Python Libraries for Financial Analysis and Portfolio Management

 

import statsmodels.api as sm

import numpy as np

# Generate some sample data

x = np.random.rand(100)

y = 2 * x + np.random.randn(100)

# Fit a linear regression model

model = sm.OLS(y, sm.add_constant(x)).fit()

print("Regression coefficients:", model.params)

print("R-squared:", model.rsquared)

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import pandas as pd

# Create a simple DataFrame

data = {'Name': ['Alice', 'Bob', 'Charlie'],

        'Age': [25, 30, 35],

        'Salary': [50000, 60000, 70000]}

df = pd.DataFrame(data)

# Perform data analysis

print("DataFrame head:")

print(df.head())

print("\nAverage salary:", df['Salary'].mean())

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import numpy as np

# Create a simple array

arr = np.array([1, 2, 3, 4, 5])

# Perform numerical operations

print("Sum:", np.sum(arr))

print("Mean:", np.mean(arr))

print("Standard deviation:", np.std(arr))

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from ibapi.client import EClient

from ibapi.wrapper import EWrapper

class MyWrapper(EWrapper):

    def __init__(self):

        super().__init__()

class MyClient(EClient):

    def __init__(self, wrapper):

        EClient.__init__(self, wrapper)

app = MyClient(MyWrapper())

app.connect("127.0.0.1", 7497, clientId=1)

app.run()

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import numpy as np

from scipy import optimize

# Define a simple objective function

def objective(x):

    return x**2 + 10*np.sin(x)

# Optimize the objective function

result = optimize.minimize(objective, x0=0)

print("Minimum value found at:", result.x)

print("Objective function value at minimum:", result.fun)

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from riskfolio.Portfolio import Portfolio

# Create a simple portfolio

data = {'Asset1': [0.05, 0.1, 0.15],

        'Asset2': [0.08, 0.12, 0.18],

        'Asset3': [0.06, 0.11, 0.14]}

portfolio = Portfolio(returns=data)

# Perform portfolio optimization

portfolio.optimize()

print("Optimal weights:", portfolio.w)

print("Expected return:", portfolio.mu)

print("Volatility:", portfolio.sigma)

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