  </tbody>\n</table>\n<p>4282 rows × 2 columns</p>\n</div>"
     },
     "metadata": {}
    }
   ],
   "execution_count": null
  },
  {
   "cell_type": "code",
   "metadata": {
    "tags": [],
    "deepnote_to_be_reexecuted": false,
    "source_hash": "5828b6c4",
    "execution_start": 1632709667475,
    "execution_millis": 2,
    "cell_id": "00018-95b5c257-aab9-4fa8-8918-2252a9363143",
    "deepnote_cell_type": "code"
   },
   "source": "price.info()",
   "outputs": [
    {
     "name": "stdout",
     "text": "<class 'pandas.core.frame.DataFrame'>\nInt64Index: 4282 entries, 0 to 4281\nData columns (total 2 columns):\n #   Column    Non-Null Count  Dtype              \n---  ------    --------------  -----              \n 0   date      4282 non-null   datetime64[ns, UTC]\n 1   PriceUSD  4084 non-null   float64            \ndtypes: datetime64[ns, UTC](1), float64(1)\nmemory usage: 100.4 KB\n",
     "output_type": "stream"
    }
   ],
   "execution_count": null
  },
  {
   "cell_type": "code",
   "metadata": {
    "tags": [],
    "deepnote_to_be_reexecuted": false,
    "source_hash": "37f22610",
    "execution_start": 1632709667528,
    "execution_millis": 880,
    "cell_id": "00019-67bca498-352b-4bd4-93e4-bf36c9222240",
    "deepnote_cell_type": "code"
   },
   "source": "# plot price for historical data\n\nplt.figure(figsize = (12, 5))\n#plt.plot(price['PriceUSD'])\nplt.plot(price['date'], price['PriceUSD'])\n#plt.xticks(range(0, df.shape[0], 180), df['date'].loc[::180], rotation=45)\n#plt.xticks(range(0, price.shape[0], 180), price['date'].loc[::180], rotation=45)\nplt.title('{} Historical Price'.format(coin_name), fontsize=14, fontweight='bold')\nplt.xlabel('Date', fontsize=14)\nplt.ylabel('Price in USD', fontsize=14)\n\n\ntime_index_split = int(np.round((len(price)*.90)))   \n\nplt.axvline(x=price.iloc[time_index_split]['date'] , color='r', label='axvline - full height')\n#plt.legend(['historical price', 'cutoff at 90%'])\n\n\narrow_start = price.iloc[time_index_split]['date'] #+ timedelta(days = 730)\ntext_start = price.iloc[time_index_split -2500]['date'] #+ timedelta() \n\nplt.annotate('end of 90% potential training set', xy=(arrow_start, price['PriceUSD'].max()/2), xytext=(text_start, price['PriceUSD'].max()/2),\n            arrowprops=dict(facecolor='black', shrink=0.05), fontsize = 14\n            )\n\n\n\nplt.grid()\nplt.show()",
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 864x360 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light",
      "image/png": {
       "width": 740,
       "height": 338
      }
     },
     "output_type": "display_data"
    }
   ],
   "execution_count": null
  },
  {
   "cell_type": "markdown",
   "source": "### Data preparation",
   "metadata": {
    "tags": [],
    "is_collapsed": false,
    "cell_id": "00020-8192daa0-04dc-439a-9187-6928bd0d4c30",
    "deepnote_cell_type": "text-cell-h3"
   }
  },
  {
   "cell_type": "markdown",
   "source": "Delete nan(s)",
   "metadata": {
    "tags": [],
    "is_collapsed": false,
    "cell_id": "00021-3c91bebb-a319-4c02-9610-8abc6d37892f",
    "deepnote_cell_type": "text-cell-p"
   }
  },
  {
   "cell_type": "code",
   "metadata": {
    "tags": [],
    "deepnote_to_be_reexecuted": false,
    "source_hash": "abb3d3e5",
    "execution_start": 1632709668405,
    "execution_millis": 93,
    "cell_id": "00022-4fa953b4-5ceb-4b0b-bcc3-41aae5bab7c4",
    "deepnote_cell_type": "code"
   },
   "source": "# note: might be better to remove nans from the master dataframe, but keep this for now\nprice = price.dropna()\nprice",
   "outputs": [
    {
     "output_type": "execute_result",
     "execution_count": 12,
     "data": {
      "application/vnd.deepnote.dataframe.v2+json": {
       "row_count": 4084,
       "column_count": 2,
       "columns": [
        {
         "name": "date",
         "dtype": "datetime64[ns, UTC]",
         "stats": {
          "unique_count": 4084,
          "nan_count": 0,
          "min": "2010-07-18 00:00:00+00:00",
          "max": "2021-09-21 00:00:00+00:00",
          "histogram": [
           {
            "bin_start": 1279411200000000000,
            "bin_end": 1314688320000000000,
            "count": 409
           },
           {
            "bin_start": 1314688320000000000,
            "bin_end": 1349965440000000000,
            "count": 408
           },
           {
            "bin_start": 1349965440000000000,
            "bin_end": 1385242560000000000,
            "count": 408
           },
           {
            "bin_start": 1385242560000000000,
            "bin_end": 1420519680000000000,
