Algorithmic Trading

FinMem: A Performance-Enhanced LLM Trading Agent with Layered Memory and Character Design ArXiv ID: 2311.13743 “View on arXiv” Authors: Unknown Abstract Recent advancements in Large Language Models (LLMs) have exhibited notable efficacy in question-answering (QA) tasks across diverse domains. Their prowess in integrating extensive web knowledge has fueled interest in developing LLM-based autonomous agents. While LLMs are efficient in decoding human instructions and deriving solutions by holistically processing historical inputs, transitioning to purpose-driven agents requires a supplementary rational architecture to process multi-source information, establish reasoning chains, and prioritize critical tasks. Addressing this, we introduce \textsc{“FinMem”}, a novel LLM-based agent framework devised for financial decision-making. It encompasses three core modules: Profiling, to customize the agent’s characteristics; Memory, with layered message processing, to aid the agent in assimilating hierarchical financial data; and Decision-making, to convert insights gained from memories into investment decisions. Notably, \textsc{“FinMem”}’s memory module aligns closely with the cognitive structure of human traders, offering robust interpretability and real-time tuning. Its adjustable cognitive span allows for the retention of critical information beyond human perceptual limits, thereby enhancing trading outcomes. This framework enables the agent to self-evolve its professional knowledge, react agilely to new investment cues, and continuously refine trading decisions in the volatile financial environment. We first compare \textsc{“FinMem”} with various algorithmic agents on a scalable real-world financial dataset, underscoring its leading trading performance in stocks. We then fine-tuned the agent’s perceptual span and character setting to achieve a significantly enhanced trading performance. Collectively, \textsc{“FinMem”} presents a cutting-edge LLM agent framework for automated trading, boosting cumulative investment returns. ...

Reinforcement Learning with Maskable Stock Representation for Portfolio Management in Customizable Stock Pools ArXiv ID: 2311.10801 “View on arXiv” Authors: Unknown Abstract Portfolio management (PM) is a fundamental financial trading task, which explores the optimal periodical reallocation of capitals into different stocks to pursue long-term profits. Reinforcement learning (RL) has recently shown its potential to train profitable agents for PM through interacting with financial markets. However, existing work mostly focuses on fixed stock pools, which is inconsistent with investors’ practical demand. Specifically, the target stock pool of different investors varies dramatically due to their discrepancy on market states and individual investors may temporally adjust stocks they desire to trade (e.g., adding one popular stocks), which lead to customizable stock pools (CSPs). Existing RL methods require to retrain RL agents even with a tiny change of the stock pool, which leads to high computational cost and unstable performance. To tackle this challenge, we propose EarnMore, a rEinforcement leARNing framework with Maskable stOck REpresentation to handle PM with CSPs through one-shot training in a global stock pool (GSP). Specifically, we first introduce a mechanism to mask out the representation of the stocks outside the target pool. Second, we learn meaningful stock representations through a self-supervised masking and reconstruction process. Third, a re-weighting mechanism is designed to make the portfolio concentrate on favorable stocks and neglect the stocks outside the target pool. Through extensive experiments on 8 subset stock pools of the US stock market, we demonstrate that EarnMore significantly outperforms 14 state-of-the-art baselines in terms of 6 popular financial metrics with over 40% improvement on profit. ...

Predicting risk/reward ratio in financial markets for asset management using machine learning ArXiv ID: 2311.09148 “View on arXiv” Authors: Unknown Abstract Financial market forecasting remains a formidable challenge despite the surge in computational capabilities and machine learning advancements. While numerous studies have underscored the precision of computer-generated market predictions, many of these forecasts fail to yield profitable trading outcomes. This discrepancy often arises from the unpredictable nature of profit and loss ratios in the event of successful and unsuccessful predictions. In this study, we introduce a novel algorithm specifically designed for forecasting the profit and loss outcomes of trading activities. This is further augmented by an innovative approach for integrating these forecasts with previous predictions of market trends. This approach is designed for algorithmic trading, enabling traders to assess the profitability of each trade and calibrate the optimal trade size. Our findings indicate that this method significantly improves the performance of traditional trading strategies as well as algorithmic trading systems, offering a promising avenue for enhancing trading decisions. ...

