13 posts tagged with "quantitative-trading"

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One of the core ideas in quantitative finance is that model predictions—often called “scores”—can be mapped to expected returns on an instrument. In Qlib, these scores are evaluated using metrics like the Information Coefficient (IC) and Rank IC to show how well the scores predict future returns. Essentially, the higher the score, the more profit the instruments—if your IC is positive and statistically significant, the highest-scored stocks should, on average, outperform the lower-scored ones.

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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.

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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.

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AdaRNN is a specialized PyTorch model designed to adaptively learn from non-stationary financial time series—where market distributions evolve over time. Originally proposed in the paper AdaRNN: Adaptive Learning and Forecasting for Time Series, it leverages both GRU layers and transfer-loss techniques to mitigate the effects of distributional shift. This article demonstrates how AdaRNN can be applied within Microsoft’s Qlib—an open-source, AI-oriented platform for quantitative finance.

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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.

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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.