2389 lines
3.8 MiB
Plaintext
2389 lines
3.8 MiB
Plaintext
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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Projet d'Analyse Boursière\n",
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"\n",
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"### Remarque: Vous êtes libre de considérer ceci comme un exercice complet ou simplement de voir la vidéo des solutions comme une revue de code pour le projet. Ce projet est conçu pour être assez stimulant car il introduira quelques nouveaux concepts par le biais de quelques astuces !\n",
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"\n",
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"Bienvenue à votre premier projet! Ce projet est destiné à couronner la première moitié du cours, qui a principalement porté sur l'apprentissage des bibliothèques que nous utilisons dans ce cours, la deuxième moitié du cours traitera beaucoup plus des techniques financières et des plateformes de trading quantitatif.\n",
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"\n",
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"Nous analyserons les données sur les actions de quelques compagnies automobiles du 1er janvier 2012 au 1er janvier 2017. Gardez à l'esprit que ce projet est principalement fait pour pratiquer vos compétences avec matplotlib, pandas et numpy. Ne déduisez pas des conseils de trading financier de l'analyse que nous faisons ici !\n",
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"\n",
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"### Partie 0: Importations\n",
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"\n",
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"**Importez les différentes bibliothèques dont vous aurez besoin - vous pouvez toujours revenir ici ou importer au fur et à mesure :)**"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"outputs": [],
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"source": [
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"import numpy as np\n",
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"import pandas as pd\n",
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"import matplotlib.pyplot as plt\n",
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"%matplotlib inline"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"____\n",
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"## Partie 1: Obtenir les données\n",
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"\n",
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"### Action Tesla (Ticker: TSLA sur le NASDAQ)\n",
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"\n",
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"**Note ! Tout le monde ne travaillera pas sur un ordinateur qui lui donnera un accès complet pour télécharger les informations boursières en utilisant pandas_datareader (pare-feu, permissions d'administration, etc...). Pour cette raison, le fichier csv pour Tesla est fourni dans un dossier data à l'intérieur de ce dossier. Il s'appelle Tesla_Stock.csv. N'hésitez pas à l'utiliser avec read_csv !**\n",
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"\n",
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"**Utilisez pandas_datareader pour obtenir les informations historiques sur les actions de Tesla du 1er janvier 2012 au 1er janvier 2017.**\n",
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"\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {},
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"outputs": [],
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"source": [
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"import datetime"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {},
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"outputs": [],
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"source": [
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"start = datetime.datetime(2012, 1, 1)\n",
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"end = datetime.datetime(2017, 1, 1)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"metadata": {},
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"outputs": [],
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"source": [
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"tesla=pd.read_csv(\"Tesla_Stock.csv\")\n",
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"tesla.set_index('Date',inplace=True)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"metadata": {},
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"outputs": [
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{
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"output_type": "execute_result",
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"data": {
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"text/plain": [
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" High Low Open Close Volume Adj Close\n",
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"Date \n",
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"2012-01-03 29.500000 27.650000 28.940001 28.080000 928100 28.080000\n",
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"2012-01-04 28.670000 27.500000 28.209999 27.709999 630100 27.709999\n",
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"2012-01-05 27.930000 26.850000 27.760000 27.120001 1005500 27.120001\n",
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"2012-01-06 27.790001 26.410000 27.200001 26.910000 986300 26.910000\n",
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"2012-01-09 27.490000 26.120001 27.000000 27.250000 897000 27.250000"
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],
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"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>Adj Close</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 </tr>\n </thead>\n <tbody>\n <tr>\n <th>2012-01-03</th>\n <td>29.500000</td>\n <td>27.650000</td>\n <td>28.940001</td>\n <td>28.080000</td>\n <td>928100</td>\n <td>28.080000</td>\n </tr>\n <tr>\n <th>2012-01-04</th>\n <td>28.670000</td>\n <td>27.500000</td>\n <td>28.209999</td>\n <td>27.709999</td>\n <td>630100</td>\n <td>27.709999</td>\n </tr>\n <tr>\n <th>2012-01-05</th>\n <td>27.930000</td>\n <td>26.850000</td>\n <td>27.760000</td>\n <td>27.120001</td>\n <td>1005500</td>\n <td>27.120001</td>\n </tr>\n <tr>\n <th>2012-01-06</th>\n <td>27.790001</td>\n <td>26.410000</td>\n <td>27.200001</td>\n <td>26.910000</td>\n <td>986300</td>\n <td>26.910000</td>\n </tr>\n <tr>\n <th>2012-01-09</th>\n <td>27.490000</td>\n <td>26.120001</td>\n <td>27.000000</td>\n <td>27.250000</td>\n <td>897000</td>\n <td>27.250000</td>\n </tr>\n </tbody>\n</table>\n</div>"
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},
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"metadata": {},
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"execution_count": 5
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}
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],
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"source": [
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"tesla.head()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Autres entreprises automobiles\n",
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"\n",
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"**Répétez les mêmes étapes pour extraire les données pour Ford et GM (General Motors)**"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"metadata": {},
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"outputs": [],
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"source": [
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"ford=pd.read_csv(\"Ford_Stock.csv\")\n",
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"ford.set_index('Date',inplace=True)\n",
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"gm=pd.read_csv(\"GM_Stock.csv\")\n",
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"gm.set_index('Date',inplace=True)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/html": [
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"<div>\n",
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"<style scoped>\n",
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" vertical-align: middle;\n",
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" }\n",
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"\n",
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" .dataframe tbody tr th {\n",
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" vertical-align: top;\n",
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" }\n",
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"\n",
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" .dataframe thead th {\n",
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" text-align: right;\n",
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" }\n",
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"</style>\n",
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"<table border=\"1\" class=\"dataframe\">\n",
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" <thead>\n",
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" <tr style=\"text-align: right;\">\n",
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" <th></th>\n",
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" <th>High</th>\n",
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" <th>Low</th>\n",
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" <th>Open</th>\n",
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" <th>Close</th>\n",
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" <th>Volume</th>\n",
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" <th>Adj Close</th>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>Date</th>\n",
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" <th></th>\n",
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" <th></th>\n",
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" <th></th>\n",
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" <th></th>\n",
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" <th></th>\n",
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" <th></th>\n",
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" </tr>\n",
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" </thead>\n",
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" <tbody>\n",
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" <tr>\n",
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" <th>2012-01-03</th>\n",
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" <td>11.25</td>\n",
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" <td>10.99</td>\n",
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" <td>11.00</td>\n",
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" <td>11.13</td>\n",
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" <td>45709900.0</td>\n",
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" <td>7.673051</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>2012-01-04</th>\n",
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" <td>11.53</td>\n",
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" <td>11.07</td>\n",
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" <td>11.15</td>\n",
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" <td>11.30</td>\n",
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" <td>79725200.0</td>\n",
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" <td>7.790251</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>2012-01-05</th>\n",
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" <td>11.63</td>\n",
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" <td>11.24</td>\n",
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" <td>11.33</td>\n",
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" <td>11.59</td>\n",
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" <td>67877500.0</td>\n",
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" <td>7.990177</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>2012-01-06</th>\n",
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" <td>11.80</td>\n",
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" <td>11.52</td>\n",
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" <td>11.74</td>\n",
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" <td>11.71</td>\n",
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" <td>59840700.0</td>\n",
