Benchmark-as-a-Service (BaaS)
Benchmark-as-a-Service (BaaS) exposes the Benchmark X evaluation engine as programmable infrastructure.
It allows external systems to run, verify, and consume AI trading benchmarks without building their own execution, scoring, or trust layers.
Think of BaaS as:
“Benchmark X, but callable.”
Why BaaS Exists (Dev Rationale)
Many teams want benchmarking, but not the full platform:
Funds want internal model evaluation
Exchanges want neutral performance rankings
AI teams want reproducible comparisons
Research groups want real-market validation
Building this from scratch requires:
Real execution infra
Risk enforcement
Scoring logic
Anti-gaming mechanisms
BaaS exposes all of that without leaking system internals.
What BaaS Provides
At a system level, BaaS provides access to:
Battle Room creation
Strategy registration (restricted)
Real-market execution
BX Score computation
Reputation-aware evaluation
Deterministic result delivery
Consumers get results and guarantees, not raw control.
Core BaaS Primitives
1. Benchmark Job
A benchmark job is the atomic unit of BaaS.
It defines:
Market(s)
Time window
Capital constraints
Risk profile
Strategy set
Visibility level (public / private)
Once submitted, a job is immutable.
2. Strategy Handle
External strategies are referenced via handles, not code.
A handle may represent:
Internal Benchmark X strategies
Approved external strategies
Wrapped internal models
The execution engine treats all handles identically.
3. Evaluation Output
Each job produces:
Full performance metrics
BX Score per strategy
Risk and behavior breakdowns
Execution summaries
Verifiable audit logs
Outputs are versioned and timestamped.
Typical BaaS Flow
From a developer’s perspective:
Client submits benchmark job
System validates parameters
Execution engine schedules Battle Room
Strategies trade in real markets
Data is collected and verified
BX Scores are computed
Results are delivered via API or webhook
No intermediate access is granted.
Access Control Model
BaaS uses capability-based access, not blanket permissions.
Access is scoped by:
Organization
Job type
Market coverage
Data granularity
Frequency limits
This prevents:
Abuse
Reverse-engineering
Benchmark farming
Data Exposure Guarantees
BaaS guarantees that consumers never receive:
Strategy logic
Execution secrets
Order-level private parameters
Other clients’ private data
What is exposed is:
Aggregated performance
Verified metrics
Deterministic scores
This keeps the benchmark neutral.
Pricing & Cost Model (Dev View)
BaaS pricing is usage-based, not subscription-based by default.
Costs scale with:
Execution time
Market count
Strategy count
Data depth
Frequency
Internally, this maps directly to T1 compute credits.
If compute is consumed → cost is incurred.
Failure Handling & Guarantees
BaaS jobs may fail due to:
Market downtime
Execution adapter failure
Strategy violation
Risk breach
System behavior:
Partial results may be returned
Failures are explicitly flagged
Jobs are never silently retried
Audit logs explain termination reason
This avoids ambiguous benchmarks.
Determinism & Reproducibility
All BaaS jobs are:
Parameter-locked
Versioned
Replayable
If the same job is run under the same conditions, differences only come from the market, not the system.
This is critical for:
Research
Compliance
Institutional usage
Who Should Use BaaS
BaaS is designed for:
Quant funds
Exchanges
AI labs
Data platforms
Institutional allocators
It is not designed for:
Retail copy trading
Signal resale
Ad-hoc experimentation
Why BaaS Strengthens the Core System
BaaS:
Increases system utilization
Generates real revenue
Stress-tests the benchmark engine
Improves scoring robustness
Keeps Benchmark X relevant beyond its own UI
It turns Benchmark X from a platform into infrastructure.
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