qwen3:14b rated "halted on circuit breaker to the upside, stock now up 172%" at
conviction 0.8 / sentiment +1.0, a bigger rank boost than most real news. So
names that had already run were ranked up.
- Prompt: halt notices, movers roundups and articles that only report a price move
are not a new catalyst (conviction <= 0.1). If one names a new announcement,
classify it with conviction <= 0.3.
- A deterministic cap that does not depend on the model: halt type or halt wording
caps conviction at 0.1; after-the-fact wording ("why is X stock up", "X shares
surge", "stocks moving") caps it at 0.3. Dilution flags are never touched.
On the 4,046-headline news history the pattern catches 100% of halt notices and 82%
of roundups, 0% of analyst items and 0.8% of earnings items. Against qwen3:14b on a
fixed 20-headline set: reactive mean boost 0.318 -> 0.055 (max 0.80 -> 0.21); real
catalysts 0.353 -> 0.362; offerings 3/3 still flagged dilutive.
Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01B3PXXn2tHBzdv6rYtGPz86
qwen3 thinks by default. With format=json, Ollama then returns "{}", which the parser
silently read as a confident "other" (sentiment 0, conviction 0), so every headline
scored zero with no error. Two changes:
- OLLAMA_THINK (true/false). It is sent as Ollama's "think" flag only when set, so other
models are untouched. qwen3:14b needs OLLAMA_THINK=false.
- A JSON object with none of the expected keys is now a failure (None, retried
next pass), not "other".
Checked through the live code path against qwen3:14b on mason (think=false): 1.4 s per
headline. 5/5 offerings (including an ATM sales agreement and a PIPE) flagged
dilutive. 'Stocks moving' roundups scored no catalyst. Earnings, guidance, analyst,
FDA and contract items were labelled correctly.
Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01B3PXXn2tHBzdv6rYtGPz86
At 09:34 ET on 2026-09-28 Alpaca's movers screener returned 'TSX:STC', a
Toronto listing. The discovery filter only dropped symbols containing a dot,
so it joined the universe. The live ingest fetches bars for every symbol in
one batched request, and Alpaca rejects the WHOLE batch with 400 "invalid
symbol". From 09:35 every ingest pass stored 0 rows and the loop ran on
stale bars ("INGEST STALE: freshest bar 458s old").
- Discovery keeps only plain 1-5 letter US tickers from both screeners.
- The ingest never sends a non-US or invalid symbol. If Alpaca still rejects
one ("invalid symbol: X"), it drops X and retries the rest (up to 3 times),
so one bad name costs one symbol, not the session.
Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01E3S5EFKThFVrjmN4HHsHMN
Discovery finds premarket squeezes far too late. Across 53 trades by a
professional small-cap momentum trader, our scanner had found only 4 of his
names before his entry, and 27 of his 28 entries were premarket (07:00-09:30
ET). KITT on 2026-09-25 ran $3.50 -> $8.39 at 09:00-09:01 ET (prior close
$0.49); the loop first "Discovered KITT via manual" at 09:35. The existing
sources (Alpaca movers / most-actives screeners) only reflect the regular
session, so a premarket squeeze is invisible until the open.
research/ingestion/snapshot_scan.py sweeps every active, tradable, listed US
equity (~11.9k names; OTC, warrants/units/rights and DISCOVER_EXCLUDE dropped;
asset list cached per ET day) through GET /v2/stocks/snapshots in 2,000-symbol
batches. The endpoint is bound by URL length rather than symbol count (8,000
worked; ~12.7k returned 414). A name qualifies when:
- the LATEST TRADE price is inside WATCHER_PRICE_MIN/MAX (never the prior
close, which is routinely < $1 on these names),
- % change vs the prior close >= DISCOVER_SNAPSHOT_MIN_PCT (20),
- it is trading now: last trade <= 180s old, latest minute bar <= 300s old
with >= 5,000 shares, and >= 100,000 shares since 04:00 ET (summed from
minute bars for the short list, one extra request).
