Quantitative Finance

Orders are where trading systems stop sounding intelligent and start spending money. A model can narrate the market beautifully. It can explain momentum, liquidity, volatility regimes, inventory pressure, and the great moral tragedy of being early. None of that matters if the final system places the wrong limit order, sizes too aggressively, fills only in a fantasy simulator, or wins a backtest because it tried enough variants to accidentally find one that looked divine. ...

TL;DR for operators Portfolio teams do not need another chatbot that confidently explains why yesterday’s price move was “driven by sentiment.” They need a system that can split research work into specialised roles, force disagreement into the open, log the reasoning trail, and turn messy inputs into a decision that a human can inspect before money moves. ...

TL;DR for operators Most trend-following signal libraries behave like kitchen drawers: MACD, crossovers, momentum mixes, Bollinger Bands, short lookbacks, long lookbacks, “robust” blends, and a few legacy knobs nobody wants to delete because they once looked clever in 2017. Sebastien Valeyre’s paper argues that much of this complexity may be unnecessary for medium-frequency cross-asset futures trend following.1 The paper tests whether the theoretical Sharpe-ratio curve derived by Grebenkov and Serror for EMA trend following is visible in real data. It is. Using daily returns for 70 futures instruments across commodities, FX, stock indices, and bonds from May 1990 to December 2023, the empirical Sharpe curve for an Agnostic Risk Parity portfolio fitted with normalized EMA signals lines up closely with the theoretical curve. ...

TL;DR for operators The paper behind this article is not a victory lap for AI stock prediction. It is a more useful thing: a controlled comparison of how different optimisers behave when a MambaStock model tries to forecast one-week-ahead S&P 500 returns.1 The operational read is simple. If your priority is lowest forecast error in this setup, the safer family is still adaptive or momentum-based optimisation: RMSProp, Adam, Nesterov, and SGD with momentum. If your priority is fast experimentation across many hyperparameter settings, Lion deserves attention because it trains quickly and tolerates a broader region of settings. If your priority is Lion-like speed without quite so much convergence thrashing, Roaree is interesting: it smooths Lion’s hard sign update and improves Lion’s test error and training stability. ...

TL;DR for operators Discounting is the quiet plumbing of derivatives. Most option-pricing systems assume a risk-free asset sits somewhere in the background, calmly providing the rate at which future payoffs become present prices. This paper asks what happens when that safe haven is unavailable, unreliable, or merely too theoretical to be useful. Its answer is not to abandon discounting, but to manufacture it from the relative dynamics of two risky assets.1 ...

TL;DR for operators Options calibration has a familiar operational problem: the model that is fast enough to run every day is usually the model that assumes the market is behaving politely. The market, naturally, has other hobbies. This paper compares two ways of calibrating implied volatility surfaces. The first is the classical route: use model-specific analytical approximations for Heston and rough Bergomi. The second is the rough-path route: represent volatility as a linear functional of the truncated signature of a primary stochastic process.1 ...

TL;DR for operators Bitcoin is not simply “more volatile” than traditional assets. That is the easy answer, and therefore the suspicious one. A recent paper compares Bitcoin, GBP/USD, gold, and natural gas using two complexity tools: Refined Composite Multiscale Sample Entropy (RCMSE) and Multifractal Detrended Fluctuation Analysis (MF-DFA).1 The result is more interesting than the usual crypto-volatility sermon. Bitcoin has the highest summed multiscale entropy complexity, at 74.66, and the widest multifractal spectrum, at 0.62. Natural gas, despite showing high volatility in the return distribution, has the lowest values on both measures: 51.48 for summed RCMSE complexity and 0.21 for spectrum width. ...

TL;DR for operators A recent paper, Dependency Network-Based Portfolio Design with Forecasting and VaR Constraints, proposes a portfolio engine that first turns the S&P 500 into a dependency network, then strips that network down to a minimum spanning tree, then selects the five most central stocks, then allocates capital using risk-aware weights, and finally uses ARIMA or neural autoregressive forecasts to decide whether those positions deserve exposure on a given day.1 ...

TL;DR for operators Markowitz variance is usually treated as the clean mathematical backbone of portfolio risk. Olkhov’s paper asks a narrower and more awkward question: what if that familiar covariance formula is only what remains after trade-volume randomness has been quietly set to zero?1 The paper’s answer is mechanism-first. It constructs a buy-and-hold portfolio as if it were a synthetic single traded security. To do that, it rescales the observed market trades of each constituent so their normalised volumes match the investor’s actual holdings, then aggregates those normalised trade values and volumes into portfolio-level trade series. Once the portfolio has its own synthetic trade values $Q(t_i)$, volumes $W(t_i)$, and implied prices $s(t_i)$, its variance can be computed in the same market-based way as the variance of any single security. ...

TL;DR for operators American options are awkward because the model must decide not only what the contract is worth, but also where exercise becomes optimal. That turns pricing into a free-boundary problem, which is exactly the kind of thing that makes high-dimensional PDE methods start sweating through their nice academic shirts. Jasper Rou’s paper extends Time Deep Gradient Flow (TDGF) to American basket put options under multidimensional Black-Scholes and Heston models.1 The useful trick is not “throw a neural network at finance”. We have tried that spell before; it produces PowerPoint before it produces risk control. The actual mechanism is more specific: TDGF trains a neural PDE solver through time steps, only applies the PDE loss in the continuation region, and builds the payoff floor into the network so the model cannot price below the intrinsic value. ...