Python in the AI era: A strategic asset in modern investment management

The programming language Python is quickly becoming an essential tool in investment management, enabling analysts and portfolio managers to perform complex data analysis, develop trading strategies, and optimise portfolios.

The global investment landscape is undergoing a seismic transformation. As artificial intelligence (AI) reshapes the mechanics of research, portfolio management, and client servicing, investment professionals face a clear mandate: adapt or be left behind. Among the most critical tools enabling this transition is Python, a versatile, open-source programming language increasingly adopted by investment professionals to enhance research, automate workflows, and build robust investment strategies.

Unlocking the power of Python in financial advice

Python’s rise in investment circles is no coincidence. Its synergy with AI and data science makes it a natural fit for financial advice firms aiming to streamline operations, elevate investment analysis, and deliver highly tailored client outcomes. The key drivers of Python’s dominance include:

• Automated data collection and integration: Python can connect to financial data providers (e.g. Morningstar, FactSet, ASX) and platforms (e.g. Xplan, Hub24) via APIs to automatically import market data, fund performance, or client portfolios. This eliminates manual spreadsheet work and ensures data is timely and consistent across systems.
• Portfolio construction and optimisation: Using Python libraries like PyPortfolioOpt or cvxpy, advisers can construct and optimise portfolios based on specific risk, income, or diversification goals.
• AI and machine learning integration: Python is the primary language used in machine learning frameworks such as TensorFlow, PyTorch, and scikit-learn, making it integral to developing predictive models and insights.
• Client reporting and visualisation: Python can automate the generation of customised reports in Excel or PDF, complete with performance charts, asset allocation visuals, and risk-return metrics. Tools like matplotlib, plotly, and xlsxwriter enable scalable reporting that’s consistent, visually engaging, and white-labelled for advice firms.

One of the most persistent inefficiencies in advice practices today is the sheer volume of duplicated manual work. From re-keying data across platforms, to generating repetitive reports and maintaining documentation, advisers often find themselves spending more time on process than on clients. Python offers a powerful solution. By automating routine workflows – such as portfolio rebalancing, data reconciliation, and report generation – firms can dramatically reduce time spent on low-value tasks. This not only improves operational efficiency but frees advisers to focus on delivering deeper, more strategic client conversations and proactive investment decisions. In an industry where trust and personalisation are paramount, shifting time from administration to advice is a competitive advantage.

Simple use-cases in investment management

Financial analysts often spend a significant portion of their time gathering and cleaning data. Python simplifies this through packages like pandas-datareader, yfinance, and requests, which allow direct access to financial data via APIs.

Example: Downloading Stock Prices

import yfinance as yf
data = yf.download(“AAPL”, start=”2020-01-01″, end=”2024-12-31″)

This snippet retrieves historical Apple stock prices and outputs a clean DataFrame, ready for analysis or visualisation. It can be executed directly in Google Colab without requiring any local installation.

Explanation

import yfinance as yf

This line imports a Python library called yfinance.

• yfinance is a popular tool used by investors and analysts to pull financial data from Yahoo Finance.
• The word as yf just creates a shortcut, so instead of typing yfinance every time, you can type yf.

data = yf.download(“AAPL”, start=”2020-01-01″, end=”2024-12-31″)

This line downloads historical stock price data for Apple Inc. (whose stock ticker is AAPL) from Yahoo Finance.

• yf.download() is a command to fetch data.
• “AAPL” tells it which company’s data you want. Here, it’s Apple Inc.
• start=”2020-01-01″ sets the beginning date of the data: January 1, 2020.
• end=”2024-12-31″ sets the ending date: December 31, 2024.
• The result is stored in a variable called data, which behaves like a spreadsheet (called a DataFrame in Python).

Other Illustrative Functions

Looking ahead: Python as a strategic differentiator

In the AI age, investment edge is no longer defined by access to data alone – but by how quickly and intelligently that data is used. Python is not merely a technical tool; it is a strategic enabler that empowers investment professionals to move from reactive to predictive, from generic to bespoke.

Firms that fail to adopt such tools risk falling behind in both performance and relevance. Atchison’s forward-looking approach – grounded in Python’s capabilities – ensures that our clients benefit from innovation, precision, and responsiveness that traditional models struggle to deliver.

Atchison has fully embraced Python as a foundational component of our investment process. Our in-house research, portfolio construction, and risk oversight functions are deeply integrated with Python-powered tools, allowing us to:

• Conduct faster investment research and prototyping
• Build and refine portfolios with higher precision
• Perform real-time analytics tailored to clients’ specific needs
• Respond swiftly to macroeconomic shifts with data-led insight

By embedding Python across the value chain, we are positioned to not just keep pace with the AI-driven era – but to lead within it.

Ye Peng is a data scientist and developer at Atchison.