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abFunctions/ml_03_stock_preduction_roku.ipynb Secret

Created July 9, 2021 05:00
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ml_03_stock_preduction_roku.ipynb
{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Tutorial url: https://www.youtube.com/watch?v=lncoLfue_Y4"
]
},
{
"cell_type": "code",
"execution_count": 140,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"import pandas as pd\n",
"import datetime\n",
"import sklearn"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [],
"source": [
"# using data set containing Roku daily stock price over the past 5 years\n",
"dataset = pd.read_csv('ROKU_daily.csv',index_col=\"Date\",parse_dates=True)"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Open</th>\n",
" <th>High</th>\n",
" <th>Low</th>\n",
" <th>Close</th>\n",
" <th>Adj Close</th>\n",
" <th>Volume</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Date</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2017-09-28</th>\n",
" <td>15.800000</td>\n",
" <td>23.500000</td>\n",
" <td>15.750000</td>\n",
" <td>23.500000</td>\n",
" <td>23.500000</td>\n",
" <td>39265900</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2017-09-29</th>\n",
" <td>26.740000</td>\n",
" <td>29.799999</td>\n",
" <td>25.469999</td>\n",
" <td>26.540001</td>\n",
" <td>26.540001</td>\n",
" <td>44294700</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2017-10-02</th>\n",
" <td>25.200001</td>\n",
" <td>26.280001</td>\n",
" <td>23.260000</td>\n",
" <td>23.559999</td>\n",
" <td>23.559999</td>\n",
" <td>16008400</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2017-10-03</th>\n",
" <td>23.010000</td>\n",
" <td>23.020000</td>\n",
" <td>20.770000</td>\n",
" <td>20.809999</td>\n",
" <td>20.809999</td>\n",
" <td>13678500</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2017-10-04</th>\n",
" <td>21.290001</td>\n",
" <td>22.600000</td>\n",
" <td>20.820000</td>\n",
" <td>20.850000</td>\n",
" <td>20.850000</td>\n",
" <td>9345700</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2021-06-21</th>\n",
" <td>365.049988</td>\n",
" <td>385.790009</td>\n",
" <td>363.100006</td>\n",
" <td>382.730011</td>\n",
" <td>382.730011</td>\n",
" <td>6227000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2021-06-22</th>\n",
" <td>382.589996</td>\n",
" <td>405.679993</td>\n",
" <td>382.000000</td>\n",
" <td>403.500000</td>\n",
" <td>403.500000</td>\n",
" <td>6686300</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2021-06-23</th>\n",
" <td>406.501007</td>\n",
" <td>424.339996</td>\n",
" <td>406.501007</td>\n",
" <td>421.700012</td>\n",
" <td>421.700012</td>\n",
" <td>8639900</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2021-06-24</th>\n",
" <td>430.494995</td>\n",
" <td>431.779999</td>\n",
" <td>419.549988</td>\n",
" <td>423.579987</td>\n",
" <td>423.579987</td>\n",
" <td>6575100</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2021-06-25</th>\n",
" <td>426.174988</td>\n",
" <td>431.975006</td>\n",
" <td>420.299988</td>\n",
" <td>430.940002</td>\n",
" <td>430.940002</td>\n",
" <td>4987500</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>942 rows × 6 columns</p>\n",
"</div>"
],
"text/plain": [
" Open High Low Close Adj Close \\\n",
"Date \n",
"2017-09-28 15.800000 23.500000 15.750000 23.500000 23.500000 \n",
"2017-09-29 26.740000 29.799999 25.469999 26.540001 26.540001 \n",
"2017-10-02 25.200001 26.280001 23.260000 23.559999 23.559999 \n",
"2017-10-03 23.010000 23.020000 20.770000 20.809999 20.809999 \n",
"2017-10-04 21.290001 22.600000 20.820000 20.850000 20.850000 \n",
"... ... ... ... ... ... \n",
"2021-06-21 365.049988 385.790009 363.100006 382.730011 382.730011 \n",
"2021-06-22 382.589996 405.679993 382.000000 403.500000 403.500000 \n",
"2021-06-23 406.501007 424.339996 406.501007 421.700012 421.700012 \n",
