Aura/docs/design/INDEX.md

aura design ledger — INDEX

The ledger records the load-bearing design contracts and their rationale. Each contract states what it guarantees, what it forbids, and why. A change that breaks a contract is a design decision (amend the contract here with its new rationale), never a silent refactor.

Provenance: contracts C1–C18 were settled in the initial rough-sketch design interview (2026-06-03), walking the design tree root-to-leaf (C16–C18 and the C10 refinement to a broker-independent position table came in follow-up turns; C10 was later reframed in cycle 0007 — the intent/exposure stream is the primary output, the position table a derived layer — see C10). The CLAUDE.md Domain invariants section is the compressed, always-loaded summary of the subset that agents must never violate; this file is the fuller form with rationale.

Vocabulary: a contract is one ledger entry. A cycle is one pipeline round; a milestone is a tracker container spanning many cycles (the first milestone is the walking skeleton: ingest → one signal → exposure/intent → sim-optimal broker → synthetic pip-equity signal-quality metric).

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Foundation — what aura is

aura is a framework and a playground for traders. A human and (primarily) LLMs author trading nodes directly in Rust; the engine backtests them deterministically and massively in parallel, composes them fractally, validates them (sweep / Monte-Carlo / walk-forward), and freezes a validated strategy into a standalone bot with a broker connection.

The predecessor RustAst (myc) tried this as a custom DSL and failed: too slow, too buggy, and LLMs author far better in Rust than in an unfamiliar DSL. aura inverts it — engine in Rust, strategies in Rust — but keeps RustAst's concepts (synchronous reactive streams, bounded-lookback series, run-counting, SoA). RustAst is a conceptual reference, not a dependency. The one reused component is data-server (the first data source).

What sets aura apart is not the single backtest — every quant system does that — but the World: the meta-level where harnesses are dynamically constructed and families of them orchestrated and explored (walk-forward, sweeps, Monte-Carlo, comparison). The deterministic single-harness engine is the substrate; the World is the product (C20–C22).

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External components

Two sibling projects live outside this repo and are named throughout the contracts. Their concrete location is recorded here so the repo is the single source of truth — not session memory.

• data-server — ~/dev/libs/data-server, Gitea Brummel/data-server (http://192.168.178.103:3000/Brummel/data-server.git). aura's first data source and the one reused component (Foundation; C3, C11, C12). A standalone leaf crate (deps: chrono, regex, zip) that loads Pepperstone M1/tick binary files and shares them lock-free as Arc<[T]> chunks (CHUNK_SIZE = 1024) via SymbolChunkIter::next_chunk (an inherent method driven by while let, not the Iterator trait); stream_*_windowed(from_ms, to_ms) provides C12's data-window with inclusive Unix-ms bounds. Records are AoS (M1Parsed/TickParsed, time_ms: i64 Unix-ms). The ingestion boundary (C3) therefore transposes these AoS records into aura's SoA columns (C7) and normalizes time_ms → canonical epoch-ns at that one boundary. Pulled in as a cargo git dependency when the ingestion task starts; deliberately absent from the bare skeleton so the workspace compiles without the network.

• RustAst (myc) — ~/dev/RustAst, Gitea Brummel/RustAst (http://192.168.178.103:3000/Brummel/RustAst.git). The predecessor DSL attempt (Foundation): a conceptual reference, never a dependency — its DSL authoring surface and Value/HM-inference machinery are exactly what aura rejects (C17). But its src/ast/rtl/ layer is a working reference implementation of the very streaming substrate aura rebuilds, and is worth reading before authoring aura-core — these are not just concepts, they exist as code:

