Category: Stock Market

  • A Market-Neutral Strategy

    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…

  • A Comprehensive Analysis of Best Trading Technical Indicators w/ TA-Lib – Tesla ’23

    A Comprehensive Analysis of Best Trading Technical Indicators w/ TA-Lib – Tesla ’23

    This study presents a comprehensive stock technical analysis guide for Tesla (TSLA) using the TA-Lib Python library. It explores the use of over 200 technical indicators, analyses historical data, and offers insight for both swing traders and long-term holders. The content includes detailed explanations and plots for various momentum, volume, volatility, and trend indicators, providing…

  • Real-Time Stock Sentiment Analysis w/ NLP Web Scraping

    Real-Time Stock Sentiment Analysis w/ NLP Web Scraping

    Stock sentiment analysis is gaining popularity as a technique to understand public opinions on specific assets. This study uses NLP web scraping in Python to extract stock sentiments from financial news headlines on FinViz. The sentiment analysis can help determine investor opinions and potential impacts on stock prices, though it is not a standalone predictor.

  • Sales Forecasting: tslearn, Random Walk, Holt-Winters, SARIMAX, GARCH, Prophet, and LSTM

    Sales Forecasting: tslearn, Random Walk, Holt-Winters, SARIMAX, GARCH, Prophet, and LSTM

    The data science project involves evaluating various sales forecasting algorithms in Python using a Kaggle time-series dataset. The forecasting algorithms include tslearn, Random Walk, Holt-Winters, SARIMA, GARCH, Prophet, LSTM and Di Pietro’s Model. The goal is to predict next month’s sales for a list of shops and products, which slightly changes every month. The best…

  • Plotly Dash TA Stock Market App

    Plotly Dash TA Stock Market App

    The post explains how to deploy a Plotly Dash stock market app in Python with the dashboard of user-defined stock prices. This includes technical indicators like volume, MACD, and stochastic. The steps include selecting a stock ticker symbol (NVDA), retrieving stock data from yfinance API, adding Moving Averages, saving the stock chart in HTML form,…

  • Dividend-NG-BTC Diversify Big Tech

    Dividend-NG-BTC Diversify Big Tech

    SEO Title: Can Dividends, Natural Gas and Crypto Diversify Big Techs? Ultimately, we need to answer the following fundamental question: Can Dividend Kings, NGUSD and BTC-USD Diversify Growth Tech assets? Dividends are very popular among investors, especially those who want a steady stream of income from their investments. Some companies choose to share their profits…

  • Returns-Volatility Domain K-Means Clustering and LSTM Anomaly Detection of S&P 500 Stocks

    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…

  • NVIDIA Returns-Drawdowns MVA & RNN Mean Reversal Trading

    NVIDIA Returns-Drawdowns MVA & RNN Mean Reversal Trading

    The study presents a machine learning-focused analytical approach to optimize NVIDIA’s stock performance using moving average crossovers and aims at comparing the outcomes with simple RNN mean reversal trading strategies. The steps taken involve preparing the stock data, calculating moving averages and drawdowns, plotting heatmaps of returns and drawdowns, and predicting returns and cumulative returns…

  • Oracle Monte Carlo Stock Simulations

    Oracle Monte Carlo Stock Simulations

    Oracle Corporation’s significant developments in Generative AI have led to lucrative partnerships with Nvidia and Elon Musk’s xAI. Having secured contracts exceeding $4 billion for its Generation 2 Cloud designed for AI model training, Oracle’s earnings doubled in Q4 2023. Monte Carlo simulations align with Zacks Rank 3-Hold for ORCL, implying bullish potential with projected…

  • NVIDIA Rolling Volatility: GARCH & XGBoost

    NVIDIA Rolling Volatility: GARCH & XGBoost

    This post examines the prediction of NVIDIA stock volatility using two models: the Generalized Autoregressive Conditional Heteroscedasticity (GARCH) and the Extreme Gradient Boosting (XGBoost). Both models are compared in terms of MSE and MAPE. The post discovers that the machine learning-based XGBoost model outperforms the GARCH model in NVDA volatility forecasting, showing the effectiveness of…

  • IQR-Based Log Price Volatility Ranking of Top 19 Blue Chips

    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.

  • Multiple-Criteria Technical Analysis of Blue Chips in Python

    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.

  • Blue-Chip Stock Portfolios for Quant Traders

    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…

  • Time Series Forecasting of Hourly U.S.A. Energy Consumption – PJM East Electricity Grid

    Time Series Forecasting of Hourly U.S.A. Energy Consumption – PJM East Electricity Grid

    Table of Contents PJME Data Let’s set the working directory YOURPATH and import the following key libraries Let’s read the input csv file in our working directory Let’s plot the time series Data Preparation Output: (113926, 1, 9) (113926,) (31439, 1, 9) (31439,) LSTM TSF Let’s plot the LSTM train/test val_loss history Output: MSE: 1811223.125…

  • EUR/USD Forecast: Prophet vs JPM

    EUR/USD Forecast: Prophet vs JPM

    JP Morgan (JPM) analysis predicts the EUR/USD exchange rate to hold at 1.08 in December 2023, while ING forecasts suggest rates of $1.00 throughout 2023 and $1.02 in Q1 2024, rising to $1.10 by Q4 2024. Using the FB Prophet model, predictions show a hold at 1.08 +/- 0.07 in December 2023, aligning with JPM’s…

  • WA House Price Prediction: EDA-ML-HPO

    WA House Price Prediction: EDA-ML-HPO

    A predictive model of house sale prices in King County, Washington, was developed using multiple supervised machine learning (ML) regression models, including LinearRegression, SGDRegressor, RandomForestRegressor, XGBRegressor, and AdaBoostRegressor. The best-performing model, XGBRegressor, explained 90.6% of the price variance, with a RMSE of $18472.7. These results, valuable to local realtors, indicate houses with a waterfront are…

  • Datapane Stock Screener App from Scratch

    Datapane Stock Screener App from Scratch

    This content provides a quick guide for value investors to use the Datapane stock screener API in Python. It includes instructions for installation, importing standard libraries, setting the stock ticker, downloading stock Adj Close price, and creating visualizations. The post also describes how to build a powerful report using Datapane’s layout components.

  • Risk-Aware Strategies for DCA Investors

    Risk-Aware Strategies for DCA Investors

    Dollar-Cost Averaging (DCA) is an investment approach that involves investing a fixed amount regularly, regardless of market price. It offers benefits such as risk reduction and market downturn resilience. It’s useful for beginners and can be combined with other strategies for a disciplined investment approach. References include Investopedia and Yahoo Finance.

  • Working with FRED API in Python: U.S. Recession Forecast & Beyond

    Working with FRED API in Python: U.S. Recession Forecast & Beyond

    The FRED API, or Federal Reserve Economic Data, provides over 267,000 economic time series from 80 sources, offering a wealth of data to promote economic education and research. It encompasses U.S. economic and financial data, including interest rates, monetary indicators, exchange rates, and regional economic data. Additionally, we analyzed correlations, trained currency exchange prediction models,…

  • Joint Analysis of Bitcoin, Gold and Crude Oil Prices

    Joint Analysis of Bitcoin, Gold and Crude Oil Prices

    The content discusses a comprehensive analysis on a joint time-series analysis of Bitcoin, Gold and Crude Oil prices from 2021 to 2023. It explores data processing, exploratory data analysis before running a range of statistical tests, ARIMA models fitting, and finally, using the Markowitz portfolio optimization method. It then presents a detailed analysis, including data…