Demos

Live, in-browser computations backing the papers on this site.

• The imbalance symmetry
  The imbalanced and balanced market making problems are solved numerically and independently; after the skew shift δ and the carry multiplier M(q), one lands on the other to within 10⁻¹³. The dots on the line are the theorem.
• Optimal and sub-optimal market makers
  Three dealers face the same enquiry stream and the same competing quotes: the optimal policy, constant-width linear-skew with no flow term, and no skew at all. The flow term is worth a few percent of profit; ignoring skew entirely is ruinous.
• The term structure of the flow skew
  The finite-horizon behavior of the equivalence, computed by its own exact spectral representation: the mid displacement is zero at maturity and climbs to −δ at the spectral-gap rate; the spread carries a finite-horizon transient; and a dealer who marks her terminal book at the flow-adjusted price has no transient at all.
• Learning faster with the symmetry
  Reinforcement learning and its inverse across cycling flow regimes. One Q-learner pools all experience into a single balanced-frame policy via the δ shift and reaches in ten thousand enquiries what the per-regime baseline hasn't reached in eighty thousand; the inverse estimator cuts error severalfold by de-tilting quotes — until the second-order bias floor of the approximate symmetry appears.

More demonstrations will appear here as the program's papers are written up.