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" <td>8.072903</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>2012-01-09</th>\n",
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" <td>11.95</td>\n",
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" <td>11.70</td>\n",
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" <td>11.83</td>\n",
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" <td>11.80</td>\n",
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" <td>53981500.0</td>\n",
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" <td>8.134951</td>\n",
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" </tr>\n",
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" </tbody>\n",
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"</table>\n",
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"</div>"
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],
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"text/plain": [
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" High Low Open Close Volume Adj Close\n",
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"Date \n",
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"2012-01-03 11.25 10.99 11.00 11.13 45709900.0 7.673051\n",
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|
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"2012-01-04 11.53 11.07 11.15 11.30 79725200.0 7.790251\n",
|
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"2012-01-05 11.63 11.24 11.33 11.59 67877500.0 7.990177\n",
|
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|
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"2012-01-06 11.80 11.52 11.74 11.71 59840700.0 8.072903\n",
|
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|
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"2012-01-09 11.95 11.70 11.83 11.80 53981500.0 8.134951"
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]
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},
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"execution_count": 7,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": []
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"metadata": {},
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"outputs": [
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{
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"output_type": "execute_result",
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"data": {
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"text/plain": [
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" High Low Open Close Volume Adj Close\n",
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"Date \n",
|
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"2012-01-03 11.25 10.99 11.00 11.13 45709900.0 7.687118\n",
|
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|
|
"2012-01-04 11.53 11.07 11.15 11.30 79725200.0 7.804530\n",
|
|||
|
|
"2012-01-05 11.63 11.24 11.33 11.59 67877500.0 8.004824\n",
|
|||
|
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"2012-01-06 11.80 11.52 11.74 11.71 59840700.0 8.087703\n",
|
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|
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"2012-01-09 11.95 11.70 11.83 11.80 53981500.0 8.149862"
|
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],
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"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>Adj Close</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 </tr>\n </thead>\n <tbody>\n <tr>\n <th>2012-01-03</th>\n <td>11.25</td>\n <td>10.99</td>\n <td>11.00</td>\n <td>11.13</td>\n <td>45709900.0</td>\n <td>7.687118</td>\n </tr>\n <tr>\n <th>2012-01-04</th>\n <td>11.53</td>\n <td>11.07</td>\n <td>11.15</td>\n <td>11.30</td>\n <td>79725200.0</td>\n <td>7.804530</td>\n </tr>\n <tr>\n <th>2012-01-05</th>\n <td>11.63</td>\n <td>11.24</td>\n <td>11.33</td>\n <td>11.59</td>\n <td>67877500.0</td>\n <td>8.004824</td>\n </tr>\n <tr>\n <th>2012-01-06</th>\n <td>11.80</td>\n <td>11.52</td>\n <td>11.74</td>\n <td>11.71</td>\n <td>59840700.0</td>\n <td>8.087703</td>\n </tr>\n <tr>\n <th>2012-01-09</th>\n <td>11.95</td>\n <td>11.70</td>\n <td>11.83</td>\n <td>11.80</td>\n <td>53981500.0</td>\n <td>8.149862</td>\n </tr>\n </tbody>\n</table>\n</div>"
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},
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"metadata": {},
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"execution_count": 7
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}
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],
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"source": [
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"ford.head()"
|
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]
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},
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{
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"cell_type": "code",
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"execution_count": 9,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/html": [
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"<div>\n",
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"<style scoped>\n",
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" vertical-align: middle;\n",
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" }\n",
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"\n",
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" .dataframe tbody tr th {\n",
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" vertical-align: top;\n",
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" }\n",
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" .dataframe thead th {\n",
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" text-align: right;\n",
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" }\n",
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"</style>\n",
|
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"<table border=\"1\" class=\"dataframe\">\n",
|
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" <thead>\n",
|
|||
|
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" <tr style=\"text-align: right;\">\n",
|
|||
|
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" <th></th>\n",
|
|||
|
|
" <th>High</th>\n",
|
|||
|
|
" <th>Low</th>\n",
|
|||
|
|
" <th>Open</th>\n",
|
|||
|
|
" <th>Close</th>\n",
|
|||
|
|
" <th>Volume</th>\n",
|
|||
|
|
" <th>Adj Close</th>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
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" <tr>\n",
|
|||
|
|
" <th>Date</th>\n",
|
|||
|
|
" <th></th>\n",
|
|||
|
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" <th></th>\n",
|
|||
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" <th></th>\n",
|
|||
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" <th></th>\n",
|
|||
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" <th></th>\n",
|
|||
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" <th></th>\n",
|
|||
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|
" </tr>\n",
|
|||
|
|
" </thead>\n",
|
|||
|
|
" <tbody>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>2012-01-03</th>\n",
|
|||
|
|
" <td>21.180000</td>\n",
|
|||
|
|
" <td>20.750000</td>\n",
|
|||
|
|
" <td>20.830000</td>\n",
|
|||
|
|
" <td>21.049999</td>\n",
|
|||
|
|
" <td>9321300.0</td>\n",
|
|||
|
|
" <td>16.299799</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>2012-01-04</th>\n",
|
|||
|
|
" <td>21.370001</td>\n",
|
|||
|
|
" <td>20.750000</td>\n",
|
|||
|
|
" <td>21.049999</td>\n",
|
|||
|
|
" <td>21.150000</td>\n",
|
|||
|
|
" <td>7856700.0</td>\n",
|
|||
|
|
" <td>16.377232</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>2012-01-05</th>\n",
|
|||
|
|
" <td>22.290001</td>\n",
|
|||
|
|
" <td>20.959999</td>\n",
|
|||
|
|
" <td>21.100000</td>\n",
|
|||
|
|
" <td>22.170000</td>\n",
|
|||
|
|
" <td>17880600.0</td>\n",
|
|||
|
|
" <td>17.167059</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>2012-01-06</th>\n",
|
|||
|
|
" <td>23.030001</td>\n",
|
|||
|
|
" <td>22.240000</td>\n",
|
|||
|
|
" <td>22.260000</td>\n",
|
|||
|
|
" <td>22.920000</td>\n",
|
|||
|
|
" <td>18234500.0</td>\n",
|
|||
|
|
" <td>17.747812</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>2012-01-09</th>\n",
|
|||
|
|
" <td>23.430000</td>\n",
|
|||
|
|
" <td>22.700001</td>\n",
|
|||
|
|
" <td>23.200001</td>\n",
|
|||
|
|
" <td>22.840000</td>\n",
|
|||
|
|
" <td>12084500.0</td>\n",
|
|||
|
|
" <td>17.685862</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" </tbody>\n",
|
|||
|
|
"</table>\n",
|
|||
|
|
"</div>"
|
|||
|
|
],
|
|||
|
|
"text/plain": [
|
|||
|
|
" High Low Open Close Volume Adj Close\n",
|
|||
|
|
"Date \n",
|
|||
|
|
"2012-01-03 21.180000 20.750000 20.830000 21.049999 9321300.0 16.299799\n",
|
|||
|
|
"2012-01-04 21.370001 20.750000 21.049999 21.150000 7856700.0 16.377232\n",
|
|||
|
|
"2012-01-05 22.290001 20.959999 21.100000 22.170000 17880600.0 17.167059\n",
|
|||
|
|
"2012-01-06 23.030001 22.240000 22.260000 22.920000 18234500.0 17.747812\n",
|
|||
|
|
"2012-01-09 23.430000 22.700001 23.200001 22.840000 12084500.0 17.685862"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"execution_count": 9,
|
|||
|
|
"metadata": {},
|
|||
|
|
"output_type": "execute_result"
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": []
|
|||
|
|
},
|
|||
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|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 8,
|
|||
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"metadata": {},
|
|||
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"outputs": [
|
|||
|
|
{
|
|||
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"output_type": "execute_result",
|
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"data": {
|
|||
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"text/plain": [
|
|||
|
|
" High Low Open Close Volume Adj Close\n",
|
|||
|
|
"Date \n",
|
|||
|
|
"2012-01-03 21.180000 20.750000 20.830000 21.049999 9321300.0 16.103352\n",
|
|||
|
|
"2012-01-04 21.370001 20.750000 21.049999 21.150000 7856700.0 16.179853\n",
|
|||
|
|
"2012-01-05 22.290001 20.959999 21.100000 22.170000 17880600.0 16.960161\n",
|
|||
|
|
"2012-01-06 23.030001 22.240000 22.260000 22.920000 18234500.0 17.533915\n",
|
|||
|
|
"2012-01-09 23.430000 22.700001 23.200001 22.840000 12084500.0 17.472712"
|
|||
|
|
],
|
|||
|
|
"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>Adj Close</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 </tr>\n </thead>\n <tbody>\n <tr>\n <th>2012-01-03</th>\n <td>21.180000</td>\n <td>20.750000</td>\n <td>20.830000</td>\n <td>21.049999</td>\n <td>9321300.0</td>\n <td>16.103352</td>\n </tr>\n <tr>\n <th>2012-01-04</th>\n <td>21.370001</td>\n <td>20.750000</td>\n <td>21.049999</td>\n <td>21.150000</td>\n <td>7856700.0</td>\n <td>16.179853</td>\n </tr>\n <tr>\n <th>2012-01-05</th>\n <td>22.290001</td>\n <td>20.959999</td>\n <td>21.100000</td>\n <td>22.170000</td>\n <td>17880600.0</td>\n <td>16.960161</td>\n </tr>\n <tr>\n <th>2012-01-06</th>\n <td>23.030001</td>\n <td>22.240000</td>\n <td>22.260000</td>\n <td>22.920000</td>\n <td>18234500.0</td>\n <td>17.533915</td>\n </tr>\n <tr>\n <th>2012-01-09</th>\n <td>23.430000</td>\n <td>22.700001</td>\n <td>23.200001</td>\n <td>22.840000</td>\n <td>12084500.0</td>\n <td>17.472712</td>\n </tr>\n </tbody>\n</table>\n</div>"
|
|||
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|
},
|
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"metadata": {},
|
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"execution_count": 8
|
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}
|
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],
|
|||
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"source": [
|
|||
|
|
"gm.head()"
|
|||
|
|
]
|
|||
|
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},
|
|||
|
|
{
|
|||
|
|
"cell_type": "markdown",
|
|||
|
|
"metadata": {},
|
|||
|
|
"source": [
|
|||
|
|
"## Partie 2: Visualisation des données\n",
|
|||
|
|
"\n",
|
|||
|
|
"**Il est temps de visualiser les données.**\n",
|
|||
|
|
"\n",
|
|||
|
|
"**Suivez et recréez les graphiques ci-dessous en suivant les instructions et les explications.**"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "markdown",
|
|||
|
|
"metadata": {},
|
|||
|
|
"source": [
|
|||
|
|
"____\n",
|
|||
|
|
"\n",
|
|||
|
|
"**Recréez ce tracé linéaire de tous les prix à l'ouverture des différentes actions! Astuce: Pour la légende, utilisez le paramètre label et plt.legend()**"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
