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Some notes about python and Zen of Python

Posted by: admin 4 days ago

Explore Python syntax

Python is a flexible programming language used in a wide range of fields, including software development, machine learning, and data analysis. Python is one of the most popular programming languages for data professionals, so getting familiar with its fundamental syntax and semantics will be useful for your future career. In this reading, you will learn about Python’s syntax and semantics, as well as where to find resources to further your learning.

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Understanding Tier 1 Capital, common equeity tier 1 Capital, and risk weighted asset through asking the right question

Posted by: admin 2 weeks, 5 days ago

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Week 1 to week 6 in learning VAR

Posted by: admin 3 weeks, 3 days ago

Week 1: Introduction to Time Series Analysis

Overview of Time Series Data:

python

# Load necessary libraries import pandas as pd import matplotlib.pyplot as plt # Load and visualize time series data data = pd.read_csv('your_time_series_data.csv') plt.figure(figsize=(10, 6)) plt.plot(data['Date'], data['Value']) plt.title('Time Series Data') plt.xlabel('Date') plt.ylabel('Value') plt.show()

Time Series Components:

python

# Decomposition of time series data from statsmodels.tsa.seasonal import seasonal_decompose result = seasonal_decompose(data['Value'], model='additive', period=12) result.plot() plt.show()

Statistical Properties of Time Series:

python

# Stationarity check using Augmented Dickey-Fuller test from statsmodels.tsa.stattools import adfuller result = adfuller(data['Value']) print('ADF Statistic:', result[0]) print('p-value:', result[1]) print('Critical Values:', result[4])

Week 2: Fundamentals of Vector Autoregression (VAR)

Introduction to VAR Model:

python

# Import VAR model from statsmodels from statsmodels.tsa.vector_ar.var_model import VAR # Create VAR model model = VAR(data)

Estimation and Interpretation:

python

# Fit the VAR model results = model.fit() # Summary of the VAR model print(results.summary())

Granger Causality and Lag Selection:

python

# Granger causality test from statsmodels.tsa.stattools import grangercausalitytests max_lag = 4 # maximum lag to test causality granger_test_result = grangercausalitytests(data, max_lag)

Week 3: Implementing VAR in Python

Building a VAR Model in Python:

python

# Implementing VAR model using statsmodels library model = VAR(data) results = model.fit(maxlags=4) # fitting the model with selected maximum lag

Visualization and Forecasting with VAR:

python

# Plotting results and visualizing time series forecasts results.plot_forecast(10)

Implementing Impulse Response Analysis:

python

# Impulse Response Analysis irf = results.irf(10) irf.plot(orth=False)

This breakdown provides code snippets for key concepts covered in the weekly plan. For the complete course, you would expand upon these snippets, incorporate explanations, provide datasets, and encourage students to apply these techniques to various time series datasets and financial data, ensuring they understand the theory and practical implementation of VAR models in Python.

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Learning Vector Autoregression in 6 weeks

Posted by: admin 3 weeks, 3 days ago

Here is the program

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week 6 Finance with Python

Posted by: admin 3 weeks, 5 days ago

Week 6: Financial Projects and Advanced Topics

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Latest posts

Some notes about python and Zen of Python

Recent news

Explore Python syntax

Understanding Tier 1 Capital, common equeity tier 1 Capital, and risk weighted asset through asking the right question

Recent news

Week 1 to week 6 in learning VAR

Recent news

Week 1: Introduction to Time Series Analysis

Overview of Time Series Data:

python

# Load necessary libraries import pandas as pd import matplotlib.pyplot as plt # Load and visualize time series data data = pd.read_csv('your_time_series_data.csv') plt.figure(figsize=(10, 6)) plt.plot(data['Date'], data['Value']) plt.title('Time Series Data') plt.xlabel('Date') plt.ylabel('Value') plt.show()

Time Series Components:

python

# Decomposition of time series data from statsmodels.tsa.seasonal import seasonal_decompose result = seasonal_decompose(data['Value'], model='additive', period=12) result.plot() plt.show()

Statistical Properties of Time Series:

python

# Stationarity check using Augmented Dickey-Fuller test from statsmodels.tsa.stattools import adfuller result = adfuller(data['Value']) print('ADF Statistic:', result[0]) print('p-value:', result[1]) print('Critical Values:', result[4])

Week 2: Fundamentals of Vector Autoregression (VAR)

Introduction to VAR Model:

python

# Import VAR model from statsmodels from statsmodels.tsa.vector_ar.var_model import VAR # Create VAR model model = VAR(data)

Estimation and Interpretation:

python

# Fit the VAR model results = model.fit() # Summary of the VAR model print(results.summary())

Granger Causality and Lag Selection:

python

# Granger causality test from statsmodels.tsa.stattools import grangercausalitytests max_lag = 4 # maximum lag to test causality granger_test_result = grangercausalitytests(data, max_lag)

Week 3: Implementing VAR in Python

Building a VAR Model in Python:

python

# Implementing VAR model using statsmodels library model = VAR(data) results = model.fit(maxlags=4) # fitting the model with selected maximum lag

Visualization and Forecasting with VAR:

python

# Plotting results and visualizing time series forecasts results.plot_forecast(10)

Implementing Impulse Response Analysis:

python

# Impulse Response Analysis irf = results.irf(10) irf.plot(orth=False)

3 weeks, 5 days ago

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