Local news model: ML-NEWS XAU transformer scores headlines (news_model.py, mode primary)
The owner's DeBERTa-v3-small two-head ONNX model (golden set 72/78, ~72% agreement with DeepSeek on unseen days) replaces DeepSeek for headline scoring. DeepSeek keeps event direction, the summary and the dashboard endpoint. - news_model.py: vendored ML-NEWS runtime (lazy onnxruntime/tokenizers, so the bot starts without them) + load-once, never-raising singleton. - news_engine: _enrich_headline_scores mode switch (off/fallback/primary), _apply_model_sentiment (gold head only: xau_score + XAU entry, never USD; not-relevant shows 0.0; never touches _llm_sent_cache), _audit_with_deepseek (archive-only labels, deepseek_audit_per_day). - Owner decision: primary mode writes nothing to logs/newsdata.db except audit copies — the model's output is not training data. - app_config: news_model section (path/threads/mode/deepseek_audit_per_day) + NEWS_MODEL_MODE env override; validated at boot. - news_archive: model rows carry teacher_model xau_transformer@<trained_at>. - deploy/README-news-model.md (ops for bot + dashboard daemon), unit gets a commented NEWS_MODEL_MODE=off line; requirements + onnxruntime, tokenizers. - test_all.py Module 38 (15 tests, one against the real bundle). Suite 581. - CLAUDE.md, spec status note, handoff marked implemented. Books unaffected: news never enters signals; no signal file touched.
This commit is contained in:
Harwinder Sekhon
@@ -23,7 +23,7 @@ cp config.json.example config.json # then fill i
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venv/bin/pip install cupy-cuda12x "nvidia-cuda-runtime-cu12==12.9.*" # optional, dev box: backtester GPU backend
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# (CuPy from pip needs that header wheel to compile kernels)
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venv/bin/python test_all.py # QA suite, no network; prints its own count
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# (566 tests / 37 modules as of 2026-09-27)
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# (581 tests / 38 modules as of 2026-09-27)
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venv/bin/python main.py --pair XAUUSD --timeframe H1 --detail # one signal report in the terminal
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# --json full dump, --panel adds the advisory LLM panel,
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# --timeframe M5 | M15 | H1 | H4
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@@ -106,8 +106,9 @@ direction with the two confirmation timeframes. Agreement needs 2 of 3. Disagree
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penalty and blocks the top grades.
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**6. News context (warning only).** `news_engine.py` reads the Forex Factory calendar (MyFXBook
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RSS as fallback) and four RSS feeds, scores headlines for gold, and can ask DeepSeek to improve
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the scores. This produces warnings and a text summary. **By design it never changes a signal.**
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RSS as fallback) and four RSS feeds and scores headlines for gold: the keyword scorer first, then
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the local XAU transformer (`news_model.py`, mode `primary`) or DeepSeek (modes `off` / `fallback`)
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overwrites those scores. This produces warnings and a text summary. **By design it never changes a signal.**
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The backtester runs with news off, so the live decision must be purely technical too.
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**7. Penalties, then the signal and the grade.** When the timeframes agree, confidence gains 10
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@@ -270,11 +271,12 @@ Arrows show who imports whom (generated from the code on 2026-09-15).
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| File | Role |
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|---|---|
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| `news_engine.py` | Calendar + RSS headline sentiment with gold-specific keywords; class-level caches; optional DeepSeek enrichment; display and warnings only |
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| `news_engine.py` | Calendar + RSS headline sentiment with gold-specific keywords; class-level caches; headline scores upgraded by the local XAU model (`news_model.py`, mode `primary` live since 2026-09-27) or by DeepSeek (modes `off` / `fallback`, `_enrich_headline_scores`); DeepSeek keeps event direction and the summary; display and warnings only |
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| `llm_client.py` | Thin `requests` client for DeepSeek (`deepseek-v4-flash`, `thinking: disabled`, one attempt, UTC-day request cap). Every caller has a non-LLM fallback |
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| `news_archive.py` | Durable headline corpus in `logs/newsdata.db` (one row per distinct headline, upgrade-only scores, provenance of the scorer). Its own database on purpose: a `signals.db` rotation in 2026-09 destroyed a 3-week corpus of 4,054 headlines and `logs/` is gitignored. Hooked at the end of `news_engine._fetch_news_sentiment_raw`, before the ±0.1 neutral-headline filter, so neutral headlines are kept for training; do not move the hook. Since 2026-09-17 the corpus is complete for a gold scorer: `xau_score` holds DeepSeek's gold score for every LLM-scored row (it used to survive only for gold-keyword headlines, so 930 of the first 1,546 rows had no gold label; older rows read NULL), headlines naming no tracked currency are scored and archived too (`currencies_json` `[]`; they are never returned to the platform), and every new headline goes to DeepSeek in the cycle it arrives, 25 per call |
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| `news_archive.py` | Durable headline corpus in `logs/newsdata.db` (one row per distinct headline, upgrade-only scores, provenance of the scorer). Its own database on purpose: a `signals.db` rotation in 2026-09 destroyed a 3-week corpus of 4,054 headlines and `logs/` is gitignored. Hooked at the end of `news_engine._fetch_news_sentiment_raw`, before the ±0.1 neutral-headline filter, so neutral headlines are kept for training; do not move the hook. Since 2026-09-17 the corpus is complete for a gold scorer: `xau_score` holds DeepSeek's gold score for every LLM-scored row (it used to survive only for gold-keyword headlines, so 930 of the first 1,546 rows had no gold label; older rows read NULL), headlines naming no tracked currency are scored and archived too (`currencies_json` `[]`; they are never returned to the platform), and every new headline goes to DeepSeek in the cycle it arrives, 25 per call. **Frozen in `news_model.mode` `primary` (live since 2026-09-27)**: the local model's output is not training data, so nothing is archived except the optional DeepSeek audit copies (`deepseek_audit_per_day`, shipped 0); `off` / `fallback` archive as before, a `fallback` model row carrying `teacher_model` `xau_transformer@<trained_at>` |
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| `agent_panel.py` | Advisory persona panel (macro by default; technical / risk / contrarian and the chair available through `agent_panel.personas` for research). Attached post-hoc. v2 (2026-09-17, `PANEL_PROMPT_VERSION`): every persona sees a code-computed `context` block (prior move in ATR, distance from the 24 h extreme, candle size, stop/target in ATR, the class base rate from `agent_panel_base_rates.json`, event timing against the expected hold) and returns `p_win` plus one reason each way; stance is derived symmetrically (≥ 60 support, ≤ 40 oppose); the verdict is the equal-weight mean (quorum 3, `verdict_source: "mean"`, default). `macro` (stance verbatim) and `chair` (synthesizer + `_apply_oppose_guard`) remain for comparison. The prompt version is logged and hashed. Persona weights are not to be tuned before about 300 graded outcomes with a held-out half |
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| `violent_watch.py` | Display-only violent-move warning and heads-up. Attached post-hoc |
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| `news_model.py` | The local headline scorer (2026-09-27): vendored CPU runtime of the owner's ML-NEWS XAU transformer (DeBERTa-v3-small, two heads: moves gold? / up or down?, ONNX; `onnxruntime` + `tokenizers` imported lazily inside `load_model`, so the bot starts without them) plus a load-once, never-raising singleton (`get_news_model`, `teacher_tag`, `warm_up_in_background`). Gold head only: `news_engine._apply_model_sentiment` writes `xau_score` and XAU's per-currency entry, never USD. Bundle (280 MB) lives outside the tree at `news_model.path`. Spec and deviations: `docs/news_model_integration_spec_2026-09-27.md`; ops: `deploy/README-news-model.md` |
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### Logging and evaluation
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@@ -311,7 +313,7 @@ Arrows show who imports whom (generated from the code on 2026-09-15).
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| `app_config.py` | Loads `config.json` once per process; env var → config.json → refuse; validates keys |
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| `config.json.example` | The schema of `config.json` (which is gitignored) |
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| `deploy/README-dashboard.md`, `deploy/gold-dashboard.service` | Live-server checklist and systemd unit for the dashboard daemon |
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| `test_all.py` | The QA suite (36 modules; Module 36 is the GPU backend parity module, Module 37 the CUDA decision-stage parity module) |
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| `test_all.py` | The QA suite (38 modules; Module 36 is the GPU backend parity module, Module 37 the CUDA decision-stage parity module, Module 38 the local news model) |
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---
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@@ -374,6 +376,7 @@ gates live in `config.py` and the pair factory, never in `config.json`. Blocks (
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| `violent_watch` | `enabled`, `alert_line`, `headsup`, `max_headsup_per_day`, `headsup_min_level` (env `VIOLENT_WATCH_ENABLED`). Off by default |
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| `signal_log` | `enabled`, `path`, `log_blocked_scans` (env `SIGNAL_LOG_ENABLED`, `SIGNAL_LOG_PATH`, `SIGNAL_LOG_BLOCKED_SCANS`) |
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| `news_archive` | `enabled` (default true), `path` (env `NEWS_ARCHIVE_ENABLED`, `NEWS_ARCHIVE_PATH`) |
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| `news_model` | `path` (bundle directory, required when mode ≠ off), `threads` (default 4), `mode` (`off` default = DeepSeek/keyword path; `fallback` = model only where DeepSeek failed; `primary` = model scores every headline, no DeepSeek headline calls), `deepseek_audit_per_day` (default 0; primary only, archive-only DeepSeek labels). Env `NEWS_MODEL_MODE` overrides `mode` per process (the dashboard unit). Live: `primary` on both processes |
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| `log_level` | Logging level |
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A legacy top-level `market_outlook` block is tolerated and ignored with one warning (the feature
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@@ -389,7 +392,9 @@ day, 30 kept), `logs/dashboard.log`. Read the databases with `mode=ro`, never `i
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## 6. Testing
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- `venv/bin/python test_all.py` runs everything with mocked network. Expect
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`RESULTS: 566 passed, 0 failed, 566 total` (2026-09-27). On a box with a visible CUDA device
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`RESULTS: 581 passed, 0 failed, 581 total` (2026-09-27). Module 38's last test loads the
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real news-model bundle when `NEWS_MODEL_DIR` (or the configured `news_model.path`) exists and
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prints SKIP otherwise. On a box with a visible CUDA device
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Module 36 fails if the GPU backend falls back (the reason is in the message); with
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`FB2_BACKEND=cpu` or no CUDA the GPU-path tests print a notice and the NumPy twins are tested.
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Module 37 (the CUDA decision stage) adds about 85 s on the GPU because its references replay
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@@ -568,6 +573,13 @@ MD5s. Scorecards from it are in-book statistics, never decisions.
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Interior gaps in history are logged, never fatal, for the same reason.
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- Existing databases still contain an empty-going-forward `outlooks` table from the removed
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market-outlook feature. Nothing reads it.
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- The local news model has a gold head only. `_apply_model_sentiment` may write `xau_score`, XAU's
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per-currency entry and, for a headline naming no other currency, the overall score; USD and every
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other currency keep the keyword value (the pair sentiment is XAU − USD). It never touches
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`_llm_sent_cache`, and a not-relevant headline displays 0.0 while `xau_score` keeps the raw
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number. In `primary` mode nothing is archived but the DeepSeek audit copies, and the dashboard
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process never archives in any mode (its DB writes are off), so a retrain needs
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`deepseek_audit_per_day > 0`, a spell in `fallback`, or a labelling run in ML-NEWS.
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- The news score is a trailing echo of the last day's price move (r ≈ 0.67 with the past day,
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≈ 0.03 with the next). Both news gating flags stay off.