  • rtl/series/data.rs — RingBuffer<T> with financial-style indexing (index 0 = newest), total_count (the run-count of C5) and lookback_limit (C8's pre-sized window); the ScalarValue marker trait ("flat scalars only, no String/Record" = C7's closed scalar set); ScalarSeries<f64|i64|bool> and the SoA RecordSeries for composites (C7's "OHLCV = a bundle of base columns").
  • rtl/series/mod.rs — create_typed_series, dispatching element type → storage backend (the type-specialized factory of C19).
  • rtl/streams/mod.rs — Signal { cycle_id, value } (C4's cycle clock) and the Stream / Observer / ObservableStream push traits (the reactive model of C4/C5).
  • rtl/streams/register.rs — the RTL "register" / delay node (the one explicit feedback path of C5/C9) plus a seeded, reproducible OHLC generator (C12's seed-as-input).

  aura reimplements these natively and sharpens them: types are monomorphized and edges type-erased to the four scalar kinds with direct dispatch (C7) instead of carrying boxed Values, and input history is shared zero-copy as Arc<[T]> (C12) instead of a VecDeque<Value>. RustAst shows the shape; aura makes it fast and deterministic.

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Contracts

C1 — Determinism and disjoint parallelism

Guarantee. A backtest is a deterministic, synchronous, non-concurrent event loop that reaches a unique state after each input tick. Same input (incl. seed) → bit-identical run. Two backtests are fully disjoint and run concurrently without locking. Forbids. Concurrency within a single sim; any nondeterministic input that is not captured as an explicit input (see C11, C12). Why. Real money rides on backtest results; reproducibility and an audit trail are non-negotiable. Speed comes from parallelism across sims, which disjointness makes lock-free.

C2 — Causality / no look-ahead

Guarantee. A node sees only the past. Input history is a read-only window that ends at the current cursor; a resampler emits a bar only once it is complete. Forbids. Any node access to data with timestamp > now; emitting a partial / still-forming bar. Why. Look-ahead is the cardinal backtester bug — a fast backtester that leaks the future is worse than none. Making the future physically absent from what a node receives beats merely discouraging it.

C3 — One merge, at ingestion only

Guarantee. Heterogeneous timestamped sources are k-way-merged by timestamp into one chronological cycle stream at the ingestion boundary; source-native time units (e.g. data-server's Unix-time_ms) are normalized there to the canonical epoch-ns timestamp of C7. Forbids. Any merge / as-of join inside the graph. Why. A single ordered timeline is the mechanism that makes heterogeneous-rate sources (news daily-bias + M5 + ticks) causally combinable without leaking the future. Keeping the merge at one boundary keeps the graph semantics simple.

C4 — Cycle granularity

Guarantee. The clock is data-driven: one input record = one cycle, advanced in global timestamp order, with a monotonic cycle_id. Ties (same timestamp, multiple sources) break by source declaration order. Forbids. A fixed time-grid clock; nondeterministic tie ordering. Why. The market is an irregular event sequence; a grid is arbitrary and either wastes empty cycles or clumps ticks. Backtest and live differ only in the origin of records, not the cycle semantics. Tie determinism preserves C1.

C5 — Freshness-gated recompute and sample-and-hold

Guarantee. The cycle_id advances everywhere (a cheap counter), but a node re-evaluates only when ≥1 of its own inputs is fresh this cycle (detected by run-count); otherwise it holds its last output. Stale inputs contribute their last (held) value. Forbids. Recomputing every node every cycle ("push all" is true for the clock, not for *recompute"); treating a held value as missing. Why. Total recompute does not scale to many sparse high-frequency sources; freshness-gating is the performance discipline that keeps the synchronous model fast.

C6 — Firing policy A and B, per input group

Guarantee. A node declares, per input group, one of two firing policies: A fire-on-any-fresh + hold (latest / as-of join — e.g. tick × held daily-bias); B all-fresh barrier (synchronizing join — e.g. O/H/L/C from four separate 15m sources: the candle is complete only when all four are fresh). A single node may mix an A input and a B group. Forbids. A single global firing mode; forcing per-node-only granularity. Why. Both are genuinely needed; RustAst implemented only B. Per-input-group granularity is required by the OHLC-plus-bias case where one node needs both. Realization (cycle 0004). Firing is tagged per input — Firing::{Any, Barrier(u8)} on InputSpec — and inputs sharing a Barrier id form a group; a mode-A input is its own trivial group, so "per input group" and the per-input tag coincide. The barrier's synchronization token is the cycle timestamp, not the cycle_id: under C4 four same-timestamp sources are four distinct cycles, so RustAst's cycle_id-equality barrier could never fire across them. A group fires when every member's last push carries the current cycle's timestamp, guarded by "≥1 member fresh this cycle" (so a group completed earlier does not re-fire). This fires both the multi-source bar and the within-source diamond rejoin (every push in a cycle carries that cycle's timestamp).

