final delivery, flake OK
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Liste+dactions+-+P7+Python+-+Feuille+1.csv
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Liste+dactions+-+P7+Python+-+Feuille+1.csv
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Actions #,Coût par action (en euros),Bénéfice (après 2 ans)
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Action-1,20,5%
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Action-2,30,10%
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Action-3,50,15%
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Action-4,70,20%
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Action-5,60,17%
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Action-6,80,25%
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Action-7,22,7%
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Action-8,26,11%
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Action-9,48,13%
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Action-10,34,27%
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Action-11,42,17%
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Action-12,110,9%
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Action-13,38,23%
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Action-14,14,1%
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Action-15,18,3%
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Action-16,8,8%
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Action-17,4,12%
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Action-18,10,14%
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Action-19,24,21%
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Action-20,114,18%
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README.md
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README.md
@ -1,6 +1,6 @@
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# AlgoInvest&Trade
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Choix optimal parmi une liste d'actions
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Déterminer un choix optimal d'actions caractérisées par un coût et un rendement, en fonction d'un coût maximum pour un profit maximal
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## Introduction
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@ -15,66 +15,56 @@ Ces instructions vous permettent de :
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```
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paquets : python 3.11, python3.11-venv, git
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modules : python requests, BeautifulSoup, csv, os
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modules : csv
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```
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### Installation
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Voici les étapes à suivre pour avoir un environnement d'exécution opérationnel :
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créer l'environnement virtuel
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1. créer l'environnement virtuel :
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```
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python3.11 -m venv env
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source env/bin/activate
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```
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cloner le dépôt, aller dans le bon dossier
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2. cloner le dépôt :
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```
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git clone https://mcstn.fr/gitea/Yann/Projet2.git
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cd Projet2/rendu
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```
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installer les modules
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```
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pip install -r requirements.txt
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git clone https://mcstn.fr/gitea/Yann/Projet7.git
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```
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## Exécution
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Pour l'algorithme bruteforce sur le dataset0,
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exécuter la commande :
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```
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python3 main.py
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python3 bruteforce.py
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```
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Pour l'algorithme de DP, executer la commande :
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```
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python3 optimized.py
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```
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## Résultat
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Les fichiers sont placés dans un répertoire "resultat"
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Le programme récupère les catégories sur la page d'accueil de l'URL, puis, pour chaque catégorie :
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1. affiche la catégorie traitée, le nombre de catégories restantes, de livres présents, traités au total et restants
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2. crée un dossier du nom de la catégorie, y enregistre les images des livres nommées en fonction du titre
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3. crée un fichier csv au nom de la catégorie, avec :
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- product_page_url
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- universal_ product_code (upc)
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- title
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- price_including_tax
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- price_excluding_tax
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- number_available
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- product_description
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- category
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- review_rating
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- image_url
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Optimized traite par défaut les datasets 1 et 2;
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Décommenter la ligne du dataset0 dans le main() si besoin
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```
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$ time python3.11 main.py
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1000 à traiter répartis en 50 catégories.
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$ time python optimized.py
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[ ... ]
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DATASET 1
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Cost: 499.43 €
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Profit: 196.84 €
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Shares : ['Share-HITN', 'Share-GRUT']
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Traitement terminé.
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DATASET 2
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Cost: 497.67 €
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Profit: 194.90 €
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Shares : ['Share-GEBJ', 'Share-LFXB', 'Share-FWBE', 'Share-PLLK', 'Share-ZKSN', 'Share-ZOFA', 'Share-PATS', 'Share-DWSK', 'Share-ALIY', 'Share-ECAQ', 'Share-FAPS', 'Share-JGTW', 'Share-QLWT', 'Share-OPBR', 'Share-ANFX', 'Share-IJFT', 'Share-JWGF']
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real 20m17,783s
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user 4m30,695s
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sys 0m3,172s
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real 0m0,852s
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user 0m0,832s
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sys 0m0,018s
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```
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## Auteur
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@ -1,5 +1,8 @@
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import csv
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MAX_COST = 500
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FILE = "Liste+dactions+-+P7+Python+-+Feuille+1.csv"
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def listFromFile(csv_file):
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"""
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get data from a csv file and :
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@ -17,6 +20,7 @@ def listFromFile(csv_file):
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item[2] = item[1] * float(item[2].strip("%")) / 100
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return liste
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def powerset(itemList):
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"""
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Generate every subset (combination) for a given list
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@ -29,6 +33,7 @@ def powerset(itemList):
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result.extend(newsubsets)
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return result
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def transformData(dataset):
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"""
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Transform in a list of dict with computed values as gain, ratio
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@ -37,19 +42,21 @@ def transformData(dataset):
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:return: a sorted list of dict
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"""
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tmpset = [{'nom': x[0], 'cout': x[1],
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'rendement': x[2],
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'gain': x[1] * x[2] / 100,
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'ratio1': x[2] / x[1],
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'ratio2': (x[1] * x[2] / 100) / x[1]}
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for x in dataset if
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x[1] > 0.0 and x[2] > 0.0]
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'rendement': x[2],
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'gain': x[1] * x[2] / 100,
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'ratio1': x[2] / x[1],
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'ratio2': (x[1] * x[2] / 100) / x[1]}
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for x in dataset if x[1] > 0.0 and x[2] > 0.0]
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return sorted(tmpset, key=lambda x: x['gain'], reverse=True)
