Initial single-cost backtest at τ=0.25 cost=0.25 revealed the binding
constraint: mean_ret = -0.176, pre-cost EV ≈ +0.074, so the model has
a real directional edge but K=6000 price moves are too small to clear
1-tick round-trip cost. The cost-vs-edge balance is the real Phase 1d.4
verdict question, not whether the model has signal.
Two enhancements per the insight block in the previous run:
1. **Cost sweep**: GpuBacktest::run now takes `costs: &[f32]` and
produces (C × T) rows instead of T. The smoke runs at five costs:
- 0.0 frictionless upper bound (theoretical max Sharpe)
- 0.0625 quarter-tick (very aggressive execution)
- 0.125 half-tick (professional desk)
- 0.25 1 tick = $12.50/contract (retail / pessimistic)
- 0.50 2 ticks (very pessimistic)
Tells us the break-even cost where Sharpe crosses zero.
2. **Annualised Sharpe**: per-trade Sharpe × sqrt(trades_per_year).
trades_per_year = n_trades × (seconds_per_year / test_time_span_seconds).
test_time_span_seconds derived from first/last test sequence end-bar
timestamps via FxCacheReader::record_timestamp. Standard Sharpe-time-
scaling assumption (trades roughly i.i.d.); imperfect when signals
cluster in correlated regimes, but the right ballpark for comparison
with industry benchmarks.
Output adds per-cost-band "best operating point" tables plus a clear
"REALISTIC VERDICT" line at cost=0.125 (half-tick — what a professional
desk would actually pay) with three gates:
- Sharpe_ann > 2.0 → deployable
- 0.5 < Sharpe_ann ≤ 2.0 → marginal
- Sharpe_ann ≤ 0.5 → fail at realistic cost
The "FRICTIONLESS UPPER BOUND" line reports the intrinsic edge — what
the model could theoretically achieve at zero cost. Even if realistic
Sharpe fails, this number tells us whether the model has anything to
optimise toward at deployment.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
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