Base/TradeDeLaBase.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Bienvenidos al Notebook del trade de la base\n",
    "\n",
    "En este notebook vamos a calcular la base para distintos contratos futuros de criptomonedas, concretamente Bitcoin y Ethereum en Binance. Vamos a usar la librería [ccxt](https://github.com/ccxt/ccxt) para descargar los datos. No obstante, podéis usar multitud de librerías, incluso fabricar vuestros propios códigos para comunicaros con los mercados y pedir los datos mediante API o sockets.\n",
    "\n",
    "<span style=\"color:blue\">Importante: instalar las librerías para que el código funcione.</span>. Simplemente ejecutad el siguiente comando desde una terminal en la misma ruta del código: python -m pip install -r requirements.txt."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importamos las liberías para trabajar con datos y gráficos\n",
    "from datetime import datetime\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.dates\n",
    "from datetime import datetime\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import ccxt # Librería para descargar los datos de criptomonedas\n",
    "\n",
    "# https://stackoverflow.com/questions/47404653/pandas-0-21-0-timestamp-compatibility-issue-with-matplotlib \n",
    "from pandas.plotting import register_matplotlib_converters\n",
    "register_matplotlib_converters()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "binance = ccxt.binance() # Creamos el objeto para comunicarnos con Binance"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Vamos a descargar los precios para Bitcoin (BTC) del futuro más cercano en el tiempo y también del spot. Los precios de los futuros históricos no están disponibles desde ccxt. \n",
    "\n",
    "Mediante el método \"fetch_ohlcv\" descargamos las barras de mercado cada 8h. Las barras de marcado resumen los datos para un rango temporal, en este caso de 8 horas (8h). El resumen consiste en:\n",
    "- El precio de apertura del intervalo (la o de ohlcv viene de open).\n",
    "- El precio más alto del intervalo (la h de ohlcv viene de high).\n",
    "- El precio más bajo del intervalo (la l de ohlcv viene de low).\n",
    "- El precio de cierre del intervalo (la c de ohlcv viene close).\n",
    "- el volumen total del intervalo (la v de ohlcv viene de volumen)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "nombre_futuro = 'BTCUSDT_230929'\n",
    "nombre_spot = 'BTCUSDT'\n",
    "btc_fut = binance.fetch_ohlcv(nombre_futuro, '8h')\n",
    "btc_spot = binance.fetch_ohlcv(nombre_spot, '8h')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Creamos un dataframe de pandas con los datos que devuelve ccxt, una para el spot y otro para el futuro."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "btc_fut_df = pd.DataFrame(btc_fut, columns = ['ts', 'o', 'h', 'l', 'c', 'v'])\n",
    "btc_spot_df = pd.DataFrame(btc_spot, columns = ['ts', 'o', 'h', 'l', 'c', 'v'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Transformamos el índice de los dataframe con la fecha en formato datetime para poder trabajar con ella."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "btc_fut_df['ts'] = btc_fut_df['ts'].apply(lambda x: datetime.utcfromtimestamp(x / 1000).strftime('%Y-%m-%d %H:%M:%S'))\n",
    "btc_spot_df['ts'] = btc_spot_df['ts'].apply(lambda x: datetime.utcfromtimestamp(x / 1000).strftime('%Y-%m-%d %H:%M:%S'))\n",
    "\n",
    "btc_fut_df.set_index('ts', inplace=True)\n",
    "btc_spot_df.set_index('ts', inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Las cinco primeras filas de los precios del futuro. Como podéis observar las filas van de 8 horas en 8 horas."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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>o</th>\n",
       "      <th>h</th>\n",
       "      <th>l</th>\n",
       "      <th>c</th>\n",
       "      <th>v</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ts</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>2023-06-25 00:00:00</th>\n",
       "      <td>30711.4</td>\n",
       "      <td>31939.0</td>\n",
       "      <td>30711.4</td>\n",
       "      <td>31081.1</td>\n",
       "      <td>84.014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-06-25 08:00:00</th>\n",
       "      <td>31081.7</td>\n",
       "      <td>31264.8</td>\n",
       "      <td>30965.1</td>\n",
       "      <td>31124.6</td>\n",
       "      <td>112.503</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-06-25 16:00:00</th>\n",
       "      <td>31134.4</td>\n",
       "      <td>31236.0</td>\n",
       "      <td>30651.0</td>\n",
       "      <td>30880.3</td>\n",
