7 posts tagged with "python"

Introduction​

Qlib is an AI-oriented, open-source platform from Microsoft that simplifies the entire quantitative finance process. By leveraging PyTorch, Qlib can seamlessly integrate modern neural networks—like Multi-Layer Perceptrons (MLPs)—to process large datasets, engineer alpha factors, and run flexible backtests. In this post, we focus on a PyTorch MLP pipeline for Alpha360 data in the US market, examining a single YAML configuration that unifies data ingestion, model training, and performance evaluation.

Introduction​

In the realm of quantitative finance, machine learning and deep learning are revolutionizing how researchers and traders discover alpha, manage portfolios, and adapt to market shifts. Qlib by Microsoft is a powerful open-source framework that merges AI techniques with end-to-end finance workflows.

This article demonstrates how Qlib automates an AI-driven quant workflow—from data ingestion and feature engineering to model training and backtesting—using a single YAML configuration for a LightGBM model. Specifically, we’ll explore the AI-centric aspects of how qrun orchestrates the entire pipeline and highlight best practices for leveraging advanced ML models in your quantitative strategies.

Introduction​

In the intricate world of algorithmic trading, seamless integration between trading platforms and broker APIs is paramount.

One common issue when interfacing with Interactive Brokers (IB) API is encountering the error:

ERROR:root:Error - ReqId: 1, Code: 200, Message: No security definition has been found for the request

This error typically arises due to incorrect exchange mapping, preventing Interactive Brokers (IB) from recognizing the requested security. This article delves into the importance of accurate exchange mapping within the VeighNa trading platform, provides a detailed overview of IB's symbol rules, explains the updatePortfolio method, and offers guidance on implementing correct mappings to avoid such errors.

Introduction​

In the realm of algorithmic trading, execution algorithms play a pivotal role in optimizing trade orders to minimize market impact and slippage. One such algorithm is the Sniper Algorithm, which is designed to execute trades discreetly and efficiently by capitalizing on favorable market conditions.

This article aims to review and understand the implementation of the Sniper Algorithm as provided in the VeighNa trading platform's open-source repository. By dissecting the code and explaining its components, we hope to provide clarity on how the algorithm functions and how it can be utilized in practical trading scenarios.

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.