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dodoxandClaude Sonnet 5 c4178fe779 Add tools/undervalued.py: trading strategy, goods priced below production cost 
Finds manufactured goods currently selling below their implied
production cost (live input cost vs output price) - candidates to buy
and hold for a price-reversion bounce, the mirror of the "is this
worth building" question saturation.py already answers.

Backtested first (326 output tickers, 287 deduplicated dip-onset
episodes across 112 distinct tickers): +3mo forward return mean
+21.3%/median +8.9%/73.2% hit rate at >=10 units/day volume - real
signal, not noise, but only ~0.10-0.24%/day, far below the 14-29%/day
marginal ROI this session's production builds found. Documented
prominently in the tool's own docstring and CLAUDE.md so it doesn't
get mistaken for a primary strategy - it's for excess idle capital
once better-yielding builds are exhausted, not before.

Promoted daily_prices() out of tools/history.py into puga/history.py
so both tools share it, per the self-improve rule.

Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
2026-10-09 10:28:34 +02:00

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Roadmap

Legend: [ ] todo, [x] done. Build order matters; each step is usable on its own.

  1. Repo skeleton, git (master), venv, CLAUDE.md, mechanics from PRUNplanner.

  2. puga/fio.py: cached FIO client (TTL per endpoint, keys from .env), plus puga/prunplanner.py for api.prunplanner.org/data/*. Inspect what exchanges/ adds (VWAP?) vs FIO /exchange/all.

  3. (price + book tools done; cxpc trend history not needed, PRUNplanner has vwap7/30) puga/market.py + tools/price.py, tools/book.py: prices across all CX, trends from cxpc, order-book walk for real fill price at quantity N.

  4. puga/econ.py (22 tests pass, expected values from PRUNplanner's suite): efficiency stack, workforce consumption, hab LP, extraction (port the formulas from docs/mechanics.md, with tests against PRUNplanner values).

  5. (v1 done; see saturation-design.md for refinements) tools/scan.py (replaces prun_scan.py): depth-aware ROI. Per recipe report: buildings the market can absorb (output demand + traded volume, input supply), ROI at fill price for N buildings, absorbable profit/day. Filter --min-buildings. Both CX-local and universe. 5b. [ ] tools/arb.py (replaces prun_cxarb.py): inter-CX arbitrage on market.walk at real fill size; capital required, fuel/shipping cost from state, profit per trip within cash. Reason: old script uses top-of-book spread x min(supply,demand); on 2 of 4 top hits it overstated profit 7 to 11x (WAI, SCN).

  6. tools/chain.py: full bill of materials, make-vs-buy per node.

  7. tools/whatif.py: marginal ROI of adding a building to a base you already run (whole-plan diff via simulate(), built dict from state/company.yaml; flags newly-caused workforce overloads).

  8. tools/planet.py: rank planets for a resource (FIO bulk Resources/BuildRequirements data), founding cost from real environment-extras materials, jump distance via tools/route.py. tools/found.py (full founding workflow/writeback) still todo. 13b. [x] puga/network.py + tools/network.py: multi-base chain modelling (per-base simulate, net transfers against real freight, cm_free for kits). Worked example: plans/chains/nike_deimos_lst.yaml (Nike LST -> Deimos PP2/BSE). Known gap: reports whole-plan profit per base, not the marginal addition; caller must diff manually.

  9. tools/route.py: jump graph (FIO /systemstars, Dijkstra by straight-line ly, jump count alongside). tools/haul.py (fuel cost, profit per trip) still todo. 9b. [x] (dry-run default, --apply after user yes) tools/plan_push.py: create/update [PuGa] plans in PRUNplanner from YAML (Api-Key auth; see decisions.md for guardrails).

  10. tools/state.py sync (sites, production efficiency, storage, ships, cash, permits): inventory, production, ships into state/.

  11. Docs to fill: docs/apex.md, docs/fio.md, docs/playbook.md (question type -> tool), docs/decisions.md.

  12. tools/buy.py: cash-aware shopping list (construction gap vs empire state's built count, plus N days of net operating stock via simulate(), priced at real fill cost, checked against cash/reserve).

  13. tools/sell.py: ask-post-price + expected-clear-time advisor from 30d traded-volume percentiles, vs hitting the bids now.

  14. tools/runway.py: days-of-runway per material from simulate()'s net consumption, base storage plus auto-detected inbound ship cargo (tools/state.py now tags each ship with its live flight origin/destination, and each STORE with its planet, needed once a 2nd base existed).

  15. tools/portfolio.py: choose the set of 1-building opportunities for one base that maximises total profit given shared whole-building housing (HB1/HB2/...), capex, permits and (for demolish-later) 60-day value decay. Persistence check exists (tools/persistence.py); wire it into the ranking.

  16. tools/undervalued.py: trading (not building) strategy - manufactured goods priced below implied production cost, candidates for a buy-and-hold-for-reversion bet. Backtested 2026-10-09 (326 output tickers, 287 deduplicated dip-onset episodes, 112 distinct tickers): +3mo forward return mean +21.3%/median +8.9%/73.2% hit rate at >=10 units/day volume - roughly 0.10-0.24%/day, far below the 14-29%/day marginal ROI on production builds found this session. Only worth excess idle capital, not a primary strategy.

Open questions

  • PRUNplanner auth is known (Authorization: Api-Key <key>). FIO REST and FIO API are separate services with separate keys (separate services with separate keys; an earlier "one API" reading was wrong). Keys are in .env. Verified 2026-09-18: FIO_REST_KEY works on rest.fnar.net as Authorization: <key> (own /sites, /storage etc. readable); FIO_API_KEY gives 401 there, host unknown. PRUNplanner key works (plans 0, empires 1, cx prefs 1 on his account). Still unknown: the OpenAPI spec at https://doc.fnar.net/api.json has no securitySchemes. Probe with read-only calls.
  • (resolved) PRUNplanner exchanges/ has vwap_daily/7d/30d and traded volume; merged into puga/market.py. Trend history via cxpc still todo.
  • Extraction: concentration to daily rate formula.
  • (resolved 2026-09-25) Real price-band filtering (NarrowPriceBandLow/High, WidePriceBandLow/High) implemented in puga/saturation.py (in_band, effective_demand, effective_supply band param) and wired into tools/scan.py stage 2. Was flagged as an unimplemented refinement in saturation-design.md since day one; the player noticed a stale out-of-band bid in a live screenshot and asked the right question.
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