"2021-06-24 430.494995 431.779999 419.549988 423.579987 423.579987 \n",
"2021-06-25 426.174988 431.975006 420.299988 430.940002 430.940002 \n",
"\n",
" Volume \n",
"Date \n",
"2017-09-28 39265900 \n",
"2017-09-29 44294700 \n",
"2017-10-02 16008400 \n",
"2017-10-03 13678500 \n",
"2017-10-04 9345700 \n",
"... ... \n",
"2021-06-21 6227000 \n",
"2021-06-22 6686300 \n",
"2021-06-23 8639900 \n",
"2021-06-24 6575100 \n",
"2021-06-25 4987500 \n",
"\n",
"[942 rows x 6 columns]"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dataset"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Open False\n",
"High False\n",
"Low False\n",
"Close False\n",
"Adj Close False\n",
"Volume False\n",
"dtype: bool"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# check to see if any of the data is not applicable. Returns bool, detects missing values (true means no missing values)\n",
"dataset.isna().any()"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"DatetimeIndex: 942 entries, 2017-09-28 to 2021-06-25\n",
"Data columns (total 6 columns):\n",
" # Column Non-Null Count Dtype \n",
"--- ------ -------------- ----- \n",
" 0 Open 942 non-null float64\n",
" 1 High 942 non-null float64\n",
" 2 Low 942 non-null float64\n",
" 3 Close 942 non-null float64\n",
" 4 Adj Close 942 non-null float64\n",
" 5 Volume 942 non-null int64 \n",
"dtypes: float64(5), int64(1)\n",
"memory usage: 51.5 KB\n"
]
}
],
"source": [
"# print out the basic info of the data set \n",
"dataset.info()"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<AxesSubplot:xlabel='Date'>"
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 1152x432 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# plotting open price over the past 5 years\n",
"dataset['Open'].plot(figsize=(16,6))"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [],
"source": [
"# convert column \"a\" of a DataFrame\n",
"# dataset[\"Close\"] = dataset[\"Close\"].str.replace(',','').astype(float)"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {},
"outputs": [],
"source": [
"# dataset[\"Volume\"] = dataset[\"Volume\"].str.replace(',','').astype(float)"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Open</th>\n",
" <th>High</th>\n",
" <th>Low</th>\n",
" <th>Close</th>\n",
" <th>Adj Close</th>\n",
" <th>Volume</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Date</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2017-09-28</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2017-09-29</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2017-10-02</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2017-10-03</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2017-10-04</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2017-10-05</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2017-10-06</th>\n",
" <td>22.274286</td>\n",
" <td>24.395715</td>\n",
" <td>21.345714</td>\n",
" <td>22.924286</td>\n",
" <td>22.924286</td>\n",
" <td>1.943051e+07</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2017-10-09</th>\n",
" <td>23.424286</td>\n",
" <td>24.660000</td>\n",
" <td>22.461428</td>\n",
" <td>23.072857</td>\n",
" <td>23.072857</td>\n",
" <td>1.492693e+07</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2017-10-10</th>\n",
" <td>23.202858</td>\n",
" <td>24.060000</td>\n",
" <td>22.065714</td>\n",
" <td>22.557143</td>\n",
" <td>22.557143</td>\n",
" <td>9.667586e+06</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2017-10-11</th>\n",
" <td>23.004286</td>\n",
" <td>23.781429</td>\n",
" <td>22.057143</td>\n",
" <td>22.571429</td>\n",
" <td>22.571429</td>\n",
" <td>8.159143e+06</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2017-10-12</th>\n",
" <td>23.098572</td>\n",
" <td>23.968572</td>\n",
" <td>22.440000</td>\n",
" <td>22.974286</td>\n",
" <td>22.974286</td>\n",
