#!/usr/bin/env python3 """Measure PASSIVE market-making economics on ES MBP-10 (markout / adverse selection). Companion to scripts/measure_ofi_ic.py. The OFI study showed a spread-CROSSING directional strategy is structurally unprofitable (predicted move ~0.01-0.06 ticks vs 1-tick cost). The exploitable structure (OFI→impact, R²~0.5-0.7) lives in the spread, capturable only by PROVIDING liquidity. This harness measures whether earning the spread is viable after adverse selection. Method (standard market-making markout / adverse-selection study): * Decode clean MBP-10; maintain best bid/ask/mid from levels[0]. * Each TRADE (action='T') fills a hypothetical PASSIVE order at the touch: - side=ASK (sell aggressor, hits bid) -> we BUY at bid (pos=+1, long) - side=BID (buy aggressor, lifts ask) -> we SELL at ask (pos=-1, short) (optimistic: assumes we win the queue on every trade — gives the UPPER BOUND per-fill economics; if even this is unprofitable, passive MM is dead.) * Per fill, PnL(Δ) in ticks = pos·(mid_{t+Δ} − fill_px)/tick − fee = half_spread (≈0.5 tick) + markout − fee markout = pos·(mid_{t+Δ} − mid_t) (NEGATIVE = adverse selection). * Viable iff half_spread + markout − fee > 0. * OFI-CONDITIONING: split fills by whether recent OFI agrees with our position (long & OFI>0, or short & OFI<0). If OFI-aligned fills have far better markout, OFI-conditioned quoting (rest only on the favorable side) is the real strategy — using the tiny OFI signal for adverse-selection AVOIDANCE, not for crossing. Usage: python3 scripts/measure_passive_mm.py [--n-cap N] [--fee-ticks F] [--ofi-window-sec W] python3 scripts/measure_passive_mm.py --self-test """ import argparse import sys import time import numpy as np TICK = 0.25 TICK_USD = 12.50 PX_SCALE = 1e9 def compute_ofi(bid_px, bid_sz, ask_px, ask_sz): """Best-level OFI per consecutive event (Cont-Kukanov-Stoikov). len-1, aligned to n>=1.""" pb_n, pb_p = bid_px[1:], bid_px[:-1] qb_n, qb_p = bid_sz[1:].astype(np.float64), bid_sz[:-1].astype(np.float64) pa_n, pa_p = ask_px[1:], ask_px[:-1] qa_n, qa_p = ask_sz[1:].astype(np.float64), ask_sz[:-1].astype(np.float64) e_bid = qb_n * (pb_n >= pb_p) - qb_p * (pb_n <= pb_p) e_ask = qa_p * (pa_n >= pa_p) - qa_n * (pa_n <= pa_p) return e_bid + e_ask def self_test(): # sell aggressor (agg=-1) → passive BUY at bid; bid=100 ask=100.25 mid=100.125; # future mid 100.25 → markout +0.5 tick, half_spread +0.5 tick, net +1.0 tick. bid, ask = 100.0, 100.25 mid = (bid + ask) / 2 pos = +1 # passive buy fill_px = bid mid_fut = 100.25 half = pos * (mid - fill_px) markout = pos * (mid_fut - mid) net_ticks = pos * (mid_fut - fill_px) / TICK assert abs(half / TICK - 0.5) < 1e-9, half assert abs(markout / TICK - 0.5) < 1e-9, markout assert abs(net_ticks - 1.0) < 1e-9, net_ticks print(f"passive-MM self-test PASS: half={half/TICK:.2f}t markout={markout/TICK:.2f}t net={net_ticks:.2f}t") return 0 def decode(path, n_cap): import zstandard import databento_dbn as d ts = np.zeros(n_cap, np.int64) bpx = np.zeros(n_cap, np.int64); apx = np.zeros(n_cap, np.int64) bsz = np.zeros(n_cap, np.int64); asz = np.zeros(n_cap, np.int64) inst = np.zeros(n_cap, np.int32) is_trade = np.zeros(n_cap, np.bool_) agg = np.zeros(n_cap, np.int8) # +1 buy-aggressor(side=BID), -1 sell-aggressor(side=ASK) dec = d.DBNDecoder(); dctx = zstandard.ZstdDecompressor() i = 0; t0 = time.time(); done = False with open(path, "rb") as fh: reader = dctx.stream_reader(fh) while not done: chunk = reader.read(1 << 22) if not chunk: break dec.write(chunk) for rec in dec.decode(): if "MBP10" not in type(rec).