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|
|||
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"execution_count": 9,
|
|||
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|
"metadata": {},
|
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|
"outputs": [
|
|||
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|
{
|
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|
|||
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|
"data": {
|
|||
|
|
"text/plain": [
|
|||
|
|
"<matplotlib.legend.Legend at 0x18cc5569100>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {},
|
|||
|
|
"execution_count": 9
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"output_type": "display_data",
|
|||
|
|
"data": {
|
|||
|
|
"text/plain": "<Figure size 1152x576 with 1 Axes>",
|
|||
|
|
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|
|
"image/png": "iVBORw0KGgoAAAANSUhEUgAAA6UAAAHwCAYAAABe/ckrAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8vihELAAAACXBIWXMAAAsTAAALEwEAmpwYAADslElEQVR4nOzdd5icZdU/8O89vW7NtvSekIQ0QqhSpAsCoigWxPKqvCp2fbGj/vTF7mvDjlgRBRVFUEB6CyQCSSC9brK9T2/374+nzDOzM7uzs9N25vu5Li6m773Z2dnnPOfc5wgpJYiIiIiIiIjKwVTuBRAREREREVHtYlBKREREREREZcOglIiIiIiIiMqGQSkRERERERGVDYNSIiIiIiIiKhsGpURERERERFQ2DEqJiIhmGCHEfCGETwhhLvdaiIiIpotBKRER1SwhxNuEENuFEAEhRLcQ4hYhREOZ1iKFEH412DwmhPhWtqBTSnlESumRUsZLvU4iIqJCY1BKREQ1SQjxUQBfBfBxAPUATgWwAMD9QghbmZa1TkrpAXAegDcBeFf6A4QQlpKvioiIqIgYlBIRUc0RQtQB+AKAG6SU90kpo1LKQwBeDyUwfYv6uJuEEH8SQvxBCDEmhNgmhFhneJ3ZQog7hRB9QoiDQogPGO67SQhxhxDiV+pzdwohNuWyPinlLgCPAVgjhFioZlHfKYQ4AuDfhtss6tdqEkLcKoQ4LoQYEkL8xbCOy4QQzwshhoUQTwoh1k7334+IiKiQGJQSEVEtOh2AA8BdxhullD4A9wK4wHDzFQD+CKAJwO8A/EUIYRVCmAD8DcALAOZAyW5+SAhxkeG5lwO4HUADgLsBfD+XxQkhVgF4BYD/GG4+G8AJAC7K8JRfA3ABWA2gFcC31dfZCOAXAN4DoBnAjwHcLYSw57IOIiKiUmBQSkREtWgWgH4pZSzDfV3q/ZqtUso/SSmjAL4FJZg9FcDJAFqklF+UUkaklAcA/BTANYbnPi6l/Ie69/PXANZhYtuEEENQgt2fAbjVcN9NUkq/lDJofIIQogPAJQCul1IOqVnfR9S73wXgx1LKZ6SUcSnlbQDC6vqJiIgqAvelEBFRLeoHMEsIYckQmHao92uOaheklAkhRCeA2QAkgNlCiGHDY81Qym413YbLAQCOLF9Ts1FKuc94gxBi3DrSzAMwKKUcynDfAgDXCSFuMNxmU9dPRERUERiUEhFRLXoKSsbwKgB3aDcKIdxQso6fMjx2nuF+E4C5AI4DiAE4KKVcVooFQwmCMzkKoEkI0SClHM5w35ellF8u6sqIiIimgeW7RERUc6SUI1AaHX1PCHGxukd0IZS9o51QSm01JwkhrlKbCn0ISjD7NIAtAEaFEP8jhHAKIcxCiDVCiJNL/L10QdkH+0MhRKP6vZyl3v1TANcLIU4RCrcQ4lIhhLeUayQiIpoIg1IiIqpJUsqvQcmIfgPAKIBnoGQWz5NShg0P/SuANwAYAnAtgKvUfZtxAK8GsB7AQSglvz+DMl6m1K4FEAWwC0AvlOAZUsrnoOwr/T6U9e8D8LYyrI+IiCgrIWW2aiAiIqLaJoS4CcBSKeVbyr0WIiKiasVMKREREREREZUNg1IiIiIiIiIqG5bvEhERERERUdlMmikVQjiEEFuEEC8IIXYKIb6g3t4khLhfCLFX/X+j4TmfFELsE0LsFkJcVMxvgIiIiIiIiGauSTOlQpna7ZZS+oQQVgCPA/gglNlug1LKm4UQNwJolFL+jxBiFYDfA9gMZTj3AwCWq10KiYiIiIiIiHSWyR4glajVp161qv9JAFcAOEe9/TYADwP4H/X229V2+geFEPugBKhPZfsas2bNkgsXLszrGyAiIiIiIqLKtnXr1n4pZUum+yYNSgFACGEGsBXAUgA/kFI+I4RoUwd2Q0rZJYRoVR8+B8pQcU2neltWCxcuxHPPPZfLUoiIiIiIiGiGEUIcznZfTt13pZRxKeV6AHMBbBZCrJno62V6iQyLercQ4jkhxHN9fX25LIOIiIiIiIiqzJRGwkgph6GU6V4MoEcI0QEA6v971Yd1AphneNpcAMczvNZPpJSbpJSbWloyZnGJiIiIiIioyuXSfbdFCNGgXnYCOB/ALgB3A7hOfdh1AP6qXr4bwDVCCLsQYhGAZQC2FHjdREREREREVAVy2VPaAeA2dV+pCcAdUsq/CyGeAnCHEOKdAI4AuBoApJQ7hRB3AHgJQAzA+9h5l4iIiIiIalU0GkVnZydCoVC5l1J0DocDc+fOhdVqzfk5k46EKYVNmzZJNjoiIiIiIqJqdPDgQXi9XjQ3N0OZuFmdpJQYGBjA2NgYFi1alHKfEGKrlHJTpudNaU8pERERERERTU0oFKr6gBQAhBBobm6eckaYQSkREREREVGRVXtAqsnn+8xpTikRERERERHNPAMDAzjvvPMAAN3d3TCbzdCmn2zZsgU2m23C5//yl7/Ec889h+9///tFWyODUiIiIiIioirV3NyM559/HgBw0003wePx4GMf+1h5F5WG5btEREREREQ1ZOvWrTj77LNx0kkn4aKLLkJXVxcA4Lvf/S5WrVqFtWvX4pprrhn3vL/97W845ZRTsGHDBpx//vno6ekpyHqYKSUiIiIiIiqRL/xtJ146PlrQ11w1uw6ff/XqnB4rpcQNN9yAv/71r2hpacEf/vAHfPrTn8YvfvEL3HzzzTh48CDsdjuGh4fHPffMM8/E008/DSEEfvazn+FrX/savvnNb057/QxKiYiIiIiIakQ4HMaOHTtwwQUXAADi8Tg6OjoAAGvXrsWb3/xmXHnllbjyyivHPbezsxNveMMb0NXVhUgkMm7sS74YlBIREREREZVIrhnNYpFSYvXq1XjqqafG3XfPPffg0Ucfxd13340vfelL2LlzZ8r9N9xwAz7ykY/g8ssvx8MPP4ybbrqpIGvinlIiIiIiIqIaYbfb0dfXpwel0WgUO3fuRCKRwNGjR3Huuefia1/7GoaHh+Hz+VKeOzIygjlz5gAAbrvttoKtiZlSIiIiIiKiGmEymfCnP/0JH/jABzAyMoJYLIYPfehDWL58Od7ylrdgZGQEUkp8+MMfRkNDQ8pzb7rpJlx99dWYM2cOTj31VBw8eLAgaxJSyoK80HRs2rRJPvfcc+VeBhERERERUcG9/PLLOOGEE8q9jJLJ9P0KIbZKKTdlejzLd4mIiIiIiKhsGJQSERFVqd3dY1h44z145sBAuZdCRESUFYNSIiKiKrX18BAA4M5tnWVeCRERUXYMSomIiKpUk9sKAOj3Rcq8EiIiouwYlBIREVWpWEJpZjjgC5d5JURERNkxKCUiIqpSwUgcADOlRERU2RiUEhERValQLAEA6BtjppSIqNb19PTgTW96ExYvXoyTTjoJp512Gv785z/j4YcfhhACP//5z/XH/uc//4EQAt/4xjdKsjYGpURERFUqpGZKI/EEHtvbhx3HRsq8IiIiKgcpJa688kqcddZZOHDgALZu3Yrbb78dnZ1KI7wTTzwRf/jDH/TH33777Vi3bl3J1seglIiIqEqFonH98rU/34LLvvd4GVdDRETl8u9//xs2mw3XX3+9ftuCBQtwww03AADmz5+PUCiEnp4eSClx33334ZJLLinZ+iwl+0pERERUUsFoHFazQLPbju7REAAgEkvAZuE5aSKisrn3RqB7e2Ffs/1E4JKbs969c+dObNy4ccKXeN3rXoc//vGP2LBhAzZu3Ai73V7YNU6Af5WIiIiqVDAah8NixiyvTb/t+HCwjCsiIqJK8L73vQ/r1q3DySefrN/2+te/Hn/84x/x+9//Hm984xtLuh5mSomIiKpUKJqAw2ZGiyd5tvucbzyMd5+1GJ961QllXBmVWzSewD+2d+GytbNhNolyL4eotkyQ0SyW1atX484779Sv/+AHP0B/fz82bdqk39be3g6r1Yr7778f//d//4cnn3yyZOtjppSIiKhKhaJxOKwmNLptKbc/8FJPmVZEleKeF7vwwdufx48e2V/upRBRCbzyla9EKBTCLbfcot8WCATGPe6LX/wivvr
|
|||
|
|
},
|
|||
|
|
"metadata": {
|
|||
|
|
"needs_background": "light"
|
|||
|
|
}
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"tesla['Open'].plot(label='Tesla',figsize=(16,8),title='Open Price')\n",
|
|||
|
|
"gm['Open'].plot(label='GM')\n",
|
|||
|
|
"ford['Open'].plot(label='Ford')\n",
|
|||
|
|
"plt.legend()"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 12,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"text/plain": [
|
|||
|
|
"<matplotlib.legend.Legend at 0x1152f6550>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"execution_count": 12,
|
|||
|
|
"metadata": {},
|
|||
|
|
"output_type": "execute_result"
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"image/png": "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
|
|||
|
|
"text/plain": [
|
|||
|
|
"<Figure size 1152x576 with 1 Axes>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {
|
|||
|
|
"needs_background": "light"
|
|||
|
|
},
|
|||
|
|
"output_type": "display_data"
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": []
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "markdown",
|
|||
|
|
"metadata": {},
|
|||
|
|
"source": [
|
|||
|
|
"____"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "markdown",
|
|||
|
|
"metadata": {},
|
|||
|
|
"source": [
|
|||
|
|
"**Tracez le volume des actions négociées chaque jour.**"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 10,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"output_type": "execute_result",
|
|||
|
|
"data": {
|
|||
|
|
"text/plain": [
|
|||
|
|
"<matplotlib.legend.Legend at 0x18cc5db3550>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {},
|
|||
|
|
"execution_count": 10
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"output_type": "display_data",
|
|||
|
|
"data": {
|
|||
|
|
"text/plain": "<Figure size 1152x576 with 1 Axes>",
|
|||
|
|
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},
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"metadata": {
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"needs_background": "light"
|
|||
|
|
}
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"tesla['Volume'].plot(label='Tesla',figsize=(16,8),title='Volume Traded')\n",
|
|||
|
|
"gm['Volume'].plot(label='GM')\n",
|
|||
|
|
"ford['Volume'].plot(label='Ford')\n",
|
|||
|
|
"plt.legend()"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 13,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"text/plain": [
|
|||
|
|
"<matplotlib.legend.Legend at 0x114b9ac50>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"execution_count": 13,
|
|||
|
|
"metadata": {},
|
|||
|
|
"output_type": "execute_result"
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
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|
|
"text/plain": [
|
|||
|
|
"<Figure size 1152x576 with 1 Axes>"
|
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|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {
|
|||
|
|
"needs_background": "light"
|
|||
|
|
},
|
|||
|
|
"output_type": "display_data"
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": []
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "markdown",
|
|||
|
|
"metadata": {},
|
|||
|
|
"source": [
|
|||
|
|
"**Intéressant, il semble que Ford ait eu un très gros pic fin 2013. Quelle était la date de ce volume d'échange maximum pour Ford ?**\n",
|
|||
|
|
"\n",
|
|||
|
|
"**Bonus: Que s'est-il passé ce jour-là?**"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 14,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"text/plain": [
|
|||
|
|
"Timestamp('2013-12-18 00:00:00')"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"execution_count": 14,
|
|||
|
|
"metadata": {},
|
|||
|
|
"output_type": "execute_result"
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": []
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 11,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"output_type": "execute_result",
|
|||
|
|
"data": {
|
|||
|
|
"text/plain": [
|
|||
|
|
"'2013-05-14'"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {},
|
|||
|
|
"execution_count": 11
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"tesla[\"Volume\"].idxmax()"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "markdown",
|
|||
|
|
"metadata": {},
|
|||
|
|
"source": [
|
|||
|
|
"____"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "markdown",
|
|||
|
|
"metadata": {},
|
|||
|
|
"source": [
|
|||
|
|
"*La visualisation du prix à l'ouverture des séries temporelles donne l'impression que Tesla a toujours eu beaucoup plus de valeur en tant qu'entreprise que GM et Ford. Mais pour vraiment comprendre cela, il faudrait regarder la capitalisation boursière totale de la société, et pas seulement le cours de l'action. Malheureusement, nos données actuelles n'ont pas cette information du nombre total d'unités d'actions présentes. Mais ce que nous pouvons faire comme simple calcul pour essayer de représenter l'argent total échangé serait de multiplier la colonne 'Volume' par le cours de l'action. Rappelez-vous que ce n'est pas encore la capitalisation boursière réelle, c'est juste une représentation visuelle de la quantité totale d'argent échangé en utilisant la série temporelle. (par exemple 100 unités d'actions à 10 dollars chacune contre 100 000 unités d'actions à 1 dollars chacune)*"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "markdown",
|
|||
|
|
"metadata": {},
|
|||
|
|
"source": [
|
|||
|
|
"**Créez une nouvelle colonne pour chaque dataframe appelée \"Total Traded\" qui est le prix d'ouverture multiplié par le volume négocié.**"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 12,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [],
|
|||
|
|
"source": [
|
|||
|
|
"tesla['Total Traded'] = tesla['Open']*tesla['Volume']\n",
|
|||
|
|
"ford['Total Traded'] = ford['Open']*ford['Volume']\n",
|
|||
|
|
"gm['Total Traded'] = gm['Open']*gm['Volume']"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 16,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [],
|
|||
|
|
"source": []
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "markdown",
|
|||
|
|
"metadata": {},
|
|||
|
|
"source": [
|
|||
|
|
"**Tracez ce total négocié (Total Traded) par rapport à l'index de temps.**"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 13,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"output_type": "execute_result",
|
|||
|
|
"data": {
|
|||
|
|
"text/plain": [
|
|||
|
|
"<matplotlib.legend.Legend at 0x18cc5759d00>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {},
|
|||
|
|
"execution_count": 13
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"output_type": "display_data",
|
|||
|
|
"data": {
|
|||
|
|
"text/plain": "<Figure size 1152x576 with 1 Axes>",
|
|||
|
|
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|
"image/png": "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
|
|||
|
|
},
|
|||
|
|
"metadata": {
|
|||
|
|
"needs_background": "light"
|
|||
|
|
}
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"tesla['Total Traded'].plot(label='Tesla',figsize=(16,8),title='Total Traded')\n",
|
|||
|
|
"gm['Total Traded'].plot(label='GM')\n",
|
|||
|
|
"ford['Total Traded'].plot(label='Ford')\n",
|
|||
|
|
"plt.legend()"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 18,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"text/plain": [
|
|||
|
|
"Text(0, 0.5, 'Total Traded')"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"execution_count": 18,
|
|||
|
|
"metadata": {},
|
|||
|
|
"output_type": "execute_result"
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"image/png": "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
|
|||
|
|
"text/plain": [
|
|||
|
|
