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Python Quant Developer Roadmap

From Python basics to live trading — a structured path for aspiring quantitative developers in financial markets.

high confidence reviewed
7 steps · 8 connections
1

Python Fundamentals

Learn Python syntax, data structures, control flow, and functions. Practice with coding exercises focused on numerical computation.

2

Pandas & NumPy

Master data manipulation with Pandas and numerical computing with NumPy. Focus on time series operations and array-based calculations.

pandasnumpy
3

Market Data APIs

Access real-time and historical market data via APIs. Learn to fetch OHLCV data, handle rate limits, and store data for backtesting.

yfinanceakshare
4

Data Analysis & Visualization

Explore market data patterns, calculate technical indicators, and build visualizations using Plotly and mplfinance.

plotlymplfinanceta-lib
5

Backtesting Frameworks

Validate trading strategies using historical data. Learn to define strategy classes, run simulations, and interpret performance metrics.

backtraderziplinevectorbt
6

Portfolio & Risk Management

Optimize portfolio allocations using mean-variance optimization, risk parity, and Black-Litterman models.

pyportfoliooptriskfolio-lib
7

Live Trading & Execution

Connect to brokerage APIs, implement order management, and deploy strategies to production with paper trading first.

alpaca-pyib-insyncccxt

Dependency Paths

python-basicspandas-numpy
pandas-numpymarket-data
market-datadata-analysis
market-databacktesting
data-analysisbacktesting
backtestingportfolio-optimization
portfolio-optimizationlive-trading
backtestinglive-trading

Verified by

editor

Last verified

2026-06-24