C7 — Four scalar base types, streamed as SoA

Guarantee. Only i64, f64, bool, timestamp (newtype over i64, epoch-ns UTC) are streamed, as columnar Structure-of-Arrays. Composite streams (OHLCV) are bundles of base columns — this is the node-output model too: a node emits a record of 1..K base columns (C8), each forwarded field-wise to a consumer slot; the bundle is structural, never a fifth scalar type. Edges are type-erased to these four kinds; the type check is paid once at wiring/sim-start, then the topology is frozen per sim → direct dispatch, no per-event allocation. Forbids. Streaming non-scalars (String, Records, tables, calendars) — those live as metadata beside the hot path; dyn Any payloads; per-event heap allocation; topology mutation mid-sim. Why. Maximal streaming performance (SIMD/cache) needs a tiny closed scalar set and SoA. The open set is composites (schemas of columns), not scalar types. Type-erasure at the edge is also forced by the cdylib boundary (C13).

C8 — The node contract

Guarantee. A node implements schema() (declares each input's scalar type, required lookback depth, and firing group, and the node's own tunable parameters — typed, with ranges, which aggregate into the blueprint's param-space the optimizer sweeps, C12/C19/C20) + eval(ctx) -> Option<&[Scalar]>. The engine provides read-only, zero-copy windows into each input's SoA ring buffer (ctx.f64_in(x)[k], sized at wiring); a node may additionally keep its own mutable series for derived/intermediate state. None/Void return = filter / not-yet-warmed-up. A node is a producer, a consumer, or both: a producer/transformer exposes one output port, whose payload is a record of 1..K base-scalar columns (a scalar is the degenerate K=1 record; an eval returns a borrowed row, one value per column); a pure consumer (sink) — chart, equity, logger — has no output. Sources are pure producers; sinks are pure consumers. Forbids. A node sizing/growing its input lookback at runtime; more than one output port per node; a fifth scalar type or a heterogeneous output payload (a record is a bundle of base columns, C7); copy-on-read of input history. Why. Engine-provided windows mean LLM-authored code cannot mis-manage lookback bookkeeping, and history passes through zero-copy. Fixed, pre-sized buffers suit deterministic, pre-dimensioned sims (no realloc in the hot loop). Realization (cycle 0005). NodeSchema.output is a Vec<FieldSpec> (named base columns; length 1 = scalar). Binding is field-wise only: Edge::from_field selects one producer column per edge; consuming a whole record is N edges (no "bind whole record" mechanism). The K fields of one record are co-fresh by construction (one eval, one timestamp), so C6 is untouched. eval returns Option<&[Scalar]> — a borrowed row into a node-owned buffer — so the forward path allocates nothing per cycle (C7). Realization (cycle 0006). The pure-consumer (sink) half of this contract is now realized at the substrate: recording is a node role, not a type. A recording node reads its typed input windows + ctx.now() in eval and pushes the record to a destination it holds as a field (a channel, a chart handle) — an out-of-graph side effect. There is no Sink type, trait, or engine flag: a node that only records returns None (pure consumer), and a node may record and return an output the engine forwards in the same eval (the "both" case). Encoding & return contract. A pure consumer declares output: vec![] — the empty record is the sink declaration; there is no separate type, trait, or marker. Its eval returns None or a zero-width Some(&[]), and the run loop debug-asserts the returned row's width equals the declared output width (row.len() == schema.output.len()). Field-wise wiring resolves Edge::from_field against the producer's output at bootstrap, so no edge can bind a field of a zero-output node — it fails with BadIndex — making a sink structurally unwireable as an in-graph producer; its only output is the out-of-graph side effect. In-graph routing stays engine-owned data (the edge table); the escape out of the graph is the node's own side effect — and that boundary is the determinism / graph-as-data boundary (C1/C7).

