The Essential Prerequisites in Quant Finance

A practical guide to the math, coding, and finance basics you can’t skip.

Every week, I get the same question: “How can I get started in quantitative finance and algorithmic trading?”

The truth is, the field can feel overwhelming. Do you need a PhD in math? Advanced coding skills? Years of market experience?

The reality is simpler: before building complex models or trading strategies, you only need a solid foundation in three pillars:

• Mathematics – to analyze and model data

• Programming – to implement and test ideas

• Finance – to understand how markets really work

This newsletter is not a full course. Instead, it’s a checklist of prerequisites: the essentials you need to master before moving forward.

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Pillar 1: Mathematics & Statistics (Concise & Actionable)

To build a strong foundation in quantitative finance, focus on mastering a short yet impactful set of math and stats concepts:

1. Descriptive Statistics – mean, variance, correlation: essential for analyzing data patterns.

2. Probability Distributions – especially Normal, Poisson, Exponential: these are everywhere in financial models.

3. Inferential Techniques – hypothesis testing, p-values, confidence intervals: to draw meaningful conclusions from limited data.

4. Regression Analysis – begin with linear regression to understand relationships; a preview of logistic regression adds context for classification tasks.

5. Optimization Fundamentals – comprehension of optimization basics: criterion, optimization method, optimization example…

Pro Tip: Even grasping the core six gives you enough toolkit to begin working on simple quant strategies and progress quickly.

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Pillar 2: Programming

In quantitative finance, coding is not optional, it’s the tool that allows you to transform raw ideas into testable strategies. You don’t need to be a software engineer, but you do need to be comfortable with Python and data manipulation.

Here are the core skills to focus on:

1. Python Basics

   • Variables, data types (lists, dictionaries, arrays)

   • Functions, loops, conditionals

   • Working with libraries and modules

2. Data Handling with Python

   • NumPy: numerical arrays, vectorized operations

   • Pandas: DataFrames, indexing, filtering, grouping

   • Handling missing data, merging datasets

3. Data Visualization

   • Matplotlib and Seaborn: plotting time series, histograms, scatter plots

   • Building visual intuition for financial data

4. Foundations of Machine Learning

   • Using scikit-learn for simple models (train/test split, regression, classification)

   • Understanding overfitting and evaluation metrics

5. Practical Workflow

   • Using Jupyter Notebooks for exploration

   • Git for version control

   • Writing clean, reusable code (functions, scripts)

📌 With just these fundamentals, you’ll be able to:

• Import financial data from APIs,

• Clean and explore it,

• Build simple indicators and strategies,

• And backtest them on historical datasets.

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Pillar 3: Finance

Even with strong math and coding skills, you can’t succeed in quant trading without understanding how markets actually work. You don’t need to be a CFA, but you do need to master the basics of financial instruments, market mechanics, and risk measures.

Here’s the essential checklist:

1. Financial Instruments

   • Equities (stocks), Bonds, ETFs

   • Derivatives (Futures, Options – at least the basic idea)

2. Market Microstructure

   • Order book: bids, asks, depth

   • Bid-ask spread and liquidity

   • Slippage (why execution price ≠ expected price)

3. Order Types

   • Market orders

   • Limit orders

   • Stop-loss and stop-limit orders

4. Market Metrics & Indicators

   • Returns (absolute, percentage, log returns)

   • Volatility (standard deviation, realized volatility)

   • Sharpe ratio and basic risk-adjusted performance metrics

5. Market Dynamics

   • Trading sessions and time zones

   • Economic calendar and macro events (e.g., FOMC, earnings reports)

   • Impact of news and announcements on prices

📌 With these fundamentals, you’ll be able to:

• Understand how your code interacts with real markets,

• Avoid beginner mistakes (like misusing order types),

• And properly evaluate whether a strategy is profitable after costs and risks.

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Getting started in quantitative finance doesn’t require advanced degrees or decades of experience. What you truly need is a solid foundation in three pillars:

• Math to understand and model data,

• Programming to implement and test your ideas,

• Finance to navigate how markets actually work.

Mastering these essentials will give you the confidence to move from theory to practice, whether it’s backtesting a strategy, analyzing data, or placing your first trades.

Remember: it’s not about learning everything at once, but about building step by step. Each concept you master brings you closer to becoming a real quant.

👉 If you want to go deeper into each step of the strategy building process, with real-life projects, ready-to-use templates, and 1:1 mentoring, that’s exactly what the Alpha Quant Program is for.

It’s the full roadmap I use to turn ideas into live strategies.