stocks.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Fetching Stock Prices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import yfinance as yf\n",
    "import datetime\n",
    "from joblib import Parallel, delayed\n",
    "import multiprocessing\n",
    "import json \n",
    "import json.decoder\n",
    "import os \n",
    "import requests\n",
    "import pandas_datareader\n",
    "import alpha_vantage.timeseries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "ts = alpha_vantage.timeseries.TimeSeries(key='QVEGLYRJUK0ABVMT',output_format='pandas')\n",
    "data, meta_data = ts.get_intraday('GOOGL','1min','full')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>1. open</th>\n",
       "      <th>2. high</th>\n",
       "      <th>3. low</th>\n",
       "      <th>4. close</th>\n",
       "      <th>5. 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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2023-01-12 20:00:00</th>\n",
       "      <td>91.12</td>\n",
       "      <td>91.21</td>\n",
       "      <td>91.12</td>\n",
       "      <td>91.21</td>\n",
       "      <td>842.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-01-12 19:59:00</th>\n",
       "      <td>91.14</td>\n",
       "      <td>91.15</td>\n",
       "      <td>91.14</td>\n",
       "      <td>91.15</td>\n",
       "      <td>568.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-01-12 19:58:00</th>\n",
       "      <td>91.12</td>\n",
       "      <td>91.12</td>\n",
       "      <td>91.12</td>\n",
       "      <td>91.12</td>\n",
       "      <td>428.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-01-12 19:56:00</th>\n",
       "      <td>91.16</td>\n",
       "      <td>91.16</td>\n",
       "      <td>91.16</td>\n",
       "      <td>91.16</td>\n",
       "      <td>379.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-01-12 19:52:00</th>\n",
       "      <td>91.16</td>\n",
       "      <td>91.19</td>\n",
       "      <td>91.16</td>\n",
       "      <td>91.19</td>\n",
       "      <td>1129.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-12-30 04:11:00</th>\n",
       "      <td>87.66</td>\n",
       "      <td>87.67</td>\n",
       "      <td>87.66</td>\n",
       "      <td>87.67</td>\n",
       "      <td>497.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-12-30 04:08:00</th>\n",
       "      <td>87.65</td>\n",
       "      <td>87.69</td>\n",
       "      <td>87.65</td>\n",
       "      <td>87.69</td>\n",
       "      <td>1689.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-12-30 04:05:00</th>\n",
       "      <td>87.49</td>\n",
       "      <td>87.55</td>\n",
       "      <td>87.49</td>\n",
       "      <td>87.50</td>\n",
       "      <td>1330.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-12-30 04:02:00</th>\n",
       "      <td>87.65</td>\n",
       "      <td>87.70</td>\n",
       "      <td>87.65</td>\n",
       "      <td>87.70</td>\n",
       "      <td>541.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-12-30 04:01:00</th>\n",
       "      <td>87.59</td>\n",
       "      <td>87.59</td>\n",
       "      <td>87.59</td>\n",
       "      <td>87.59</td>\n",
       "      <td>405.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5636 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     1. open  2. high  3. low  4. close  5. volume\n",
       "date                                                              \n",
       "2023-01-12 20:00:00    91.12    91.21   91.12     91.21      842.0\n",
       "2023-01-12 19:59:00    91.14    91.15   91.14     91.15      568.0\n",
       "2023-01-12 19:58:00    91.12    91.12   91.12     91.12      428.0\n",
       "2023-01-12 19:56:00    91.16    91.16   91.16     91.16      379.0\n",
       "2023-01-12 19:52:00    91.16    91.19   91.16     91.19     1129.0\n",
       "...                      ...      ...     ...       ...        ...\n",
       "2022-12-30 04:11:00    87.66    87.67   87.66     87.67      497.0\n",
       "2022-12-30 04:08:00    87.65    87.69   87.65     87.69     1689.0\n",
       "2022-12-30 04:05:00    87.49    87.55   87.49     87.50     1330.0\n",
       "2022-12-30 04:02:00    87.65    87.70   87.65     87.70      541.0\n",
       "2022-12-30 04:01:00    87.59    87.59   87.59     87.59      405.0\n",
       "\n",
       "[5636 rows x 5 columns]"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "env_json = {\n",
    "    'Current_day': 1\n",
    "}\n",
    "with open('env.json',\"w\") as outfile:\n",