C9 — Fractal, acyclic composition

Guarantee. A composite is itself a Node that wires a sub-graph and exposes one output; signal, combined signal, and (with execution) strategy are all the same abstraction, nestable arbitrarily. The dataflow graph is a DAG; the only feedback path is an explicit delay/state node (the RTL "register"). Wiring is written in Rust (builder API); the built graph is introspectable runtime data. Forbids. Implicit dataflow cycles (combinational loops); special-casing "signal-of-signals" as separate mechanics. Why. Self-application of one contract gives unlimited composition with no adapter zoo. Acyclicity keeps the synchronous reactive model well-defined; forcing feedback through a visible delay node keeps the per-cycle determinism intact and the one legitimate feedback path explicit. Graph-as-data enables visualization, freezing, and re-parameterization for sweeps.

C10 — Strategy output is an intent/exposure stream; position management is a decoupled derived layer; brokers are downstream nodes

Guarantee. A strategy's primary, backtestable output is not an equity curve, nor a position-event table, but an intent / exposure stream: the DAG expresses exactly one state at time t (C8 — a node emits at most one record per eval), so a strategy emits one signed, bounded exposure f64 ∈ [-1, +1] per cycle (per instrument; the portfolio is multi-instrument). Exposure is the desired fractional position; position sizing and risk live in the decision/sizing node that shapes a raw signal score into exposure. The chain is signals (scores) → decision/sizing node → exposure stream.

Signal quality is evaluated by the (a) sim-optimal broker — a downstream consumer node (C8/C9) that consumes the exposure stream plus the relevant price stream and integrates exposure(t-1) · (price(t) − price(t-1)) into a synthetic equity stream in pips: deterministic, frictionless, perfect-fill (no currency, no real-broker constraints). The exposure held into a cycle (decided at t-1) earns that cycle's return, so the integration is causal (C2 — no look-ahead). Pip PnL uses per-instrument pip metadata (reference data beside the hot path, C7/C15). This pip-equity curve measures the signal's quality, not an execution-modelled P&L; it is the primary research loop — backtest one signal, combine it with another, backtest the combination.

Position management — turning the exposure stream into a broker-independent, time-ordered table of position events (pure scalar columns, C7: event_ts: timestamp, action: i64 buy / sell / close, position_id: i64, instrument_id: i64, volume: f64 unsigned — direction is the action; a position's open time is its opening event's event_ts, no separate open_ts; a close references a position_id and may be partial via its own volume) — is a decoupled, derivable, downstream layer. The events are the first difference of the exposure state, materialized as a computed table (where multiple events may share one event_ts — e.g. a reversal's close + open), never as a per-eval node output (which C8 caps at one record per cycle). This layer feeds (b) realistic broker nodes (Pepperstone, …): downstream consumer nodes that consume the position-event table plus prices, apply real spread / commission / slippage / lot / margin, may reject or modify positions, and produce a currency equity stream for viability and deployment. The position-event table stays broker-independent — one table feeds many realistic brokers, giving directly comparable currency curves. Live: a realistic broker node consumes the events in real time and routes orders as a side effect; reconciliation with the real account is an external adapter.

