Explaining reinforcement learning agent for high-frequency trading in quantitative finance
High-frequency trading (HFT) has emerged as a prominent domain within quantitative trading, leveraging advanced algorithms to exploit microsecond-level market inefficiencies, particularly evident in the volatile Cryptocurrency (Crypto) market. Despite its potential, HFT faces challenges such as low...
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| フォーマット: | Final Year Project (FYP) |
| 言語: | English |
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Nanyang Technological University
2024
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| オンライン･アクセス: | https://hdl.handle.net/10356/174971 |