A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
Study uses RNN for real-time crypto price prediction and trading optimization.
FinGPT uses LLMs for real-time market sentiment analysis.
The paper analyzes real-time methods to detect rapidly varying liquidity in markets.
A new framework enables real-time task trade-off control.
Deep RL shows promise in algo trading, but more research needed.
Optimizes real-time data processing in HFT algorithms using machine learning.
ALPE improves mid-price forecasting in HFT with real-time data.
We describe an end-to-end real-time S&P futures trading system. Inner-shell stochastic nonlinear dynamic models are developed, and Canonical Momenta Indicators (CMI) are derived from a fitted Lagrangian used by outer-shell trading models dependent on these indicators. Recursive and adaptive optimization using Adaptive …
Agent Trading Arena trains LLMs in real-time financial markets to improve numerical reasoning.
Neural process model improves real-time condition monitoring signal prediction.
Flexible algorithm of multicurrency trade on Forex market has been built on the grounds of non-linear stochastic wavelets (NSW) model. Probability of the loss-free trade has been evaluated. Results of the algorithm's real-time testing and issues of the algorithm's development are discussed.
GC 2022 challenges real-time trend detection in financial tick data.
AI-Trader benchmarks LLMs in live financial markets, revealing poor trading performance.
Trading system uses NP-hard optimization to select stocks for high Sharpe ratio trading.
Study combines sentiment analysis with traditional models for better S&P 500 trading.
Motivated by the practical challenge in monitoring the performance of a large number of algorithmic trading orders, this paper provides a methodology that leads to automatic discovery of the causes that lie behind a poor trading performance. It also gives theoretical foundations to a generic framework for real-time tra…
Exchanges acquire excess processing capacity to accommodate trading activity surges associated with zero-sum high-frequency trader (HFT) "duels." The idle capacity's opportunity cost is an externality of low-latency trading. We build a model of decentralized exchanges (DEX) with flexible capacity. On DEX, HFTs acquire …
FinBloom enhances LLMs for real-time financial queries.
In order to reduce signalling, traders may resort to limiting access to dark venues and imposing limits on minimum fill sizes they are willing to trade. However, doing this also restricts the liquidity available to the trader since an ever increasing quantity of orders are traded by algos in clips. An alternative is to…
Statistical arbitrage strategies, such as pairs trading and its generalizations, rely on the construction of mean-reverting spreads enjoying a certain degree of predictability. Gaussian linear state-space processes have recently been proposed as a model for such spreads under the assumption that the observed process is…
The algorithmic trading comes from digitalisation of the processing of trading assets on financial markets. Since 1980 the computerization of the stock market offers real time processing of financial information. This technological revolution has offered processes and mathematic methods to identify best return on trans…
New framework detects crypto wash trading using liquidity measures.
FutureQuant Transformer predicts price ranges and volatility for futures trading.
Study predicts ad conversions based on URL embeddings.
MM-DREX adapts LLM experts for financial trading via dynamic routing.
Electricity accounts for 25% of global greenhouse gas emissions. Reducing emissions related to electricity consumption requires accurate measurements readily available to consumers, regulators and investors. In this case study, we propose a new real-time consumption-based accounting approach based on flow tracing. This…
Ever growing volume and velocity of data coupled with decreasing attention span of end users underscore the critical need for real-time analytics. In this regard, anomaly detection plays a key role as an application as well as a means to verify data fidelity. Although the subject of anomaly detection has been researche…
Data driven methods for time series forecasting that quantify uncertainty open new important possibilities for robot tasks with hard real time constraints, allowing the robot system to make decisions that trade off between reaction time and accuracy in the predictions. Despite the recent advances in deep learning, it i…
We propose a mathematical model of momentum risk-taking, which is essentially real-time risk management focused on short-term volatility of stock markets. Its implementation, our fully automated momentum equity trading system presented systematically, proved to be successful in extensive historical and real-time experi…
The paper proposes a framework for modeling and analysis of the dynamics of supply, demand, and clearing prices in power system with real-time retail pricing and information asymmetry. Real-time retail pricing is characterized by passing on the real-time wholesale electricity prices to the end consumers, and is shown t…
Dynamic sentiment analysis improves stock trading strategies.
ATLAS uses LLMs to adaptively trade by optimizing prompts and coordinating agents.
Paper develops a fast Bayesian method to predict toxic trades in financial transactions.
The imbalance of buying and selling functions profoundly in the formation of market trends, however, a fine-granularity investigation of the imbalance is still missing. This paper investigates a unique transaction dataset that enables us to inspect the imbalance of buying and selling on the man-times level at high freq…
Model predicts and optimizes trading of electricity price spreads across multiple zones.
Dynamic portfolio optimization is the process of sequentially allocating wealth to a collection of assets in some consecutive trading periods, based on investors' return-risk profile. Automating this process with machine learning remains a challenging problem. Here, we design a deep reinforcement learning (RL) architec…
ContestTrade uses competitive teams to improve LLM trading performance.
Model predicts option movements using residual transactions for better market timing.
In most illiquid markets, there is no obvious proxy for the market price of an asset. The European corporate bond market is an archetypal example of such an illiquid market where mid-prices can only be estimated with a statistical model. In this OTC market, dealers / market makers only have access, indeed, to partial i…
Background-Foreground classification is a well-studied problem in computer vision. Due to the pixel-wise nature of modeling and processing in the algorithm, it is usually difficult to satisfy real-time constraints. There is a trade-off between the speed (because of model complexity) and accuracy. Inspired by the reject…
Trading large volumes of a financial asset in order driven markets requires the use of algorithmic execution dividing the volume in many transactions in order to minimize costs due to market impact. A proper design of an optimal execution strategy strongly depends on a careful modeling of market impact, i.e. how the pr…
Adversarial attacks can fool algorithmic trading systems.
Study compares statistical and machine learning models for detecting crypto trading anomalies.
Detecting real-time price impact in algo trading
Study finds short-term trading signals can enhance alpha in U.S. S&P 500 portfolios.
Real-time personalization for HAR models learns from new users without prior data.
HedgeAgents boosts financial trading with balanced strategies.
This survey analyzes deep learning methods for real-time semantic image segmentation.