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stock_prices = [100, 101, 102, 98, 97]

for i in range(1, len(stock_prices)):

daily_return = (stock_prices[i] - stock_prices[i-1]) / stock_prices[i-1]

print("For Loop : " + str(daily_return))

principal = 500

rate = 0.07

years = 0

while principal < 1000:

principal *= (1 + rate)

years += 1

print("While Loop: " + str(years))

principal=1000

rate=0.05

years=0

while principal<2000:

principal*= (1+rate)

years+=1

print(years)

stock_prices=[105, 107, 104, 106, 103]

for i in range(1,len(stock_prices)):

daily_return=(stock_prices[i]-stock_prices[i-1])/stock_prices[i]

print("Exercice 1 " + str(daily_return))

stock_prices = [105, 107, 104, 106, 103]

daily_returns = []

for i in range(1, len(stock_prices)):

daily_return = (stock_prices[i] - stock_prices[i - 1]) / stock_prices[i]

daily_returns.append(daily_return)

average_return = sum(daily_returns) / len(daily_returns)

print("Average return" + str(average_return))

#nombre d'années nécessaire pour avoir P = 1000

principal = 500

rate = 0.07

year = 0

while principal < 1000 :

principal *= (1+rate)

year += 1

print("Nombres d'années: " + str(year) + "et le principal est de: " + str(principal))

#Final value of the investment

principal = 500

rate = 0.07

year = 0

while principal < 1000 :

principal *= (1+rate)

year += 1

if principal >= 1000 :

print(principal)

#test

test = 500*(1.07)**11

print(test)

#conditional statments

pe_ratio = 20

if pe_ratio < 15:

print("Buy the stock.")

elif pe_ratio > 25:

print("Sell the stock.")

else:

print("Hold the stock.")

bond_yield = 4

if bond_yield >= 4 :

print("Buy the bond")

else :

print("Don't buy the bond")

pe_ratio = 17

if pe_ratio < 15:

print("Buy the stock")

elif pe_ratio > 20:

print("Sell the stock")

else:

print("Hold the stock")

pe_ratio = 15

if 14 < pe_ratio < 16:

print("Buy the stock")

elif 23 < pe_ratio < 27:

print("Sell the stock")

else:

print("Hold the stock")

#Object oriented programming

class Bond:

def __init__(self, par_value, coupon_rate, maturity):

self.par_value = par_value

self.coupon_rate = coupon_rate

self.maturity = maturity

def current_yield(self, market_price):

return (self.coupon_rate*self.par_value)/market_price

ten_year_note = Bond(1000, 0.025, 10)

yield_on_note = ten_year_note.current_yield(950)

print (yield_on_note)

class Stock:

def __init__(self, stock_name, current_price, dividend):

self.stock_name = stock_name

self.current_price = current_price

self.dividend = dividend

def yield_dividend (self):

return (self.dividend / self.current_price)

APPL = Stock("APPL", 200, 0.75)

print(APPL.yield_dividend())

class CurrencyConverter:

def __init__(self):

self.rates = {}

def conversion_rate(self, source, target, rate):

key = (source, target) #permet de combiner deux devises EX source USD target EUR = key(USD/EUR)

self.rates[key] = rate

def convert(self, amount, source, target):

key = (source, target)

if key in self.rates:

conversion_rate = self.rates[key]

converted_amount = amount * conversion_rate

return converted_amount

else :

print("Conversion rate not available.")

# Currency converter test

converter = CurrencyConverter()

converter.conversion_rate("USD","EUR", 0.8)

print(converter.rates)

converter.conversion_rate("USD","JPY",140)

print(converter.rates)

USDtoEUR = converter.convert(100,"USD","EUR")

print("100 USD is equal to :", USDtoEUR, "EUR")

USDtoJPY = converter.convert(250,"USD","JPY")

print("250 USD is equal to : ", USDtoJPY,"JPY")

#Mathematical Tools

import numpy as np

prices = np.array([100, 102, 104, 101, 99, 98])

returns = (prices[1:] - prices[:-1]) / prices[:-1]

print("Daily returns:", returns)

annual_volatility = np.std(returns) * np.sqrt(252) #% Assuming 252 trading days

print("Annualized volatility:", annual_volatility)

# Exercice 1 : Stock price simulations

np.random.seed(0)

daily_returns = np.random.normal(0.001, 0.02, 1000) # (moyenne, écart-type, taille)

stock_prices = [100]

for r in daily_returns: # r est la variation quotidienne des rendements pour le jour en cours.

stock_prices.append(stock_prices[-1]*(1+r))

print(stock_prices[-1])

#Exercice 2 : Portfolio variance

import numpy as np

values = np.array([0.1, 0.2])

weights = np.array([0.4, 0.6])

average = np.average(values, weights=weights)

variance = np.average((values-average)**2, weights=weights)

print(variance)