       "      <td>131.964</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-06-26 00:00:00</th>\n",
       "      <td>30881.2</td>\n",
       "      <td>31000.0</td>\n",
       "      <td>30436.6</td>\n",
       "      <td>30830.8</td>\n",
       "      <td>181.676</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-06-26 08:00:00</th>\n",
       "      <td>30832.0</td>\n",
       "      <td>31080.0</td>\n",
       "      <td>30534.5</td>\n",
       "      <td>30841.3</td>\n",
       "      <td>133.316</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           o        h        l        c        v\n",
       "ts                                                              \n",
       "2023-06-25 00:00:00  30711.4  31939.0  30711.4  31081.1   84.014\n",
       "2023-06-25 08:00:00  31081.7  31264.8  30965.1  31124.6  112.503\n",
       "2023-06-25 16:00:00  31134.4  31236.0  30651.0  30880.3  131.964\n",
       "2023-06-26 00:00:00  30881.2  31000.0  30436.6  30830.8  181.676\n",
       "2023-06-26 08:00:00  30832.0  31080.0  30534.5  30841.3  133.316"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "btc_fut_df.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Las cinco primeras filas de los precios spot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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>o</th>\n",
       "      <th>h</th>\n",
       "      <th>l</th>\n",
       "      <th>c</th>\n",
       "      <th>v</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ts</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>2023-04-15 08:00:00</th>\n",
       "      <td>30433.29</td>\n",
       "      <td>30470.00</td>\n",
       "      <td>30285.0</td>\n",
       "      <td>30325.27</td>\n",
       "      <td>7940.29532</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-04-15 16:00:00</th>\n",
       "      <td>30325.27</td>\n",
       "      <td>30360.19</td>\n",
       "      <td>30202.0</td>\n",
       "      <td>30295.09</td>\n",
       "      <td>7588.17359</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-04-16 00:00:00</th>\n",
       "      <td>30295.10</td>\n",
       "      <td>30399.40</td>\n",
       "      <td>30120.0</td>\n",
       "      <td>30344.97</td>\n",
       "      <td>6305.55476</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-04-16 08:00:00</th>\n",
       "      <td>30344.97</td>\n",
       "      <td>30362.00</td>\n",
       "      <td>30160.4</td>\n",
       "      <td>30332.98</td>\n",
       "      <td>8318.72236</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-04-16 16:00:00</th>\n",
       "      <td>30332.98</td>\n",
       "      <td>30549.99</td>\n",
       "      <td>30192.0</td>\n",
       "      <td>30304.65</td>\n",
       "      <td>11807.71610</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            o         h        l         c            v\n",
       "ts                                                                     \n",
       "2023-04-15 08:00:00  30433.29  30470.00  30285.0  30325.27   7940.29532\n",
       "2023-04-15 16:00:00  30325.27  30360.19  30202.0  30295.09   7588.17359\n",
       "2023-04-16 00:00:00  30295.10  30399.40  30120.0  30344.97   6305.55476\n",
       "2023-04-16 08:00:00  30344.97  30362.00  30160.4  30332.98   8318.72236\n",
       "2023-04-16 16:00:00  30332.98  30549.99  30192.0  30304.65  11807.71610"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "btc_spot_df.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A continuación juntamos ambas tablas haciendo coincidir las fechas y nos quedamos con el precio de apertura."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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>precio_spot</th>\n",
       "      <th>precio_futuro</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ts</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2023-06-25 00:00:00</th>\n",
       "      <td>30527.44</td>\n",
       "      <td>30711.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-06-25 08:00:00</th>\n",
       "      <td>30723.78</td>\n",
       "      <td>31081.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-06-25 16:00:00</th>\n",
       "      <td>30585.16</td>\n",
       "      <td>31134.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-06-26 00:00:00</th>\n",
       "      <td>30462.67</td>\n",
       "      <td>30881.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-06-26 08:00:00</th>\n",
       "      <td>30404.28</td>\n",
       "      <td>30832.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     precio_spot  precio_futuro\n",
       "ts                                             \n",
