Optimization Of Trading Strategies Research Articles

Applications and issues of artificial intelligence in the financial sector

This paper investigates the application of artificial intelligence in the financial sector, analyzing the existing technical, ethical, and legal issues, and proposing corresponding solutions. The research background highlights the widespread use of AI technology in the financial industry and the efficiency and cost benefits it brings. The research focuses on challenges related to data quality, feature engineering, model complexity, real-time capability, computational resources, and data privacy protection. The research method includes literature review and case analysis, revealing the applications of AI technology in stock prediction, risk management, trading strategy optimization, and customer service. The results indicate that effective data cleaning, automated feature engineering, model simplification and regularization techniques, the use of interpretability tools like LIME and SHAP, and the introduction of fairness evaluation standards can significantly enhance AI model performance and transparency. The conclusion points out that these measures can not only solve the current technical and ethical issues of AI in the financial sector but also promote the widespread application and standardization of AI technology in other fields.

Pricing Mechanism and Trading Strategy Optimization for Microgrid Cluster Based on CVaR Theory

With the increasing penetration rate of renewable energy generation, the uncertainty of renewable energy output in microgrid cluster (MGC) leads to significant fluctuations in transaction volume, which may lead to the risk of transaction default. This paper proposes a day-ahead two layer trading model for microgrid cluster based on price trading mechanism and Conditional value-at-risk (CVaR) theory. Firstly, the upper-layer establishes an objective to minimize the overall power fluctuation of the microgrid cluster using Demand response (DR) with a penalty mechanism. The microgrid cluster adopts an internal pricing mechanism and adjusts transaction prices based on internal supply-demand conditions to guide microgrids’ participation in intracluster trading, thereby encouraging the microgrid to use the flexible resources to reduce power fluctuation. Secondly, the lower-layer optimization establishes an optimization model with the objective of minimizing the comprehensive operating cost of the microgrid cluster. The model employs backward scenario reduction techniques to obtain multiple sets of typical scenarios for renewable energy generation, and the CVaR theory is introduced to quantify the potential risk of transaction default. Finally, the effectiveness of the proposed models is verified through case studies considering various application scenarios.

High-frequency forecasting of the crude oil futures price with multiple timeframe predictions fusion

In the abundant literature about crude oil futures price forecasting, researchers generally predicted the crude oil price movements from the perspective of only a single timeframe. In addition, the trading strategies of their trading models were generally designed to be less sophisticated, and their prediction models lacked interpretability. To fill these gaps, a price direction fused prediction and trading approach has been proposed for high-frequency prediction of the Chinese crude oil futures. In the proposed approach, the MTXGBoost (Multiple Timeframes eXtreme Gradient Boosting) is developed and utilized for predictions fusion under multiple timeframes, and the NSGA-II (Non-dominated Sorting Genetic Algorithm-II) is integrated for trading strategy optimization. Moreover, the SHAP (Shapley Additive exPlanation) approach is also employed to interpret how the proposed approach made predictions. Experimental results show that the approach proposed in this research averagely produced a direction prediction accuracy of 78.69%, an accumulated return of 23.17%, and a maximum drawdown of 1.00%, demonstrating that it can produce an excellent profit with small trading risks. Therefore, the proposed approach can be employed as an intelligent, efficient, and reliable decision support system for market investors, energy-related companies, and government departments to make crude oil related decisions.

Stochastic optimization of trading strategies in sequential electricity markets

Quantity and price risks determine key uncertainties market participants face in electricity markets with increased volatility, for instance due to high shares of renewables. In the time from day-ahead until real-time, there lies a large variation in best available information, such as between forecasts and realizations of uncertain parameters like renewable feed-in and electricity prices. This uncertainty reflects on both the market outcomes and the quantity of renewable generation, making the determination of sound trading strategies across different market segments a complex task. The scope of the paper is to optimize day-ahead and intraday trading decisions jointly for a portfolio with controllable and intermittent renewable generation under consideration of risk. We include a reserve market, a day-ahead market and an intraday market in stochastic modeling and develop a multi-stage stochastic Mixed Integer Linear Program. We assess the profitability as well as the risk exposure, quantified by the conditional value at risk metric, of trading strategies following different risk preferences. We conclude that a risk-neutral trader mainly relies on the opportunity of higher expected profits in intraday trading, whereas risk can be hedged effectively by trading on the day-ahead. Finally, we show that reserve market participation implies various rationales, including the relation of expected reserve prices among each other, the relation of expected reserve prices to spot market prices, as well as the relation of the spot market prices among each other.

