Skip to main content
WeiMindWeiMind
ChineseView Course
Quant Radar

Research Directions

High-Frequency and Market Microstructure Latest Activity

Research on order books, market making, transaction costs, and execution algorithms.

17 related activitiesLast 30 days 16 itemsFeatured among them 0 items

Updated

High-Frequency and Market Microstructurerelated activities

20 OctTueIssue 1–17 items
  1. arXiv q-fin32

    Research on Branching Signature Models

    The paper proposes a branching signature model that generalizes the classical geometric rough path based on the branching rough path framework, proving its universal approximation theorem, which can approximate high-level signatures through iterative combinations of low-level signature mappings. The model can explicitly extend the original path to a high-dimensional space via the mapping Ψ, implementing it as the classical geometric signature of the extended path, and its effectiveness is verified in two applications: rough volatility calibration and optimal execution.

    Read original
  2. arXiv q-fin54

    Partial Identification of FIFO Passive Execution under Aggregated Order Books: Different Fill Outcomes from the Same Book

    This arXiv paper defines the recovery of tick-level queue history from aggregated L2 limit order book snapshots as a conditional partial identification problem. It constructs a path-preserving compiler that only varies among three cancellation allocation rules—front-of-queue, quantity-weighted random, and back-of-queue—while fixing the aggregated path, reconciled market removals, latent order partitioning, and new order arrivals.

    Read original
  3. arXiv q-fin46

    Research on cooperative multi-agent reinforcement learning for market making in prediction market order books

    The study addresses market-making challenges in Polymarket's thin order books, using the JaxMARL-HFT framework to train cooperative multi-agent market-making models on 417 real markets. The market-making agents are split into quoters and risk managers, trained using recurrent independent proximal policy optimization (IPPO) combined with RNN policies. Testing showed that the improved single-agent market-making model quoted more frequently and had smaller out-of-sample losses, but none of the models achieved positive average portfolio value in out-of-sample evaluation, with only single-seed training completed.

    Read original
  4. arXiv q-fin64

    arXiv Paper: The Control Layer Behind Rule-Following Trading Agents Across Cross-Market Quotes

    This arXiv paper tests the degree to which 4 AI trading agents adhere to their own strategies and risk rules, based on 2627 closed trades from February 12 to August 27, 2026. 18% of the trades were live orders on Hyperliquid, while the rest were simulated at real-time prices, with a uniform round-trip fee of 0.09%.

    Read original
  5. arXiv q-fin46

    Probabilistic Order Book Forecasting with Conditional Diffusion Models

    This study introduces LOBGrad, a conditional DDPM framework for probabilistic order book forecasting. The framework combines an autoregressive RNN encoder with a diffusion-based generator, and establishes an excess-risk oracle inequality for the single-step DDPM noise prediction objective under exponential mixing and regularity assumptions.

    Read original
  6. arXiv q-fin35

    Topological Compression of Belief States in Prediction Markets for Event Forecasting

    This study introduces topological data analysis (TDA) and persistent homology (PH) into forecasting models in the field of prediction markets, exploring whether the underlying structure of market belief states has predictive value. The research findings show that single-parameter PH can improve the predictive power and calibration accuracy of linear forecasting models, and that two-parameter multi-persistent homology can produce similar gains for nonlinear models. The paper is 41 pages long with 19 figures, and belongs to the field of Trading and Market Microstructure (q-fin.TR).

    Read original
  7. arXiv q-fin37

    Bias Correction of Transaction Cost Models for Characteristic Function-Based Estimators

    To address the need for transaction cost estimation in scenarios without quote data, researchers developed an empirical characteristic function method with first-order bias correction for spread estimation, and established the asymptotic distribution of the corrected estimator under appropriate conditions. Numerical experiments show that this method has a bias-variance trade-off compared to the Roll model, and offers computational efficiency advantages over the Hasbrouck model.