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- The level-breach rescan and the candle loop both skip the settlement-pause window
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@@ -632,6 +644,7 @@ One line per validated change or verdict, newest first. Full evidence in the nam
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| Date | Decision | Where the evidence is |
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|---|---|---|
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| 2026-09-27 | Local news scorer, round two: the owner's ML-NEWS model is now a DeBERTa-v3-small two-head transformer (gold-relevant? then bullish/bearish), distilled from 17,245 DeepSeek gold labels (Sep 8–22 archive + a 2019–2026 Reuters/CNBC/MarketWatch crawl labelled by DeepSeek, 560 calls). Golden set 72/78 (DeepSeek 78, keywords 24, the 09-17 TF-IDF 39), agreement with DeepSeek on unseen live days ~72% (ceiling 90%), 19 ms/headline on one CPU thread as int8-embedding ONNX; passed the four promotion criteria fixed on 09-22. OWNER DECISIONS: it is to score headlines here in mode `primary` with NO DeepSeek for headline scoring (DeepSeek stays for event direction, summaries, the dashboard endpoint); model files live OUTSIDE this tree at a configured path (280 MB, never in git); shadow mode declined (the archive already allows offline replay). IMPLEMENTED the same day (branch `sql-integration`): `news_model.py` (vendored runtime + never-raising singleton, lazy `onnxruntime` / `tokenizers`), `news_model` config section + `NEWS_MODEL_MODE` env, `news_engine._enrich_headline_scores` / `_apply_model_sentiment` (gold-only rules A–F) / `_audit_with_deepseek`, archive provenance for `model` rows, `deploy/README-news-model.md`, Module 38 (15 tests, one against the real bundle: bullish / neutral labels, 7 ms per headline on the dev box). Two further owner decisions during implementation: **`primary` mode stops writing `newsdata.db`** (only the DeepSeek audit sample, shipped 0, is archived) and **both processes load the model**. Books unaffected (news never enters signals); no signal file touched, no parity event. ML-NEWS `news_transformer.py` received the same two `load_model` edits (commit `9496cd9` there) | `docs/news_model_integration_spec_2026-09-27.md`; `~/Development/ML-NEWS/eval/RESULTS_2026-09-27.md` |
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| 2026-09-27 | Single "conviction" number vs the two-number grade (owner question: one number that is easier to understand and to decide on). Pre-registered; switch `signals.grading_mode` (`pair` default = the shipped rule, byte-identical on both scripts, all five pins reproduced; `min` = min(\|score\|, 0.6 × conf); `product` = \|score\| × conf / 100; thresholds from the control's corner points, no tuning; mirrored in K2, twin MD5-equal to the reference on both new paths). FAILED on M5 and M15: K-MIN moves 43–54 trades for −1.2R / −2.9R (a, b, e / a, b, d); K-PROD −29R on M15 with PF 1.29 and W3 DD 28.9R, neutral on M5. H1 / H4 K-PROD +11.9R / +17.0R on W3 by taking 40–100 extra low-score trades (H1 PF 1.98 → 1.75, DD doubles): a hypothesis, not evidence. Nothing ships; the knob stays research-only. Code review (high) before the runs: 6 findings fixed, 1 recorded. SHIPPED the display half of the question (owner request the same day): alerts lead with the grade and print score / confidence as words (`main.plain_words`, same line on Telegram, `/status`, CLI and the dashboard card); books untouched. Suite 566 | `backtest/research/conviction_campaign_2026-09-27.md` |
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| 2026-09-26 | Quant review + live log 2026-09-04 → 09-25 (100 setups replayed on the scans' stored candles): stop-hunt claim REFUTED (71% of stop-outs ran ≥ 1R past the stop, same as the books); −25.1R of the −37.8R raw live book were consecutive-bar re-entries the backtester's cooldown never takes; the cooldown view sits at the 3rd–5th percentile of the books' three-week windows. SHIPPED: 5-bar alert cooldown; alert wording (trail first as the tested exit, TP1 a guide with its R, planned RR, validated-book line per class); M15 grade-A SELL block (`sell_min_quality` `A+, B`, PASSED a–f: W2 +154.15R/813 PF 1.53 vs +146.14/931 PF 1.43, OOS +68.5 vs +60.67, W3 DD +8%); 1R printed-target floor in every generator after the gates (`tp_print_floor_rr`, owner rule "TP1 at least 1:1"; label-only re-pin of all four timeframes). FAILED: M15 `min_rr_ratio` 1.0 (trade-identical, −2.0R); a pre-gate target floor (−24.6R). OWNER'S CALL: M5 `tp_floor_rr` 1.0 adds +48.9R on W2 with both halves up but max DD 10.1 → 15.9R (ladder 0.6–1.0 non-monotone); not shipped. Telegram / dashboard AI block rewritten for a new trader (owner rule): one plain headline against the usual odds, the chance "in 100", one reason for and one against, no verdict word or confidence figure; persona prompts ban trading jargon (`PANEL_PROMPT_VERSION` v2.1). Two delivery bugs from the DB checks fixed: the macro-source panel verdict never stored its probability (all v2 rows NULL); the level-breach rescan re-logged the 20:45 M15 bar three times every evening after the settlement pause (guard added to `_handle_breach`). Indicator grid and volume / daily-direction screens (Part C) not run | `backtest/research/rr_and_sell_campaign_2026-09-26.md` |
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| 2026-09-18 | Full-GPU research twin SHIPPED: `backtest/cudaBasedBacktesting.py` + `backtest/cuda_signal_kernels.py` (owner decisions: research only, `gpu_backtest.py` stays the base reference, every research flag supported, the one exemption from instructions item 6 recorded there). Proof: the five reference MD5s, four 6-month acceptance books (2026-03-18 → 2026-09-18, M5 and M15, $0.65 and $0.30), the M15 OOS `--export-features` CSV and `--record --record-bars` tables, and ten research-flag variants, all byte-identical to the reference script; Module 37 (16 tests) pins every stage against the live code. Books take 0.8–2.4 s instead of 13–115 s. Rule added: any signal change is a parity event for the twin | `backtest/research/cuda_backtester_2026-09-18.md` |
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@@ -0,0 +1,81 @@
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# Handoff: the local news model and ForexBot2 (2026-09-27)
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**Status: IMPLEMENTED later the same day** (`news_model.py`, `news_model` config section, Module 38,
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`deploy/README-news-model.md`). Two decisions were added during implementation: `primary` mode writes
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nothing to `logs/newsdata.db` except the DeepSeek audit sample, and both processes load the model. The
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authoritative record is the status note at the top of `docs/news_model_integration_spec_2026-09-27.md`
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and the CLAUDE.md ledger row; this file is kept as the historical handoff and can be deleted.
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Read this before touching headline scoring. It records what exists, what the owner decided, and what
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was left to build at the time of writing.
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## What exists
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The owner's news model lives in a separate project, `~/Development/ML-NEWS`
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(git `ssh://git@192.168.0.11:222/harry/News-ML-Decoder.git`). On 2026-09-27 it became a
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DeBERTa-v3-small two-head transformer, exported to ONNX for CPU:
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- Question 1: does this headline move the gold price? Question 2: if so, up or down?
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- Trained to copy DeepSeek's gold score on 17,245 headlines: the Sep 8–22 `newsdata.db` archive plus a
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2019–2026 Reuters / CNBC / MarketWatch crawl that DeepSeek labelled (560 calls).
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- Golden set 72/78. DeepSeek 78, the keyword scorer 24, the 2026-09-17 TF-IDF model 39.
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- Agreement with DeepSeek on unseen live-feed days about 72% (DeepSeek agrees with itself about 90%).
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- 19 ms per headline on one CPU thread, 9 ms on four. Needs only `onnxruntime`, `tokenizers`, `numpy`.
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- Bundle: `ML-NEWS/models/xau_transformer/{model.onnx (271 MB), tokenizer.json, meta.json}`.
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- Full results: `~/Development/ML-NEWS/eval/RESULTS_2026-09-27.md`.
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It passed the four promotion criteria fixed in advance on 2026-09-22 (golden ≥ 55/78, distractors ≥ 6/8,
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unseen-days agreement ≥ 62.8%, latency ≤ 20 ms). This supersedes the 2026-09-17 verdict "a fallback,
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not a replacement" in the CLAUDE.md ledger.
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## What the owner decided (2026-09-27)
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1. **Use it as the primary headline scorer, with no DeepSeek calls for headline scoring.** DeepSeek
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stays for everything else: calendar-event direction (`_llm_event_direction`), the news summary, the
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dashboard's on-demand summary endpoint.
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2. **Model files live outside the ForexBot2 tree**, for example `/home/Forex/models/xau_transformer`,
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copied by hand, path set in `config.json`. Never in git. ForexBot2 keeps deploying by `git pull`.
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3. **No shadow mode.** The archive already stores every headline with its score, so offline replay
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covers the comparison.
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4. Accepted consequence: with DeepSeek off for headlines, the archive stops receiving fresh DeepSeek
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labels, which is what ML-NEWS retrains on. An optional config knob (`deepseek_audit_per_day`,
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default 0) can label a few headlines a day for the archive only.
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## What is left to build
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Everything. The specification is `docs/news_model_integration_spec_2026-09-27.md` in this repo
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(identical to `ML-NEWS/docs/FOREXBOT2_INTEGRATION_SPEC.md`). In one screen:
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| File | Change |
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|---|---|
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| `requirements.txt` | add `onnxruntime`, `tokenizers` (no "torch"/"transformers" anywhere in the file; a test greps for them) |
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| `news_model.py` (new) | vendored copy of ML-NEWS `news_transformer.py` + a load-once singleton that never raises; onnxruntime imported lazily |
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| `app_config.py` | optional `news_model` section: `path`, `threads`, `mode` (`off`/`fallback`/`primary`), `deepseek_audit_per_day`; `NEWS_MODEL_MODE` env override |
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| `news_engine.py` | `_apply_model_sentiment` + a mode switch at the call site (~line 945); model score goes into `xau_score` and the XAU entry only, never into USD |
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| `news_archive.py` | store `teacher_model` for `model` rows (the `model` rank already exists) |
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| `test_all.py` | 11 tests listed in the spec |
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| `deploy/README-news-model.md` (new) | ops for both the bot and the dashboard daemon |
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| `config.json` (live) | the new section |
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Rules that must survive any implementation:
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- The model has a gold head only. **Never write its score into a non-XAU currency**:
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`get_pair_sentiment` computes `XAU_mean − USD_mean` and the signal aggregator acts on it.
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- A headline the model calls not-relevant shows 0.0, whatever the raw score; the raw score still goes
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to `xau_score` for training data.
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- Never write model results into `_llm_sent_cache`; that cache decides what is re-sent to DeepSeek.
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- If the model fails to load, `primary` falls back to the DeepSeek path for that cycle, not to keywords.
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- Both the bot and `dashboard_publisher.py` build a `NewsEngine`; each process that loads the model
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holds about 0.6 GB. Decide per process with the env override.
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- News never enters signals. CLAUDE.md §7 applies unchanged; no book is affected by any of this.
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## How to verify when it is built
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`venv/bin/python test_all.py` green; one news cycle in `primary` mode archives only `score_source='model'`
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rows with `xau_score` set and zero DeepSeek headline calls; renaming the model directory away still
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starts both processes and yields `llm` rows; `mode="off"` reproduces today's rows exactly.
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## Re-verify before editing
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Every file:line number in the spec was checked on 2026-09-27 against `6af1237` and drifts with each
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commit. Check them first.
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+87
-1
@@ -90,7 +90,7 @@ _KNOWN_TOP = {"telegram", "data_providers", "scanning", "pairs", "log_level",
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# config.json files still carry the block and must keep
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# loading; load_app_config logs one warning when it is there.
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"market_outlook",
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"violent_watch", "signal_log", "news_archive"}
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"violent_watch", "signal_log", "news_archive", "news_model"}
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_KNOWN_OANDA = {
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# "account_id" is TOLERATED but ignored: it belonged to the auto-
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# execution daemon removed 2026-09-03 (last commit 31002cb). The candle
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@@ -111,6 +111,8 @@ _KNOWN_VIOLENT_WATCH = {"enabled", "alert_line", "headsup",
|
||||
"max_headsup_per_day", "headsup_min_level"}
|
||||
_KNOWN_SIGNAL_LOG = {"enabled", "path", "log_blocked_scans"}
|
||||
_KNOWN_NEWS_ARCHIVE = {"enabled", "path"}
|
||||
_KNOWN_NEWS_MODEL = {"path", "threads", "mode", "deepseek_audit_per_day"}
|
||||
_NEWS_MODEL_MODES = ("off", "fallback", "primary")
|
||||
_KNOWN_TELEGRAM = {"token", "chat_id", "channel_id", "alert_min_quality"}
|
||||
_KNOWN_SCANNING = {"pairs", "timeframes"}
|
||||
_KNOWN_PAIR_KEYS = {"blocked_hours_utc"}
|
||||
@@ -150,6 +152,7 @@ class AppConfig:
|
||||
default_factory=lambda: dict(_VIOLENT_WATCH_DEFAULTS))
|
||||
signal_log_settings: Dict[str, Any] = field(default_factory=dict)
|
||||
news_archive_settings: Dict[str, Any] = field(default_factory=dict)
|
||||
news_model_settings: Dict[str, Any] = field(default_factory=dict)
|
||||
log_level: str = "INFO"
|
||||
|
||||
|
||||
@@ -405,6 +408,30 @@ def load_app_config(path: str = CONFIG_PATH) -> AppConfig:
|
||||
"config.json: news_archive.path must be a non-empty string")
|
||||
app.news_archive_settings = dict(narch)
|
||||
|
||||
nm = raw.get("news_model", {})
|
||||
_check_keys(nm, _KNOWN_NEWS_MODEL, '"news_model"')
|
||||
if "mode" in nm and nm["mode"] not in _NEWS_MODEL_MODES:
|
||||
raise AppConfigError(
|
||||
'config.json: news_model.mode must be "off", "fallback" or "primary"')
|
||||
if "threads" in nm and (not isinstance(nm["threads"], int)
|
||||
or isinstance(nm["threads"], bool)
|
||||
or nm["threads"] < 1):
|
||||
raise AppConfigError(
|
||||
"config.json: news_model.threads must be a positive integer")
|
||||
if "deepseek_audit_per_day" in nm and (
|
||||
not isinstance(nm["deepseek_audit_per_day"], int)
|
||||
or isinstance(nm["deepseek_audit_per_day"], bool)
|
||||
or nm["deepseek_audit_per_day"] < 0):
|
||||
raise AppConfigError(
|
||||
"config.json: news_model.deepseek_audit_per_day must be a "
|
||||
"non-negative integer")
|
||||
if nm.get("mode", "off") != "off" and (
|
||||
not isinstance(nm.get("path"), str) or not nm.get("path")):
|
||||
raise AppConfigError(
|
||||
"config.json: news_model.path is required when news_model.mode "
|
||||
"is not off")
|
||||
app.news_model_settings = dict(nm)
|
||||
|
||||
level = raw.get("log_level", app.log_level)
|
||||
if level not in _VALID_LOG_LEVELS:
|
||||
raise AppConfigError(
|
||||
@@ -569,6 +596,65 @@ def signal_log_path(app: Optional[AppConfig] = None) -> str:
|
||||
return path
|
||||
|
||||
|
||||
# ── Local news model (env → config.json → default) ───────────────────
|
||||
#
|
||||
# The owner's ML-NEWS XAU transformer scores headlines locally
|
||||
# (news_model.py). "off" is today's DeepSeek/keyword path; "fallback" runs
|
||||
# the model only on rows DeepSeek left keyword-scored; "primary" scores
|
||||
# every headline with the model and never calls DeepSeek for headlines
|
||||
# (except the optional audit sample). NEWS_MODEL_MODE lets one process
|
||||
# (the dashboard daemon's systemd unit) pick its own mode: each process
|
||||
# that loads the model holds ~0.6 GB.
|
||||
|
||||
def news_model_mode(app: Optional[AppConfig] = None) -> str:
|
||||
env = os.environ.get("NEWS_MODEL_MODE")
|
||||
if env:
|
||||
mode = env.strip().lower()
|
||||
if mode in _NEWS_MODEL_MODES:
|
||||
return mode
|
||||
logger.warning(
|
||||
'NEWS_MODEL_MODE=%r is not one of %s; using config.json',
|
||||
env, _NEWS_MODEL_MODES)
|
||||
app = app or get_app_config()
|
||||
mode = app.news_model_settings.get("mode", "off")
|
||||
return mode if mode in _NEWS_MODEL_MODES else "off"
|
||||
|
||||
|
||||
def news_model_path(app: Optional[AppConfig] = None) -> Optional[str]:
|
||||
"""Absolute path to the bundle directory (model.onnx, tokenizer.json,
|
||||
meta.json), or None when not configured. A relative value resolves
|
||||
against the repo root like news_archive_path."""
