Tag: AAPL
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A Market-Neutral Strategy
The work aims to solve the problem of Markowitz portfolio optimization for a one-year investment horizon through the pairs trading cointegrated strategy. Market-neutral trading strategies seek to generate returns independent of market swings to achieve a zero beta against its relevant market index. Statistical arbitrage (SA), pairs trading, and APO signals are analyzed. The study…
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Dividend-NG-BTC Diversify Big Tech
SEO Title: Can Dividends, Natural Gas and Crypto Diversify Big Techs? The objective of this project is to implement several basic Quant Trading (QT) optimization ideas by analyzing tech growth, dividend stocks and highly volatile assets (commodities and crypto) in terms of risk/return optimization scenarios and (potential) diversification options. We will use the financial APIs…
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Returns-Volatility Domain K-Means Clustering and LSTM Anomaly Detection of S&P 500 Stocks
This study aims to implement and evaluate the K-means algorithm for ranking/clustering S&P 500 stocks based on average annualized return and volatility. The second goal is to detect anomalies in the best performing S&P 500 stocks using the Isolation Forest algorithm. Additionally, anomalies in the S&P 500 historical stock price time series data will be…
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IQR-Based Log Price Volatility Ranking of Top 19 Blue Chips
The focus is on risk assessment of top blue chips. We determine market regimes using standard deviation (STD) of log-domain stock prices.
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Multiple-Criteria Technical Analysis of Blue Chips in Python
Blue chip stocks are the stocks of well-known, high-quality companies. We demonstrate that the proposed approach can help optimize the blue-chip portfolios comprehensively.
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Blue-Chip Stock Portfolios for Quant Traders
This post delves into optimizing blue-chip stock portfolios using Python fintech libraries for private DIY self-traders. It includes steps for examining trading signals, comparing stock returns, performing analyses, and implementing forecast models. The content covers AAPL trading signals, risk vs. ROI analysis, a 4-stock portfolio, Monte-Carlo predictions, SPY return/volatility, and SPY Prophet forecast. The examples…
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Data Visualization in Python – 1. Stock Technical Indicators
Featured Photo by Monstera on Pexels. In this post, we will put forward advanced integrated data visualization (AIDV) in the stock trading decisions and stock price prediction. The objective is to implement and visualize most popular stock technical indicators that are commonly used in technical analysis. From the point of view of algo trading, this…
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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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Bear vs. Bull Portfolio Risk/Return Optimization QC Analysis
Based on the Portfolio Allocation and Optimization Algorithm discussed earlier and the related portfolio management, let’s run the Bear vs. Bull QC test of the portfolio P=[MSFT, AAPL, NDAQ] in terms of the Risk/Return Ratio (RRR). We have got a Sharpe ratio of less than one that is considered unacceptable or bad. The risk the…
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Inflation-Resistant Stocks to Buy
Inflation-Resistant Stocks to Buy AAPL Example Python workflow Download 3 historical datasets – stock price and monthly/annual CPI Compute the monthly/annual stock performance (%) and CPI rate (%) Apply linear regression to the stock vs CPI performance cross-plot Check the slope or gradient of the linear trend – positive, negative or zero.
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ML/AI Regression for Stock Prediction – AAPL Use Case
1. Install Yahoo finance library 2. Call all dependencies that we will use for this exercise 3. Define the ticker you will use 4. Let’s look at the data table 5. Data Exploration Phase 6. Data Preparation, Pre-Processing & Manipulation 7. Apply Linear Regression 8. Perform ML QC Analysis 9. Final Output