# Exercice 3 : Efficient frontier

# `rng=np.random.default_rng(seed=42)` is creating a random number generator object using the

# `default_rng` function from the `numpy.random` module. The `seed` parameter is set to 42, which

# ensures that the random numbers generated will be the same each time the code is run. This is useful

# for reproducibility, as it allows you to obtain the same random numbers for testing or debugging

# purposes.

rng=np.random.default_rng(seed=42)

weights = rng.random((5, 2))

print("Weights are :",weights)

returns=np.array([0.1, 0.15])

rendement=returns*weights

rendement2 =rendement.sum(axis=1)

#print ("rendement:", rendement)

print ("Returns in function of the weights are :", rendement2)

#Data Visualization Tools

#Exercise 1: Plotting Stock Prices using Matplotlib

import matplotlib.pyplot as plt

import seaborn as sns

dates = [1,2,3,4,5,6,7,8,9,10]

stock_prices = [105, 103, 106, 109, 108, 107, 110, 112, 111, 113]

#plt.plot(stock_prices)

#plt.title('Stock Prices over 10 days')

#plt.xlabel('Days')

#plt.ylabel('Stock Prices')

#plt.show()

stock_prices2 = [107, 108, 107, 107, 106, 108, 109, 108, 109, 110]

plt.plot(dates,stock_prices, label="stock_prices1", color = "blue")

plt.plot(dates,stock_prices2, label="stock_prices2", color = "orange")

plt.title('Stock Prices 1 & 2 over 10 days')

plt.legend ("Stock1 = blue")

plt.legend("Stock2 = orange")

plt.xlabel('Days')

plt.ylabel('Stock Prices')

plt.show()

#Exercise 2: Visualizing Distributions using Seaborn

import matplotlib.pyplot as plt

import seaborn as sns

returns = [0.05, -0.02, 0.03, -0.01, 0.02, 0.03, -0.03, 0.01, 0.04, -0.01]

sns.histplot(returns, bins=5, kde=True)

plt.title(’Distribution of Stock Returns’)

plt.show()

#Financial Times Series

import matplotlib.pyplot as plt

import seaborn as sns

import numpy as np

dates = ["4th January", "5th January", "6th January"]

stock_prices1 = [155, 156, 153]

def calculate_average(prices):

return sum(prices)/len(prices)

average_prices = calculate_average(stock_prices1)

print('Average stock prices :', {average_prices})

#on cherche à trouver le jour associé à la valeur la plus haute.

#Pour cela on utilise la fonction index pour garder en mémoire une valeur.

max_prices_index = stock_prices1.index(max(stock_prices1))

dates_highprice = dates[max_prices_index]

print(dates_highprice)

dates.append("7th January")

dates.append("8th January")

stock_prices1.append(157)

stock_prices1.append(158)

# Idée : calculer la moving average pour avoir la tendance globale sur la timeline

# pourquoi ça ne fonctionne pas c'est la formule du quizz

# moving_average = rolling(window=5).mean()

if not stock_prices1:

print("Aucune donnée de prix n'est disponible.")

else:

n = len(dates)

def moving_average(stock_prices1, n):

ret = np.cumsum(stock_prices1)

ret [n:] = ret[n:] - ret[:-n]

return ret[n-1:]/n

mov_avg = moving_average(stock_prices1, 5)

if mov_avg > average_prices:

print("la tendance est croissante")

elif mov_avg < average_prices :

print ("la tendance est décroissante")

else :

print ("la tendance est stable")

#Histogramme

import matplotlib.pyplot as plt

import seaborn as sns

returns = [0.05, -0.02, 0.03, -0.01, 0.02, 0.03, -0.03, 0.01, 0.04, -0.01]

# `sns.histplot()` is a function from the seaborn library in Python that is used to create a histogram

# plot. A histogram is a graphical representation of the distribution of a dataset. It displays the

# frequency of occurrences of different values in a dataset by dividing the data into bins and

# counting the number of values that fall into each bin.

sns.histplot(returns, bins = 5, kde=True)

plt.title("Distribution of Stock Returns")

plt.show()

Exercices on Basic Data

-----------------------

total_shares = int(input("Numbers of shares you want to buy :"))

price = float(input("Share price :"))

total_value = total_shares * price

print(total_value)

annual_rate_percentage = float(input("Annual Interest Rate in percentage :"))

annual_rate_float = annual_rate_percentage / 100

formatted_interest_rate = f"{annual_rate_float:.2f}"

print(formatted_interest_rate)

stock_price = float(input("Stock prices :"))

final_price = round(stock_price)

print(final_price)

compagnyname = input("What is the compagny's name ? ")

ticker = input("What is the compagny's ticker ?")

compagnyCEO = input("What is the compagny CEO name ?")

print("The compagny's name is ", compagnyname, ", its ticker is ", ticker, " and its CEO is ", compagnyCEO, "." )

bond_rating = "AAA"

# == est un comparateur entre ce qu'il y a gauche et a droite du signe.

#Si la valeur de bond_rating est égale à "AAA", la condition est vraie, et le code à l'intérieur du bloc if est exécuté.

if bond_rating == AAA :

bond_rating = AA+

print(bond_rating)