|
||||
app = app or get_app_config()
|
||||
path = app.news_model_settings.get("path")
|
||||
if not isinstance(path, str) or not path:
|
||||
return None
|
||||
if not os.path.isabs(path):
|
||||
path = os.path.join(os.path.dirname(os.path.abspath(__file__)), path)
|
||||
return path
|
||||
|
||||
|
||||
def news_model_threads(app: Optional[AppConfig] = None) -> int:
|
||||
"""ONNX intra-op threads (measured 2026-09-27: 1 → 19 ms per headline,
|
||||
4 → 9 ms, 16 → 13 ms)."""
|
||||
app = app or get_app_config()
|
||||
try:
|
||||
return max(1, int(app.news_model_settings.get("threads", 4)))
|
||||
except (TypeError, ValueError):
|
||||
return 4
|
||||
|
||||
|
||||
def news_model_audit_per_day(app: Optional[AppConfig] = None) -> int:
|
||||
"""primary mode only: headlines per UTC day also sent to DeepSeek so
|
||||
the archive keeps receiving fresh training labels. Archive-only, never
|
||||
displayed. 0 = no DeepSeek headline calls at all (owner decision
|
||||
2026-09-27)."""
|
||||
app = app or get_app_config()
|
||||
try:
|
||||
return max(0, int(app.news_model_settings.get("deepseek_audit_per_day", 0)))
|
||||
except (TypeError, ValueError):
|
||||
return 0
|
||||
|
||||
|
||||
# ── News-archive resolution helpers (env → config.json → default) ────
|
||||
|
||||
def news_archive_enabled(app: Optional[AppConfig] = None) -> bool:
|
||||
|
||||
@@ -65,5 +65,11 @@
|
||||
"enabled": true,
|
||||
"path": "logs/newsdata.db"
|
||||
},
|
||||
"news_model": {
|
||||
"path": "/home/Forex/models/xau_transformer",
|
||||
"threads": 4,
|
||||
"mode": "off",
|
||||
"deepseek_audit_per_day": 0
|
||||
},
|
||||
"log_level": "INFO"
|
||||
}
|
||||
|
||||
@@ -19,7 +19,9 @@ git pull
|
||||
venv/bin/python test_all.py # optional sanity: suite must be green
|
||||
```
|
||||
|
||||
No new Python dependencies are required.
|
||||
Two optional Python packages (`onnxruntime`, `tokenizers`) arrive with the local news model;
|
||||
`venv/bin/pip install -r requirements.txt` after the pull. See `deploy/README-news-model.md`,
|
||||
which also says when to keep this daemon on DeepSeek (`NEWS_MODEL_MODE=off` in the unit).
|
||||
|
||||
## 2. Make the web folder writable
|
||||
|
||||
|
||||
@@ -0,0 +1,185 @@
|
||||
# Deploying the local news model (live server)
|
||||
|
||||
Since 2026-09-27 ForexBot2 can score news headlines with the owner's ML-NEWS gold transformer
|
||||
instead of DeepSeek. The model runs on the CPU inside the bot process (and inside the dashboard
|
||||
daemon), needs two extra Python packages and a 280 MB model folder that lives **outside** the git
|
||||
tree. DeepSeek keeps its other jobs: the direction of a calendar surprise, the news summary and the
|
||||
dashboard's on-demand summary endpoint.
|
||||
|
||||
What the model is, in one paragraph: a DeBERTa-v3-small transformer with two heads (does this
|
||||
headline move gold? if so, up or down?) exported to ONNX, trained to copy DeepSeek's gold score on
|
||||
17,245 headlines. On the 78-headline golden set it scores 72 (DeepSeek 78, the old keyword scorer
|
||||
24); on unseen live days it agrees with DeepSeek about 72% of the time (DeepSeek agrees with itself
|
||||
about 90%). It scores one headline in about 10–20 ms on one CPU thread. It is **gold only**: every
|
||||
other currency's headline sentiment stays with the keyword scorer in `primary` mode.
|
||||
|
||||
Owner decisions this deployment implements:
|
||||
|
||||
- Mode `primary`: the model scores every headline; **no DeepSeek calls for headline scoring**.
|
||||
- **The news archive stops growing.** In `primary` mode the model's own output is not archived (it is
|
||||
not training data). Only the optional DeepSeek audit sample (`deepseek_audit_per_day`, shipped 0)
|
||||
writes rows. `logs/newsdata.db` stays as it is; nothing is migrated or deleted.
|
||||
- Both processes (bot and dashboard daemon) load the model so Telegram and the dashboard show the same
|
||||
headline scores. Each ONNX session holds about 0.6 GB of RAM.
|
||||
|
||||
## 0. Check memory
|
||||
|
||||
```bash
|
||||
free -m
|
||||
```
|
||||
|
||||
Each process that loads the model needs about 0.7 GB of headroom; two processes about 1.5 GB. If the
|
||||
box cannot spare that, keep the model on the bot only: uncomment the `Environment=NEWS_MODEL_MODE=off`
|
||||
line in `deploy/gold-dashboard.service` (step 5). The dashboard then keeps scoring its headlines with
|
||||
DeepSeek and its scores may differ from the bot's. The dashboard process never writes the archive in
|
||||
any mode, so this choice does not affect training data.
|
||||
|
||||
## 1. Code and dependencies
|
||||
|
||||
```bash
|
||||
cd /home/Forex/ForexBot2
|
||||
git pull
|
||||
venv/bin/pip install -r requirements.txt # adds onnxruntime and tokenizers
|
||||
```
|
||||
|
||||
`requirements.txt` must never contain the training stack (no torch, no transformers); the test suite
|
||||
checks that. The live box runs Python 3.9, where pip resolves onnxruntime 1.19.2 and tokenizers
|
||||
0.22.2; the bundle of 2026-09-27 scores identically there and on the dev box (1.30.0 / 0.23.2).
|
||||
|
||||
## 2. Copy the model files (outside git)
|
||||
|
||||
On the machine that holds ML-NEWS (`~/Development/ML-NEWS/models/xau_transformer/`), copy exactly
|
||||
three files. Skip the `torch/` and `torch_final/` subfolders; they are training checkpoints.
|
||||
|
||||
```bash
|
||||
ssh Forex@LIVE 'mkdir -p /home/Forex/models/xau_transformer'
|
||||
rsync -av --progress \
|
||||
~/Development/ML-NEWS/models/xau_transformer/{model.onnx,tokenizer.json,meta.json} \
|
||||
Forex@LIVE:/home/Forex/models/xau_transformer/
|
||||
```
|
||||
|
||||
Verify on the live box:
|
||||
|
||||
```bash
|
||||
sha256sum /home/Forex/models/xau_transformer/model.onnx
|
||||
# bundle of 2026-09-27: 9e7a64d3ae1df335059ce829cafcf6e4544b85a4f72cc88923c2e42c48e975a0
|
||||
sha256sum /home/Forex/models/xau_transformer/tokenizer.json
|
||||
# bundle of 2026-09-27: 045f93f80eb9ad18e559647563fb827dea59d93258fdb8b922de82bc53666d16
|
||||
python3 -c "import json; print(json.load(open('/home/Forex/models/xau_transformer/meta.json'))['trained_at'])"
|
||||
# 2026-09-27T23:15:30.403442+00:00
|
||||
```
|
||||
|
||||
The files should be owned by (or readable by) the account the bot runs as. Read-only is enough.
|
||||
|
||||
## 3. Configure
|
||||
|
||||
Add one block to `/home/Forex/ForexBot2/config.json` (any position; `config.json.example` shows it).
|
||||
Leave the `deepseek` block as it is.
|
||||
|
||||
```json
|
||||
"news_model": {
|
||||
"path": "/home/Forex/models/xau_transformer",
|
||||
"threads": 2,
|
||||
"mode": "primary",
|
||||
"deepseek_audit_per_day": 0
|
||||
}
|
||||
```
|
||||
|
||||
(`threads` is clamped to the core count; the live box has 2 cores, hence 2. Measured there: load
|
||||
0.8 s, 16 ms per headline, 633 MB resident for the session.)
|
||||
|
||||
| Key | Meaning |
|
||||
|---|---|
|
||||
| `path` | folder with `model.onnx`, `tokenizer.json`, `meta.json`; required when `mode` is not `off` |
|
||||
| `threads` | ONNX threads per process. Measured: 1 thread 19 ms per headline, 4 threads 9 ms, 16 threads 13 ms. 4 is right for a 4–8 core box |
|
||||
| `mode` | `off` = today's DeepSeek/keyword path, nothing loads. `fallback` = DeepSeek first, the model only for headlines DeepSeek failed on. `primary` = the model scores everything, DeepSeek never sees a headline (except the audit sample) |
|
||||
| `deepseek_audit_per_day` | `primary` only: headlines per UTC day also sent to DeepSeek and archived as `llm` rows (one call of up to 25 per cycle at most). They are training labels for the next ML-NEWS retrain; the bot never displays them. 0 = no DeepSeek headline calls at all |
|
||||
|
||||
The environment variable `NEWS_MODEL_MODE=off|fallback|primary` overrides `mode` for one process. It
|
||||
exists so the dashboard daemon's systemd unit can pick its own mode.
|
||||
|
||||
A config error (unknown key, bad mode, missing path) stops the bot at boot with a clear message, like
|
||||
every other `config.json` mistake.
|
||||
|
||||
## 4. Smoke test without starting the bot
|
||||
|
||||
```bash
|
||||
cd /home/Forex/ForexBot2
|
||||
venv/bin/python -c "
|
||||
import news_model as m
|
||||
b = m.get_news_model()
|
||||
print(m.score_news(b, 'Gold jumps as dollar slides'))
|
||||
print(m.score_news(b, 'Red Lobster endless shrimp losses'))"
|
||||
```
|
||||
|
||||
Expect a log line `news model loaded from /home/Forex/models/xau_transformer (trained_at 2026-09-27…,
|
||||
threshold 0.2, 4 threads)`, then `label: bullish` for the first headline and `label: neutral` for the
|
||||
second. If instead you see `news model unavailable, DeepSeek/keyword path stays: …`, the message names
|
||||
the cause (missing file, missing package, bad JSON). The bot would still start in that state and
|
||||
would score with DeepSeek for the whole process lifetime; fix the cause and restart.
|
||||
|
||||
Then run the suite. With the model folder configured, the integration test loads the real bundle:
|
||||
|
||||
```bash
|
||||
venv/bin/python test_all.py # expect RESULTS: … passed, 0 failed
|
||||
```
|
||||
|
||||
## 5. Restart both processes
|
||||
|
||||
Bot: restart it the way it is run on this box (nohup / screen / its unit). Dashboard:
|
||||
|
||||
```bash
|
||||
sudo systemctl restart gold-dashboard
|
||||
```
|
||||
|
||||
If the dashboard must not load the model (step 0), first uncomment the
|
||||
`Environment=NEWS_MODEL_MODE=off` line in `deploy/gold-dashboard.service`, then
|
||||
`sudo cp deploy/gold-dashboard.service /etc/systemd/system/ && sudo systemctl daemon-reload` before
|
||||
the restart.
|
||||
|
||||
## 6. What to check afterwards
|
||||
|
||||
- **Logs** (`logs/telegram.log`, `logs/dashboard.log`): exactly one `news model loaded` line per
|
||||
process, no `news model unavailable` line. If the level is DEBUG you also see
|
||||
`news archive: primary mode, 0 DeepSeek audit rows stored, N model/keyword rows not archived` every
|
||||
news cycle.
