/Stock_Analysis_For_Quant

Different Types of Stock Analysis in Python, R, Matlab, Excel, Power BI

Primary LanguageJupyter Notebook

Stock Analysis for Quants

This is Stock Analysis project in Excel, Power BI, Matlab, Python, and R language with different types of analysis such as data analysis, technical analysis, fundamental analysis, quantitative analysis, and different types of trading strategies. In addition, this is for quantitative researching and analyzing in trading and investment. Quantitative analysis (QA) is a technique that use mathematices and statistical modeling, measurement, and research for understanding financial behaviors. Many different types of technical indicators and stock strategies in Excel, Python, and R language. Using timeseries, forecasting, machine learning, and deep learning for this research project in different type of programming languages. 📈 📉

Prerequistes

Programming Language and Software

Python 3.5+

R 3.0.0 +

Matlab R2016a

Excel 2016

Power BI

List of Trading Strategies

Description: There are many various methods used to accomplish different strategy; therefore, each with appropriate market environments and risks inherent in the strategy. Trading strategy is a technique of buying and selling in the markets that is based on predefined rules used to make trading decisions.

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Trend-following Strategies
Algorithmic Trading Strategies
Statistical Arbitrage
Arbitrage Opportunities
Index Fund Rebalancing
Mathematical Model-based Strategies
Trading Range (Mean Reversion)
Fundamental Analysis
Technical Analysis
Swing Trading Strategy Scalping (Trading)
Day Trading
Trading the News
Trading the Signals Social Trading
Value Investing
Performance Analysis
Quantitative Analysis

List of Portfolio Strategies

Description: Portfolio strategies is an investment method for investors to use their assets to achieve their financial goals.

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Long-term Investment
Short-term Investment
Buy and Hold
Rebalance Portfolio
Value Investment
Momentum Investment
Core and Satellite
The Dave Ramsey Portfolio
Capital Asset Pricing Model (CAPM)
Modern Portfolio Theory (MPT)
Post-Modern Portfolio Theory (PMPT)
Portfolio Allocation
Portfolio Optimization
Markowitz Portfolio Optimization Theory Minimum-Variance Portfolios (Global Minimum-variance Portfolio)
Global Portfolio Optimization (The Black Litterman)
Tactical Asset Allocation
Constant-Weighting Asset Allocation
Strategic Asset Allocation
Dynamic Asset Allocation
Insured Asset Allocation
Integrated Asset Allocation
ETFs Asset Allocation
Bonds Asset Allocation
Mutual Funds Asset Allocation
Commodities Asset Allocation
Portfolio Insurance
Constant Proportion Portfolio Insurance (CPPI)
Presidental Stock Portfolio
Obama Stock Portfolio
Trump Stock Portfolio

List Type of Risks

Description: Risk measures are statistical method to defined the individual stock or together to perform a risk assessment.

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Trade Risk
Position Size Risk
Market Risk
Margin Risk
Liquidity Risk
Overnight Risk
Volatility Risk

List of Risk-Adjusted Returns Ratios Measurement

Description: Risk-Adjusted Returns Ratios is an investment's return by measuring how much risk is involved in producing that return.

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Appraisal Ratio
Bernardo Ledoit Ratio
Burke Ratio
Calmar Ratio
Conditional Sharpe Ratio
Gain Loss Ratio
Information Ratio
K appa Three Ratio
Martin Ratio
Modigliani Ratio
Omega Ratio
Pain Ratio
Risk-adjusted return on capital (RAROC)
Sterling Ratio
Sharpe Ratio
Sortino Ratio
Treynor Ratio
Upside Potential Ratio

Credits:

Developed by Tin Hang and other contributors (sharing knowledge) from schools, books, and blogs.

Links to research paper for quant:

https://www.researchgate.net
https://www.academia.edu/
https://quant.stackexchange.com/questions/38886/what-are-the-quantitative-finance-papers-that-we-should-all-have-in-our-shelves

Disclaimer

⚠️

Do not use this code for investing or trading in the stock market. However, if you are interest in the stock market, you should read 📚 books, research paper, and 💻 blog that relate to stock market, investment, or finance. On the other hand, if you into quant, read books about machine learning or deep learning. Books about 📘 machine trading, algorithmic trading, and quantitative trading. Do experimental on stock historical price and test different strategies or method to see if it works or not. Learn from it and take notes 📓.

◼️ This is not get rich quick.

◼️ This is not financial advisor.

◼️ This is for researching and educational purposes.