"<Figure size 1152x576 with 1 Axes>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {
|
|||
|
|
"needs_background": "light"
|
|||
|
|
},
|
|||
|
|
"output_type": "display_data"
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": []
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "markdown",
|
|||
|
|
"metadata": {},
|
|||
|
|
"source": [
|
|||
|
|
"**Intéressant, il semble qu'il y ait eu une énorme quantité d'argent échangé pour Tesla début 2014. Quelle date c'était et que s'est-il passé ?**"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 19,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"text/plain": [
|
|||
|
|
"Timestamp('2014-02-25 00:00:00')"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"execution_count": 19,
|
|||
|
|
"metadata": {},
|
|||
|
|
"output_type": "execute_result"
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": []
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 14,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"output_type": "execute_result",
|
|||
|
|
"data": {
|
|||
|
|
"text/plain": [
|
|||
|
|
"'2014-02-25'"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {},
|
|||
|
|
"execution_count": 14
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"tesla[\"Total Traded\"].idxmax()"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "markdown",
|
|||
|
|
"metadata": {},
|
|||
|
|
"source": [
|
|||
|
|
"____"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "markdown",
|
|||
|
|
"metadata": {},
|
|||
|
|
"source": [
|
|||
|
|
"**Entraînons-nous à tracer des moyennes glissantes ou mobiles (MA - Moving Averages). Tracez MA50 et MA200 pour GM.**"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 15,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"output_type": "execute_result",
|
|||
|
|
"data": {
|
|||
|
|
"text/plain": [
|
|||
|
|
"<matplotlib.legend.Legend at 0x18cc5c43df0>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {},
|
|||
|
|
"execution_count": 15
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"output_type": "display_data",
|
|||
|
|
"data": {
|
|||
|
|
"text/plain": "<Figure size 1152x576 with 1 Axes>",
|
|||
|
|
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|
|||
|
|
},
|
|||
|
|
"metadata": {
|
|||
|
|
"needs_background": "light"
|
|||
|
|
}
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"gm['Open'].plot(label=\"Open\", figsize=(16,8))\n",
|
|||
|
|
"gm['Open'].rolling(50).mean().plot(label=\"MA50\")\n",
|
|||
|
|
"gm['Open'].rolling(200).mean().plot(label=\"MA200\")\n",
|
|||
|
|
"plt.legend()"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 21,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"text/plain": [
|
|||
|
|
"<matplotlib.axes._subplots.AxesSubplot at 0x114e24a50>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"execution_count": 21,
|
|||
|
|
"metadata": {},
|
|||
|
|
"output_type": "execute_result"
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
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|
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"text/plain": [
|
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|
|
"<Figure size 1152x576 with 1 Axes>"
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]
|
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},
|
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"metadata": {
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"needs_background": "light"
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},
|
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"output_type": "display_data"
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}
|
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],
|
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"source": []
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},
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{
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"cell_type": "markdown",
|
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|
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"metadata": {},
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"source": [
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|
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"______"
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|
|
]
|
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|
|
},
|
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|
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{
|
|||
|
|
"cell_type": "markdown",
|
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|
|
"metadata": {},
|
|||
|
|
"source": [
|
|||
|
|
"**Enfin, voyons s'il existe une relation entre ces actions car elles sont tous liées à l'industrie automobile. Nous pouvons le voir facilement à travers un diagramme de dispersion. Importez la matrice de dispersion de pandas.plotting et utilisez-la pour créer un diagramme de dispersion du prix d'ouverture de toutes les actions. Vous devrez peut-être réorganiser les colonnes dans un nouveau dataframe unique. Vous trouverez des conseils et des informations ici: https://pandas.pydata.org/pandas-docs/stable/visualization.html#scatter-matrix-plot**"
|
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|
|
]
|
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|
|
},
|
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|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 16,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [],
|
|||
|
|
"source": [
|
|||
|
|
"from pandas.plotting import scatter_matrix"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
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|
|
"execution_count": 17,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [],
|
|||
|
|
"source": [
|
|||
|
|
"car_comp = pd.concat([tesla['Open'],gm['Open'],ford['Open']],axis=1)\n",
|
|||
|
|
"car_comp.columns = ['Tesla Open','GM Open','Ford Open']"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 18,
|
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|
|
"metadata": {},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"output_type": "display_data",
|
|||
|
|
"data": {
|
|||
|
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"text/plain": "<Figure size 720x720 with 9 Axes>",
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"image/png": "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
|
|||
|
|
},
|
|||
|
|
"metadata": {
|
|||
|
|
"needs_background": "light"
|
|||
|
|
}
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"scatter_matrix(car_comp, alpha=0.2, figsize=(10, 10), hist_kwds={'bins':50});"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 25,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAfEAAAHoCAYAAABZ69YuAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8QZhcZAAAgAElEQVR4nOy9R6xlWZae9+3jzfXm+fARact0uW52VzebTfZAECVIIkERoCYaUYI0ogBC4kiARhI04EQCBGokUAQHggAJJCGiQUENku3KdLnMqKzM8PH8u+/a483eGuwbLyMyI7MysyOquzLPN3rv2nPvO++svdb+17+EUoqGhoaGhoaGXz6MP+8DaGhoaGhoaPhsNEG8oaGhoaHhl5QmiDc0NDQ0NPyS0gTxhoaGhoaGX1KaIN7Q0NDQ0PBLShPEGxoaGhoafkmx/rwP4NMwGo3U1atX/7wPo+FzxIMHD2jOqYYXSXNONbxovv/970+UUuPn3fdLFcSvXr3K9773vT/vw2j4HPHNb36zOacaXijNOdXwohFCPPyo+36pgnhDQ8MXA6UUj6cpeVWz2/cJnOZS1fD5YxYXnEU5Xd9ms+N9ptdo9sQbGhr+whHlFYu0JCslk1Xx5304DQ0vhZNVRl5KTpc5tfxs7qlNEG9oaPiFUNWSWVxQVPK590/jgrcPF9yfRPi2iW0JADp+k4U3/MUmzisWSfmR99dSUdaSeVLw06Mlj6cJAB3PBiB0TUxDfKb3bv47GhoafiE8OE9I8grHNnhlo43xgYvWyTLj3mlMJRWebfLqZptaKiyzyTUa/uKhlEJKRV5J7p3FAGzWLhvtZ8viRSW5cxqtA3mNbZrMk5KNTs1Oz2ej7f6ZzvEmiDc0NLxUaqnIy5p/+94Zx8uMUcuhqCQ7PZ9Ry714XOCYVFLhOyZ5KRFCYJmfLTtp+GJR1hKpFK5lIqXi4TShqCSXBi9HT5GVNT98NOPOWcxG22Gj7WMa4rkl8bSsL26318E6dE2c9c9/1kXqL3UQv/rf/IuLnx/893/9z/FIGhoanodSirtnEYfzhAeTGCHgPCoQCBZpSc+3OY8LPNvkyjDENARZKdnouD//xRsa0AH1zmmEUnB5ECAMiLIK0OdaMHjxYW6ZlTyepUyjgjivcC2Ta+PWh7JwgI5n0Qtsilqy2/NxLQMhXtzi9Jc6iDc0NPziKSqJaYhPtIeXljVJXuOYJv3QwTQMLg98bEswarkcLTLm673EW5st9voBoC+S86SgFzgv9bM0/PKT5BV5WSOEICkrxi0XxzIoa0nHt1/Ke/Z8h82OyzTKUUrhmPr9nvc/IYTg0iC4+H2RlCCg+4KOrQniDQ0Nn5hZXLA/SzENwc2NFo710aXAZVby6DwhyktGLZfffnVMXSte3e7QcvWlZ5XpAC4EGOvsZJmVPJxo4U8l1TMl94aGDzJNCn56vEQgGIQOlmnw6lYbpdQLzXifJsordno+O72AaZzj2xbmJ3ivJ/8/oKsG3eDPHsibIN7Q0PCJmMYFtw8XKHQmklX1xwbxJK9RCrq+QzewKVcK09ICtmAYooCdrt6z9Gzj4rXUU+J1qT5b203D55vDeUqcV2x2XOK8JnRsTAPypzofhBAopV6KOPJokSKlXnx+Za9HUtSfKLN++nx+Ued2E8QbGhp+LmUteetgDkqQljVXhzZt9+MvH4PQYZ4UKAXj0GWVVeSlxLMM3jleIZXi8jBgED5bMu8GNjvSo1aKcZOFN3yAvKo5XmQs05K0rNnr+yzTAkMYbHXf35Ouasnds5iikuz1ffrhi9uaabs2i7Sk5Vp4tolnm5/oeYPQQSod/F/U8XxugngjcmtoeHkczlOKSrHKSr6023lmj+8JSimKSmqFuW0ilaKSCqV0yfPWRouyViRFxTTWZfRVVl30ygJkRcV3Hswoa8m3rg5eWjm04ZcX2zCYJwWLtMIw4OowJHRtrA+ow7NKUlSSrKw5j3P6ocMqK9mfpfi2yeVB8KE2x0/K5WFAUUnsT9k9kVeSRVriWsYLK/d/boJ4Q0PDi+PBJOZ4mXJj3GLc9jCEYLPjMWpJro7CDz0+L2t+7+0TDhYp14YhNzZCtro+TyqGRa1bxhxLYBk2ba+kkpLhB7KR/XnKeaQd2h6cx2x1PcpasbnupT1dZZxHBYPQ+cw2lQ0vHikVJ6sMgWCz437m4HS6zEjLms2O95HZrWEIro5CFmmJYxkczFIenidkZUVSVtwYt6gltD0TheJ0lSEMncFPY60m/8HDGfd6Hr9xY/SJs+gP8nFbSR/F2SonLWrSoqYX2LS9Zk+8oaHhBRPnFW8dLChrRZTV/NXXXHZ7vu7jrpXOsGvJWZQjlL6o3jmNuHMacR7nOIZgt+8TOiZbXY+ilmy23y+Lz5ICqRTj9ocv1OO2yyovSYuaKyOfk0UOgAB2ej6nyxyl9MWwCeJ/cTiPiwt7XNsUDD/DNkhW1pws9d9bqexisVjVkrioabnWhfr7yjDUWzXAwSxGGHCyyBm3Pb7/YMpWN+AsAt/WTmhxVpFXkq5v8/bhgkVa4Vol985i3tjpvJgv4SOYrDImccEwdGm5FvOkxDLFZ148fJAmiDc0NDyDbRo4tkFRVxhCC3As0yCrJNOo4GCeUlQ1j2YpZ8uMvb7PWZRzvEiJ8pL+lR6jloMQgnH72Yu5lIrDeYZUijunEXlZk5aSvYHPV/e61FLxxlabo2XOo/OUYejQC5wLk4yWa3G8yPAdk/1ZwiB0muEofwFwnhKO2Z8hQwWw1m2LWVmjeL8s/u7JimmiPQU2Oi6uaaCApKi1y99ZRJLX5HXN6TJDoohLSVHWDAOb42WGYQi+vNth0PL41pU+RaUwDeh+BkvfWiruTyJOlzm7fZ8rww9Xpp6QlTV/dO+ctJC4lmDQcul5Npf74cU5/WelOfsbGhqeQSrFG1sd9mcJrmXyg0dz2r7FMi3xbYvHswSl4L3jFQo4i3IUimVe0fIsVrne897seB8qqxqGwHcMHkwS3jqY8/A8ZhKX3BqHTOOcS/2QsyinrCTDtsMgdLg8DGh79jojq0iKipNlyrvHCscy2O377PWDZ0RNDb8YyloySwparsX1cYgQfOZFlWUaXB0G/PR4SZTVHM1TWp7Fo2lCWkjun0V8Za/HJMpZpRXLrMQ2hW55FGCZupRvGIKDecrJIuPdkxVHiwzXMnBMg7/9rcuMOz7/zpublFJ9pnJ2WtYcL3POljlRXtH27A+JM58Q5xVpITlcpCySkqujkAeTmC/tdvn2zSFbXf8zfVdP0wTxhoYGAKZxzg8fzTlZZry+3WGRVex0bfYXMY4VYBkGXd9mq+MhBCzTnF7g8v37M87inINZyo1RSF0pzuOcd45g3HEZtVyKSrLMtJr3+qiFUPDuyZJlWlPXCqUEi7hkFNY4lsmw7dL1HLa63sWFtpKKslYss4ppXJAUkqzU084sw2Cj7X5moVLDZ+PRNCHJa05FzuvbnU81xONkmXG20oKz3Z4OZgpwTJNVVnL7aMmVtYCy41n4ts5cz6OceaoD+PF5ztEixXO0175hGASOQZRVmIbAd0yirMIKbX52vOJ0lbHR9vAci8+65Atsk5ZjMjX0cZUfMdAHwLUMQsfk6jBgFepzVSn9GfanSRPEGxoaXgy1VPzR3XP2ZylZUTNuF2x1PDq+pVXllaIb2lweBiAUD88Tvro3YNxxOFvl1ChMw2Cv7zNsO5hCUErJyTJj1HJ5eB6TlRLLFLy+3WHc8Wi5Nq/vaBHSKHSopOLtozm+ZTEMu2x1vWd6bz3bxBTgOyZ7dkBSVhzMU5KiZpmVzwTwqpZMk4LAsS6MZRpePE++cSHe//nnkZU1+7OUR+cJo5bDNCrY6eqqTehabHRcjuY6uz5d5Vwfh4xaLqFjkpQV753C+apAGNDyLDqFgzBgbxDgWQa+bfErl/tkZcX5qkDWkNU1rmXwr26fcnUU8O2b42eO6dMoxQ1D8CuX+1wdhdyfxJyuckopL9wGn3C2ynn7YEFc6g6MX706ICtr/uXbJzgv0Hq1ObsbGhrWbS8
|
|||
|
|
"text/plain": [
|
|||
|
|
"<Figure size 576x576 with 9 Axes>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {
|
|||
|
|
"needs_background": "light"
|
|||
|
|
},
|
|||
|
|
"output_type": "display_data"
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": []
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "markdown",
|
|||
|
|
"metadata": {},
|
|||
|
|
"source": [
|
|||
|
|
"_____\n",
|
|||
|
|
"### Bonus: Tâche de visualisation ! (C'est difficile !)\n",
|
|||
|
|
"**Créons maintenant un graphique en chandelier! Regardez la vidéo si vous ne parvenez pas à recréer cette visualisation, il y a plusieurs étapes à suivre! Référez-vous à la vidéo pour comprendre comment interpréter et lire ce graphique. Conseils: https://matplotlib.org/examples/pylab_examples/finance_demo.html**\n",
|
|||
|
|
"\n",
|
|||
|
|
"**Créer un graphique de chandeliers pour Ford en janvier 2012 (trop de dates, ce ne sera pas idéal pour un graphique en chandelier)**"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 26,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [],
|
|||
|
|
"source": [
|
|||
|
|
"# Code ici"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 27,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"image/png": "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
|
|||
|
|
"text/plain": [
|
|||
|
|
"<Figure size 432x288 with 1 Axes>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {
|
|||
|
|
"needs_background": "light"
|
|||
|
|
},
|
|||
|
|
"output_type": "display_data"
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": []
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "markdown",
|
|||
|
|
"metadata": {},
|
|||
|
|
"source": [
|
|||
|
|
"____\n",
|
|||
|
|
"# Partie 3: Analyse financière de base\n",
|
|||
|
|
"\n",
|
|||
|
|
