An artifact is not a publication. It needs a token to download, expires
after ninety days, and nothing outside GitHub can link to it, so a number
that only lives in an artifact is a number nobody can check.
A third job merges a green run's two payloads into
benchmarks/vs_vectorbt/results/latest.json and commits it. The two are
stored side by side rather than folded into one table: they run on two
runners, and timings from two machines are not rows of the same table.
Only a run where both measuring jobs came back green is published, and a
dispatch that pins an old version measures and reports without becoming
the published number.
Two gaps a reader could name without running anything: costs appeared on one
workload out of four, and nothing in the suite was a portfolio.
Costs could not simply be switched on across the board, and the reason is
measured. On FractionOfEquity sizing a 5 bps fee puts the engines 1.3e-4 of
capital apart and 2 bps of slippage 2.1e-5, against a 1e-9 tolerance, while the
round-trip counts stay identical: the trading agrees, the cost arithmetic does
not, because one charges the fee on top of the notional and the other reserves
it out of cash first. In fixed units both land exactly, to 1e-15. So
`sma_cross_costs` carries a fee and slippage on the headline signal, sized in
units, and the price of that is visible rather than hidden: x48.0 against x50.6
at 100k bars.
`multi_asset` runs five independent series in one shared book. It is the
workload manifoldbt does worst on, and it is here for that reason: going from
one asset to five costs it 6.1x and vectorbt 1.4x, so the ratio falls from x36.7
to x8.8 at a million bars. Broadcasting a column per asset is close to free;
walking five books is not. A portfolio is also what people actually run, and a
suite that only measures where it wins is not evidence.
Both are capped where a materialised five-column simulation would stop measuring
the engine and start measuring the swap file, and `ema_rsi_fees` keeps the
ceiling it got for going bankrupt.
Repetitions go from two to three: the floor at which a median is a median rather
than the mean of two.
The three points were sized before this runner had ever run one. It has now,
so they are sized from what it measured: 87.5 us per combination for manifoldbt
at 20,000 bars, 1.16 ms for vectorbt, 1.34 ms for raptorbt, and 15.0 ms for
raptorbt at 200,000 bars.
20,000 x 5,000 stays, because it is the only one of the three vectorbt can hold:
it materialises 1.57 MB per combination at that length, so 5,000 already costs
it 2.5 GB. The other two grow to 20,000 and 10,000 combinations and put it out
of scope, which is where a sweep stops being a speed comparison and becomes a
capability one.
raptorbt sets the budget, not manifoldbt. With no fan-out API its sweep is a
Python loop costing a full backtest per cell, so the large point goes deep in
combinations on a short series rather than the reverse: 20,000 combinations on
20,000 bars costs it 27 s a call, where 5,000 combinations on a million bars
would cost it 25 minutes.
Also: sweeps, not grids. `run_sweep`, `run_sweep_lite` and `--sweep` are what
the product calls this, and a second word for the same thing is a second thing
to learn. `grid` is kept only where it means the parameter space itself.
Five consecutive red runs, two unrelated causes.
Four of them never reached an engine: the workflow installs the tag it is
handed, but 0.18.0rc1 and rc2 were previews that never reached PyPI.
Pre-releases are now skipped, and the report step checks for its input file
instead of dying on a missing one and reporting the wrong cause twice.
The fifth came from adding 10M bars, which broke a workload whose validity
depended on the ladder stopping at 5M. ema_rsi_fees sizes in fixed units and
pays 5 bps a side, so over 10M one-minute bars the fees compound into the whole
account: -15% of capital at 1M, -74% at 5M, exactly -100% at 10M, where fees
reach 99,611 of the 100,000 it started with. Both engines then sit at zero and
disagree by 9,085 round-trips about how many worthless trades to book on a dead
account. Workloads can now declare a ceiling, and the runner skips past it out
loud.
Fewer points per axis: three series lengths instead of five, a decade apart
each step. 10k measured the clock rather than the work, and 5M sat between two
points that already bracketed it. sma_cross crosses on 30/150 rather than
10/50, worth about 15% on the ratio because it books a third of the trades.
Grids get their own job, licensed through ci_activate.py, which refuses to run
unlicensed rather than time a wait. Three points, not a matrix: across the
plane the four-core ratio moves only between x32 and x38.
The harness compared two engines everywhere; it now compares N against a
reference. manifoldbt is the reference: every parity check and every ratio is
a challenger against it, never two challengers against each other.
raptorbt 0.9.0 joins on three of the four workloads. Its sma_cross comes back
bit-identical to the reference's final equity, and its rsi matches to the last
bit; its ema seeds on a different warmup and it has no fixed-quantity sizing,
so the fee workload records it as unsupported with the reason rather than
leaving a blank cell. On the bracket it diverges in its own documented way: it
never re-arms while the entry level holds, so it books exactly the reference's
round-trips minus the ones that re-enter on the exit bar.
Python moves to 3.12, which raptorbt pins rather than we do: it is built
against pyo3 0.20.3, whose maximum supported CPython is 3.12. Timings from runs
before this change are therefore not directly comparable.
The bar matrix gains 10M and the repetition default drops from 7 to 2. Measured,
those two almost cancel: the job stays around 16 minutes. macOS keeps its old
ceiling, since 10M bars adds 1.55 GB on vectorbt's side alone and that runner
has 7 GB.
A speed claim a reader cannot reproduce is a screenshot. This harness
installs manifoldbt from PyPI like any user would, generates its own data,
and gates every timing behind a parity check: a workload where the two
engines disagree publishes nothing and fails the run.
It lives here rather than in the engine repository because it benchmarks the
published wheel, not the source. Anyone can fork this repository and press
"Run workflow" to get the same table on their own runner.
The workflow runs on demand, weekly, and on every published release, so a
version that gets slower says so in public.
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