13 posts tagged with "quantitative-trading"

Introduction​

Backtesting is essential for validating trading strategies, especially in the high-frequency and volatile world of stocks like NVIDIA (NVDA). Using VeighNa, an open-source algorithmic trading system, provides traders with the flexibility to thoroughly test strategies and optimize for performance. In this guide, we'll walk through setting up VeighNa, backtesting a simple Moving Average Crossover strategy on NVIDIA, explaining the strategy in detail, troubleshooting common installation issues, and optimizing your strategy.

Introduction​

In the fast-paced world of algorithmic trading, accessing reliable and timely financial data is essential for backtesting strategies, optimizing models, and making data-driven trading decisions. Automating data collection can streamline your workflow and ensure that you have access to the most recent market information. In this guide, we’ll walk through how to automate the collection of stock data using Python and yfinance, and how to upload this data to Hugging Face for convenient access and future use.

Although this article uses NVIDIA stock data as an example, the process is applicable to any publicly traded company or financial instrument. By integrating data collection and storage into one automated pipeline, traders and analysts can focus on what matters most—developing strategies and maximizing returns.

Introduction​

Algorithmic trading is transforming the financial landscape, and frameworks like VeighNa combined with Interactive Brokers (IB) offer traders the tools they need to optimize their trading strategies and automate execution across global markets. However, setting up these tools on macOS, particularly on Apple Silicon (M1/M2), can be tricky due to package compatibility issues. This guide will walk you through the installation process of VeighNa with IB on macOS, highlighting all the potential gotchas we encountered, along with their solutions.