Data Investment Academy

Data Investment Academy 2026-11-10 22:28:27

Financial Report Data Mining Basics: Turn Hundred-Page Earnings Reports into Databases — a Beginner-Friendly Guide to Mining Signals During Earnings Season

Earnings season means more than revenue and net profit. In this beginner tutorial from the Data Investing Academy, learn to read filings differently: use XBRL structured data to extract line items in bulk, mine note fields—accounts receivable, inventory, contract liabilities—for leading signals, read sentiment in management discussions, and apply a three-step workflow with common pitfalls to turn public reports into personal data research assets.

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Data Investment Academy 2026-11-08 13:49:20

A Beginner's Guide to Quantitative Trading Strategies: Trend Following, Mean Reversion, and Momentum Explained — Your First Lesson in Choosing the Right Strategy Type

Quantitative trading is not a mysterious black box — its core is understanding the market assumptions and risk-return structure behind each strategy type. This article explains the logic, use cases, and common pitfalls of four classic quant strategies: trend following, mean reversion, momentum, and arbitrage. It also offers four self-check questions to help beginners replace subjective judgment with data rules and take the first step toward a data-driven trading system.

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Data Investment Academy 2026-11-03 13:30:54

Introduction to Portfolio Construction: Build Your First Diversified Portfolio with a Data-Driven Mindset — A Practical Asset Allocation Guide Every Beginner Needs

A portfolio construction tutorial for investing beginners: starting from correlation, the core concept of diversification, it details a four-step asset allocation process, five portfolio health-check metrics, and three common pitfalls, plus a three-month practice roadmap for building and maintaining your first diversified portfolio with data-driven thinking.

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Data Investment Academy 2026-10-28 19:10:12

Getting Started with Financial Databases: From Free Data Sources to Professional Terminals — Build Your Investment Database from Scratch

Financial databases are the infrastructure of data-driven investing. This article systematically reviews four major database categories—macroeconomic, market trading, company fundamentals, and alternative data—compares the strengths and weaknesses of mainstream free and paid data sources, and summarizes five practical tips and three common pitfalls for beginners when retrieving data, helping investors with no prior background efficiently build their own data workflows and take a solid first step

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Data Investment Academy 2026-10-28 19:09:50

A Beginner's Guide to Building Risk Awareness: The First Essential Lesson for Data Investors — Protect Your Principal Before Talking About Returns

Risk awareness is the first mandatory lesson for data investors. This article systematically outlines the three major sources of risk in data investing—data and model risk, market and liquidity risk, and behavioral and execution risk—and presents five practical, actionable training methods: setting account red lines, risk-based position sizing, a risk-reward ratio checklist, stress testing, and trading journal reviews, helping novice investors learn to protect their capital and keep drawdowns un

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Data Investment Academy 2026-10-26 08:26:42

A Beginner's Guide to Stock Fundamental Analysis: Understanding a Company's True Value Through Data — A Four-Step Analysis Framework Every New Investor Needs

A systematic introduction to stock fundamental analysis for complete beginners: covering the three data pillars of financial statements, core financial ratios, and valuation methods, it breaks down the four-step analysis framework for beginners and the three most common pitfalls, and shows how to leverage financial report databases and quantitative backtesting tools to validate fundamental logic in the era of data intelligence—helping investors build a reusable, data-driven analysis system that

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Data Investment Academy 2026-10-23 10:40:23

A Beginner's Guide to Financial Data Collection: Understand Data Sources and Common Pitfalls to Take Your First Step Toward Data-Driven Investing

Data collection is the starting point of data-driven investing, and its quality directly determines the reliability of subsequent analysis and decision-making. This article systematically reviews the four major data types—market data, fundamentals, macroeconomic data, and alternative data; compares the three main acquisition channels—free public sources, programmatic interfaces, and professional databases; and provides an in-depth analysis of common pitfalls such as survivorship bias, look-ahead

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Data Investment Academy 2026-10-22 08:26:18

Investment Data Tool Recommendation: Building a Data Investment Toolbox from Scratch, A Five-Level Guide for Beginner Investors

In the era of data-driven investment, the right tool is the first step for novice investors to move towards rational decision-making. This article systematically organizes a five-layer tool system, covering data collection, storage and organization, quantitative analysis, visualization monitoring, and AI assistance, including paths for free public data sources, open-source interfaces, the Python ecosystem, and personal dashboard setup. It also provides selection principles prioritizing data qual

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Data Investment Academy 2026-10-21 11:29:15

Financial Data Cleaning and Preprocessing in Practice: Don't Let Dirty Data Ruin Your Investment Decisions, Essential Basics for Data Investors

Financial data is full of missing values, outliers, duplicate records, and inconsistent terminology, which can lead to false conclusions in direct analysis, resulting in overestimation and real losses in trading. This article is for beginner data investors and systematically explains the four core steps of financial data cleaning and preprocessing, analyzes quantitative traps such as reweighting, survivorship bias, and look-ahead bias, and provides practical tools and standardized workflow recom

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Data Investment Academy 2026-10-20 07:42:25

Introduction to Technical Pattern Recognition: Validate the true win rate of top, shoulders, and bottoms with data thinking, say goodbye to guessing price movements

Technical patterns are the first lesson most novice investors encounter in technical analysis, but also the area where misconceptions are most concentrated. This article deconstructs the logic of bullish and bearish games behind classic patterns such as head and shoulders, double bottoms, and triangles from a data perspective, explaining how to use rules, sample statistics, and win-loss ratio thinking to validate the effectiveness of patterns, and analyzing the three novice traps: hindsight bias

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