Category: Stock Market
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Data Visualization in Python – 1. Stock Technical Indicators
Featured Photo by Monstera on Pexels. In this project, we will implement the following Technical Indicators in Python: Conventionally, we will look at the following three main groups of technical indicators: Input Stock Data Let’s set the working directory VIZ import osos.chdir(‘VIZ’)os. getcwd() and import the key libraries import datetime as dtimport pandas as pdimport…
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Top 6 Reliability/Risk Engineering Learnings
The content provides a review of Eric Marsden’s e-learning Python courseware on risk engineering, loss prevention and safety management. It includes discussions of various topics such as the failure of light bulbs, electronic components, large computing facility maintenance, and oil field pumps. The content also delves into stock market risk analysis like Value at Risk…
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Gold Price Linear Regression
This content focuses on predicting gold prices using machine learning algorithms in Python. With an 80% R2-score and a Sharpe ratio of 2.33, it suggests a potential 8% revenue from an investment starting in December 2022. The forecasted next-day price for SPDR Gold Trust Shares is $185.136, aligning with Barchart’s “100% BUY” signal.
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Deep Reinforcement Learning (DRL) on $MO 8.07% DIV USA Stock Data 2022-23
This study applies the Deep Reinforcement Learning (DRL) algorithm to USA stocks with +4% DIV in 2022-23, focusing on Altria Group, Inc. The study addresses accurate stock price predictions and the challenges in traditional methods. Recent advances in DRL have shown improved accuracy in stock forecasting, making it suitable for turbulent markets and investment decision-making.
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JPM Breakouts: Auto ARIMA, FFT, LSTM & Stock Indicators
The post discusses predicting JPM stock prices for 2022-2023 using several predictive models like ARIMA, FFT, LSTM, and Technical Trading Indicators (TTIs) such as EMA, RSI, OBV, and MCAD. The ARIMA model used historical data, while the partial spectral decompositions of stock prices served as features for the FFT model. TTIs were calculated to validate…
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Applying a Risk-Aware Portfolio Rebalancing Strategy to ETF, Energy, Pharma, and Aerospace/Defense Stocks in 2023
The post discusses applying Guillen’s algorithm for risk-aware portfolio rebalancing, using Python. It incorporates five different stocks with specific weight allocations within an initial portfolio of $1,000,000. The post demonstrates setting the parameters for portfolio, importing required libraries, downloading input data, setting algorithmic rules for rebalancing, calculation of shares and portfolio values, and plotting visualizations.…
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Post-SVB Risk Aware Investing
The recent collapse of Silicon Valley Bank and its repercussions have prompted a reevaluation of risk-aware investing in the US financial sector. The crisis has exposed the vulnerability of banks invested in long-term fixed income assets, highlighting the importance of diversification and risk management. Market indicators suggest continued volatility and uncertainty, urging investors to exercise…
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LSTM Price Predictions of 4 Tech Stocks
The given content explains the process of using Exploratory Data Analysis (EDA) and Long Short-Term Memory (LSTM) Sequential model for comparing the risk/return of four major tech stocks: Apple, Google, Microsoft, and Amazon, considering the tech scenario in 2023. The analysis involves examining stock price patterns, their correlations, risk-return assessment, and predicting stock prices using…
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Portfolio Optimization of 20 Dividend Growth Stocks
The post discusses implementing a stochastic optimization algorithm to create a balanced portfolio of 20 dividend growth stocks for maximum return within defined risk tolerance. By analyzing daily stock and benchmark data, the algorithm optimizes the portfolio to outperform the benchmark index and achieve desired risk-reward outcomes. The results facilitate spreading investment capital across diverse…
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Towards Max(ROI/Risk) Trading
This post compares 1-year ROI/Risk of selected stocks vs ETF using stock analyzer functions. It includes comparing prices, visualizing annual risk and return, and examining correlation matrix of stock returns. It provides insights for selecting CPB stock for trading based on low correlation with ^GSPC, high return (~20%), and low risk (~23%).