__name__: continue l0 = rec.levels[0] ts[i] = rec.ts_event bpx[i] = l0.bid_px; apx[i] = l0.ask_px bsz[i] = l0.bid_sz; asz[i] = l0.ask_sz inst[i] = rec.instrument_id a = str(rec.action); s = str(rec.side) if a.endswith("TRADE") or a == "T": is_trade[i] = True if s.endswith("BID") or s == "B": agg[i] = 1 elif s.endswith("ASK") or s == "A": agg[i] = -1 i += 1 if i >= n_cap: done = True break dt = time.time() - t0 return ts[:i], bpx[:i], apx[:i], bsz[:i], asz[:i], inst[:i], is_trade[:i], agg[:i], dt def analyze(path, n_cap, fee_ticks, ofi_window_sec): ts, bpx, apx, bsz, asz, inst, is_trade, agg, dt = decode(path, n_cap) n = len(ts) ids, cnts = np.unique(inst, return_counts=True) dom = ids[cnts.argmax()] m = inst == dom ts, bpx, apx, bsz, asz, is_trade, agg = ts[m], bpx[m], apx[m], bsz[m], asz[m], is_trade[m], agg[m] valid = (bpx > 0) & (apx > 0) & (bpx < apx) ts, bpx, apx, bsz, asz, is_trade, agg = (a[valid] for a in (ts, bpx, apx, bsz, asz, is_trade, agg)) mid = (bpx + apx) / 2.0 / PX_SCALE print(f"\n=== {path.split('/')[-1]} ===") print(f"decoded {n} events in {dt:.1f}s | dominant={dom} → {valid.sum()} clean front-month events") # event-level OFI (aligned to events 1..), prepend 0 for event 0 ofi = np.empty(len(ts)); ofi[0] = 0.0 ofi[1:] = compute_ofi(bpx, bsz, apx, asz) cum_ofi = np.cumsum(ofi) # passive fills = trades with a known aggressor fill = is_trade & (agg != 0) fi = np.flatnonzero(fill) pos = (-agg[fi]).astype(np.float64) # passive position: opposite the aggressor fill_px = np.where(pos > 0, bpx[fi], apx[fi]) / PX_SCALE mid_fill = mid[fi] half_ticks = pos * (mid_fill - fill_px) / TICK nbuy = int((pos > 0).sum()); nsell = int((pos < 0).sum()) print(f"passive fills: {len(fi)} (buy={nbuy} sell={nsell}) | " f"avg half-spread earned = {half_ticks.mean():.3f} ticks (${half_ticks.mean()*TICK_USD:.2f})") # OFI in the window before each fill (adverse-selection avoidance signal) w_ns = int(ofi_window_sec * 1e9) j0 = np.searchsorted(ts, ts[fi] - w_ns, side="left") ofi_w = cum_ofi[fi] - cum_ofi[j0] aligned = np.sign(ofi_w) == np.sign(pos) # OFI agrees with our passive direction print(f"OFI-window={ofi_window_sec}s | OFI-aligned fills: {100*aligned.mean():.1f}% " f"fee={fee_ticks} ticks/side (${fee_ticks*TICK_USD:.2f})") horizons = [0.1, 1.0, 5.0, 10.0, 60.0] print(f"\nMARKOUT (ticks) and NET per-fill PnL = half_spread + markout − fee") print(f"{'Δ(s)':>6} {'markout':>9} {'net_all':>9} {'net_$':>8} {'net_aligned':>12} {'net_adverse':>12} {'sel_$/fill':>11}") rows = [] for d_s in horizons: idx2 = np.searchsorted(ts, ts[fi] + int(d_s * 1e9), side="left") idx2 = np.clip(idx2, 0, len(mid) - 1) mid_fut = mid[idx2] markout = pos * (mid_fut - mid_fill) / TICK net = half_ticks + markout - fee_ticks net_aligned = net[aligned].mean() if aligned.any() else float("nan") net_adverse = net[~aligned].mean() if (~aligned).any() else float("nan") # "selective" MM: only take OFI-aligned fills sel_usd = (net[aligned].mean() * TICK_USD) if aligned.any() else float("nan") rows.append((d_s, markout.mean(), net.mean())) print(f"{d_s:>6} {markout.mean():>9.4f} {net.mean():>9.4f} {net.mean()*TICK_USD:>7.2f}$ " f"{net_aligned:>12.4f} {net_adverse:>12.4f} {sel_usd:>10.2f}$") print("\nINTERPRETATION:") print(" net_all > 0 → naive passive MM (quote both sides at touch) is viable at that horizon.") print(" net_aligned >> net_adverse → OFI-conditioned quoting (rest only on the favorable") print(" side / pull the adverse side) converts the tiny OFI signal into MM edge.") print(" net_all < 0 but net_aligned > 0 → naive MM loses, but SELECTIVE OFI-driven MM works.") return rows def main(): ap = argparse.ArgumentParser() ap.add_argument("file", nargs="?") ap.add_argument("--n-cap", type=int, default=8_000_000) ap.add_argument("--fee-ticks", type=float, default=0.1, help="per-side fee in ticks (~$1.25=0.1t)") ap.add_argument("--ofi-window-sec", type=float, default=1.0) ap.add_argument("--self-test", action="store_true") args = ap.parse_args() if args.self_test: return self_test() if not args.file: ap.error("file required (or --self-test)") analyze(args.file, args.n_cap, args.fee_ticks, args.ofi_window_sec) return 0 if __name__ == "__main__": sys.exit(main())