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98a6f9a0b1
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>
71 lines
3.9 KiB
Markdown
71 lines
3.9 KiB
Markdown
# M2 — Data feeds (market data pipeline)
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## 1. Component
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The economy organ's sensory nervous system: **live RSS feeds, price streams, and on-chain data**
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flowing into both the Marketplace (M1) and the Sims (M3). Sits between them — the Marketplace
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produces execution data (fills, positions, P&L) that feeds back into Sims, and Sims produce
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predictions that inform Traders operating through the Marketplace. Data Feeds is the bridge.
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## 2. Status / certainty
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DESIGN-FIRST · ABSENT. Role C3; implementation C1.
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## 3. Language & location
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TBD · `src/economy/feeds/`. Needs async I/O for streaming data (WebSockets, SSE, RSS polling).
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Pony actors are a natural fit (async, backpressure-aware). Python or Rust for API client libs.
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## 4. Does / does-not
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- **Does:** ingest live market data from external sources (RSS, price APIs, DEX subgraphs,
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on-chain event logs); normalize heterogeneous data into a common internal format; distribute
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to Sims (M3) for prediction and to Traders (M5) for decision-making; ingest Marketplace (M1)
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execution data (fills, portfolio state) and feed it back into Sims for calibration; maintain
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time-series history within session (ring buffer).
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- **Does-not:** predict (Sims do); trade (Marketplace does); filter or editorialize data — it
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delivers raw, normalized feeds. Interpretation is the consumer's job.
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## 5. Interface contract
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- `subscribe(feed_type: FeedType, consumer_id) -> subscription_handle`.
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`FeedType` ∈ { `price_tick`, `rss_news`, `on_chain_event`, `dex_pool_state`, `execution_fill`,
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`portfolio_state` }.
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- `publish(feed_type, data_point: NormalizedDatum)` — internal; sources push into the pipeline.
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- `query_history(feed_type, time_range) -> [NormalizedDatum]` — sims and traders can pull
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historical data within the session window.
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- `NormalizedDatum { feed_type, source, timestamp, payload, confidence }` — common shape.
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`confidence` ∈ [0.0, 1.0] — data source reliability (exchange-reported price = high; RSS
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sentiment = lower).
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## 6. Dependencies & stubs
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- External data sources (price APIs, RSS, RPC nodes) — *stub:* canned market data replay.
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- M1 Marketplace — execution data source (fills, positions); *stub:* canned fills.
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- M3 Sims — primary consumer; *stub:* print data points.
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- M5 Traders — secondary consumer; *stub:* print data points.
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## 7. Invariants / laws
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- **L1 (C4):** data feeds are **raw and unnormalized in meaning** — the pipeline normalizes
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*format* (schema, timestamps, units) but never interprets, filters, or editorialize content.
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- **L2 (C4):** **bidirectional flow** — external data flows in (market → sims/traders), and
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internal execution data flows back (marketplace → sims). Both directions use the same
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`NormalizedDatum` shape.
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- **L3 (C3):** **backpressure, not drop** — if a consumer is slow, buffer up to a cap, then
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apply backpressure to the source. Never silently drop data points.
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- **L4 (C3):** every datum carries a **source and timestamp** — consumers can always trace
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where data came from and when.
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## 8. Build steps
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1. Define `NormalizedDatum` and `FeedType` shapes.
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2. Implement the pub/sub pipeline (subscribe, publish, distribute).
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3. Wire external source adapters (start with one price API + one RSS feed).
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4. Wire M1 execution data feedback loop.
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5. Implement session-scoped time-series history (ring buffer).
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## 9. Tests
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Normalization: heterogeneous inputs produce uniform `NormalizedDatum` output. Pub/sub: subscriber
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receives published data. History: query returns correct time range. Backpressure: slow consumer
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does not cause data loss. Bidirectional: marketplace fills reach sims via the feed.
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## 10. Open items
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- Which price APIs / RSS sources to support initially (CoinGecko? DeFiLlama? specific DEX
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subgraphs?).
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- History buffer size / eviction policy.
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- Whether feeds need authentication / rate limiting management.
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- Latency requirements (how fresh must data be for each consumer type?).
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