|
||||
- **`top`**: each process about 0.6 GB more resident memory than before; CPU near 0% between scans
|
||||
(thread spinning is disabled in the runtime).
|
||||
- **DeepSeek spend**: the daily request count no longer moves with the headline count; only the event
|
||||
direction, the summary and the panel use it.
|
||||
- **Archive frozen** (the intended state with audit 0):
|
||||
|
||||
```bash
|
||||
sqlite3 -readonly logs/newsdata.db "SELECT MAX(first_seen), COUNT(*) FROM headlines;"
|
||||
```
|
||||
|
||||
The newest `first_seen` stops advancing after the restart. With `deepseek_audit_per_day` > 0 you
|
||||
see at most that many new rows a day, all `score_source = 'llm'`.
|
||||
- **Telegram / dashboard**: headline scores still appear; the wording and layout are unchanged, only
|
||||
the numbers now come from the model. A headline the model calls irrelevant to gold shows as neutral.
|
||||
|
||||
## 7. Rollback
|
||||
|
||||
Set `"mode": "fallback"` (DeepSeek first, the model only fills gaps and the archive grows again) or
|
||||
`"mode": "off"` (exactly the pre-2026-09-27 behaviour) in `config.json`, or export
|
||||
`NEWS_MODEL_MODE=off` for one process, and restart. No data migration in either direction.
|
||||
|
||||
## 8. Retraining cadence
|
||||
|
||||
When ML-NEWS ships a new bundle, copy the three files over the old ones (step 2), check the new
|
||||
`trained_at`, and restart both processes. Rows written in `fallback` mode carry
|
||||
`teacher_model = xau_transformer@<trained_at>`, so the archive shows which bundle scored them.
|
||||
|
||||
With `deepseek_audit_per_day` at 0 the archive receives no new DeepSeek labels, so the next retrain
|
||||
needs one of: a positive audit value for a few weeks, a spell in `fallback` mode, or a separate
|
||||
labelling run in ML-NEWS on freshly crawled headlines (`eval/eval_archive.py --scorer transformer` on a
|
||||
copy of the live archive gives the model-versus-DeepSeek agreement on whatever labelled rows exist).
|
||||
|
||||
## Known limits
|
||||
|
||||
- The model copies DeepSeek's opinion; it has not learned from price moves. DeepSeek itself is only
|
||||
about 90% consistent with itself, and the model agrees with it about 72% of the time.
|
||||
- Gold only. In `primary` mode every other currency's headline sentiment is the keyword scorer (24 of
|
||||
78 on the golden set), which is what those currencies got whenever DeepSeek was unavailable before.
|
||||
- News never enters signals (CLAUDE.md section 7). No trade, grade or backtest book changes with any of
|
||||
this; the model only changes the headline lines people read.
|
||||
@@ -15,6 +15,10 @@ Type=simple
|
||||
User=Forex
|
||||
WorkingDirectory=/home/Forex/ForexBot2
|
||||
Environment=DASHBOARD_OUT_DIR=/var/www/trade
|
||||
# The local news model (deploy/README-news-model.md) loads in this process
|
||||
# too (~0.6 GB RSS) so the dashboard shows the bot's headline scores.
|
||||
# Uncomment to keep this daemon on DeepSeek when memory is short:
|
||||
#Environment=NEWS_MODEL_MODE=off
|
||||
ExecStart=/home/Forex/ForexBot2/venv/bin/python dashboard_publisher.py
|
||||
Restart=always
|
||||
RestartSec=30
|
||||
|
||||
@@ -0,0 +1,535 @@
|
||||
# Specification: run the ML-NEWS XAU transformer inside ForexBot2
|
||||
|
||||
Status: **IMPLEMENTED 2026-09-27** (ForexBot2 branch `sql-integration`, on top of `6af1237`; suite
|
||||
Module 38). The text below is the specification as written before the implementation; file:line
|
||||
numbers refer to `6af1237` and have drifted. Where the implementation differs, this list wins:
|
||||
|
||||
- **`primary` mode does not archive the cycle's rows.** Owner decision during implementation: once the
|
||||
local model scores, `logs/newsdata.db` stops growing, because the model's own output is not training
|
||||
data. Only the DeepSeek audit copies (`deepseek_audit_per_day`, shipped 0) are written. `off` and
|
||||
`fallback` archive as before (a `fallback` model row carries `model_teacher`). §5.4 and §9 read
|
||||
accordingly.
|
||||
- `_audit_with_deepseek` **returns** the `llm` copies; there is no `_pending_audit_rows` attribute.
|
||||
The mode switch of §5.2 lives in `NewsEngine._enrich_headline_scores(scored, mode)`, which returns
|
||||
those copies, so the call site and the tests share one function.
|
||||
- `config.json.example` ships `"mode": "off"`; the live server sets `primary`
|
||||
(`deploy/README-news-model.md`).
|
||||
- §8 step 0 was wrong: the dashboard daemon never writes the archive in any mode
|
||||
(`set_db_writes_for_process(False)`), so its DeepSeek rows are not "free audit labels". Both
|
||||
processes load the model (owner decision); `NEWS_MODEL_MODE=off` stays as the memory fallback.
|
||||
- `warm_up_in_background()` is implemented (module-level once flag; a `NewsEngine` is built per scan).
|
||||
- `news_model.get_news_model()` also treats a missing `path` as a load failure (one warning).
|
||||
- `requirements.txt` ranges: `onnxruntime>=1.19,<2`, `tokenizers>=0.22,<1`. The live box runs
|
||||
Python 3.9, where only 1.19.2 / 0.22.2 install; verified on 2026-09-28 to score the bundle
|
||||
identically to the dev box's 1.30.0 / 0.23.2 (golden pair, four probe headlines).
|
||||
- ML-NEWS `news_transformer.py` received the §3.4 edits in the same session; the vendored copy differs
|
||||
only in `load_model` having no default directory.
|
||||
|
||||
Repos:
|
||||
- **ML-NEWS** `~/Development/ML-NEWS` (git `ssh://git@192.168.0.11:222/harry/News-ML-Decoder.git`).
|
||||
Produces the model. Nothing in ForexBot2 imports from it.
|
||||
- **ForexBot2** `~/Development/ForexBot2`, live at `/home/Forex/ForexBot2`, deployed by `git pull`.
|
||||
Consumes the model.
|
||||
|
||||
---
|
||||
|
||||
## 1. Context and goal
|
||||
|
||||
### 1.1 What the model is
|
||||
`ML-NEWS/models/xau_transformer/` holds a fine-tuned `microsoft/deberta-v3-small` with two heads,
|
||||
exported to ONNX:
|
||||
|
||||
| File | Size | Purpose |
|
||||
|---|---|---|
|
||||
| `model.onnx` | 271 MB | encoder + heads; int8 embedding table, fp32 compute |
|
||||
| `tokenizer.json` | 8 MB | HF `tokenizers` fast tokenizer |
|
||||
| `meta.json` | 4 KB | thresholds, `trained_at`, `relevance_threshold`, `export_checks`, training stats |
|
||||
|
||||
Inputs: one headline, ≤ 64 tokens. Outputs per headline (from `news_transformer.score_batch`):
|
||||
|
||||
```python
|
||||
{"headline": str, "p_relevant": float, "p_bullish": float,
|
||||
"relevant": bool, # p_relevant >= meta["relevance_threshold"] (0.2 for the shipped bundle)
|
||||
"direction": "bullish"|"bearish",
|
||||
"label": "bullish"|"bearish"|"neutral", # neutral when not relevant
|
||||
"score": float} # p_relevant * (2*p_bullish - 1), in [-1, 1]
|
||||
```
|
||||
|
||||
`score` is on the same scale as DeepSeek's `xau` score that ForexBot2 already stores in
|
||||
`xau_score`. The model was trained to copy DeepSeek (`deepseek-v4-flash`, prompt sha1 `d32e04c6…`, the
|
||||
same prompt as `news_engine._LLM_SENTIMENT_PROMPT_SHA1`) on 17,245 headlines. Quality on
|
||||
2026-09-27: golden set 72/78 (DeepSeek 78, keyword scorer 24), agreement with DeepSeek on unseen live
|
||||
days ~72%, CPU latency 19 ms/headline on 1 thread, 9 ms on 4. Full numbers: `ML-NEWS/eval/RESULTS_2026-09-27.md`.
|
||||
|
||||
**The model scores gold only.** It has no head for USD, EUR or anything else. This drives most of
|
||||
the rules in §5.
|
||||
|
||||
### 1.2 How ForexBot2 scores headlines today
|
||||
`news_engine.py`, inside `NewsEngine._fetch_news_sentiment_raw` (called via
|
||||
`_cached_fetch("_headlines_cache", …)` at :822, which holds `NewsEngine._cache_lock`, :629-647):
|
||||
|
||||
1. RSS items become `NewsSentiment` objects (:891-910) with the **keyword scorer** filling
|
||||
`sentiment_score` and `sentiment_by_currency[cur]` for each currency in `currencies_mentioned`,
|
||||
`score_source="keyword"`, `xau_score=None`.
|
||||
2. `scored = headlines + unmatched` (:944); `self._apply_llm_sentiment(scored)` (:945, in try/except).
|
||||
`_apply_llm_sentiment` (:1032-1065) sends headlines not in `NewsEngine._llm_sent_cache` to DeepSeek
|
||||
in batches of `_LLM_BATCH_CAP = 25` via `self._llm_headline_scores(texts)` (:1009) and, on success,
|
||||
overwrites `sentiment_score`, `sentiment_by_currency` (XAU gets the gold score, every other currency
|
||||
the overall score), sets `xau_score`, `score_source="llm"`. A failed batch stops sending; those
|
||||
headlines stay keyword-scored and **stay out of the cache**, so they are re-sent next window.
|
||||
3. `news_archive.archive_headlines(scored, teacher_model=…, teacher_prompt_sha1=…)` (:958-964).
|
||||
The archive UPSERT (news_archive.py:203-260) keeps the higher-ranked source:
|
||||
`_SOURCE_RANK = {"unknown": 0, "keyword": 1, "model": 2, "llm": 3}` (:107). `"model"` is already
|
||||
reserved for "a trained local artifact" (news_engine.py:223-227). `build_row` (:323-354) stores
|
||||
`teacher_model`/`teacher_prompt_sha1` only when `source == "llm"`.
|
||||
4. Downstream consumers of the scores: `get_fundamental_context` (drops |score| <
|
||||
`NEUTRAL_HEADLINE_THRESHOLD = 0.1`, :256), `get_pair_sentiment` (:553-557, computes
|
||||
`XAU_mean − USD_mean`), `signal_aggregator.py:1677/1723` (acts on that when
|
||||
`sentiment_affects_signals`), `agent_panel.py`, the dashboard.
|
||||
|
||||
Two processes run this code: the bot (`venv/bin/python telegram_bot.py`, CLAUDE.md:30) and the
|
||||
dashboard daemon (`dashboard_publisher.py:401-403` builds its own `NewsEngine`, `:503` calls
|
||||
`get_fundamental_context()`; unit `deploy/gold-dashboard.service`). They share `config.json` and the
|
||||
archive DB.
|
||||
|
||||
### 1.3 Decisions taken by the owner (2026-09-27)
|
||||
- **Mode `primary`, no DeepSeek for headline scoring.** The model scores every headline. DeepSeek stays
|
||||
for everything else it does (`_llm_event_direction` for calendar surprises, the news summary, the
|
||||
dashboard's on-demand `news_summary.php`).
|
||||
- **Model files live outside the ForexBot2 tree**, path in `config.json`.
|
||||
- Accepted consequence: the archive stops receiving fresh DeepSeek labels, so the ML-NEWS weekly
|
||||
retrain and the "replay and compare" check lose their data source. Mitigation kept optional:
|
||||
`deepseek_audit_per_day` (§4) labels a few headlines a day for the archive only. Default 0.
|
||||
|
||||
### 1.4 Non-goals
|
||||
- Removing the DeepSeek client or its non-headline uses.
|
||||
- Scoring non-gold currencies with the model.
|
||||
- Changing ML-NEWS training. (The one ML-NEWS change is §3.4, a runtime option.)
|
||||
- Training from price moves.
|
||||
|
||||
---
|
||||
|
||||
## 2. Target layout on the live server
|
||||
|
||||
```
|
||||
/home/Forex/ForexBot2/ # git pull as today
|
||||
news_model.py # NEW, vendored runtime (§3)
|
||||
requirements.txt # + onnxruntime, tokenizers (§3.1)
|
||||
app_config.py, news_engine.py, news_archive.py, test_all.py # edited (§4, §5, §6, §7)
|
||||
config.json # + "news_model" section (§4)
|
||||
deploy/README-news-model.md # NEW ops checklist (§8)
|
||||
deploy/gold-dashboard.service # maybe + Environment=NEWS_MODEL_MODE=off (§8)
|
||||
/home/Forex/models/xau_transformer/ # NEW, outside git, copied by hand
|
||||
model.onnx tokenizer.json meta.json
|
||||
```
|
||||
|
||||
Memory: each process that loads the model holds ~0.5–0.7 GB RSS for the ONNX session. Two processes
|
||||
→ ~1.5 GB. `free -m` before deciding whether the dashboard daemon also runs it (§8 step 0).