"Il est maintenant temps de se concentrer sur quelques calculs financiers clés. Cela vous servira de transition vers la deuxième moitié du cours. Vous n'avez qu'à suivre les instructions, il s'agira principalement d'un exercice de conversion d'une équation ou d'un concept mathématique en code à l'aide de python et pandas, ce que nous ferons souvent lorsque nous travaillerons avec des données quantitatives! Si vous vous sentez perdu dans cette section, ne vous inquiétez pas! Allez simplement au notebook (ou vidéo) sur les solutions et traitez-la comme une revue de code, utilisez le style d'apprentissage qui vous convient le mieux!\n",
|
|||
|
|
"\n",
|
|||
|
|
"Commençons !\n",
|
|||
|
|
"____"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "markdown",
|
|||
|
|
"metadata": {},
|
|||
|
|
"source": [
|
|||
|
|
"## Rendement quotidien en pourcentage\n",
|
|||
|
|
"Nous commencerons par calculer la rendement quotidienne en pourcentage. Le rendement (en %) est défini par la formule suivante :"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "markdown",
|
|||
|
|
"metadata": {},
|
|||
|
|
"source": [
|
|||
|
|
"$ r_t = \\frac{p_t}{p_{t-1}} -1$"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "markdown",
|
|||
|
|
"metadata": {},
|
|||
|
|
"source": [
|
|||
|
|
"Cela définit r_t (rendement à l'instant t) comme étant égal au prix à l'instant t divisé par le prix à l'instant t-1 (la veille) moins 1. En gros, cela vous informe simplement de votre pourcentage de gain (ou de perte) si vous avez acheté l'action le jour et l'avez ensuite vendue le lendemain. Bien que cela ne soit pas nécessairement utile pour tenter de prédire les valeurs futures du titre, c'est très utile pour analyser la volatilité du titre. Si les rendements quotidiens ont une large distribution, le titre est plus volatil d'un jour à l'autre. Calculons les pourcentages de rendement, puis traçons un histogramme et décidons quel titre est le plus stable!"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "markdown",
|
|||
|
|
"metadata": {},
|
|||
|
|
"source": [
|
|||
|
|
"**Créez une nouvelle colonne pour chaque dataframe appelée 'returns'. Cette colonne sera calculée à partir de la colonne de prix à la fermeture'Close'. Il y a deux façons de faire cela, soit un simple calcul en utilisant la méthode .shift() qui suit la formule ci-dessus, ou vous pouvez aussi utiliser la méthode pct_change intégrée à pandas.**"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 19,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"output_type": "execute_result",
|
|||
|
|
"data": {
|
|||
|
|
"text/plain": [
|
|||
|
|
" High Low Open Close Volume Adj Close \\\n",
|
|||
|
|
"Date \n",
|
|||
|
|
"2012-01-03 29.500000 27.650000 28.940001 28.080000 928100 28.080000 \n",
|
|||
|
|
"2012-01-04 28.670000 27.500000 28.209999 27.709999 630100 27.709999 \n",
|
|||
|
|
"2012-01-05 27.930000 26.850000 27.760000 27.120001 1005500 27.120001 \n",
|
|||
|
|
"2012-01-06 27.790001 26.410000 27.200001 26.910000 986300 26.910000 \n",
|
|||
|
|
"2012-01-09 27.490000 26.120001 27.000000 27.250000 897000 27.250000 \n",
|
|||
|
|
"\n",
|
|||
|
|
" Total Traded returns \n",
|
|||
|
|
"Date \n",
|
|||
|
|
"2012-01-03 2.685921e+07 NaN \n",
|
|||
|
|
"2012-01-04 1.777512e+07 -0.013177 \n",
|
|||
|
|
"2012-01-05 2.791268e+07 -0.021292 \n",
|
|||
|
|
"2012-01-06 2.682736e+07 -0.007743 \n",
|
|||
|
|
"2012-01-09 2.421900e+07 0.012635 "
|
|||
|
|
],
|
|||
|
|
"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>Adj Close</th>\n <th>Total Traded</th>\n <th>returns</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 </tr>\n </thead>\n <tbody>\n <tr>\n <th>2012-01-03</th>\n <td>29.500000</td>\n <td>27.650000</td>\n <td>28.940001</td>\n <td>28.080000</td>\n <td>928100</td>\n <td>28.080000</td>\n <td>2.685921e+07</td>\n <td>NaN</td>\n </tr>\n <tr>\n <th>2012-01-04</th>\n <td>28.670000</td>\n <td>27.500000</td>\n <td>28.209999</td>\n <td>27.709999</td>\n <td>630100</td>\n <td>27.709999</td>\n <td>1.777512e+07</td>\n <td>-0.013177</td>\n </tr>\n <tr>\n <th>2012-01-05</th>\n <td>27.930000</td>\n <td>26.850000</td>\n <td>27.760000</td>\n <td>27.120001</td>\n <td>1005500</td>\n <td>27.120001</td>\n <td>2.791268e+07</td>\n <td>-0.021292</td>\n </tr>\n <tr>\n <th>2012-01-06</th>\n <td>27.790001</td>\n <td>26.410000</td>\n <td>27.200001</td>\n <td>26.910000</td>\n <td>986300</td>\n <td>26.910000</td>\n <td>2.682736e+07</td>\n <td>-0.007743</td>\n </tr>\n <tr>\n <th>2012-01-09</th>\n <td>27.490000</td>\n <td>26.120001</td>\n <td>27.000000</td>\n <td>27.250000</td>\n <td>897000</td>\n <td>27.250000</td>\n <td>2.421900e+07</td>\n <td>0.012635</td>\n </tr>\n </tbody>\n</table>\n</div>"
|
|||
|
|
},
|
|||
|
|
"metadata": {},
|
|||
|
|
"execution_count": 19
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"tesla['returns'] = (tesla['Close'] / tesla['Close'].shift(1) ) - 1\n",
|
|||
|
|
"tesla.head()"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 29,
|
|||
|
|
"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>High</th>\n",
|
|||
|
|
" <th>Low</th>\n",
|
|||
|
|
" <th>Open</th>\n",
|
|||
|
|
" <th>Close</th>\n",
|
|||
|
|
" <th>Volume</th>\n",
|
|||
|
|
" <th>Adj Close</th>\n",
|
|||
|
|
" <th>Total Traded</th>\n",
|
|||
|
|
" <th>returns</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",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" </thead>\n",
|
|||
|
|
" <tbody>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>2012-01-03</th>\n",
|
|||
|
|
" <td>29.500000</td>\n",
|
|||
|
|
" <td>27.650000</td>\n",
|
|||
|
|
" <td>28.940001</td>\n",
|
|||
|
|
" <td>28.080000</td>\n",
|
|||
|
|
" <td>928100</td>\n",
|
|||
|
|
" <td>28.080000</td>\n",
|
|||
|
|
" <td>2.685921e+07</td>\n",
|
|||
|
|
" <td>NaN</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>2012-01-04</th>\n",
|
|||
|
|
" <td>28.670000</td>\n",
|
|||
|
|
" <td>27.500000</td>\n",
|
|||
|
|
" <td>28.209999</td>\n",
|
|||
|
|
" <td>27.709999</td>\n",
|
|||
|
|
" <td>630100</td>\n",
|
|||
|
|
" <td>27.709999</td>\n",
|
|||
|
|
" <td>1.777512e+07</td>\n",
|
|||
|
|
" <td>-0.013177</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>2012-01-05</th>\n",
|
|||
|
|
" <td>27.930000</td>\n",
|
|||
|
|
" <td>26.850000</td>\n",
|
|||
|
|
" <td>27.760000</td>\n",
|
|||
|
|
" <td>27.120001</td>\n",
|
|||
|
|
" <td>1005500</td>\n",
|
|||
|
|
" <td>27.120001</td>\n",
|
|||
|
|
" <td>2.791268e+07</td>\n",
|
|||
|
|
" <td>-0.021292</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>2012-01-06</th>\n",
|
|||
|
|
" <td>27.790001</td>\n",
|
|||
|
|
" <td>26.410000</td>\n",
|
|||
|
|
" <td>27.200001</td>\n",
|
|||
|
|
" <td>26.910000</td>\n",
|
|||
|
|
" <td>986300</td>\n",
|
|||
|
|
" <td>26.910000</td>\n",
|
|||
|
|
" <td>2.682736e+07</td>\n",
|
|||
|
|
" <td>-0.007743</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>2012-01-09</th>\n",
|
|||
|
|
" <td>27.490000</td>\n",
|
|||
|
|
" <td>26.120001</td>\n",
|
|||
|
|
" <td>27.000000</td>\n",
|
|||
|
|
" <td>27.250000</td>\n",
|
|||
|
|
" <td>897000</td>\n",
|
|||
|
|
" <td>27.250000</td>\n",
|
|||
|
|
" <td>2.421900e+07</td>\n",
|
|||
|
|
" <td>0.012635</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" </tbody>\n",
|
|||
|
|
"</table>\n",
|
|||
|
|
"</div>"
|
|||
|
|
],
|
|||
|
|
"text/plain": [
|
|||
|
|
" High Low Open Close Volume Adj Close \\\n",
|
|||
|
|
"Date \n",
|
|||
|
|
"2012-01-03 29.500000 27.650000 28.940001 28.080000 928100 28.080000 \n",
|
|||
|
|
"2012-01-04 28.670000 27.500000 28.209999 27.709999 630100 27.709999 \n",
|
|||
|
|
"2012-01-05 27.930000 26.850000 27.760000 27.120001 1005500 27.120001 \n",
|
|||
|
|
"2012-01-06 27.790001 26.410000 27.200001 26.910000 986300 26.910000 \n",
|
|||
|
|
"2012-01-09 27.490000 26.120001 27.000000 27.250000 897000 27.250000 \n",
|
|||
|
|
"\n",
|
|||
|
|
" Total Traded returns \n",
|
|||
|
|
"Date \n",
|
|||
|
|
"2012-01-03 2.685921e+07 NaN \n",
|
|||
|
|
"2012-01-04 1.777512e+07 -0.013177 \n",
|
|||
|
|
"2012-01-05 2.791268e+07 -0.021292 \n",
|
|||
|
|
"2012-01-06 2.682736e+07 -0.007743 \n",
|
|||
|
|
"2012-01-09 2.421900e+07 0.012635 "
|
|||
|
|
]
|
|||
|
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},
|
|||
|
|
"execution_count": 29,
|
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|
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}
|
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|
],
|
|||
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"source": []
|
|||
|
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},
|
|||
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|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 20,
|
|||
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"metadata": {},
|
|||
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"outputs": [
|
|||
|
|
{
|
|||
|
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|
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"data": {
|
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"text/plain": [
|
|||
|
|
" High Low Open Close Volume Adj Close \\\n",
|
|||
|
|
"Date \n",
|
|||
|
|
"2012-01-03 29.500000 27.650000 28.940001 28.080000 928100 28.080000 \n",
|
|||
|
|
"2012-01-04 28.670000 27.500000 28.209999 27.709999 630100 27.709999 \n",
|
|||
|
|
"2012-01-05 27.930000 26.850000 27.760000 27.120001 1005500 27.120001 \n",
|
|||
|
|
"2012-01-06 27.790001 26.410000 27.200001 26.910000 986300 26.910000 \n",
|
|||
|
|
"2012-01-09 27.490000 26.120001 27.000000 27.250000 897000 27.250000 \n",
|
|||
|
|
"\n",
|
|||
|
|
" Total Traded returns \n",
|
|||
|
|
"Date \n",
|
|||
|
|
"2012-01-03 2.685921e+07 NaN \n",
|
|||
|
|
"2012-01-04 1.777512e+07 -0.013177 \n",
|
|||
|
|
"2012-01-05 2.791268e+07 -0.021292 \n",
|
|||
|
|
"2012-01-06 2.682736e+07 -0.007743 \n",
|
|||
|
|
"2012-01-09 2.421900e+07 0.012635 "
|
|||
|
|
],
|
|||
|
|
"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>Adj Close</th>\n <th>Total Traded</th>\n <th>returns</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 </tr>\n </thead>\n <tbody>\n <tr>\n <th>2012-01-03</th>\n <td>29.500000</td>\n <td>27.650000</td>\n <td>28.940001</td>\n <td>28.080000</td>\n <td>928100</td>\n <td>28.080000</td>\n <td>2.685921e+07</td>\n <td>NaN</td>\n </tr>\n <tr>\n <th>2012-01-04</th>\n <td>28.670000</td>\n <td>27.500000</td>\n <td>28.209999</td>\n <td>27.709999</td>\n <td>630100</td>\n <td>27.709999</td>\n <td>1.777512e+07</td>\n <td>-0.013177</td>\n </tr>\n <tr>\n <th>2012-01-05</th>\n <td>27.930000</td>\n <td>26.850000</td>\n <td>27.760000</td>\n <td>27.120001</td>\n <td>1005500</td>\n <td>27.120001</td>\n <td>2.791268e+07</td>\n <td>-0.021292</td>\n </tr>\n <tr>\n <th>2012-01-06</th>\n <td>27.790001</td>\n <td>26.410000</td>\n <td>27.200001</td>\n <td>26.910000</td>\n <td>986300</td>\n <td>26.910000</td>\n <td>2.682736e+07</td>\n <td>-0.007743</td>\n </tr>\n <tr>\n <th>2012-01-09</th>\n <td>27.490000</td>\n <td>26.120001</td>\n <td>27.000000</td>\n <td>27.250000</td>\n <td>897000</td>\n <td>27.250000</td>\n <td>2.421900e+07</td>\n <td>0.012635</td>\n </tr>\n </tbody>\n</table>\n</div>"
|
|||
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|
},
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"metadata": {},
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"execution_count": 20
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|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"# méthode 2: en utilisant pct_changes\n",
|
|||
|
|
"tesla['returns'] = tesla['Close'].pct_change()\n",
|
|||
|
|
"tesla.head()"
|
|||
|
|
]
|
|||
|
|
},
|
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{
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"cell_type": "code",
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"execution_count": 31,
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"<table border=\"1\" class=\"dataframe\">\n",
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" <thead>\n",
|
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|
|
" <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>Adj Close</th>\n",
|
|||
|
|
" <th>Total Traded</th>\n",
|
|||
|
|
" <th>returns</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",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" </thead>\n",
|
|||
|
|
" <tbody>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>2012-01-03</th>\n",
|
|||
|
|
" <td>29.500000</td>\n",
|
|||
|
|
" <td>27.650000</td>\n",
|
|||
|
|
" <td>28.940001</td>\n",
|
|||
|
|
" <td>28.080000</td>\n",
|
|||
|
|
" <td>928100</td>\n",
|
|||
|
|
" <td>28.080000</td>\n",
|
|||
|
|
" <td>2.685921e+07</td>\n",
|
|||
|
|
" <td>NaN</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>2012-01-04</th>\n",
|
|||
|
|
" <td>28.670000</td>\n",
|
|||
|
|
" <td>27.500000</td>\n",
|
|||
|
|
" <td>28.209999</td>\n",
|
|||
|
|
" <td>27.709999</td>\n",
|
|||
|
|
