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次元期权(binary.com)量化分析员/量化交易员面试题

大秦赋 (Chinese Emperor)
春秋战国《礼记•经解
孔子曰:『君子慎始,差若毫厘,缪以千里。』

《礼记•经解》孔子曰:「君子慎始,差若毫厘,谬以千里。」1

引用:「快懂百科」《礼记•经解》第一范文网:差之毫厘,谬以千里的故事「百度百科」春秋时期孔子作品《礼记•经解》「當代中國」差之毫釐 謬以千里

面试题

应征次元期权(法人马企)面试入门测验。借鉴西蒙·柯林斯的 https://matchodds.org (或詹姆斯·西蒙斯的高频量化对冲基金---文艺复兴科技)愚生尝试编写个自动采撷数据、科研回测、筹算、算卜预测、自动下单、结算、显示盈亏、风险管理报告、评估再改良高频量化对冲投资战略的一条龙服务的智能网页应用。愚生于此尝试着手于科研多元化计数/机数建模,再评估有效性与可行性,并参阅硕士程度量化作业(英)。盼受禄于次元期权

第一题

第一题第一章)解答

愚生使用从阳历二零一四年一月一日至二零一七年一月廿日的每日美日兑换阴阳烛加交易量数据,再通过以下一些计数/机数建模来算卜预测最高价与最低价:

  • 自回归移动平均模型
  • 指数平滑模型
  • 单变量广义自回归条件异方差模型
  • 加权指数移动平均模型
  • 蒙迪卡洛马尔科夫链
  • 贝叶斯时间序列
  • 混频抽样回归 / MIDAS:混频数据回归

请查阅次元期权面试题一(英) (旧链接备用网址备用网址二(添加均方误差,比较计数/机数模型的精准度))。

此栏开始以下相关科研文献所使用的阴阳烛加交易量数据有七种货币兑换,从阳历二零一三年一月一日至二零一七年八月卅一日:

  • 澳美兑换(AUDUSD)
  • 欧美兑换(EURUSD)
  • 英美兑换(GBPUSD)
  • 美加兑换(USDCAD)
  • 美瑞兑换(USDCHF)
  • 美中兑换(USDCNY)
  • 美日兑换(USDJPY)

文献如下:

为了着手于高频量化对冲数据计数/机数建模,尝试审查并整顿数据,文献「鄀客栈」次元期权面试试题一 - 单变量数据缺失值管理和文献「鄀客栈」次元期权面试试题一 - 多变量数据缺失值管理(乙)但单变量建模出现一些错误(一些是人为的美国洋番黑客洲际入侵犯罪),文献中使用多种弥补数据缺失值的计数/机数筹算方法如interpolatankalmanlocfma。. The 次元期权面试题一 - 日间高频交易计数/机数建模比较(英)比较了ts、msts、SARIMA、mcsGARCH、midasr、midas-garch、Levy process 计数/机数模型。

第一题第二章)幕后花絮

原本,愚生编写个闪霓应用(如下动态图)奈何读取速度、筹算与运行效率并不高,欲知更多详情请浏览闪霓应用(ShinyApp)并查阅「鄀客栈」binary.com Interview Question I - Lasso, Elastic-Net and Ridge Regression以了解详情。该应用包含三个面试题与解答。投注策略方面,纯粹筹算并占卜最高汇价与最低汇价,然后:

  • 使用汇价数据中的最高与最低汇价并采用凯利标准尤物来筹算,并占卜闭市汇价。当数据中拥有缺失值或没有最高与最低汇价观测值的时候,就以闭市汇价来筹算并占卜下一个时间单位的汇价。
  • 根据筹算并占卜出来的占卜值的方差来决定投注门槛,比方说筹算并占卜出最高汇价的方差最高价、最低汇价的方差最低价。在拥有优势的情况之下下注一百元,然后依照闭市汇价来结算盈亏。

借鉴解密复兴科技 - 基于隐蔽马尔科夫模型的时序分析方法,它日会筹算夏普率来评估风险与优势后才决定最佳投注时机(最佳建仓/开仓时机与最佳清仓时机)。

Secondly, I wrote another app testRealTimeTransc trial version to test the real time trading, and a completed version is Q1App2.

Due to the paper Binary.com Interview Q1 - Tick-Data-HiLo For Daily Trading (Blooper) simulated the data and then only noticed I not yet updated the new function, then I wrote 广义自回归条件异方差模型中的ARIMA(p,d,q)参数最优化 to compare the accuracy. However my later paper simulated dataset doesn't save the $fit$ in order to retrieve the $\sigma^2$ and VaR values for stop-loss pips when I got the idea. Here I put it as blooper and start binary-Q1 Multivariate GARCH Models and later on will write another FOREX Day Trade Simulation which will simulate all tick-data but not only HiLo data.