|
||||
|
||||
---
|
||||
|
||||
## 3. `news_model.py` (new, ForexBot2)
|
||||
|
||||
### 3.1 Dependencies
|
||||
Add to `requirements.txt`:
|
||||
```
|
||||
onnxruntime>=1.30,<2
|
||||
tokenizers>=0.23,<1
|
||||
```
|
||||
Pin to the versions that verified the bundle in ML-NEWS `.venv` (check `pip show` there before
|
||||
writing). `tokenizer.json` compatibility across older `tokenizers` releases is not guaranteed.
|
||||
**Do not write the words "torch" or "transformers" anywhere in requirements.txt**, not even in a
|
||||
comment: `test_all.py:11437-11462` greps the file for them and fails.
|
||||
|
||||
### 3.2 Source
|
||||
Copy `ML-NEWS/news_transformer.py` → `ForexBot2/news_model.py`. Keep verbatim: `clean_headline`,
|
||||
`_sigmoid`, `load_model`, `score_batch`, `score_news`, constants `MAX_LENGTH = 64`,
|
||||
`RELEVANT_MIN = 0.25`, `RELEVANT_MAX_IRRELEVANT = 0.10`. Drop the CLI `main` or keep it, either is fine.
|
||||
Add a docstring line: `Vendored from ML-NEWS news_transformer.py at commit <sha>; bundle trained_at
|
||||
<meta.json trained_at>.`
|
||||
|
||||
**Imports.** `onnxruntime` and `tokenizers` must stay imported *inside* `load_model` (as in
|
||||
news_transformer.py:58-59). Module top imports only `numpy`, `json`, `logging`, `os`, `pathlib`,
|
||||
`threading`. Reason: `news_engine.py` will `import news_model` at its top; if onnxruntime is missing on
|
||||
a box, the bot must still start.
|
||||
|
||||
### 3.3 Singleton
|
||||
```python
|
||||
_lock = threading.Lock()
|
||||
_bundle: Optional[dict] = None
|
||||
_failed: bool = False # tried and failed; don't retry, don't re-log
|
||||
|
||||
def get_news_model() -> Optional[dict]:
|
||||
"""The loaded bundle, or None. Loads once per process. Never raises."""
|
||||
global _bundle, _failed
|
||||
if _bundle is not None or _failed:
|
||||
return _bundle
|
||||
with _lock:
|
||||
if _bundle is not None or _failed:
|
||||
return _bundle
|
||||
try:
|
||||
import app_config
|
||||
path = app_config.news_model_path()
|
||||
threads = app_config.news_model_threads()
|
||||
b = load_model(path, threads=threads)
|
||||
logger.info("news model loaded from %s (trained_at %s, threshold %s, %d threads)",
|
||||
path, b["meta"].get("trained_at"), b["meta"].get("relevance_threshold"), threads)
|
||||
_bundle = b
|
||||
except Exception as e: # ImportError, FileNotFoundError, bad JSON, ORT errors
|
||||
logger.warning("news model unavailable, DeepSeek/keyword path stays: %s: %s",
|
||||
type(e).__name__, e)
|
||||
_failed = True
|
||||
return _bundle
|
||||
|
||||
def reset_for_tests() -> None: # clears _bundle/_failed; used by test_all.py
|
||||
```
|
||||
|
||||
Optional: `warm_up_in_background()` that calls `get_news_model()` in a daemon thread at
|
||||
`NewsEngine.__init__`, so the first news cycle isn't blocked by the ~3–5 s session creation under
|
||||
`_cache_lock`. Nice to have, not required.
|
||||
|
||||
### 3.4 Two changes inside `load_model` (also apply to ML-NEWS `news_transformer.py` so the copies stay identical)
|
||||
```python
|
||||
options = ort.SessionOptions()
|
||||
if threads:
|
||||
options.intra_op_num_threads = max(1, min(int(threads), os.cpu_count() or 1))
|
||||
options.inter_op_num_threads = 1
|
||||
# ORT intra-op threads spin-wait after each run by default; in a mostly idle
|
||||
# bot process that is 4 cores at 100% between cycles.
|
||||
options.add_session_config_entry("session.intra_op.allow_spinning", "0")
|
||||
```
|
||||
|
||||
### 3.5 Contract `score_batch(bundle, headlines: list[str]) -> list[dict]`
|
||||
Unchanged from ML-NEWS. Empty or whitespace-only headline raises `ValueError` from `clean_headline`;
|
||||
callers pass already-non-empty `NewsSentiment.headline`, but §5 still wraps per-batch in try/except.
|
||||
Batch size: `_apply_model_sentiment` sends all of a cycle's headlines in one call (typically 30–150);
|
||||
`score_batch` pads to the longest in the batch, ≤ 64 tokens, so one call of 150 is ~1 s on 4 threads.
|
||||
|
||||
---
|
||||
|
||||
## 4. Configuration
|
||||
|
||||
### 4.1 `config.json` section (add to `config.json.example` too)
|
||||
```json
|
||||
"news_model": {
|
||||
"path": "/home/Forex/models/xau_transformer",
|
||||
"threads": 4,
|
||||
"mode": "primary",
|
||||
"deepseek_audit_per_day": 0
|
||||
}
|
||||
```
|
||||
|
||||
| Key | Type | Default if absent | Meaning |
|
||||
|---|---|---|---|
|
||||
| `path` | str | required when mode ≠ off | directory with the three bundle files |
|
||||
| `threads` | int ≥ 1 | 4 | ORT intra-op threads (measured: 1→19 ms, 4→9 ms, 16→13 ms) |
|
||||
| `mode` | `"off"`/`"fallback"`/`"primary"` | `"off"` | see §5.2 |
|
||||
| `deepseek_audit_per_day` | int ≥ 0 | 0 | primary mode only: headlines/day to also send to DeepSeek, archive-only |
|
||||
|
||||
Whole section absent → `mode="off"` → today's behaviour exactly.
|
||||
|
||||
Environment override: `NEWS_MODEL_MODE=off|fallback|primary` wins over config.json. Purpose: the
|
||||
dashboard daemon's systemd unit can turn the model off for that process only. Pattern to copy:
|
||||
`NEWS_ARCHIVE_ENABLED` in `app_config.py:574-583`.
|
||||
|
||||
### 4.2 `app_config.py` edits (follow the `news_archive` section, :397-406, accessors :574/:588)
|
||||
1. `_KNOWN_TOP` (:86-92): add `"news_model"`. **Required**; `_check_keys(raw, _KNOWN_TOP, "top level")`
|
||||
at :181 makes boot fail on an unknown top-level key.
|
||||
2. `_KNOWN_NEWS_MODEL = {"path", "threads", "mode", "deepseek_audit_per_day"}` next to
|
||||
`_KNOWN_NEWS_ARCHIVE` (:113).
|
||||
3. `AppConfig` dataclass (~:147): `news_model_settings: Dict[str, Any] = field(default_factory=dict)`.
|
||||
4. In `load_app_config`, after the `news_archive` block:
|
||||
```python
|
||||
nm = raw.get("news_model", {})
|
||||
_check_keys(nm, _KNOWN_NEWS_MODEL, '"news_model"')
|
||||
if "mode" in nm and nm["mode"] not in ("off", "fallback", "primary"):
|
||||
raise AppConfigError('config.json: news_model.mode must be "off", "fallback" or "primary"')
|
||||
if "threads" in nm and (not isinstance(nm["threads"], int) or isinstance(nm["threads"], bool) or nm["threads"] < 1):
|
||||
raise AppConfigError("config.json: news_model.threads must be a positive integer")
|
||||
if "deepseek_audit_per_day" in nm and (not isinstance(nm["deepseek_audit_per_day"], int) or nm["deepseek_audit_per_day"] < 0):
|
||||
raise AppConfigError("config.json: news_model.deepseek_audit_per_day must be a non-negative integer")
|
||||
if nm.get("mode", "off") != "off" and not isinstance(nm.get("path"), str):
|
||||
raise AppConfigError("config.json: news_model.path is required when mode is not off")
|
||||
app.news_model_settings = dict(nm)
|
||||
```
|
||||
5. Accessors:
|
||||
```python
|
||||
def news_model_mode() -> str: # env NEWS_MODEL_MODE overrides; default "off"
|
||||
def news_model_path() -> Optional[str]
|
||||
def news_model_threads() -> int # default 4
|
||||
def news_model_audit_per_day() -> int # default 0
|
||||
```
|
||||
`NewsConfig` in `config.py:169` is **not** touched: tests construct it bare as `NewsConfig()`, and it
|
||||
holds feed/threshold settings, not paths. `news_engine.py` reads the new settings through `app_config`
|
||||
the same way it reaches DeepSeek through `get_llm_client()`.
|
||||
|
||||
---
|
||||
|
||||
## 5. `news_engine.py` changes
|
||||
|
||||
### 5.1 New method `_apply_model_sentiment`
|
||||
Place next to `_apply_llm_sentiment` (:1032). Runs under `_cache_lock` like it.
|
||||
|
||||
```python
|
||||
def _apply_model_sentiment(self, headlines: List[NewsSentiment]) -> int:
|
||||
"""Score with the local XAU transformer every headline that is still
|
||||
keyword-scored. Returns the number scored. Never raises.
|
||||
|
||||
The model has a gold head only. It writes xau_score always, XAU's
|
||||
per-currency score when XAU is mentioned, and the overall score only
|
||||
when the headline names no other currency. USD/EUR/... entries keep
|
||||
their keyword score: get_pair_sentiment subtracts USD from XAU and
|
||||
must not see the same number on both sides.
|
||||
"""
|
||||
bundle = news_model.get_news_model()
|
||||
if bundle is None or not headlines:
|
||||
return 0
|
||||
todo = [h for h in headlines if h.score_source == "keyword"]
|
||||
if not todo:
|
||||
return 0
|
||||
try:
|
||||
results = news_model.score_batch(bundle, [h.headline for h in todo])
|
||||
except Exception as e:
|
||||
logger.debug(f"news model scoring failed, keyword scores stand: {e}")
|
||||
return 0
|
||||
for h, r in zip(todo, results):
|
||||
raw = float(r["score"]) # archive value, unmodified
|
||||
shown = raw if r["relevant"] else 0.0 # display value, see rule D
|
||||
h.xau_score = raw
|
||||
if "XAU" in h.currencies_mentioned:
|
||||
h.sentiment_by_currency["XAU"] = shown
|
||||
if set(h.currencies_mentioned) <= {"XAU"}: # [] or ["XAU"]
|
||||
h.sentiment_score = shown
|
||||
h.score_source = "model"
|
||||
return len(todo)
|
||||
```
|
||||
|
||||
Rules, with the reason each exists:
|
||||
|
||||
- **A. Never write the model score into a non-XAU currency.** `get_pair_sentiment` (:553-557) forms
|
||||
`XAU_mean − USD_mean`; `signal_aggregator.py:1677/1723` acts on it. The DeepSeek path puts its
|
||||
*overall* score into USD; the model has no overall score, so USD keeps the keyword number.
|
||||
- **B. `sentiment_score` from the model only when the headline names no other currency.** The archive's
|
||||
`score` column is `sentiment_score`, and the ML-NEWS replay eval reads it; for a gold-only headline it
|
||||
should be the model's number, for "Fed hikes, dollar jumps" it stays keyword so the row is honest
|
||||
about what the model did and didn't score. `score_source="model"` therefore means "XAU score is the
|
||||
model's" — amend the dataclass comment at :223-227 to say so.
|
||||
- **C. `xau_score` is always the raw signed score.** That column exists for training data
|
||||
(:229-237); keep it unfiltered.
|
||||
- **D. Display fields get 0.0 when `relevant` is False.** A headline the model calls not-relevant can
|
||||
still carry |score| up to ~0.2 from a confident direction head; `NEUTRAL_HEADLINE_THRESHOLD = 0.1`
|
||||
(:256) would let 0.15 through as "leaning bearish". Zeroing the shown value makes "neutral" mean
|
||||
neutral. (0.1 happens to equal the model's irrelevant band `RELEVANT_MAX_IRRELEVANT`, so the two
|
||||
notions agree.)
|
||||
- **E. Do not touch `NewsEngine._llm_sent_cache`.** That cache decides what is re-sent to DeepSeek. In
|
||||
`fallback` mode a headline that failed DeepSeek must stay uncached so it is retried next window and
|
||||
its archive row upgrades `model` → `llm` through the rank rule (news_archive.py:203-260).
|
||||
- **F. Only `score_source == "keyword"` rows are touched.** Never overwrite an `llm` row in memory.