Commodities Trading through Deep Policy Gradient Methods ArXiv ID: 2309.00630 “View on arXiv” Authors: Unknown Abstract Algorithmic trading has gained attention due to its potential for generating superior returns. This paper investigates the effectiveness of deep reinforcement learning (DRL) methods in algorithmic commodities trading. It formulates the commodities trading problem as a continuous, discrete-time stochastic dynamical system. The proposed system employs a novel time-discretization scheme that adapts to market volatility, enhancing the statistical properties of subsampled financial time series. To optimize transaction-cost- and risk-sensitive trading agents, two policy gradient algorithms, namely actor-based and actor-critic-based approaches, are introduced. These agents utilize CNNs and LSTMs as parametric function approximators to map historical price observations to market positions.Backtesting on front-month natural gas futures demonstrates that DRL models increase the Sharpe ratio by $83%$ compared to the buy-and-hold baseline. Additionally, the risk profile of the agents can be customized through a hyperparameter that regulates risk sensitivity in the reward function during the optimization process. The actor-based models outperform the actor-critic-based models, while the CNN-based models show a slight performance advantage over the LSTM-based models. ...

Learning Not to Spoof ArXiv ID: 2306.06087 “View on arXiv” Authors: Unknown Abstract As intelligent trading agents based on reinforcement learning (RL) gain prevalence, it becomes more important to ensure that RL agents obey laws, regulations, and human behavioral expectations. There is substantial literature concerning the aversion of obvious catastrophes like crashing a helicopter or bankrupting a trading account, but little around the avoidance of subtle non-normative behavior for which there are examples, but no programmable definition. Such behavior may violate legal or regulatory, rather than physical or monetary, constraints. In this article, I consider a series of experiments in which an intelligent stock trading agent maximizes profit but may also inadvertently learn to spoof the market in which it participates. I first inject a hand-coded spoofing agent to a multi-agent market simulation and learn to recognize spoofing activity sequences. Then I replace the hand-coded spoofing trader with a simple profit-maximizing RL agent and observe that it independently discovers spoofing as the optimal strategy. Finally, I introduce a method to incorporate the recognizer as normative guide, shaping the agent’s perceived rewards and altering its selected actions. The agent remains profitable while avoiding spoofing behaviors that would result in even higher profit. After presenting the empirical results, I conclude with some recommendations. The method should generalize to the reduction of any unwanted behavior for which a recognizer can be learned. ...

From Data to Trade: A Machine Learning Approach to Quantitative Trading ArXiv ID: ssrn-4315362 “View on arXiv” Authors: Unknown Abstract “Machine learning has revolutionized the field of quantitative trading, enabling traders to develop and implement sophisticated trading strategies that lev Keywords: Machine Learning, Quantitative Trading, Algorithmic Trading, Time Series Forecasting, Financial Markets, Multi-Asset Complexity vs Empirical Score Math Complexity: 3.0/10 Empirical Rigor: 2.0/10 Quadrant: Philosophers Why: The paper is a broad, introductory survey of ML concepts in quantitative trading with minimal advanced mathematics or original derivations, and lacks any code, backtests, or specific empirical results. flowchart TD A["Research Goal"] --> B["Data Collection"] A --> C["ML Model Selection"] B --> D["Feature Engineering"] C --> D D --> E["Model Training"] E --> F["Backtesting"] F --> G["Key Findings"]