            "count": 409
           },
           {
            "bin_start": 1420519680000000000,
            "bin_end": 1455796800000000000,
            "count": 408
           },
           {
            "bin_start": 1455796800000000000,
            "bin_end": 1491073920000000000,
            "count": 408
           },
           {
            "bin_start": 1491073920000000000,
            "bin_end": 1526351040000000000,
            "count": 409
           },
           {
            "bin_start": 1526351040000000000,
            "bin_end": 1561628160000000000,
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      "text/plain": "                          date      PriceUSD\n198  2010-07-18 00:00:00+00:00      0.085840\n199  2010-07-19 00:00:00+00:00      0.080800\n200  2010-07-20 00:00:00+00:00      0.074736\n201  2010-07-21 00:00:00+00:00      0.079193\n202  2010-07-22 00:00:00+00:00      0.058470\n...                        ...           ...\n4277 2021-09-17 00:00:00+00:00  47213.794129\n4278 2021-09-18 00:00:00+00:00  48204.013652\n4279 2021-09-19 00:00:00+00:00  47218.473996\n4280 2021-09-20 00:00:00+00:00  42855.510345\n4281 2021-09-21 00:00:00+00:00  40526.489376\n\n[4084 rows x 2 columns]",
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     },
     "metadata": {}
    }
   ],
   "execution_count": null
  },
  {
   "cell_type": "markdown",
   "source": "Normalization",
   "metadata": {
    "tags": [],
    "is_collapsed": false,
    "cell_id": "00023-716f41c0-a9cd-4d70-9498-1b7909200c06",
    "deepnote_cell_type": "text-cell-p"
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  {
   "cell_type": "code",
   "metadata": {
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    "deepnote_to_be_reexecuted": false,
    "source_hash": "efda84d3",
    "execution_start": 1632709668496,
    "execution_millis": 3,
    "cell_id": "00024-dc1043f9-c7f8-4bfa-b74a-8a6651e4fc7b",
    "deepnote_cell_type": "code"
   },
   "source": "scaler = MinMaxScaler()\n\n# Fit the scaler with the portion of the dataset that will go to the training set\ntrain_split_value = 0.9\nnum_train = int(np.round(train_split_value * price.shape[0]))\n\n#scaler.fit(price[:num_train][['PriceUSD']])\n#normalized_price = scaler.transform(price[['PriceUSD']])\n\n\nscaler.fit(price[:num_train][['PriceUSD']].values)\nnormalized_price = pd.DataFrame()\nnormalized_price['date'] = price['date']\nnormalized_price['Price'] = scaler.transform(price[['PriceUSD']])",
   "outputs": [],
   "execution_count": null
  },
  {
   "cell_type": "code",
   "metadata": {
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    "deepnote_to_be_reexecuted": false,
    "source_hash": "c038211b",
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    "cell_id": "00025-96b0741a-89f6-4806-bd99-0a6ef59d514e",
    "deepnote_cell_type": "code"
   },
   "source": "normalized_price",
   "outputs": [
    {
     "output_type": "execute_result",
     "execution_count": 14,
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       ]
      },
      "text/plain": "                          date         Price\n198  2010-07-18 00:00:00+00:00  1.797278e-06\n199  2010-07-19 00:00:00+00:00  1.540665e-06\n200  2010-07-20 00:00:00+00:00  1.231901e-06\n201  2010-07-21 00:00:00+00:00  1.458837e-06\n202  2010-07-22 00:00:00+00:00  4.037146e-07\n...                        ...           ...\n4277 2021-09-17 00:00:00+00:00  2.403902e+00\n4278 2021-09-18 00:00:00+00:00  2.454319e+00\n4279 2021-09-19 00:00:00+00:00  2.404140e+00\n4280 2021-09-20 00:00:00+00:00  2.181998e+00\n4281 2021-09-21 00:00:00+00:00  2.063416e+00\n\n[4084 rows x 2 columns]",
      "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>date</th>\n      <th>Price</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>198</th>\n      <td>2010-07-18 00:00:00+00:00</td>\n      <td>1.797278e-06</td>\n    </tr>\n    <tr>\n      <th>199</th>\n      <td>2010-07-19 00:00:00+00:00</td>\n      <td>1.540665e-06</td>\n    </tr>\n    <tr>\n      <th>200</th>\n      <td>2010-07-20 00:00:00+00:00</td>\n      <td>1.231901e-06</td>\n    </tr>\n    <tr>\n      <th>201</th>\n      <td>2010-07-21 00:00:00+00:00</td>\n      <td>1.458837e-06</td>\n    </tr>\n    <tr>\n      <th>202</th>\n      <td>2010-07-22 00:00:00+00:00</td>\n      <td>4.037146e-07</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>4277</th>\n      <td>2021-09-17 00:00:00+00:00</td>\n      <td>2.403902e+00</td>\n    </tr>\n    <tr>\n      <th>4278</th>\n      <td>2021-09-18 00:00:00+00:00</td>\n      <td>2.454319e+00</td>\n    </tr>\n    <tr>\n      <th>4279</th>\n      <td>2021-09-19 00:00:00+00:00</td>\n      <td>2.404140e+00</td>\n    </tr>\n    <tr>\n      <th>4280</th>\n      <td>2021-09-20 00:00:00+00:00</td>\n      <td>2.181998e+00</td>\n    </tr>\n    <tr>\n      <th>4281</th>\n      <td>2021-09-21 00:00:00+00:00</td>\n      <td>2.063416e+00</td>\n    </tr>\n  </tbody>\n</table>\n<p>4084 rows × 2 columns</p>\n</div>"