    "    json.dump(env_json,outfile)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "tmp_stocks = pd.read_csv('nasdaq.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "symbols = list(tmp_stocks['Symbol'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "tmp_json = {}\n",
    "counter = 0 \n",
    "sub_count = 0 \n",
    "for idx, i in enumerate(symbols):\n",
    "    if \"^\" in i: \n",
    "        continue \n",
    "    if sub_count % 500 == 0:\n",
    "        counter += 1 \n",
    "        tmp_json[counter] = []\n",
    "    tmp_json[counter].append(i)\n",
    "    sub_count += 1 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "14"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(tmp_json)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(\"stocks.json\",\"w\") as outfile:\n",
    "    json.dump(tmp_json,outfile)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "      <th>volume</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2022-12-30 09:31:00</th>\n",
       "      <td>2.9000</td>\n",
       "      <td>2.9100</td>\n",
       "      <td>2.9000</td>\n",
       "      <td>2.9100</td>\n",
       "      <td>255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-12-30 09:32:00</th>\n",
       "      <td>2.9100</td>\n",
       "      <td>2.9100</td>\n",
       "      <td>2.9100</td>\n",
       "      <td>2.9100</td>\n",
       "      <td>1000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-12-30 09:34:00</th>\n",
       "      <td>2.9101</td>\n",
       "      <td>2.9101</td>\n",
       "      <td>2.9101</td>\n",
       "      <td>2.9101</td>\n",
       "      <td>1000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-12-30 09:35:00</th>\n",
       "      <td>2.9200</td>\n",
       "      <td>2.9200</td>\n",
       "      <td>2.9200</td>\n",
       "      <td>2.9200</td>\n",
       "      <td>517</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-12-30 09:45:00</th>\n",
       "      <td>2.9200</td>\n",
       "      <td>2.9200</td>\n",
       "      <td>2.9200</td>\n",
       "      <td>2.9200</td>\n",
       "      <td>250</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
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       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-01-12 15:56:00</th>\n",
       "      <td>3.0000</td>\n",
       "      <td>3.0000</td>\n",
       "      <td>3.0000</td>\n",
       "      <td>3.0000</td>\n",
       "      <td>1735</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-01-12 15:57:00</th>\n",
       "      <td>3.0000</td>\n",
       "      <td>3.0000</td>\n",
       "      <td>3.0000</td>\n",
       "      <td>3.0000</td>\n",
       "      <td>643</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-01-12 15:58:00</th>\n",
       "      <td>3.0000</td>\n",
       "      <td>3.0000</td>\n",
       "      <td>2.9998</td>\n",
       "      <td>2.9998</td>\n",
       "      <td>8280</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-01-12 15:59:00</th>\n",
       "      <td>3.0100</td>\n",
       "      <td>3.0100</td>\n",
       "      <td>3.0100</td>\n",
       "      <td>3.0100</td>\n",
       "      <td>620</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-01-12 16:00:00</th>\n",
       "      <td>3.0200</td>\n",
       "      <td>3.0200</td>\n",
       "      <td>3.0100</td>\n",
       "      <td>3.0100</td>\n",
       "      <td>1517</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>606 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                       open    high     low   close  volume\n",
       "2022-12-30 09:31:00  2.9000  2.9100  2.9000  2.9100     255\n",
       "2022-12-30 09:32:00  2.9100  2.9100  2.9100  2.9100    1000\n",
       "2022-12-30 09:34:00  2.9101  2.9101  2.9101  2.9101    1000\n",
       "2022-12-30 09:35:00  2.9200  2.9200  2.9200  2.9200     517\n",
       "2022-12-30 09:45:00  2.9200  2.9200  2.9200  2.9200     250\n",
       "...                     ...     ...     ...     ...     ...\n",
       "2023-01-12 15:56:00  3.0000  3.0000  3.0000  3.0000    1735\n",
       "2023-01-12 15:57:00  3.0000  3.0000  3.0000  3.0000     643\n",
       "2023-01-12 15:58:00  3.0000  3.0000  2.9998  2.9998    8280\n",
       "2023-01-12 15:59:00  3.0100  3.0100  3.0100  3.0100     620\n",
       "2023-01-12 16:00:00  3.0200  3.0200  3.0100  3.0100    1517\n",
       "\n",
       "[606 rows x 5 columns]"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pandas_datareader.data.DataReader('AAIC' , 'av-intraday', start=datetime.datetime(2022,1,1),end=datetime.datetime(2023,1,14),api_key='QVEGLYRJUK0ABVMT',)"
   ]
  }
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