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7. Trader decision-making based on individual and social learning with competing trading strategies

Author

Zhang, Junhuan, McBurney, Peter John, and Musial-Gabrys, Katarzyna Malgorzata

Abstract

This thesis reports on research exploring the impact of social networks on the strategies and performance of traders in artificial financial markets. A Social Network Artificial stoCk marKet (SNACK) model is developed to do this. The SNACK model investigates how traders may make decisions about trading strategies based on individual learning and social learning in continuous double auction markets with different communication networks. The case of no-network is also considered, for comparison. Two reinforcement learning methods for multi-armed bandit problems, Epsilon-greedy and SoftMax, are applied to help traders to make decisions by learning from the experiences of themselves and their neighbours. Moreover, a random learning strategy is applied for comparison with these two reinforcement learning methods. There are four main statistical analyses of the simulation data from the SNACK model. First, I examine the influence of the proportion of rational traders on the performance of the financial markets and traders. This shows that the proportion of rational traders can impact market outcomes, which include market price, number of trades, buyers' profit, sellers' profit, and market efficiency. Second, the Tripartite Domination Conjecture about the convergence of proportions of traders choosing each trading strategy shows that the proportions of buyers and sellers selecting each trading strategy are stable after a fixed number of trading rounds with social networks and no-network. Third, the relationship between the proportion of rational traders and the financial markets is quantified. The findings show that there may be a strong sine and exponential mixture or polynomial quantitative relationship between the proportions of rational traders and the mean values of market outcomes with different social networks and no-network. The fourth study is how social networks impact the performance of traders and of the financial markets. These four studies are explored under SoftMax learning strategy. In addition, another small study of the influences of different machine learning methods for the decision making processes on the performance of market outcomes is given in the appendix. In summary, this research contributes to an understanding of the relationship between social networks and the performance of both traders and marketplaces, in artificial stock markets.

Published
2015

9. Cryptocurrency Price Bubble Detection Using Log-Periodic Power Law Model and Wavelet Analysis

Author

Zhang, Junhuan, Wang, Haodong, Chen, Jing, and Liu, Anqi

Abstract

In this article, we establish a method to detect and formulate price bubbles in the cryptocurrency markets. This method identifies abnormal crashes through violations of the exponential decaying property. Confirmations of bubble bursts within these anomalies are obtained through wavelet analysis. By decomposing the cryptocurrency price into the high-frequency and low-frequency factors, we distinguish the price regimes versus the periods with bubbles and crashes in both time and frequency domains. In addition, we apply the log-periodic power law model to fit the bubble formation. In the analysis of eight cryptocurrencies—Bitcoin, Ethereum, Litecoin, Antshares, Ethereum Classic, Dash, Monero, and OmiseGO—from 15 May 2018 to 28 November 2022, we identify 24 bubbles. Some of them exhibit a significant and strong exponential growth pattern.

Published
2024
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12. Molecular fingerprinting and relative relationship of apricot cultivars in China by simple sequence repeat (SSR) markers

Author

Sun Haoyuan, Yang Li, Wang Yu zhu, Chen Xuan, and Zhang Junhuan

Subjects
Phylogenetic tree, Genetic relationship, Biology, Sequence repeat, Applied Microbiology and Biotechnology, Horticulture, DNA profiling, Botany, Genetics, Microsatellite, Cultivar, Molecular Fingerprinting, Agronomy and Crop Science, Molecular Biology, Biotechnology
Abstract

In order to construct DNA fingerprint of apricot in China, the simple sequence repeats (SSR) analysis of 130 majority cultivars were performed. 104 alleles from 10 SSR loci were combined, which became the identification key of apricot cultivars. DNA fingerprint of these varieties had been primarily established. The identifying efficiency was also investigated using SSR data from three pair primers. The results demonstrate that 124 cultivars could be distinguished. There was a high efficiency in the identification of apricot germplasms using SSR markers and some cultivars with closely relationship (the similarity coefficient higher than 0.525) also could be separated from each other. These results can be expected to distinguish among all other apricot cultivars, and represent a practical system for apricot cultivar identification at any development stage, which would provide evidences for protection of breeder’s intellectual rights. In addition, cluster phylogenetic analysis was conducted based on all the SSR data. The ‘Jingjia No.1’ and its male parent ‘Shanhuangxing’ had been clustered within one group; ‘Longwangmao’ and ‘Yiwofeng’ which was called ‘small Longwangmao’ were clustered into the same group. This result further suggests that it was reliable to study the genetic relationship among these apricot cultivars using SSR markers with these primers. Key words: Apricot, fingerprinting, SSR.

Published
2012

16. The apricot (Prunus armeniacaL.) genome elucidates Rosaceae evolution and beta-carotenoid synthesis

Author

Jiang, Fengchao, Zhang, Junhuan, Wang, Sen, Yang, Li, Luo, Yingfeng, Gao, Shenghan, Zhang, Meiling, Wu, Shuangyang, Hu, Songnian, Sun, Haoyuan, and Wang, Yuzhu

Abstract

Apricots, scientifically known as Prunus armeniacaL, are drupes that resemble and are closely related to peaches or plums. As one of the top consumed fruits, apricots are widely grown worldwide except in Antarctica. A high-quality reference genome for apricot is still unavailable, which has become a handicap that has dramatically limited the elucidation of the associations of phenotypes with the genetic background, evolutionary diversity, and population diversity in apricot. DNA from P. armeniacawas used to generate a standard, size-selected library with an average DNA fragment size of ~20 kb. The library was run on Sequel SMRT Cells, generating a total of 16.54 Gb of PacBio subreads (N50 = 13.55 kb). The high-quality P. armeniacareference genome presented here was assembled using long-read single-molecule sequencing at approximately 70× coverage and 171× Illumina reads (40.46 Gb), combined with a genetic map for chromosome scaffolding. The assembled genome size was 221.9 Mb, with a contig NG50 size of 1.02 Mb. Scaffolds covering 92.88% of the assembled genome were anchored on eight chromosomes. Benchmarking Universal Single-Copy Orthologs analysis showed 98.0% complete genes. We predicted 30,436 protein-coding genes, and 38.28% of the genome was predicted to be repetitive. We found 981 contracted gene families, 1324 expanded gene families and 2300 apricot-specific genes. The differentially expressed gene (DEG) analysis indicated that a change in the expression of the 9-cis-epoxycarotenoid dioxygenase (NCED) gene but not lycopene beta-cyclase (LcyB) gene results in a low β-carotenoid content in the white cultivar “Dabaixing”. This complete and highly contiguous P. armeniacareference genome will be of help for future studies of resistance to plum pox virus(PPV) and the identification and characterization of important agronomic genes and breeding strategies in apricot.

Published
2019
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