# Build your trading system
import pandas as pd
import numpy as np
class TradingSystem:
def __init__(self, initial_capital=100000):
self.capital = initial_capital
self.positions = {}
def calculate_signals(self, data):
"""Your challenge: Implement signal generation"""
signals = []
return signals
def backtest(self, data):
"""Run backtest on historical data"""
signals = self.calculate_signals(data)
return results
# Instant feedback as you code
system = TradingSystem()
results = system.backtest(market_data)
print(f"Sharpe Ratio: {results.sharpe:.2f}")Learn Quantitative Finance Like a Pro Learn Quantitative Engineering Like a Pro Learn Quantitative Development Like a Pro
Master the skills needed to build trading systems, pricing models, and risk management tools, and land your dream job in quantitative finance.
Three Pillars of Quantitative Finance
Master finance, technology, and mathematics through hands-on courses with real code examples
Finance Fundamentals
Build a comprehensive understanding of financial markets, instruments, and economic principles that drive quantitative finance.
Key Topics:
- Portfolio Theory & Asset Allocation
- Derivatives Pricing & Hedging
- Fixed Income & Credit Risk
- Market Microstructure
- Risk Metrics & Regulation
# Portfolio Optimization
import numpy as np
from scipy.optimize import minimize
def optimize_portfolio(returns, risk_free_rate=0.02):
mean_returns = returns.mean() * 252
cov_matrix = returns.cov() * 252
def sharpe_ratio(weights):
port_ret = np.sum(mean_returns * weights)
port_std = np.sqrt(
np.dot(weights.T, np.dot(cov_matrix, weights))
)
return -(port_ret - risk_free_rate) / port_std
constraints = {'type': 'eq', 'fun': lambda x: np.sum(x) - 1}
bounds = tuple((0, 1) for _ in range(len(returns.columns)))
result = minimize(sharpe_ratio,
[1/len(returns.columns)] * len(returns.columns),
method='SLSQP', bounds=bounds,
constraints=constraints)
return result.xTechnology
Master the programming languages, frameworks, and infrastructure that power modern quantitative finance systems.
Key Topics:
- Python for Data Analysis & Modeling
- C++ for High-Frequency Trading
- Cloud Infrastructure & Distributed Systems
- Database Design & Optimization
- API Development & Integration
# Async Market Data Pipeline
import asyncio
import aiohttp
import pandas as pd
async def fetch_market_data(endpoints):
async with aiohttp.ClientSession() as session:
tasks = [session.get(ep) for ep in endpoints]
responses = await asyncio.gather(*tasks)
data = [await r.json() for r in responses]
return pd.DataFrame(data)
# Concurrent data fetching
endpoints = [
'/quotes/AAPL', '/quotes/GOOGL', '/quotes/MSFT'
]
market_data = await fetch_market_data(endpoints)Mathematics
Apply advanced mathematical techniques to model financial phenomena, optimize strategies, and solve complex problems.
Key Topics:
- Stochastic Calculus & Itô's Lemma
- Linear Algebra & Matrix Operations
- Optimization & Convex Programming
- Statistical Methods & Hypothesis Testing
- Monte Carlo Simulations
# Monte Carlo Option Pricing
import numpy as np
def price_option(S0, K, T, r, sigma, n_sims=100000):
random_shocks = np.random.normal(
0, 1, (n_sims, int(T * 252))
)
dt = T / 252
prices = S0 * np.exp(np.cumsum(
(r - 0.5 * sigma**2) * dt +
sigma * np.sqrt(dt) * random_shocks, axis=1
))
payoffs = np.maximum(prices[:, -1] - K, 0)
option_price = np.exp(-r * T) * np.mean(payoffs)
return option_price
price = price_option(S0=100, K=105, T=0.25, r=0.05, sigma=0.2)Land a job of one of the top quant firms
How It Works
Get started in minutes, not days
Create Your Account
Sign up in seconds and get immediate access to the platform. No credit card required to explore.
Choose Your Path
Pick from Finance, Technology, or Mathematics streams — or follow all three. The curriculum adapts to your level.
