CryptoNotebook2.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "import auth\n",
    "import api\n",
    "import pprint\n",
    "auth.login()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pprint"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "btc_data = api.getHistoricals('BTC', span='week')['data_points']\n",
    "eth_data = api.getHistoricals('ETH', span='week')['data_points']\n",
    "ltc_data = api.getHistoricals('LTC', span='week')['data_points']\n",
    "bch_data = api.getHistoricals('BCH', span='week')['data_points']\n",
    "doge_data = api.getHistoricals('DOGE', span='week')['data_points']\n",
    "etc_data = api.getHistoricals('ETC', span='week')['data_points']\n",
    "bsv_data = api.getHistoricals('BSV', span='week')['data_points']\n",
    "\n",
    "\n",
    "btc_data = [float(point['low_price']) for point in btc_data]\n",
    "eth_data = [float(point['low_price']) for point in eth_data]\n",
    "ltc_data = [float(point['low_price']) for point in ltc_data]\n",
    "bch_data = [float(point['low_price']) for point in bch_data]\n",
    "doge_data = [float(point['low_price']) for point in doge_data]\n",
    "etc_data = [float(point['low_price']) for point in etc_data]\n",
    "bsv_data = [float(point['low_price']) for point in bsv_data]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(8720.811442, 168.58, 58.39, 366.885, 0.002337, 10.123949, 298.113688)\n",
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      "(8845.907739, 168.96, 58.189435, 356.040831, 0.002338, 10.144475, 287.376041)\n",
      "(8817.732801, 169.04, 58.215, 356.91, 0.002337, 10.204746, 292.230013)\n",
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      "(9001.61784, 170.46, 59.0, 366.14, 0.002366, 10.5015, 298.302901)\n",
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      "(8922.288666, 170.095178, 57.788352, 361.16, 0.00234, 10.727, 276.664269)\n",
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      "(9279.985205, 175.285, 60.395811, 381.24, 0.002386, 11.577555, 290.774662)\n",
      "(9257.67307, 175.32, 60.123022, 378.525, 0.002377, 11.58655, 290.905)\n",
      "(9288.395, 175.48, 60.433635, 379.75, 0.002388, 11.627544, 292.11)\n",
      "(9306.425359, 175.32, 60.372865, 380.938125, 0.002375, 11.624553, 290.593611)\n",
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      "(9293.564241, 176.42, 60.857471, 386.175, 0.00239, 12.11652, 293.900488)\n",
      "(9308.496571, 176.869361, 61.007824, 389.205, 0.002408, 12.316, 294.492007)\n",
      "(9284.363581, 175.595, 60.55, 383.02, 0.00241, 12.066184, 282.943576)\n",
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      "(9225.411411, 174.485, 59.93, 376.42, 0.002364, 11.966029, 281.950764)\n",
      "(9235.488759, 174.11468, 59.721618, 379.405, 0.00237, 11.95436, 281.182015)\n",
      "(9225.965643, 174.435, 60.135, 380.905, 0.002355, 12.076004, 281.150855)\n",
      "(9259.873989, 174.64, 59.822397, 380.92, 0.002358, 12.240121, 283.81)\n",
      "(9234.671317, 173.749786, 59.152349, 376.67, 0.002349, 12.028398, 278.09)\n",
      "(9223.785116, 174.075, 59.29, 374.94, 0.002347, 11.842572, 278.9)\n",
      "(9287.672428, 175.385, 59.771597, 381.705, 0.002362, 12.138574, 286.925)\n",
      "(9305.601887, 175.49, 60.012486, 381.795, 0.002369, 12.123333, 287.19)\n",
      "(9284.968993, 175.03, 60.04, 380.255, 0.002367, 12.028, 282.602397)\n",
      "(9307.180591, 175.045, 59.991723, 380.78, 0.002366, 12.117473, 285.365)\n",
      "(9288.089909, 174.905, 59.86, 381.775, 0.002364, 12.099, 286.073005)\n",
      "(9274.325246, 174.341385, 59.795, 379.873226, 0.002356, 12.204643, 288.94403)\n",
