Backtest-Minimos-Maximos.ipynb

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   "source": [
    "# Back-test : Estrategia Maximos y minimos historicos\n",
    "\n",
    "En este backtest voy a realizar una estrategia basada en los los precios maximos y minimos que vamos encontrando en las ultimas 50 rondas. En caso de que el Low sea igual al valor minimo de las ultimas 50 rondas, compro la accion. En cuanto al maximo, si estoy comprado y la acción alcanza el maximo de las ultimas 50 ruedas, vendo la misma."
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "source": [
    "### Carga de las Librerias\n",
    "\n",
    "* Pandas : para mostrar los datos en un dataframe y poder analizarlo.\n",
    "* pandas_datareader : para importar los datos de \"Yahoo Finance\"\n",
    "* datetime : para poder trabajar con fechas\n",
    "* numpy :nos va a ser util para trabajar con datos NaN\n",
    "* itertools : para realizar todas las iteraciones de los cruces"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 562,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pandas_datareader import data\n",
    "import pandas as pd\n",
    "import datetime as dt\n",
    "from datetime import date\n",
    "import numpy as np"
   ]
  },
  {
   "source": [
    "* Creamos la funcion ajustar_columnas , la cual va a recibir los datos descargados anteriormente.\n",
    "\n",
    "* Esta funcion se va a encargar de ajustar las columnas ,usando el coeficiente que surge entre los datos de cierre ajustados / datos de cierre.\n",
    "\n",
    "* Una vez ajustadas todas las columnas, borra las que quedaron sin ajustar\n",
    "\n",
    "* Por ultimo crea una columna \"Dif_Close\" con la difenrencia entre el cierre de ayer y hoy. El producto acumulado de esta diferencia lo voy guardando en la columna \"Buy_and_Hold\"."
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 564,
   "metadata": {},
   "outputs": [],
   "source": [
    "def ajustar_columnas(dataframe):\n",
    "    dataframe['coef']=dataframe['Adj Close'] / dataframe['Close']\n",
    "    dataframe['High']=dataframe['High']*dataframe.coef\n",
    "    dataframe['Low']=dataframe['Low']*dataframe.coef\n",
    "    dataframe['Open']=dataframe['Open']*dataframe.coef\n",
    "    dataframe['Close']=dataframe['Adj Close']\n",
    "    dataframe=dataframe.drop(['coef','Adj Close'],axis=1) #Borro las columnas que ya no necesito\n",
    "    dataframe['Dif_Close']=dataframe.Close.pct_change()\n",
    "    \n",
    "    return dataframe"
   ]
  },
  {
   "source": [
    "## Estrategia Maximos y minimos historicos\n",
    "\n",
    "La funcion \"agrega_min_max_hitoricos\" recibe el dataframe y en principio hace un doble bucle, en el primero irá marcando los 10 minimos valores de los ultmimas 50 ruedas, y en el segundo hará lo mismo pero marcando los maximos."
