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bench: fix the red runs, trim the matrix, add a grid job (#7)
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.
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@@ -85,11 +85,47 @@ store)`, the documented entry point, not through an internal fast path.
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| Workload | What it exercises | vectorbt | raptorbt |
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|---|---|---|---|
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| `sma_cross` | SMA 10/50 crossover, long-only, no cost | exact | exact |
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| `ema_rsi_fees` | EMA 12/26 crossover with an RSI(14) filter and a 5 bps taker fee | exact | unsupported |
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| `sma_cross` | SMA 30/150 crossover, long-only, no cost | exact | exact |
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| `ema_rsi_fees` | EMA 12/26 crossover with an RSI(14) filter and a 5 bps taker fee, capped at 1M bars | exact | unsupported |
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| `sma_cross_metrics` | the same simulation, plus max drawdown, Sharpe, Sortino and volatility | exact | exact |
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| `bracket_sl_tp` | the same entry with a 15 bps stop and a 30 bps target | documented | documented |
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### Why the fee workload stops at 1M bars
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A workload can stop being a comparison before it stops running. `ema_rsi_fees`
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sizes in fixed units and pays 5 bps a side, and at 1-minute resolution it turns
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over often enough that the fees compound into the account: measured, it ends at
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-15% of capital on 1M bars, -74% on 5M, and exactly -100% on 10M, where fees
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reach 99,611 of the 100,000 it started with.
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Past that point the engines still agree on the equity, because both are sitting
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at zero, and disagree by thousands of round-trips about how many more worthless
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trades to book on a dead account. That is a fact about a bankrupt strategy, not
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about either engine, so the workload carries a ceiling and the runner skips it
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above that with the reason printed. The other workloads have no ceiling.
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### What the windows are, and why they moved
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`sma_cross` crosses on 30/150 rather than 10/50. The two were measured against
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each other on the same 5M bars, and the slower pair is worth about 15% on the
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ratio (x267 against x232 with a performance summary) because it books a third of
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the trades and manifoldbt's cost, unlike vectorbt's, moves with the trade count.
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That is a real effect and a small one, and it is worth knowing which way the
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knobs turn before anyone quotes a number:
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| Turn up | Effect on the ratio | Why |
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|---|---|---|
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| series length | **widens** | vectorbt materialises the simulation; its cost is linear in bars |
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| asking for the summary | **widens sharply** | it has to build the equity curve it deferred |
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| number of trades | narrows | near-free for vectorbt's per-bar loop, real for manifoldbt |
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| number of indicators | narrows | same reason |
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Measured at 5M bars on four levels of turnover, the ratio runs from x40 at
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480,000 round-trips to x71 at 2,500. The floor matters more than the peak: even
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in the busiest configuration tested, with half a million round-trips, the gap
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holds at x40, and x151 with a performance summary.
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Each of those runs across a range of series lengths. Two further axes, cold
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start and memory, are measured in their own processes because they cannot be
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measured honestly inside the main one.
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@@ -254,8 +290,30 @@ On the raptorbt side specifically:
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- `bench.py` - the runner
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- `sweep_child.py` - one parameter-grid point, in its own process
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- `report.py` - JSON to Markdown, and to the GitHub job summary
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- `ci_activate.py` - activates the licence the grid job needs, and refuses to
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continue without one
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- `ci/bench-vs-vectorbt.yml` - the workflow, deployed to the public repository
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## Parameter grids
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Grids run in their own CI job, because they need a licence: an unlicensed
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fan-out call waits out a fixed interval before doing any work, so a stopwatch
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would be timing the wait rather than the engine. The harness refuses to produce
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a number in that state rather than producing a wrong one.
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Three points, chosen from a measured map of the plane rather than picked. Across
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bars from 5,000 to 200,000 and grids from 500 to 10,000 combinations, the ratio
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on four cores moves only between x32 and x38, so a denser matrix would spend
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runner time re-measuring the same number. What the three points carry is the
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shape: two grid sizes at one series length, and one grid vectorbt cannot hold at
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all (it materialises the simulation per combination, measured at 3.93 MB per
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combination on 50,000 bars).
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Grid ratios are much more sensitive to core count than single backtests are,
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because manifoldbt is the only one of the three that fans out across cores:
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measured on the same point, x38 on four cores and x146 on twenty. Numbers from
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the CI job are four-core numbers, and they are the conservative ones.
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The directory is still named `vs_vectorbt` and the workflow file still
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`bench-vs-vectorbt.yml`: renaming either would break the path the public
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repository runs and start a fresh, empty run history.
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@@ -55,6 +55,19 @@ from workloads import ( # noqa: E402
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unsupported_by,
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)
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def _in_range(key: str, bars: int) -> bool:
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"""Is this workload still a comparison at this series length?
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A ceiling is not a performance limit, it is a validity one. The fee workload
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bankrupts its account past a certain length, and two engines agreeing that a
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dead account is worth zero is not a measurement. Skipping is loud in the run
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output rather than silent, because a table that is short by one row reads
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like a choice.
