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Insights, updates, and deep dives on algorithmic trading, quantitative finance, and the Q314 platform.
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Seven Statistical Frameworks Behind Systematic Hedge Fund Research
Why quantitative investing is less about one “black box” model than about assigning different modelling frameworks to different research, forecasting and risk tasks
AI for Trading is Here: But ChatGPT is the Wrong Approach
Paradigm shift. There is a fundamental change happening in how people interact with financial information. AI is here. Increasingly, individuals are turning to ChatGPT not just for explanations, but for direction. Questions that would previously have gone to an advisor, a broker, or hours of research are now condensed into a single prompt: “What should I invest in?” On the surface, this feels efficient. The answers are immediate, articulate, and often sound well reasoned. Indeed, data from organisations such as Pew Research Center and Statista suggests that a growing share of users are already using AI tools in this way, including for financial decision-making. You may have even tried it yourself (we know we have!). But there is a fundamental issue here, and a dangerous one. This type of interface creates a sense of capability that the underlying system simply does not have. ChatGPT is extremely capable for many things (and we like using it too!) but not as a financial advisor, as an LLM is natural-language based not quantitative like an ML would be, and more importantly it can be biased.
Get Started in Quant Trading: 5 Strategies To Build First
Get Started in Quant Trading: 5 Strategy Types To Build First Quantitative trading can appear intimidating to newcomers. The field is often associated with complex mathematics, PhD-level research teams, and institutional trading infrastructure. In reality, many successful systematic strategies are built on relatively simple statistical principles. The complexity typically lies not in the concept of the strategy itself, but in the discipline of implementing it consistently, testing it rigorously, and executing it without emotional interference. For traders transitioning from discretionary trading into systematic trading, the most effective starting point is to focus on a small number of well-understood strategy types. These strategy types form the backbone of many professional quantitative portfolios and provide a strong foundation for experimentation and refinement. This article outlines five core strategy types that beginners can build and test as their first systematic trading systems.
Why Systematic and Algorithmic Trading Benefits Investors
Financial markets have evolved dramatically over the past two decades. Advances in computing power, data availability, and market infrastructure have fundamentally changed how trading decisions are made. Increasingly, the most sophisticated participants in financial markets rely not on discretionary judgement, but on systematic and algorithmic approaches. For investors, the shift toward systematic trading reflects a simple reality, human decision making in financial markets is often flawed. Quantitative strategies offer a structured alternative, one that replaces emotion and intuition with data, rules, and statistical validation.