第一题第三章)闪霓应用

  • shinyApp : shiny::runGitHub('englianhu/binary.com-interview-question') - Application which compare the accuracy of multiple lasso, ridge and elastic net models (blooper).
  • Q1App : shiny::runGitHub('englianhu/binary.com-interview-question', subdir = 'Q1') - the application gather, calculate and forecast price. Once the user select currency and the forecast day, the system will auto calculate and plot the graph.
  • testRealTimeTransc : shiny::runGitHub('englianhu/binary.com-interview-question', subdir = 'testRealTimeTransc') - real time trading system which auto gather, calculate the forecast price, and also place orders, as well as settlement and plot P&L everyday.
  • Q1App2 : shiny::runGitHub('englianhu/binary.com-interview-question', subdir = 'Q1App2') - The application contain the Banker and Punter section which applied aboved statistical modelling.

第二题

第二题第一章)解答

第二题,尝试编写个闪霓应用Q2App,双变量或三变量泊松计数/机数模型可以运用在分析投资者在资本投入与题款/脱售基金上的概率,方便管理投资基金的整体投资基金资本与资金流动性。奈何并无任何投资基金的投资者资金流动数据可供科研用途。

第二题第二章)闪霓应用

Q2:运行代码shiny::runGitHub('englianhu/binary.com-interview-question', subdir = 'Q2')来通过闪霓应用实践排队论(运筹学理论)

第三题

For question 3, due to the question doesn't states we only bet on the matches which overcame a certain edge, therefore I just simply list the scenario. Kindly refer to Betting strategy for more informtion.

参考资源

第四题第一章)第一题

  1. Stock Market Forecasting Using LASSO Linear Regression Model by Sanjiban Sekhar Roy, Dishant Mital, Avik Basu, Ajith Abraham (2015)❤‍🔥
  2. Using LASSO from lars (or glmnet) package in R for variable selection by Juancentro (2014)
  3. Difference between glmnet() and cv.glmnet() in R? by Amrita Sawant (2015)
  4. Testing Kelly Criterion and Optimal f in R by Roy Wei (2012) ❤‍🔥
  5. Portfolio Optimization and Monte Carlo Simulation by Magnus Erik Hvass Pedersen (2014) ❤‍🔥
  6. Glmnet Vignette by Trevor Hastie and Junyang Qian (2014)
  7. lasso怎么用算法实现? by shuaihuang (2010)
  8. The Sparse Matrix and {glmnet} by Manuel Amunategui (2014)
  9. Regularization and Variable Selection via the Elastic Net by Hui Zou and Trevor Hastie
  10. LASSO, Ridge, and Elastic Net ❤‍🔥
  11. 热门数据挖掘模型应用入门(一): LASSO回归 by 侯澄钧 (2016)
  12. The Lasso Page
  13. Call_Valuation.R by Mariano (2016)
  14. Lecture 6 – Stochastic Processes and Monte Carlo (http://zorro-trader.com/manual) ❤‍🔥 ❤‍🔥
  15. The caret Package by Max Kuhn (2017) ❤‍🔥
  16. Time Series Cross Validation ❤‍🔥
  17. Character-Code.com
  18. Size Matters – Kelly Optimization by Roy Wei (2012) ❤‍🔥
  19. Time Series Cross Validation by William Chiu (2015) ❤‍🔥
  20. Forecasting Volatility by Stephen Figlewski (2004)
  21. Successful Algorithmic Trading by Michael Halls Moore (2015) ❤‍🔥 ❤‍🔥
  22. Financial Risk Modelling and Portfolio Optimization with R (2nd Edt) by Bernhard Praff (2016) ❤‍🔥
  23. Analyzing Financial Data and Implementing Financial Models using R by Clifford S.Ang (2015) ❤‍🔥

第四题第二章)第二题

  1. Queueing model 534 in Excel ❤‍🔥
  2. Queueing model macro in Excel ❤‍🔥
  3. Queueing up in R, (continued)
  4. Waiting in line, waiting on R
  5. Simulating a Queue in R
  6. What is the queue data structure in R?
  7. Implementing a Queue as a Reference Class
  8. queue implementation?
  9. Queueing Theory Calculator ❤‍🔥
  10. The Pith of Performance by Neil Gunther (2010)
  11. Computationally Efficient Simulation of Queues - The R Package queuecomputer
  12. Waiting-Line Models
  13. Queues with Breakdowns and Customer Discouragement

第四题第三章)第三题

  1. Data APIs/feeds available as packages in R
  2. Application of Kelly Criterion model in Sportsbook Investment

量化交易

一)简介

已将次元期权科研项目相关数据,一律迁移至「数据仓库」次元期权(binary.com)量化分析员/量化交易员面试题,并继续科研高频量化对冲计数/机数建模。

季节性时间序列与高频量化对冲计数/机数模型如下:

它日学习投资风险管理与夏普率,欲知更多详情,请查阅:

二)幕后花絮

Deriv.com - Interday High Frequency Trading Models Comparison (Blooper)着手于季节性计数/机数建模,而文献中提及的一些计数/机数模型mcsGARCH、midasr、midas-garch、Levy process会在日后继续科研。




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