Timeline for How to use PCA for trading
Current License: CC BY-SA 3.0
5 events
| when toggle format | what | by | license | comment | |
|---|---|---|---|---|---|
| May 2, 2013 at 9:54 | comment | added | Lisa Ann | Let you have a multifactorial model which takes as inputs about 10 ~ 20 exogenous weakly stationary variables. Then you can use PCA to get just 3 ~ 4 orthogonal variables in order to simplify your model without losing too much information (it maybe first 3 ~ 4 principal components explain more than 90% of the 10 ~ 20 original variables' total variance). For instance, technical traders often use lot of t.a. indicators, such as MACD, RSI, stochastic and so on: it's likely the first principal component of these indicators explain more than 95% of all indicators' variance. | |
| Apr 29, 2013 at 13:27 | answer | added | SRKX | timeline score: 0 | |
| Apr 29, 2013 at 13:16 | answer | added | Lucas Morin | timeline score: 10 | |
| Apr 29, 2013 at 12:00 | comment | added | chrisaycock | Sounds like a hammer looking for a nail. | |
| Apr 29, 2013 at 11:00 | history | asked | ManInMoon | CC BY-SA 3.0 |