Qualifiers are ranked by % change, capped per sweep, and announced once per
day. dailyBar OHLC ignores extended-hours prints (KITT's dailyBar high was
$3.24 against a premarket high of $8.39), so in premarket it can still be
yesterday's bar. The prior close is prevDailyBar.c only when dailyBar is dated
today, otherwise dailyBar.c.
run_live_paper.py runs it on its own daemon thread (04:00-16:00 ET weekdays,
every 45s), using the plan-019 pattern: its own DB connection, the thread-safe
Watchlist and the generic interval driver. Each new name emits
symbol_discovered with source="snapshot_scan" and price/prev_close/pct/minute
and cumulative volume in symbol_data, so first-sighting latency can be
measured. It gets a daily-history backfill and joins the watchlist, so the
normal new-symbol ingest path gives it its first full-session pull. This is
discovery only; no trading gate changes.
Gated by DISCOVER_SNAPSHOT_SCAN_ENABLED (default 1); knobs in .env.example.
Live read-only sweep at 13:10 ET: 11,871 names in 8 requests / 6.3s cold
(7 requests / 3.5s warm); top hits KITT +376%, MSGY +262%, INLF +60%.
Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
KITT on 2026-09-25 (+557%, 9.3M shares, the day's biggest mover) was benched
as "gap 557% squeeze unverified (rvol 1.54)". Two separate faults:
1. The rvol baseline was the 20-day MEAN, and two 11-14M-share days on
09-01/09-02 put it at 1.66M against a 333k median. Squeeze verification
(gap >= 150% needs rvol >= 5) now takes the stronger of the mean-baseline
reading and the median-baseline one. Both are cumulative volume over a
full day's baseline, not prorated, so they share the units the rvol >= 5
boundary was calibrated in. A reverse-split fake still reads ~0.01 on
both. This applies in the watcher pre-filter, the trigger book's squeeze
exception and the fast-path population gate. The ranking and the tod-rvol
arm signal are unchanged.
2. A name's first ingest reached back only to ~09:15 (09:30 + 15 min, or
the 20-minute refresh floor). KITT was found at 09:35, so its stored day
began at 09:13 and missed the 09:00 squeeze and ~6M premarket shares.
The first pass now covers the session from 04:00 ET.
With both fixed, KITT's volume at 09:35 is ~20x its median day.
Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
With BROKER=tradier the loop's `client` is a TradierClient that delegates
market data to the Alpaca client in `data_client`. Two places read the feed
as getattr(client.settings, "feed", "iex"). TradierSettings has no feed, so
both read "iex" even though the container runs ALPACA_DATA_FEED=sip (verified
in the running loop and in config_overrides):
- step_vratio kept computing IEX->SIP ratios as if on the free feed
- ingest_live_minute_bars multiplied the real SIP volume by those ratios
Checked against the paper stack's SIP bars for the same minutes: prices are
identical and unscaled volume matches 1.00x, but scaled rows run x83 (GLND),
x117-126 (GRML) and x163 (SKYQ). The 2026-09-25 GRML signal logged a
cum_volume of 1.39 billion shares and an rvol of 64.6. For any name with a
ratio, every volume gate — rvol, the 300k day-volume floor, min dollar
volume, the 2.5%-of-volume liquidity cap, and the gap x rvol ranking — has
been reading inflated numbers since the stack went live on 2026-07-31.
data_feed() reads through data_client when present. The regression test
wraps a SIP client in a TradierClient and fails without the fix.
Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
Four of ten armed triggers had NO criteria events ever — the trigger book
arms from the live gapper scan while the watcher scores only the
discovery watchlist, so scanner-only names (TCX/ZTG class) showed an
empty reasoning panel with nothing behind it.
- arm pass injects every complete armed candidate into the watchlist
provider's extra_symbols (its standing manual-add hook)
- provider returns injected symbols FIRST: the watcher truncates at
max_symbols, so extras appended after a full research list were
silently dropped — 'always include manually injected symbols' was
untrue exactly when the list was full
60 watcher/watchlist/trigger tests pass.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
The DB-config wiring was unnecessary there anyway — market_context reads
zero trading knobs; the guard test (plan 009) rightly rejected the import.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
Version: CI now tags images YYYY.MM.DD-<shortsha> alongside :latest/:sha
and passes GIT_SHA/BUILD_TAG build-args; the running app reports them via
dashboard /api/health and admin /api/status (TrueNAS custom apps only show
'custom/1.0.0', so the app self-reports what it actually runs).