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 565,
   "metadata": {},
   "outputs": [],
   "source": [
    "def agrega_min_max_hitoricos(dataframe):    \n",
    "    \n",
    "    minimos_maximos=['minimos_historicos','maximos_historicos']\n",
    "    \n",
    "    for min_max in minimos_maximos:\n",
    "        if min_max == 'minimos_historicos':            \n",
    "            minimos=list(dataframe.Low.iloc[-50:].sort_values(ascending=False).iloc[-10:])\n",
    "            indice=list(dataframe.Low.iloc[-50:].sort_values(ascending=False).iloc[-10:].index)   \n",
    "            dataframe.loc[indice,'minimos_historicos'] =minimos \n",
    "\n",
    "        if min_max == 'maximos_historicos':            \n",
    "            maximos=list(dataframe.High.iloc[-50:].sort_values(ascending=True).iloc[-10:])\n",
    "            indice=list(dataframe.High.iloc[-50:].sort_values(ascending=True).iloc[-10:].index)  \n",
    "            dataframe.loc[indice,'maximos_historicos'] =maximos    \n",
    "\n",
    "    return dataframe\n"
   ]
  },
  {
   "source": [
    "El objetivo de la estrategia se basa en comprar cuando el valor minimo del dia se encuentre dentro de los 10 minimos de las ultimas 50 ruedas. La señal de venta se dara cuando el maximo valor del dia atraviese uno de los 10 maximos valores de las ultimas 50 ruedas"
   ],
   "cell_type": "markdown",
   "metadata": {}
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  {
   "cell_type": "code",
   "execution_count": 566,
   "metadata": {},
   "outputs": [],
   "source": [
    "def trade_valores_historicos(dataframe):  \n",
    "     \n",
    "    dataframe['Senal']=np.where(dataframe.Low <= dataframe['minimos_historicos'] ,1,np.where((dataframe.High >= dataframe['maximos_historicos']) ,-1,np.NaN))\n",
    "    dataframe['Senal']=dataframe.Senal.ffill()\n",
    "    dataframe['Posicion'] = dataframe.Senal.shift(1)    # mi posicion se activará al otro dia de la señal.\n",
    "    dataframe['Trade'] = np.where( dataframe.Posicion==1 & (dataframe.Posicion!= dataframe.Posicion.shift())  ,1 ,np.where( (dataframe.Posicion!= dataframe.Posicion.shift()) & dataframe.Posicion.shift()==1  ,-1 ,0)) # Cuando mi posicion me da compra y no estoy comprado, entro en la posicion con Trade=1. Cuando estoy comprado y la posicion da venta, el trade queda en -1 . Cuando no ocurre ninguno de los dos casos, el trade es cero ( esto se puede dar o porque sigo comprado , o porque todavia no entré en ninguna posicion)\n",
    "    return dataframe"
   ]
  },
  {
   "source": [
    "## Obtenemos los datos de cada estrategia"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "source": [
    "* Vamos a crear la funcion datos_estrategia que va a recibir el dataframe.\n"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 576,
   "metadata": {},
   "outputs": [],
   "source": [
    "def datos_estrategia(dataframe):     \n",
    "    \n",
    "    dataframe['Retornos'] = np.where(( (dataframe.Trade!=0 ) ),dataframe.Dif_Close -0.0085 ,\n",
    "    np.where(dataframe.Posicion==1,dataframe.Dif_Close,0) )    \n",
    "    dataframe['capital_estrategia'] = (dataframe.Retornos + 1).cumprod() * 100 \n",
    "    \n",
    "    return dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 587,
   "metadata": {},
   "outputs": [],
   "source": [
    "def resultado_estrategia(dataframe):\n",
    "  \n",
    "    dataframe_estrategia=dataframe # Creamos una copia del dataframe ya que vamos a hacer modificaciones, y no queremos perder el original.\n",
    "    resultado={} #creamos un diccionario para ir guardando los resultados\n",
    "    resultado['Beneficio Bruto'] = round(dataframe_estrategia.iloc[-1]['capital_estrategia']-100,2) \n",
    "    dataframe_estrategia=dataframe_estrategia[dataframe.Trade !=0] # Filtramos el dataframe para obtener solo las lineas donde se operó.     \n",
    "    resultado['trades']=dataframe_estrategia['Trade'].count()  # Cantidad de trades\n",
    "\n",
