finnhub.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import time\n",
    "import os\n",
    "from datetime import datetime\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "import finnhub"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "api_key = os.environ['FINNHUB_KEY']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "finnhub_client = finnhub.Client(api_key=api_key)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Script for harvesting data from finnhub"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "symbol_data = pd.DataFrame(finnhub_client.forex_symbols('OANDA'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['OANDA:USD_TRY',\n",
       " 'OANDA:GBP_NZD',\n",
       " 'OANDA:EUR_SEK',\n",
       " 'OANDA:XAU_AUD',\n",
       " 'OANDA:USD_JPY',\n",
       " 'OANDA:CAD_HKD',\n",
       " 'OANDA:USD_SGD',\n",
       " 'OANDA:USD_CZK',\n",
       " 'OANDA:USD_PLN',\n",
       " 'OANDA:XAU_NZD',\n",
       " 'OANDA:EUR_HKD',\n",
       " 'OANDA:USD_SEK',\n",
       " 'OANDA:HKD_JPY',\n",
       " 'OANDA:CAD_SGD',\n",
       " 'OANDA:XAG_NZD',\n",
       " 'OANDA:AUD_HKD',\n",
       " 'OANDA:GBP_HKD',\n",
       " 'OANDA:GBP_JPY',\n",
       " 'OANDA:AUD_CAD',\n",
       " 'OANDA:GBP_CAD',\n",
       " 'OANDA:EUR_DKK',\n",
       " 'OANDA:USD_THB',\n",
       " 'OANDA:CAD_JPY',\n",
       " 'OANDA:NZD_CHF',\n",
       " 'OANDA:AUD_SGD',\n",
       " 'OANDA:XAG_AUD',\n",
       " 'OANDA:EUR_USD',\n",
       " 'OANDA:XAG_SGD',\n",
       " 'OANDA:XAG_GBP',\n",
       " 'OANDA:USD_CHF',\n",
       " 'OANDA:USD_MXN',\n",
       " 'OANDA:USD_CNH',\n",
       " 'OANDA:SGD_CHF',\n",
       " 'OANDA:EUR_CHF',\n",
       " 'OANDA:EUR_PLN',\n",
       " 'OANDA:TRY_JPY',\n",
       " 'OANDA:EUR_CZK',\n",
       " 'OANDA:CAD_CHF',\n",
       " 'OANDA:NZD_HKD',\n",
       " 'OANDA:CHF_ZAR',\n",
       " 'OANDA:GBP_CHF',\n",
       " 'OANDA:NZD_CAD',\n",
       " 'OANDA:XAG_CHF',\n",
       " 'OANDA:USD_NOK',\n",
       " 'OANDA:EUR_JPY',\n",
       " 'OANDA:AUD_USD',\n",
       " 'OANDA:EUR_SGD',\n",
       " 'OANDA:AUD_CHF',\n",
       " 'OANDA:USD_DKK',\n",
       " 'OANDA:USD_HUF',\n",
       " 'OANDA:XAG_CAD',\n",
       " 'OANDA:EUR_HUF',\n",
       " 'OANDA:XAG_JPY',\n",
       " 'OANDA:NZD_USD',\n",
       " 'OANDA:XAU_EUR',\n",
       " 'OANDA:SGD_JPY',\n",
       " 'OANDA:EUR_NZD',\n",
       " 'OANDA:XAU_CHF',\n",
       " 'OANDA:NZD_SGD',\n",
       " 'OANDA:ZAR_JPY',\n",
       " 'OANDA:GBP_SGD',\n",
       " 'OANDA:XAU_GBP',\n",
       " 'OANDA:EUR_NOK',\n",
       " 'OANDA:USD_ZAR',\n",
       " 'OANDA:USD_CAD',\n",
       " 'OANDA:XAU_JPY',\n",
       " 'OANDA:CHF_HKD',\n",
       " 'OANDA:EUR_CAD',\n",
       " 'OANDA:XAU_HKD',\n",
       " 'OANDA:EUR_AUD',\n",
       " 'OANDA:XAU_CAD',\n",
       " 'OANDA:USD_HKD',\n",
       " 'OANDA:NZD_JPY',\n",
       " 'OANDA:AUD_JPY',\n",
       " 'OANDA:EUR_GBP',\n",
       " 'OANDA:GBP_PLN',\n",
       " 'OANDA:GBP_USD',\n",
       " 'OANDA:XAU_SGD',\n",
       " 'OANDA:CHF_JPY',\n",
       " 'OANDA:XAG_EUR',\n",
       " 'OANDA:GBP_ZAR',\n",
       " 'OANDA:EUR_TRY',\n",
       " 'OANDA:AUD_NZD',\n",
       " 'OANDA:EUR_ZAR',\n",
       " 'OANDA:GBP_AUD',\n",
       " 'OANDA:XAU_XAG',\n",
       " 'OANDA:XAG_HKD']"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "currencies = list(symbol_data[symbol_data['description'].str.contains('/')]['symbol'])\n",
    "currencies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SUCCESS OANDA:XAU_XAG 1200000000   \r"
     ]
    }
   ],
   "source": [
    "directory = 'data/OANDA'\n",
    "\n",
    "for currency in currencies:\n",
    "    # start = int(datetime.timestamp(datetime.now())) - 60*60*24*90 # just for testing\n",
    "    if currency[-7] == 'X':\n",
    "        start = 1200000000 # jan 10 2008 (metals seems to start at 2008)\n",
    "    else:\n",
    "        start = 1104537600 # jan 1 2005\n",
    "\n",
    "    end = int(datetime.timestamp(datetime.now()))\n",
    "    data = None\n",
    "    \n",
    "    # check if file already exists\n",
    "    filename = directory + f\"/{currency[-7:]}.csv\"\n",
    "    if os.path.exists(filename):\n",
    "        continue\n",
    "\n",
    "    while end > start:\n",
    "        time.sleep(.4) # to stay away from the rate limit of 150 calls per min\n",
    "        \n",
    "        # except the api call so that it can be retried (sometimes runs into server errors)\n",
