Tutorial.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# AIOKraken Tutorial\n",
    "For more information, please check http://github.com/asmodehn/aiokraken\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Obtaining file:///opt/Projects/aiokraken\n",
      "Requirement already satisfied: aiohttp in /home/alexv/.local/share/virtualenvs/aiokraken-bMbyeTfo/lib/python3.7/site-packages (from aiokraken==2.0.0) (3.6.0)\n",
      "Requirement already satisfied: aiodns in /home/alexv/.local/share/virtualenvs/aiokraken-bMbyeTfo/lib/python3.7/site-packages (from aiokraken==2.0.0) (2.0.0)\n",
      "Requirement already satisfied: cchardet in /home/alexv/.local/share/virtualenvs/aiokraken-bMbyeTfo/lib/python3.7/site-packages (from aiokraken==2.0.0) (2.1.4)\n",
      "Requirement already satisfied: chardet<4.0,>=2.0 in /home/alexv/.local/share/virtualenvs/aiokraken-bMbyeTfo/lib/python3.7/site-packages (from aiohttp->aiokraken==2.0.0) (3.0.4)\n",
      "Requirement already satisfied: yarl<2.0,>=1.0 in /home/alexv/.local/share/virtualenvs/aiokraken-bMbyeTfo/lib/python3.7/site-packages (from aiohttp->aiokraken==2.0.0) (1.3.0)\n",
      "Requirement already satisfied: async-timeout<4.0,>=3.0 in /home/alexv/.local/share/virtualenvs/aiokraken-bMbyeTfo/lib/python3.7/site-packages (from aiohttp->aiokraken==2.0.0) (3.0.1)\n",
      "Requirement already satisfied: attrs>=17.3.0 in /home/alexv/.local/share/virtualenvs/aiokraken-bMbyeTfo/lib/python3.7/site-packages (from aiohttp->aiokraken==2.0.0) (19.1.0)\n",
      "Requirement already satisfied: multidict<5.0,>=4.0 in /home/alexv/.local/share/virtualenvs/aiokraken-bMbyeTfo/lib/python3.7/site-packages (from aiohttp->aiokraken==2.0.0) (4.5.2)\n",
      "Requirement already satisfied: pycares>=3.0.0 in /home/alexv/.local/share/virtualenvs/aiokraken-bMbyeTfo/lib/python3.7/site-packages (from aiodns->aiokraken==2.0.0) (3.0.0)\n",
      "Requirement already satisfied: idna>=2.0 in /home/alexv/.local/share/virtualenvs/aiokraken-bMbyeTfo/lib/python3.7/site-packages (from yarl<2.0,>=1.0->aiohttp->aiokraken==2.0.0) (2.8)\n",
      "Requirement already satisfied: cffi>=1.5.0 in /home/alexv/.local/share/virtualenvs/aiokraken-bMbyeTfo/lib/python3.7/site-packages (from pycares>=3.0.0->aiodns->aiokraken==2.0.0) (1.12.3)\n",
      "Requirement already satisfied: pycparser in /home/alexv/.local/share/virtualenvs/aiokraken-bMbyeTfo/lib/python3.7/site-packages (from cffi>=1.5.0->pycares>=3.0.0->aiodns->aiokraken==2.0.0) (2.19)\n",
      "Installing collected packages: aiokraken\n",
      "  Found existing installation: aiokraken 2.0.0\n",
      "    Uninstalling aiokraken-2.0.0:\n",
      "      Successfully uninstalled aiokraken-2.0.0\n",
      "  Running setup.py develop for aiokraken\n",
      "Successfully installed aiokraken\n",
      "\u001b[33mWARNING: You are using pip version 19.2.2, however version 19.2.3 is available.\n",
      "You should consider upgrading via the 'pip install --upgrade pip' command.\u001b[0m\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "%pip install -e .\n",
    "from aiokraken.rest.client import RestClient\n",
    "from aiokraken.rest.api import Server"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "kraken = RestClient(Server())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/0/public/Time\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Tue Oct  1 15:00:31 2019"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "await kraken.time()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         time    open    high     low   close    vwap      volume  count\n",