    "    dataframe_estrategia=dataframe_estrategia[['Posicion','capital_estrategia']]\n",
    "    dataframe_estrategia['resultado_trade']=np.where((dataframe_estrategia.Posicion==-1)&(dataframe_estrategia.Posicion.shift()==1),        dataframe_estrategia['capital_estrategia']-dataframe_estrategia['capital_estrategia'].shift(),0 ) # Restamos el capital que obtuvimos luego de una venta ,con el capital anterior en el momento de compra. Este calculo si es positivo ,nos indica que el Trade fue ganador, de lo contrario perdimos.\n",
    "    resultado['trades_ganadores']=dataframe_estrategia.resultado_trade[dataframe_estrategia.resultado_trade >= 0].count()\n",
    "    resultado['suma_trades_ganadores']=round(dataframe_estrategia.resultado_trade[dataframe_estrategia.resultado_trade >= 0].sum(),2)\n",
    "    resultado['trades_perdedores']=dataframe_estrategia.resultado_trade[dataframe_estrategia.resultado_trade < 0].count()\n",
    "    resultado['suma_trades_perdedores']=abs(round(dataframe_estrategia.resultado_trade[dataframe_estrategia.resultado_trade < 0].sum(),2))\n",
    "\n",
    "    if resultado['trades_ganadores'] ==0:\n",
    "        resultado['Ratio Acierto'] = 0\n",
    "        resultado['Profit Factor'] = 0\n",
    "\n",
    "    elif resultado['trades_perdedores']==0:\n",
    "        resultado['Ratio Acierto'] = 100\n",
    "        resultado['Profit Factor'] = 100\n",
    "\n",
    "    else:\n",
    "        resultado['Ratio Acierto'] = round(100 * resultado['trades_ganadores'] / resultado['trades'], 2)\n",
    "        resultado['Profit Factor'] = round( resultado['suma_trades_ganadores'] / resultado['suma_trades_perdedores'], 2)\n",
    "    # cuando  trades_ganadores o trades_perdedores sea igual a cero ,completamos manualmente el Ratio Acierto y Profit Factor, ya que de lo contrario nos daria error por division por cero.  \n",
    "    resultado['capital_estrategia']=round(dataframe['capital_estrategia'].iloc[-1],2)\n",
    "    \n",
    "    return resultado"
   ]
  },
  {
   "source": [
    "# Corremos la estrategia\n"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "source": [
    "Primero vamos a descargar 2 datasets para la misma accion. Uno con datos anteriores al momento desde el cual queremos comprobar la estrategia y otro con los datos desde la fecha de inicio de la estrategia hasta la actual.\n",
    "\n",
    "El motivo de descargar los datos en 2 tramos ,es que si corremos la estrategia solo con datos actuales, en el primer bucle debe detectar minimos y maximos, y como no encuentra datos anteriores ,aquellos datos van a ser minimos y maximos , y esto nos daria errores.\n",
    "\n",
    "Por este motivo  , vamos a ir agregando de a uno los datos del datos_futuros al datos_antiguos. De esta manera, en cada bucle, vamos a tener todos los datos viejos mas el nuevo dato. Por lo tanto ,cada iteracion agregará una fecha nueva al dataframe datos_antiguos.\n",
    "\n"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 584,
   "metadata": {
    "tags": [
     "outputPrepend"
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   "outputs": [
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     "text": [
      "e documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  dataframe['Senal']=dataframe.Senal.ffill()\n",
      "<ipython-input-566-01c9ab704fa8>:5: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  dataframe['Posicion'] = dataframe.Senal.shift(1)    # mi posicion se activará al otro dia de la señal.\n",
      "<ipython-input-566-01c9ab704fa8>:6: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  dataframe['Trade'] = np.where( dataframe.Posicion==1 & (dataframe.Posicion!= dataframe.Posicion.shift())  ,1 ,np.where( (dataframe.Posicion!= dataframe.Posicion.shift()) & dataframe.Posicion.shift()==1  ,-1 ,0)) # Cuando mi posicion me da compra y no estoy comprado, entro en la posicion con Trade=1. Cuando estoy comprado y la posicion da venta, el trade queda en -1 . Cuando no ocurre ninguno de los dos casos, el trade es cero ( esto se puede dar o porque sigo comprado , o porque todavia no entré en ninguna posicion)\n",
