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Run this module

cd "Python Basics - NumPy"
python "numpy_tutorial.py"

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Python Basics – NumPy

Overview

Covers the NumPy primitives that appear in virtually every quant codebase — from vectorised return calculations to portfolio variance via the quadratic form. All examples use realistic financial data so the connection between the NumPy API and actual quant work is immediate.

Concepts Covered

  • Array creation, dtypes, shapes, and 2-D OHLC matrices
  • Vectorised daily and log returns with np.diff and np.log
  • Descriptive statistics: mean, std, Sharpe ratio, skewness — all without loops
  • Broadcasting: applying portfolio weights across a returns matrix without explicit iteration
  • Covariance matrices and annualised portfolio volatility via the quadratic form (w @ cov @ w)
  • Boolean indexing for P&L analysis: win rate, average win/loss, profit factor

Files

  • numpy_tutorial.py: Annotated walkthrough script; each section prints labelled output alongside the corresponding NumPy call

How to Run

pip install numpy
python numpy_tutorial.py

Sections

Section What it demonstrates
Arrays and dtypes 1-D price array, 2-D OHLC matrix, shape/ndim/dtype
Vectorised returns np.diff, np.log, cumulative return
Descriptive statistics Mean, vol, Sharpe, min/max, skewness — 252-day sim
Broadcasting Weight × returns matrix without a Python loop
Covariance & portfolio variance np.cov, quadratic form, annualised vol
Boolean indexing Daily P&L filtering, win rate, profit factor

Practice Ideas

  • Extend the covariance section with three real tickers loaded from CSV
  • Add kurtosis to the descriptive statistics section
  • Compute a rolling 20-day Sharpe using np.lib.stride_tricks

Next Steps

  • Continue to Python Basics - Pandas/ for time-series resampling and signal generation on top of NumPy arrays
  • Apply these primitives in Quantitative Methods - Statistics/ and Risk Metrics/

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