Start Building
Write real code, build complete systems, and get instant feedback. Progress from fundamentals to production-level projects.
Why Become a Quant?
Quantitative finance offers a unique combination of high compensation, cutting-edge technology, and global career opportunities that make it one of the most sought-after professions today.
Exceptional Salaries
Quantitative finance professionals command some of the highest salaries in both tech and finance. Entry-level quants often earn $150K+, with senior roles reaching $500K+ and beyond.
Cutting-Edge Technology
Work with the latest technologies—machine learning, cloud computing, distributed systems, and high-performance computing. Gain experience that's highly transferable to top tech companies.
Global Recognition
Quantitative finance is respected worldwide. Your skills are valued in financial centers from New York to London, Singapore to Tokyo, opening doors to international career opportunities.
Highly Sought After
Top hedge funds, investment banks, and tech companies actively recruit quants. The combination of technical expertise and financial acumen makes you a rare and valuable asset.
A Launchpad for Your Career
The skills you develop as a quant—advanced programming, mathematical modeling, data analysis, and financial expertise—open doors to diverse career paths. Whether you want to stay in finance, transition to tech, or start your own venture, quantitative finance experience is highly valued across industries.
Everything You Need to Succeed
Comprehensive courses covering all aspects of quantitative finance and engineering
Market Data Analysis
Work with real-time and historical market data, build data pipelines, and perform statistical analysis.
Pricing Models
Master Black-Scholes, Monte Carlo simulations, and other quantitative pricing models used in finance.
Algorithmic Trading
Build and backtest trading strategies, implement execution algorithms, and optimize portfolio performance.
Risk Management
Understand VaR, stress testing, portfolio optimization, and other risk management techniques.
Python & C++
Learn the programming languages used in quantitative finance, from Python for research to C++ for HFT.
Interactive Learning
Hands-on coding exercises, real-world projects, and interactive simulations to reinforce your learning.
Code Directly in Your Browser
No setup required. Write Python, execute instantly, and get real-time feedback on your solutions.
# Build your trading system
import pandas as pd
import numpy as np
class TradingSystem:
def __init__(self, initial_capital=100000):
self.capital = initial_capital
self.positions = {}
def calculate_signals(self, data):
"""Your challenge: Implement signal generation"""
# TODO: Add your logic here
signals = []
return signals
def backtest(self, data):
"""Run backtest on historical data"""
signals = self.calculate_signals(data)
# Backtest logic...
return results
# Instant feedback as you code
system = TradingSystem()
results = system.backtest(market_data)
print(f"Sharpe Ratio: {results.sharpe:.2f}")calculate_signals method to generate buy/sell signals based on moving averages.Trusted by Aspiring Quants
Our students have gone on to work at leading quantitative finance firms worldwide
“The portfolio optimisation and risk management courses were exactly what I needed to prepare for my interviews. I went from knowing basic Python to confidently implementing Black-Scholes models and Monte Carlo simulations. Landed the role three months after starting.”
“I'd been self-teaching from textbooks and YouTube for over a year with little progress. Quantt's interactive approach completely changed that — being able to write and test code in the browser, with instant feedback, made everything click. The stochastic calculus content is genuinely world-class.”
“What sets Quantt apart is the build projects. I built a complete trading engine, an options pricing model, and a factor model — all within the platform. These weren't toy examples, they were production-quality systems. I showcased them in every interview.”
“I transitioned from software engineering into quantitative finance using Quantt. The technology stream leveraged my existing coding skills while the finance stream filled in the gaps. The structured learning path meant I always knew what to study next.”
“Easily the best value for money in quant finance education. CQF quoted me £20,000 — Quantt covers much of the same practical material for a fraction of the cost. The annual plan is an absolute no-brainer if you're serious about breaking into the industry.”
“The mathematics stream is outstanding. I came from a physics background and needed to bridge the gap into financial mathematics. The progression from calculus through stochastic calculus to Monte Carlo methods was perfectly paced. Highly recommended.”
The Best Place to Learn Quantitative Finance
Other platforms fall short. Here's how Quantt solves the problems others can't.
Codecademy & DataCamp
While they offer interactive coding, their courses cover general programming and data science. They lack the specialized focus on quantitative finance, trading systems, and financial modeling that professionals need.