" <td>630100</td>\n",
|
|||
|
|
" <td>27.709999</td>\n",
|
|||
|
|
" <td>1.777512e+07</td>\n",
|
|||
|
|
" <td>-0.013177</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>2012-01-05</th>\n",
|
|||
|
|
" <td>27.930000</td>\n",
|
|||
|
|
" <td>26.850000</td>\n",
|
|||
|
|
" <td>27.760000</td>\n",
|
|||
|
|
" <td>27.120001</td>\n",
|
|||
|
|
" <td>1005500</td>\n",
|
|||
|
|
" <td>27.120001</td>\n",
|
|||
|
|
" <td>2.791268e+07</td>\n",
|
|||
|
|
" <td>-0.021292</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>2012-01-06</th>\n",
|
|||
|
|
" <td>27.790001</td>\n",
|
|||
|
|
" <td>26.410000</td>\n",
|
|||
|
|
" <td>27.200001</td>\n",
|
|||
|
|
" <td>26.910000</td>\n",
|
|||
|
|
" <td>986300</td>\n",
|
|||
|
|
" <td>26.910000</td>\n",
|
|||
|
|
" <td>2.682736e+07</td>\n",
|
|||
|
|
" <td>-0.007743</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>2012-01-09</th>\n",
|
|||
|
|
" <td>27.490000</td>\n",
|
|||
|
|
" <td>26.120001</td>\n",
|
|||
|
|
" <td>27.000000</td>\n",
|
|||
|
|
" <td>27.250000</td>\n",
|
|||
|
|
" <td>897000</td>\n",
|
|||
|
|
" <td>27.250000</td>\n",
|
|||
|
|
" <td>2.421900e+07</td>\n",
|
|||
|
|
" <td>0.012635</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" </tbody>\n",
|
|||
|
|
"</table>\n",
|
|||
|
|
"</div>"
|
|||
|
|
],
|
|||
|
|
"text/plain": [
|
|||
|
|
" High Low Open Close Volume Adj Close \\\n",
|
|||
|
|
"Date \n",
|
|||
|
|
"2012-01-03 29.500000 27.650000 28.940001 28.080000 928100 28.080000 \n",
|
|||
|
|
"2012-01-04 28.670000 27.500000 28.209999 27.709999 630100 27.709999 \n",
|
|||
|
|
"2012-01-05 27.930000 26.850000 27.760000 27.120001 1005500 27.120001 \n",
|
|||
|
|
"2012-01-06 27.790001 26.410000 27.200001 26.910000 986300 26.910000 \n",
|
|||
|
|
"2012-01-09 27.490000 26.120001 27.000000 27.250000 897000 27.250000 \n",
|
|||
|
|
"\n",
|
|||
|
|
" Total Traded returns \n",
|
|||
|
|
"Date \n",
|
|||
|
|
"2012-01-03 2.685921e+07 NaN \n",
|
|||
|
|
"2012-01-04 1.777512e+07 -0.013177 \n",
|
|||
|
|
"2012-01-05 2.791268e+07 -0.021292 \n",
|
|||
|
|
"2012-01-06 2.682736e+07 -0.007743 \n",
|
|||
|
|
"2012-01-09 2.421900e+07 0.012635 "
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"execution_count": 31,
|
|||
|
|
"metadata": {},
|
|||
|
|
"output_type": "execute_result"
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": []
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 21,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [],
|
|||
|
|
"source": [
|
|||
|
|
"ford['returns'] = ford['Close'].pct_change(1)\n",
|
|||
|
|
"gm['returns'] = gm['Close'].pct_change(1)"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 33,
|
|||
|
|
"metadata": {},
|
|||
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|
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|
|||
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|
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" vertical-align: middle;\n",
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|
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|
"\n",
|
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|
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" .dataframe tbody tr th {\n",
|
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" vertical-align: top;\n",
|
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|
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" }\n",
|
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|
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"\n",
|
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|
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" .dataframe thead th {\n",
|
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" text-align: right;\n",
|
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|
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|
|
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|
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|
|
"<table border=\"1\" class=\"dataframe\">\n",
|
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|
" <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>Adj Close</th>\n",
|
|||
|
|
" <th>Total Traded</th>\n",
|
|||
|
|
" <th>returns</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",
|
|||
|
|
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|
|||
|
|
" </thead>\n",
|
|||
|
|
" <tbody>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>2012-01-03</th>\n",
|
|||
|
|
" <td>11.25</td>\n",
|
|||
|
|
" <td>10.99</td>\n",
|
|||
|
|
" <td>11.00</td>\n",
|
|||
|
|
" <td>11.13</td>\n",
|
|||
|
|
" <td>45709900.0</td>\n",
|
|||
|
|
" <td>7.673051</td>\n",
|
|||
|
|
" <td>5.028089e+08</td>\n",
|
|||
|
|
" <td>NaN</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>2012-01-04</th>\n",
|
|||
|
|
" <td>11.53</td>\n",
|
|||
|
|
" <td>11.07</td>\n",
|
|||
|
|
" <td>11.15</td>\n",
|
|||
|
|
" <td>11.30</td>\n",
|
|||
|
|
" <td>79725200.0</td>\n",
|
|||
|
|
" <td>7.790251</td>\n",
|
|||
|
|
" <td>8.889359e+08</td>\n",
|
|||
|
|
" <td>0.015274</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>2012-01-05</th>\n",
|
|||
|
|
" <td>11.63</td>\n",
|
|||
|
|
" <td>11.24</td>\n",
|
|||
|
|
" <td>11.33</td>\n",
|
|||
|
|
" <td>11.59</td>\n",
|
|||
|
|
" <td>67877500.0</td>\n",
|
|||
|
|
" <td>7.990177</td>\n",
|
|||
|
|
" <td>7.690521e+08</td>\n",
|
|||
|
|
" <td>0.025664</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>2012-01-06</th>\n",
|
|||
|
|
" <td>11.80</td>\n",
|
|||
|
|
" <td>11.52</td>\n",
|
|||
|
|
" <td>11.74</td>\n",
|
|||
|
|
" <td>11.71</td>\n",
|
|||
|
|
" <td>59840700.0</td>\n",
|
|||
|
|
" <td>8.072903</td>\n",
|
|||
|
|
" <td>7.025298e+08</td>\n",
|
|||
|
|
" <td>0.010354</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>2012-01-09</th>\n",
|
|||
|
|
" <td>11.95</td>\n",
|
|||
|
|
" <td>11.70</td>\n",
|
|||
|
|
" <td>11.83</td>\n",
|
|||
|
|
" <td>11.80</td>\n",
|
|||
|
|
" <td>53981500.0</td>\n",
|
|||
|
|
" <td>8.134951</td>\n",
|
|||
|
|
" <td>6.386011e+08</td>\n",
|
|||
|
|
" <td>0.007686</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" </tbody>\n",
|
|||
|
|
"</table>\n",
|
|||
|
|
"</div>"
|
|||
|
|
],
|
|||
|
|
"text/plain": [
|
|||
|
|
" High Low Open Close Volume Adj Close Total Traded \\\n",
|
|||
|
|
"Date \n",
|
|||
|
|
"2012-01-03 11.25 10.99 11.00 11.13 45709900.0 7.673051 5.028089e+08 \n",
|
|||
|
|
"2012-01-04 11.53 11.07 11.15 11.30 79725200.0 7.790251 8.889359e+08 \n",
|
|||
|
|
"2012-01-05 11.63 11.24 11.33 11.59 67877500.0 7.990177 7.690521e+08 \n",
|
|||
|
|
"2012-01-06 11.80 11.52 11.74 11.71 59840700.0 8.072903 7.025298e+08 \n",
|
|||
|
|
"2012-01-09 11.95 11.70 11.83 11.80 53981500.0 8.134951 6.386011e+08 \n",
|
|||
|
|
"\n",
|
|||
|
|
" returns \n",
|
|||
|
|
"Date \n",
|
|||
|
|
"2012-01-03 NaN \n",
|
|||
|
|
"2012-01-04 0.015274 \n",
|
|||
|
|
"2012-01-05 0.025664 \n",
|
|||
|
|
"2012-01-06 0.010354 \n",
|
|||
|
|
"2012-01-09 0.007686 "
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"execution_count": 33,
|
|||
|
|
"metadata": {},
|
|||
|
|
"output_type": "execute_result"
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": []
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 34,
|
|||
|
|
"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>High</th>\n",
|
|||
|
|
" <th>Low</th>\n",
|
|||
|
|
" <th>Open</th>\n",
|
|||
|
|
" <th>Close</th>\n",
|
|||
|
|
" <th>Volume</th>\n",
|
|||
|
|
" <th>Adj Close</th>\n",
|
|||
|
|
" <th>Total Traded</th>\n",
|
|||
|
|
" <th>MA50</th>\n",
|
|||
|
|
" <th>MA200</th>\n",
|
|||
|
|
" <th>returns</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",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" </thead>\n",
|
|||
|
|
" <tbody>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>2012-01-03</th>\n",
|
|||
|
|
" <td>21.180000</td>\n",
|
|||
|
|
" <td>20.750000</td>\n",
|
|||
|
|
" <td>20.830000</td>\n",
|
|||
|
|
" <td>21.049999</td>\n",
|
|||
|
|
" <td>9321300.0</td>\n",
|
|||
|
|
" <td>16.299799</td>\n",
|
|||
|
|
" <td>1.941627e+08</td>\n",
|
|||
|
|
" <td>NaN</td>\n",
|
|||
|
|
" <td>NaN</td>\n",
|
|||
|
|
" <td>NaN</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>2012-01-04</th>\n",
|
|||
|
|
" <td>21.370001</td>\n",
|
|||
|
|
" <td>20.750000</td>\n",
|
|||
|
|
" <td>21.049999</td>\n",
|
|||
|
|
" <td>21.150000</td>\n",
|
|||
|
|
" <td>7856700.0</td>\n",
|
|||
|
|
" <td>16.377232</td>\n",
|
|||
|
|
" <td>1.653835e+08</td>\n",
|
|||
|
|
" <td>NaN</td>\n",
|
|||
|
|
" <td>NaN</td>\n",
|
|||
|
|
" <td>0.004751</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>2012-01-05</th>\n",
|
|||
|
|
" <td>22.290001</td>\n",
|
|||
|
|
" <td>20.959999</td>\n",
|
|||
|
|
" <td>21.100000</td>\n",
|
|||
|
|
" <td>22.170000</td>\n",
|
|||
|
|
" <td>17880600.0</td>\n",
|
|||
|
|
" <td>17.167059</td>\n",
|
|||
|
|
" <td>3.772807e+08</td>\n",
|
|||
|
|
" <td>NaN</td>\n",
|
|||
|
|
" <td>NaN</td>\n",
|
|||
|
|
" <td>0.048227</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>2012-01-06</th>\n",
|
|||
|
|
" <td>23.030001</td>\n",
|
|||
|
|
" <td>22.240000</td>\n",
|
|||
|
|
" <td>22.260000</td>\n",
|
|||
|
|
" <td>22.920000</td>\n",
|
|||
|
|
" <td>18234500.0</td>\n",
|
|||
|
|
" <td>17.747812</td>\n",
|
|||
|
|
" <td>4.059000e+08</td>\n",
|
|||
|
|
" <td>NaN</td>\n",
|
|||
|
|
" <td>NaN</td>\n",
|
|||
|
|
" <td>0.033829</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>2012-01-09</th>\n",
|
|||
|
|
" <td>23.430000</td>\n",
|
|||
|
|
" <td>22.700001</td>\n",
|
|||
|
|
" <td>23.200001</td>\n",
|
|||
|
|
" <td>22.840000</td>\n",
|
|||
|
|
" <td>12084500.0</td>\n",
|
|||
|
|
" <td>17.685862</td>\n",
|
|||
|
|
" <td>2.803604e+08</td>\n",
|
|||
|
|
" <td>NaN</td>\n",
|
|||
|
|
" <td>NaN</td>\n",
|
|||
|
|
" <td>-0.003490</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" </tbody>\n",
|
|||
|
|
"</table>\n",
|
|||
|
|
"</div>"
|
|||
|
|
],
|
|||
|
|
"text/plain": [
|
|||
|
|
" High Low Open Close Volume Adj Close \\\n",
|
|||
|
|
"Date \n",
|
|||
|
|
"2012-01-03 21.180000 20.750000 20.830000 21.049999 9321300.0 16.299799 \n",
|
|||
|
|
"2012-01-04 21.370001 20.750000 21.049999 21.150000 7856700.0 16.377232 \n",
|
|||
|
|
"2012-01-05 22.290001 20.959999 21.100000 22.170000 17880600.0 17.167059 \n",
|
|||
|
|
"2012-01-06 23.030001 22.240000 22.260000 22.920000 18234500.0 17.747812 \n",
|
|||
|
|
"2012-01-09 23.430000 22.700001 23.200001 22.840000 12084500.0 17.685862 \n",
|
|||
|
|
"\n",
|
|||
|
|
" Total Traded MA50 MA200 returns \n",
|
|||
|
|
"Date \n",
|
|||
|
|
"2012-01-03 1.941627e+08 NaN NaN NaN \n",
|
|||
|
|
"2012-01-04 1.653835e+08 NaN NaN 0.004751 \n",
|
|||
|
|
"2012-01-05 3.772807e+08 NaN NaN 0.048227 \n",
|
|||
|
|
"2012-01-06 4.059000e+08 NaN NaN 0.033829 \n",
|
|||
|
|
"2012-01-09 2.803604e+08 NaN NaN -0.003490 "
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"execution_count": 34,
|
|||
|
|
"metadata": {},
|
|||
|
|
"output_type": "execute_result"
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": []
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "markdown",
|
|||
|
|
"metadata": {},
|
|||
|
|
"source": [
|
|||
|
|
"**Maintenant, tracez un histogramme des rendements de chaque entreprise. Soit vous les faites séparément, soit vous les empilez les uns sur les autres. Quelle est l'action la plus \"volatile\"? (selon la variance des rendements quotidiens, nous discuterons de la volatilité de façon beaucoup plus détaillée dans les prochaines sections).**"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 22,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"output_type": "execute_result",
|
|||
|
|
"data": {
|
|||
|
|
"text/plain": [
|
|||
|
|
"<AxesSubplot:>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {},
|
|||
|
|
"execution_count": 22
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"output_type": "display_data",
|
|||
|
|
"data": {
|
|||
|
|
"text/plain": "<Figure size 432x288 with 1 Axes>",
|
|||
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|
|||
|
|
},
|
|||
|
|
"metadata": {
|
|||
|
|
"needs_background": "light"
|
|||
|
|
}
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"tesla['returns'].hist(bins=50)"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 35,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"text/plain": [
|
|||
|
|
"<matplotlib.axes._subplots.AxesSubplot at 0x115bc8810>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"execution_count": 35,
|
|||
|
|
"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": []
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 36,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"text/plain": [
|
|||
|
|
"<matplotlib.axes._subplots.AxesSubplot at 0x115bc89d0>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"execution_count": 36,