      "<ipython-input-566-01c9ab704fa8>:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  dataframe['Senal']=np.where(dataframe.Low <= dataframe['minimos_historicos'] ,1,np.where((dataframe.High >= dataframe['maximos_historicos']) ,-1,np.NaN))\n",
      "<ipython-input-566-01c9ab704fa8>:4: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  dataframe['Senal']=dataframe.Senal.ffill()\n",
      "<ipython-input-566-01c9ab704fa8>:5: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  dataframe['Posicion'] = dataframe.Senal.shift(1)    # mi posicion se activará al otro dia de la señal.\n",
      "<ipython-input-566-01c9ab704fa8>:6: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  dataframe['Trade'] = np.where( dataframe.Posicion==1 & (dataframe.Posicion!= dataframe.Posicion.shift())  ,1 ,np.where( (dataframe.Posicion!= dataframe.Posicion.shift()) & dataframe.Posicion.shift()==1  ,-1 ,0)) # Cuando mi posicion me da compra y no estoy comprado, entro en la posicion con Trade=1. Cuando estoy comprado y la posicion da venta, el trade queda en -1 . Cuando no ocurre ninguno de los dos casos, el trade es cero ( esto se puede dar o porque sigo comprado , o porque todavia no entré en ninguna posicion)\n",
      "<ipython-input-566-01c9ab704fa8>:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  dataframe['Senal']=np.where(dataframe.Low <= dataframe['minimos_historicos'] ,1,np.where((dataframe.High >= dataframe['maximos_historicos']) ,-1,np.NaN))\n",
      "<ipython-input-566-01c9ab704fa8>:4: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  dataframe['Senal']=dataframe.Senal.ffill()\n",
      "<ipython-input-566-01c9ab704fa8>:5: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  dataframe['Posicion'] = dataframe.Senal.shift(1)    # mi posicion se activará al otro dia de la señal.\n",
      "<ipython-input-566-01c9ab704fa8>:6: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  dataframe['Trade'] = np.where( dataframe.Posicion==1 & (dataframe.Posicion!= dataframe.Posicion.shift())  ,1 ,np.where( (dataframe.Posicion!= dataframe.Posicion.shift()) & dataframe.Posicion.shift()==1  ,-1 ,0)) # Cuando mi posicion me da compra y no estoy comprado, entro en la posicion con Trade=1. Cuando estoy comprado y la posicion da venta, el trade queda en -1 . Cuando no ocurre ninguno de los dos casos, el trade es cero ( esto se puede dar o porque sigo comprado , o porque todavia no entré en ninguna posicion)\n",
      "<ipython-input-566-01c9ab704fa8>:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  dataframe['Senal']=np.where(dataframe.Low <= dataframe['minimos_historicos'] ,1,np.where((dataframe.High >= dataframe['maximos_historicos']) ,-1,np.NaN))\n",
      "<ipython-input-566-01c9ab704fa8>:4: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  dataframe['Senal']=dataframe.Senal.ffill()\n",
      "<ipython-input-566-01c9ab704fa8>:5: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  dataframe['Posicion'] = dataframe.Senal.shift(1)    # mi posicion se activará al otro dia de la señal.\n",
      "<ipython-input-566-01c9ab704fa8>:6: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  dataframe['Trade'] = np.where( dataframe.Posicion==1 & (dataframe.Posicion!= dataframe.Posicion.shift())  ,1 ,np.where( (dataframe.Posicion!= dataframe.Posicion.shift()) & dataframe.Posicion.shift()==1  ,-1 ,0)) # Cuando mi posicion me da compra y no estoy comprado, entro en la posicion con Trade=1. Cuando estoy comprado y la posicion da venta, el trade queda en -1 . Cuando no ocurre ninguno de los dos casos, el trade es cero ( esto se puede dar o porque sigo comprado , o porque todavia no entré en ninguna posicion)\n",
      "<ipython-input-566-01c9ab704fa8>:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  dataframe['Senal']=np.where(dataframe.Low <= dataframe['minimos_historicos'] ,1,np.where((dataframe.High >= dataframe['maximos_historicos']) ,-1,np.NaN))\n",