      "(9266.955, 172.253517, 59.48, 377.671615, 0.002371, 12.154, 284.295)\n",
      "(9124.2742, 170.04, 57.94, 369.125, 0.002316, 11.586357, 281.377157)\n",
      "(9220.341033, 171.088351, 58.495, 372.8, 0.002342, 11.770267, 285.025)\n",
      "(9210.456625, 171.26, 58.595, 373.035, 0.002333, 11.713914, 286.87)\n",
      "(9209.865645, 171.565, 58.62, 373.175, 0.002337, 11.7835, 286.125)\n",
      "(9246.06, 172.575, 59.035, 380.09, 0.002347, 11.935, 289.060764)\n",
      "(9271.034839, 172.8, 59.32, 380.37, 0.002358, 12.117163, 288.2)\n",
      "(9260.877318, 172.73, 59.16, 380.3, 0.002352, 12.078687, 289.875)\n",
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      "(9315.182211, 174.5, 61.245, 385.66, 0.002359, 12.391, 299.29)\n",
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      "(9276.332201, 173.42, 60.71, 379.875, 0.002339, 12.07687, 292.347546)\n",
      "(9308.255, 174.23, 61.63, 382.3, 0.002359, 12.2375, 296.455)\n",
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      "(9291.06, 174.835, 63.38, 380.52, 0.002357, 11.801714, 289.81)\n",
      "(9172.33, 174.25, 65.18, 377.49, 0.002338, 11.581955, 286.702555)\n",
      "(9320.17, 176.365, 66.755, 383.69, 0.002357, 11.8505, 292.47)\n",
      "(9310.96, 178.795, 67.054864, 390.475, 0.002376, 12.114679, 293.71)\n",
      "(9428.096318, 179.66, 67.163148, 389.81, 0.002364, 12.027069, 292.42)\n",
      "(9446.001984, 180.59, 67.930617, 389.45, 0.002367, 12.107, 293.450046)\n",
      "(9473.117661, 182.685, 67.711075, 387.823547, 0.002392, 12.00627, 293.62)\n",
      "(9484.443774, 182.600605, 67.757935, 389.095, 0.002395, 12.114126, 294.705)\n",
      "(9441.85, 182.535, 66.96, 388.695, 0.002387, 12.100678, 294.88)\n",
      "(9423.972449, 183.065, 67.315, 389.32, 0.002394, 12.0935, 293.005)\n",
      "(9418.560879, 182.149806, 66.8842, 386.44, 0.002392, 11.9525, 290.755)\n",
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      "(9202.128608, 176.05, 65.75, 362.74668, 0.002362, 10.912819, 258.555434)\n",
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      "(9273.290339, 178.45, 67.21, 371.69, 0.002356, 11.1795, 269.775)\n",
      "(9296.4, 178.068415, 67.325, 372.63, 0.002359, 11.206, 269.17)\n",
      "(9330.275, 180.32, 68.12, 375.14, 0.002362, 11.35, 275.59)\n",
      "(9368.1, 181.835, 70.572067, 381.575, 0.002369, 11.479, 281.917897)\n",
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      "(9266.15, 179.66, 69.545, 374.725, 0.002353, 11.228094, 273.56)\n",
      "(9275.843368, 180.27, 69.045, 376.04, 0.00237, 11.4795, 275.005)\n",
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      "(9333.01785, 181.865, 70.327293, 377.695, 0.002368, 11.482984, 278.715)\n",
      "(9330.18, 181.85, 70.52, 376.485, 0.002366, 11.4745, 277.675)\n",
      "(9320.24, 181.108002, 70.355, 376.38, 0.002368, 11.4645, 277.015)\n",
      "(9308.877246, 181.45, 70.67677, 377.015, 0.002363, 11.461287, 277.098015)\n",
      "(9325.3, 181.5, 70.05, 376.1, 0.002368, 11.471, 276.480609)\n",
      "(9328.505, 181.78, 70.025, 376.485, 0.002371, 11.479404, 278.345)\n",
      "(9346.885, 182.32, 70.34, 376.96, 0.002371, 11.479, 277.03)\n",
      "(9290.985, 180.385, 69.035, 372.365, 0.002366, 11.212849, 273.625)\n",
      "(9269.95, 180.255, 68.955, 372.325, 0.002363, 11.203, 272.765)\n",
      "(9206.567768, 179.045, 67.435, 368.985045, 0.002357, 11.157, 270.55)\n",
      "(9208.525, 179.285, 68.04, 367.904222, 0.002386, 11.136507, 271.27)\n",
      "(9121.14651, 178.44, 67.51, 367.46, 0.002391, 11.093, 268.296338)\n",
      "(9249.714137, 180.49, 68.355, 372.63, 0.002379, 11.321, 271.69)\n",