" <td>6.711629e+06</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2017-10-13</th>\n",
" <td>23.450000</td>\n",
" <td>24.197143</td>\n",
" <td>22.744286</td>\n",
" <td>23.284286</td>\n",
" <td>23.284286</td>\n",
" <td>5.798943e+06</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2017-10-16</th>\n",
" <td>23.674286</td>\n",
" <td>24.340000</td>\n",
" <td>22.838572</td>\n",
" <td>23.310000</td>\n",
" <td>23.310000</td>\n",
" <td>5.260086e+06</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2017-10-17</th>\n",
" <td>23.647143</td>\n",
" <td>24.215715</td>\n",
" <td>22.767143</td>\n",
" <td>23.151429</td>\n",
" <td>23.151429</td>\n",
" <td>4.513229e+06</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2017-10-18</th>\n",
" <td>23.405714</td>\n",
" <td>23.808572</td>\n",
" <td>22.544286</td>\n",
" <td>22.795714</td>\n",
" <td>22.795714</td>\n",
" <td>3.550043e+06</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2017-10-19</th>\n",
" <td>22.885714</td>\n",
" <td>23.427143</td>\n",
" <td>22.351429</td>\n",
" <td>22.667143</td>\n",
" <td>22.667143</td>\n",
" <td>2.770871e+06</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2017-10-20</th>\n",
" <td>22.637143</td>\n",
" <td>23.155715</td>\n",
" <td>22.151429</td>\n",
" <td>22.411429</td>\n",
" <td>22.411429</td>\n",
" <td>2.242857e+06</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2017-10-23</th>\n",
" <td>22.362857</td>\n",
" <td>22.822857</td>\n",
" <td>21.708571</td>\n",
" <td>21.945715</td>\n",
" <td>21.945715</td>\n",
" <td>2.126100e+06</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2017-10-24</th>\n",
" <td>21.884286</td>\n",
" <td>22.322857</td>\n",
" <td>21.184286</td>\n",
" <td>21.481429</td>\n",
" <td>21.481429</td>\n",
" <td>2.041100e+06</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2017-10-25</th>\n",
" <td>21.411429</td>\n",
" <td>21.830714</td>\n",
" <td>20.661429</td>\n",
" <td>21.014286</td>\n",
" <td>21.014286</td>\n",
" <td>2.016714e+06</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Open High Low Close Adj Close \\\n",
"Date \n",
"2017-09-28 NaN NaN NaN NaN NaN \n",
"2017-09-29 NaN NaN NaN NaN NaN \n",
"2017-10-02 NaN NaN NaN NaN NaN \n",
"2017-10-03 NaN NaN NaN NaN NaN \n",
"2017-10-04 NaN NaN NaN NaN NaN \n",
"2017-10-05 NaN NaN NaN NaN NaN \n",
"2017-10-06 22.274286 24.395715 21.345714 22.924286 22.924286 \n",
"2017-10-09 23.424286 24.660000 22.461428 23.072857 23.072857 \n",
"2017-10-10 23.202858 24.060000 22.065714 22.557143 22.557143 \n",
"2017-10-11 23.004286 23.781429 22.057143 22.571429 22.571429 \n",
"2017-10-12 23.098572 23.968572 22.440000 22.974286 22.974286 \n",
"2017-10-13 23.450000 24.197143 22.744286 23.284286 23.284286 \n",
"2017-10-16 23.674286 24.340000 22.838572 23.310000 23.310000 \n",
"2017-10-17 23.647143 24.215715 22.767143 23.151429 23.151429 \n",
"2017-10-18 23.405714 23.808572 22.544286 22.795714 22.795714 \n",
"2017-10-19 22.885714 23.427143 22.351429 22.667143 22.667143 \n",
"2017-10-20 22.637143 23.155715 22.151429 22.411429 22.411429 \n",
"2017-10-23 22.362857 22.822857 21.708571 21.945715 21.945715 \n",
"2017-10-24 21.884286 22.322857 21.184286 21.481429 21.481429 \n",
"2017-10-25 21.411429 21.830714 20.661429 21.014286 21.014286 \n",
"\n",
" Volume \n",
"Date \n",
"2017-09-28 NaN \n",
"2017-09-29 NaN \n",
"2017-10-02 NaN \n",
"2017-10-03 NaN \n",
"2017-10-04 NaN \n",
"2017-10-05 NaN \n",
"2017-10-06 1.943051e+07 \n",
"2017-10-09 1.492693e+07 \n",
"2017-10-10 9.667586e+06 \n",
"2017-10-11 8.159143e+06 \n",
"2017-10-12 6.711629e+06 \n",
"2017-10-13 5.798943e+06 \n",
"2017-10-16 5.260086e+06 \n",
"2017-10-17 4.513229e+06 \n",
"2017-10-18 3.550043e+06 \n",
"2017-10-19 2.770871e+06 \n",
"2017-10-20 2.242857e+06 \n",
"2017-10-23 2.126100e+06 \n",
"2017-10-24 2.041100e+06 \n",
"2017-10-25 2.016714e+06 "
]
},
