bench: add a cost workload and a multi-asset one, and go to three repetitions (#10)

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.

benchmarks/vs_vectorbt/data.py

@@ -61,6 +61,19 @@ def make_ohlcv(
     )
 
 
+def make_universe(rows: int, count: int, *, seed: int = DEFAULT_SEED) -> dict:
+    """`count` independent series, keyed by the symbol id each engine will use.
+
+    Independent, not correlated: a portfolio of copies of one asset would let a
+    position-sizing bug cancel itself out across the book, which is exactly the
+    class of mistake a multi-asset workload exists to catch. The seeds are
+    spaced far apart and derived from the same base, so the whole universe is
+    reproducible from `seed` alone and the first symbol is bit-identical to the
+    single-asset series of the same length.
+    """
+    return {i + 1: make_ohlcv(rows, seed=seed + 1000 * i) for i in range(count)}
+
+
 def digest(df: pd.DataFrame) -> str:
     """Short content fingerprint of the bars, recorded in the result envelope.