    "        try:\n",
    "            response = finnhub_client.forex_candles(currency, '1', start, end)\n",
    "        except:\n",
    "            print(\"PAUSED\", currency, end, end = '\\t\\r')\n",
    "            time.sleep(90)\n",
    "            finnhub_client = finnhub.Client(api_key=api_key)\n",
    "            continue\n",
    "        \n",
    "        if response['c'] is None:\n",
    "            end = end - 14400 # 4 hours\n",
    "            print(\"NO DATA\", currency, end, end = '\\t\\r')\n",
    "            continue\n",
    "\n",
    "        fx = pd.DataFrame(response)\n",
    "        fx = fx.rename(columns = {'o': 'open', 'h': 'high', 'l': 'low', 'c': 'close', 'v': 'volume'})\n",
    "        fx['datetime'] = pd.to_datetime(fx['t'], unit='s')\n",
    "        fx = fx.set_index('datetime')\n",
    "        fx = fx.drop(columns = ['s', 't'])\n",
    "\n",
    "        data = pd.concat((fx, data))\n",
    "\n",
    "        end = response['t'][0]\n",
    "        print(\"SUCCESS\", currency, end, end = '\\t\\r')\n",
    "    \n",
    "    data.to_csv(filename)\n",
    "    del data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Appendix\n",
    "Just looking at finnhub data and see how it corresponds with broker (coinexx) data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='datetime'>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "response = pd.DataFrame(finnhub_client.forex_candles('OANDA:EUR_USD', '1', 1230768000, int(datetime.timestamp(datetime.now()))))\n",
    "\n",
    "fx = pd.DataFrame(response).rename(columns = {'o': 'open', 'h': 'high', 'l': 'low', 'c': 'close'})\n",
    "fx['datetime'] = pd.to_datetime(fx['t'], unit='s')\n",
    "fx = fx.set_index('datetime')\n",
    "fx['close'][-500:].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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>close</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>open</th>\n",
       "      <th>s</th>\n",
       "      <th>t</th>\n",
       "      <th>v</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>datetime</th>\n",
       "      <th></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>2023-02-10 20:59:00</th>\n",
       "      <td>1.06783</td>\n",
       "      <td>1.06796</td>\n",
       "      <td>1.06777</td>\n",
       "      <td>1.06792</td>\n",
       "      <td>ok</td>\n",
       "      <td>1676062740</td>\n",
       "      <td>65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-02-10 21:00:00</th>\n",
       "      <td>1.06793</td>\n",
       "      <td>1.06794</td>\n",
       "      <td>1.06781</td>\n",
       "      <td>1.06782</td>\n",
       "      <td>ok</td>\n",
       "      <td>1676062800</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-02-10 21:01:00</th>\n",
       "      <td>1.06791</td>\n",
       "      <td>1.06794</td>\n",
       "      <td>1.06790</td>\n",
       "      <td>1.06794</td>\n",
       "      <td>ok</td>\n",
       "      <td>1676062860</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-02-10 21:02:00</th>\n",
       "      <td>1.06790</td>\n",
       "      <td>1.06794</td>\n",
       "      <td>1.06789</td>\n",
       "      <td>1.06789</td>\n",
       "      <td>ok</td>\n",
       "      <td>1676062920</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-02-10 21:03:00</th>\n",
       "      <td>1.06773</td>\n",
       "      <td>1.06789</td>\n",
       "      <td>1.06768</td>\n",
       "      <td>1.06785</td>\n",
       "      <td>ok</td>\n",
       "      <td>1676062980</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-02-10 21:04:00</th>\n",
       "      <td>1.06775</td>\n",
       "      <td>1.06779</td>\n",
       "      <td>1.06772</td>\n",
       "      <td>1.06772</td>\n",
       "      <td>ok</td>\n",
       "      <td>1676063040</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-02-10 21:05:00</th>\n",
       "      <td>1.06765</td>\n",
       "      <td>1.06774</td>\n",
       "      <td>1.06762</td>\n",
       "      <td>1.06774</td>\n",
       "      <td>ok</td>\n",
       "      <td>1676063100</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-02-10 21:06:00</th>\n",
       "      <td>1.06773</td>\n",
       "      <td>1.06774</td>\n",
       "      <td>1.06768</td>\n",
       "      <td>1.06768</td>\n",
       "      <td>ok</td>\n",
