Ajout de sections pour la visualisation des données et RShiny dans le README

- Changed execution_count from 3 to null for a cleaner notebook state.
- Simplified the normality test logic by using a conditional expression to determine the p-value calculation, improving code readability.

M2/Data Visualisation/init.R

@@ -0,0 +1,24 @@
+# Liste des packages à installer
+packages_to_install <- c(
+  "lattice", "grid", "ggplot2", "gridExtra", "locfit", "scales",
+  "formattable", "RColorBrewer", "plotly", "dplyr", "tidyr",
+  "rmarkdown", "ggthemes", "cowplot", "kableExtra", "ggridges",
+  "colorspace", "sf", "mapview", "tidyverse", "readxl", "readr",
+  "giscoR", "gapminder", "GGally", "ggfortify", "lubridate", "zoo",
+  "xts", "forecast", "feasts", "tseries", "tsibble", "fable"
+)
+
+# Fonction pour installer les packages manquants
+install_if_absent <- function(package_name) {
+  if (!requireNamespace(package_name, quietly = TRUE)) {
+    install.packages(package_name)
+    message(paste("Package", package_name, "installé avec succès."))
+  } else {
+    message(
+      paste("Package", package_name, "déjà installé, installation ignorée.")
+    )
+  }
+}
+
+# Appliquer la fonction à la liste de packages
+lapply(packages_to_install, install_if_absent)

M2/Risks Management/TP1/portef_v3_4_2.py

@@ -1,144 +0,0 @@
-"""
-Created on Thu Oct  3 15:57:44 2024
-
-@author: turinici
-"""
-
-
-"""
-This program uses historical data in the format in :
-    https://turinici.com/wp-content/uploads/cours/common/close_cac40_historical.csv
-
-It can also be downloaded form yahoo finance in daily
-prices (at least the "close") if possible at lest 5 years
-
-Idea: use yahoo e.g., yfinance package
-"pip install yfinance"
-
-
-Then the code does :
-
-1'/ order by increasing date
-
-2/ plot price histogram and returns  (with "log" and/or "actuarial")
-
-3/ test normality of : prices, log returns, actuarial returns
-    for instance can use scipy.stats.normaltest
-
-4/ shows the random versus optimal results
-
-TODO : replace "None" by what is required to implement the task.
-
-"""
-import matplotlib.pyplot as plt
-import numpy as np
-import pandas as pd
-from scipy.stats import kstest, normaltest  # type: ignore
-
-#from scipy.special import softmax
-
-# take csv from www course :we suppose it is available locally))
-data = pd.read_csv('M2/Risks Management/TP1/close_cac40_historical.csv', sep=';', index_col = 'Date')
-data.head()
-# order by increasing date, keep variable 'data'
-data = data.sort_index(ascending=True)
-data.head()
-data.tail()
-
-# plot histogram of prices
-_ = data.hist(bins=30, figsize = (15,15))
-
-def normality_test(data,kolmogorov_smirnov=False,level=0.01,print_results=True):
-    """
-    Tests normality of each column of dataframe "data".
-    Inputs:
-        kolmogorov_smirnov= false: use "normaltest", otherswise use kstest, both from scipy.stats
-        level = p-value threshold level for the conclusions
-
-    Outputs:  the number of yes/no in the results
-    """
-
-    pvalues = []
-    for cols in data.keys():
-        pv = kstest(data[cols].dropna(), 'norm', args=(data[cols].mean(), data[cols].std())).pvalue if kolmogorov_smirnov else normaltest(data[cols].dropna()).pvalue
-        pvalues.append(pv)
-        res = 'normal' if pv >= level else 'not normal'
-        print("Test pval=", pv, 'res=', res)
-    normalok = sum([1 for pv in pvalues if pv >= level])
-    normalnotok = sum([1 for pv in pvalues if pv < level])
-    if (print_results):
-        print("no. of normal = ", normalok)
-        print("no. of not normal = ", normalnotok)
-    return normalok, normalnotok
-
-normality_test(data)
-
-# use 'data' to compute returns
-# returns = data.pct_change() #actuarial
-returns = np.log(data/data.shift(1))
-
-_ = returns.hist(bins = int(np.sqrt(returns.shape[0])), figsize = (15,15)) # type: ignore
-
-normality_test(returns.tail(25*3)) # type: ignore # test last 3 months
-
-###########################################################
-print('normality tests for increments, not returns!!')
-increments = data - data.shift(1)
-_ = increments.hist(bins=int(np.sqrt(increments.shape[0])), figsize = (15,15))
-
-normality_test(increments.tail(25*3))
-########################################################################
-
-
-
-#%%
-nb = 10  #will work with nb stocks
-all_returns = returns.copy() #backup
-nb_all = all_returns.shape[1]
-if (nb > nb_all):
-    print("too many number of stocks, revert to max")
-    nb = nb_all
-#choose the stock names
-nb_stocks_names = np.random.choice(all_returns.keys(), nb, replace=False) # type: ignore
-returns_small = all_returns.loc[:, nb_stocks_names] # type: ignore
-
-#%%
-#compute avg and cov of returns
-mean_returns = returns_small.mean()
-cov_matrix = returns_small.cov()
-rdt_list = []
-std_list = []
-for _ in range(500):
-    #sample at random some "allocation"
-    allocation = np.random.random(nb)
-    rdt_port = allocation@mean_returns
-    std_port = np.sqrt(allocation@cov_matrix@allocation)
-    rdt_list.append(rdt_port)
-    std_list.append(std_port)
-
-
-inverse_cov = np.linalg.inv(cov_matrix)
-# compute and draw the efficient frontier on the same graph
-onesM = np.ones_like(mean_returns)
-#compute 'a' and 'b' using formulas from the course
-a = onesM.T @ inverse_cov @ onesM
-b = onesM.T @ inverse_cov @ mean_returns
-
-# plot the frontier and its symmetric w/r to origin
-sigmarange = np.linspace(1. / np.sqrt(a) + 1.e-10, 1.1 * np.max(std_list), 47)
-# compute the return of the optimal portfolio for sigma in sigmarange
-# will use the "factor" auxiliary variable
-factor = np.sqrt(sigmarange**2 - 1. / a)
-optimal_return = b / a + np.sqrt(sigmarange**2 - 1. / a) * factor
-
-fig = plt.figure('perf')
-plt.scatter(std_list, rdt_list)
-plt.plot(sigmarange, optimal_return, 'r-')
-plt.xlabel('std')
-plt.ylabel('rdt')
-#plt.xlim([0,.2])
-#plt.ylim([-.05,.05])
-plt.show()
-
-
-# %%

README.md

@@ -29,6 +29,7 @@ The projects are organized into two main sections:
   - `Statistical Learning`
 
 - `M2`
+  - `Data Visualisation`
   - `Linear Models`
   - `Machine Learning`
   - `Risks Management`
@@ -50,4 +51,5 @@ The projects are organized into two main sections:
 - [RMarkdown](https://rmarkdown.rstudio.com): A dynamic tool for combining code, results, and narrative into high-quality documents and presentations.
 - [FactoMineR](https://factominer.free.fr/): An R package focused on multivariate exploratory data analysis (e.g., PCA, MCA, CA).
 - [ggplot2](https://ggplot2.tidyverse.org): A grammar-based graphics package for creating complex and elegant visualizations in R.
+- [RShiny](https://shiny.rstudio.com): A web application framework for building interactive web apps directly from R.
 - and my 🧠.