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 · 25 shown
LLM agents that act on live Web data must combine heterogeneous evidence, remember what they have done and improve from outcomes without weakening their own safeguards.
PAPER REPORTS · Live paper portfolio, Apr 8 to Sep 10, 2026 (102 trading days): - Return +5.0% vs SPY +12.4%. · Live paper portfolio, June 22 to July 29, 2026: -0.3%, vs SMH -24.6% over the same window.
The value of a forecast depends on its accuracy, but also on its actionability. In trading, an investor must translate return forecasts into positions while accounting for trading costs and the lingering impact of earlier trades.
OUR BACKTEST · Sharpe 0.07 · Return +8.8% · Max DD -55.7%
Automated markets increasingly host autonomous, learning trading agents and are powered by formulaic automated market makers (AMMs).
PAPER REPORTS · Rolling 7-day annualized Sharpe ratio of net strategy returns ranges roughly between -20 and +20 for every agent…
We study long-term investment in an Ornstein-Uhlenbeck diffusion model with exponential utility. We solve the finite-horizon problem explicitly and show that information delay qualitatively alters long-horizon behavior.
OUR BACKTEST · Sharpe -0.06 · Return -0.6% · Max DD -3.9%
Portfolio decisions are often made under uncertainty about expected returns, and investors revise their beliefs as new information arrives.
OUR BACKTEST · Sharpe 1.07 · Return +152.8% · Max DD -57.4%
We develop a framework for an investor who trades until she either reaches a financial goal or an exogenous deadline arrives. Analogous to utility functions over wealth, we measure satisfaction with the timing of reaching a goal by a discount function.
OUR BACKTEST · Sharpe 0.63 · Return +44.3% · Max DD -26.3%
We investigate a continuous-time financial market where the asset price exhibits weak (sublinear) mean reversion and has a nonzero drift.
OUR BACKTEST · Sharpe 0.09 · Return +0.0% · Max DD -0.1%
We study the joint dynamics of the best bid and ask prices with a spread-gated Hawkes-flocking model.
Abstract. In this paper, infinite horizon stochastic difference equations and backward stochastic difference equations with fractional noise are studied. The main difficulty comes from the self-dependence of fractional noise on an infinite horizon.
OUR BACKTEST · Sharpe 0.59 · Return +40.5% · Max DD -26.3%
Mitigating \emph{drawdown}, the decline in wealth from its running peak, presents a canonical problem in path-dependent risk control.
PAPER REPORTS · Figure 1 reports higher estimated expected terminal return E[R_pi] for the drawdown-modulated policy than for the… · Maximum drawdown is guaranteed by construction: P(d* <= d_max) = 1 and V(k) >= (1 - d_max)V_max(k) > 0 for all k with…
OUR BACKTEST · Sharpe -1.60 · Return -0.0% · Max DD -0.0%
We examine log-optimal portfolio allocation when the long-run price of an asset follows a power-law trajectory, $P(t)=At^α$, and its instantaneous return variance decays as $σ^2(t)=σ_0^2 t^{-2γ}$.
Abstract We develop a continuous-time framework in which the risk associated with leverage is parameterized by a dimensionless drawdown-risk index relative to an arbitrary reference asset.
PAPER REPORTS · μ_{0|w} = 0.145 with σ_{0|w} = 0.274 and s_{0|w} = 0.665 for the scaled growth-optimal Kelly strategy k_{0|e1}w* on the… · μ_{e1|w} = 0.096 with σ_{e1|w} = 0.223 and s_{e1|w} = 0.543 for w = e1 + k_{0|e1}w*_{e1} (index-relative…
OUR BACKTEST · Sharpe 0.67 · Return +59.2% · Max DD -35.9%
Retail proprietary-trading firms sell a two-stage product: a paid evaluation that must reach a profit target before breaching a trailing drawdown, then a funded account that must survive a minimum window and a consistency rule before a payout.
PAPER REPORTS · Adversarial implementation of a published practitioner rule set (not the authors' own edge, but the authors' own… · Best in-sample payout rate achievable by the authors' payout-rate optimisation: 4.6% (Lucid Flex) versus a 9.5%…
Managing drawdown, the peak-to-trough decline in an investment portfolio's value, is a precondition for long-term survival in practical investment management.
