We consider a basic model of multi-period trading, which can be used to evaluate the performance of a trading strategy. We describe a framework for single-period optimization, where the trades in each period are found by solving a convex optimization problem that trades off expected return, risk, transaction cost and h…
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48 results for “single trade”
Unified market-based description of returns and variances of trades.
problem Market-based variance of trades and market portfolio.
method Unified market-based approach to describe returns and variances of trades and market portfolio.
result Market-based variance accounts for random volumes of trades and differs from Markowitz's portfolio variance.
We describe how the market-based average and volatility of the "actual" return, which the investors gain within their market sales, depend on the statistical moments, volatilities, and correlations of the current and past market trade values. We describe three successive approximations. First, we derive the dependence …
We develop a single-period model for a large economic agent who trades with market makers at their utility indifference prices. A key role is played by a pair of conjugate saddle functions associated with the description of Pareto optimal allocations in terms of the utility function of a representative market maker.
The paper explores a Multi-Objective RL approach for trading that generalizes reward functions.
problem Improving performance in single-asset trading through adaptive reward functions.
method Developed a Multi-Objective Deep Reinforcement Learning algorithm to generalize reward functions and discount factors.
result The Multi-Objective algorithm demonstrates increased predictive stability and better performance in sparse reward scenarios.
The book explores essential stats and psychology for quantitative trading.
problem Developing a quantitative trading system.
method Logical progression through articles on statistics, quantitative trading, and psychology.
result Essential elements for quantitative trading systems.
Optimizes portfolio with two controls to minimize trades and maintain signal integrity.
problem Optimizing a single-asset portfolio with transaction costs and signal autocorrelation.
method Formulated an optimization problem to minimize trades while maintaining signal integrity and achieving maximum return.
result Locally optimal solution minimizes trades and achieves maximum return, with a quantifiable improvement based on threshold and autocorrelation removed.
Single Curve Collapse of the Price Impact Function for the New York Stock Exchangecond-mat.stat-mech
We study the average price impact of a single trade executed in the NYSE. After appropriate averaging and rescaling, the data for the 1000 most highly capitalized stocks collapse onto a single function, giving average price shift as a function of trade size. This function increases as a power that is the order of 1/2 f…
Paper improves financial trading models using GPU parallelism.
problem Challenges in policy instability and sampling bottlenecks in reinforcement learning for financial tasks.
method Revisits ensemble methods with massively parallel simulations on GPUs.
result Significantly improved computational efficiency and robustness of financial decision-making strategies.
Study finds optimal boundaries for hedging a perpetual American put option.
problem Hedging perpetual American put options using delta hedging is impractical.
method Considered a seller of a perpetual American put option with a single trade.
result Determined optimal trading boundaries and hedging strategy.
Generalizes insider trading model to multiple assets.
problem Modeling informed trading in a multi-asset context.
method Formulated an infinite-dimensional Bayesian trading game.
result Obtained a parsimonious equilibrium with closed-form solutions.
A new framework enables real-time task trade-off control.
problem Conflict between multiple related tasks in a fixed model capacity.
method Formulates MTL as a preference-conditioned multiobjective optimization problem; uses a hypernetwork-based neural network.
result A single model can handle different trade-off preferences among multiple tasks.
A quasi-centralized limit order book (QCLOB) is a limit order book (LOB) in which financial institutions can only access the trading opportunities offered by counterparties with whom they possess sufficient bilateral credit. We perform an empirical analysis of a recent, high-quality data set from a large electronic tra…
We confirm and substantially extend the recent empirical result of Andersen et al. \cite{Andersen2015}, where it is shown that the amount of risk exchanged in the E-mini S\&P futures market (i.e. price times volume times volatility) scales like the 3/2 power of the number of trades . We show that this 3/2-law ho…
SentARL uses sentiment features to improve trading profits.
problem Improving profit stability in single-asset trading.
method Sentiment-Aware Reinforcement Learning (SentARL) system.
result SentARL consistently outperforms baselines across multiple assets and conditions.
We extend the framework of trading strategies of Gatheral [2010] from single stocks to a pair of stocks. Our trading strategy with the executions of two round-trip trades can be described by the trading rates of the paired stocks and the ratio of their trading periods. By minimizing the potential cost arising from cros…
Market-based portfolio variance measures risks using trade data.
problem Measuring portfolio risks using traditional methods ignores trade volume randomness.
method Uses time series of trades with securities and portfolio to assess variance.
result Portfolio variance can be decomposed into securities' contributions, accounting for trade volume randomness.