Structural break-aware pairs trading strategy using deep reinforcement learning.

Pairs trading is an effective statistical arbitrage strategy considering the spread of paired stocks in a stable cointegration relationship. Nevertheless, rapid market changes may break the relationship (namely structural break), which further leads to tremendous loss in intraday trading. In this paper, we design a two-phase pairs trading strategy optimization framework, namely structural break-aware pairs trading strategy (SAPT), by leveraging machine learning techniques. Phase one is a hybrid model extracting frequency- and time-domain features to detect structural breaks. Phase two optimizes pairs trading strategy by sensing important risks, including structural breaks and market-closing risks, with a novel reinforcement learning model. In addition, the transaction cost is factored in a cost-aware objective to avoid significant reduction of profitability. Through large-scale experiments in real Taiwan stock market datasets, SAPT outperforms the state-of-the-art strategies by at least 456% and 934% in terms of profit and Sortino ratio, respectively.

Machine Learning Methods in Algorithmic Trading Strategy Optimization – Design and Time Efficiency

Abstract The main aim of this paper was to formulate and analyse the machine learning methods, fitted to the strategy parameters optimization specificity. The most important problems are the sensitivity of a strategy performance to little parameter changes and numerous local extrema distributed over the solution space in an irregular way. The methods were designed for the purpose of significant shortening of the computation time, without a substantial loss of strategy quality. The efficiency of methods was compared for three different pairs of assets in case of moving averages crossover system. The problem was presented for three sets of two assets’ portfolios. In the first case, a strategy was trading on the SPX and DAX index futures; in the second, on the AAPL and MSFT stocks; and finally, in the third case, on the HGF and CBF commodities futures. The methods operated on the in-sample data, containing 16 years of daily prices between 1998 and 2013 and was validated on the out-of-sample period between 2014 and 2017. The major hypothesis verified in this paper is that machine learning methods select strategies with evaluation criterion near the highest one, but in significantly lower execution time than the brute force method (Exhaustive Search).

Trading strategy optimization for a prosumer in continuous double auction-based peer-to-peer market: A prediction-integration model

With increasing prosumers employed with flexible resources, advanced demand-side management has become of great importance. To this end, integrating demand-side flexible resources into electricity markets is a significant trend for smart energy systems. The continuous double auction (CDA) market is viewed as a promising P2P (peer to peer) market mechanism to enable interactions among demand side prosumers and consumers in distribution grids. To achieve optimal operations and maximize profits, prosumers in the electricity market must act as price makers to simultaneously optimize their operations and trading strategies. However, the CDA-based market is difficult to model explicitly because of its information-based clearing mechanism and the stochastic bidding behaviors of its participants. To facilitate prosumers actively participating in the CDA market, this paper proposes a novel prediction-integration strategy optimization (PISO) model. A surrogate market prediction model based on Extreme Learning Machine (ELM) is developed, which learns the interaction relationship between prosumer bidding actions and market responses from historical transaction data. Moreover, the prediction model can be conveniently transformed and integrated into the prosumer operation optimization model in the form of constraints. Therefore, prosumer operations and market trading strategies can be jointly optimized through the proposed approach, facilitating the integration of flexible resources into electricity markets. Numerical studies demonstrate the effectiveness of the proposed model by comparing with existing CDA trading strategies under various market conditions.

COSMOS trader – Chaotic Neuro-oscillatory multiagent financial prediction and trading system

