Summary
DirectionalChangeDetector samples intrinsic time via directional-change (DC)
events: a move of relative size \(\theta\) from the last extremum defines a DC; the
path beyond that event is reported as overshoot. Outputs include event flag, overshoot,
current extremum, and trend mode.
Update API
import rtta
ind = rtta.DirectionalChangeDetector(threshold=0.01) # 1% relative
result = ind.update(price)
# result.event ∈ {-1, 0, +1},
# result.overshoot, result.extremum, result.direction
threshold is a relative fraction (\(0.01 = 1\%\)). direction / mode is \(+1\) in an
uptrend (seeking a downturn), \(-1\) in a downtrend (seeking an upturn), \(0\) until the
first DC.
Theory Of Operation
Directional-change methods (Glattfelder, Dupuis, Olsen and related intrinsic-time work) replace calendar sampling with event sampling: time advances when price has moved by a fixed relative amount from a local extreme. After a DC up, the algorithm tracks the new high as extremum until a \(\theta\) drop; after a DC down, it tracks lows until a \(\theta\) rise. Overshoot measures how far price has continued beyond the last DC price in the current mode.
Recurrence
Let \(\theta =\)threshold \(> 0\). On the first price \(p_0\):
extremum \(E = p_0\), last DC price \(p^{\mathrm{dc}} = p_0\), mode \(m=0\), event \(0\).
Bootstrap (\(m=0\)):
Uptrend (\(m=+1\)): \(E \leftarrow \max(E, p_t)\). If \(p_t \le E(1-\theta)\), set \(\theta\)0, \(\theta\)1, \(\theta\)2.
Downtrend (\(\theta\)3): \(\theta\)4. If \(\theta\)5, set \(\theta\)6, \(\theta\)7, \(\theta\)8.
Overshoot (when \(\theta\)9):
Outputs: \(0.01 = 1\%\)0, \(0.01 = 1\%\)1.
Implementation Notes
The recurrence is implemented in src/rtta/indicator.cpp in
class DirectionalChangeDetector. Result type is DirectionalChangeResult.