Enactive Drift Regulated Adaptation in Chaotic and Non-Stationary Time-Series Data
Anomaly: 0.00
Prediction: 0.00
Active Attractor: –
# Attractors: 0
Attractors L/R/G:
0 /
0 /
0
Accuracy: 1.00
Mean accuracy: –
Regime F1: –
Regime F1 L/R/G:
– /
– /
–
State entropy: –
Model horizon: – samples
(~– s)
Mean horizon: – samples (~– s)
Macro horizon: – samples
(~– s)
Mean macro duration: – samples
Next regime shift in: – samples
(~– s)
Local next shift: – samples (~– s)
Regional next shift: – samples (~– s)
Global next shift (meta): – samples (~– s)
Predicted shift severity: –
Regime transitions: –
Regimes L/R/G:
– /
– /
–
Regime stats L/R/G: count
– /
– /
–, F1
– /
– /
–
Next regime: –
Mean accuracy: –
MAE: –
MSE: –
MAE@24: –
MSE@24: –
MAE@48: –
MSE@48: –
MAE@96: –
MSE@96: –
MAE@192: –
MSE@192: –
MAE@336: –
MSE@336: –
MAE@720: –
MSE@720: –
Skill L/R/G: – /
– /
–
Stress L/R/G: – /
– /
–
Active scale: –
Trading: Position FLAT, Pos +0.000,
Equity 1.000,
Equity $ 0.00,
Local regime –,
Exp. return 0.00000
Samples: 0 | Turnover: 0.00 | Trades: 0 | Avg hold: 0
| PnL/trade: 0.000 | Avg return/step: 0.000000
Baseline (hold long): 1.000 | Baseline (random): 1.000
Training equity (sim diagnostic): 1.000
Follow-sim shadow equity: 1.000 (Δ sim: 0.000 | Δ live: 0.000)
Decomp: pos live – | pos sim – | ret live(frac) – | ret sim –
pnl live(frac) – + pnl live(sim) – | cost live – | pnl sim –
Blend weights:
wH (HDMIC) 0.50,
wT (RNN) 0.50
(enabled)
DL instrumentation:
mean|NN Δ| 0.00000,
mean|Xform Δ| 0.00000,
corr(sign(NN Δ), ret) –,
corr(sign(Xform Δ), ret) –
DL trend (RNN):
p(up) 0.00,
p(down) 0.00,
p(flat) 0.00
Buy in: – samples (~– s)
Sell in: – samples (~– s)
Live timers:
Buy in – (~– s),
Hold for – (~– s),
Sell in – (~– s)
Prep window:
– samples (~– s)