Trends and Applications of Machine Learning in QuantitativeFinance ArXiv ID: ssrn-3397005 “View on arXiv” Authors: Unknown Abstract Recent advances in machine learning are finding commercial applications across many industries, not least the finance industry. This paper focuses on applicatio Keywords: machine learning, algorithmic trading, predictive analytics, quantitative finance, Multi-Asset Complexity vs Empirical Score Math Complexity: 4.5/10 Empirical Rigor: 3.0/10 Quadrant: Philosophers Why: The paper is a broad literature review of ML applications in finance, focusing on conceptual categorization rather than novel mathematical derivations or empirical backtesting. It outlines common algorithms and use cases but lacks implementation details, statistical metrics, or specific experimental results. flowchart TD G["Research Goal: Evaluate ML in Quant Finance"] --> D["Data Sources"] D --> M["Key Methodology"] D --> C["Computational Processes"] M --> F["Key Findings/Outcomes"] C --> F subgraph D ["Data/Inputs"] D1["Multi-Asset Market Data"] D2["Historical Price & Volatility"] end subgraph M ["Methodology Steps"] M1["Algorithmic Trading Strategies"] M2["Predictive Analytics"] end subgraph C ["Computational Processes"] C1["Deep Learning Models"] C2["Reinforcement Learning"] end subgraph F ["Outcomes"] F1["Enhanced Portfolio Optimization"] F2["Improved Risk Management"] F3["Commercial Applications in Finance"] end

A Backtesting Protocol in the Era of Machine Learning ArXiv ID: ssrn-3275654 “View on arXiv” Authors: Unknown Abstract Machine learning offers a set of powerful tools that holds considerable promise for investment management. As with most quantitative applications in finance, th Keywords: Machine Learning, Investment Management, Quantitative Finance, Asset Pricing, Algorithmic Trading Complexity vs Empirical Score Math Complexity: 3.0/10 Empirical Rigor: 7.0/10 Quadrant: Street Traders Why: The paper focuses on a research protocol for backtesting and data mining, with moderate empirical rigor involving practical concerns like overfitting and data scarcity, but lacks advanced mathematical derivations, centering instead on statistical concepts and real-world data challenges. flowchart TD A["Research Goal: Develop robust backtesting protocol for ML in finance"] --> B["Data: Cross-sectional stock data & fundamental features"] B --> C["Methodology: ML pipelines with walk-forward validation"] C --> D["Computation: Model training, hyperparameter tuning, & signal generation"] D --> E["Risk Controls: Transaction costs, liquidity constraints, & overfitting tests"] E --> F["Key Outcomes: Generalizable signals & realistic performance metrics"] F --> G["Implication: ML requires rigorous validation to avoid false discoveries"]

Advances in Financial Machine Learning: Lecture 1/10 (seminar slides) ArXiv ID: ssrn-3270329 “View on arXiv” Authors: Unknown Abstract Machine learning (ML) is changing virtually every aspect of our lives. Today ML algorithms accomplish tasks that until recently only expert humans could perform Keywords: machine learning, algorithmic trading, predictive analytics, data science, fintech, Multi-Asset / Quantitative Strategies Complexity vs Empirical Score Math Complexity: 3.5/10 Empirical Rigor: 2.0/10 Quadrant: Philosophers Why: The excerpt presents a high-level critique of econometric methods compared to machine learning, but it focuses on theoretical arguments and conceptual pitfalls rather than advancing novel mathematical techniques or presenting concrete backtesting results. flowchart TD A["Research Goal: Apply ML to Financial Markets"] --> B["Methodology: Identify Financial Signals & Features"] B --> C["Data Inputs: High-Frequency Trading & Market Data"] C --> D["Computation: Training Algorithms & Model Validation"] D --> E["Outcomes: Predictive Analytics for Multi-Asset Strategies"]

Advances in Financial Machine Learning: Lecture 8/10 (seminar slides) ArXiv ID: ssrn-3270269 “View on arXiv” Authors: Unknown Abstract Machine learning (ML) is changing virtually every aspect of our lives. Today ML algorithms accomplish tasks that until recently only expert humans could perform Keywords: Machine Learning (ML), Predictive Analytics, Algorithmic Trading, Big Data, Equities Complexity vs Empirical Score Math Complexity: 8.0/10 Empirical Rigor: 3.0/10 Quadrant: Lab Rats Why: The excerpt features advanced statistical methods and formal derivations for detecting structural breaks and entropy estimation, but it lacks implementation details, backtests, or code, focusing instead on theoretical presentations suitable for academic exploration. flowchart TD Q["Research Goal: Can ML beat markets?"] D["Input: Big Data Equities"] P["Computational Process: Algorithmic Trading Models"] F["Outcome: Predictive Analytics"] E["Key Finding: Risk/Overfitting Constraints"] Q --> D D --> P P --> F F --> E