     },
     "metadata": {}
    }
   ],
   "execution_count": null
  },
  {
   "cell_type": "code",
   "metadata": {
    "tags": [],
    "deepnote_to_be_reexecuted": false,
    "source_hash": "ab1c9e5c",
    "execution_start": 1632709668608,
    "execution_millis": 548,
    "cell_id": "00026-1139e1e6-9167-4ed0-ab9f-8f0eb1d39566",
    "deepnote_cell_type": "code"
   },
   "source": "plt.plot(normalized_price['date'], normalized_price['Price'])",
   "outputs": [
    {
     "output_type": "execute_result",
     "execution_count": 15,
     "data": {
      "text/plain": "[<matplotlib.lines.Line2D at 0x7fd9a66c9dd0>]"
     },
     "metadata": {}
    },
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light",
      "image/png": {
       "width": 377,
       "height": 248
      }
     },
     "output_type": "display_data"
    }
   ],
   "execution_count": null
  },
  {
   "cell_type": "markdown",
   "source": "Split data for training and testing",
   "metadata": {
    "tags": [],
    "is_collapsed": false,
    "cell_id": "00027-b43953ab-0c06-4839-b469-c9df6aed213b",
    "deepnote_cell_type": "text-cell-p"
   }
  },
  {
   "cell_type": "code",
   "metadata": {
    "tags": [],
    "deepnote_to_be_reexecuted": false,
    "source_hash": "df1f30be",
    "execution_start": 1632709669133,
    "execution_millis": 7,
    "cell_id": "00028-bacf240b-4302-4403-9062-ddfebd17765f",
    "deepnote_cell_type": "code"
   },
   "source": "def data_sequences(dataset, dates, target_size):\n    d = []\n    dt = []\n\n    for idx in range(len(dataset) - target_size):\n        d.append(dataset[idx : idx+target_size])\n        dt.append(dates[idx : idx+target_size])\n\n    return np.array(d), np.array(dt)\n\n\ndef split_dataset(dataset, dates, target_size, train_split=0.9):\n\n    data, dt = data_sequences(dataset, dates, target_size)\n    print(data.shape)\n\n    num_train = int(np.round(train_split_value * price.shape[0]))\n\n    window_size_adj_split_index = num_train - target_size\n\n    X_train = data[:window_size_adj_split_index, :-1, :]\n    y_train = data[:window_size_adj_split_index, -1, :]\n    print(X_train.shape)\n\n    X_test = data[window_size_adj_split_index:, :-1, :]\n    y_test = data[window_size_adj_split_index:, -1, :]\n    print(X_test.shape)\n\n    dt = dt[window_size_adj_split_index:, -1, :]\n    print(dt.shape)\n\n    return X_train, y_train, X_test, y_test, dt\n",
   "outputs": [],
   "execution_count": null
  },
  {
   "cell_type": "markdown",
   "source": "# Model",
   "metadata": {
    "tags": [],
    "is_collapsed": false,
    "cell_id": "00029-e951c5fe-3100-4e50-89c0-b1267c2efabd",
    "deepnote_cell_type": "text-cell-h1"
   }
  },
  {
   "cell_type": "markdown",
   "source": "### Build the model",
   "metadata": {
    "tags": [],
    "is_collapsed": false,
    "cell_id": "00030-242d86e8-656e-4181-bde9-5eb2c2574393",
    "deepnote_cell_type": "text-cell-h3"
   }
  },
  {
   "cell_type": "code",
   "metadata": {
    "tags": [],
    "cell_id": "00029-d7e5c1bf-32d7-42f9-b137-9e624ab620a3",
    "deepnote_to_be_reexecuted": false,
    "source_hash": "5d60f940",
    "execution_start": 1632709669149,
    "execution_millis": 1,
    "deepnote_cell_type": "code"
   },
   "source": "def build_model(X_train, window, num_neurons = 128, activation_function = 'linear'):\n    \n    model = Sequential([\n        LSTM(units=num_neurons, input_shape=(window, X_train.shape[-1]), return_sequences=True),\n        LeakyReLU(alpha=0.8),\n        LSTM(units=num_neurons, return_sequences=True),\n        LeakyReLU(alpha=0.8),\n        Dropout(0.2), \n        LSTM(units=int(num_neurons/2), return_sequences=False),\n        Dropout(0.2), \n        Dense(units=1, activation=activation_function)\n    ])\n\n    return model\n\n\ndef epochs_loss(model, loss_function = 'mean_squared_error', optimizer='adam'):\n     #'mean_absolute_error'\n    model.compile(\n        loss=loss_function, \n        optimizer='adam'\n    )\n\n    return model",
   "outputs": [],
   "execution_count": null
  },
  {
   "cell_type": "markdown",
   "source": "### Prediction",
   "metadata": {
    "tags": [],
    "is_collapsed": false,
    "cell_id": "00032-7fa24245-8783-4e75-8939-608607c75ad9",