       "      <td>1676063160</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-02-10 21:07:00</th>\n",
       "      <td>1.06779</td>\n",
       "      <td>1.06782</td>\n",
       "      <td>1.06772</td>\n",
       "      <td>1.06775</td>\n",
       "      <td>ok</td>\n",
       "      <td>1676063220</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-02-10 21:08:00</th>\n",
       "      <td>1.06779</td>\n",
       "      <td>1.06783</td>\n",
       "      <td>1.06779</td>\n",
       "      <td>1.06779</td>\n",
       "      <td>ok</td>\n",
       "      <td>1676063280</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-02-10 21:09:00</th>\n",
       "      <td>1.06776</td>\n",
       "      <td>1.06778</td>\n",
       "      <td>1.06776</td>\n",
       "      <td>1.06777</td>\n",
       "      <td>ok</td>\n",
       "      <td>1676063340</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-02-10 21:10:00</th>\n",
       "      <td>1.06773</td>\n",
       "      <td>1.06778</td>\n",
       "      <td>1.06772</td>\n",
       "      <td>1.06778</td>\n",
       "      <td>ok</td>\n",
       "      <td>1676063400</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-02-10 21:11:00</th>\n",
       "      <td>1.06773</td>\n",
       "      <td>1.06775</td>\n",
       "      <td>1.06771</td>\n",
       "      <td>1.06774</td>\n",
       "      <td>ok</td>\n",
       "      <td>1676063460</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-02-10 21:12:00</th>\n",
       "      <td>1.06783</td>\n",
       "      <td>1.06785</td>\n",
       "      <td>1.06772</td>\n",
       "      <td>1.06775</td>\n",
       "      <td>ok</td>\n",
       "      <td>1676063520</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-02-10 21:13:00</th>\n",
       "      <td>1.06778</td>\n",
       "      <td>1.06783</td>\n",
       "      <td>1.06774</td>\n",
       "      <td>1.06783</td>\n",
       "      <td>ok</td>\n",
       "      <td>1676063580</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-02-10 21:14:00</th>\n",
       "      <td>1.06781</td>\n",
       "      <td>1.06782</td>\n",
       "      <td>1.06778</td>\n",
       "      <td>1.06779</td>\n",
       "      <td>ok</td>\n",
       "      <td>1676063640</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-02-10 21:15:00</th>\n",
       "      <td>1.06782</td>\n",
       "      <td>1.06783</td>\n",
       "      <td>1.06781</td>\n",
       "      <td>1.06782</td>\n",
       "      <td>ok</td>\n",
       "      <td>1676063700</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       close     high      low     open   s           t   v\n",
       "datetime                                                                   \n",
       "2023-02-10 20:59:00  1.06783  1.06796  1.06777  1.06792  ok  1676062740  65\n",
       "2023-02-10 21:00:00  1.06793  1.06794  1.06781  1.06782  ok  1676062800  45\n",
       "2023-02-10 21:01:00  1.06791  1.06794  1.06790  1.06794  ok  1676062860  24\n",
       "2023-02-10 21:02:00  1.06790  1.06794  1.06789  1.06789  ok  1676062920  12\n",
       "2023-02-10 21:03:00  1.06773  1.06789  1.06768  1.06785  ok  1676062980  34\n",
       "2023-02-10 21:04:00  1.06775  1.06779  1.06772  1.06772  ok  1676063040  28\n",
       "2023-02-10 21:05:00  1.06765  1.06774  1.06762  1.06774  ok  1676063100  31\n",
       "2023-02-10 21:06:00  1.06773  1.06774  1.06768  1.06768  ok  1676063160   6\n",
       "2023-02-10 21:07:00  1.06779  1.06782  1.06772  1.06775  ok  1676063220  20\n",
       "2023-02-10 21:08:00  1.06779  1.06783  1.06779  1.06779  ok  1676063280   9\n",
       "2023-02-10 21:09:00  1.06776  1.06778  1.06776  1.06777  ok  1676063340   4\n",
       "2023-02-10 21:10:00  1.06773  1.06778  1.06772  1.06778  ok  1676063400  15\n",
       "2023-02-10 21:11:00  1.06773  1.06775  1.06771  1.06774  ok  1676063460  11\n",
       "2023-02-10 21:12:00  1.06783  1.06785  1.06772  1.06775  ok  1676063520  30\n",
       "2023-02-10 21:13:00  1.06778  1.06783  1.06774  1.06783  ok  1676063580  15\n",
       "2023-02-10 21:14:00  1.06781  1.06782  1.06778  1.06779  ok  1676063640  14\n",
       "2023-02-10 21:15:00  1.06782  1.06783  1.06781  1.06782  ok  1676063700  15"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fx.between_time('20:59', '21:15')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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