Quantttt — Master Quantitative Trading

How it works

Learn. Practice. Master.

A structured path from first principles to interview-ready quant knowledge — no fluff, just rigorous math with real intuition.

📖

Study the Theory

Proof-based lessons on GBM, Itô's Lemma, BSM PDE, Greeks, and stochastic volatility. Rigorous but readable, with Indian market context throughout.

🧩

Solve MCQ Problems

Practice problems modelled on real quant interviews and NSE/NISM exams. Instant feedback with full worked solutions. All examples in ₹.

📈

Track Your Progress

Build streaks, earn topic badges, and identify gaps. Designed to get you ready for quant desks, prop firms, and MFE programme interviews.

Curriculum

What You'll Learn

Four pillars. One goal: make you think quantitatively. Start with Math — everything else builds on it.

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Mathematics

Probability, Stochastic Processes, Linear Algebra, Calculus

● Active

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Finance

BSM, Heston, Greeks, Vol Models, NPTEL courses

● Active

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Programming

Algorithms, DSA, and low-latency systems for quant developers

● Active

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Machine Learning

Mathematical ML, Deep Learning, Reinforcement Learning

● Active

Mathematics — Choose a track

Beginner → Advanced ● Live

Probability Theory

Sample spaces, axioms, Bayes' theorem, random variables, distributions, limit theorems — the language of uncertainty. Built for traders.

Intermediate → Advanced ● Live

Stochastic Processes

Markov chains, Poisson processes, renewal theory, CTMCs, Brownian motion — where the maths starts to smell like money.

Advanced 🔒 Coming 2026

Linear Algebra

Vectors, matrices, decompositions, PCA, least squares — the engine behind every ML algorithm in finance.

Advanced 🔒 Coming 2026

Multivariable Calculus

Partial derivatives, chain rule, multiple integrals, vector identities — the language of optimisation.

Finance — Choose a track

Intermediate → Advanced ● Live

Option Pricing

From Black-Scholes to Heston — GBM, PDE derivation, Greeks, implied volatility, stochastic volatility, characteristic functions, calibration.

Advanced 🔒 Coming 2026

Fixed Income & Rates

Bond pricing, yield curves, duration, convexity, interest rate models — the other half of every derivatives desk.

Advanced 🔒 Coming 2026

Portfolio Theory

Mean-variance optimisation, CAPM, factor models, risk parity — rigorous treatment of how capital gets allocated.

Advanced 🔒 Coming 2026

Market Microstructure

Order books, bid-ask spreads, price impact, execution algorithms — what actually happens inside NSE every millisecond.

Programming — Choose a track

Undergraduate → Advanced ● Live

Introduction to Algorithms

Correctness proofs, asymptotic analysis, sorting, hashing, trees, graphs, shortest paths, dynamic programming — the algorithmic foundation every quant developer needs.

Graduate ● Live

Design of Algorithms

Randomised algorithms, network flow, linear programming, NP-completeness, approximation algorithms — the graduate-level toolkit for hard problems.

Advanced 🔒 Coming 2026

C++ for Low-Latency Systems

Memory model, cache efficiency, RAII, lock-free data structures, order book implementation — the language of high-frequency trading.

Machine Learning — Choose a track

Foundational → Advanced ● Live

Mathematical Foundations of ML

Probabilistic framing, ERM, MLE, MAP, EM algorithm, bias-variance tradeoff, SVMs, neural networks — built from scratch with full rigour.

Intermediate 🔒 Coming 2026

Deep Learning

Backpropagation, CNNs, RNNs, LSTMs, attention mechanisms, transformers — and what they're actually doing mathematically.

Advanced 🔒 Coming 2026

Reinforcement Learning

MDPs, Q-learning, policy gradients, multi-armed bandits — with direct applications to algorithmic trading and portfolio optimisation.

Practice Arena

Try a Real Problem

Every concept is reinforced with problems that mirror actual quant interview questions — with full step-by-step solutions.

quan(t)⁴.com / BSM / problem-04

★ Medium

MediumBlack-Scholes

A Nifty call option has spot $S_0 =$ ₹50,000, strike $K =$ ₹52,000, maturity $T = 0.25$ years, risk-free rate $r = 6.5\%$, and volatility $\sigma = 18\%$. Which is closest to the BSM call price?

$$C = S_0 \cdot N(d_1) - K \cdot e^{-rT} \cdot N(d_2)$$ $$d_1 = \frac{\ln(S_0/K) + (r + \sigma^2/2)\,T}{\sigma\sqrt{T}}, \quad d_2 = d_1 - \sigma\sqrt{T}$$

₹520

₹890

₹1,240

₹1,880

Step-by-step solution:

Step 1 — Compute $d_1$:
$\ln(50000/52000) \approx -0.0392$
$r + \sigma^2/2 = 0.065 + 0.0162 = 0.0812$
$d_1 = \dfrac{-0.0392 + 0.0812 \times 0.25}{0.18 \times 0.5} = \mathbf{-0.210}$

Step 2 — Compute $d_2$:
$d_2 = -0.210 - 0.09 = \mathbf{-0.299}$

Step 3 — Normal CDF values:
$N(-0.210) \approx 0.417 \quad | \quad N(-0.299) \approx 0.382$

Step 4 — Call price:
$C = 50{,}000 \times 0.417 - 52{,}000 \times e^{-0.01625} \times 0.382$
$= 20{,}850 - 19{,}610 =$ ₹1,240 ✓

💡Use $(r + \sigma^2/2)$ in $d_1$ — the Itô correction is critical

📊$d_2 = d_1 - \sigma\sqrt{T}$ is always a quick sanity check

🔁Put via parity: $P = C - S_0 + Ke^{-rT}$

Learn Quant.
Trade Smarter.
Think in Numbers.

Study the Theory

Solve MCQ Problems

Track Your Progress

Probability Theory

Stochastic Processes

Linear Algebra

Multivariable Calculus

Option Pricing

Fixed Income & Rates

Portfolio Theory

Market Microstructure

Introduction to Algorithms

Design of Algorithms

C++ for Low-Latency Systems

Mathematical Foundations of ML

Deep Learning

Reinforcement Learning

Ready to think
quantitatively?

Learn Quant.Trade Smarter.Think in Numbers.

Study the Theory

Solve MCQ Problems

Track Your Progress

Probability Theory

Stochastic Processes

Linear Algebra

Multivariable Calculus

Option Pricing

Fixed Income & Rates

Portfolio Theory

Market Microstructure

Introduction to Algorithms

Design of Algorithms

C++ for Low-Latency Systems

Mathematical Foundations of ML

Deep Learning

Reinforcement Learning

Ready to thinkquantitatively?

Learn Quant.
Trade Smarter.
Think in Numbers.

Ready to think
quantitatively?