So the two broker classes attach at two different points: the sim-optimal broker on the exposure stream (signal quality, pips, now), realistic brokers on the derived position-event table (execution viability, currency, later). Both are ordinary downstream nodes (C8/C9); several can attach at once for directly comparable curves. Forbids. Treating an equity curve as the strategy's output; making the position-event table the strategy's direct DAG output (it is derived, not emitted per eval — a decision instant may need >1 event, which C8 forbids) or the measure of signal quality; baking a broker into the strategy; a special external broker subsystem (a broker is an ordinary node); storing open_ts (derive it from the opening event); a signed-volume direction trick in the event table (use action); broker-specific assumptions leaking into the strategy logic. Why. The DAG is a synchronous reactive graph: at time t it holds exactly one state, and a node emits at most one record per eval (C8). A sequence of position events — where one decision instant (a stop-and-reverse) needs a close and an open at the same event_ts — cannot be the DAG's per-cycle output without violating C8. The state the DAG can express faithfully is the desired exposure (one value per cycle); the position events are its first difference, a derived consequence. Decoupling them resolves the C8↔C10 impedance and matches how signal research actually proceeds: you first measure a signal's quality (does this exposure, held over time, make pips?) independently of any execution model; only later do you model position management and real-broker frictions for deployment. The sim-optimal pip curve is a level, currency-free playing field for comparing and combining signals; the derived position-event table + realistic broker nodes then test real-world viability. Modelling brokers as nodes (not a bespoke subsystem) keeps them within the one Node/graph abstraction (C9). This supersedes the earlier "the strategy's output is the position-event table" framing (cycle 0007 reframe).

C11 — Generalized sources; record-then-replay determinism boundary

Guarantee. A source is anything that produces timestamped scalar streams — market data (data-server) and non-financial sources (e.g. a news-agent node emitting a bias) are treated identically. Anything nondeterministic, external, or slow (LLM/news/web) is materialized into a recorded, timestamped stream before it enters the engine; backtest replays the recording, live computes fresh in real time and records it for future backtests. A bias enters as a value held until the next event (firing policy A). Forbids. Any live external call inside a backtest replay. Why. It is the only model compatible with reproducible backtests — LLM calls are nondeterministic and far too slow per-cycle. Per ~/.claude/CLAUDE.md, external LLM (IONOS) calls happen only at the recording/live-source edge, with explicit per-session consent, never inside a sim.

C12 — The atomic sim unit and the four orchestration axes

Guarantee. The atomic unit is (frozen topology + param-set + data-window + RNG-seed) → deterministic run → metrics. Parameters are typed, ranged, runtime values injected at graph build (no recompile per param-set; the optimizer sees a generic vector of typed ranges). Raw data is shared read-only across sims via Arc<[T]> (data-server is built for this). Four axes orchestrate the atomic unit: (1) param-sweep (grid/random), (2) optimization (argmax metric), (3) walk-forward (rolling in-sample optimize + out-of-sample test), (4) Monte-Carlo (N seeded realizations perturbing input). MC = sweep over seeds; each realization is itself deterministic given its seed. Forbids. Baking a specific search strategy (Bayesian/genetic) into the primitive — those are pluggable policies atop the atomic unit; recompiling on a param change. Why. A stable primitive + orchestration axes keeps "wahnsinnig schnell" (embarrassingly parallel across the unit) cleanly separated from search policy. Seed-as-input reconciles Monte-Carlo with C1. The "frozen topology" of the atomic unit is one harness instance, selected by the harness's structural axes (C20); the structural experiment matrix is the outer orchestration over this dimension, the tuning sweep the inner (C19/C20).

C13 — Hot-reload is authoring-only; deploy is frozen

Guarantee. A node/strategy is authored as a native Rust cdylib, hot-reloaded during the authoring loop (Rust-ABI; host and node built with the same toolchain). The live/deploy bot is a statically-linked, versioned, frozen artifact. Forbids. Hot-swapping a running live bot; loading third-party / foreign- toolchain plugins. Why. Hot-reload makes the research loop fast; a live artifact must be frozen and reproducible (audit trail: this bot = this commit). A sweep pays no hot-reload tax — params are runtime data (C12), so the cdylib loads once.