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"""
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ceiling = WORKLOADS[key].max_bars
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return ceiling is None or bars <= ceiling
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# 2: timings, parity and speedups became per-engine maps when the harness grew
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# past two engines. `report.py` reads version 1 files as well, so the results
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# archived under results/ stay readable.
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@@ -655,15 +668,31 @@ def main() -> int:
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paired = [k for k in SCOPE_PAIR if k in args.workloads]
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singles = [k for k in args.workloads if k not in paired]
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def skip(key: str, bars: int) -> None:
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print(" {k:18s} {b:>12,} bars ... skipped beyond this workload's "
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"ceiling of {c:,} bars".format(
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k=key, b=bars, c=WORKLOADS[key].max_bars))
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results = []
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failures = 0
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for bars in args.bars:
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if len(paired) > 1:
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for entry in measure_pair(paired, bars, args.reps, workdir, active):
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runnable = [k for k in paired if _in_range(k, bars)]
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for key in paired:
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if key not in runnable:
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skip(key, bars)
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if len(runnable) > 1:
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for entry in measure_pair(runnable, bars, args.reps, workdir, active):
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results.append(entry)
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failures += announce(entry)
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for key in singles + (paired if len(paired) == 1 else []):
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elif runnable:
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entry = measure(runnable[0], bars, args.reps, workdir, active)
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results.append(entry)
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failures += announce(entry)
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for key in singles:
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for bars in args.bars:
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if not _in_range(key, bars):
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skip(key, bars)
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continue
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entry = measure(key, bars, args.reps, workdir, active)
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results.append(entry)
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failures += announce(entry)
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@@ -672,7 +701,8 @@ def main() -> int:
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# a cold-start table cannot come back missing a column because the workload
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# happened to be one somebody sits out.
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probe_workload = next(
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(k for k in args.workloads if all(supported(k, n) for n in active)),
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(k for k in args.workloads
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if all(supported(k, n) for n in active) and _in_range(k, 20_000)),
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args.workloads[0],
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)
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@@ -59,6 +59,10 @@ class Workload:
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title: str
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why: str
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params: Dict[str, Any] = field(default_factory=dict)
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# Longest series this workload is valid on, or None for no ceiling. A
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# workload can stop being a comparison before it stops running: see the fee
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# workload, whose account this exists to keep alive.
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max_bars: int | None = None
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# Engine name -> Note. An engine with no entry here is expected to agree
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# with the reference down to float-reordering noise, and a disagreement is
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# a failure that withholds the timing.
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@@ -70,11 +74,11 @@ WORKLOADS: Dict[str, Workload] = {
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for w in (
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Workload(
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key="sma_cross",
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title="SMA 10/50 crossover, long-only, no cost",
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title="SMA 30/150 crossover, long-only, no cost",
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why="The canonical baseline. Unambiguous indicator, no fee policy, "
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"no stop semantics: if the engines disagree here, nothing else "
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"in the suite is worth reading.",
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params=dict(fast=10, slow=50, alloc=1.0),
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params=dict(fast=30, slow=150, alloc=1.0),
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),
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Workload(
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key="ema_rsi_fees",
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@@ -87,6 +91,14 @@ WORKLOADS: Dict[str, Workload] = {
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"engines on a wiped-out account compares rounding noise.",
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params=dict(fast=12, slow=26, rsi_period=14, rsi_lo=30.0, rsi_hi=70.0,
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units=5.0, fee_bps=5.0),
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# The account has to survive, or the engines are being compared on
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# rounding noise around zero. Measured: -15% of capital at 1M bars,
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# -74% at 5M, and exactly -100% at 10M, where fees reach 99,611 of
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# the 100,000 started with. Past that the two disagree by thousands
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# of round-trips while both sit at zero equity, which is a fact
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# about a bankrupt strategy and not about either engine. The ceiling
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# is set where the comparison still means something.
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max_bars=1_000_000,
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notes={
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"raptorbt": Note(
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"unsupported",
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@@ -110,7 +122,7 @@ WORKLOADS: Dict[str, Workload] = {
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),
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Workload(
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key="sma_cross_metrics",
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title="SMA 10/50 crossover, with a performance summary",
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title="SMA 30/150 crossover, with a performance summary",
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why="The same simulation as `sma_cross`, but both engines are asked "
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"for what a user actually reads: max drawdown, Sharpe, Sortino "
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"and volatility alongside the return. manifoldbt computes them "
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@@ -120,7 +132,7 @@ WORKLOADS: Dict[str, Workload] = {
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"`sma_cross` above is the same work without the summary, and the "
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"two are reported side by side so the reader can see what the "
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"summary costs each engine.",
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params=dict(fast=10, slow=50, alloc=1.0, metrics=True),
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params=dict(fast=30, slow=150, alloc=1.0, metrics=True),
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),
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Workload(
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key="bracket_sl_tp",
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