    "deepnote_cell_type": "text-cell-h3"
   }
  },
  {
   "cell_type": "code",
   "metadata": {
    "tags": [],
    "cell_id": "00030-ed292d7a-30c6-4cfc-88aa-debde46641fb",
    "deepnote_to_be_reexecuted": false,
    "source_hash": "5ff4f117",
    "execution_start": 1632709669170,
    "execution_millis": 1078935,
    "deepnote_cell_type": "code"
   },
   "source": "windows = [1, 7, 30, 90] #, 200] #, 365]\n\nmae = []\nlosses = []\npredictions = []\ntest_dates = []\n\nfor wd in windows:\n    #X_train, y_train, X_test, y_test = split_dataset(normalized_price, target_size = wd + 1, train_split=train_split_value)\n\n    X_train, y_train, X_test, y_test, date_test = split_dataset(normalized_price[['Price']], \n                                                                normalized_price[['date']], \n                                                                target_size = wd + 1, \n                                                                train_split=train_split_value)\n    \n    model = build_model(X_train, wd, num_neurons = 128, activation_function = 'linear')\n    model = epochs_loss(model, loss_function = 'mean_squared_error', optimizer='adam')\n    print(model.summary())\n\n    history = model.fit(\n        X_train, \n        y_train, \n        epochs=25, \n        batch_size=64, \n        shuffle=False,\n        validation_split=0.1\n    )\n\n    y_pred = model.predict(X_test)\n    y_pred_inverse = scaler.inverse_transform(y_pred)\n    y_test_inverse = scaler.inverse_transform(y_test)\n    #scaler.inverse_transform(y_pred)\n\n    test_dates.append(date_test)\n    predictions.append(y_pred_inverse)\n    #predictions.append(list(itertools.chain(*y_pred_inverse)))\n    mae.append(tf.keras.metrics.mean_absolute_error(y_pred_inverse.T, y_test_inverse.T).numpy())\n    losses.append((history.history['loss'], history.history['val_loss']))",
   "outputs": [
    {
     "name": "stdout",
     "text": "(4082, 2, 1)\n(3674, 1, 1)\n(408, 1, 1)\n(408, 1)\n2021-09-27 02:27:50.397182: I tensorflow/compiler/jit/xla_cpu_device.cc:41] Not creating XLA devices, tf_xla_enable_xla_devices not set\n2021-09-27 02:27:50.397419: W tensorflow/stream_executor/platform/default/dso_loader.cc:60] Could not load dynamic library 'libcuda.so.1'; dlerror: libcuda.so.1: cannot open shared object file: No such file or directory\n2021-09-27 02:27:50.397443: W tensorflow/stream_executor/cuda/cuda_driver.cc:326] failed call to cuInit: UNKNOWN ERROR (303)\n2021-09-27 02:27:50.397462: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:156] kernel driver does not appear to be running on this host (p-465bb6a2-b771-4fe2-b2d2-aed723f7eb7d): /proc/driver/nvidia/version does not exist\n2021-09-27 02:27:50.397675: I tensorflow/core/platform/cpu_feature_guard.cc:142] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 AVX512F FMA\nTo enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n2021-09-27 02:27:50.397902: I tensorflow/compiler/jit/xla_gpu_device.cc:99] Not creating XLA devices, tf_xla_enable_xla_devices not set\nModel: \"sequential\"\n_________________________________________________________________\nLayer (type)                 Output Shape              Param #   \n=================================================================\nlstm (LSTM)                  (None, 1, 128)            66560     \n_________________________________________________________________\nleaky_re_lu (LeakyReLU)      (None, 1, 128)            0         \n_________________________________________________________________\nlstm_1 (LSTM)                (None, 1, 128)            131584    \n_________________________________________________________________\nleaky_re_lu_1 (LeakyReLU)    (None, 1, 128)            0         \n_________________________________________________________________\ndropout (Dropout)            (None, 1, 128)            0         \n_________________________________________________________________\nlstm_2 (LSTM)                (None, 64)                49408     \n_________________________________________________________________\ndropout_1 (Dropout)          (None, 64)                0         \n_________________________________________________________________\ndense (Dense)                (None, 1)                 65        \n=================================================================\nTotal params: 247,617\nTrainable params: 247,617\nNon-trainable params: 0\n_________________________________________________________________\nNone\nEpoch 1/25\n2021-09-27 02:27:51.399568: I tensorflow/compiler/mlir/mlir_graph_optimization_pass.cc:116] None