C14 — Headless core, two faces

Guarantee. The engine is a UI-agnostic library. Two faces sit on it: a programmatic/CLI face (the primary surface for the LLM and automation — author a node, run a sim/sweep, emit structured metrics) and a visual face for human exploration. Visualization is only a downstream consumer node on the streams. Forbids. Any UI/pixel knowledge inside the engine. Why. The LLM drives programmatically, the human visually; a headless core serves both. The visual face is the playground (C22) — egui-native, in-process zero-copy from the SoA columns; it is staged after the runnable substrate but is core to aura's identity, not optional.

C15 — Resampling-as-node; sessions/calendars

Guarantee. A resampler is a node (finer stream → coarser bar stream), clock-sensitive, emitting a completed bar only at the boundary (C2). Calendars and instrument specs are metadata (non-scalar, beside the hot path); session context is exposed as scalar streams via a SessionNode (bars_since_open: i64, in_session: bool, session_open_ts: timestamp). "3rd 15m candle after session open" is then a plain node checking bars_since_open == 3. Forbids. Streaming the calendar; special-casing session logic outside the stream model. Why. Keeps the line consistent — everything a signal needs arrives as a stream; reference data feeds source/session nodes from beside the hot path.

C16 — Engine / project separation; three-tier node reuse

Guarantee. aura is the reusable engine; each research project is a separate external repo that depends on aura via cargo (the game-engine / game split). Node reuse is cargo-native, in three tiers: aura-std (universal blocks, ship with the engine) / shared node crates (cross-project-reusable, their own repos, pulled as cargo git deps) / project-local nodes/ (experimental, project-specific). A reusable node is an rlib dependency; the hot-reload unit stays the project-side cdylib that composes it (consistent with C13). Concretely a project is a Rust crate — a cdylib library of node / strategy / experiment blueprints — plus a static Aura.toml (project context: data paths, instrument/pip metadata, default broker & window, runs dir). During research the aura host loads and runs it (C13 hot-reload); for deploy the chosen strategy + broker freeze into a standalone binary. A project is therefore always a Rust program built on the engine. Forbids. Project-specific signals in the aura repo (it keeps at most example/fixture nodes under examples/ for its own tests); a multi-project manager inside aura; a bespoke node registry/marketplace (cargo + Gitea is the package mechanism). Why. The engine/game split keeps the engine sharp and reusable while each project versions its own research with its own forward-queue. Promotion (local → shared → std) is the ordinary Rust reuse gradient, no new mechanism.

C17 — Authoring surface

Guarantee. All logic — nodes, strategies, and experiments/harnesses — is authored in native Rust through Claude Code + the skills pipeline: the human describes, Claude writes the Rust, builds it, runs it via the aura CLI, and reports metrics. Declarative config (Aura.toml) carries only static project context (data paths, instrument/pip metadata, defaults, runs dir), never logic. aura ships no embedded coding-LLM. IONOS LLMs are used only as a runtime data source (news-agent bias, C11), gated by per-session consent, never in the code path. Forbids. An in-app LLM chat that generates node code inside aura; using IONOS (weaker models) as the authoring brain. Why. LLMs author Rust well in Claude Code — that is the fix to RustAst's failure; making weaker models the coding brain reintroduces the very problem. Keeps aura's scope an engine + playground, not an LLM-IDE.

C18 — Project management: one repo = one project, plus a run registry

Guarantee. Management has two planes. (1) Code & forward-queue: git (commit = identity; the frozen bot is a commit) + Gitea (ideas/hypotheses as the forward-queue, a research thrust = a milestone, the idea → experimental → validated → deployed label gradient). (2) Experiments & results: an Aura-native run registry — one record per run = a manifest (node-commit + params + data-window + seed + broker profile) + metrics, queryable, with lineage (composite ← signals; run ← inputs). Determinism (C1/C12) makes a run reproducible from its tiny manifest, so the registry stores manifests + metrics and re-derives full results on demand. Depth: structured (promotion/status, lineage, run-diff). Forbids. Storing results not reproducible from a recorded manifest; duplicating git/Gitea inside aura; a multi-project workspace manager. Why. Comparing experiments over time is the heart of the research loop and has no home in git/Gitea; determinism makes a structured registry cheap. Sequencing: the walking skeleton emits a manifest + metrics per run from day one; the registry/index is a later milestone over manifests that already exist.

