Image Representation of Time Series for Reinforcement Learning Trading Agent

The availability of diverse data has increased the demand for expertise in algorithmic trading strategies. Reinforcement learning has shown interesting applicability in a wide range of tasks, especially in some challenging problems as trading, where slow model convergence, inference speed, and reduced model accuracy appear as barriers in this type of application. In this paper, we propose the transformation of time series into images considering a transfer learning based on a semi-supervised model with deep Q learning agents, where labels were generated by an evolutionary algorithm to improve both training speed and performance measures.

Deep Reinforcement Learning Agent for S&P 500 Stock Selection

This study investigated the performance of a trading agent based on a convolutional neural network model in portfolio management. The results showed that with real-world data the agent could produce relevant trading results, while the agent’s behavior corresponded to that of a high-risk taker. The data used were wide in comparison with earlier reported research and was based on the full set of the S&P 500 stock data for twenty-one years supplemented with selected financial ratios. The results presented are new in terms of the size of the data set used and with regards to the model used. The results provide direction and offer insight into how deep learning methods may be used in constructing automatic trading systems.

Adaptive Quantitative Trading: An Imitative Deep Reinforcement Learning Approach

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.

MAHARAJA OVERBECK: SATU PANDANGAN MENGENAI HAK DAN KEDUDUKAN PEMERINTAH BORNEO UTARA (1877-1881)

Gustavos Overbeck adalah seorang saudagar, pengembara, dan diplomat. Apabila beliau memajak seluruh wilayah Borneo Utara pada tahun 1877, Sultan Brunei telah melantik beliau sebagai Maharaja Sabah, Raja Gaya dan Sandakan. Tujuan Overbeck pergi ke Borneo Utara hanya untuk berniaga demi mencari keuntungan. Pada tahun 1854, beliau menjadi ejen perdagangan kepada sebuah syarikat British, iaitu Dent & Company di Hong Kong. Pada tahun 1864-1866, beliau pernah dilantik sebagai konsul Austria. Pada awal tahun 1876, Overbeck telah membeli hak konsesi syarikat American Trading Company yang memiliki konsesi ke atas Borneo Utara. Overbeck bercita-cita untuk menubuhkan sebuah pemerintahan koloni. Pada bulan November 1877, beliau belayar ke Borneo Utara dengan tujuan untuk memperoleh hak wilayah dan eksploitasi sumbernya. Beliau menghadap Sultan Brunei untuk memperbaharui konsesi yang hampir tamat tempoh pada ketika itu. Pada 29 Disember 1877, satu perjanjian konsesi ke atas Borneo Utara dengan Sultan Abdul Momin telah termeterai. Perjanjian tersebut memberi hak sepenuhnya kepada Overbeck untuk memerintah Borneo Utara. Kertas kerja ini menggunakan kaedah kajian kepustakaan dan arkib. Makalah ini akan menjelaskan kejayaan dan kegagalan Overbeck untuk memanfaat kedudukan beliau sebagai pemerintah di Borneo Utara sehingga tahun 1881. Gustavos Overbeck was a merchant, traveler, and diplomat. When he occupied the whole province of North Borneo in 1877, the Sultan of Brunei appointed him as Maharaja of Sabah, and Raja of Gaya and Sandakan. Overbeck's intention to go to North Borneo was only to do business for profit. In 1854, he became a trading agent for a British company, the Dent & Company in Hong Kong. In 1864-1866 he was appointed as consul of Austria. In early 1876, Overbeck purchased the concession rights of the American Trading Company, which had concessions over North Borneo. Overbeck aspired to establish a colony. In November 1877 he sailed to North Borneo with the aim of securing territorial rights and exploiting its resources. He met the Sultan of Brunei to renew the concession that was about to expire. On December 29, 1877, a concession agreement for North Borneo was been signed with Sultan Abdul Momin. The agreement gives Overbeck full rights to govern North Borneo. This paper will utilize library and archival research methods. This paper will explain the success and failure of Overbeck to leverage his position as ruler of North Borneo until 1881.