of the MLIR optimization passes are enabled (registered 2)\n2021-09-27 02:27:51.420150: I tensorflow/core/platform/profile_utils/cpu_utils.cc:112] CPU Frequency: 2499995000 Hz\n52/52 [==============================] - 7s 49ms/step - loss: 0.0047 - val_loss: 0.0995\nEpoch 2/25\n52/52 [==============================] - 1s 14ms/step - loss: 0.0179 - val_loss: 0.1117\nEpoch 3/25\n52/52 [==============================] - 1s 13ms/step - loss: 0.0111 - val_loss: 0.0338\nEpoch 4/25\n52/52 [==============================] - 1s 14ms/step - loss: 0.0087 - val_loss: 0.0032\nEpoch 5/25\n52/52 [==============================] - 1s 13ms/step - loss: 0.0014 - val_loss: 0.0028\nEpoch 6/25\n52/52 [==============================] - 1s 14ms/step - loss: 6.6610e-04 - val_loss: 8.5096e-04\nEpoch 7/25\n52/52 [==============================] - 1s 15ms/step - loss: 4.0487e-04 - val_loss: 3.2753e-04\nEpoch 8/25\n52/52 [==============================] - 1s 14ms/step - loss: 3.1833e-04 - val_loss: 3.4660e-04\nEpoch 9/25\n52/52 [==============================] - 1s 14ms/step - loss: 2.6074e-04 - val_loss: 0.0013\nEpoch 10/25\n52/52 [==============================] - 1s 13ms/step - loss: 2.2062e-04 - val_loss: 0.0014\nEpoch 11/25\n52/52 [==============================] - 1s 13ms/step - loss: 1.0191e-04 - val_loss: 3.9435e-04\nEpoch 12/25\n52/52 [==============================] - 1s 14ms/step - loss: 8.4067e-05 - val_loss: 3.0863e-04\nEpoch 13/25\n52/52 [==============================] - 1s 14ms/step - loss: 9.1494e-05 - val_loss: 2.6479e-04\nEpoch 14/25\n52/52 [==============================] - 1s 14ms/step - loss: 8.6499e-05 - val_loss: 2.6837e-04\nEpoch 15/25\n52/52 [==============================] - 1s 14ms/step - loss: 8.0163e-05 - val_loss: 2.8247e-04\nEpoch 16/25\n52/52 [==============================] - 1s 13ms/step - loss: 1.4132e-04 - val_loss: 7.2476e-04\nEpoch 17/25\n52/52 [==============================] - 1s 14ms/step - loss: 2.0364e-04 - val_loss: 0.0019\nEpoch 18/25\n52/52 [==============================] - 1s 15ms/step - loss: 2.9725e-04 - val_loss: 0.0042\nEpoch 19/25\n52/52 [==============================] - 1s 14ms/step - loss: 4.2195e-04 - val_loss: 0.0017\nEpoch 20/25\n52/52 [==============================] - 1s 14ms/step - loss: 8.8382e-04 - val_loss: 8.7310e-04\nEpoch 21/25\n52/52 [==============================] - 1s 14ms/step - loss: 0.0010 - val_loss: 7.2718e-04\nEpoch 22/25\n52/52 [==============================] - 1s 15ms/step - loss: 6.5301e-04 - val_loss: 8.4509e-04\nEpoch 23/25\n52/52 [==============================] - 1s 13ms/step - loss: 6.2303e-04 - val_loss: 9.0399e-04\nEpoch 24/25\n52/52 [==============================] - 1s 14ms/step - loss: 5.7362e-04 - val_loss: 0.0013\nEpoch 25/25\n52/52 [==============================] - 1s 13ms/step - loss: 5.3640e-04 - val_loss: 9.2271e-04\n(4076, 8, 1)\n(3668, 7, 1)\n(408, 7, 1)\n(408, 1)\nModel: \"sequential_1\"\n_________________________________________________________________\nLayer (type)                 Output Shape              Param #   \n=================================================================\nlstm_3 (LSTM)                (None, 7, 128)            66560     \n_________________________________________________________________\nleaky_re_lu_2 (LeakyReLU)    (None, 7, 128)            0         \n_________________________________________________________________\nlstm_4 (LSTM)                (None, 7, 128)            131584    \n_________________________________________________________________\nleaky_re_lu_3 (LeakyReLU)    (None, 7, 128)            0         \n_________________________________________________________________\ndropout_2 (Dropout)          (None, 7, 128)            0         \n_________________________________________________________________\nlstm_5 (LSTM)                (None, 64)                49408     \n_________________________________________________________________\ndropout_3 (Dropout)          (None, 64)                0         \n_________________________________________________________________\ndense_1 (Dense)              (None, 1)                 65        \n=================================================================\nTotal params: 247,617\nTrainable params: 247,617\nNon-trainable params: 0\n_________________________________________________________________\nNone\nEpoch 1/25\n52/52 [==============================] - 9s 75ms/step - loss: 0.0025 - val_loss: 0.0109\nEpoch 2/25\n52/52 [==============================] - 3s 53ms/step - loss: 0.0121 - val_loss: 0.0021\nEpoch 3/25\n52/52 [==============================] - 3s 50ms/step - loss: 0.0198 - val_loss: 0.0021\nEpoch 4/25\n52/52 [==============================] - 3s 51ms/step - loss: 0.0043 - val_loss: 0.0685\nEpoch 5/25\n52/52 [==============================] - 3s 51ms/step - loss: 0.0101 - val_loss: 0.0099\nEpoch 6/25\n52/52 [==============================] - 3s 50ms/step - loss: 0.0026 - val_loss: 0.0074\nEpoch 7/25\n52/52 [==============================] - 3s 50ms/step - loss: 5.6141e-04 - val_loss: 0.0062\nEpoch 8/25\n52/52 [==============================] - 3s 50ms/step - loss: 4.0349e-04 - val_loss: 9.7216e-04\nEpoch 9/25\n52/52 [==============================] - 3s 51ms/step - loss: 3.4923e-04 - val_loss: 0.0015\nEpoch 10/25\n52/52 [==============================] - 3s 52ms/step - loss: 4.0372e-04 - val_loss: 0.0017\nEpoch 11/25\n52/52 [==============================] - 3s 51ms/step - loss: 2.8954e-04 - val_loss: 0.0010\nEpoch 12/25\n52/52 [==============================] - 3s 50ms/step - loss: 2.2694e-04 - val_loss: 0.0012\nEpoch 13/25\n52/52 [==============================] - 3s 50ms/step - loss: 2.3240e-04 - val_loss: 0.0014\nEpoch 14/25\n52/52 [==============================] - 3s 50ms/step - loss: 2.4113e-04 - val_loss: 0.0029\nEpoch 15/25\n52/52 [==============================] - 3s 51ms/step - loss: 2.9087e-04 - val_loss: 0.0041\nEpoch 16/25\n52/52 [==============================] - 3s 51ms/step - loss: 2.3264e-04 - val_loss: 0.0051\nEpoch 17/25\n52/52 [==============================] - 3s 51ms/step - loss: 3.0972e-04 - val_loss: 0.0029\nEpoch 18/25\n52/52 [==============================] - 3s 51ms/step - loss: 4.7419e-04 - val_loss: 0.0017\nEpoch 19/25\n52/52 [==============================] - 3s 51ms/step - loss: 5.6212e-04 - val_loss: 0.0013\nEpoch 20/25\n52/52 [==============================] - 3s 51ms/step - loss: 2.8318e-04 - val_loss: 0.0036\nEpoch 21/25\n52/52 [==============================] - 3s 50ms/step - loss: 3.2383e-04 - val_loss: 0.0092\nEpoch 22/25\n52/52 [==============================] - 3s 50ms/step - loss: 3.0766e-04 - val_loss: 0.0031\nEpoch 23/25\n52/52 [==============================] - 3s 50ms/step - loss: 0.0014 - val_loss: 0.0010\nEpoch 24/25\n52/52 [==============================] - 3s 50ms/step - loss: 3.8360e-04 - val_loss: 9.0751e-04\nEpoch 25/25\n52/52 [==============================] - 3s 51ms/step - loss: 2.5023e-04 - val_loss: 8.9149e-04\n(4053, 31, 1)\n(3645, 30, 1)\n(408, 30, 1)\n(408, 1)\nModel: \"sequential_2\"\n_________________________________________________________________\nLayer (type)                 Output Shape              Param #   \n=================================================================\nlstm_6 (LSTM)                (None, 30, 128)           66560     \n_________________________________________________________________\nleaky_re_lu_4 (LeakyReLU)    (None, 30, 128)           0         \n_________________________________________________________________\nlstm_7 (LSTM)                (None, 30, 128)           131584    \n_________________________________________________________________\nleaky_re_lu_5 (LeakyReLU)    (None, 30, 128)           0         \n_________________________________________________________________\ndropout_4 (Dropout)          (None, 30, 128)           0         \n_________________________________________________________________\nlstm_8 (LSTM)                (None, 64)                49408     \n_________________________________________________________________\ndropout_5 (Dropout)          (None, 64)                0         \n_________________________________________________________________\ndense_2 (Dense)              (None, 1)                 65        \n=================================================================\nTotal params: 247,617\nTrainable params: 247,617\nNon-trainable params: 0\n_________________________________________________________________\nNone\nEpoch 1/25\n52/52 [==============================] - 14s 206ms/step - loss: 0.0017 - val_loss: 0.0814\nEpoch 2/25\n52/52 [==============================] - 10s 190ms/step - loss: 0.0082 - val_loss: 0.0038\nEpoch 3/25\n52/52 [==============================] - 10s 190ms/step - loss: 0.0047 - val_loss: 0.0034\nEpoch 4/25\n52/52 [==============================] - 10s 189ms/step - loss: 0.0051 - val_loss: 0.0031\nEpoch 5/25\n52/52 [==============================] - 10s 188ms/step - loss: 0.0030 - val_loss: 0.0079\nEpoch 6/25\n52/52 [==============================] - 10s 188ms/step - loss: 0.0033 - val_loss: 0.0042\nEpoch 7/25\n52/52 [==============================] - 10s 185ms/step - loss: 6.2018e-04 - val_loss: 0.0065\nEpoch 8/25\n52/52 [==============================] - 10s 189ms/step - loss: 3.9948e-04 - val_loss: 0.0024\nEpoch 9/25\n52/52 [==============================] - 10s 186ms/step - loss: 2.5694e-04 - val_loss: 0.0013\nEpoch 10/25\n52/52 [==============================] - 10s 189ms/step - loss: 2.4095e-04 - val_loss: 0.0012\nEpoch 11/25\n52/52 [==============================] - 10s 189ms/step - loss: 2.8144e-04 - val_loss: 0.0040\nEpoch 12/25\n52/52 [==============================] - 10s 185ms/step - loss: 9.6930e-04 - val_loss: 0.0183\nEpoch 13/25\n52/52 [==============================] - 10s 187ms/step - loss: 0.0011 - val_loss: 0.0013\nEpoch 14/25\n52/52 [==============================] - 10s 185ms/step - loss: 0.0013 - val_loss: 0.0011\nEpoch 15/25\n52/52 [==============================] - 9s 183ms/step - loss: 2.0246e-04 - val_loss: 0.0013\nEpoch 16/25\n52/52 [==============================] - 10s 190ms/step - loss: 2.3131e-04 - val_loss: 0.0036\nEpoch 17/25\n52/52 [==============================] - 10s 190ms/step - loss: 4.4157e-04 - val_loss: 0.0169\nEpoch 18/25\n52/52 [==============================] - 10s 184ms/step - loss: 7.6047e-04 - val_loss: 0.0010\nEpoch 19/25\n52/52 [==============================] - 10s 183ms/step - loss: 0.0020 - val_loss: 0.0010\nEpoch 20/25\n52/52 [==============================] - 10s 184ms/step - loss: 2.8013e-04 - val_loss: 9.6350e-04\nEpoch 21/25\n52/52 [==============================] - 10s 185ms/step - loss: 2.4165e-04 - val_loss: 0.0014\nEpoch 22/25\n52/52 [==============================] - 10s 186ms/step - loss: 2.3678e-04 - val_loss: 0.0053\nEpoch 23/25\n52/52 [==============================] - 10s 187ms/step - loss: 5.6268e-04 - val_loss: 0.0136\nEpoch 24/25\n52/52 [==============================] - 10s 190ms/step - loss: 5.7105e-04 - val_loss: 0.0014\nEpoch 25/25\n52/52 [==============================] - 10s 188ms/step - loss: 0.0017 - val_loss: 8.4095e-04\n(3993, 91, 1)\n(3585, 90, 1)\n(408, 90, 1)\n(408, 1)\nModel: \"sequential_3\"\n_________________________________________________________________\nLayer (type)                 Output Shape              Param #   \n=================================================================\nlstm_9 (LSTM)                (None, 90, 128)           66560     \n_________________________________________________________________\nleaky_re_lu_6 (LeakyReLU)    (None, 90, 128)           0         \n_________________________________________________________________\nlstm_10 (LSTM)               (None, 90, 128)           131584    \n_________________________________________________________________\nleaky_re_lu_7 (LeakyReLU)    (None, 90, 128)           0         \n_________________________________________________________________\ndropout_6 (Dropout)          (None, 90, 128)           0         \n_________________________________________________________________\nlstm_11 (LSTM)               (None, 64)                49408     \n_________________________________________________________________\ndropout_7 (Dropout)          (None, 64)                0         \n_________________________________________________________________\ndense_3 (Dense)              (None, 1)                 65        \n=================================================================\nTotal params: 247,617\nTrainable params: 247,617\nNon-trainable params: 0\n_________________________________________________________________\nNone\nEpoch 1/25\n51/51 [==============================] - 34s 584ms/step - loss: 8.9791e-04 - val_loss: 0.0079\nEpoch 2/25\n51/51 [==============================] - 29s 562ms/step - loss: 0.0168 - val_loss: 0.0055\nEpoch 3/25\n51/51 [==============================] - 28s 556ms/step - loss: 0.0022 - val_loss: 0.0067\nEpoch 4/25\n51/51 [==============================] - 28s 556ms/step - loss: 0.0016 - val_loss: 0.0014\nEpoch 5/25\n51/51 [==============================] - 28s 556ms/step - loss: 0.0023 - val_loss: 0.0100\nEpoch 6/25\n51/51 [==============================] - 28s 556ms/step - loss: 0.0030 - val_loss: 0.0022\nEpoch 7/25\n51/51 [==============================] - 28s 550ms/step - loss: 0.0010 - val_loss: 0.0050\nEpoch 8/25\n51/51 [==============================] - 28s 552ms/step - loss: 0.0013 - val_loss: 0.0101\nEpoch 9/25\n51/51 [==============================] - 28s 546ms/step - loss: 0.0014 - val_loss: 0.0030\nEpoch 10/25\n51/51 [==============================] - 28s 559ms/step - loss: 2.2320e-04 - val_loss: 0.0043\nEpoch 11/25\n51/51 [==============================] - 30s 580ms/step - loss: 2.1576e-04 - val_loss: 0.0023\nEpoch 12/25\n51/51 [==============================] - 31s 609ms/step - loss: 3.2272e-04 - val_loss: 0.0028\nEpoch 13/25\n51/51 [==============================] - 29s 562ms/step - loss: 2.4459e-04 - val_loss: 0.0040\nEpoch 14/25\n51/51 [==============================] - 28s 549ms/step - loss: 2.3581e-04 - val_loss: 0.0034\nEpoch 15/25\n51/51 [==============================] - 28s 554ms/step - loss: 3.5951e-04 - val_loss: 0.0043\nEpoch 16/25\n51/51 [==============================] - 28s 552ms/step - loss: 2.8993e-04 - val_loss: 0.0056\nEpoch 17/25\n51/51 [==============================] - 28s 553ms/step - loss: 3.0913e-04 - val_loss: 0.0044\nEpoch 18/25\n51/51 [==============================] - 28s 555ms/step - loss: 6.0772e-04 - val_loss: 0.0029\nEpoch 19/25\n51/51 [==============================] - 28s 550ms/step - loss: 8.4417e-04 - val_loss: 0.0016\nEpoch 20/25\n51/51 [==============================] - 28s 553ms/step - loss: 7.3560e-04 - val_loss: 0.0020\nEpoch 21/25\n51/51 [==============================] - 28s 556ms/step - loss: 2.6292e-04 - val_loss: 0.0038\nEpoch 22/25\n51/51 [==============================] - 28s 550ms/step - loss: 3.0255e-04 - val_loss: 0.0091\nEpoch 23/25\n51/51 [==============================] - 28s 557ms/step - loss: 3.4543e-04 - val_loss: 0.0051\nEpoch 24/25\n51/51 [==============================] - 29s 572ms/step - loss: 6.6100e-04 - val_loss: 0.0021\nEpoch 25/25\n51/51 [==============================] - 29s 558ms/step - loss: 0.0011 - val_loss: 0.0018\n",
     "output_type": "stream"
    }
   ],
   "execution_count": null
  },
  {
   "cell_type": "code",
   "metadata": {
    "tags": [],
    "cell_id": "00034-de61f2d1-7870-4ddf-8002-6f7f8ca5f0a3",
    "deepnote_to_be_reexecuted": false,
    "source_hash": "7e94d9f5",
    "execution_start": 1632710748107,
    "execution_millis": 5,
    "deepnote_cell_type": "code"
   },
   "source": "#len(predictions[0])",
   "outputs": [],
   "execution_count": null
  },
  {
   "cell_type": "code",
   "metadata": {
    "tags": [],
    "cell_id": "00035-1eba6083-febc-41a8-9b78-ddbee1a2515d",
    "deepnote_to_be_reexecuted": false,
    "source_hash": "25eb2b9b",
    "execution_start": 1632710748115,
    "execution_millis": 0,
    "deepnote_cell_type": "code"
   },
   "source": "#np.concatenate((a, b)).T[0]",
   "outputs": [],
   "execution_count": null
  },
  {
   "cell_type": "code",
   "metadata": {
    "tags": [],
    "cell_id": "00029-a358c761-8a85-4721-942c-ec88a0eb2fcb",
    "deepnote_to_be_reexecuted": false,
    "source_hash": "4411cfd4",
    "execution_start": 1632710748125,
    "execution_millis": 730,
    "deepnote_cell_type": "code"
   },
   "source": "plt.figure(figsize = (15,8))\n\na = scaler.inverse_transform(y_train)[-1].reshape(-1,1)\nb = y_test_inverse[:-1]\n\nnaive_mae = round(tf.keras.metrics.mean_absolute_error(np.concatenate((a, b)).T, y_test_inverse.T).numpy()[0])\n\n\nplt.plot(df['date'][-len(y_test_inverse):], y_test_inverse, color='green') #, label=\"Actual Price\"\n\nplt.plot(df['date'][-len(y_test_inverse):], np.concatenate((a, b)).T[0], color='pink') #, label=\"Naive Prediction\"\n\n##plt.plot(list(itertools.chain(*y_test_inverse)), color='green') #, label=\"Actual Price\"\n##plt.plot(test, list(itertools.chain(*y_test_inverse)), color='green') #, label=\"Actual Price\"\n##print(price.index)\n\ncolors = ['blue', 'red', 'orange', 'purple', 'gray', 'black']\nleg = []\nfor idx, pred in enumerate(predictions):\n    plt.plot(df['date'][-len(y_test_inverse):], pred, color=colors[idx])\n    leg.append('prediction: window = {} days, MAE = ${}'.format(windows[idx], round(np.float_(mae[idx]))))\n\n#dates = pd.DataFrame(list(itertools.chain(*price[num_train+1:-1][['date']].values)))\n#plt.xticks(range(0, dates.shape[0], 30), dates[0].loc[::30], rotation=45)\n##plt.xticks(range(0, price.shape[0], 180), price['date'].loc[::180], rotation=45)\n\nplt.title('{} Price Prediction'.format(coin_name), fontsize=14, fontweight='bold')\nplt.xlabel('Days')\nplt.ylabel('Price (USD)')\n#plt.legend(leg, loc='best')\nplt.legend([\"Actual Price\", \"Naive Prediction: MAE = ${}\".format(naive_mae)] + leg, loc='best')\n\nplt.grid() \nplt.show()\n\n# 13486, 10276, 10762",
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 1080x576 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light",
      "image/png": {
       "width": 903,
       "height": 497
      }
     },
     "output_type": "display_data"
    }
   ],
   "execution_count": null
  },
  {
   "cell_type": "markdown",
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