|
||||
|
||||
### 5.2 Call site (:944-947)
|
||||
Replace
|
||||
```python
|
||||
scored = headlines + unmatched
|
||||
try:
|
||||
self._apply_llm_sentiment(scored)
|
||||
except Exception as e:
|
||||
logger.debug(f"LLM sentiment failed, keyword scores stand: {e}")
|
||||
```
|
||||
with
|
||||
```python
|
||||
scored = headlines + unmatched
|
||||
mode = app_config.news_model_mode()
|
||||
try:
|
||||
if mode == "primary":
|
||||
n = self._apply_model_sentiment(scored)
|
||||
if n == 0 and news_model.get_news_model() is None:
|
||||
# model unavailable this process: degrade to DeepSeek, not to keywords
|
||||
self._apply_llm_sentiment(scored)
|
||||
else:
|
||||
self._audit_with_deepseek(scored) # no-op when audit_per_day == 0
|
||||
elif mode == "fallback":
|
||||
self._apply_llm_sentiment(scored)
|
||||
self._apply_model_sentiment(scored) # only what DeepSeek left keyword-scored
|
||||
else:
|
||||
self._apply_llm_sentiment(scored)
|
||||
except Exception as e:
|
||||
logger.debug(f"headline sentiment enrichment failed, keyword scores stand: {e}")
|
||||
```
|
||||
`import news_model` and `import app_config` at the module top of news_engine.py (news_model's top
|
||||
imports only numpy, §3.2).
|
||||
|
||||
Mode semantics:
|
||||
|
||||
| mode | DeepSeek for headlines | model | when a step fails |
|
||||
|---|---|---|---|
|
||||
| `off` | as today | never | keyword scores stand (today) |
|
||||
| `fallback` | first, as today | on rows DeepSeek left keyword-scored | keyword scores stand |
|
||||
| `primary` | never (except audit) | on all rows | model missing → DeepSeek path for the cycle; model raises mid-batch → keyword |
|
||||
|
||||
### 5.3 Audit sample (primary mode, optional feature, default off)
|
||||
```python
|
||||
_audit_day: str = "" # class attrs, like the caches
|
||||
_audit_sent_today: int = 0
|
||||
|
||||
def _audit_with_deepseek(self, headlines):
|
||||
cap = app_config.news_model_audit_per_day()
|
||||
if cap <= 0 or not get_llm_client().available:
|
||||
return
|
||||
today = datetime.now(timezone.utc).date().isoformat()
|
||||
if NewsEngine._audit_day != today:
|
||||
NewsEngine._audit_day, NewsEngine._audit_sent_today = today, 0
|
||||
room = cap - NewsEngine._audit_sent_today
|
||||
if room <= 0:
|
||||
return
|
||||
cand = [h for h in headlines if h.score_source == "model"
|
||||
and hashlib.sha1(h.headline.encode()).hexdigest() not in NewsEngine._llm_sent_cache]
|
||||
batch = cand[:min(room, self._LLM_BATCH_CAP)]
|
||||
if not batch:
|
||||
return
|
||||
scores = self._llm_headline_scores([h.headline for h in batch])
|
||||
if scores is None:
|
||||
return
|
||||
NewsEngine._audit_sent_today += len(batch)
|
||||
copies = []
|
||||
for h, (overall, xau) in zip(batch, scores):
|
||||
NewsEngine._llm_sent_cache[hashlib.sha1(h.headline.encode()).hexdigest()] = (overall, xau)
|
||||
c = copy.copy(h) # the displayed object keeps the model score
|
||||
c.sentiment_score = overall
|
||||
c.sentiment_by_currency = {cur: (xau if cur == "XAU" else overall) for cur in h.currencies_mentioned}
|
||||
c.xau_score = xau
|
||||
c.score_source = "llm"
|
||||
copies.append(c)
|
||||
self._pending_audit_rows = copies # archived by the call site right after `scored`
|
||||
```
|
||||
The call site then archives `scored` first and `self._pending_audit_rows` second (same
|
||||
`teacher_model`/`teacher_prompt_sha1` as today's DeepSeek rows); the rank rule upgrades those rows
|
||||
to `llm`, the in-memory objects the bot displays keep the model's numbers. One batch per cycle at
|
||||
most, ≤ `cap` per UTC day. This is the only place DeepSeek is called for headlines in primary mode.
|
||||
|
||||
### 5.4 Archive call (:958-964)
|
||||
Pass, in addition to today's kwargs:
|
||||
```python
|
||||
model_teacher=news_model.teacher_tag() # "xau_transformer@<meta.trained_at>" or None
|
||||
```
|
||||
where `teacher_tag()` reads the loaded bundle's meta (None if not loaded). See §6.
|
||||
|
||||
### 5.5 Dataclass comment
|
||||
`NewsSentiment.score_source` comment (:223-227): add `"model" = the local XAU transformer scored
|
||||
xau_score (and XAU's per-currency score); other currencies remain keyword-scored.`
|
||||
|
||||
---
|
||||
|
||||
## 6. `news_archive.py` changes
|
||||
|
||||
No schema change. `_SOURCE_RANK` (:107) and `_STORED_RANK` (:203-206) already rank `model` = 2.
|
||||
|
||||
- `archive_headlines(headlines, *, teacher_model=None, teacher_prompt_sha1=None, origin="live",
|
||||
model_teacher=None)` — new kwarg, passed through to `build_row`.
|
||||
- `build_row` (:323-354):
|
||||
```python
|
||||
"teacher_model": (teacher_model if source == "llm"
|
||||
else model_teacher if source == "model" else None),
|
||||
"teacher_prompt_sha1": (teacher_prompt_sha1 if source in ("llm", "model") else None),
|
||||
```
|
||||
For a model row `teacher_prompt_sha1` is the DeepSeek prompt the model was distilled from (the same
|
||||
constant the bot already passes), which is the truthful provenance.
|
||||
- Effect: a retrain is visible in the data (`teacher_model` changes), and the UPSERT's
|
||||
`COALESCE(excluded.teacher_model, headlines.teacher_model)` (:253-260) only applies when the new
|
||||
rank ≥ stored rank, so an `llm` row's teacher is never replaced by a model tag.
|
||||
|
||||
---
|
||||
|
||||
## 7. Tests (`test_all.py`)
|
||||
|
||||
The suite is a plain script: `@test(name)` (:51-64) runs each function at definition time and
|
||||
tallies; the file ends with `sys.exit(1 if FAIL > 0 else 0)`. Run: `venv/bin/python test_all.py`.
|
||||
Stubbing patterns in use: instance-level `ne._llm_headline_scores = lambda texts: …` with
|
||||
`lc._client = _StubLLM({...})` and `finally` cleanup (:5196-5215); module-level swap
|
||||
`saved = _ne_mod.get_llm_client; _ne_mod.get_llm_client = stub; … finally restore` (:11391-11424).
|
||||
|
||||
Add, using the same patterns and a fake bundle (`news_model.score_batch` swapped on the module):
|
||||
|
||||
1. **model scores keyword rows only.** Two `NewsSentiment`s, one `score_source="llm"`; after
|
||||
`_apply_model_sentiment`, the llm one is byte-identical, the keyword one has `score_source="model"`,
|
||||
`xau_score` set, `sentiment_by_currency["XAU"]` = score, `["USD"]` unchanged (rule A).
|
||||
2. **overall score rule (B).** `currencies_mentioned=["XAU"]` → `sentiment_score` = model score;
|
||||
`["USD","XAU"]` → `sentiment_score` unchanged; `[]` → model score.
|
||||
3. **relevance zeroing (D).** stub returns `relevant=False, score=-0.18` → `sentiment_by_currency["XAU"]
|
||||
== 0.0`, `xau_score == -0.18`.
|
||||
4. **cache untouched (E).** `_llm_sent_cache` empty before and after.
|
||||
5. **failure isolation.** stub raises → all rows still `keyword`, method returns 0, no exception.
|
||||
`news_model.get_news_model()` returning None → returns 0.
|
||||
6. **primary degrades to DeepSeek.** `get_news_model` → None, `_llm_headline_scores` stubbed → rows
|
||||
end up `llm`.
|
||||
7. **audit sample.** `deepseek_audit_per_day=3`, 10 model rows → exactly 3 sent, displayed objects
|
||||
still `model`, `_pending_audit_rows` has 3 `llm` copies; second call same day sends 0.
|
||||
8. **config.** unknown key in `news_model` → `AppConfigError`; `mode="banana"` → error; section
|
||||
absent → `news_model_mode() == "off"`; `NEWS_MODEL_MODE=off` env beats `"mode": "primary"`.
|
||||
9. **archive.** `build_row` of a `model` headline with `model_teacher="xau_transformer@T"` stores it;
|
||||
an `llm` row stores the DeepSeek teacher; existing rank tests still pass.
|
||||
10. **import hygiene.** update the docstring of the test at :11437-11462 (it says numpy-only); add
|
||||
an assertion that `requirements.txt` lists `onnxruntime` and `tokenizers`; keep its "no torch /
|
||||
transformers" checks.
|
||||
11. **integration, skipped when the bundle is absent** (`os.environ.get("NEWS_MODEL_DIR")` or the
|
||||
configured path missing → print SKIP): load, `score_news(b, "Fed cuts rates, signals more
|
||||
easing")["label"] == "bullish"`, `score_news(b, "Red Lobster endless shrimp losses")["label"] ==
|
||||
"neutral"`, latency of 20 calls < 1 s.
|
||||
|
||||
`test_all.py:3675-3690` loads `config.json.example` for pair output only; adding the section there is
|
||||
safe.
|
||||
|
||||
---
|
||||
|
||||
## 8. Ops checklist → `deploy/README-news-model.md` (new)
|
||||
|
||||
Covers **both** processes: the bot (`telegram_bot.py`) and the dashboard daemon
|
||||
(`deploy/gold-dashboard.service`).
|
||||
|
||||
0. `free -m`. Each model-loading process needs ~0.7 GB headroom. If only the bot should score with
|
||||
the model, add `Environment=NEWS_MODEL_MODE=off` to `deploy/gold-dashboard.service` (the dashboard
|
||||
then keeps using DeepSeek for its headlines; those `llm` rows land in the shared archive and act
|
||||
as free audit labels).
|
||||
1. `cd /home/Forex/ForexBot2 && git pull && venv/bin/pip install -r requirements.txt`.
|
||||
2. `mkdir -p /home/Forex/models/xau_transformer` and copy `model.onnx`, `tokenizer.json`, `meta.json`
|
||||
from the ML-NEWS machine (rsync, ~280 MB). Verify `sha256sum model.onnx` matches the source.
|
||||
Owner: the account the bot runs as; read-only is enough.
|
||||
3. Add the `news_model` section to `config.json` (§4.1) with `"mode": "primary"`. Leave the
|
||||
`deepseek` section unchanged.
|
||||
4. Smoke test without starting the bot:
|
||||
```
|
||||
venv/bin/python -c "import news_model as m; b=m.get_news_model(); print(m.score_news(b,'Gold jumps as dollar slides'))"
|
||||
```
|
||||
Expect `label: bullish`, and the "news model loaded" log line.
|
||||
5. `venv/bin/python test_all.py` — green (integration test runs because the dir exists).
|
||||
6. Restart the bot; restart the dashboard unit. Check:
|
||||
- log: one "news model loaded" line per process, no "news model unavailable";
|
||||
- `top`: RSS ~+0.6 GB per process, idle CPU ~0% between scans (spinning disabled);
|
||||
- archive: new rows have `score_source='model'`, `xau_score` set, `teacher_model`
|
||||
`xau_transformer@…`; DeepSeek daily counter for headlines stays at 0 (audit off).
|
||||
7. Rollback: set `"mode": "fallback"` or `"off"` (or the env var), restart. No data migration needed.
|
||||
8. Retrain cadence: when ML-NEWS ships a new bundle, copy the three files over the old ones and
|
||||
restart both processes. `meta.json.trained_at` changes, so the archive shows which model scored which
|
||||
rows. If `deepseek_audit_per_day` is set, ML-NEWS's `eval/eval_archive.py --scorer transformer` on a
|
||||
copy of the live archive gives the model-vs-DeepSeek agreement on those rows.
|
||||
|
||||
---
|
||||
|
||||
## 9. Acceptance criteria (end-to-end)
|
||||
|
||||
- With `mode="primary"` and DeepSeek enabled, one live news cycle archives only `model` rows (plus
|
||||
≤ `deepseek_audit_per_day` `llm` rows), and the cycle's added latency is < 1 s for ~100 headlines.
|
||||
- With the model directory renamed away, both processes start, log one warning each, and the cycle's
|
||||
rows are `llm` (DeepSeek path), not `keyword`.
|
||||
- With `onnxruntime` uninstalled in a scratch venv, `python -c "import news_engine"` succeeds.
|
||||
- A headline naming USD and XAU ends with `sentiment_by_currency["USD"]` equal to the keyword value
|
||||
and `["XAU"]` equal to the model value.
|
||||
- `mode="off"` reproduces today's archive rows exactly (`llm`/`keyword`, same teacher fields).
|
||||
- `venv/bin/python test_all.py` exits 0.
|
||||
|
||||
## 10. Known limits to state in the README
|
||||
- The model copies DeepSeek's opinion (72% agreement on unseen live days, 72/78 golden); it does not
|
||||
learn from price. DeepSeek itself is ~90% self-consistent.
|
||||
- Gold only. Every other currency's sentiment remains the ~31%-accurate keyword scorer in primary
|
||||
mode, which is what those currencies get today whenever DeepSeek is unavailable.
|
||||
- With audit off, no new DeepSeek labels accrue; the ML-NEWS retrain loop needs either
|
||||
`deepseek_audit_per_day > 0`, the dashboard left on DeepSeek, or a separate labelling run.