       "2023-06-25 00:00:00     30527.44        30711.4\n",
       "2023-06-25 08:00:00     30723.78        31081.7\n",
       "2023-06-25 16:00:00     30585.16        31134.4\n",
       "2023-06-26 00:00:00     30462.67        30881.2\n",
       "2023-06-26 08:00:00     30404.28        30832.0"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.concat([btc_spot_df.o, btc_fut_df.o], axis=1, sort=True)\n",
    "df.columns = ['precio_spot', 'precio_futuro']\n",
    "df = df.dropna()\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 6))\n",
    "fechas = [datetime.strptime(ax, '%Y-%m-%d %H:%M:%S') for ax in df.index]\n",
    "plt.plot(fechas, df.precio_spot.values, label=nombre_spot, linewidth=2)\n",
    "plt.plot(fechas, df.precio_futuro.values, label=nombre_futuro, linewidth=2)\n",
    "plt.xlabel('Fecha')\n",
    "plt.ylabel('Dólares ($)')\n",
    "plt.title('Spot vs futuro')\n",
    "plt.grid(True)\n",
    "plt.legend()\n",
    "plt.gcf().autofmt_xdate()\n",
    "plt.savefig('btc_spot_fut.png', format='png', dpi = 500)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Vamos a calcular la base como <font size=5> $\\frac{precio_f - precio_s}{precio_s}$ $\\times$ 100 </font>, donde:\n",
    "- $precio_f$ es el precio del futuro\n",
    "- $precio_s$ es el precio del spot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ts\n",
       "2023-06-25 00:00:00    0.602605\n",
       "2023-06-25 08:00:00    1.164961\n",
       "2023-06-25 16:00:00    1.795773\n",
       "2023-06-26 00:00:00    1.373911\n",
       "2023-06-26 08:00:00    1.406776\n",
       "dtype: float64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "base_df = (df.precio_futuro - df.precio_spot) / df.precio_spot * 100\n",
    "base_df.head() "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Es importante anualizar las bases anteriores. Si lo pensáis, cada uno de los porcentajes anteriores corresponde a un número de días distinto. Los primeros porcentajes van desde junio hasta finales de septiembre. Es decir, no es lo mismo un 1% mensual que un 1% trimestral. Es por ello que anualizamos los porcentajes de la siguiente manera:\n",
    "\n",
    "1. Calculamos el porcentaje diario como: base_df / número de días\n",
    "2. Multiplicamos por 365\n",
    "\n",
    "El número de días sería entre la fecha de vencimiento del futuro (29/09/2023) y la fecha en la que se calcula la base. Por tanto, la fórmula para anualizar la base sería:\n",
    "\n",
    "<font size=3> base anualizada = $\\frac{base}{\\text{numero de dias}}$ $\\times$ 365 </font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "fechas = [datetime.strptime(ax, '%Y-%m-%d %H:%M:%S') for ax in base_df.index]\n",
    "fecha_futuro = datetime(2023, 9, 29, 0, 0)\n",
    "dias_para_vencimiento = np.array([(fecha_futuro - ax).days for ax in fechas])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ts\n",
       "2023-06-25 00:00:00    2.291156\n",
       "2023-06-25 08:00:00    4.475902\n",
       "2023-06-25 16:00:00    6.899548\n",
       "2023-06-26 00:00:00    5.278711\n",
       "2023-06-26 08:00:00    5.462480\n",
       "dtype: float64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "base_anualizada_df = base_df / dias_para_vencimiento * 365\n",
    "base_anualizada_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 6))\n",
    "plt.plot(fechas, base_anualizada_df.values, linewidth=2)\n",
    "plt.xlabel('Fecha')\n",
    "plt.ylabel('Rendimiento anual (%)')\n",
    "plt.title('Base para ' + nombre_futuro)\n",
    "plt.grid(True)\n",
    "plt.gcf().autofmt_xdate()\n",
    "plt.savefig('base_btc.png', format='png', dpi = 500)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "import dill\n",
    "filename = 'globalsave.pkl'\n",
    "dill.dump_session(filename)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import dill\n",
    "filename = 'globalsave.pkl'\n",
    "dill.load_session(filename)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ts\n",
       "2023-06-25 00:00:00    2.291156\n",
       "2023-06-25 08:00:00    4.475902\n",
       "2023-06-25 16:00:00    6.899548\n",
       "2023-06-26 00:00:00    5.278711\n",
       "2023-06-26 08:00:00    5.462480\n",
       "                         ...   \n",
       "2023-09-27 08:00:00   -3.963408\n",
       "2023-09-27 16:00:00    4.690086\n",
       "2023-09-28 00:00:00   -8.857544\n",
       "2023-09-28 08:00:00        -inf\n",
       "2023-09-28 16:00:00        -inf\n",
       "Length: 288, dtype: float64"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "base_anualizada_df"
   ]
  },
  {
   "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": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}