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def selectActions(actionList, maximal_cost):
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"""
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:param actionList: takes a list of combinations and a max
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:return: a list of selected combinations where cost is under max
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select combination corresponding to max cost
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:param actionList: list of combinations
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:param maximal_cost: maximal cost
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:return: a list of selected items
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"""
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best = []
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for i in actionList:
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@ -66,17 +73,15 @@ def selectActions(actionList, maximal_cost):
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return sortedBest.pop(0)
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def main():
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actions = listFromFile(FILE)
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power_actions = powerset(actions)
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selected_actions = selectActions(power_actions, MAX_COST)
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# tri des actions sur le rendement
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print("Cost:", selected_actions[1], "€")
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print("Profit: %.2f €" % selected_actions[0])
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print(f"Shares: {[x[0] for x in selected_actions[2]]}")
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actions = listFromFile("/home/b/Documents/OCR/projet7/actions.csv")
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power_actions = powerset(actions)
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selected_actions = selectActions(power_actions, 500)
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print("Nombre d'actions:", len(actions))
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print("Nb de combinaisons:", len(power_actions))
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#tri des actions sur le rendement
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print("Gain: %.2f €" % selected_actions[0])
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print("Cout:", selected_actions[1], "€")
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print("Actions sélectionnées:", selected_actions[2:])
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if __name__ == '__main__':
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main()
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1002
dataset1_Python+P7.csv
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1002
dataset1_Python+P7.csv
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File diff suppressed because it is too large
Load Diff
1001
dataset2_Python+P7.csv
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1001
dataset2_Python+P7.csv
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File diff suppressed because it is too large
Load Diff
74
optimized.py
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optimized.py
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import csv
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MAX_COST = 500
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DATASET1 = "dataset1_Python+P7.csv"
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DATASET2 = "dataset2_Python+P7.csv"
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DATASET0 = "Liste+dactions+-+P7+Python+-+Feuille+1.csv"
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def listFromFile(csv_file):
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"""
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liste.pop(0)
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for item in liste:
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item[1] = float(item[1])
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if item[2][-1] == "%":
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item[2] = item[2].strip("%")
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item[2] = float(item[2])
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return liste
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def transformData(dataset):
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"""
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Transform in a list of dict with computed values as gain, ratio
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:return: a sorted list of dict
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"""
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tmpset = [{'nom': x[0], 'cout': x[1],
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'rendement': x[2],
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'gain': x[1] * x[2] / 100,
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'ratio1': x[2] / x[1],
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'ratio2': (x[1] * x[2] / 100) / x[1]} for x in dataset if
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x[1] > 0.0 and x[2] > 0.0]
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'rendement': x[2],
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'gain': x[1] * x[2] / 100,
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'ratio1': x[2] / x[1],
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'ratio2': (x[1] * x[2] / 100) / x[1]}
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for x in dataset if x[1] > 0.0 and x[2] > 0.0]
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return sorted(tmpset, key=lambda x: x['gain'], reverse=True)
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def get_results(filepath, maximum, nbr):
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"""
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load, transform data then run the algorithm and print results
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:param filepath: full path to csv
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:param maximum: maximum cost
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:param nbr: set number
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:return: print results
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"""
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action_list = transformData(listFromFile(filepath))
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maximum_gain, selection = sacADosFloat(action_list, maximum)
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print("\nDATASET", nbr)
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print(f"Cost: {sum(x['cout'] for x in selection):.2f} €")
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print("Profit: %.2f €" % maximum_gain)
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print(f"Shares : {[x['nom'] for x in selection]}")
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def sacADosFloat(actions, maximum_cost):
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"""
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Use dynamic approach
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if i == 0 or w == 0:
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table[i][w] = 0.0
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elif actions[i-1]['cout'] <= w:
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table[i][w] = max(actions[i-1]['gain'] + table[i-1][int(w-actions[i-1]['cout'])], table[i-1][w])
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table[i][w] = (
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max(
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actions[i-1]['gain'] +
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table[i-1][int(w-actions[i-1]['cout'])],
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table[i-1][w]
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)
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)
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else:
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table[i][w] = table[i-1][w]
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# Select
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# Selection
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w = maximum_cost
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selected_actions = []
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for i in range(n, 0, -1):
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@ -65,25 +99,11 @@ def sacADosFloat(actions, maximum_cost):
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return table[n][int(maximum_cost)], selected_actions
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actions = transformData(listFromFile("/home/b/Documents/OCR/projet7/ph3/dataset1_Python+P7.csv"))
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actions2 = transformData(listFromFile("/home/b/Documents/OCR/projet7/ph3/dataset2_Python+P7.csv"))
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def main():
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# get_results(DATASET0, MAX_COST, 0)
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get_results(DATASET1, MAX_COST, 1)
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get_results(DATASET2, MAX_COST, 2)
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maximum_cost = 500
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maximum_gain1, selection1 = sacADosFloat(actions, maximum_cost)
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maximum_gain2, selection2 = sacADosFloat(actions2, maximum_cost)
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print("\nDATASET 1")
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print(f"Cout: {sum(x['cout'] for x in selection1):.2f}")
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#print(f"Rendement: {sum((x['cout']*x['rendement']/100)for x in actions_selectionnees):.2f}")
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print("Gain: %.2f" % maximum_gain1)
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print(f"Actions sélectionnées: {[x['nom'] for x in selection1]}")
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print("\nDATASET 2")
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print(f"Cout: {sum(x['cout'] for x in selection2):.2f}")
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#print(f"Rendement: {sum((x['cout']*x['rendement']/100)for x in actions_selectionnees2):.2f}")
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print("Gain: %.2f" % maximum_gain2)
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print(f"Actions sélectionnées: {[x['nom'] for x in selection2]}")
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if __name__ == '__main__':
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main()
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