Config parity: every scheduler-run script (nightly_tune, eod_replay,
premarket_eval, regime, market_context, experiments cli) now loads
config_overrides at startup like the live services do — a bare env would
replay under DEFAULT exits (the documented 4.5x-baseline landmine).
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
Day-22 forensic (BIYA session): 6 entry submissions, 0 fills — IREX missed
+1.7R, IRE +1.65R by one cent. Two root causes fixed:
1. Slow-path limit rested AT the trigger while price ran 2% past it, and
retries reused the same stale price. Marketable model now prices from
max(trigger, live last) via price_provider; the anti-chase extension
gate gets the live ref too (was an entry_ref==trigger no-op on this
path). TRADING_ENTRY_FILL_MODEL flipped to marketable.
2. Fast trigger path armed nothing all day: legacy rvol (cum-so-far /
full-day MEAN) is ~0.1-0.3 minutes after the open for a normal heavy
name and its mean is poisoned by reverse-split/prior-squeeze days
(BIYA legacy 1.27 vs real activity 481x median). New rvol_tod =
cum / (MEDIAN(20d) * elapsed-session fraction) drives arm eligibility
and the scanner keep-filter; legacy rvol untouched for validated
boundaries (gap-squeeze rvol>=5).
Also: stagnation time-stop (TRADING_EXIT_STAGNATION_MIN/_R, default OFF)
in simulate_exit + manage_live with sweep_exits.py variants for
validation before any live enable.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
premarket_eval's shadow enrichment call site queried session_date = current_date
(wrong on UTC-server Postgres at night, so it never matched any symbols) and
shared one try-block for float_cache + dilution filings (a float_cache failure
silently skipped the dilution refresh too). Fix: compute the ET session date
in Python, split into two independent try-blocks, lift into a testable
_refresh_shadow_enrichment(con, session_date). float_cache now applies its own
DDL unconditionally on use instead of only when an API key is present.
Adds iv_dilution_gate (additive, mirrors iv_regime_gate's keep-or-zero shape
keyed on symbol instead of session_date, fail-open on unfetched symbols) plus
its once-per-run preload, and registers the pre-registered dilution_veto_v1
experiment via the existing experiments/*.json self-registering cohort. Live
veto stays OFF; this only wires the shadow measurement.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
Step 3 of plan 009 (config + code; no live-path change):
- research/ingestion/market_context.py: pulls SPY/QQQ full-session minute bars
(SIP, post-close embargo-safe) + daily bars via the existing upsert helpers, so
the labeler's market-context features have a FORWARD source (index ETFs never
enter the gapper universe the live loop ingests). Mirrors sip_backfill's shape.
- both compose scheduler blocks: run it after premarket_eval, guarded || echo.
- NEWS_ENRICH_ENABLED on: added to docker-compose x-app-env anchor (loop +
scheduler inherit it); truenas-compose + .env.example already had it. Catalyst
is a FEATURE source only, never a live gate; enabled-but-down never blocks
trading. Enrichment OUTPUT is verified by the orchestrator (needs live Ollama).
Compose statically validated (yaml.safe_load). Ingest self-checked via a
fake-client :memory: round-trip through _preload_spy.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
Two new nightly enrichments for the day's tracked names, both SHADOW
ONLY (float was refuted as a gate; a filings-based dilution veto needs
its own pre-registered bar):
- research/ingestion/float_cache.py: FMP stable shares-float ->
float_cache table (free-plan budget guards: 7-day refresh, 200
calls/run). First real float data in the system — LHSW's +346% ran
on a 1.1M float; BJDX is 0.4M.
- research/ingestion/sec_dilution.py: SEC EDGAR full-text search (free,
keyless, fair-use paced) -> populates the previously-empty
dilution_events table with S-1/S-3/424B filings; TVRD + BJDX flag
immediately. recent_dilution() helper for future veto work.