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Donchian Channel Trading Systems
This article explores the application of algo trading using Python for Altria Group, Inc., a Dividend King diversifying beyond smoking. The historical data for Altria is used to test and contrast trading strategies based on the Donchian Channel indicator. The key is to compare the highest total return when using the Donchian Channel Breakout versus…
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SARIMAX Crude Oil Prices Forecast – 2. Brent
This study focuses on validating the EIA energy forecast for the 2023 Brent crude oil spot price using SARIMAX time-series cross-validation. It includes prerequisites, data loading, ETS decomposition, ADF test, SARIMAX modeling, predictions, model evaluation, and summary. The predictions align with the EIA forecast, with discrepancies within predicted confidence intervals.
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Comparison of Global Growth Stocks – 2. AZN
Summary: A comprehensive QC assessment of top growth stocks in Q1’23 was conducted, focusing on A-rated AstraZeneca PLC (AZN) in the biopharmaceutical industry. The company’s financial indicators, technical analysis, and algorithmic trading signals were analyzed. Backtesting showed a 34% profit from investing in AZN, outperforming the benchmark by 50%.
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Comparison of U.S. Growth Stocks – 1. WMT
The U.S. labor market and consumer spending are robust despite economic challenges. Bank of America reports a 5.1% rise in credit and debit card spending in January. The focus is on A-rated growth stocks like Walmart Inc. (WMT), with promising metrics and technical indicators supporting a Strong Buy sentiment. Algo trading with DI shows successful…
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Turkey/Syria Earthquake Live Knowledge Update & Charity Guide
Turkey has recently experienced a high frequency of earthquakes, the largest being a magnitude of 7.8 near Nurdağı, Gaziantep, on February 6th, 2023. This was one of the most powerful earthquakes in the country’s history, resulting in over 22,000 deaths and significant damage across Southern Turkey and Northern Syria. The aftermath left many survivors homeless,…
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SARIMAX Crude Oil Prices Forecast – 1. WTI
The content discusses a detailed forecast of Brent and WTI oil prices for 2023, using Python, SARIMAX and Time Series Analysis. The data indicates volatility in the oil market starting 2023, with prices set to decrease from 2022 levels. Experts also warn of a potential US recession in 2023, which could further impact the oil…
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Stocks to Watch in 2023: MarketBeat Ideas
Let’s review the 10 best stocks to own in 2023 (brought to you by Marketbeat.com). Featured Photo by Alesia Kozik on Pexels. Uncertainty is the Only Certainty for 2023: It’s Hard to Own Stocks When the Market is Falling: Focus on Opportunity, Value and Dividends in 2023: On a sector basis, the energy sector looks…
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Stock Market ’22 Round Up & ’23 Outlook: Zacks Strategy vs Seeking Alpha Tactics
Featured Photo by Pixabay Contents: Zacks Market Research 2022 has been a strong year for jobs: Commodity markets: Energy: Global Investments: In the Zacks October 2022 Chief Investment Officer (CIO) survey, the CIOs made it fairly clear how they felt about investing outside the US. Answer: not great. Corporate High Yield and Investment Grade Bonds:In…
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XOM SMA-EMA-RSI Golden Crosses ’22
Featured Photo by Johannes Plenio on Pexels. Today we will discuss the XOM stock using most basic technical trading indicators (TTIs) within the Python library ta-lib. Recall that this library is widely used by algo traders requiring to perform technical analysis of financial market data. It includes 150+ indicators such as ADX, MACD, RSI, Stochastic,…
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A Roadmap from Data Science to BI via ML
The blog post presents a comprehensive roadmap to Data Science (DS), providing an overview of career prospects, the field’s intersections with Mathematics, Statistics, and Computer Science, and its business relevance. The text details the earning potential of data scientists and the steps towards becoming one, including Data Analysis, Machine Learning, and Business Intelligence. It highlights…
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