"execution_count": 33,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# get 7 day rolling mean (starts on day 7, iterates daily following with 7-day rolling avg)\n",
"dataset.rolling(7).mean().head(20)"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<AxesSubplot:xlabel='Date'>"
]
},
"execution_count": 35,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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\n",
"text/plain": [
"<Figure size 1152x432 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# plot out rolling 30-day avg in comparison to daily close\n",
"dataset[\"Open\"].plot(figsize=(16,6))\n",
"dataset.rolling(window=30).mean()[\"Close\"].plot()"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<AxesSubplot:xlabel='Date'>"
]
},
"execution_count": 36,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 1152x432 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"dataset['Close: 30 Day Mean'] = dataset['Close'].rolling(window=30).mean()\n",
"dataset[['Close','Close: 30 Day Mean']].plot(figsize=(16,6))"
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<AxesSubplot:xlabel='Date'>"
]
},
"execution_count": 38,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 1152x432 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# optional: specify a minimum number of periods\n",
"dataset['Close'].expanding(min_periods=1).mean().plot(figsize=(16,6))"
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Open</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Date</th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2017-09-28</th>\n",
" <td>15.800000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2017-09-29</th>\n",
" <td>26.740000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2017-10-02</th>\n",
" <td>25.200001</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2017-10-03</th>\n",
" <td>23.010000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2017-10-04</th>\n",
" <td>21.290001</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2021-06-21</th>\n",
" <td>365.049988</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2021-06-22</th>\n",
" <td>382.589996</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2021-06-23</th>\n",
" <td>406.501007</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2021-06-24</th>\n",
" <td>430.494995</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2021-06-25</th>\n",
" <td>426.174988</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>942 rows × 1 columns</p>\n",
"</div>"
],
"text/plain": [
" Open\n",
"Date \n",
"2017-09-28 15.800000\n",
"2017-09-29 26.740000\n",
"2017-10-02 25.200001\n",
"2017-10-03 23.010000\n",
"2017-10-04 21.290001\n",
"... ...\n",
"2021-06-21 365.049988\n",
"2021-06-22 382.589996\n",
"2021-06-23 406.501007\n",
"2021-06-24 430.494995\n",
"2021-06-25 426.174988\n",
"\n",
"[942 rows x 1 columns]"
]
},
"execution_count": 40,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Create dataframe of the training set\n",
"training_set=dataset['Open']\n",
"training_set=pd.DataFrame(training_set)\n",
"training_set"
]
},
{
"cell_type": "code",
"execution_count": 62,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Open False\n",
"High False\n",
"Low False\n",
"Close False\n",
"Adj Close False\n",
"Volume False\n",
"Close: 30 Day Mean True\n",
"dtype: bool"
]
},
"execution_count": 62,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# DATA PREPROCESSING\n",
"# Data cleaning\n",
"dataset.isna().any()"
]
},
{
"cell_type": "code",
"execution_count": 64,
"metadata": {},
"outputs": [],
"source": [
"# Feature scaling\n",
"from sklearn.preprocessing import MinMaxScaler\n",
"sc = MinMaxScaler(feature_range = (0,1))\n",
"training_set_scaled = sc.fit_transform(training_set)"
]
},
{
"cell_type": "code",
"execution_count": 67,
"metadata": {},
"outputs": [],
"source": [
"# Creating a data structure with 60 timesteps and 1 output\n",