Exercices on Collection

-----------------------

list_tickers = ["BTC", "ETH", "SOL"]

list_tickers.append("DODGE")

list_tickers.pop(1)

print(list_tickers)

stock_info = {

"ticker" : "ABC",

"price" : 100,

"volume" : 1000,

"outstanding" : 1000000

}

print(stock_info)

daily_closing_price = [103, 102, 110, 115, 112, 108, 112]

average_closing_price = sum(daily_closing_price)/ len(daily_closing_price)

average_closing_price = f"{average_closing_price:.4f}"

print(average_closing_price)

dictionary = {

"ticker": "ABC",

"stock_info" : {

"price" : 10,

"market cap" : 1000000,

"sector" : "technology",

}

print(dictionary)

#10

tickers = ["ABC","DEF","GHI","JKL"]

daily = [1.2, -0.4, 1.3, 0.4]

dictionary = dict(zip(tickers, daily))

print(dictionary)

Exercices on Loops

------------------

#11

stock_prices = [123, 126, 119, 118, 122, 127, 130]

for i in range(1, len(stock_prices)):

price_change = stock_prices[0] - stock_prices[i]

print(f"Day {i}: Price change = {price_change}")

#12

tickers = ["ABC","DEF","GHI","JKL"]

current_price = [123, 126, 119, 118]

dict = {}

for i in range (len(tickers)):

dict[tickers[i]] = [current_price[i]]

print(dict)

#13

budget = 1000

stock_price = 100

total = 0

while budget >= stock_price:

total += stock_price

budget -= stock_price

nb_shares = total/ stock_price

print(nb_shares)

#14

rates = [0.32, 0.21, 0.05, 0.12, 0.08]

for i in rates:

investment = 1000 * i

print(f"With an interest rate of {i} the investment value is {investment}. ")

#15

import random

daily_closing_prices = []

daily_closing_prices = [random.uniform(50, 200) for _ in range(30)]

average_price = sum(daily_closing_prices) / len(daily_closing_prices)

print("Average Stock Price for the Month:", average_price)

while average_price >= 100:

highest_price = max(daily_closing_prices)

daily_closing_prices.remove(highest_price)

print("Removed highest price: ",highest_price)

average_price = sum(daily_closing_prices) / len(daily_closing_prices)

print("New average is ", average_price)

print("Now the average is beyond $100")

EXERCICES ON CONDITIONAL STATEMENTS

-----------------------------------

#16

credit_loan = (646)

if credit_loan >= 700:

print("Approve the loan")

elif credit_loan >= 650 :

print ("Conditionally approve the loan")

else:

print("decline the loan")

#17

dividend_yield = (0.07)

if dividend_yield > 0.05:

print("high yield")

elif dividend_yield > 0.02:

print("moderate yield")

else :

print("low yield")

#18

stock_price = (120)

opening_price = (134)

if opening_price < stock_price * 0.95:

print("buy")

elif opening_price > stock_price * 1.1:

print("sell")

#19

bond_rating = str("AA+")

rating = ["AAA", "AA+", "BBB"]

if bond_rating in rating:

print("investment grade")

else:

print("non-investment grade")

#20

portfolio = (100)

loss = (0.03)

if loss >= 0.02:

print("have to sell 10 percent of the total portfolio's asset")

portfolio = portfolio * 0.9

print("new portfolio's value is ", portfolio)

else:

print("no significant variation")

EXERCICES ON OBJECTED ORIENTED PROGRAMMING

------------------------------------------

#21

class Stock:

def __init__ (self, ticker, base_price, current_price):

self.ticker = ticker

self.base_price = base_price

self.current_price = current_price

def method(self):

change_price = (self.base_price - self.current_price) / self.current_price

print(change_price)

#22

class Stock:

def __init__ (self, ticker, base_price, current_price):

self.ticker = ticker

self.base_price = base_price

self.current_price = current_price

def calculate_value(self):

return self.current_price

class Portfolio:

def __init__ (self):

self.stock = []

def add_stock(self, stock):

self.stock.append(stock)

print("new stock add")

def remove_stock(self, ticker):

for stock in self.stock:

if stock.ticker == ticker:

self.stock.remove(stock)

print("stock remove")

break

def total_value(self):

total_value = 0

for stock in self.stock:

total_value += stock.calculate_value()

return total_value

print(total_value)

#23

class BankAccount:

def __init__ (self, account_number, balance):

self.account_number = account_number

self.balance = balance

def deposit(self, deposit_ammount):

deposit_ammount = float(input("Deposit Ammount :"))

if deposit_ammount > 0:

self.balance += deposit_ammount

print("New balance is :", self.balance)

else :

print("No deposit ammount.")

def withdraw(self, withdraw_ammount):

withdraw_ammount = float(input("Withdraw Ammount :"))

if withdraw_ammount > 0 and withdraw_ammount <= self.balance:

self.balance -= withdraw_ammount

print("New balance is :", self.balance)

elif withdraw_ammount > self.balance:

print("Insufficient funds.")

else :

print("No withdraw ammount.")

def check(self, balance):

checkverif = input("Do you want to check the balance ? (Yes/ No):")

if checkverif == "Yes":

print(self.balance)

elif checkverif == "No":

print("no balance check")

else :

print("Erreur Value")

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