|
|||
|
|
"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": []
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 37,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"text/plain": [
|
|||
|
|
"<matplotlib.axes._subplots.AxesSubplot at 0x116019a90>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"execution_count": 37,
|
|||
|
|
"metadata": {},
|
|||
|
|
"output_type": "execute_result"
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
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"text/plain": [
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"<Figure size 432x288 with 1 Axes>"
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]
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},
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"metadata": {
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"needs_background": "light"
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},
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"output_type": "display_data"
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}
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],
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"source": []
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},
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{
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"cell_type": "code",
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"execution_count": 23,
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"metadata": {},
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"outputs": [
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{
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"output_type": "execute_result",
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"data": {
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},
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"metadata": {},
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"execution_count": 23
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},
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{
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"output_type": "display_data",
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"data": {
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"text/plain": "<Figure size 720x576 with 1 Axes>",
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"image/png": "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
|
|||
|
|
},
|
|||
|
|
"metadata": {
|
|||
|
|
"needs_background": "light"
|
|||
|
|
}
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"tesla['returns'].hist(bins=100,label='Tesla',figsize=(10,8),alpha=0.5)\n",
|
|||
|
|
"gm['returns'].hist(bins=100,label='GM',alpha=0.5)\n",
|
|||
|
|
"ford['returns'].hist(bins=100,label='Ford',alpha=0.5)\n",
|
|||
|
|
"plt.legend()"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 38,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"text/plain": [
|
|||
|
|
"<matplotlib.legend.Legend at 0x115f4ba50>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"execution_count": 38,
|
|||
|
|
"metadata": {},
|
|||
|
|
"output_type": "execute_result"
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"image/png": "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
|
|||
|
|
"text/plain": [
|
|||
|
|
"<Figure size 720x576 with 1 Axes>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {
|
|||
|
|
"needs_background": "light"
|
|||
|
|
},
|
|||
|
|
"output_type": "display_data"
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": []
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "markdown",
|
|||
|
|
"metadata": {},
|
|||
|
|
"source": [
|
|||
|
|
"**Essayez aussi de tracer un KDE au lieu dun 'histogrammes pour avoir un autre point de vue. Quelle action a le tracé le plus large?**"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 24,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"output_type": "execute_result",
|
|||
|
|
"data": {
|
|||
|
|
"text/plain": [
|
|||
|
|
"<matplotlib.legend.Legend at 0x18cc5e71700>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {},
|
|||
|
|
"execution_count": 24
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"output_type": "display_data",
|
|||
|
|
"data": {
|
|||
|
|
"text/plain": "<Figure size 864x432 with 1 Axes>",
|
|||
|
|
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|
|||
|
|
},
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|||
|
|
"metadata": {
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|||
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|
"needs_background": "light"
|
|||
|
|
}
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"tesla['returns'].plot(kind='kde',label='Tesla',figsize=(12,6))\n",
|
|||
|
|
"gm['returns'].plot(kind='kde',label='GM')\n",
|
|||
|
|
"ford['returns'].plot(kind='kde',label='Ford')\n",
|
|||
|
|
"plt.legend()"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 39,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"text/plain": [
|
|||
|
|
"<matplotlib.legend.Legend at 0x1165b8490>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"execution_count": 39,
|
|||
|
|
"metadata": {},
|
|||
|
|
"output_type": "execute_result"
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"image/png": "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
|
|||
|
|
"text/plain": [
|
|||
|
|
"<Figure size 864x432 with 1 Axes>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {
|
|||
|
|
"needs_background": "light"
|
|||
|
|
},
|
|||
|
|
"output_type": "display_data"
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": []
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "markdown",
|
|||
|
|
"metadata": {},
|
|||
|
|
"source": [
|
|||
|
|
"**Essayez aussi de créer des diagrammes en boîtes comparant les rendements.**"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 40,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"text/plain": [
|
|||
|
|
"<matplotlib.axes._subplots.AxesSubplot at 0x1a185efe50>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"execution_count": 40,
|
|||
|
|
"metadata": {},
|
|||
|
|
"output_type": "execute_result"
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"image/png": "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
|
|||
|
|
"text/plain": [
|
|||
|
|
"<Figure size 576x792 with 1 Axes>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {
|
|||
|
|
"needs_background": "light"
|
|||
|
|
},
|
|||
|
|
"output_type": "display_data"
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": []
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "markdown",
|
|||
|
|
"metadata": {},
|
|||
|
|
"source": [
|
|||
|
|
"## Comparaison des rendements quotidiens entre les actions\n",
|
|||
|
|
"\n",
|
|||
|
|
"**Créez un graphique matriciel de dispersion pour voir la corrélation entre les rendements quotidiens de chaque titre. Cela permet de répondre à la question de savoir dans quelle mesure les sociétés automobiles sont liées entre elles. Le marché considère-t-il Tesla comme une entreprise de technologie plutôt que comme une entreprise automobile ?**"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 41,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"image/png": "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
|
|||
|
|
"text/plain": [
|
|||
|
|
"<Figure size 576x576 with 9 Axes>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {
|
|||
|
|
"needs_background": "light"
|
|||
|
|
},
|
|||
|
|
"output_type": "display_data"
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": []
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "markdown",
|
|||
|
|
"metadata": {},
|
|||
|
|
"source": [
|
|||
|
|
"**Il semble que Ford et GM aient une sorte de relation, traçons juste ces deux-là dans un diagramme de dispersion pour voir cela de plus près !**"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 42,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"text/plain": [
|
|||
|
|
"<matplotlib.axes._subplots.AxesSubplot at 0x1a18b72110>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"execution_count": 42,
|
|||
|
|
"metadata": {},
|
|||
|
|
"output_type": "execute_result"
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"image/png": "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
|
|||
|
|
"text/plain": [
|
|||
|
|
"<Figure size 720x576 with 1 Axes>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {
|
|||
|
|
"needs_background": "light"
|
|||
|
|
},
|
|||
|
|
"output_type": "display_data"
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": []
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "markdown",
|
|||
|
|
"metadata": {},
|
|||
|
|
"source": [
|
|||
|
|
"____\n",
|
|||
|
|
"## Rendements quotidiens cumulatifs\n",
|
|||
|
|
"\n",
|
|||
|
|
"Super ! Nous pouvons maintenant voir quel titre a eu le plus grand écart de rendement quotidien (vous auriez dû vous rendre compte que c'était Tesla, notre graphique original du cours de l'action aurait dû aussi le montrer).\n",
|
|||
|
|
"\n",
|
|||
|
|
"Avec les rendements cumulatifs quotidiens, la question à laquelle nous essayons de répondre est la suivante: si j'avais investi 1$ dans l'entreprise au début de la série temporelle, combien vaudrait-elle aujourd'hui? Cette question est différente de celle du prix de l'action à la journée courante, car elle tiendra compte des rendements quotidiens. N'oubliez pas que notre simple calcul ici ne tiendra pas compte des actions qui redonnent un dividende. Examinons quelques exemples simples:"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "markdown",
|
|||
|
|
"metadata": {},
|
|||
|
|
"source": [
|
|||
|
|
"Disons qu'il y a une action \"ABC\" qui est activement négociée en bourse. ABC a les prix suivants correspondant aux dates indiquées:"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "markdown",
|
|||
|
|
"metadata": {},
|
|||
|
|
"source": [
|
|||
|
|
" Date Prix\n",
|
|||
|
|
" 01/01/2018 10\n",
|
|||
|
|
" 01/02/2018 15\n",
|
|||
|
|
" 01/03/2018 20\n",
|
|||
|
|
" 01/04/2018 25"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "markdown",
|
|||
|
|
"metadata": {},
|
|||
|
|
"source": [
|
|||
|
|
"**Rendement quotidien**: Le rendement quotidien est le profit ou perte réalisé par l'action par rapport à la veille. (C'est ce qu'on vient de calculer ci-dessus). Une valeur supérieure à 1 indique un profit, de même qu'une valeur inférieure à 1 indique une perte. Il est également exprimé en pourcentage pour mieux transmettre l'information. (Exprimé en pourcentage, si la valeur est supérieure à 0, le titre vous a donné un profit, sinon une perte). Ainsi, pour l'exemple ci-dessus, les rendements quotidiens seraient"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "markdown",
|
|||
|
|
"metadata": {},
|
|||
|
|
"source": [
|
|||
|
|
" Date Rendement quotidien %Rendement quotidien\n",
|
|||
|
|
" 01/01/2018 10/10 = 1 - \n",
|
|||
|
|
" 01/02/2018 15/10 = 3/2 50%\n",
|
|||
|
|
" 01/03/2018 20/15 = 4/3 33%\n",
|
|||
|
|
" 01/04/2018 25/20 = 5/4 20%"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "markdown",
|
|||
|
|
"metadata": {},
|
|||
|
|
"source": [
|
|||
|
|
"**Rendement Cumulé**: Bien que les rendements quotidiens soient utiles, ils ne donnent pas à l'investisseur un aperçu immédiat des gains qu'il a réalisés jusqu'à présent, surtout si le titre est très volatil. Le rendement cumulatif est calculé par rapport au jour où l'investissement est effectué. Si le rendement cumulatif est supérieur à 1, vous faites des profits, sinon vous êtes en perte. Donc, pour l'exemple ci-dessus, les gains cumulatifs sont les suivants:"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "markdown",
|
|||
|
|
"metadata": {},
|
|||
|
|
"source": [
|
|||
|
|
" Date Rendement cumulé %rendement cumulé\n",
|
|||
|
|
" 01/01/2018 10/10 = 1 100 % \n",
|
|||
|
|
" 01/02/2018 15/10 = 3/2 150 %\n",
|
|||
|
|
" 01/03/2018 20/10 = 2 200 %\n",
|
|||
|
|
" 01/04/2018 25/10 = 5/2 250 %"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "markdown",
|
|||
|
|
"metadata": {},
|
|||
|
|
"source": [
|
|||
|
|
"La formule pour un rendement quotidien cumulatif est la suivante :\n",
|
|||
|
|
"\n",
|
|||
|
|
"$ i_i = (1+r_t) * i_{t-1} $\n",
|
|||
|
|
"\n",
|
|||
|
|
"Ici, nous pouvons voir que nous ne faisons que multiplier notre investissement précédent à i à t-1 par 1+notre pourcentage de rendement. Pandas rend cela très simple à calculer avec sa méthode cumprod(). En utilisant quelque chose de la manière suivante :\n",
|
|||
|
|
"\n",
|
|||
|
|
" df[daily_cumulative_return] = (1 + df[pct_daily_return]).cumprod()\n",
