Fundamental and Technical Stock Analysis: A Machine Learning Approach featured image

Fundamental and Technical Stock Analysis: A Machine Learning Approach

Explore the power of machine learning in fundamental and technical stock analysis. Learn how to leverage historical data, financial statements, and predictive modeling techniques to make informed investment decisions. Discover the potential of SARIMAX and AdaBoost algorithms in forecasting stock prices. Gain insights into discount rate calculations, NPV, and IRR to evaluate investment opportunities. Enhance your understanding of stock market trends and improve your trading strategies using this comprehensive guide.

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Portfolio featured for the period 15th May 2023 to 19th May 2023

Investment Update: Our Weekly Portfolio Strategy for May 15th, 2023

In this article, we analyze a previously suggested portfolio based on BSE SENSEX companies and provide recommendations for a new portfolio for the upcoming week. The article includes a detailed analysis of the performance of the previous portfolios and provides new portfolio recommendations based on Sharpe Ratio and Machine Learning optimization.

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Portfolio-ML-Featured-Image

Investing in BSE SENSEX Companies for Weekly Returns: Strategies and Tips

Are you a risk-taker or a risk-averse investor? Are you looking for investment advice that can help you make the most out of your stock portfolio while minimizing risks? If so, you might want to consider using Machine Learning (ML) methodologies.

In this article, we will explore how Machine Learning can help you build a profitable investment portfolio on BSE SENSEX companies. We will cover two different portfolios, one that maximizes the Sharpe Ratio and the other that uses ML to perform mean-variance optimization. We will also discuss how cross-validation can be used to choose the best model and achieve better performance.

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Portfolio Allocation Featured Image

Maximizing Returns and Minimizing Risk: Creating an Optimized Portfolio in PYTHON

This article is significant for individuals interested in creating an optimized portfolio of stocks using Python. It provides a comprehensive guide on how to gather historical data on stocks, calculate the mean historical return and covariance matrix, and use the Efficient Frontier, Sharpe ratio, and Discrete Allocation methods to create an optimized portfolio. By leveraging the “yfinance,” “expected_returns,” “risk_models,” and “pypfopt” libraries, users can automate the process of portfolio optimization and allocation. However, it’s important to note that stock investments always carry a level of risk, and users should conduct proper research on the stocks and markets in question. Overall, this code is a great starting point for individuals looking to create an optimized portfolio and streamline their investment process.

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Invest Like a Pro - Use Machine Learning Algorithms to Optimize Your Portfolio featured image

Invest Like a Pro: Use Machine Learning Algorithms to Optimize Your Stock Portfolio

Discover how to use machine learning algorithms to optimize your investment portfolio using a mean-variance approach. Learn how to download stock price data from Yahoo Finance, perform mean-variance optimization with a linear regression model, and choose the best model using cross-validation. Achieve better performance and reduce risk in your investment portfolio with machine learning.

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Portfolio Featured

Monthly Portfolio for the period : October – November

The recommended investment portfolio includes a weighted ratio of different equities such as HDFCBANK.NS, INFY.NS, ITC.NS, RCOM.NS, MARUTI.NS, and WIPRO.NS. The portfolio offers an estimated monthly return of 1.53%, with a low estimated risk of 0.58%. The investment is well diversified, capped at 25% for each equity to avoid concentration risk.

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portfolio update

Earning record breaking 13.73% monthly return, Monthly portfolio report

Anomalies Post’s portfolio gave an astonishing monthly return of 13.73%, as promised. Moreover, the post is happy to announce that the accumulated wealth of investors have risen from the value of Rs. 100,106 (as on 4th May 2015) to Rs. 123,950 (as on 5th October 2015), giving 23.82%, overall return in 5 month period.

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