Repo containing Jupyter Notebooks for Financial Analysis
The repo makes use of the following tools:
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Python Fundamentals:
- Basic syntax, data types, variables, and control structures.
- Functions, modules, and packages.
- File handling and input/output operations.
- Exception handling.
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NumPy: NumPy is a fundamental library for scientific computing in Python. It provides support for efficient numerical operations and multidimensional arrays, which are essential for quantitative analysis.
- Array manipulation and indexing.
- Mathematical operations and functions.
- Linear algebra operations.
- Statistical analysis and random number generation.
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Pandas: Pandas is a powerful library for data manipulation and analysis. It provides data structures like DataFrames, which are widely used in quantitative finance.
- Data ingestion and cleaning.
- Data indexing, selection, and filtering.
- Data aggregation and grouping.
- Time series analysis.
- Merging, joining, and reshaping data.
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Matplotlib and Seaborn: These libraries are used for data visualization in Python.
- Basic plotting with Matplotlib.
- Customizing plots and adding annotations.
- Creating line plots, scatter plots, histograms, bar plots, etc.
- Statistical visualization with Seaborn.
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Scipy: Scipy is a library for scientific and technical computing. It provides a wide range of functions for optimization, interpolation, signal processing, and more.
- Numerical integration and differentiation.
- Optimization techniques.
- Interpolation and curve fitting.
- Signal processing and filtering.
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Statsmodels: Statsmodels is a library focused on statistical modeling and econometrics. It provides various statistical models and functions for quantitative analysis.
- Regression analysis.
- Time series analysis.
- Hypothesis testing.
- Panel data analysis.
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QuantLib: QuantLib is a powerful open-source library for quantitative finance. It provides functions and models for pricing derivatives, interest rate modeling, risk management, and more.
- Pricing options and derivatives.
- Interest rate modeling and yield curve construction.
- Monte Carlo simulation.
- Risk management and VaR calculations.
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Machine Learning Libraries: Familiarize yourself with machine learning libraries such as scikit-learn and TensorFlow. Machine learning techniques can be applied in areas like predictive modeling, algorithmic trading, and risk assessment.
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Financial Data APIs: Understand how to retrieve financial data using APIs like AlphaVantage, Yahoo Finance, or Quandl. Learn techniques for data preprocessing and handling time series data.
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Algorithmic Trading: Gain knowledge of algorithmic trading concepts and tools, including backtesting frameworks like Zipline or Backtrader, and execution platforms like Interactive Brokers API or Alpaca.
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Database Connectivity: Learn how to interact with databases like SQL or NoSQL systems to retrieve, store, and analyze financial data efficiently.
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Parallel Computing and Distributed Systems: Gain expertise in leveraging parallel computing techniques and distributed systems to accelerate complex computations, such as Monte Carlo simulations or large-scale data processing.