    Read original
19 OctMon
  1. arXiv q-fin47

    AI Agents in Automated Markets

    The research team built behavioral models for three types of trading agents—rational arbitrageurs, manipulators, and retail momentum followers—based on reinforcement learning, and conducted a pilot study on hourly cryptocurrency data (AAVE, ETC, SUI). The results show that the behavioral characteristics of different agents have differentiated impacts on market pressure, liquidity, volatility, and risk-adjusted returns. The research conclusions support formulating differentiated regulatory rules for different behavioral types, rather than uniformly restricting volatility or turnover rates.

    Read original
  2. arXiv q-fin44

    OrderFlowDiT: A Transport-Regularized Diffusion Transformer for Probabilistic Limit Order Supply Prediction

    Researchers propose OrderFlowDiT, a conditional diffusion Transformer trained with an unbalanced optimal transport (UOT) auxiliary loss, to predict the conditional distribution of limit order submissions over the next five minutes. Across 1,950 validation conditions covering 30 U.S. stocks, adding UOT reduces the full-field transport cost by 45.8% and the transport energy score by 26.1% compared with training using only mean squared error, with improvements in both metrics for every stock.

    Read original
  3. arXiv q-fin43

    From Alpha to Execution: Trading Horizon, Market Regimes, and Execution Schedules under Concave Impact and Hurst Scaling

    This study analyzes trade execution under concave power-law impact and Hurst-scaled price fluctuations. It determines the optimal execution horizon by maximizing the Sharpe ratio of realized trading P&L and identifies conditions for an interior optimum. The derived execution schedule can reduce envelope costs by 10-17% compared to a TWAP schedule over the same duration. The results are published on arXiv.

    Read original
  4. arXiv q-fin46

    Cross-Product Modeling in Continuous Intraday Electricity Markets Based on Hawkes Processes

    This paper proposes a multivariate state-dependent Hawkes process (SDHP) to model cross-product liquidity dynamics between quarter-hourly and hourly contracts in the Dutch EPEX SPOT continuous intraday market. The model is calibrated using Level 3 order book data from the first month of each quarter in 2024, dividing trading sessions into normal and stress states. The study finds that approximately 95% of orders are driven by their own order flow, with cross-excitation primarily flowing from quarter-hourly contracts to hourly contracts, and this dominance significantly weakens in stress states.

    Read original
  5. arXiv q-fin55

    Portfolio Arbitrage in Prediction Markets: Empirical Analysis Based on the Polymarket Order Book

    This study proposes a framework for identifying and quantifying arbitrage opportunities between logically related prediction market contracts, demonstrating that arbitrage exists when prices fall outside the convex hull of feasible return vectors, and solving for the optimal arbitrage portfolio via integer programming. The study recovered 1.9 million logical relationships through semantic analysis of platform metadata and settlement rules, constructing a constraint system covering more than 255,000 contracts.

    Read original
  6. arXiv q-fin51

    arXiv Paper: Basis-Only Funding Rates Cannot Achieve Dividend Fairness — Settlement Design for Crypto-Equity Perpetuals

    This 16-page paper, accepted by MARBLE 2026, argues that crypto-exchange stock-like perpetual contracts calculate funding rates from the real-time basis and cannot replicate shareholder dividend cash flows for traders entering or exiting at arbitrary times. The authors derive a fair funding-rate identity, convert point-in-time fairness into an optimal stopping condition, and use measure theory to show that predictable dividends are atomic events. Absolutely continuous funding-rate paths cannot replicate this atom over a holding interval and must be matched with discrete event credits.

    Read original
11 OctSun
  1. Quantocracy24

    Quantocracy's Latest Quantitative Research Link Roundup as of October 10, 2026

    Quantocracy has released its latest roundup of quantitative research links as of October 10, 2026, featuring four related research pieces. It notes that the 30-year U.S. Treasury yield closed at 5.60% in the first week of October, the highest level since 2002; the remaining content covers research discussions on momentum portfolio development, pre-market patterns, and market stop-loss hunting.

    Read original
21 SeptMon
24 JulFri