      "<ipython-input-566-01c9ab704fa8>:4: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  dataframe['Senal']=dataframe.Senal.ffill()\n",
      "<ipython-input-566-01c9ab704fa8>:5: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  dataframe['Posicion'] = dataframe.Senal.shift(1)    # mi posicion se activará al otro dia de la señal.\n",
      "<ipython-input-566-01c9ab704fa8>:6: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  dataframe['Trade'] = np.where( dataframe.Posicion==1 & (dataframe.Posicion!= dataframe.Posicion.shift())  ,1 ,np.where( (dataframe.Posicion!= dataframe.Posicion.shift()) & dataframe.Posicion.shift()==1  ,-1 ,0)) # Cuando mi posicion me da compra y no estoy comprado, entro en la posicion con Trade=1. Cuando estoy comprado y la posicion da venta, el trade queda en -1 . Cuando no ocurre ninguno de los dos casos, el trade es cero ( esto se puede dar o porque sigo comprado , o porque todavia no entré en ninguna posicion)\n",
      "<ipython-input-566-01c9ab704fa8>:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  dataframe['Senal']=np.where(dataframe.Low <= dataframe['minimos_historicos'] ,1,np.where((dataframe.High >= dataframe['maximos_historicos']) ,-1,np.NaN))\n",
      "<ipython-input-566-01c9ab704fa8>:4: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  dataframe['Senal']=dataframe.Senal.ffill()\n",
      "<ipython-input-566-01c9ab704fa8>:5: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  dataframe['Posicion'] = dataframe.Senal.shift(1)    # mi posicion se activará al otro dia de la señal.\n",
      "<ipython-input-566-01c9ab704fa8>:6: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  dataframe['Trade'] = np.where( dataframe.Posicion==1 & (dataframe.Posicion!= dataframe.Posicion.shift())  ,1 ,np.where( (dataframe.Posicion!= dataframe.Posicion.shift()) & dataframe.Posicion.shift()==1  ,-1 ,0)) # Cuando mi posicion me da compra y no estoy comprado, entro en la posicion con Trade=1. Cuando estoy comprado y la posicion da venta, el trade queda en -1 . Cuando no ocurre ninguno de los dos casos, el trade es cero ( esto se puede dar o porque sigo comprado , o porque todavia no entré en ninguna posicion)\n",
      "<ipython-input-566-01c9ab704fa8>:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  dataframe['Senal']=np.where(dataframe.Low <= dataframe['minimos_historicos'] ,1,np.where((dataframe.High >= dataframe['maximos_historicos']) ,-1,np.NaN))\n",
      "<ipython-input-566-01c9ab704fa8>:4: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  dataframe['Senal']=dataframe.Senal.ffill()\n",
      "<ipython-input-566-01c9ab704fa8>:5: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  dataframe['Posicion'] = dataframe.Senal.shift(1)    # mi posicion se activará al otro dia de la señal.\n",
      "<ipython-input-566-01c9ab704fa8>:6: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  dataframe['Trade'] = np.where( dataframe.Posicion==1 & (dataframe.Posicion!= dataframe.Posicion.shift())  ,1 ,np.where( (dataframe.Posicion!= dataframe.Posicion.shift()) & dataframe.Posicion.shift()==1  ,-1 ,0)) # Cuando mi posicion me da compra y no estoy comprado, entro en la posicion con Trade=1. Cuando estoy comprado y la posicion da venta, el trade queda en -1 . Cuando no ocurre ninguno de los dos casos, el trade es cero ( esto se puede dar o porque sigo comprado , o porque todavia no entré en ninguna posicion)\n",
      "<ipython-input-566-01c9ab704fa8>:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  dataframe['Senal']=np.where(dataframe.Low <= dataframe['minimos_historicos'] ,1,np.where((dataframe.High >= dataframe['maximos_historicos']) ,-1,np.NaN))\n",
      "<ipython-input-566-01c9ab704fa8>:4: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  dataframe['Senal']=dataframe.Senal.ffill()\n",
      "<ipython-input-566-01c9ab704fa8>:5: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  dataframe['Posicion'] = dataframe.Senal.shift(1)    # mi posicion se activará al otro dia de la señal.\n",