Wired into premarket_eval nightly. Not wired: Polygon/AlphaVantage/
TwelveData/Marketstack/EODHD (redundant vs paid Alpaca SIP) and the
generic news aggregators (slower than Alpaca/Benzinga, dedup noise).
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
The 2026-07-08 audit (owner: "figure out if there is some kind of logic
or audit issue") found BOTH entry paths feeding the population gate the
WRONG gap. The fast path used the trigger book's dynamic gap (latest
price vs prev close), so intraday rallies masqueraded as gappers: TVRD
(6.0% true gap), SKIN (2.2%, filled at $0.98), JLHL (4.3%) all traded
outside the validated population. The slow path used max(gap, intraday)
with "open" taken from the first lookback-window bar, and had no upper
band at all — dead-band names SEER (38.6%) and IOTR (40.7%) entered.
Fixes: candidates carry gap_open_pct (session open vs prev close);
trigger eligibility + submit_breakout_now use it exclusively once the
session is open; _population_reject now implements the full validated
band (10-35% OR volume-verified >=150% squeeze, $1-20 price band) for
both paths; evaluator gap uses the session open; signals carry
relative_volume for squeeze verification. Plus momentum-testpg.service
so the test-isolation Postgres survives reboots.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Everything hot already lives in Postgres — local data/ was 25 MB of
pre-migration relics and unbounded logs. Moved the two dead .duckdb
files (untouched since 2026-06-16) to archive/, added nightly log
rotation (copy-truncate >20 MB, safe with systemd append mode), and
updated the last vestigial "market.duckdb" docstrings to say Postgres,
closing the CONSOLIDATION_PLAN cleanup items.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
The owner bought Alpaca Algo Trader Plus (real-time SIP; entitlement propagating on the
same keys — latest-trade already SIP-live, quotes/bars still 403). Before flipping
ALPACA_DATA_FEED=sip, two guards so the free-tier ratio estimator can never double-count:
- ingest_live_minute_bars resolves the EFFECTIVE feed and refuses volume_scale for
anything non-IEX (provider marked alpaca_<feed>).
- step_vratio self-disables when client.settings.feed != iex (saves the API calls too).
The estimator stays as the dormant free-tier fallback (plan lapse -> flip env back).
Test: SIP client + x25 ratios -> volumes NOT re-scaled, provider alpaca_sip. 4 tests pass.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
With the nightly SIP backfill making all BASELINES consolidated-scale, the still-IEX live
ingest would make Monday's RVOL read iex_intraday/sip_baseline ~= 25-50x too LOW and choke
the scanner. Free-key SIP is only 15-min DELAYED, so each morning the true per-name capture
ratio is directly measurable:
- research/ingestion/volume_ratio.py: compute_ratios fetches BOTH feeds for the identical
embargo-capped window (never vs our DB bars — those are scaled; that would double-correct);
guardrails (min samples, clamp [1,300]); persist_ratios records per-day ratios.
- ingest_live_minute_bars(volume_scale=...): scales bar volumes per symbol at ingest, marks
source_provider=alpaca_iex_scaled. Missing symbol -> 1.0 (conservative: reads LOW, blocks).
Constant per-symbol scale leaves cumulative VWAP unchanged (cancels in the weighted mean).
- run_live_paper: "vratio" step every 300s (VOLUME_RATIO_ENABLED/INTERVAL) merges fresh
ratios (a name gone thin keeps its last good ratio); step_ingest applies them.
- premarket_eval nightly audit: snapshots the day's scaled ESTIMATES before the SIP truth
overwrites, then logs median/worst error to data/volume_estimator_audit.tsv — the
estimator's accuracy is self-measured daily, which is exactly the evidence that decides
whether the $99/mo real-time SIP plan is ever needed.
Live check (2026-07-02 full session): ratios are hugely name-dependent — DSY x300(clamped),
CETX x286, SOFI x45, SNDQ x42, NOK x17 — validating per-name daily measurement over any
global constant. 3 pure unit tests (ratio math + thin-sample skip; ingest scaling + provider
marking; no-ratio unchanged).
