Tag: Stock markets

  • Leveraging Predictive Uncertainties of Time Series Forecasting Models

    Leveraging Predictive Uncertainties of Time Series Forecasting Models

    Featured Image via Canva. Table of Contents Introduction Random Simulation Tests TSLA Stock 43 Days TSLA Stock 300 Days Housing in the United States Industrial Production Federal Funds Rate Data S&P 500 Absolute Returns Number of Airline Passengers- 1. Holt-Winters Number of Airline Passengers- 2. Prophet Average Temperature in India Monthly Sales Data Analysis QC…

  • 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…

  • 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,…

  • 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.

  • NLP & Stock Impact of ChatGPT-Related Tweets

    NLP & Stock Impact of ChatGPT-Related Tweets

    This Python project extends a recent study on half a million tweets about OpenAI’s language model, ChatGPT. It uncovers public sentiment about this rapidly growing app and examines its impact on the future of AI-powered LLMs, including stock influences. The project uses data analysis techniques such as text processing, sentiment analysis, identification of key influencers,…

  • 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.

  • Top 6 Reliability/Risk Engineering Learnings

    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…

  • Deep Reinforcement Learning (DRL) on $MO 8.07% DIV USA Stock Data 2022-23

    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.

  • JPM Breakouts: Auto ARIMA, FFT, LSTM & Stock Indicators

    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…

  • LSTM Price Predictions of 4 Tech Stocks

    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…

  • Energy E&P: XOM Technical Analysis Nov ’22

    Energy E&P: XOM Technical Analysis Nov ’22

    Featured Photo by Kayden The last few years offer a case study of how quickly energy markets can shift. According to our recent post, Oil & Gas Exploration and Production (E&P) stays the top energy sector, remaining firmly at Very Attractive to Buy. In the light of what we know about E&P business, let’s take…

  • Invest in AI via Macroaxis Sep ’22 Update

    Invest in AI via Macroaxis Sep ’22 Update

    Invest in AI via Macroaxis Sep ’22 Update 4 AI pillars AI thematic idea 20 stocks Asset Allocation Market Capitalization (%) Instrument Composition Market Elasticity Risk/Return Ratio Asse ratings Technical Analysis Correlation Matrix Takeaways Business headlines

  • A Weekday Market Research Update

    A Weekday Market Research Update

    Market technical analysis, research and ideas.

  • Keras LSTM Stock Prediction

    Despite the vast amount of historical stock data, the high noise to signal ratio and varied market conditions cause inadequate stock price predictions. A solution to these challenges is Long Short Term Memory (LSTM). LSTMs offer a range of adjustable parameters without the necessity for fine-tuning, and efficiently handle sequential data.