      "<ipython-input-566-01c9ab704fa8>:6: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  dataframe['Trade'] = np.where( dataframe.Posicion==1 & (dataframe.Posicion!= dataframe.Posicion.shift())  ,1 ,np.where( (dataframe.Posicion!= dataframe.Posicion.shift()) & dataframe.Posicion.shift()==1  ,-1 ,0)) # Cuando mi posicion me da compra y no estoy comprado, entro en la posicion con Trade=1. Cuando estoy comprado y la posicion da venta, el trade queda en -1 . Cuando no ocurre ninguno de los dos casos, el trade es cero ( esto se puede dar o porque sigo comprado , o porque todavia no entré en ninguna posicion)\n",
      "<ipython-input-566-01c9ab704fa8>:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  dataframe['Senal']=np.where(dataframe.Low <= dataframe['minimos_historicos'] ,1,np.where((dataframe.High >= dataframe['maximos_historicos']) ,-1,np.NaN))\n",
      "<ipython-input-566-01c9ab704fa8>:4: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  dataframe['Senal']=dataframe.Senal.ffill()\n",
      "<ipython-input-566-01c9ab704fa8>:5: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  dataframe['Posicion'] = dataframe.Senal.shift(1)    # mi posicion se activará al otro dia de la señal.\n",
      "<ipython-input-566-01c9ab704fa8>:6: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  dataframe['Trade'] = np.where( dataframe.Posicion==1 & (dataframe.Posicion!= dataframe.Posicion.shift())  ,1 ,np.where( (dataframe.Posicion!= dataframe.Posicion.shift()) & dataframe.Posicion.shift()==1  ,-1 ,0)) # Cuando mi posicion me da compra y no estoy comprado, entro en la posicion con Trade=1. Cuando estoy comprado y la posicion da venta, el trade queda en -1 . Cuando no ocurre ninguno de los dos casos, el trade es cero ( esto se puede dar o porque sigo comprado , o porque todavia no entré en ninguna posicion)\n",
      "<ipython-input-576-13a539a457da>:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  dataframe['Retornos'] = np.where(( (dataframe.Trade!=0 ) ),dataframe.Dif_Close -0.0085 ,\n",
      "<ipython-input-576-13a539a457da>:5: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  dataframe['capital_estrategia'] = (dataframe.Retornos + 1).cumprod() * 100\n",
      "<ipython-input-584-2b4b737a81b4>:35: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  estrategia['Buy_and_Hold']=np.NaN\n",
      "D:\\Anaconda\\lib\\site-packages\\pandas\\core\\indexing.py:1745: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  isetter(ilocs[0], value)\n"
     ]
    }
   ],
   "source": [
    "\n",
    "accion='GGAL'\n",
    "start_date = dt.datetime(2015, 1, 1)\n",
    "end_date = dt.datetime(2020,12 , 31)\n",
    "hoy = dt.datetime(2021,5, 1)\n",
    "\n",
    "datos_antiguos=data.DataReader(accion,'yahoo',start_date,end_date) # datos antiguos sobre los cuales se iran agregando los nuevos datos \n",
    "datos_futuros=data.DataReader(accion,'yahoo',end_date,hoy) # datos sobre los cual hago el bucle, y los voy agregando a los viejos\n",
    "datos_antiguos=ajustar_columnas(datos_antiguos)\n",
    "datos_futuros=ajustar_columnas(datos_futuros)\n",
    "datos_antiguos['minimos_historicos']=np.NaN # creo una columna para cada dataframe con valores NaN\n",
    "datos_antiguos['maximos_historicos']=np.NaN\n",
    "datos_futuros['minimos_historicos']=np.NaN\n",
    "datos_futuros['maximos_historicos']=np.NaN\n",
    "\n",
    "for dia_nuevo in range(len(datos_futuros)):    #corro un nuevo bucle por cada dia del nuevo dataframe\n",
    "    indice=datos_futuros.index[dia_nuevo] #obtengo el indice de los datos nuevos, para ir agregandolo a los viejos\n",
    "    if dia_nuevo==0:\n",
    "        primer_indice=indice\n",
    "\n",
    "    #datos_nuevos_bucle=list(datos_futuros[['High', 'Low', 'Open', 'Close', 'Volume','Adj Close','minimos_historicos','maximos_historicos']].loc[indice])\n",
    "    datos_nuevos_bucle=datos_futuros.loc[indice]\n",
    "    datos_antiguos.loc[indice]=datos_nuevos_bucle # agrega el nuevo dia al dataframe    \n",
    "    #datos_a_correr=datos_antiguos.loc[primer_indice:]  \n",