|
||||
+15
-5
@@ -324,9 +324,17 @@ def build_row(headline: Any, *, teacher_model: Optional[str] = None,
|
||||
teacher_prompt_sha1: Optional[str] = None,
|
||||
origin: str = "live",
|
||||
first_seen: Optional[str] = None,
|
||||
last_seen: Optional[str] = None) -> Optional[Dict[str, Any]]:
|
||||
last_seen: Optional[str] = None,
|
||||
model_teacher: Optional[str] = None) -> Optional[Dict[str, Any]]:
|
||||
"""Flatten one ``NewsSentiment`` (or an equivalent object) into a row.
|
||||
|
||||
Provenance: an ``llm`` row stores ``teacher_model`` (the DeepSeek
|
||||
model name); a ``model`` row stores ``model_teacher`` (the local
|
||||
scorer's tag, ``xau_transformer@<trained_at>``, news_model.teacher_tag).
|
||||
Both carry ``teacher_prompt_sha1``: for a model row it is the DeepSeek
|
||||
prompt the model was distilled from, which is the truthful lineage.
|
||||
Keyword rows carry neither.
|
||||
|
||||
Returns None when the object carries no usable headline text."""
|
||||
text = getattr(headline, "headline", None)
|
||||
if not isinstance(text, str) or not text.strip():
|
||||
@@ -349,9 +357,10 @@ def build_row(headline: Any, *, teacher_model: Optional[str] = None,
|
||||
getattr(headline, "currencies_mentioned", None)),
|
||||
"keywords_json": _json_or_none(getattr(headline, "keywords", None)),
|
||||
"score_source": source,
|
||||
"teacher_model": teacher_model if source == "llm" else None,
|
||||
"teacher_model": (teacher_model if source == "llm"
|
||||
else model_teacher if source == "model" else None),
|
||||
"teacher_prompt_sha1": (
|
||||
teacher_prompt_sha1 if source == "llm" else None),
|
||||
teacher_prompt_sha1 if source in ("llm", "model") else None),
|
||||
"origin": origin,
|
||||
"xau_score": _finite_score(getattr(headline, "xau_score", None)),
|
||||
}
|
||||
@@ -364,7 +373,8 @@ _archives_lock = threading.Lock()
|
||||
|
||||
def archive_headlines(headlines: Any, *, teacher_model: Optional[str] = None,
|
||||
teacher_prompt_sha1: Optional[str] = None,
|
||||
origin: str = "live") -> None:
|
||||
origin: str = "live",
|
||||
model_teacher: Optional[str] = None) -> None:
|
||||
"""Persist a batch of scored headlines if the news_archive config
|
||||
block enables it. NEVER raises — a logging failure must not break a
|
||||
scan."""
|
||||
@@ -377,7 +387,7 @@ def archive_headlines(headlines: Any, *, teacher_model: Optional[str] = None,
|
||||
for headline in headlines:
|
||||
row = build_row(headline, teacher_model=teacher_model,
|
||||
teacher_prompt_sha1=teacher_prompt_sha1,
|
||||
origin=origin)
|
||||
origin=origin, model_teacher=model_teacher)
|
||||
if row is not None:
|
||||
rows.append(row)
|
||||
if not rows:
|
||||
|
||||
+184
-15
@@ -8,6 +8,7 @@ Key principle: High-impact news events override technical signals.
|
||||
A perfect technical setup can be destroyed by an unexpected NFP print.
|
||||
"""
|
||||
|
||||
import copy
|
||||
import hashlib
|
||||
import html
|
||||
import logging
|
||||
@@ -23,7 +24,9 @@ from typing import Dict, List, Optional, Tuple
|
||||
|
||||
import requests
|
||||
|
||||
import app_config
|
||||
import news_archive
|
||||
import news_model
|
||||
from config import NewsConfig
|
||||
from llm_client import get_llm_client
|
||||
|
||||
@@ -222,18 +225,23 @@ class NewsSentiment:
|
||||
publisher: Optional[str] = None # Google News <source> text, else None
|
||||
# Which scorer produced sentiment_score / sentiment_by_currency:
|
||||
# "keyword" (the built-in scorer, ~31% on the 2026-08-06 golden set),
|
||||
# "llm" (DeepSeek, 78/78 on the same set) or "model" (a trained local
|
||||
# artifact). Without it the two are indistinguishable in the archive
|
||||
# and a model trained on the mix inherits the keyword scorer's errors.
|
||||
# "llm" (DeepSeek, 78/78 on the same set) or "model" (the local XAU
|
||||
# transformer, news_model.py, 72/78; since 2026-09-27). "model" means
|
||||
# the model scored xau_score and XAU's per-currency entry; every
|
||||
# other currency's entry, and sentiment_score when the headline names
|
||||
# another currency, remain keyword-scored — the model has a gold head
|
||||
# only. Without the tag the scorers are indistinguishable in the
|
||||
# archive and a model trained on the mix inherits the keyword errors.
|
||||
score_source: str = "keyword"
|
||||
# DeepSeek's gold-specific score for THIS headline, whatever currencies
|
||||
# it names. sentiment_by_currency["XAU"] carries it only when the
|
||||
# headline matched a gold keyword; for a pure Fed / dollar / yields
|
||||
# headline it used to be discarded, which left 930 of the first 1,546
|
||||
# archived DeepSeek rows without a gold label (eval 2026-09-17).
|
||||
# Nothing displayed or averaged reads it — the archive stores it as
|
||||
# training data for a local scorer. None until DeepSeek has scored
|
||||
# the headline (the keyword scorer never sets it).
|
||||
# The gold-specific score for THIS headline (DeepSeek's, or the local
|
||||
# model's raw signed score), whatever currencies it names.
|
||||
# sentiment_by_currency["XAU"] carries it only when the headline
|
||||
# matched a gold keyword; for a pure Fed / dollar / yields headline it
|
||||
# used to be discarded, which left 930 of the first 1,546 archived
|
||||
# DeepSeek rows without a gold label (eval 2026-09-17). Nothing
|
||||
# displayed or averaged reads it — the archive stores it as training
|
||||
# data. None until DeepSeek or the model has scored the headline (the
|
||||
# keyword scorer never sets it).
|
||||
xau_score: Optional[float] = None
|
||||
|
||||
|
||||
@@ -384,6 +392,12 @@ class NewsEngine:
|
||||
#: client's max_tokens (~10 tokens per headline, 2000 max).
|
||||
_LLM_BATCH_CAP = 25
|
||||
|
||||
# primary-mode DeepSeek audit sample (news_model.deepseek_audit_per_day):
|
||||
# how many model-scored headlines went to DeepSeek today, archive-only.
|
||||
# Written under _cache_lock like _llm_sent_cache.
|
||||
_audit_day: str = ""
|
||||
_audit_sent_today: int = 0
|
||||
|
||||
# Per-source fetch health for status displays (telegram /status).
|
||||
# name → {"ok": ts|None, "err": ts|None, "detail": str, "extra": str}.
|
||||
# All writers run under _cache_lock (fetches happen inside
|
||||
@@ -398,6 +412,14 @@ class NewsEngine:
|
||||
self._session.headers.update({
|
||||
"User-Agent": "Mozilla/5.0 (compatible; ForexIntel/1.0)"
|
||||
})
|
||||
# Create the local scorer's ONNX session off the scan thread so
|
||||
# the first news cycle does not pay 3-5 s under _cache_lock.
|
||||
# Idempotent per process (a NewsEngine is built for every scan).
|
||||
try:
|
||||
if app_config.news_model_mode() != "off":
|
||||
news_model.warm_up_in_background()
|
||||
except Exception as e: # never let the warm-up cost a scan
|
||||
logger.debug(f"news model warm-up skipped: {e}")
|
||||
|
||||
def get_fundamental_context(
|
||||
self, currencies: Optional[List[str]] = None
|
||||
@@ -941,10 +963,13 @@ class NewsEngine:
|
||||
# currency-matched headlines so display, per-currency means, the
|
||||
# panel and the summary see exactly what they saw before.
|
||||
scored = headlines + unmatched
|
||||
mode = app_config.news_model_mode()
|
||||
audit_rows: List[NewsSentiment] = []
|
||||
try:
|
||||
self._apply_llm_sentiment(scored)
|
||||
audit_rows = self._enrich_headline_scores(scored, mode)
|
||||
except Exception as e:
|
||||
logger.debug(f"LLM sentiment failed, keyword scores stand: {e}")
|
||||
logger.debug(f"headline sentiment enrichment failed, keyword "
|
||||
f"scores stand: {e}")
|
||||
|
||||
# Archive BEFORE any downstream filtering. get_fundamental_context
|
||||
# drops every headline inside ±NEUTRAL_HEADLINE_THRESHOLD, so a
|
||||
@@ -955,16 +980,65 @@ class NewsEngine:
|
||||
# arguments are evaluated BEFORE that function's own try/except, so
|
||||
# a raising get_llm_client() would escape it. Archiving is a side
|
||||
# effect — it must never cost us the headlines we just fetched.
|
||||
#
|
||||
# In "primary" mode the local model is the scorer and its output
|
||||
# is not training data, so the cycle's rows are NOT archived
|
||||
# (owner decision 2026-09-27: stop writing newsdata.db once the
|
||||
# model scores). Only the DeepSeek audit copies, the one source of
|
||||
# fresh labels, are stored; with deepseek_audit_per_day 0 the
|
||||
# archive is frozen. "off" and "fallback" archive as before.
|
||||
try:
|
||||
teacher = getattr(get_llm_client(), "_model", None)
|
||||
if mode == "primary":
|
||||
if audit_rows:
|
||||
news_archive.archive_headlines(
|
||||
scored,
|
||||
teacher_model=getattr(get_llm_client(), "_model", None),
|
||||
audit_rows, teacher_model=teacher,
|
||||
teacher_prompt_sha1=_LLM_SENTIMENT_PROMPT_SHA1)
|
||||
logger.debug(
|
||||
f"news archive: primary mode, {len(audit_rows)} DeepSeek "
|
||||
f"audit rows stored, {len(scored)} model/keyword rows "
|
||||
f"not archived")
|
||||
else:
|
||||
news_archive.archive_headlines(
|
||||
scored, teacher_model=teacher,
|
||||
teacher_prompt_sha1=_LLM_SENTIMENT_PROMPT_SHA1,
|
||||
model_teacher=news_model.teacher_tag())
|
||||
except Exception as e:
|
||||
logger.debug(f"news archive skipped, headlines unaffected: {e}")
|
||||
|
||||
return headlines
|
||||
|
||||
def _enrich_headline_scores(
|
||||
self, scored: List[NewsSentiment], mode: str
|
||||
) -> List[NewsSentiment]:
|
||||
"""Upgrade keyword scores on `scored` per the news_model mode.
|
||||
Returns the DeepSeek audit copies to archive (primary mode only,
|
||||
usually empty). Runs under _cache_lock like the scorers it calls.
|
||||
|
||||
| mode | DeepSeek for headlines | local model |
|
||||
|----------|------------------------|----------------------------|
|
||||
| off | every new headline | never |
|
||||
| fallback | first, as before | rows DeepSeek left keyword |
|
||||
| primary | audit sample only | every row |
|
||||
|
||||
primary with the model unavailable in this process (missing
|
||||
files, onnxruntime not installed) degrades to the DeepSeek path
|
||||
for the cycle, never straight to keywords; a model failure mid-
|
||||
batch leaves keyword scores standing (see _apply_model_sentiment).
|
||||
"""
|
||||
if mode == "primary":
|
||||
n = self._apply_model_sentiment(scored)
|
||||
if n == 0 and news_model.get_news_model() is None:
|
||||
self._apply_llm_sentiment(scored)
|
||||
return []
|
||||
return self._audit_with_deepseek(scored)
|
||||
if mode == "fallback":
|
||||
self._apply_llm_sentiment(scored)
|
||||
self._apply_model_sentiment(scored)
|
||||
return []
|
||||
self._apply_llm_sentiment(scored)
|
||||
return []
|
||||
|
||||
def _analyze_headline_sentiment(
|
||||
self, headline: str, for_currency: Optional[str] = None
|
||||
) -> float:
|
||||
@@ -1074,6 +1148,101 @@ class NewsEngine:
|
||||
h.xau_score = xau
|
||||
h.score_source = "llm"
|
||||
|
||||
# ── Local XAU transformer (news_model.py, since 2026-09-27) ──────
|
||||
|
||||
def _apply_model_sentiment(self, headlines: List[NewsSentiment]) -> int:
|
||||
"""Score with the local XAU transformer every headline that is
|
||||
still keyword-scored. Returns the number scored. Never raises.
|
||||
|
||||
The model has a gold head only. It writes xau_score always, XAU's
|
||||
per-currency score when XAU is mentioned, and the overall score
|
||||
only when the headline names no other currency. USD/EUR/...
|
||||
entries keep their keyword score: get_pair_sentiment subtracts
|
||||
USD from XAU and must not see the same number on both sides.