SAFLe solves federated learning's trade-off between non-linearity and scalability.
problem Federated Learning's high communication overhead and performance collapse on non-IID data.
method SAFLe introduces a structured head of bucketed features and sparse, grouped embeddings, mathematically equivalent to a high-dimensional linear regression.
result SAFLe achieves a new state-of-the-art in analytic FL, outperforming linear AFL and multi-round DeepAFL.
The worldwide trade network has been widely studied through different data sets and network representations with a view to better understanding interactions among countries and products. Here we investigate international trade through the lenses of the single-layer, multiplex, and multi-layer networks. We discuss diffe…
Modeling insider trading with transaction costs and fair pricing.
problem Maximizing profits for an informed trader in a market with transaction costs and fair pricing.
method Single auction model and continuous time analogue, analyzing equilibrium behavior with polynomial and differential equations.
result Equilibrium trading strategy and pricing rules are affected by transaction costs, revealing an information gap.
By studying all the trades and best bids/asks of ultra high frequency snapshots recorded from the order books of a basket of 10 futures assets, we bring qualitative empirical evidence that the impact of a single trade depends on the intertrade time lags. We find that when the trading rate becomes faster, the return var…
Variety and Volatility in Financial Marketscond-mat.stat-mech
We study the price dynamics of stocks traded in a financial market by considering the statistical properties both of a single time series and of an ensemble of stocks traded simultaneously. We use the stocks traded in the New York Stock Exchange to form a statistical ensemble of daily stock returns. For each tradin…
Method orders Pareto solutions using transformed objective scores.
problem Lack of orderability in multi-objective optimization solutions.
method Probability integral transform to map objectives to scores.
result Pareto efficient solutions can be ordered in score space.
Multi-task learning is a powerful method for solving multiple correlated tasks simultaneously. However, it is often impossible to find one single solution to optimize all the tasks, since different tasks might conflict with each other. Recently, a novel method is proposed to find one single Pareto optimal solution with…
In this paper, the survival function of waiting times between orders and the corresponding trades in a double-auction market is studied both by means of experiments and of empirical data. It turns out that, already at the level of order durations, the survival function cannot be represented by a single exponential, thu…
Sentiment analysis from LLMs improves financial trading performance.
problem Improving dynamic strategy optimization in financial markets.
method Integration of sentiment analysis from LLMs into RL frameworks.
result Sentiment-enhanced RL models outperform traditional RL models in net worth and cumulative profit.
We analyze a proprietary dataset of trades by a single asset manager, comparing their price impact with that of the trades of the rest of the market. In the context of a linear propagator model we find no significant difference between the two, suggesting that both the magnitude and time dependence of impact are univer…
We propose a prediction model based on the minority game in which traders continuously evaluate a complete set of trading strategies with different memory lengths using the strategies' past performance. Based on the chosen trading strategy they determine their prediction of the movement for the following time period of…
We consider the optimal trade execution strategies for a large portfolio of single stocks proposed by Almgren (2003). This framework accounts for a nonlinear impact of trades on average market prices. The results of Almgren (2003) are based on the assumption that no shares of assets per unit of time are trade at the be…
We study a stochastic control approach to managed futures portfolios. Building on the Schwartz 97 stochastic convenience yield model for commodity prices, we formulate a utility maximization problem for dynamically trading a single-maturity futures or multiple futures contracts over a finite horizon. By analyzing the a…
Using a proprietary dataset of meta-orders and prediction signals, and assuming a quasi-linear impact model, we deconvolve market impact from past correlated trades and a predictable return component to elicit the temporal dependence of the market impact of a single daily meta-order, over a ten day horizon in various e…
Using the most comprehensive source of commercially available data on the US National Market System, we analyze all quotes and trades associated with Dow 30 stocks in 2016 from the vantage point of a single and fixed frame of reference. We find that inefficiencies created in part by the fragmentation of the equity mark…
Proposes deep mixture models for probabilistic price movement forecasting in high-frequency trading.
problem Probabilistic forecasting of price movements in high-frequency trading.
method Deep recurrent neural networks with probabilistic mixture models.
result Outperforms benchmark models in both metric-based and simulated trading scenarios.