C19 — Bootstrap: blueprint → instance (recursive)

Guarantee. Construction is a distinct phase, recursive at every level. Each node type has a factory params → sized concrete node (e.g. SMA(length) sizes its ring buffer). A blueprint is the param-generic, input-role-generic graph-as-data produced by running a Rust builder (C9); it carries free numeric params (declared ranges) and free input roles. The bootstrap binds (blueprint + param-set + data bindings + seed) into a concrete, frozen instance — buffers sized, topology fixed. This is precisely the "wiring / graph build" that C7 ("sized at wiring", "topology frozen per sim") and C12 ("params injected at graph build") already reference. The same machinery applies recursively up to the harness (C20). A sweep builds many instances from one blueprint; instances are disjoint (C1). Forbids. Params that change topology (a topology change is a different blueprint — Fork A, C7 "frozen"); resizing buffers after bootstrap; running a sim against an un-bootstrapped blueprint. Why. Separating the param-generic blueprint from the param-bound instance is what makes one strategy reusable across a whole sweep and lets the optimizer mutate "the 20" by rebuilding an instance (cheap; no recompile, C12) instead of rewriting code. Naming the build phase makes the implicit "wiring" of C7/C12 explicit.

C20 — Strategy ↔ harness; the harness is the root sim graph

Guarantee. A strategy is a reusable composite-node blueprint (C9): broker-, data-, and viz-independent, with inputs declared as named roles (symbol-agnostic where possible) and the intent/exposure stream (C10) as output. A harness (the experimental setup) is the root sim graph — sources bound to the strategy's input roles + the strategy + attached broker node(s) + sinks — and is itself produced by the bootstrap (C19). A harness instance is C1's disjoint unit (RustAst's "root scope"). The harness has two kinds of parameterization: structural axes (which strategy, which instrument(s), which broker(s), which window) whose variation selects different instances — the experiment matrix; and tuning params (the strategy's numeric params) swept within a fixed structure (Fork A). The same strategy blueprint is reused across backtest, sweep, visual workspaces, and the frozen live bot — each a different harness. Both strategy and harness/experiment are authored in Rust via builder APIs (C17); the experiment matrix is ordinary Rust control flow (loops/conditionals), not a config schema. Ontologically, a node (incl. a strategy composite) is an open fragment — free input roles + ≤1 output (C8/C9) — that does not run alone; a harness is the closed root graph: a strategy with its input roles bound to sources and its output terminated in broker/sink nodes, under a clock. A harness is therefore not a node (no free inputs, no output; it does not fit eval) — it is the closure that runs, C1's disjoint unit / the root scope. Harnesses do not nest as nodes; the World (C21) orchestrates them as objects. Forbids. Embedding data sources / brokers / sinks inside a strategy; a declarative experiment mini-DSL (logic is Rust — C17); modelling the harness as a node (it is the closed root scope, not an open composable node). Why. Reusability needs the strategy to be a context-free blueprint that many harnesses embed. Modelling the harness as a root graph keeps it within the one Node/graph abstraction (C9) and makes "10 strategies in one environment" and "one strategy × N instruments" plain nested loops over the structural axes. Rust authoring (not config) preserves full programmatic power — conditional/adaptive matrices, generated axes, custom wiring — and avoids re-introducing the DSL trap C17 rejects.