|
|||
|
|
" "
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "markdown",
|
|||
|
|
"metadata": {},
|
|||
|
|
"source": [
|
|||
|
|
"**Créez une colonne de rendement quotidien cumulatif (cumulative daily return) pour le dataframe de chaque société automobile.**"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
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"cell_type": "code",
|
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|
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"execution_count": 25,
|
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"metadata": {},
|
|||
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"outputs": [
|
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|
|
{
|
|||
|
|
"output_type": "execute_result",
|
|||
|
|
"data": {
|
|||
|
|
"text/plain": [
|
|||
|
|
" High Low Open Close Volume Adj Close \\\n",
|
|||
|
|
"Date \n",
|
|||
|
|
"2012-01-03 29.500000 27.650000 28.940001 28.080000 928100 28.080000 \n",
|
|||
|
|
"2012-01-04 28.670000 27.500000 28.209999 27.709999 630100 27.709999 \n",
|
|||
|
|
"2012-01-05 27.930000 26.850000 27.760000 27.120001 1005500 27.120001 \n",
|
|||
|
|
"2012-01-06 27.790001 26.410000 27.200001 26.910000 986300 26.910000 \n",
|
|||
|
|
"2012-01-09 27.490000 26.120001 27.000000 27.250000 897000 27.250000 \n",
|
|||
|
|
"\n",
|
|||
|
|
" Total Traded returns Cumulative Return \n",
|
|||
|
|
"Date \n",
|
|||
|
|
"2012-01-03 2.685921e+07 NaN NaN \n",
|
|||
|
|
"2012-01-04 1.777512e+07 -0.013177 0.986823 \n",
|
|||
|
|
"2012-01-05 2.791268e+07 -0.021292 0.965812 \n",
|
|||
|
|
"2012-01-06 2.682736e+07 -0.007743 0.958333 \n",
|
|||
|
|
"2012-01-09 2.421900e+07 0.012635 0.970442 "
|
|||
|
|
],
|
|||
|
|
"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>Adj Close</th>\n <th>Total Traded</th>\n <th>returns</th>\n <th>Cumulative Return</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 </tr>\n </thead>\n <tbody>\n <tr>\n <th>2012-01-03</th>\n <td>29.500000</td>\n <td>27.650000</td>\n <td>28.940001</td>\n <td>28.080000</td>\n <td>928100</td>\n <td>28.080000</td>\n <td>2.685921e+07</td>\n <td>NaN</td>\n <td>NaN</td>\n </tr>\n <tr>\n <th>2012-01-04</th>\n <td>28.670000</td>\n <td>27.500000</td>\n <td>28.209999</td>\n <td>27.709999</td>\n <td>630100</td>\n <td>27.709999</td>\n <td>1.777512e+07</td>\n <td>-0.013177</td>\n <td>0.986823</td>\n </tr>\n <tr>\n <th>2012-01-05</th>\n <td>27.930000</td>\n <td>26.850000</td>\n <td>27.760000</td>\n <td>27.120001</td>\n <td>1005500</td>\n <td>27.120001</td>\n <td>2.791268e+07</td>\n <td>-0.021292</td>\n <td>0.965812</td>\n </tr>\n <tr>\n <th>2012-01-06</th>\n <td>27.790001</td>\n <td>26.410000</td>\n <td>27.200001</td>\n <td>26.910000</td>\n <td>986300</td>\n <td>26.910000</td>\n <td>2.682736e+07</td>\n <td>-0.007743</td>\n <td>0.958333</td>\n </tr>\n <tr>\n <th>2012-01-09</th>\n <td>27.490000</td>\n <td>26.120001</td>\n <td>27.000000</td>\n <td>27.250000</td>\n <td>897000</td>\n <td>27.250000</td>\n <td>2.421900e+07</td>\n <td>0.012635</td>\n <td>0.970442</td>\n </tr>\n </tbody>\n</table>\n</div>"
|
|||
|
|
},
|
|||
|
|
"metadata": {},
|
|||
|
|
"execution_count": 25
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"tesla[\"Cumulative Return\"] = (1 + tesla[\"returns\"]).cumprod()\n",
|
|||
|
|
"tesla.head()"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 44,
|
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"metadata": {},
|
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"outputs": [
|
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{
|
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|
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"text/html": [
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|
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"<div>\n",
|
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"<style scoped>\n",
|
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" .dataframe tbody tr th:only-of-type {\n",
|
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" vertical-align: middle;\n",
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" }\n",
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"\n",
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" .dataframe tbody tr th {\n",
|
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" vertical-align: top;\n",
|
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" }\n",
|
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|
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" .dataframe thead th {\n",
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" text-align: right;\n",
|
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" }\n",
|
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"</style>\n",
|
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|
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"<table border=\"1\" class=\"dataframe\">\n",
|
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|
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" <thead>\n",
|
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|
|
" <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>Adj Close</th>\n",
|
|||
|
|
" <th>Total Traded</th>\n",
|
|||
|
|
" <th>returns</th>\n",
|
|||
|
|
" <th>Cumulative Return</th>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>Date</th>\n",
|
|||
|
|
" <th></th>\n",
|
|||
|
|
" <th></th>\n",
|
|||
|
|
" <th></th>\n",
|
|||
|
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" <th></th>\n",
|
|||
|
|
" <th></th>\n",
|
|||
|
|
" <th></th>\n",
|
|||
|
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" <th></th>\n",
|
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|
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" <th></th>\n",
|
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|
|
" <th></th>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" </thead>\n",
|
|||
|
|
" <tbody>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>2012-01-03</th>\n",
|
|||
|
|
" <td>29.500000</td>\n",
|
|||
|
|
" <td>27.650000</td>\n",
|
|||
|
|
" <td>28.940001</td>\n",
|
|||
|
|
" <td>28.080000</td>\n",
|
|||
|
|
" <td>928100</td>\n",
|
|||
|
|
" <td>28.080000</td>\n",
|
|||
|
|
" <td>2.685921e+07</td>\n",
|
|||
|
|
" <td>NaN</td>\n",
|
|||
|
|
" <td>NaN</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>2012-01-04</th>\n",
|
|||
|
|
" <td>28.670000</td>\n",
|
|||
|
|
" <td>27.500000</td>\n",
|
|||
|
|
" <td>28.209999</td>\n",
|
|||
|
|
" <td>27.709999</td>\n",
|
|||
|
|
" <td>630100</td>\n",
|
|||
|
|
" <td>27.709999</td>\n",
|
|||
|
|
" <td>1.777512e+07</td>\n",
|
|||
|
|
" <td>-0.013177</td>\n",
|
|||
|
|
" <td>0.986823</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>2012-01-05</th>\n",
|
|||
|
|
" <td>27.930000</td>\n",
|
|||
|
|
" <td>26.850000</td>\n",
|
|||
|
|
" <td>27.760000</td>\n",
|
|||
|
|
" <td>27.120001</td>\n",
|
|||
|
|
" <td>1005500</td>\n",
|
|||
|
|
" <td>27.120001</td>\n",
|
|||
|
|
" <td>2.791268e+07</td>\n",
|
|||
|
|
" <td>-0.021292</td>\n",
|
|||
|
|
" <td>0.965812</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>2012-01-06</th>\n",
|
|||
|
|
" <td>27.790001</td>\n",
|
|||
|
|
" <td>26.410000</td>\n",
|
|||
|
|
" <td>27.200001</td>\n",
|
|||
|
|
" <td>26.910000</td>\n",
|
|||
|
|
" <td>986300</td>\n",
|
|||
|
|
" <td>26.910000</td>\n",
|
|||
|
|
" <td>2.682736e+07</td>\n",
|
|||
|
|
" <td>-0.007743</td>\n",
|
|||
|
|
" <td>0.958333</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>2012-01-09</th>\n",
|
|||
|
|
" <td>27.490000</td>\n",
|
|||
|
|
" <td>26.120001</td>\n",
|
|||
|
|
" <td>27.000000</td>\n",
|
|||
|
|
" <td>27.250000</td>\n",
|
|||
|
|
" <td>897000</td>\n",
|
|||
|
|
" <td>27.250000</td>\n",
|
|||
|
|
" <td>2.421900e+07</td>\n",
|
|||
|
|
" <td>0.012635</td>\n",
|
|||
|
|
" <td>0.970442</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" </tbody>\n",
|
|||
|
|
"</table>\n",
|
|||
|
|
"</div>"
|
|||
|
|
],
|
|||
|
|
"text/plain": [
|
|||
|
|
" High Low Open Close Volume Adj Close \\\n",
|
|||
|
|
"Date \n",
|
|||
|
|
"2012-01-03 29.500000 27.650000 28.940001 28.080000 928100 28.080000 \n",
|
|||
|
|
"2012-01-04 28.670000 27.500000 28.209999 27.709999 630100 27.709999 \n",
|
|||
|
|
"2012-01-05 27.930000 26.850000 27.760000 27.120001 1005500 27.120001 \n",
|
|||
|
|
"2012-01-06 27.790001 26.410000 27.200001 26.910000 986300 26.910000 \n",
|
|||
|
|
"2012-01-09 27.490000 26.120001 27.000000 27.250000 897000 27.250000 \n",
|
|||
|
|
"\n",
|
|||
|
|
" Total Traded returns Cumulative Return \n",
|
|||
|
|
"Date \n",
|
|||
|
|
"2012-01-03 2.685921e+07 NaN NaN \n",
|
|||
|
|
"2012-01-04 1.777512e+07 -0.013177 0.986823 \n",
|
|||
|
|
"2012-01-05 2.791268e+07 -0.021292 0.965812 \n",
|
|||
|
|
"2012-01-06 2.682736e+07 -0.007743 0.958333 \n",
|
|||
|
|
"2012-01-09 2.421900e+07 0.012635 0.970442 "
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"execution_count": 44,
|
|||
|
|
"metadata": {},
|
|||
|
|
"output_type": "execute_result"
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": []
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "markdown",
|
|||
|
|
"metadata": {},
|
|||
|
|
"source": [
|
|||
|
|
"**Tracez maintenant les colonnes de rendement cumulatif en fonction de l'index de la série temporelle. Quel titre a affiché le rendement le plus élevé pour un dollar investi? Lequel a affiché le rendement le plus faible?**"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 26,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [],
|
|||
|
|
"source": [
|
|||
|
|
"ford[\"Cumulative Return\"] = (1 + ford[\"returns\"]).cumprod()\n",
|
|||
|
|
"gm[\"Cumulative Return\"] = (1 + gm[\"returns\"]).cumprod()"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 27,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"output_type": "execute_result",
|
|||
|
|
"data": {
|
|||
|
|
"text/plain": [
|
|||
|
|
"<matplotlib.legend.Legend at 0x18cc83def40>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {},
|
|||
|
|
"execution_count": 27
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"output_type": "display_data",
|
|||
|
|
"data": {
|
|||
|
|
"text/plain": "<Figure size 1152x576 with 1 Axes>",
|
|||
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},
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|||
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|
"metadata": {
|
|||
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|
"needs_background": "light"
|
|||
|
|
}
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"tesla['Cumulative Return'].plot(label='Tesla',figsize=(16,8),title='Cumulative Return')\n",
|
|||
|
|
"gm['Cumulative Return'].plot(label='GM')\n",
|
|||
|
|
"ford['Cumulative Return'].plot(label='Ford')\n",
|
|||
|
|
"plt.legend()"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 28,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"output_type": "execute_result",
|
|||
|
|
"data": {
|
|||
|
|
"text/plain": [
|
|||
|
|
"'2014-09-04'"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {},
|
|||
|
|
"execution_count": 28
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"tesla['Cumulative Return'].idxmax()"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 46,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"text/plain": [
|
|||
|
|
"<matplotlib.legend.Legend at 0x1a18def450>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"execution_count": 46,
|
|||
|
|
"metadata": {},
|
|||
|
|
"output_type": "execute_result"
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
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|
|||
|
|
"text/plain": [
|
|||
|
|
"<Figure size 1152x576 with 1 Axes>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {
|
|||
|
|
"needs_background": "light"
|
|||
|
|
},
|
|||
|
|
"output_type": "display_data"
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": []
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "markdown",
|
|||
|
|
"metadata": {},
|
|||
|
|
"source": [
|
|||
|
|
"# Bon travail!\n",
|
|||
|
|
"\n",
|
|||
|
|
"Voilà pour l'analyse de base, qui conclut cette moitié du cours, beaucoup plus axée sur l'apprentissage des outils du métier. La deuxième moitié du cours est celle où nous nous plongerons vraiment dans les fonctionnalités conçues pour les séries temporelles, l'analyse quantitative, le trading algorithmique, et bien plus encore!"
|
|||
|
|
]
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"metadata": {
|
|||
|
|
"anaconda-cloud": {},
|
|||
|
|
"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": "3.8.5-final"
|
|||
|
|
}
|
|||
|
|
},
|
|||
|
|
"nbformat": 4,
|
|||
|
|
"nbformat_minor": 1
|
|||
|
|
}
|