Unified routing and arbitrage with concave continuation.
problem Combining routing and arbitrage in financial markets.
method Extending AMM trade functions to negative inputs via concave continuation.
result Unified approach unifies routing and arbitrage.
Risk Constrained Trading Strategies for Stochastic Generation with a Single-Price Balancing Marketq-fin.GN
Due to the limited predictability of wind power and other stochastic generation, trading this energy in competitive electricity markets is challenging. This paper derives revenue-maximising and risk-constrained strategies for stochastic generators participating in electricity markets with a single-price balancing mecha…
In the seminal paper on optimal execution of portfolio transactions, Almgren and Chriss (2001) define the optimal trading strategy to liquidate a fixed volume of a single security under price uncertainty. Yet there exist situations, such as in the power market, in which the volume to be traded can only be estimated and…
TradingAgents uses LLM-powered multi-agent framework for financial trading.
problem Lack of collaborative dynamics in multi-agent financial trading systems.
method Inspired by real-world trading firms, TradingAgents features specialized LLM-powered agents and a risk management team.
result Framework outperforms baseline models in trading performance metrics.
Study shows time matters in automated trading, improving simple strategies over complex ones.
problem Effects of reaction speed and trading urgency on automated trading strategies.
method Simulated financial markets with public limit order book and continuous double auction matching. Examined reaction speed and trading urgency.
result Simple strategies outperform complex ones when considering reaction speed and trading urgency.
We explore the competitive effects of reaction time of automated trading strategies in simulated financial markets containing a single exchange with public limit order book and continuous double auction matching. A large body of research conducted over several decades has been devoted to trading agent design and simula…
Previous studies of the stock price response to trades focused on the dynamics of single stocks, i.e. they addressed the self-response. We empirically investigate the price response of one stock to the trades of other stocks in a correlated market, i.e. the cross-responses. How large is the impact of one stock on other…
Paper proposes a deep reinforcement learning model for forex trading that considers transaction costs.
problem Trading in forex markets with high transaction costs and non-stationary data.
method Deep reinforcement learning model considering transaction costs and online learning.
result Maximizes profit while keeping transaction costs low in non-stationary markets.
This paper addresses practical challenges in portfolio optimisation for automated trading.
problem Implementing optimal portfolio weights into real trades with transaction costs and lot sizes.
method Two-stage framework: optimises portfolio weights first, then generates realistic trades.
result The two-stage approach effectively converts optimal portfolios into actionable trades, mitigating practical difficulties.
A Generalization of the Robust Positive Expectation Theorem for Stock Trading via Feedback Controlq-fin.ST
The starting point of this paper is the so-called Robust Positive Expectation (RPE) Theorem, a result which appears in literature in the context of Simultaneous Long-Short stock trading. This theorem states that using a combination of two specially-constructed linear feedback trading controllers, one long and one short…
Symmetry alteration of ensemble return distribution in crash and rally days of financial marketscond-mat.stat-mech
We select the stocks traded in the New York Stock Exchange and we form a statistical ensemble of daily stock returns for each of the trading days of our database from the stock price time series. We study the ensemble return distribution for each trading day and we find that the symmetry properties of the ensem…
We employ a 2x3 factorial experiment to study two central factors in the design of prediction markets (PMs) for idea evaluation: the overall design of the PM, and the elasticity of market prices set by a market maker. The results show that 'multi-market designs' on which each contract is traded on a separate PM lead to…
We present an agent based model of a single asset financial market that is capable of replicating several non-trivial statistical properties observed in real financial markets, generically referred to as stylized facts. While previous models reported in the literature are also capable of replicating some of these stati…
FlowHFT learns adaptive trading strategies from multiple models for diverse market conditions.
problem Traditional HFT models are limited by specific market conditions and cannot adapt to dynamic markets.
method FlowHFT uses flow matching policy to learn from multiple expert models and adapt to various market scenarios.
result FlowHFT consistently outperforms individual expert models in multiple market conditions.
Derivative traders are usually required to scan through hundreds, even thousands of possible trades on a daily basis. Up to now, not a single solution is available to aid in their job. Hence, this work aims to develop a trading recommendation system, and apply this system to the so-called Mid-Curve Calendar Spread (MCC…