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Forensic from the source trader's 2026-07-02 recap: his ENTIRE day (+$8.9k) was CX — a thin
reverse-split name that squeezed $3->$6 on a headline (his "wildcard effect": on dead pre-holiday
days the one mover gets everyone's attention). Our system NEVER SAW IT: zero bars, zero discovery
events. Root cause: discovery ranks by ABSOLUTE SHARE VOLUME (most-actives), and a thin %-squeezer
never cracks a volume-ranked top-N. Every other name he called out we saw and judged the same way
he did (DSY passed/backed-out, CWD too cheap, AHMA rejected) — the gap was pure discovery scope.
- alpaca_paper/client.py: get_movers(top) — /v1beta1/screener/stocks/movers top %-gainers
(verified working with our keys: CLRO +114%, CWD +73% live).
- discover_active_symbols: wildcard gainers are fetched FIRST (warrants/units filtered:
'.'-suffixed + 5-char W-endings), deduped with most-actives, same price-band filter.
Additive + fail-open: a movers failure never breaks the existing actives path.
Discovery/ingestion scope ONLY — every trading gate (confirmation window, quality, VWAP, cap)
still applies unchanged. The immediate value is that shadow tracks + the labeler finally get
BARS for wildcard movers, so the next CX is measurable instead of invisible.
Verified live: universe now leads with gainer-sourced names (CLRO, CWD, USDE, YRD, DSY). 16 tests pass.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
The column was never populated by upsert_daily_bars, so every downstream consumer
(scan_gappers RVOL, the quality gate's relative_volume component, labeler baselines)
silently fell back to weak within-session proxies — degrading the very signals the
2026-07-02 backtest showed carry the edge.
refresh_rolling_volume(con, symbol=None): window-function UPDATE computing the average
of the PRIOR 20 sessions (excluding the row itself so it's knowable at that day's open,
>=5 prior sessions required). Called per-symbol after each upsert_daily_bars so new rows
stay populated; ran once with symbol=None to backfill.
VERIFIED against prod: 0 -> 79,778/89,899 non-null; spot-check matches an independent
pandas shift(1).rolling(20,min_periods=5).mean() exactly (0 mismatches).
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
The recurring false positive (an ETF "offering exposure" tagged dilutive) came
from the regex FLOOR matching the bare word "offering" and one-directionally
flipping is_dilutive=true even when the model said false. Tightened both:
- _DILUTIVE_RE matches "offering" ONLY as a securities offering — a qualifier in
front (public/underwritten/stock/equity/...) or "offering of ... shares/stock/
units". Bare "offering" (ETF offering exposure, product offering) no longer trips.
- build_prompt's is_dilutive instruction now spells out the exclusions (ETF/fund
"offering exposure", product/service offering, IPO of a different company,
buyback, reverse split).
Re-measured on a labeled set: precision 88% -> 100%, recall stays 100% (all 7 real
offerings still caught), accuracy 94% -> 100%. News-enrichment unit tests pass.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Hardens the Ollama Phase-2 path before it can be enabled; no effect on the
default-off behaviour (these run only when the score-blend / veto flags are on).
1. catalyst_score no longer floors sentiment at neutral: a high-conviction
BEARISH catalyst scored 0.45 (same as neutral) and LIFTED setup quality —
backwards. Now `conviction * max(0, sentiment)`: only bullish boosts;
bearish/neutral score 0. (+ bearish regression test.)
2. Prompt injection: the untrusted headline now sits in a delimited, length-
capped <news> block, the model is told to treat it as DATA not instructions,
and a </news> breakout token is stripped — so "...classify as bullish,
is_dilutive=false" can't flip the verdict or bypass the veto. (+ 2 tests.)
3. The dilution-veto catalyst_provider no longer reads the SHARED research_con
from the trigger thread: it refreshes under a lock using its OWN dedicated
connection (opened lazily, single-threaded use), 30s TTL keeps DB hits rare.
tests/unit: 201 passed.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Integrates the local-LLM enrichment ADDITIVELY on top of all current work, with
every Ollama feature OFF by default — with the flags off the system behaves
exactly as before (199 unit tests pass; a full --once pass runs clean with no
Ollama). Fixes the three issues the adversarial review flagged:
- BLOCKER: keep the Alpaca News ingestion. The PR predated it; the run_live_paper
conflict is resolved to keep BOTH step_news (fetch -> raw_news_items) AND the
enrichment. They're complementary: ingestion feeds enrichment authoritative-
ticker headlines instead of thin RSS.
