Research
Every research paper we have written up, newest paper first. New arrivals are screened automatically for topical fit with systematic trading; the ones that clear it are read, written up, and — where the idea is testable on our data — backtested. Particular focus on alpha hypotheses that can be formalised and tested. The Radar also covers portfolio construction, market risk and execution where the research is directly relevant to systematic investment processes. Follow new entries by RSS.
16,915 papers screened · 588 on the radar · 30 shown
Realistic market impact is essential for generative limit order book (LOB) models to serve as world models for trading agents. If a simulator misprices this mechanism, agents learn policies that are optimal in the simulator but wrong in the real market.
Value-at-Risk (VaR) is a widely adopted quantile-based risk measure in practice, yet incorporating VaR objectives into Markov decision processes (VaR MDPs) remains a longstanding challenge.
PAPER REPORTS · Simulated alpha-VaR of total execution revenue, dynamic vs. optimal static strategy (Regime 2, Almgren-Chriss…
Classical execution models assume linear impact, diffusive prices, and an exogenous horizon. We study execution under concave power-law impact and Hurst-scaled price fluctuations, and make two contributions.
OUR BACKTEST · Sharpe -0.29 · Return -9.4% · Max DD -22.3%
We develop a framework to identify and quantify arbitrage across logically related prediction-market contracts.
PAPER REPORTS · Accumulated take, settled windows, 23 Jun-3 Aug 2026, net of spreads and taker fees at displayed depth, held to… · First take, settled windows, same period and costs: guaranteed return 2.7% ($1.10M on $40.6M); at resolution 3.2%…
In its traditional form, the smart order router only cares about price when deciding where to send a trade; send the order to the exchange currently showing the tightest spread.
PAPER REPORTS · Mean effective execution cost 6.561 bp (latency-aware router) vs 6.548 bp (price-only baseline). · Hit rate vs baseline: 2.2% of 14,400 held-out rows (latency-aware cheaper than price-only).
Market making under realistic order-flow dynamics is challenging due to the clustered, self-exciting nature of order arrivals, which are naturally modeled by non-Markovian Hawkes processes.
PAPER REPORTS · HSN mean cumulative episodic reward, simulated (no market data), multi-asset cross-excitation cs=0.7: 0.0548±.0007 over… · HSN, simulated power-law kernel ε=0.15: 0.0216±.0004 mean cumulative reward, +32.5% vs the best baseline (p<0.001).
Renewed attention to the identity of Bitcoin’s pseudonymous creator has revived an old worry: that the roughly 1.148 million BTC mined by Satoshi and never moved represent a major tail risk for Bitcoin. This paper argues that the worry is overstated.
This paper develops a reproducible framework for predicting gold-price direction and converting the forecasts into a currency-hedged SHFE-COMEX spread-trading strategy.
PAPER REPORTS · Hedged (USD/CNH) walk-forward strategy: annual return 14.8%, volatility 7.1%, Sharpe 2.08, max drawdown 4.2%, win rate… · Unhedged version, same setup: annual return 11.2%, volatility 9.0%, Sharpe 1.24, max drawdown 12.7%, win rate 61.3%, 52…
Flashblocks divide a block's priority gas auction into shorter sequential auctions that commit transaction order before the block is complete. We ask how this auction cadence affects bidding and competition among automated arbitrageurs, or searchers.
Reinforcement learning (RL) is increasingly used for financial optimal-control problems when complex dynamics make analytical strategies difficult to obtain.
PAPER REPORTS · Proposed method (frozen analytical policy + residual PPO), execution cost halved from 0.0012 to 0.0006, σ_ξ=100, full… · Same method at a_true=0.0012: change 0.00019 [-0.00041,0.00079]; at a_true=0.0018: change 0.00019 [-0.00034,0.00071]…
Deep reinforcement-learning policies for order execution can vary substantially across training seeds, so apparent architectural gains may reflect favourable training realisations rather than reproducible properties of the architecture.
PAPER REPORTS · Mean implementation shortfall, out-of-sample Jan 16–20 2023, frictionless replay with no commissions or market impact,… · Per-run CVaR-95 under the same setup: DDQL 110.8, MoE K=8 122.2, DDQL-cap8 104.1 bps.