      "(9259.9, 180.615, 68.477489, 371.415, 0.002387, 11.2995, 272.76)\n",
      "(9311.43, 184.66495, 70.14, 376.84, 0.002402, 11.5035, 278.77)\n",
      "(9316.872974, 185.61, 69.795, 375.13, 0.002409, 11.455, 280.025)\n",
      "(9331.05, 186.105, 70.255, 376.705, 0.002424, 11.487, 280.81)\n",
      "(9344.085, 188.01, 70.44, 378.395, 0.002424, 11.4745, 282.215)\n",
      "(9340.897714, 189.5, 70.605469, 379.595, 0.002421, 11.464, 281.18)\n",
      "(9367.959426, 189.725, 71.515, 380.31, 0.002431, 11.4525, 282.22)\n",
      "(9370.17, 189.615, 71.505, 380.055, 0.002432, 11.3875, 281.215)\n",
      "(9320.868701, 188.72, 70.935, 376.17, 0.002444, 11.286, 278.045)\n",
      "(9402.597632, 190.445, 71.36, 378.0, 0.002442, 11.3975, 279.885)\n",
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      "(9367.96132, 189.91, 71.059471, 380.995, 0.002435, 11.437, 280.485)\n",
      "(9400.665, 190.465, 71.33, 381.46, 0.00245, 11.4595, 281.105)\n",
      "(9396.24, 190.7, 71.215, 380.755, 0.002444, 11.4595, 280.975)\n",
      "(9399.045, 190.43, 71.18819, 380.34, 0.002443, 11.467, 279.81)\n",
      "(9385.622249, 190.89, 71.32, 379.96, 0.002436, 11.4815, 280.93)\n",
      "(9316.123909, 187.916737, 70.030722, 379.176574, 0.002421, 11.408751, 279.833309)\n",
      "(9287.9, 187.365, 69.52, 375.88, 0.00241, 11.2765, 276.303907)\n",
      "(9257.188644, 186.47, 69.122619, 373.075, 0.002401, 11.253, 274.495)\n",
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      "(9218.107462, 188.44, 70.085285, 378.08, 0.002418, 11.743021, 278.157828)\n",
      "(9306.126324, 189.04, 70.214104, 379.515, 0.002426, 11.7745, 279.53)\n",
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      "(9336.112296, 189.6666, 70.380269, 381.095, 0.002426, 11.928185, 282.447381)\n",
      "(9321.26, 188.585, 70.045, 379.66, 0.002434, 11.8535, 282.44)\n",
      "(9326.49, 188.635, 70.185, 380.015, 0.002428, 11.874, 280.17)\n",
      "(9302.205, 187.62, 69.795, 379.685, 0.002425, 11.817, 279.97)\n",
      "(9261.155, 186.275168, 69.31, 377.195, 0.00241, 11.715715, 276.135)\n",
      "(9272.075, 186.565, 69.225, 376.535, 0.002395, 11.7375, 276.74)\n",
      "(9299.553003, 188.245, 69.581128, 379.83, 0.00239, 11.824, 281.145)\n",
      "(9249.169391, 186.93, 68.965231, 377.97, 0.002387, 11.765, 277.92)\n",
      "(9233.995451, 186.680284, 68.618531, 377.08, 0.002385, 11.726706, 278.72099)\n",
      "(9231.950093, 186.56, 68.490176, 377.025, 0.002378, 11.278323, 272.991048)\n",
      "(9224.585, 185.66, 68.029556, 377.365, 0.002389, 11.3585, 273.264665)\n"
     ]
    }
   ],
   "source": [
    "# create a cap-weighted index\n",
    "cap_dict = {\n",
    "    'btc': 159,\n",
    "    'eth': 18.6,\n",
    "    'ltc': 3.7,\n",
    "    'bch': 6.7,\n",
    "    'doge': .3,\n",
    "    'etc': 1.3,\n",
    "    'bsv': 5.8\n",
    "}\n",
    "weights = [cap_dict['btc'], cap_dict['eth'], cap_dict['ltc'], cap_dict['bch'], cap_dict['doge'], cap_dict['etc'], cap_dict['bsv']]\n",
    "zipped = zip(btc_data, eth_data, ltc_data, bch_data, doge_data, etc_data, bsv_data)\n",
    "index = np.zeros(len(btc_data))\n",
    "i = 0\n",
    "for item in zipped:\n",
    "    print(item)\n",
    "    index[i] = np.average(item, weights=weights)\n",
    "    i = i + 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot(data):\n",
    "    fig, ax = plt.subplots()\n",
    "    ax.plot(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "plot(index)\n",
