Category: General

Explore our collection of free quantitative research tools, interactive platforms, and trading applications covering factor investing, macroeconomic data, news sentiment, systematic strategies, and bias-free backtesting.

Man AHL has recently published a research piece titled “A Trend Following Deep Dive: Cash (Equities) Is King” (Panjabi, Bordigoni, and Buchanan, 2026) which has resonated not only with researchers in the trend-following space but also with those specializing in equity markets. The authors show that timing equity factors with momentum can provide meaningful diversification […]

If you have ever worked on quant-trading desks, or been involved in advisory work, you already know that portfolio managers and institutional clients are always keen to know whether a signal can support a certain amount of capital in production and real-world trading settings. With this new follow-up piece, we would like to present some […]

Most trading systems fail in production for reasons that have nothing to do with the strategy itself. The code works, the backtests look good, and paper trading passes, but the infrastructure around the system quietly breaks in ways most people never expect. From stale market data and rejected MOC orders to pacing violations, clock drift, and paper trading mismatches, these are some of the real production issues traders keep running into when automating through broker APIs like Interactive Brokers. Based on real conversations and production experience, here are a few lessons worth knowing before you go live.

Identical trading strategies can produce vastly different results depending on the data provider. In this study, the same backtest ranges from $226k to $726k, highlighting how intraday data quality can materially affect performance.

When Intraday Trades are carried overnight 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 constraints on purpose can sometimes be an informative experiment. In […]

Compounding is most often interrupted by investor reactions to volatility rather than by weak long-term returns. Volatility targeting seeks to stabilize portfolio risk over time, reducing behavioral errors and helping preserve long-term compounding.

This article examines whether intraday trend-following strategies are effective in Bitcoin and whether trend strength varies across the trading week. Using a high-frequency, volatility-targeted long–short benchmark, we identify a pronounced performance pickup starting Sunday evening New York time and extending into Monday, aligned with the Asian market open. We refer to this pattern as the Monday Asia Open Effect and discuss potential liquidity-driven explanations.

Volatility is fundamental to modern finance, yet it remains intrinsically unobservable. What we call “volatility” is not a directly measured quantity, but an estimate constructed from market data using a chosen methodology. Because volatility forecasts are evaluated against these constructed proxies rather than the latent volatility itself, model performance depends as much on how volatility is defined and measured as on the forecasting model used. Understanding this dependence is essential for interpreting volatility forecasts, comparing models, and applying them effectively across trading horizons and use cases.

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 and important advantage: it keeps the live trading […]