Simple rolling window implementation
Compute local information within a rolling time window of length T, such as the number of points in the window, the minimum or maximum, or the rolling sum.
An example is given below, with T = 60 and
- Time: number of seconds since epoch
- Value: price ratio (no unit)
- N_O: number of observation of in the curing sliding time window
- Roll_Sum: the current rolling sum,
- Min_Value and Max_Value the minimum/maximum in the current window:
| Time | Value | NO | Roll_Sum | Min_Val | Max_Val |
|---|---|---|---|---|---|
| 1355270609 | 1.80215 | 1 | 1.80215 | 1.80215 | 1.80215 |
| 1355270621 | 1.80185 | 2 | 3.604 | 1.80185 | 1.80215 |
| 1355270646 | 1.80195 | 3 | 5.40595 | 1.80185 | 1.80215 |
| 1355270702 | 1.80225 | 2 | 3.6042 | 1.80195 | 1.80225 |
| 1355270702 | 1.80215 | 3 | 5.40635 | 1.80195 | 1.80225 |
| 1355270829 | 1.80235 | 1 | 1.80235 | 1.80235 | 1.80235 |
| 1355270854 | 1.80205 | 2 | 3.6044 | 1.80205 | 1.80235 |
| 1355270868 | 1.80225 | 3 | 5.40665 | 1.80205 | 1.80235 |
| 1355271000 | 1.80245 | 1 | 1.80245 | 1.80245 | 1.80245 |
| 1355271023 | 1.80285 | 2 | 3.6053 | 1.80245 | 1.80285 |
| 1355271024 | 1.80275 | 3 | 5.40805 | 1.80245 | 1.80285 |
| 1355271026 | 1.80285 | 4 | 7.2109 | 1.80245 | 1.80285 |
| 1355271027 | 1.80265 | 5 | 9.01355 | 1.80245 | 1.80285 |
| 1355271056 | 1.80275 | 6 | 10.8163 | 1.80245 | 1.80285 |
| 1355271428 | 1.80265 | 1 | 1.80265 | 1.80265 | 1.80265 |
| 1355271466 | 1.80275 | 2 | 3.6054 | 1.80265 | 1.80275 |
| 1355271471 | 1.80295 | 3 | 5.40835 | 1.80265 | 1.80295 |
| 1355271507 | 1.80265 | 3 | 5.40835 | 1.80265 | 1.80295 |
| 1355271562 | 1.80275 | 2 | 3.6054 | 1.80265 | 1.80275 |
| 1355271588 | 1.80295 | 2 | 3.6057 | 1.80275 | 1.80295 |
cd Release make clean make all
cd .. ./rolling_window data.csv