Résultats de recherche pour « Quantitative Trading »

Quantitative trading (QT) , which refers to the usage of mathematical models and data-driven techniques in analyzing the financial market, has been a popular topic in both academia and financial industry since 1970s. In the last decade, reinforcement learning (RL) has garnered significant interest in many domains such as robotics and video games, owing to its outstanding ability on solving complex sequential decision making problems. RL’s impact is pervasive, recently demonstrating its ability to conquer many challenging QT tasks. It is a flourishing research direction to explore RL techniques’ potential on QT tasks. This paper aims at providing a comprehensive survey of research efforts on RL-based methods for QT tasks. More concretely, we devise a taxonomy of RL-based QT models, along with a comprehensive summary of the state of the art. Finally, we discuss current challenges and propose future research directions in this exciting field.

In recent years, considerable efforts have been devoted to developing AI techniques for finance research and applications. For instance, AI techniques (e.g., machine learning) can help traders in quantitative trading (QT) by automating two tasks: market condition recognition and trading strategies execution. However, existing methods in QT face challenges such as representing noisy high-frequent financial data and finding the balance between exploration and exploitation of the trading agent with AI techniques. To address the challenges, we propose an adaptive trading model, namely iRDPG, to automatically develop QT strategies by an intelligent trading agent. Our model is enhanced by deep reinforcement learning (DRL) and imitation learning techniques. Specifically, considering the noisy financial data, we formulate the QT process as a Partially Observable Markov Decision Process (POMDP). Also, we introduce imitation learning to leverage classical trading strategies useful to balance between exploration and exploitation. For better simulation, we train our trading agent in the real financial market using minute-frequent data. Experimental results demonstrate that our model can extract robust market features and be adaptive in different markets.

ABSTRACT___________________________________________________________________Applied general equilibrium (AGE) models, which feature multiple countries, multiple industries, and input-output linkages across industries, have been the dominant tool for evaluating the impact of trade reforms since the 1980s.We review how these models are used to perform policy analysis and document their shortcomings in predicting the industry-level effects of past trade reforms.We argue that, to improve their performance, AGE models need to incorporate product-level data on bilateral trade relations by industry and better model how trade reforms lower bilateral trade costs.We use the least traded products methodology of Kehoe et al. (2015) to provide guidance on how improvements can be made.We provide further suggestions on how AGE models can incorporate recent advances in quantitative trade theory to improve their predictive ability and better quantify the gains from trade liberalization.

With the rapid development of data science, quantitative trading models have become prevalent in financial markets. Based on Apriori algorithm, we analyze the rise characteristics of gold and bitcoin. Based on the principle of financial investment, BiLSTM with attention mechanism and VaR, the transaction decision model was established. We also establish a exponential model to identify how much money should be invested each time. Simultaneously, we apply a modified greedy algorithm to decide our investing decisions and it turns out to be quiet successful. Generally, the trading model established in this paper has good sensitivity to adapt to market changes and has strong risk resistance.

Quantitative trading through automated systems has been vastly growing in recent years. The advancement in machine learning algorithms has pushed that growth even further, where their capability in extracting high-level patterns within financial markets data is evident. Nonetheless, trading with supervised machine learning can be challenging since the system learns to predict the price to minimize the error rather than optimize a financial performance measure. Reinforcement Learning (RL), a machine learning paradigm that intersects with optimal control theory, could bridge that divide since it is a goal-oriented learning system that could perform the two main trading steps, market analysis and making decisions to optimize a financial measure, without explicitly predicting the future price movement. This survey reviews quantitative trading under the different main RL methods. We first begin by describing the trading process and how it suits the RL framework, and we briefly discuss the historical aspect of RL inception. We then abundantly discuss RL preliminaries, including the Markov Decision Process elements and the main approaches of extracting optimal policies under the RL framework. After that, we review the literature of QT under both tabular and function approximation RL. Finally, we propose directions for future research predominantly driven by the still open challenges in implementing RL on QT applications.

This paper also addresses quantitative trading where machine learning techniques are applied. Quantitative trading uses historical data and makes future predictions that inform and optimise trading strategies. There are two machine learning techniques used in quantitative trading: supervised and reinforcement learning. In a supervised learning setting, machine learning techniques such as regression analysis, classification models and time series prediction are used to forecast future possible outcomes in the market. Data such as historical prices, volume and financial indicators are used to determine trends that inform and optimise trading decisions. Reinforcement learning is another machine learning technique in quantitative trading where adaptive strategies using a reward system are designed. These strategies then analyse market dynamics and optimise trades through interaction and feedback. The third technique, deep learning, takes neural networks and uses them to process large-scale, complex data. It's particularly useful in quantitative trading as it generates more accurate predictions; for instance, neural networks are capable of learning trading signals from price and volume data and even from news headlines. By enabling computers to analyse large quantities of training data, deep learning improves the predictive accuracy of future price trends. It also helps to generate trading signals that are more robust to market conditions.

Quantitative trading through automated systems has been vastly growing in recent years. The advancement in machine learning algorithms has pushed that growth even further, where their capability in extracting high-level patterns within financial markets data is evident. Nonetheless, trading with supervised machine learning can be challenging since the system learns to predict the price to minimize the error rather than optimize a financial performance measure. Reinforcement Learning (RL), a machine learning paradigm that intersects with optimal control theory, could bridge that divide since it is a goal-oriented learning system that could perform the two main trading steps, market analysis and making decisions to optimize a financial measure, without explicitly predicting the future price movement. This survey reviews quantitative trading under the different main RL methods. We first begin by describing the trading process and how it suits the RL framework, and we briefly discuss the historical aspect of RL inception. We then abundantly discuss RL preliminaries, including the Markov Decision Process elements and the main approaches of extracting optimal policies under the RL framework. After that, we review the literature of QT under both tabular and function approximation RL. Finally, we propose directions for future research predominantly driven by the still open challenges in implementing RL on QT applications.