    "    # # Filtro los datos desde la primer fecha de la estrategia hasta el final. En la primera iteracion \n",
    "    datos_con_minimos_maximos=agrega_min_max_hitoricos(datos_antiguos)\n",
    "    datos_con_minimos_maximos=datos_con_minimos_maximos.loc[primer_indice:]\n",
    "    datos_final=trade_valores_historicos(datos_con_minimos_maximos)      \n",
    "\n",
    "estrategia=datos_estrategia(datos_final)\n",
    "\n",
    "datos_futuros['Buy_and_Hold'] = (datos_futuros.Dif_Close + 1).cumprod() * 100 # hacemos estos pasos para cargar los datos de la estrategia Buy and Hold en el dataframe llamado estrategia\n",
    "datos_futuros.loc[datos_futuros.index[0],'Buy_and_Hold']=100\n",
    "Buy_and_Hold_indice=list(datos_futuros.index)  \n",
    "Buy_and_Hold_valores=list(datos_futuros.Buy_and_Hold)\n",
    "estrategia['Buy_and_Hold']=np.NaN\n",
    "estrategia.loc[Buy_and_Hold_indice,'Buy_and_Hold'] =Buy_and_Hold_valores \n",
    "\n",
    "resultados = resultado_estrategia(estrategia)        \n"
   ]
  },
  {
   "source": [
    "## Visualizamos los resultados"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 585,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "{'Beneficio Bruto': 3.84,\n",
       " 'trades': 6,\n",
       " 'trades_ganadores': 5,\n",
       " 'suma_trades_ganadores': 7.49,\n",
       " 'trades_perdedores': 1,\n",
       " 'suma_trades_perdedores': 1.18,\n",
       " 'Ratio Acierto': 83.33,\n",
       " 'Profit Factor': 6.35,\n",
       " 'capital_estrategia': 103.84}"
      ]
     },
     "metadata": {},
     "execution_count": 585
    }
   ],
   "source": [
    "resultados"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 586,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "                High       Low      Open     Close     Volume  Dif_Close  \\\n",
       "Date                                                                       \n",
       "2020-12-31  8.878040  8.441578  8.808603  8.669728   696800.0        NaN   \n",
       "2021-01-04  8.679648  8.134070  8.679648  8.213427  1137500.0  -0.052632   \n",
       "2021-01-05  8.332462  8.034875  8.104312  8.124150   469000.0  -0.010870   \n",
       "2021-01-06  8.471336  8.104311  8.233266  8.243186   593800.0   0.014652   \n",
       "2021-01-07  8.471337  8.044794  8.382060  8.253105   828000.0   0.001203   \n",
       "...              ...       ...       ...       ...        ...        ...   \n",
       "2021-04-26  7.380181  7.142110  7.142110  7.350422   680500.0   0.029167   \n",
       "2021-04-27  7.747206  7.340503  7.340503  7.657929   910300.0   0.041835   \n",
       "2021-04-28  8.064633  7.618251  7.657930  7.727367   887800.0   0.009067   \n",
       "2021-04-29  7.796804  7.538895  7.757126  7.568654   371800.0  -0.020539   \n",
       "2021-04-30  7.499216  7.360342  7.479377  7.429779   322700.0  -0.018349   \n",
       "\n",
       "            minimos_historicos  maximos_historicos  Senal  Posicion  Trade  \\\n",
       "Date                                                                         \n",
       "2020-12-31                 NaN            8.878040   -1.0       NaN      0   \n",
       "2021-01-04                 NaN            8.679648   -1.0      -1.0      0   \n",
       "2021-01-05                 NaN                 NaN   -1.0      -1.0      0   \n",
       "2021-01-06                 NaN            8.471336   -1.0      -1.0      0   \n",
       "2021-01-07                 NaN            8.471337   -1.0      -1.0      0   \n",
       "...                        ...                 ...    ...       ...    ...   \n",
       "2021-04-26                 NaN                 NaN    1.0       1.0      0   \n",
       "2021-04-27                 NaN                 NaN    1.0       1.0      0   \n",
       "2021-04-28                 NaN            8.064633   -1.0       1.0      0   \n",
       "2021-04-29                 NaN                 NaN   -1.0      -1.0     -1   \n",