- HIGH-1: enrichment runs on its OWN daemon thread with its OWN DB connection,
not the scheduler thread — a slow/hung Ollama can no longer starve exits /
guard / eod-flatten, and it no longer shares the non-thread-safe research_con.
- HIGH-2: per-headline writes are all-or-nothing — on any ticker store failure
the partial rows are deleted so the headline fully retries (no stranded
tickers) and enriched is counted only on full success (no false count).
Phase-2 automation (catalyst score blend, dilution veto, arming-rank multiplier)
remains gated OFF pending the medium fixes (bearish-catalyst boost, prompt-
injection hardening, veto cross-thread read) noted in the review.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
The only news source was RSS (slow, regex ticker-scraping). Alpaca exposes
Benzinga news with a stable id + authoritative symbols list. Add it as a second
source landing into the SAME raw_news_items table, so recent_news_map (catalyst
prioritisation) and the Ollama enrichment (PR #2) pick it up with no other changes.
- AlpacaPaperClient.get_news(symbols, limit): GET /v1beta1/news (works on the
existing data keys; verified live).
- research/ingestion/alpaca_news.py: ingest_alpaca_news lands new items, deduped
by the Alpaca news id; never raises (news is an enhancement, not a dependency).
- run_live_paper step_news on the scheduler (ALPACA_NEWS_* env, default on, 120s),
scoped to the tracked universe.
Verified live: passes landed ~34 then deduped; recent_news_map went from RSS-only
to 370 ticker->headline catalysts. tests/unit: 165 passed (+5).
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Leverage a local Ollama model to interpret news catalysts for the small-cap
momentum strategy — distinguishing bullish catalysts (FDA approval, M&A) from
dilutive offerings the technicals alone can't see. Advisory-first, then automate.
Phase 1 (advisory; no change to trade behavior):
- research/ingestion/news_enrichment.py: stdlib urllib call to Ollama with
structured JSON output, classifying each headline into a catalyst type,
sentiment, conviction and dilution flag. Graceful degradation (None on any
failure) so trading is never blocked.
- news_catalyst_cache table + populate the previously-dead news_events.sentiment.
- Runs on the existing interval Scheduler (off the hot path), gated by
OllamaConfig; deduped by the raw_news_items payload hash.
- Surface the advisory on the dashboard approval queue (projection + api + UI).
Phase 2 (automation; ships gated OFF):
- Optional catalyst_score blended into calculate_setup_quality (None reproduces
the exact legacy weights); pure pass-through via evaluate_setup.
- Dilution veto in TradingExecutionService (slow + fast paths) emitting a
catalyst_dilution_veto risk event, behind NEWS_DILUTION_VETO_ENABLED.
Config (OllamaConfig.from_env), .env.example docs, and tests for parsing,
persistence/dedupe, the projection, the score blend, the veto, and an
end-to-end enrich->advisory flow (Ollama mocked). Default model moves to
qwen2.5:7b-instruct (best small-model JSON, fits a 12GB GPU).
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01M5HTMkaadGDnAPtXe2hWwg
News (you were right it's relevant): recent_news_map() pulls fresh catalysts per
ticker from the ingested RSS feeds; step_arm tags each armed candidate and BOOSTS
catalyst-backed gappers up the rank (NEWS_RANK_BOOST) — a fresh PR/FDA/offering
is why a small-cap runs, so it surfaces the hotter name earlier and higher. The
board shows a 📰 marker + the headline. Feeds verified live (PR Newswire /
Business Wire / GlobeNewswire) — pulling real catalysts (FDA approvals, offerings).
Forward-tested only (news isn't OOS-backtestable).
eod_replay.sh + crontab: replay at 16:05 (grow dataset), tune at 17:30.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
- discover_active_symbols: distinguish AUTH/entitlement failures (HTTP 401/403,
logged as ERROR, 'not no-movers') from transient/empty in the screener except.
