What is a worked example of Vortex?

What is Vortex, in plain terms?

Vortex (often discussed as the “Vortex indicator”) is a technical-analysis method that tries to summarize which direction—upward or downward—was more dominant over a chosen lookback period. It does this by building two directional components from price movement and then comparing them.

A “worked example of Vortex” means you see the arithmetic for a small, clearly stated dataset: the window you choose, the formulas you apply, and the intermediate values you compute. This makes it possible to reproduce the same result on your own charting platform (or in a spreadsheet) and to see exactly what assumptions change the output.

Worked example of Vortex (with every assumption stated)

Below is one self-contained scenario using an invented mini dataset. Because there is no real-time data here, the only thing that “matters” is that the calculation rules are consistent.

Assumptions

1. You use 5 bars as the lookback window (including the first bar as bar 1, then bars 2–5 as the full set for the numeric demonstration).
2. You use OHLC data (open, high, low, close) for each bar.
3. You compute true range (TR) in the standard way: TR = max(high − low, abs(high − prev_close), abs(low − prev_close)).
4. You compute directional movement using the idea of whether the current high/low is “expanding” relative to the previous bar. (Different websites describe slightly different exact steps; pick one consistent definition and verify it against your platform.)

Mini dataset (invented)

For bars 1–5 (values are arbitrary but internally consistent):

• Bar 1: H=10, L=9, C=9.5
• Bar 2: H=10.5, L=9.4, C=10
• Bar 3: H=11, L=10.2, C=10.4
• Bar 4: H=10.8, L=10.1, C=10.2
• Bar 5: H=11.2, L=10.3, C=10.9

Step 1: compute TR for bars 2–5

Using TR definition and prev_close from the prior bar:

• TR2 uses prev_close=C1=9.5: max(10.5−9.4=1.1, |10.5−9.5|=1.0, |9.4−9.5|=0.1) = 1.1
• TR3 prev_close=C2=10: max(11−10.2=0.8, |11−10|=1.0, |10.2−10|=0.2) = 1.0
• TR4 prev_close=C3=10.4: max(10.8−10.1=0.7, |10.8−10.4|=0.4, |10.1−10.4|=0.3) = 0.7
• TR5 prev_close=C4=10.2: max(11.2−10.3=0.9, |11.2−10.2|=1.0, |10.3−10.2|=0.1) = 1.0

Step 2: compute directional components (+ and −)

Here is the key place where definitions vary across implementations. A common approach is:

• Create +V from upward directional movement, often based on how much the current high exceeds the previous high (or related expansion logic).
• Create −V from downward directional movement, often based on how much the current low is below the previous low (or related expansion logic).
• Scale those directional values by TR, then aggregate over the lookback.

To keep this worked example reproducible without claiming one universal formula, use this consistent, transparent template:

1. Compute UpMove = max(H_t − H_{t−1}, 0)
2. Compute DownMove = max(L_{t−1} − L_t, 0)
3. Compute +VM = UpMove / TR_t and −VM = DownMove / TR_t
4. Sum +VM over the window and sum −VM over the window.

Using that template:

• For bar 2: UpMove=max(10.5−10,0)=0.5; DownMove=max(9.0−9.4,0)=0 ⇒ +VM2=0.5/1.1=0.4545; −VM2=0
• Bar 3: UpMove=max(11−10.5,0)=0.5; DownMove=max(9.4−10.2,0)=0 ⇒ +VM3=0.5/1.0=0.5; −VM3=0
• Bar 4: UpMove=max(10.8−11,0)=0; DownMove=max(10.2−10.1,0)=0.1 ⇒ +VM4=0/0.7=0; −VM4=0.1/0.7=0.1429
• Bar 5: UpMove=max(11.2−10.8,0)=0.4; DownMove=max(10.1−10.3,0)=0 ⇒ +VM5=0.4/1.0=0.4; −VM5=0

Aggregate across bars 2–5 (four terms in this mini example):

• Sum(+VM)=0. 4545+0. 5+0+0. 4=1. 3545
• Sum(−VM)=0+0+0. 1429+0=**0.