       "2021-04-30                 NaN                 NaN   -1.0      -1.0      0   \n",
       "\n",
       "            Retornos  capital_estrategia  Buy_and_Hold  \n",
       "Date                                                    \n",
       "2020-12-31  0.000000          100.000000    100.000000  \n",
       "2021-01-04  0.000000          100.000000     94.736840  \n",
       "2021-01-05  0.000000          100.000000     93.707092  \n",
       "2021-01-06  0.000000          100.000000     95.080096  \n",
       "2021-01-07  0.000000          100.000000     95.194508  \n",
       "...              ...                 ...           ...  \n",
       "2021-04-26  0.029167          101.733446     84.782610  \n",
       "2021-04-27  0.041835          105.989501     88.329521  \n",
       "2021-04-28  0.009067          106.950550     89.130440  \n",
       "2021-04-29 -0.029039          103.844800     87.299778  \n",
       "2021-04-30  0.000000          103.844800     85.697940  \n",
       "\n",
       "[83 rows x 14 columns]"
      ],
      "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>High</th>\n      <th>Low</th>\n      <th>Open</th>\n      <th>Close</th>\n      <th>Volume</th>\n      <th>Dif_Close</th>\n      <th>minimos_historicos</th>\n      <th>maximos_historicos</th>\n      <th>Senal</th>\n      <th>Posicion</th>\n      <th>Trade</th>\n      <th>Retornos</th>\n      <th>capital_estrategia</th>\n      <th>Buy_and_Hold</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      <th></th>\n      <th></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>2020-12-31</th>\n      <td>8.878040</td>\n      <td>8.441578</td>\n      <td>8.808603</td>\n      <td>8.669728</td>\n      <td>696800.0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>8.878040</td>\n      <td>-1.0</td>\n      <td>NaN</td>\n      <td>0</td>\n      <td>0.000000</td>\n      <td>100.000000</td>\n      <td>100.000000</td>\n    </tr>\n    <tr>\n      <th>2021-01-04</th>\n      <td>8.679648</td>\n      <td>8.134070</td>\n      <td>8.679648</td>\n      <td>8.213427</td>\n      <td>1137500.0</td>\n      <td>-0.052632</td>\n      <td>NaN</td>\n      <td>8.679648</td>\n      <td>-1.0</td>\n      <td>-1.0</td>\n      <td>0</td>\n      <td>0.000000</td>\n      <td>100.000000</td>\n      <td>94.736840</td>\n    </tr>\n    <tr>\n      <th>2021-01-05</th>\n      <td>8.332462</td>\n      <td>8.034875</td>\n      <td>8.104312</td>\n      <td>8.124150</td>\n      <td>469000.0</td>\n      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     },
     "metadata": {},
     "execution_count": 586
    }
   ],
   "source": [
    "estrategia"
   ]
  },
  {
   "source": [
    "Cargamos la libreria de graficos y definimos el tamaño de los gráficos para que se ajusten bien al notebook"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 559,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline   \n",
    "import matplotlib.pyplot as plt\n",
    "plt.rcParams['figure.figsize'] = 18, 8\n",
    "plt.style.use('ggplot')"
   ]
  },
  {
   "source": [
    "Comparativa entre la estrategia Buy and Hold, y nuestro sistema"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 574,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Date'>"
      ]
     },
     "metadata": {},
     "execution_count": 574
    },
    {
     "output_type": "display_data",
     "data": {
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\n"
     },
     "metadata": {}
    }
   ],
   "source": [
    "estrategia[['capital_estrategia','Buy_and_Hold']].plot()"
   ]
  },
  {
   "source": [
    "Graficamos los minimos y maximos de nuestro sistema. Vemos que los minimos nos dan las entradas ,y los maximos las salidas."
   ],
   "cell_type": "markdown",
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     "execution_count": 578
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\n"
     },
     "metadata": {}
    }
   ],
   "source": [
    "estrategia[['minimos_historicos','maximos_historicos','Close']].plot(figsize=(20,13))"
   ]
  }
 ]
}