    "plot(btc_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'pp' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-7-5951fbd6d1ef>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mpp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcorrcoef\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mindex\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbtc_data\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mltc_data\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0meth_data\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbch_data\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdoge_data\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0metc_data\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbsv_data\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m: name 'pp' is not defined"
     ]
    }
   ],
   "source": [
    "pp.pprint(np.corrcoef([index, btc_data, ltc_data, eth_data, bch_data, doge_data, etc_data, bsv_data]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plotDiff(data1, data2):\n",
    "    diffs = [point[0] - point[1] for point in zip(data1, data2)]\n",
    "    diffs_mean = np.mean(diffs)\n",
    "    print('average diff: {}'.format(diffs_mean))\n",
    "    diffs_std = np.std(diffs)\n",
    "    diffs_normalized = (diffs - diffs_mean) / diffs_std\n",
    "    fig, ax = plt.subplots()\n",
    "    ax.plot(diffs_normalized)\n",
    "    \n",
    "def plotRatio(data1, data2):\n",
    "    diffs = [point[0] / point[1] for point in zip(data1, data2)]\n",
    "    diffs_mean = np.mean(diffs)\n",
    "    print('average ratio: {}'.format(diffs_mean))\n",
    "    diffs_std = np.std(diffs)\n",
    "    diffs_normalized = (diffs - diffs_mean) / diffs_std\n",
    "    plt.plot(diffs_normalized)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib qt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "average ratio: 1.2224935528908936\n",
      "average ratio: 0.023619849427637285\n",
      "average ratio: 0.00859621138892942\n",
      "average ratio: 0.049681762914671045\n",
      "average ratio: 3.142022914409728e-07\n",
      "average ratio: 0.00151321717383698\n",
      "average ratio: 0.037509451851713976\n"
     ]
    }
   ],
   "source": [
    "arr = [btc_data, eth_data, ltc_data, bch_data, doge_data, etc_data, bsv_data]\n",
    "index_smoothed = moving_average(index)\n",
    "for item in arr:\n",
    "    item_smoothed = moving_average(item)\n",
    "    plotRatio(item_smoothed, index_smoothed)\n",
    "plt.legend(['btc', 'eth', 'ltc', 'bch', 'doge', 'etc', 'bsv'])\n",
    "plt.ylabel('std from mean')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "api.getCryptoQuote('ETC')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'ask_price': '0.002420',\n",
       " 'bid_price': '0.002413',\n",
       " 'mark_price': '0.002416',\n",
       " 'high_price': '0.002494',\n",
       " 'low_price': '0.002378',\n",
       " 'open_price': '0.002456',\n",
       " 'symbol': 'DOGEUSD',\n",
       " 'id': '1ef78e1b-049b-4f12-90e5-555dcf2fe204',\n",
       " 'volume': '0.000000'}"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "api.getCryptoQuote('DOGE')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "def moving_average(a, n=3) :\n",
    "    ret = np.cumsum(a, dtype=float)\n",
    "    ret[n:] = ret[n:] - ret[:-n]\n",
    "    return ret[n - 1:] / n\n",
    "\n",
    "def bollinger_lower(a, n=3):\n",
    "    cleaned = moving_average(a, n)\n",
    "    std = np.std(a)\n",
    "    return cleaned - std"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "btc_moving_data = moving_average(btc_data, n = 10)\n",
    "btc_boll_lower = bollinger_lower(btc_data, n = 10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plot(btc_moving_data)\n",
    "plot(btc_boll_lower)"
   ]
  }
 ],
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