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Run shadow signal workflow monthly (#8)
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.github/workflows/dispatch_shadow_signal.yml

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@@ -2,9 +2,9 @@ name: Long Horizon Shadow Signal
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on:
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schedule:
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# Weekly after Friday US market close. This remains shadow-only and does not
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# place trades.
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- cron: "30 2 * * 6"
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# Monthly shadow research run. This remains shadow-only and does not place
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# trades.
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- cron: "30 2 1 * *"
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workflow_dispatch:
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inputs:
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provider:

README.md

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Near-term work should focus on:
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- keeping the weekly workflow healthy
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- keeping the monthly workflow healthy
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- accumulating saved `signal_history/*.json` artifacts
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- replaying only saved artifacts, not regenerated historical AI judgments
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- improving context quality before any downstream plugin integration
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## Operating Model
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1. A weekly workflow builds a point-in-time context bundle from current market
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1. A monthly workflow builds a point-in-time context bundle from current market
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prices.
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2. The workflow creates or updates a dated long-horizon shadow-signal issue and
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embeds the context bundle as review evidence.
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Without `--prices`, the script downloads recent daily prices for the default
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universe through Yahoo's chart endpoint and writes a point-in-time context bundle
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for the weekly shadow issue. The scheduled workflow uses
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for the monthly shadow issue. The scheduled workflow uses
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`--allow-download-errors`, so external data-source failures still create an
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operator issue with the failure recorded instead of silently skipping the run.
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docs/architecture.md

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- `AiLongHorizonSignalPipelines` stores context examples, schema, validation,
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replay tooling, and shadow artifacts.
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- `CodexAuditBridge` owns provider routing and API keys.
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- GitHub Issues are the first operator notification layer for scheduled shadow
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- GitHub Issues are the first operator notification layer for monthly shadow
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signal runs.
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- The scheduled workflow builds the market context bundle before dispatching the
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bridge and embeds that bundle into the issue, because the bridge reads the

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