      "0  1569891660  7549.1  7549.6  7549.1  7549.3  7549.3  0.02025415      4\n",
      "1  1569891720  7549.3  7549.4  7549.3  7549.3  7549.3  0.05658091      5\n",
      "2  1569891780  7549.3  7549.3  7546.8  7546.8  7546.8  0.03010246      2\n",
      "3  1569891840  7546.8  7546.8  7543.3  7543.3  7546.2  0.13324434      4\n",
      "4  1569891900  7543.3  7543.3  7542.9  7542.9  7543.2  0.44721395      2\n"
     ]
    }
   ],
   "source": [
    "# polling ohlc\n",
    "ohlc = await kraken.ohlc(pair='XBTEUR')\n",
    "print(ohlc.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 864x360 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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": [
    "from IPython.display import clear_output\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from matplotlib import pyplot as plt\n",
    "import collections\n",
    "import time\n",
    "import talib\n",
    "import asyncio\n",
    "%matplotlib inline\n",
    "\n",
    "# def live_plot(data_dict, figsize=(7,5), title=''):\n",
    "#     clear_output(wait=True)\n",
    "#     plt.figure(figsize=figsize)\n",
    "#     for label,data in data_dict.items():\n",
    "#         plt.plot(data, label=label)\n",
    "#     plt.title(title)\n",
    "#     plt.grid(True)\n",
    "#     plt.xlabel('epoch')\n",
    "#     plt.legend(loc='center left') # the plot evolves to the right\n",
    "#     plt.show();\n",
    "    \n",
    "#for i in range(10):  # how to interactively stop this ?  DO we even need it ? probably not for REST interface anyway...\n",
    "clear_output(wait=True)\n",
    "plt.figure(figsize=(12,5))\n",
    "\n",
    "ohlc_df = await kraken.ohlc(pair='XBTEUR') #since=ohlc.get('last'))\n",
    "# convert what we want to draw to numeric to please mpl\n",
    "ohlc_df.dataframe.close = pd.to_numeric(ohlc_df.dataframe.close)\n",
    "#ohlc_df.time = pd.to_datetime(ohlc_df.time, unit='s')\n",
    "ohlc_df.dataframe.plot(x='time', y='close') \n",
    "\n",
    "#testing TAlib\n",
    "res = talib.MA(ohlc_df.dataframe.close)\n",
    "#res.plot()\n",
    "\n",
    "plt.legend(loc='center left') # the plot evolves to the right\n",
    "plt.show()\n",
    "\n",
    "time.sleep(10.0)\n",
    "#await asyncio.sleep(10.0)  # careful with rate limits...  # asyncio sleep allows other cells to run !?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from IPython.display import clear_output\n",
    "import numpy as np\n",
    "from matplotlib import pyplot as plt\n",
    "import collections\n",
    "%matplotlib inline\n",
    "\n",
    "def live_plot(data_dict, figsize=(7,5), title=''):\n",
    "    clear_output(wait=True)\n",
    "    plt.figure(figsize=figsize)\n",
    "    for label,data in data_dict.items():\n",
    "        plt.plot(data, label=label)\n",
    "    plt.title(title)\n",
    "    plt.grid(True)\n",
    "    plt.xlabel('epoch')\n",
    "    plt.legend(loc='center left') # the plot evolves to the right\n",
    "    plt.show();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import time\n",
    "\n",
    "data = collections.defaultdict(list)\n",
    "for i in range(100):\n",
    "    time.sleep(1.0)\n",
    "    data['foo'].append(np.random.random())\n",
    "    data['bar'].append(np.random.random())\n",
    "    data['baz'].append(np.random.random())\n",
    "    live_plot(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}