PAPER REPORTS · NASDAQ 2010.01-2025.04, zero transaction costs and zero slippage: CAST annualized Sharpe 0.523, MDD 0.114, Calmar… · CSI300 2010.01-2025.04, frictionless: CAST ASR 0.269, MDD 0.158, CR 0.104, SoR 0.257, FV $1272
Reinforcement learning trading systems published in the academic literature overwhelmingly rely on price-aggregate state representations (OHLCV bars) or limit-order-book depth features, leaving microstructure pattern theories from the practitioner literature,…
PAPER REPORTS · TSLA: +163.6% ROI, -27.5% max drawdown, 27,019 trades, held-out final 12 months of Jan 2020-Feb 2026 data, zero… · NVDA: +116.5% ROI, -47.8% max drawdown, 12,892 trades, same held-out 12-month partition, zero leverage, $0.0002/share…
We study exponential-utility maximization for high-frequency trading in a discretized fractional Brownian motion model. Using spectral methods for stationary Gaussian sequences, we derive the asymptotic growth rate of the optimal certainty equivalent.
This paper develops the first end-to-end application of cross-sectional learning-to-rank to the S&P 500 weekly options (SPXW) zero-day-to-expiration surface, integrated with margin-aware position sizing, an abstention rule driven by model uncertainty, and a…
PAPER REPORTS · Out-of-time 2025 annualized Sharpe 4.308 to 5.761 across seven sizing methods, net of Reg-T margin, tiered IBKR fees,… · Headline Edge Allocation OOT 2025: Sharpe 5.7612, Sortino 7.0291, annualized return 10.48% (excess of risk-free),…
KellyBoost is a single multi-output XGBoost model whose softmax output is the portfolio: with y the vector of per-asset holding-period returns, the training loss is - log(1 + w y), the negative log growth rate, so the fitted model is the growth-optimal…
PAPER REPORTS · KellyBoost (searched, gross of costs), 2013-01 to 2026-07, 163 monthly decisions: mean log growth 0.47 (x100 per 20-day… · KellyBoost hand-built feature pipeline, same period: logG 0.39, annualized return 5.7%, vol 22.9%, Sharpe 0.28, max DD…
PAPER REPORTS · Out-of-sample RMSE (70-30 split, 2021–2024, no transaction costs): LLF BTC 0.541, ETH 0.256, USDT 0.244, BNB 0.676, BCH… · Out-of-sample MAE: LLF BTC 0.377, ETH 0.199, BNB 0.446, BCH 0.567, LTC 0.458, ICP 0.519, MATIC 0.612, USDT 0.137 (RF…
Human capital is a central organizational input, but standard financial data reveal little about firm-specific disruptions to workforce availability, cost, skills, and continuity.
OUR BACKTEST · Sharpe 0.56 · Return +50.1% · Max DD -40.8%
A new class of software systems is transforming investment analysis. Large language model agents assembled into collaborative team structures including analysts, researchers, and risk managers are increasingly deployed across financial markets.
PAPER REPORTS · Author's own prior human practice (not the AI framework): 127.17% return vs 50.67% benchmark within sixteen months,… · Ranked first among 276 comparable peer funds nationally through the 2020 market-stress period (peer average negative);…
How much capital a trading strategy can absorb before its edge disappears is a causal question about how much is deployed, but it is answered with observational proxies that rest on incompatible assumptions.
Conformal prediction has traditionally been used to quantify prediction uncertainty.
PAPER REPORTS · DEV 2016-2021 (1,511 days), Config A: 28.45% annualised net log growth, Sharpe 1.336, max drawdown 27.68%, Calmar… · DEV 2016-2021, Config B: 25.84% annualised net log growth, Sharpe 1.386, max drawdown 20.26%, Calmar 1.376, annualised…
OUR BACKTEST · Sharpe 0.41 · Return +45.1% · Max DD -40.4%
Finite multiplicative systems often cease to evolve when a lower continuation threshold is reached,whereas standard growth-optimal benchmarks assume uninterrupted continuation.
OUR BACKTEST · Sharpe 0.68 · Return +16.4% · Max DD -8.3%
The distribution of a normal mean-variance mixture depends on the law of its positive mixing variable. We compare six parametric mixing laws with a grid nonparametric maximum likelihood estimator under the same determinant identification constraint.
PAPER REPORTS · Model-based lower-envelope CPT value at the robust optimum: -0.04478 at 5% annual reference return; -0.09245 at 10%… · Empirical holdout CPT value at the robust weights: -0.04867 at 5% reference; -0.10049 at 10% reference (holdout 483…
OUR BACKTEST · Sharpe 0.13 · Return +0.1% · Max DD -0.2%