Table of Contents
- 1. the most important ones:
- 2. Event study
- 3. Factors
- 4. Factor backtest
- 5. Forecast model
- 6. Simple forecast model
- 7. Transaction cost model
- 8. Risk model
- 9. Strategy simulation
- 10. Simulation workflow
- 11. Trade simulator interface
- 12. Report template editor
- 13. Script editor and debugger
- 14. ModelScript reference
- 15. Included Scripts
- 16. Included Cpp Scripts
- 17. Using Microsoft Visual Studio
- 18. Portfolio optimization
- 19. ClariFI portfolio optimization
- 20. Barra portfolio optimization
- 21. Northfield portfolio optimization
- 22. Axioma portfolio optimization
- 23. Portfolio attribution
- 24. Portfolio attribution process
- 25. Portfolio attribution export
- 26. Portfolio attribution report
- 27. Top down snapshot
- 28. Top down history
- 29. Cumulative Brinson attribution
- 30. Brinson attribution history
- 31. Contribution snapshot
- 32. Contribution history
- 33. Risk factor snapshot
- 34. Risk factor history
- 35. Risk attribution snapshot
- 36. Risk factor attribution
- 37. Process launcher
- 38. Cache data
- 39. multi-factor model
- 40. Fama-French Three factor model
- 41. Performance analytics:
#+AUTHORS weiwu
1 the most important ones:
get familiar with what you've done in your current job. review every detail of your project.
2 Event study
EventStudy The Event Study module is designed to evaluate the impact of discrete events on future returns. Classic examples of these events are stock splits, earnings surprises, index additions and deletions. The event study attempts to determine if there is a significant abnormal return which can be attributed to the occurrence of the event.
2.1 Event study workflow
2.2 Event study report
3 Factors
3.1 fundamental factor
3.1.1 P/E ratio
静态市盈率是市场广泛谈及的市盈率,即以目前市场价格除以已知的最近公开的每股收益后的比值。
所谓的市盈率是一个反映股票收益与风险的重要指标,也叫市价盈利率。体现的是企业按现在的盈利水平要花多少年才能收回成本。 \[ 市盈率 = frac{股价}{每股收益} \]
动态市盈率是指还没有真正实现的下一年度的预测利润的市盈率。 $动态市盈率=静态市盈率x[1/(1 + I)n]&,I为年复合增长率.
3.2 debt
3.3 profit
4 Factor backtest
5 Forecast model
6 Simple forecast model
7 Transaction cost model
8 Risk model
9 Strategy simulation
10 Simulation workflow
11 Trade simulator interface
12 Report template editor
13 Script editor and debugger
14 ModelScript reference
15 Included Scripts
16 Included Cpp Scripts
17 Using Microsoft Visual Studio
18 Portfolio optimization
19 ClariFI portfolio optimization
20 Barra portfolio optimization
21 Northfield portfolio optimization
22 Axioma portfolio optimization
23 Portfolio attribution
24 Portfolio attribution process
25 Portfolio attribution export
26 Portfolio attribution report
27 Top down snapshot
28 Top down history
29 Cumulative Brinson attribution
30 Brinson attribution history
31 Contribution snapshot
32 Contribution history
33 Risk factor snapshot
34 Risk factor history
35 Risk attribution snapshot
36 Risk factor attribution
37 Process launcher
38 Cache data
39 multi-factor model
40 Fama-French Three factor model
data factor library 在资本资产定价模型(CAPM)等传统理论下,投资组合的全部风险溢价由Beta系数表示。但是这一模型在解释股票市场回报的现实情况上,如一月效应,遇到了诸多挑战。法马和佛伦奇(1992)观察发现市值较小、市值账面比较低的两类公司更有可能取得优于市场水平的平均回报率。由此三因子模型通过引入二个新的解释变量:市净率、公司规模,与CAPM中的市场指数一同估计股票的回报水平,即: {\displaystyle r=Rf+β _{3}(Rm-Rf)+bs⋅ {\mathit {SMB}}+bv⋅ {\mathit {HML}}+α } {\displaystyle r=Rf+β _{3}(Rm-Rf)+bs⋅ {\mathit {SMB}}+bv⋅ {\mathit {HML}}+α } 其中 {\displaystyle r} r是投资组合的期望收益率, {\displaystyle Rf} Rf是市场无风险收益率, {\displaystyle Rm} Rm是市场组合的收益率,三个变量的待估系数 {\displaystyle β } β 是市场组合风险溢价、规模溢价、市净率溢价三个因素变化对期望收益率的影响,其中市场组合风险溢价的系数beta概念接近于CAPM模型中的beta系数,公司规模变量 {\displaystyle SMB} SMB是指由市值小的公司组成的投资组合回报与市值大的公司组成的投资组合回报之差,市净率溢价 {\displaystyle HML} HML是账面价值比较高的公司组成的投资组合回报与比值较低的公司投资组合回报之差。 {\displaystyle α } α 是超额收益率,在理想的情况下,投资组合的超额回报将全部被三因素解释,从而 {\displaystyle α } α 应在统计学意义上等于0.
- Book-to-Market
- size
- value
41 Performance analytics:
41.1 percentage change:
\[percentage change(P_n) = value / value.shift(1)\]
41.2 cumlative return:
\[total_return = (1+P_1)*(1+P_2)*...(1+P_n) - 1.0\]
41.3 annualized return:
\[annualized return = (1 + cumulative return)^(252/periods) - 1.0\] \[(annualized return + 1)^(periods/252) = 1 + cumulative return\]
41.4 annualized volatility:
\(\sigma = standard deviation of returns(degree of freedom N -1)\) \[annualized volatility = \sigma * \sqrt(periods)\]
41.5 annualized downrisk volatility:
41.6 downside standard deviation:
Downside deviation is a measure of downside risk that focuses on returns that fall below a minimum threshold or minimum acceptable return (MAR). It is used in the calculation of a risk measure known as the Sortino Ratio. $$MAR=\sqrt{E[(X-E[X])2]1{X<E[X]}])0.5}
41.7 sharpe ratio:
\[sharpe ratio = (E[annual returns] - interest rate)/\sigma_annual\]
41.8 sortino ratio:
\[SR = \div{annual return - R_f, Downside Risk}\]
41.9 return standard deviation:
\(\sigma = standard deviation of returns(degree of freedom N -1)\)