Udemy, YouTube, Pluralsight
These platforms rely on passive video watching. You can't code directly in the platform, test your solutions, or get real-time feedback. Learning by watching isn't the same as learning by doing.
CFA & CQF
CFA costs $1,000+ per exam level (3 levels total) plus study materials. CQF costs $20,000+ for the full program. These are excellent credentials but prohibitively expensive for many aspiring quants.
| Feature | Quantt | Generic Platforms | Video Courses | Certifications |
|---|---|---|---|---|
| Quant finance focus | ||||
| Interactive code editor | ||||
| Build real systems | ||||
| Instant feedback | ||||
| Python & C++ courses | ||||
| Structured learning path | ||||
| Affordable pricing | ||||
| Production-level projects |
Specialized. Interactive. Affordable.
Frequently Asked Questions
Everything you need to know about Quantt
The platform focuses primarily on Python, which is the industry standard for quantitative research and data analysis. You'll also learn C++ fundamentals for high-frequency trading systems, and get an introduction to Rust for modern systems programming. All coding is done directly in our browser-based editor — no local setup required.
Quantt covers three core streams: Finance (portfolio theory, derivatives pricing, fixed income, market microstructure, risk management), Technology (Python, C++, distributed systems, cloud infrastructure, database design), and Mathematics (stochastic calculus, linear algebra, optimisation, statistics, Monte Carlo methods). Each stream progresses from foundations to advanced professional-level content.
You should have basic Python programming experience — things like variables, loops, functions, and classes. No prior finance or advanced maths knowledge is required. We start each stream with foundational refreshers and build up progressively. The platform is ideal for computer science graduates, career changers, and junior professionals looking to break into quant finance.
Our in-browser code editor runs Python directly in your browser using Pyodide — no installation needed. You write code, run it instantly, and get real-time feedback. The editor supports libraries like NumPy, Pandas, and SciPy, and includes features like syntax highlighting, auto-completion, and integrated test suites that validate your solutions.
You'll build real-world systems including a complete trading engine (order book, execution, backtesting), an options pricing model (Black-Scholes, Heston stochastic volatility), a portfolio optimiser, a machine learning prediction model, a volatility forecasting system, and a factor model. Each project is broken into guided tasks with clear steps.
Generic platforms teach general programming without financial context. Video platforms are passive — you watch, not do. Quantt is purpose-built for quantitative finance: every course, exercise, and project is designed around real financial problems. You write and execute code directly in the platform, build complete systems, and get instant feedback on your solutions.
Yes. Both Monthly (£39.95/month) and Annual (£249.95/year) plans can be cancelled at any time. Annual plans save you 47% compared to monthly billing. We also offer a 30-day money-back guarantee on all plans — no questions asked.
Absolutely. Quantt is specifically designed for people transitioning into quantitative finance. The curriculum covers everything from programming foundations to advanced trading systems. Our students have gone on to work at firms including Goldman Sachs, J.P. Morgan, Citadel, Two Sigma, and Jane Street.
Choose Your Learning Path
Flexible pricing options to fit your goals and budget
Monthly
Flexible monthly subscription
- Full course access
- Interactive code editor
- Real-world challenges
- Instant feedback
- Progress tracking
- Market data analysis
- Pricing models (Black-Scholes, Monte Carlo)
- Algorithmic trading strategies
- Risk management (VaR, stress testing)
- Python & C++ courses
- Build complete systems
- Priority support
Annual
Best value - save 47%
Save £229.45 (47% off)
- Full course access
- Interactive code editor
- Real-world challenges
- Instant feedback
- Progress tracking
- Market data analysis
- Pricing models (Black-Scholes, Monte Carlo)
- Algorithmic trading strategies
- Risk management (VaR, stress testing)
- Python & C++ courses
- Build complete systems
- Priority support
Enterprise
For teams and organizations
- Everything in Annual
- Team management & analytics
- Custom integrations & APIs
- Dedicated account manager
- Custom course content
- 24/7 priority support
30-day money-back guarantee on all paid plans. No questions asked.