"X_train = []\n",
"y_train = []\n",
"for i in range(60,942):\n",
" X_train.append(training_set_scaled[i-60:i, 0])\n",
" y_train.append(training_set_scaled[i, 0])\n",
"X_train, y_train = np.array(X_train), np.array(y_train)\n",
"\n",
"# Reshaping\n",
"X_train = np.reshape(X_train, (X_train.shape[0], X_train.shape[1], 1))"
]
},
{
"cell_type": "code",
"execution_count": 81,
"metadata": {},
"outputs": [],
"source": [
"# PART II - BUILDING THE RNN\n",
"\n",
"# Importing all Keras libraries and packages (it's easier)\n",
"from keras.layers import *\n",
"from keras.models import *\n",
"import keras.backend as K #for some advanced functions"
]
},
{
"cell_type": "code",
"execution_count": 88,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Epoch 1/100\n",
"28/28 [==============================] - 26s 107ms/step - loss: 0.0351\n",
"Epoch 2/100\n",
"28/28 [==============================] - 4s 129ms/step - loss: 0.0050\n",
"Epoch 3/100\n",
"28/28 [==============================] - 3s 123ms/step - loss: 0.0042\n",
"Epoch 4/100\n",
"28/28 [==============================] - 3s 115ms/step - loss: 0.0045\n",
"Epoch 5/100\n",
"28/28 [==============================] - 4s 127ms/step - loss: 0.0043\n",
"Epoch 6/100\n",
"28/28 [==============================] - 3s 120ms/step - loss: 0.0047\n",
"Epoch 7/100\n",
"28/28 [==============================] - 3s 116ms/step - loss: 0.0037\n",
"Epoch 8/100\n",
"28/28 [==============================] - 3s 107ms/step - loss: 0.0040\n",
"Epoch 9/100\n",
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"Epoch 10/100\n",
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"Epoch 15/100\n",
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"Epoch 17/100\n",
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"Epoch 18/100\n",
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"Epoch 19/100\n",
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"Epoch 20/100\n",
"28/28 [==============================] - 3s 107ms/step - loss: 0.0023\n",
"Epoch 21/100\n",
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"Epoch 24/100\n",
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"Epoch 25/100\n",
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"Epoch 26/100\n",
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"Epoch 28/100\n",
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"Epoch 29/100\n",
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"Epoch 30/100\n",
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"Epoch 31/100\n",
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"Epoch 32/100\n",
"28/28 [==============================] - 3s 116ms/step - loss: 0.0021\n",
"Epoch 33/100\n",
"28/28 [==============================] - 3s 108ms/step - loss: 0.0026\n",
"Epoch 34/100\n",
"28/28 [==============================] - 3s 111ms/step - loss: 0.0022\n",
"Epoch 35/100\n",
"28/28 [==============================] - 3s 112ms/step - loss: 0.0023\n",
"Epoch 36/100\n",
"28/28 [==============================] - 3s 107ms/step - loss: 0.0018\n",
"Epoch 37/100\n",
"28/28 [==============================] - 3s 119ms/step - loss: 0.0017\n",
"Epoch 38/100\n",
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"Epoch 39/100\n",
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"Epoch 41/100\n",
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"Epoch 42/100\n",
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"Epoch 45/100\n",
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"Epoch 46/100\n",
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"Epoch 47/100\n",
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"Epoch 48/100\n",
"28/28 [==============================] - 3s 112ms/step - loss: 0.0019\n",
"Epoch 49/100\n",
"28/28 [==============================] - 3s 113ms/step - loss: 0.0015\n",
"Epoch 50/100\n",
