Quant Mashup - Concretum Group A Hidden Trade Around SpaceX IPO? [Concretum Group]The piece we present today stems from some internal exchange within the Concretum team ahead of the highly anticipated SpaceX IPO, an offering that has dominated headlines for a string of firsts in recent market history, from its record valuation (~$1.75 trillion) to the one that interests us most:(...) The Non-Linear Costs of Trading [Concretum Group]At Concretum Group, a relevant part of our research effort goes into developing strategies for external clients, each arriving with different requirements about what market behavior to model and, just as importantly, about how much capital a given strategy is meant to run on. This brings us to a(...) How to Build a Reliable Algo Trading Infrastructure [Concretum Group]More and more traders are using Claude Code, ChatGPT, Cursor, and other LLMs to build and automate their trading systems. It works. You can go from strategy idea to a working bot in a day. The code compiles, the backtest looks good, orders fire on paper trading, and you move to production. Then(...) When Short Sellers Create Overnight Alpha [Concretum Group]Last week, we shared some findings of an intraday short-selling signal taken from our internal research archives. Today, picking up on the same theme, we present some evidence behind an effect we believe stems from the very presence of short sellers in stocks with the same characteristics(...) Identifying Stocks to Fade [Concretum Group]Without a shade of doubt, Market Wizards books have been a staple in the upbringing of whole generations of traders and investors, and rightfully so… we ourselves have been inspired by the exceptional stories within them. The series, authored by Jack Schwager, began in 1989: what has made it so(...) How to Manage an Intraday Trend Trade [Concretum Group]In managing our book, we run trend strategies across multiple asset classes and at different speeds, with exposure ranging from slower multi-day systems to faster intraday signals. Regardless of model specifications, we keep observing the same pattern: small implementation details can produce(...) Breaking the Rules of Intraday Trading [Concretum Group]Quantitative research is, at its core, about following rules. As in any other STEM discipline (science, technology, engineering, and mathematics), precise frameworks give research rigor, discipline, and comparability. Yet, because such frameworks often remain unquestioned, challenging one of their(...) Improving Performance with Fast Alphas; A Tactical Overlay for Intraday Trend Trading [Concretum Group]Predictive signals operating at very short horizons often exhibit strong gross performance in backtests but fail to survive realistic transaction costs due to prohibitive turnover. This research note argues that the inability to monetize such signals directly does not imply the absence of economic(...) Seasonality in Bitcoin Intraday Trend Trading [Concretum Group]As our readers are aware, futures trend trading, particularly at higher frequencies, represents a core area of Concretum’s expertise, with a meaningful share of our trading risk allocated to this family of models. Over recent years, we have also published several papers presenting simple and(...) The Volatility You Can’t See [Concretum Group]Volatility is one of the most important numbers in finance, yet it has a strange feature: it cannot be directly observed. Volatility is a latent variable, meaning it is a real property of markets, but it can only be inferred from the footprints it leaves on prices. As a useful analogy, consider(...) When Execution Delays Erode Short-Term Alpha [Concretum Group]In short-term trading systems, delaying the execution of a signal can lead to a meaningful deterioration in performance. Many systematic traders design strategies under the assumption that any signal computed at the market close should be executed on the next day’s open. This workflow has a clear(...) Opportunity-Set Bias in Mean-Reversion Trading Systems [Concretum Group]In the evaluation of new signals and trading strategies, a common practice is to initiate the research process by analyzing a full set of trade statistics. The rationale behind this approach is simple: strategies exhibiting attractive trade-level metrics are considered eligible for further due(...) ChatGPT in Systematic Investing - Enhancing Risk-Adjusted Returns with LLMs [Concretum Group]This paper investigates whether large language models (LLMs) can improve cross-sectional momentum strategies by extracting predictive signals from firm-specific news. We combine daily U.S. equity returns for S&P 500 constituents with high-frequency news data and use prompt-engineered queries to(...) Building a Survivorship Bias-Free Crypto Dataset with CoinMarketCap API [Concretum Group]When you look at a chart of Bitcoin’s price from 2010 to today, it tells a story of volatility, resilience, and long-term gains. But what about the thousands of coins that launched, pumped, and then disappeared along the way? Most commonly used crypto datasets, especially those tied to current(...) Backtesting the Opening Range Breakout (ORB) Strategy using Polygon.io [Concretum Group]In this article, we will show you how to run, customize, and analyze a backtest for the Opening Range Breakout (ORB) strategy. Instead of explaining every line of code, we’ll focus on how to execute the backtest, adjust key parameters, and interpret the results. By the end, you’ll be able to:(...) How to Evaluate the Effectiveness of a Trading Strategy: p-Values and Bootstrapping Methods [Concretum Group]One common question we often receive from our readers is: “How do you evaluate the effectiveness of a trading strategy?” In this post, we’ll explore two fundamental techniques used in quantitative research to assess whether a trading strategy may genuinely offer an advantage or if its(...)