Love where you’re taking this. Here’s a clean, code-first plug-in contract for feeds, plus a messaging plan that scales from your current Redis Router up to NATS/Kafka if/when needed—while keeping Orchestra decoupled and future-proof.

1) The universal contract (what every data source must speak)

1.1 Event envelope (bus-friendly, source-agnostic)

Use this wrapper for every message, no matter the feed:

• event_type drives routing and storage.

• schema_ref is the only thing Orchestra/normalizers need to know to validate/transform.

• watermark enables exactly-once or at least-once dedupe.

• payload follows a canonical schema (see 1.2).

1.2 Canonical payload schemas (small set, versioned)

Keep these tight and versioned (JSON Schema + Pydantic types; Avro/Protobuf optional later):

• quotes/v2: bid/ask/mid, sizes, venue, symbol, tick_type.

• trades/v2: price, size, side, trade_id.

• orderbook/v1: levels, depth, side arrays.

• gex/levels/v1: walls, zero-gamma, zero-delta, timestamps.

• news/v1: headline, source, tickers[], sentiment?, url.

• calendar/v1: event_name, actual/forecast/previous, importance.

Example quotes/v2 payload:

Rule: Connectors map proprietary fields → these canonical payloads before publishing.

1.3 Connector manifest (DI-friendly, zero guessing)

Each connector ships a connector.manifest.yaml:

Orchestra reads manifests at startup, registers routes, and applies rate limits automatically.

2) Data flow (conditioning & handoff without choking)

Collector → Normalizer → Bus → Persister → Feature/Signals

1. Collector (source adapter)
   Pulls raw data; never publishes raw. Hands raw to the Normalizer via in-proc queue.

2. Normalizer (schema compiler)

   • Validates against the canonical schema_ref (JSON Schema).

   • Adds envelope, computes checksum, sets event_time/ingest_time, sequence.

   • Publishes to the bus topic (see 3).

3. Persister

   • Subscribes by event_type and writes to Timescale tables (quotes, trades, gex_levels, news, calendar).

   • Idempotent on (event_type, source_id, symbol, event_time, sequence, checksum).

4. Feature builders / Signals

   • Continuous aggs & materialized views (e.g., ZG/ZD, Markov inputs).

   • Emit derived events (e.g., signals/zgzd/v1) back onto the bus for downstream consumers (alerts, autotrade).

This guarantees Orchestra only deals in well-formed messages. Garbage never hits your core.

3) Topics, routing, retention

Naming:

Retention:

• Hot streams (quotes/trades): 24–72h on the bus → persisted in Timescale for long history.

• Control/Signals: 7–30d (small volume).

• DLQ per topic: *.dlq keeps failed messages + last error.

4) Bus choice: start simple, swap later without rewrites

• Now (fits your stack): Redis Streams + consumer groups.
  Pros: already running; microsecond latency; easy ops.
  Usage: 1 stream per topic; MAXLEN ~ caps; DLQs per topic.

• If fan-out and global WAN grow: NATS JetStream.
  Pros: blazing low-latency, simple semantics, good clustering, subjects with wildcards.
  Migration: adapter that reads/writes both for a while.

• If we hit very high throughput & replay needs (TB/day): Kafka.
  Pros: huge throughput, long retention, Connect ecosystem.
  Cons: heavier ops. Only move when metrics say so.

Design hedge: our envelope + topics + schema are bus-agnostic. Only the transport client changes.

5) Backpressure, DLQ, idempotency

• Backpressure: rate limiter per connector (from manifest). If lag rises, slow publishers; fast-fail to DLQ on hard errors.

• Idempotency: dedupe key = hash of (event_type, source_id, symbol|partition, event_time|sequence, checksum).
  Timescale writes use ON CONFLICT DO NOTHING.

• DLQ: store the bad envelope + error; auto-replayer service can fix/re-emit post-patch.

6) Orchestration interface (so jobs don’t care about sources)

Jobs never call “Barchart” or “IBKR.” They declare needs:

The runtime resolves data from Timescale (primary) and/or recent bus cache. No source names appear here—total decoupling.

7) Storage model in Timescale (simple & fast)

• Raw canonical tables (narrow, NOT JSONB for hot paths):

  • quotes(ts, symbol, bid, ask, bid_size, ask_size, source_id, ...);

  • trades(ts, symbol, price, size, side, source_id, trade_id, ...);

  • gex_levels(asof, symbol, zero_gamma, zero_delta, call_wall, put_wall, source_id, ...);

  • news(ts, ticker, headline, source_id, url, sentiment);

  • calendar(ts, ticker?, event_name, actual, forecast, importance);

• Continuous aggregates for OHLCV, spreads, features.

• Materialized views feeding Markov/alerts.

• Replay: from Timescale or archived Parquet if we add S3 later.

8) Health + governance

• Every connector exposes /health (latency, last_ok, lag).

• Status Gateway scrapes and emits health.json → Joomla/Svelte.

• Schema governance: JSON Schema in utp-schemas/, semver’d, CI-validated.

• Manifests in each connector repo; CI checks they don’t claim unsupported event_types.

9) Edge & regions (US/EU/JP chambers)

• Put Collectors/Normalizers near the venue (latency + regional auth).

• Persist locally → replicate to central Timescale or query federated views.

• Topics include realm & region so cross-region consumers can subscribe precisely.

10) Practical “Day-1” steps (fits what you’ve built)

1. Lock the envelope + canonical schemas v1.0 in utp-schemas/ (JSON Schema + Pydantic).

2. Add connector manifests to IBKR, Tasty, Barchart.

3. Update collectors to publish only canonical envelopes to utp.prod.chamber_us.*.

4. Normalizer as a common lib (utp-normalize) each connector imports.

5. Persister service writes to Timescale with idempotent UPSERTs.

6. Orchestra jobs use needs (event_type/symbols/lookback), not vendor names.

7. Keep Redis Streams now; set per-topic MAXLEN + DLQs; measure lag.

8. Status Gateway already reads health → add bus depth & persister lag metrics.

9. Begin with alert-only path (Telegram/email/WebSocket) from signals.* events; later flip execution mode per YAML flag + guardrails.

Why this wins

• Pluggable by design (your DI instincts, but with thin manifests + canonical schemas).

• Transport-agnostic: Redis→NATS→Kafka without rewriting business code.

• Operator-friendly: health, DLQs, backpressure, idempotency from day one.

• Trader-first: Chamber can run alone to produce P&L quickly; Brains and new Chambers slot in later.

If you want, I can draft the JSON Schemas for quotes/v2, trades/v2, and gex/levels/v1 and the baseline topic map so you can line up your first Chamber runs against them immediately.