"28/28 [==============================] - 3s 107ms/step - loss: 0.0013\n",
"Epoch 51/100\n",
"28/28 [==============================] - 3s 122ms/step - loss: 0.0031\n",
"Epoch 52/100\n",
"28/28 [==============================] - 3s 104ms/step - loss: 0.0026\n",
"Epoch 53/100\n",
"28/28 [==============================] - 3s 108ms/step - loss: 0.0015\n",
"Epoch 54/100\n",
"28/28 [==============================] - 3s 111ms/step - loss: 0.0013\n",
"Epoch 55/100\n",
"28/28 [==============================] - 3s 112ms/step - loss: 0.0021\n",
"Epoch 56/100\n",
"28/28 [==============================] - 3s 122ms/step - loss: 0.0015\n",
"Epoch 57/100\n",
"28/28 [==============================] - 3s 106ms/step - loss: 0.0016\n",
"Epoch 58/100\n",
"28/28 [==============================] - 3s 115ms/step - loss: 0.0016\n",
"Epoch 59/100\n",
"28/28 [==============================] - 5s 171ms/step - loss: 0.0014\n",
"Epoch 60/100\n",
"28/28 [==============================] - 6s 206ms/step - loss: 0.0014\n",
"Epoch 61/100\n",
"28/28 [==============================] - 6s 212ms/step - loss: 0.0017\n",
"Epoch 62/100\n",
"28/28 [==============================] - 6s 200ms/step - loss: 0.0014\n",
"Epoch 63/100\n",
"28/28 [==============================] - 6s 197ms/step - loss: 0.0013\n",
"Epoch 64/100\n",
"28/28 [==============================] - 4s 146ms/step - loss: 0.0016\n",
"Epoch 65/100\n",
"28/28 [==============================] - 4s 155ms/step - loss: 0.0016\n",
"Epoch 66/100\n",
"28/28 [==============================] - 5s 164ms/step - loss: 0.0015A\n",
"Epoch 67/100\n",
"28/28 [==============================] - 4s 150ms/step - loss: 0.0014\n",
"Epoch 68/100\n",
"28/28 [==============================] - 4s 137ms/step - loss: 0.0014\n",
"Epoch 69/100\n",
"28/28 [==============================] - 4s 138ms/step - loss: 0.0014\n",
"Epoch 70/100\n",
"28/28 [==============================] - 5s 178ms/step - loss: 0.0014\n",
"Epoch 71/100\n",
"28/28 [==============================] - 4s 146ms/step - loss: 0.0013\n",
"Epoch 72/100\n",
"28/28 [==============================] - 4s 151ms/step - loss: 0.0018\n",
"Epoch 73/100\n",
"28/28 [==============================] - 4s 150ms/step - loss: 0.0016\n",
"Epoch 74/100\n",
"28/28 [==============================] - 4s 152ms/step - loss: 0.0015\n",
"Epoch 75/100\n",
"28/28 [==============================] - 4s 153ms/step - loss: 0.0013\n",
"Epoch 76/100\n",
"28/28 [==============================] - 5s 189ms/step - loss: 0.0017\n",
"Epoch 77/100\n",
"28/28 [==============================] - 5s 178ms/step - loss: 0.0015\n",
"Epoch 78/100\n",
"28/28 [==============================] - 5s 193ms/step - loss: 0.0011\n",
"Epoch 79/100\n",
"28/28 [==============================] - 5s 194ms/step - loss: 0.0013\n",
"Epoch 80/100\n",
"28/28 [==============================] - 4s 153ms/step - loss: 0.0012\n",
"Epoch 81/100\n",
"28/28 [==============================] - 4s 160ms/step - loss: 0.0016\n",
"Epoch 82/100\n",
"28/28 [==============================] - 5s 175ms/step - loss: 0.0013\n",
"Epoch 83/100\n",
"28/28 [==============================] - 4s 151ms/step - loss: 0.0016\n",
"Epoch 84/100\n",
"28/28 [==============================] - 3s 110ms/step - loss: 0.0012\n",
"Epoch 85/100\n",
"28/28 [==============================] - 4s 143ms/step - loss: 0.0012\n",
"Epoch 86/100\n",
"28/28 [==============================] - 4s 134ms/step - loss: 0.0016\n",
"Epoch 87/100\n",
"28/28 [==============================] - 5s 174ms/step - loss: 0.0015\n",
"Epoch 88/100\n",
"28/28 [==============================] - 6s 199ms/step - loss: 0.0014\n",
"Epoch 89/100\n",
"28/28 [==============================] - 4s 147ms/step - loss: 0.0013\n",
"Epoch 90/100\n",
"28/28 [==============================] - 3s 103ms/step - loss: 0.0014\n",
"Epoch 91/100\n",
