Rewrite M-series: crypto trading engine + market prediction sims

The initial M-series specs were wrong (text digestion pipeline). Replaced
with the actual economy organ architecture:

  M0  hub (independent system, scoped autonomy, multi-layered braking)
  M1  Marketplace (multi-trader harness, deterministic law script, veto)
  M2  Data Feeds (RSS + live market, bridges Marketplace ↔ Sims)
  M3  Sims hub + 7 sub-specs (always-running, bounded predictions):
      M3a statistical, M3b sociological, M3c AMM/liquidity,
      M3d MEV/adversarial, M3e tokenomics/macro, M3f consensus/staking,
      M3g market microstructure
  M4  Wallets (sovereign custody, our keys only, 1:1 trader binding)
  M5  Traders (AI actors, wallet-bound, all tool calls monitored)
  M6  Conductor (supervisory AI, veto, pause/investigate, SAE intake)
  M7  SAE monitor (trader surveillance, Brain-compatible message format)

Grounded in AMM invariant mechanics, MEV game theory, SDE tokenomics,
and evolutionary consensus games. Tax stub for Verschwörern Veregeister.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

core/docs/plans/M2-data-feeds.md

@@ -0,0 +1,70 @@
+# M2 — Data feeds (market data pipeline)
+
+## 1. Component
+The economy organ's sensory nervous system: **live RSS feeds, price streams, and on-chain data**
+flowing into both the Marketplace (M1) and the Sims (M3). Sits between them — the Marketplace
+produces execution data (fills, positions, P&L) that feeds back into Sims, and Sims produce
+predictions that inform Traders operating through the Marketplace. Data Feeds is the bridge.
+
+## 2. Status / certainty
+DESIGN-FIRST · ABSENT. Role C3; implementation C1.
+
+## 3. Language & location
+TBD · `src/economy/feeds/`. Needs async I/O for streaming data (WebSockets, SSE, RSS polling).
+Pony actors are a natural fit (async, backpressure-aware). Python or Rust for API client libs.
+
+## 4. Does / does-not
+- **Does:** ingest live market data from external sources (RSS, price APIs, DEX subgraphs,
+  on-chain event logs); normalize heterogeneous data into a common internal format; distribute
+  to Sims (M3) for prediction and to Traders (M5) for decision-making; ingest Marketplace (M1)
+  execution data (fills, portfolio state) and feed it back into Sims for calibration; maintain
+  time-series history within session (ring buffer).
+- **Does-not:** predict (Sims do); trade (Marketplace does); filter or editorialize data — it
+  delivers raw, normalized feeds. Interpretation is the consumer's job.
+
+## 5. Interface contract
+- `subscribe(feed_type: FeedType, consumer_id) -> subscription_handle`.
+  `FeedType` ∈ { `price_tick`, `rss_news`, `on_chain_event`, `dex_pool_state`, `execution_fill`,
+  `portfolio_state` }.
+- `publish(feed_type, data_point: NormalizedDatum)` — internal; sources push into the pipeline.
+- `query_history(feed_type, time_range) -> [NormalizedDatum]` — sims and traders can pull
+  historical data within the session window.
+- `NormalizedDatum { feed_type, source, timestamp, payload, confidence }` — common shape.
+  `confidence` ∈ [0.0, 1.0] — data source reliability (exchange-reported price = high; RSS
+  sentiment = lower).
+
+## 6. Dependencies & stubs
+- External data sources (price APIs, RSS, RPC nodes) — *stub:* canned market data replay.
+- M1 Marketplace — execution data source (fills, positions); *stub:* canned fills.
+- M3 Sims — primary consumer; *stub:* print data points.
+- M5 Traders — secondary consumer; *stub:* print data points.
+
+## 7. Invariants / laws
+- **L1 (C4):** data feeds are **raw and unnormalized in meaning** — the pipeline normalizes
+  *format* (schema, timestamps, units) but never interprets, filters, or editorialize content.
+- **L2 (C4):** **bidirectional flow** — external data flows in (market → sims/traders), and
+  internal execution data flows back (marketplace → sims). Both directions use the same
+  `NormalizedDatum` shape.
+- **L3 (C3):** **backpressure, not drop** — if a consumer is slow, buffer up to a cap, then
+  apply backpressure to the source. Never silently drop data points.
+- **L4 (C3):** every datum carries a **source and timestamp** — consumers can always trace
+  where data came from and when.
+
+## 8. Build steps
+1. Define `NormalizedDatum` and `FeedType` shapes.
+2. Implement the pub/sub pipeline (subscribe, publish, distribute).
+3. Wire external source adapters (start with one price API + one RSS feed).
+4. Wire M1 execution data feedback loop.
+5. Implement session-scoped time-series history (ring buffer).
+
+## 9. Tests
+Normalization: heterogeneous inputs produce uniform `NormalizedDatum` output. Pub/sub: subscriber
+receives published data. History: query returns correct time range. Backpressure: slow consumer
+does not cause data loss. Bidirectional: marketplace fills reach sims via the feed.
+
+## 10. Open items
+- Which price APIs / RSS sources to support initially (CoinGecko? DeFiLlama? specific DEX
+  subgraphs?).
+- History buffer size / eviction policy.
+- Whether feeds need authentication / rate limiting management.
+- Latency requirements (how fresh must data be for each consumer type?).