feat: Phase 2 — microstructure analytics + 81 tests

New microstructure/ module with pure-function analytics:

microstructure/book.py:
  microprice() — depth-weighted mid price
  mid_price() — simple bid/ask midpoint
  order_book_imbalance() — ranged [-1, 1] volume skew
  depth_imbalance() — imbalance at fixed price distance
  spread_stats() — spread, spread_bps, mid, bid, ask
  depth_resiliency() — bid/ask volume within impact radius
  queue_depletion_prob() — Poisson fill probability at level
  batch_book_stats() — aggregate stats across snapshots

microstructure/trades.py:
  classify_lee_ready() — Lee-Ready aggressor classification
  classify_bulk_lee_ready() — batch classification with mids/bids/asks
  compute_markouts() — forward mid-price change at configurable horizons
  markout_summary() — mean/std/t-stat per side per horizon
  trade_volume_profile() — size bucket distribution
  trade_arrival_rate() — rolling trades/sec with burst detection

microstructure/toxicity.py:
  compute_vpin() — volume-synchronized informed trading probability
  compute_vpin_time_series() — rolling VPIN with alarm threshold
  fill_toxicity() — adverse price movement post-trade
  adverse_selection_ratio() — per-side adverse selection
  liquidation_clustering() — cluster detection in liquidation events

microstructure/funding.py:
  funding_regime() — classify regime (neutral/positive/negative/high)
  funding_predictability() — AR(1) autocorrelation analysis
  funding_carry_pnl() — cumulative carry PnL estimation
  basis_spread() — perp premium over spot (bps)
  basis_convergence_speed() — mean-reversion half-life via AR(1)

microstructure/signals.py:
  composite_signal() — weighted OBI + trade + VPIN + funding signal
  SignalPipeline — stateful pipeline accumulating book/trade updates
  detect_hft_regime() — regime classifier for HFT strategy selection

Bug fixes in Phase 1:
  - data/latency.py: proper linear-interpolation percentiles
  - data/normalizer.py: UTC timezone for naive datetimes
  - data/normalizer.py: detect_sequence_gap returns gap-1 (missing count)
  - microstructure/toxicity.py: consistent vpin_value key in compute_vpin

81 tests across 4 test files (store, normalizer, latency, microstructure)

data/latency.py

@@ -80,11 +80,19 @@ class LatencyTracker:
             return {"p50": 0, "p90": 0, "p95": 0, "p99": 0, "count": 0}
         vals = sorted(v for _, v in buffer)
         n = len(vals)
+
+        def _pct(p: float) -> float:
+            k = (n - 1) * p / 100
+            lo = int(k)
+            hi = min(lo + 1, n - 1)
+            frac = k - lo
+            return vals[lo] + frac * (vals[hi] - vals[lo])
+
         return {
-            "p50": round(vals[int(n * 0.50)], 2),
-            "p90": round(vals[int(n * 0.90)], 2),
-            "p95": round(vals[int(n * 0.95)], 2),
-            "p99": round(vals[int(n * 0.99)], 2),
+            "p50": round(_pct(50), 2),
+            "p90": round(_pct(90), 2),
+            "p95": round(_pct(95), 2),
+            "p99": round(_pct(99), 2),
             "max": round(vals[-1], 2),
             "count": n,
         }

data/normalizer.py

@@ -39,6 +39,8 @@ def normalize_timestamp(ts, source: str = "hl") -> int:
         return _parse_iso_ms(ts)
 
     if isinstance(ts, datetime):
+        if ts.tzinfo is None:
+            ts = ts.replace(tzinfo=timezone.utc)
         return int(ts.timestamp() * 1000)
 
     return int(time.time() * 1000)
@@ -120,5 +122,5 @@ def detect_sequence_gap(
     if diff == 1:
         return 0
     if diff > 1:
-        return min(diff, max_gap * 100)  # cap reporting size
+        return min(diff - 1, max_gap * 100)
     return -1  # duplicate or reset