Day 4 – Choosing My Path: The Start of a Focused Exploration

After going through a lot of researching I’ve narrowed down my goals for this upcoming year :

Breakdown of Core Learning Components (Approximate Durations):

1. Mathematics (3–4 months)

   • Topics: Linear algebra, probability theory, multivariable calculus, and optimization.

   • Goal: Develop a solid theoretical understanding with practical application in financial contexts.

   • Recommended Sources: MIT OCW, Khan Academy, "Introduction to Probability" by Blitzstein, and Boyd's lectures on convex optimization.

2. Programming (2–3 months concurrently)

   • Primary Language: Python (required), C++ (optional at this stage unless targeting HFT roles).

   • Focus: Data structures, numerical computation (NumPy, pandas), simulation, and model prototyping.

   • Recommended Path: Complete hands-on projects involving financial datasets, basic model backtesting, or simulations.

3. Financial Theory (3–4 months)

   • Topics: Capital Asset Pricing Model (CAPM), derivatives and options pricing, risk and portfolio theory.

   • Goal: Understand pricing models, no-arbitrage principles, and the mathematics of financial instruments.

   • Recommended Resources: “Options, Futures, and Other Derivatives” by John Hull; NPTEL or Coursera finance specializations.

4. Econometrics (Optional in core, 2 months if included)

   • Topics: Regression analysis, time series modeling, statistical testing.

   • Relevance: Especially critical for those pursuing research-oriented or data-driven quant roles.

Study Load Assumptions

• 15–20 hours/week: Suitable for students or working professionals.