Quant Trading Learning Path

Quantitative trading means making trading decisions with rules you can write down, test on data and measure. Instead of asking "does this chart look bullish?", a quant asks "when this condition occurred in the past, what happened next, how often, and is that difference real or luck?" This path builds the skills to answer those questions honestly.

Who this path is for#

It suits traders who want to test ideas before risking money, programmers moving into markets and anyone curious about how systematic funds work. You do not need a maths degree, but you will need patience with numbers and willingness to learn some code. If you are new to markets, read the first three stages of the Beginner Learning Path first.

Stage 1: The quant mindset#

1. Quantitative Trading
2. Discretionary vs Systematic Trading
3. Expectancy
4. The Trading Research Process
5. The Strategy Lifecycle
6. Why Strategies Fail

Practice: take one trading idea you believe in and rewrite it as a precise rule a computer could follow, with exact entry, exit and position size.

Stage 2: Probability and statistics#

1. Probability for Traders
2. Expected Value
3. Law of Large Numbers
4. Conditional Probability
5. Mean, Median and Mode
6. Variance and Standard Deviation
7. Covariance and Correlation
8. Sampling and Standard Error
9. Confidence Intervals
10. Hypothesis Testing and P-Values
11. Statistical Significance in Trading

Practice: flip a coin 100 times, or simulate it, and calculate how often you see a streak of six heads. Compare that with how often traders treat a six trade winning streak as proof of skill.

Stage 3: Distributions and returns#

1. Probability Distributions Explained
2. Normal Distribution
3. Fat Tails
4. Skewness and Kurtosis
5. Compounding and Geometric vs Arithmetic Returns
6. Time Series Basics
7. Stationarity, Differencing and Unit Roots
8. Autocorrelation and Partial Autocorrelation

Stage 4: Tools and data#

1. Python for Trading
2. NumPy and Pandas for Traders
3. Tick Data and OHLCV Data
4. Historical Data for Backtesting
5. Cleaning Market Data
6. Splits and Dividends in Price Data
7. Point-in-Time and Survivorship-Free Data

Practice: download daily prices for one stock, compute daily returns, and plot their distribution next to a normal curve.

Stage 5: Backtesting properly#

1. Backtesting Methodology
2. In-Sample vs Out-of-Sample Testing
3. Walk-Forward Analysis
4. Overfitting and Curve Fitting
5. Look-Ahead Bias
6. Survivorship and Selection Bias
7. Data Leakage
8. P-Hacking and Multiple Testing
9. Costs and Slippage in Backtests
10. Monte Carlo Simulation
11. Robustness and Stress Testing

Practice: backtest a simple moving average crossover, then run it again with realistic costs and on a period you did not look at while building it. Note how much the results change.

Stage 6: Measuring results#

1. Sharpe Ratio
2. Sortino Ratio
3. Maximum Drawdown
4. Profit Factor
5. Win Rate and Payoff Ratio

Stage 7: Strategy families#

1. Trend Following
2. Mean Reversion
3. Momentum Trading
4. Pairs Trading
5. Statistical Arbitrage
6. Factor Investing Explained

Stage 8: Going live#

1. Algorithmic Trading Explained
2. From Backtest to Live: Paper, Shadow and Canary
3. Risk Controls and Kill Switches
4. Monitoring Positions, P&L and Risk
5. Signal and Alpha Decay

Common mistakes on this path#

• Trusting a beautiful backtest. The better a backtest looks, the more suspicious you should be of overfitting or a data error.
• Leaving out costs. Many strategies that look profitable disappear once spreads, commissions and slippage are included.
• Testing hundreds of variations and keeping the best one. That finds luck, not edge. See P-Hacking and Multiple Testing.
• Skipping paper trading. Live execution often differs from simulated fills.

Frequently asked questions#

Do I need to code to do quant trading?#

Practically, yes. Spreadsheets can test simple ideas, but Python is the standard tool for handling data, backtesting and automation.

How much maths does quant trading need?#

For most strategies, solid probability, statistics and some linear algebra are enough. Advanced options pricing and machine learning need more.

Can quant strategies stop working?#

Yes. Edges decay as more traders find them and as markets change. Monitoring live performance against expectations is part of the job.