C21 — The World: the meta-level is the product

Guarantee. Above the harness sits the World — the project's program / "game" (C16: a Rust crate). Within it, harnesses are dynamically constructible, first-class objects: meta-programs (walk-forward, sweep, optimize, Monte-Carlo — C12's axes) construct harness instances at runtime via the bootstrap (C19), run them disjointly in parallel (C1), aggregate / compare their results, and discard the transient instances. A walk-forward rolls windows → bootstraps a harness per window → stitches out-of-sample equity + parameter stability into one meta-result. Orchestrating families of harnesses is first-class, not a headless afterthought; the run registry (C18) is the World's memory. Forbids. Relegating multi-harness orchestration (walk-forward / sweep / comparison) to second-class headless-only status; treating the single backtest as the product. Why. What happens within one harness — backtest a strategy → equity — is commodity; every quant system covers it. aura exists for the meta-level: a programmable space where harnesses are dynamically built and families of them orchestrated and explored. The deterministic single-harness engine (C1–C20) is the substrate; the World is the product.

C22 — The playground is a trace explorer; sinks are the recording mechanism

Guarantee. The World is a program: nothing is displayable until it runs, and its harnesses are transient machinery (built, run, discarded — C21). The only durable, displayable substance is the recorded trace, captured by sinks — pure consumer nodes (C8) that persist a stream (equity, position events, a node's output) into the run registry (C18). Displayable = exactly what a sink recorded; with no sink, only input params + summary metrics remain. The playground is therefore an execution viewer / trace explorer, and it plays any harness (it is the harness player, not a harness, never bound to a default one): before a run it shows the program structure (graph-as-data, C9) + param knobs; during a run, live sink streams; after a run, recorded traces + metrics from the registry — including meta-views (stitched walk-forward, sweep surfaces, multi-strategy / instrument comparison). Observability is explicit and selective — you instrument what you want to see; the choice of sinks is part of the experiment. The engine ships sample harnesses with sinks so a newcomer sees a populated trace immediately; a fresh project is empty until built, run, and instrumented. Forbids. A scene-editor model that assumes a persistent populated world; constructing or wiring topology in the UI (topology is Rust + hot-reload, C9/C17 — the UI reflects the live graph-as-data and tunes runtime params via sliders, C12); retaining un-sinked data; binding the playground to a single / default harness. Why. A program has no scene-at-rest to inspect; what persists is what it records. Making sinks the one recording-and-observability mechanism keeps "what can I see?" answerable by "what did I instrument?", and keeps the engine UI-agnostic (C14). Live param tuning (runtime values, no topology change — C12 / C19 Fork A) gives the interactive feel without a wiring DSL. Realization (cycle 0006). Sinks-as-recording-mechanism is realized at the substrate level: a recorded trace is exactly what a recording node pushed out of the graph (no engine recording registry; the constructing World holds each recording node's destination). The engine's single observe: usize affordance is removed — Harness::run returns () and recording is a node-side concern, so one run records many streams (one per recording node) instead of exactly one row. Recorded streams are sparse and timestamped (a record per fired cycle, tagged ctx.now()), matching a trace of timestamped events (C18). No new contract; the Harness API change (observe removed, run -> ()) is recorded here.

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Open architectural threads not yet resolved

• Playground & World UI surface — the playground is core (C22), egui-native; open is the concrete UI of the meta-views (walk-forward, sweep surfaces, comparison) and the run/replay clock controls.
• Parameter-space search strategies (Bayesian/genetic) — pluggable policies atop the atomic sim unit (C12), not yet designed.
• aura new scaffolder, the experiment-builder API, and Aura.toml's static-context schema — aura new scaffolds a Rust project crate (node / strategy / experiment blueprints) against the engine (C16/C20); the experiment-builder API surface (harness wiring, structural axes, sweep combinators) and Aura.toml's schema (data paths, instrument/pip metadata, default broker & window, runs dir) are not yet designed.
• aura-std contents — the crate exists (doc-only); which universal blocks land first follows the walking-skeleton's needs.
• strategies/ split — a later split, inside a project, of top-level strategies from reusable building blocks in nodes/; not a day-1 cut.
• Sequencing — the runnable single-harness substrate comes first (walking skeleton: a closed harness that runs deterministically and records via a sink); the World/meta layer (C21) and the playground trace-explorer (C22) are the differentiating layers that follow — you orchestrate and visualize a thing that must first run once.