Tapping the Markets with Python

Tackling Finance and Trading With Data Science

Photo by m. on Unsplash

From banking to Wall Street, finance relies heavily on data science and modeling to analyze markets and place strategic trades. With the rise of algorithmic trading, Python has become a dominant choice for leveraging data into financial insights thanks to its powerful libraries for math, visualization and backtesting strategies.

By exploring some Python tools for trading, we can gain an appreciation for how data science is transforming finance and opening opportunities for technology to directly influence markets.

Crunching Numbers with NumPy and pandas

Python’s NumPy and pandas form twin pillars for numerical data analysis ideal for financial datasets. NumPy offers powerful multi-dimensional arrays and mathematical functions for array-based computing:

import numpy as np

returns = np.random.normal(0.06, 0.2, 100)  
print(np.mean(returns))
print(np.std(returns))

With vectorization, we efficiently calculate statistics over arrays. pandas builds on this with its DataFrame for convenient data manipulation and cleaning. It’s adept at ingesting financial data from CSVs and databases:

import pandas as pd

df = pd.read_csv('prices.csv'…