In this paper, we extend earlier findings of supra-competitive outcomes in optimal-execution games by identifying a learned punitive mechanism that deters deviations and provides behavioural evidence of collusion.
We model market impact as the response to submitted order flow net of counterflow from latent traders, activated when price displacements from the level that would prevail without the order exceed individual thresholds.
We study the joint dynamics of the best bid and ask prices with a spread-gated Hawkes-flocking model.
Abstract This paper studies how pre-trade transparency is associated with market quality in corporate bond markets.
Automated execution algorithms are organized into schedule-based and liquidity-seeking families.
PAPER REPORTS · Relative slippage vs. arrival mid, passive Shadow-PPOV: +0.0955 ± 0.0130 ticks/contract (CME ES, calendar 2025, 246… · Relative to immediate execution: −0.4236 ± 0.0117 ticks/contract (same sample and cost assumptions)
We study strategic trading around index reconstitution in a continuous-time, multiasset game with transient cross-asset price impact and heterogeneous beliefs about future index membership.
We consider two canonical market-microstructure regularities: the long-memory of trade signs and the square-root law of meta-order impact. The point is not to propose new empirical laws, but to separate the clocks on which existing laws are defined.
Price-level limit order book (L2) data reveal aggregate liquidity but not the ordered queue required by price--time priority.
PAPER REPORTS · Passive-at-touch implementation shortfall, 1301.T, 18 held-out July 2025 dates, 1,080 episodes, zero… · Passive-at-touch implementation shortfall, 7911.T, same 18 held-out dates: 0.765 bps [0.547, 0.974] front, 0.926…
Decentralized exchanges record trading and liquidity provision on public blockchains, but empirical analysis requires interpreting these records and linking them to execution metadata.
This paper develops uniform inference and certified capacity decisions for an estimated financial stability boundary. Conditional risk, temporary cross-impact, and effective risk-bearing capacity are jointly estimated from dependent observations.
PAPER REPORTS · Capacity/regret under the baseline loss convention (simulated 60-cell design, no market data): Projected safe - planned… · Under the high convention-loss calibration: projected-safe mean regret 0.107, below pointwise delta 0.120 and plug-in…
This paper develops a mechanism through which costly changes in the representations used for portfolio choice can contribute to persistent signed order flow.
Transient impact models compose a nonlinearity with a memory kernel, and the order of composition determines the criterion for absence of price manipulation. We classify both orders.
Quantitative trading is moving from isolated predictive models toward agentic workflows that combine reasoning, tool use, memory, and feedback.
Market-order flow in financial markets exhibits long-range correlations. This is a widely known stylised fact of financial markets. A popular hypothesis for this stylised fact comes from the Lillo-Mike-Farmer (LMF) order-splitting theory.
We study the quadratic tracking problem of a general stochastic target process with absolutely continuous controls, with and without terminal constraint. We derive explicit, non-asymptotic upper bounds in terms of a Besov-type modulus of the target.
We report a market in which a positive return is visible in prices yet cannot be realized by a fixed trading policy, and we measure why.
Financial markets do not evolve uniformly through calendar time. Periods of intense information arrival accelerate market activity, while information-poor periods produce the familiar intraday lull in trading.
Parent-order execution is a core problem in algorithmic trading, where the goal is to split a large order into smaller orders while reducing execution costs.
PAPER REPORTS · Aggressive setting, April 2026, DS-v4-f: value-weighted price performance -2.26 bps vs TWAP benchmark price, i.e. · Passive setting, April 2026, DS-v4-f: -3.92 bps wbp, +1.07 bps vs TWAP and +0.71 bps vs strongest baseline; 100%…
We derive a mesoscopic model for optimal execution with limit orders that incorporates microstructural features of passive price impact.
PAPER REPORTS · Baseline simulation, η=0: mean P&L $320 (95% CI 301–339), mean implementation shortfall −$35 (CI −37 to −34), mean… · Simulation with η=0.005: mean P&L $71 (CI 49–93), shortfall −$19 (CI −21 to −17), final inventory 1,928 shares, trading…