Over the years, financial engineering ranging from the study of financial signals to the modelling of financial prediction is one of the most stimulating topics for both academia and financial community. Not only because of its importance in terms of financial and commercial values, but more it vitally poses a real challenge to worldwide researchers and quants owing to its highly chaotic and almost unpredictable nature.This paper devises an innovative Chaotic Oscillatory Multi-agent-based Neuro-computing System (a.k.a. COSMOS) for worldwide financial prediction and intelligent trading. With the adoption of author's theoretical works on Lee-oscillator with profound transient-chaotic property, COSMOS effectively integrates chaotic neural oscillator technology into: 1) COSMOS Forecaster - Chaotic FFBP-based Time-series Supervised-learning agent for worldwide financial forecast and; 2) COSMOS Trader - Chaotic RBF-based Actor-Critic Reinforcement-learning agents for the optimization of trading strategies. COSMOS not only provides a fast reinforcement learning and forecast solution, more prominently it successfully resolves the massive data over-training and deadlock problems which usually imposed by traditional recurrent neural networks and RBF networks using classical sigmoid or gaussian-based activation functions.From the implementation perspective, COSMOS is integrated with 2048-trading day time-series financial data and 39 major financial signals as input signals for the real-time prediction and intelligent agent trading of 129 worldwide financial products which consists of: 9 major cryptocurrencies, 84 forex, 19 major commodities and 17 worldwide financial indices. In terms of system performance, past 500-day average daily forecast performance of COSMOS attained less 1% forecast percentage errors and with promising results of 8–13% monthly average returns.

The Bio-Inspired Optimization of Trading Strategies and Its Impact on the Efficient Market Hypothesis and Sustainable Development Strategies

In this paper, the evolutionary algorithm for the optimization of Forex market trading strategies is proposed. The introduction to issues related to the financial markets and the evolutionary algorithms precedes the main part of the paper, in which the proposed trading system is presented. The system uses the evolutionary algorithm for optimization of a parameterized greedy strategy, which is then used as an investment strategy on the Forex market. In the proposed system, a model of the Forex market was developed, including all elements that are necessary for simulating realistic trading processes. The proposed evolutionary algorithm contains several novel mechanisms that were introduced to optimize the greedy strategy. The most important of the proposed techniques are the mechanisms for maintaining the population diversity, a mechanism for protecting the best individuals in the population, the mechanisms preventing the excessive growth of the population, the mechanisms of the initialization of the population after moving the time window and a mechanism of choosing the best strategies used for trading. The experiments, conducted with the use of real-world Forex market data, were aimed at testing the quality of the results obtained using the proposed algorithm and comparing them with the results obtained by the buy-and-hold strategy. By comparing our results with the results of the buy-and-hold strategy, we attempted to verify the validity of the efficient market hypothesis. The credibility of the hypothesis would have more general implications for many different areas of our lives, including future sustainable development policies.

Pairs trading strategy optimization using the reinforcement learning method: a cointegration approach

Recent studies show that the popularity of the pairs trading strategy has been growing and it may pose a problem as the opportunities to trade become much smaller. Therefore, the optimization of pairs trading strategy has gained widespread attention among high-frequency traders. In this paper, using reinforcement learning, we examine the optimum level of pairs trading specifications over time. More specifically, the reinforcement learning agent chooses the optimum level of parameters of pairs trading to maximize the objective function. Results are obtained by applying a combination of the reinforcement learning method and cointegration approach. We find that boosting pairs trading specifications by using the proposed approach significantly overperform the previous methods. Empirical results based on the comprehensive intraday data which are obtained from S&P500 constituent stocks confirm the efficiently of our proposed method.

Application of differential evolution algorithm for optimization of strategies based on financial time series

An approach for optimization of trading strategies (algorithms) based on indicators of financial markets and evolutionary computation is described. A new version of differential evolution algorithm for the search for optimal parameters of trading strategies for maximization of trading profit is used. The experimental results show that this approach can improve several times the profitability of the trading strategies.

Pairs Trading Strategy Optimization Using the Reinforcement Learning Method: A Cointegration Approach

Recent studies show that the growing popularity of the pairs trading strategy may pose a problem as the opportunities to trade become much smaller. Hence optimizing pairs trading strategy has gained widespread attention among high frequency traders. In this paper, by employing reinforcement learning, we examine the optimum level of pairs trading specifications over time. The reinforcement learning agent chooses the optimum level of parameters of pairs trading to maximize the objective function. Results are obtained by applying a combination of reinforcement learning method and cointegration approach. We find that boosting pairs trading specifications by using the proposed approach is significantly over perform the previous methods. Empirical results based on the intraday data which are obtained from S&P500 constituent stocks support our method.

A parallel genetic algorithm for optimization of trading strategies

An approach to optimization of trading strategies (algorithms) is described based on indicators of financial markets and on evolutionary computations. A parallel genetic algorithm is presented for searching optimal parameters of trading strategies aimed at profit maximization.