DSS
DSS, Inc.
stock NYSEAMERICAN

Been running a live system that uses phase space reconstruction(Takens) as one component of a composite signal. Curious if anyone else has gone down this road.
My implementation:
* τ selected via first minimum of mutual information (AMI), search up to τ=50
* m via false nearest neighbors, stopping at <1% FNN — getting m=3–7 depending on instrument
* Largest Lyapunov exponent estimated with the Wolf algorithm on the embedded attractor
* Positive λ → chaotic regime, use `log(1/ε)/λ` as prediction horizon
* Hurst via r|s analysis — H>0.55 + R²>0.6 on recent 20 bars → trending regime, H<0.45 → mean-reverting, rest is neutral
Running this on ETFs (SPY, QQQ, DIA, GLD). One of four components weighted equally alongside a DSS oscillator, mean level structure, and options flow.
Question: financial time series are arguably stochastic, not truly chaotic. Could Lypanunov exponent just be noise sensitivity, not actual deterministic chaos? But empirically the signal has edge in prediction horizon metric as a filter (ie short horizon = don't execute).
Has anyone else implemented Takens/FNN/Lyapunov in a live system? Did you go Wolf algorithm or Rosenstein for the exponent? Any luck with correlation dimension as an additional filter?

Been running a live system that uses phase space reconstruction(Takens) as one component of a composite signal. Curious if anyone else has gone down this road.
My implementation:
* τ selected via first minimum of mutual information (AMI), search up to τ=50
* m via false nearest neighbors, stopping at <1% FNN — getting m=3–7 depending on instrument
* Largest Lyapunov exponent estimated with the Wolf algorithm on the embedded attractor
* Positive λ → chaotic regime, use `log(1/ε)/λ` as prediction horizon
* Hurst via r|s analysis — H>0.55 + R²>0.6 on recent 20 bars → trending regime, H<0.45 → mean-reverting, rest is neutral
Running this on ETFs (SPY, QQQ, DIA, GLD). One of four components weighted equally alongside a DSS oscillator, mean level structure, and options flow.
Question: financial time series are arguably stochastic, not truly chaotic. Could Lypanunov exponent just be noise sensitivity, not actual deterministic chaos? But empirically the signal has edge in prediction horizon metric as a filter (ie short horizon = don't execute).
Has anyone else implemented Takens/FNN/Lyapunov in a live system? Did you go Wolf algorithm or Rosenstein for the exponent? Any luck with correlation dimension as an additional filter?