| 000 | 02266cam a2200277zu 4500 | ||
|---|---|---|---|
| 001 | 88817655 | ||
| 003 | FRCYB88817655 | ||
| 005 | 20250429182309.0 | ||
| 006 | m o d | ||
| 007 | cr un | ||
| 008 | 250429s2013 fr | o|||||0|0|||eng d | ||
| 020 | _a9780124016897 | ||
| 035 | _aFRCYB88817655 | ||
| 040 |
_aFR-PaCSA _ben _c _erda |
||
| 100 | 1 | _aKissell, Robert | |
| 245 | 0 | 1 |
_aThe Science of Algorithmic Trading and Portfolio Management _c['Kissell, Robert'] |
| 264 | 1 |
_bElsevier Science _c2013 |
|
| 300 | _a p. | ||
| 336 |
_btxt _2rdacontent |
||
| 337 |
_bc _2rdamdedia |
||
| 338 |
_bc _2rdacarrier |
||
| 650 | 0 | _a | |
| 700 | 0 | _aKissell, Robert | |
| 856 | 4 | 0 |
_2Cyberlibris _uhttps://international.scholarvox.com/netsen/book/88817655 _qtext/html _a |
| 520 | _aThe Science of Algorithmic Trading and Portfolio Management, with its emphasis on algorithmic trading processes and current trading models, sits apart from others of its kind. Robert Kissell, the first author to discuss algorithmic trading across the various asset classes, provides key insights into ways to develop, test, and build trading algorithms. Readers learn how to evaluate market impact models and assess performance across algorithms, traders, and brokers, and acquire the knowledge to implement electronic trading systems. This valuable book summarizes market structure, the formation of prices, and how different participants interact with one another, including bluffing, speculating, and gambling. Readers learn the underlying details and mathematics of customized trading algorithms, as well as advanced modeling techniques to improve profitability through algorithmic trading and appropriate risk management techniques. Portfolio management topics, including quant factors and black box models, are discussed, and an accompanying website includes examples, data sets supplementing exercises in the book, and large projects. Prepares readers to evaluate market impact models and assess performance across algorithms, traders, and brokers. Helps readers design systems to manage algorithmic risk and dark pool uncertainty. Summarizes an algorithmic decision making framework to ensure consistency between investment objectives and trading objectives. | ||
| 999 |
_c1326814 _d1326814 |
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