"28/28 [==============================] - 3s 101ms/step - loss: 0.0011\n",
"Epoch 92/100\n",
"28/28 [==============================] - 3s 100ms/step - loss: 0.0013\n",
"Epoch 93/100\n",
"28/28 [==============================] - 4s 127ms/step - loss: 0.0012 \n",
"Epoch 94/100\n",
"28/28 [==============================] - 5s 183ms/step - loss: 0.0011\n",
"Epoch 95/100\n",
"28/28 [==============================] - 4s 152ms/step - loss: 0.0013\n",
"Epoch 96/100\n",
"28/28 [==============================] - 4s 151ms/step - loss: 0.0011\n",
"Epoch 97/100\n",
"28/28 [==============================] - 6s 225ms/step - loss: 0.0011\n",
"Epoch 98/100\n",
"28/28 [==============================] - 6s 210ms/step - loss: 9.4430e-04\n",
"Epoch 99/100\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"28/28 [==============================] - 6s 212ms/step - loss: 0.0014\n",
"Epoch 100/100\n",
"28/28 [==============================] - 6s 211ms/step - loss: 0.0011\n"
]
},
{
"data": {
"text/plain": [
"<keras.callbacks.History at 0x7ff97b437e20>"
]
},
"execution_count": 88,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Initializing the RNN\n",
"regressor = Sequential()\n",
"\n",
"# Adding the first LSTM layer and some Dropout regularization\n",
"regressor.add(LSTM(units = 50, return_sequences = True, input_shape = (X_train.shape[1], 1)))\n",
"regressor.add(Dropout(0.2))\n",
"\n",
"# Adding a second LSTM layer and some Dropout regularization\n",
"regressor.add(LSTM(units = 50, return_sequences = True))\n",
"regressor.add(Dropout(0.2))\n",
"\n",
"# Adding a third LSTM layer and some Dropout regularization\n",
"regressor.add(LSTM(units = 50, return_sequences = True))\n",
"regressor.add(Dropout(0.2))\n",
"\n",
"# Adding a forth LSTM layer and some Dropout regularization\n",
"regressor.add(LSTM(units = 50))\n",
"regressor.add(Dropout(0.2))\n",
"\n",
"# Adding the output layer\n",
"regressor.add(Dense(units = 1))\n",
"\n",
"\n",
"# Compiling the RNN\n",
"regressor.compile(optimizer = 'adam', loss = 'mean_squared_error')\n",
"\n",
"# Fitting the RNN to the training set\n",
"regressor.fit(X_train, y_train, epochs = 100, batch_size = 32)"
]
},
{
"cell_type": "code",
"execution_count": 90,
"metadata": {},
"outputs": [],
"source": [
"# PART 3 - Making the predictions and visualizing the results\n",
"\n",
"# Getting the real stock price of 2017\n",
"dataset_test = pd.read_csv(\"ROKU_test.csv\",index_col=\"Date\",parse_dates=True)"
]
},
{
"cell_type": "code",
"execution_count": 92,
"metadata": {},
"outputs": [],
"source": [
"real_stock_price = dataset_test.iloc[:,1:2].values"
]
},
{
"cell_type": "code",
"execution_count": 94,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Open</th>\n",
" <th>High</th>\n",
" <th>Low</th>\n",
" <th>Close</th>\n",
" <th>Adj Close</th>\n",
" <th>Volume</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Date</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2020-06-26</th>\n",
" <td>124.820000</td>\n",
" <td>126.550003</td>\n",
" <td>120.260002</td>\n",
" <td>122.550003</td>\n",
" <td>122.550003</td>\n",
" <td>7984100</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2020-06-29</th>\n",
" <td>122.400002</td>\n",
" <td>123.209000</td>\n",
" <td>112.110001</td>\n",
" <td>115.050003</td>\n",
" <td>115.050003</td>\n",
" <td>12073800</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2020-06-30</th>\n",
" <td>115.099998</td>\n",
" <td>117.870003</td>\n",
" <td>113.879997</td>\n",
" <td>116.529999</td>\n",
" <td>116.529999</td>\n",
" <td>5845900</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2020-07-01</th>\n",
" <td>117.300003</td>\n",
" <td>129.440002</td>\n",
" <td>116.349998</td>\n",
" <td>128.389999</td>\n",
" <td>128.389999</td>\n",
" <td>15001800</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2020-07-02</th>\n",
" <td>128.470001</td>\n",
" <td>132.500000</td>\n",