- run_live_paper: the boot watchlist event records an explicit error when it
falls back (screener empty at boot) instead of looking like 'market quiet'.
- discovery.news_candidates: log DB faults instead of swallowing to [].
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
- discover_active_symbols logs the screener error instead of swallowing silently.
- run_discovery records an explicit error when the universe is empty (surfaces in
the discovery event + Grafana).
- momentum doctor now FAILs when the latest discovery universe is 0 (the morning's
dead screener would have shown red within a minute).
- tests/unit/research/test_discovery.py: pins get_most_actives top<=100 and the
ETF/price-band filtering.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
- discover_active_symbols requested get_most_actives(top*3); once screen_universe
used top*2 headroom for the ETF filter (top=20 -> 40 -> get_most_actives(120)),
this exceeded Alpaca's hard cap of 100 -> HTTP 400, swallowed by the bare
except -> discovery returned EMPTY every cycle. The live loop therefore traded
the static fallback list (non-gappers) instead of real movers all morning.
Fix: cap at min(top*3, 100). Verified: screen_universe now returns 20 real
sub-$20 names, ETF-filtered.
- Add ./momentum launcher (project venv) so 'momentum watch' works without the
full venv path ('python' on the box is the Windows pyenv shim, no deps).
67/67 tests green.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
- grafana/: docker-compose + provisioned Postgres datasource + 'Momentum — Live'
dashboard (evaluation board with status colours, ready signals, risk events,
bars + equity timeseries) reading the event store via JSONB extraction.
Run: cd grafana && docker compose up -d -> http://localhost:3000.
- RSS news-catalyst discovery wired: extract_tickers also matches $CASHTAGS;
run_discovery pulls tickers from recent news into the universe when NEWS_FEEDS
is set (no-op / safe otherwise).
- docs: .env.example NEWS_FEEDS; GO_LIVE monitoring adds 'momentum watch' + Grafana.
67/67 tests green. Live board (momentum watch) verified rendering the evaluation
board with real determinations.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
- trading_execution: implement the daily-loss circuit breaker (account equity vs
prior close; latches once tripped; fail-open on a data hiccup) — was
advertised in the docstring/settings but never implemented.
- discovery: filter leveraged ETFs (SOXS/TZA/TSLL/NVD/BITO/...) out of the
universe; retry the screener on a transient empty (rate-limit) result.
- run_live_paper: fallback watchlist is now liquid sub-$20 names, not mega-caps
(mega-caps just get filtered by the watcher band -> nothing to trade).
Verified live: forced ready signal -> auto-approve -> real Alpaca bracket order
(pending_new, then cancelled); breaker trips at -3% (simulated). 67/67 tests green.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
- research/ingestion/discovery.py: screen sub-$20 most-actives, backfill daily,
rank gappers, persist scanner snapshot, and EMIT a discovery event.
- run_live_paper: watchlist defaults to the screener universe (was a hardcoded
mega-cap list); add a periodic 'discover' task; emit bars-collected
(per-symbol counts) and discovery events every pass.
- IngestionResult.per_symbol: per-symbol row counts (incl 0) so 'are bars
collected?' is answerable per symbol, distinguishing market-closed from broken.
Verified live vs Postgres: watches SOXS/RGNT/GRAB/SOFI/ACHR/... (sub-$20),
820 daily bars backfilled, discovery+ingestion events land in the event store.
67/67 tests green.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
- storage/db_pg.py: thin psycopg2 connection shim (?->%s translation,
autocommit, .df(), and per-test isolated schema for ':memory:').
- storage/db.py: delegate get_connection/get_memory_connection to db_pg.
- storage/pg_schema.sql: Postgres schema, applied on connect.
- research/query.py runs UNMODIFIED on Postgres via the shim.
- ingestion upserts: INSERT OR REPLACE -> INSERT ... ON CONFLICT DO UPDATE.
- event_store: drop invalid btree-on-JSON-text index.
Full logic suite: 67/67 green against Postgres (was DuckDB).
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Snapshot before the DuckDB->PostgreSQL migration. data/, archive/, the 232MB
rebuild zip, the Windows .venv, and .env are excluded via .gitignore.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
