Category: fintech
-
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…
-
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…
-
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…
-
Anomaly Detection using the Isolation Forest Algorithm
The post describes the application of Isolation Forest, an unsupervised anomaly detection algorithm, to identify abnormal patterns in financial and taxi ride data. The challenge is to accurately distinguish normal and abnormal data points for fraud detection, fault diagnosis, and outlier identification. Using real-world datasets of financial transactions and NYC taxi rides, the algorithm successfully…
-
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…
-
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
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…
-
Improved Multiple-Model ML/DL Credit Card Fraud Detection: F1=88% & ROC=91%
In 2023, the global card industry is projected to suffer $36.13 billion in fraud losses. This has necessitated a priority focus on enhancing credit card fraud detection by banks and financial organizations. AI-based techniques are making fraud detection easier and more accurate, with models able to recognize unusual transactions and fraud. The post discusses a…
-
Unsupervised ML, K-Means Clustering & Customer Segmentation
Table of Clickable Contents Motivation Methods Open-Source Datasets This file contains the basic information (ID, age, gender, income, and spending score) about the customers. Online retail is a transnational data set which contains all the transactions occurring between 01/12/2010 and 09/12/2011 for a UK-based and registered non-store online retail. The company mainly sells unique all-occasion…
-
Top Fast-Growing Apps in 2023
The OKTA Business at Work report and blogs by Leon Zucchini discuss the fastest-growing and new app categories. Key trends include the growth of collaboration, communication, and travel apps, and the adoption of multi-cloud. Ten notable growing apps are Kandji, Grammarly, Bob, Notion, Prisma Access, Navan, GitLab, Ironclad, Terraform Cloud, and Figma. Emerging apps include…
-
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.
-
GPT & DeepLake NLP: Amazon Financial Statements
The post outlines the implementation of an AI-powered chatbot using NLP to process and analyze financial data from Amazon’s financial statements. The tool employs LlamaIndex and DeepLake to answer queries, summarize financial information, and analyze trends. This approach enhances the efficiency of data analysis, making it a valuable resource for finance and banking professionals.
-
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,…
-
Dabl Auto EDA-ML
Dabl, short for Data Analysis Baseline Library, is a high-level data exploration library in Python that automates repetitive data wrangling tasks in the early stages of supervised machine learning model development. Developed by Andreas Mueller and the scikit-learn community, it facilitates data preprocessing, advanced integrated visualization, exploratory data analysis (EDA), and ML model development, demonstrated…
-
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…
-
Overview of AWS Tech Portfolio 2023
This summary focuses on the extensive capabilities of Amazon Web Services (AWS) by 2023, highlighting its 27% year-on-year growth and a net sales increase to $127.1 billion. AWS emerges as the top cloud service provider, offering over 200 services including compute, storage, databases, networking, AI, and machine learning. It is constantly expanding operations, having opened…
-
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…
-
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…
Our Blogs 