" <td>125.195000</td>\n",
" <td>128.649994</td>\n",
" <td>128.649994</td>\n",
" <td>12158100</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Open High Low Close Adj Close \\\n",
"Date \n",
"2020-06-26 124.820000 126.550003 120.260002 122.550003 122.550003 \n",
"2020-06-29 122.400002 123.209000 112.110001 115.050003 115.050003 \n",
"2020-06-30 115.099998 117.870003 113.879997 116.529999 116.529999 \n",
"2020-07-01 117.300003 129.440002 116.349998 128.389999 128.389999 \n",
"2020-07-02 128.470001 132.500000 125.195000 128.649994 128.649994 \n",
"\n",
" Volume \n",
"Date \n",
"2020-06-26 7984100 \n",
"2020-06-29 12073800 \n",
"2020-06-30 5845900 \n",
"2020-07-01 15001800 \n",
"2020-07-02 12158100 "
]
},
"execution_count": 94,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dataset_test.head()"
]
},
{
"cell_type": "code",
"execution_count": 95,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"DatetimeIndex: 257 entries, 2020-06-26 to 2021-07-02\n",
"Data columns (total 6 columns):\n",
" # Column Non-Null Count Dtype \n",
"--- ------ -------------- ----- \n",
" 0 Open 257 non-null float64\n",
" 1 High 257 non-null float64\n",
" 2 Low 257 non-null float64\n",
" 3 Close 257 non-null float64\n",
" 4 Adj Close 257 non-null float64\n",
" 5 Volume 257 non-null int64 \n",
"dtypes: float64(5), int64(1)\n",
"memory usage: 14.1 KB\n"
]
}
],
"source": [
"dataset_test.info()"
]
},
{
"cell_type": "code",
"execution_count": 118,
"metadata": {},
"outputs": [],
"source": [
"dataset_test['Volume'] = dataset_test['Volume']"
]
},
{
"cell_type": "code",
"execution_count": 108,
"metadata": {},
"outputs": [],
"source": [
"test_set=dataset_test['Open']\n",
"test_set=pd.DataFrame(test_set)"
]
},
{
"cell_type": "code",
"execution_count": 109,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"DatetimeIndex: 257 entries, 2020-06-26 to 2021-07-02\n",
"Data columns (total 1 columns):\n",
" # Column Non-Null Count Dtype \n",
"--- ------ -------------- ----- \n",
" 0 Open 257 non-null float64\n",
"dtypes: float64(1)\n",
"memory usage: 4.0 KB\n"
]
}
],
"source": [
"test_set.info()"
]
},
{
"cell_type": "code",
"execution_count": 121,
"metadata": {},
"outputs": [],
"source": [
"# Getting the predicted stock price\n",
"dataset_total = pd.concat((dataset['Open'], dataset_test['Open']), axis = 0)\n",
"inputs = dataset_total[len(dataset_total) - len(dataset_test) - 60:].values\n",
"inputs = inputs.reshape(-1,1)\n",
"inputs = sc.transform(inputs)\n",
"X_test = []\n",
"for i in range(60, 80):\n",
" X_test.append(inputs[i-60:i, 0])\n",
"X_test = np.array(X_test)\n",
"X_test = np.reshape(X_test, (X_test.shape[0], X_test.shape[1], 1))\n",
"predicted_stock_price = regressor.predict(X_test)\n",
"predicted_stock_price = sc.inverse_transform(predicted_stock_price)"
]
},
{
"cell_type": "code",
"execution_count": 123,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"RangeIndex: 20 entries, 0 to 19\n",
"Data columns (total 1 columns):\n",
" # Column Non-Null Count Dtype \n",
"--- ------ -------------- ----- \n",
" 0 0 20 non-null float32\n",
"dtypes: float32(1)\n",
"memory usage: 208.0 bytes\n"
]
}
],
"source": [
"predicted_stock_price=pd.DataFrame(predicted_stock_price)\n",
"predicted_stock_price.info()"
]
},
{
"cell_type": "code",
"execution_count": 141,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# Visualizing the results\n",
"plt.plot(real_stock_price, color = 'red', label = 'Real Roku Stock Price')\n",
"plt.plot(predicted_stock_price, color = 'blue', label = 'Predicted Roku Stock Price')\n",
"plt.title('Roku Stock Price Prediction')\n",
"plt.xlabel('Time')\n",
"plt.ylabel('Roku Stock Price')\n",
"plt.legend()\n",
"plt.show()"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.8.5"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
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