|
||||
|
||||
Rules (spec 2026-09-27):
|
||||
A. never write the model score into a non-XAU currency;
|
||||
B. sentiment_score from the model only for [] or ["XAU"] — the
|
||||
archive's `score` column must be honest about what the model
|
||||
did and did not score;
|
||||
C. xau_score is the raw signed score (training data, unfiltered);
|
||||
D. displayed values are 0.0 when the model says not relevant — a
|
||||
confident direction head can still give |score| ~0.2, which
|
||||
NEUTRAL_HEADLINE_THRESHOLD (0.1) would show as "leaning";
|
||||
E. NewsEngine._llm_sent_cache is never touched — it decides what
|
||||
is re-sent to DeepSeek (fallback mode retries, audit sample);
|
||||
F. only score_source == "keyword" rows change; an llm row stands.
|
||||
"""
|
||||
bundle = news_model.get_news_model()
|
||||
if bundle is None or not headlines:
|
||||
return 0
|
||||
todo = [h for h in headlines if h.score_source == "keyword"]
|
||||
if not todo:
|
||||
return 0
|
||||
try:
|
||||
results = news_model.score_batch(bundle, [h.headline for h in todo])
|
||||
if len(results) != len(todo):
|
||||
raise ValueError(
|
||||
f"{len(results)} results for {len(todo)} headlines")
|
||||
except Exception as e:
|
||||
logger.debug(f"news model scoring failed, keyword scores stand: {e}")
|
||||
return 0
|
||||
for h, r in zip(todo, results):
|
||||
raw = float(r["score"]) # archive value
|
||||
shown = raw if r["relevant"] else 0.0 # display value
|
||||
h.xau_score = raw
|
||||
if "XAU" in h.currencies_mentioned:
|
||||
h.sentiment_by_currency["XAU"] = shown
|
||||
if set(h.currencies_mentioned) <= {"XAU"}: # [] or ["XAU"]
|
||||
h.sentiment_score = shown
|
||||
h.score_source = "model"
|
||||
return len(todo)
|
||||
|
||||
def _audit_with_deepseek(
|
||||
self, headlines: List[NewsSentiment]
|
||||
) -> List[NewsSentiment]:
|
||||
"""primary mode: send up to news_model.deepseek_audit_per_day
|
||||
model-scored headlines per UTC day to DeepSeek and return `llm`
|
||||
COPIES for the archive. The displayed objects keep the model's
|
||||
numbers. One batch per cycle at most; the pairs go into
|
||||
_llm_sent_cache so a headline is never audited twice. This is the
|
||||
only place DeepSeek is called for headlines in primary mode; with
|
||||
the cap at 0 (the shipped value) it returns [] without a call."""
|
||||
cap = app_config.news_model_audit_per_day()
|
||||
if cap <= 0 or not headlines or not get_llm_client().available:
|
||||
return []
|
||||
today = datetime.now(timezone.utc).date().isoformat()
|
||||
if NewsEngine._audit_day != today:
|
||||
NewsEngine._audit_day, NewsEngine._audit_sent_today = today, 0
|
||||
room = cap - NewsEngine._audit_sent_today
|
||||
if room <= 0:
|
||||
return []
|
||||
cache = NewsEngine._llm_sent_cache
|
||||
cand = [h for h in headlines if h.score_source == "model"
|
||||
and hashlib.sha1(h.headline.encode()).hexdigest() not in cache]
|
||||
batch = cand[:min(room, self._LLM_BATCH_CAP)]
|
||||
if not batch:
|
||||
return []
|
||||
scores = self._llm_headline_scores([h.headline for h in batch])
|
||||
if scores is None:
|
||||
return []
|
||||
NewsEngine._audit_sent_today += len(batch)
|
||||
copies = []
|
||||
for h, (overall, xau) in zip(batch, scores):
|
||||
cache[hashlib.sha1(h.headline.encode()).hexdigest()] = (overall, xau)
|
||||
c = copy.copy(h)
|
||||
c.sentiment_score = overall
|
||||
c.sentiment_by_currency = {
|
||||
cur: (xau if cur == "XAU" else overall)
|
||||
for cur in h.currencies_mentioned
|
||||
}
|
||||
c.xau_score = xau
|
||||
c.score_source = "llm"
|
||||
copies.append(c)
|
||||
while len(cache) > self._LLM_SENT_CACHE_MAX:
|
||||
cache.pop(next(iter(cache)))
|
||||
return copies
|
||||
|
||||
def _llm_event_direction(self, event: EconomicEvent) -> Optional[str]:
|
||||
"""Gold direction for a released surprise, e.g. "bearish: strong
|
||||
jobs lift yields". None → caller keeps the numeric description.
|
||||
|
||||
+203
@@ -0,0 +1,203 @@
|
||||
"""Local XAU headline scorer: the owner's ML-NEWS transformer on CPU.
|
||||
|
||||
Vendored from ML-NEWS news_transformer.py at commit afa26dc; bundle
|
||||
trained_at 2026-09-27T23:15:30.403442+00:00. Keep the scoring functions
|
||||
identical to that file (the one difference: ``load_model`` has no default
|
||||
directory here, the path always comes from config.json); ML-NEWS trains
|
||||
and exports, ForexBot2 only scores.
|
||||
|
||||
A fine-tuned DeBERTa-v3-small encoder with two heads, exported to ONNX
|
||||
with an int8 embedding table. Scoring needs only onnxruntime, tokenizers
|
||||
and numpy. For each headline:
|
||||
|
||||
- ``p_relevant``: probability the news moves the gold price (trained on
|
||||
DeepSeek |xau| >= 0.25 vs <= 0.10)
|
||||
- ``p_bullish``: probability that, if it does, gold goes up
|
||||
- ``relevant``: ``p_relevant >= relevance_threshold`` (from meta.json)
|
||||
- ``label``: ``bullish`` / ``bearish``, or ``neutral`` when not relevant
|
||||
- ``score``: ``p_relevant * (2 * p_bullish - 1)`` in [-1, 1], the same
|
||||
scale as DeepSeek's gold score the archive stores in ``xau_score``
|
||||
|
||||
The model has a GOLD head only. news_engine writes its score into
|
||||
``xau_score`` and the XAU per-currency entry, never into USD or any other
|
||||
currency (get_pair_sentiment subtracts USD from XAU).
|
||||
|
||||
Import contract: ``onnxruntime`` and ``tokenizers`` are imported INSIDE
|
||||
``load_model`` so ``import news_model`` (and therefore ``import
|
||||
news_engine``) succeeds on a box without them; ``get_news_model`` then
|
||||
returns None once, with one warning, and the DeepSeek path stays.
|
||||
|
||||
Configuration comes from app_config (``news_model`` section, env
|
||||
``NEWS_MODEL_MODE``): ``news_model_path()``, ``news_model_threads()``.
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import html
|
||||
import json
|
||||
import logging
|
||||
import os
|
||||
import threading
|
||||
from pathlib import Path
|
||||
from typing import Any, Iterable, Optional
|
||||
|
||||
import numpy as np
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
MAX_LENGTH = 64
|
||||
#: DeepSeek |xau| at or above this is "moves gold"; at or below
|
||||
#: RELEVANT_MAX_IRRELEVANT it is "does not"; the band between carries no
|
||||
#: relevance label in training.
|
||||
RELEVANT_MIN = 0.25
|
||||
RELEVANT_MAX_IRRELEVANT = 0.10
|
||||
|
||||
|
||||
def clean_headline(headline: str) -> str:
|
||||
"""The exact text normalisation used in training and scoring."""
|
||||
text = " ".join(html.unescape(str(headline or "")).split())
|
||||
if not text:
|
||||
raise ValueError("headline is empty")
|
||||
return text
|
||||
|
||||
|
||||
def _sigmoid(x: np.ndarray) -> np.ndarray:
|
||||
return 1.0 / (1.0 + np.exp(-x))
|
||||
|
||||
|
||||
def load_model(model_dir: str | Path,
|
||||
threads: int | None = None) -> dict[str, Any]:
|
||||
"""Load the ONNX model, tokenizer and metadata. Only load trusted files."""
|
||||
import onnxruntime as ort
|
||||
from tokenizers import Tokenizer
|
||||
|
||||
model_dir = Path(model_dir)
|
||||
meta_path = model_dir / "meta.json"
|
||||
if not meta_path.is_file():
|
||||
raise FileNotFoundError(f"model not found: {meta_path}")
|
||||
meta = json.loads(meta_path.read_text(encoding="utf-8"))
|
||||
tokenizer = Tokenizer.from_file(str(model_dir / "tokenizer.json"))
|
||||
tokenizer.enable_truncation(max_length=int(meta.get("max_length", MAX_LENGTH)))
|
||||
tokenizer.enable_padding(pad_id=int(meta["pad_token_id"]), pad_token=meta["pad_token"])
|
||||
options = ort.SessionOptions()
|
||||
if threads:
|
||||
options.intra_op_num_threads = max(1, min(int(threads), os.cpu_count() or 1))
|
||||
options.inter_op_num_threads = 1
|
||||
# ORT intra-op threads spin-wait after each run by default; in a mostly
|
||||
# idle bot process that is 4 cores at 100% between cycles.
|
||||
options.add_session_config_entry("session.intra_op.allow_spinning", "0")
|
||||
session = ort.InferenceSession(str(model_dir / meta.get("onnx_file", "model.onnx")),
|
||||
options, providers=["CPUExecutionProvider"])
|
||||
return {"meta": meta, "tokenizer": tokenizer, "session": session}
|
||||
|
||||
|
||||
def score_batch(bundle: dict[str, Any], headlines: Iterable[str]) -> list[dict[str, Any]]:
|
||||
texts = [clean_headline(h) for h in headlines]
|
||||
if not texts:
|
||||
return []
|
||||
encodings = bundle["tokenizer"].encode_batch(texts)
|
||||
ids = np.array([e.ids for e in encodings], dtype=np.int64)
|
||||
mask = np.array([e.attention_mask for e in encodings], dtype=np.int64)
|
||||
rel_logit, dir_logit = bundle["session"].run(
|
||||
["relevance_logit", "direction_logit"],
|
||||
{"input_ids": ids, "attention_mask": mask})
|
||||
p_rel = _sigmoid(rel_logit.astype(np.float64))
|
||||
p_bull = _sigmoid(dir_logit.astype(np.float64))
|
||||
threshold = float(bundle["meta"].get("relevance_threshold", 0.5))
|
||||
out = []
|
||||
for text, pr, pb in zip(texts, p_rel, p_bull):
|
||||
relevant = bool(pr >= threshold)
|
||||
direction = "bullish" if pb >= 0.5 else "bearish"
|
||||
label = direction if relevant else "neutral"
|
||||
confidence = pr * max(pb, 1 - pb) if relevant else 1 - pr
|
||||
out.append({
|
||||
"target": "XAUUSD",
|
||||
"text": text,
|
||||
"relevant": relevant,
|
||||
"p_relevant": round(float(pr), 4),
|
||||
"direction": direction,
|
||||
"p_bullish": round(float(pb), 4),
|
||||
"label": label,
|
||||
"score": round(float(pr * (2 * pb - 1)), 4),
|
||||
"confidence": round(float(confidence), 4),
|
||||
})
|
||||
return out
|
||||
|
||||
|
||||
def score_news(bundle: dict[str, Any], headline: str) -> dict[str, Any]:
|
||||
return score_batch(bundle, [headline])[0]
|
||||
|
||||
|
||||
# ── Process-wide singleton (ForexBot2 addition, not in ML-NEWS) ──────────
|
||||
#
|
||||
# One ONNX session per process (~0.6 GB RSS, 3–5 s to create). A failed
|
||||
# load is remembered: the bot logs one warning and keeps the DeepSeek /
|
||||
# keyword path for the rest of its life instead of retrying every cycle.
|
||||
|
||||
_lock = threading.Lock()
|
||||
_bundle: Optional[dict] = None
|
||||
_failed: bool = False # tried and failed; don't retry, don't re-log
|
||||
_warm_started: bool = False
|
||||
|
||||
|
||||
def get_news_model() -> Optional[dict]:
|
||||
"""The loaded bundle, or None. Loads once per process. Never raises."""
|
||||
global _bundle, _failed
|
||||
if _bundle is not None or _failed:
|
||||
return _bundle
|
||||
with _lock:
|
||||
if _bundle is not None or _failed:
|
||||
return _bundle
|
||||
try:
|
||||
import app_config
|
||||
path = app_config.news_model_path()
|
||||
if not path:
|
||||
raise FileNotFoundError("news_model.path is not configured")
|
||||
threads = app_config.news_model_threads()
|
||||
b = load_model(path, threads=threads)
|
||||
logger.info(
|
||||
"news model loaded from %s (trained_at %s, threshold %s, "
|
||||
"%d threads)", path, b["meta"].get("trained_at"),
|
||||
b["meta"].get("relevance_threshold"), threads)
|
||||
_bundle = b
|
||||
except Exception as e: # ImportError, FileNotFoundError, bad JSON, ORT
|
||||
logger.warning(
|
||||
"news model unavailable, DeepSeek/keyword path stays: %s: %s",
|
||||
type(e).__name__, e)
|
||||
_failed = True
|
||||
return _bundle
|
||||
|
||||
|
||||
def teacher_tag() -> Optional[str]:
|
||||
"""Provenance tag for archive rows scored by the loaded model, e.g.
|
||||
``xau_transformer@2026-09-27T23:15:30…``. Reads the already-loaded
|
||||
bundle only; never triggers a load. None when nothing is loaded."""
|
||||
b = _bundle
|
||||
if b is None:
|
||||
return None
|
||||
return f"xau_transformer@{b['meta'].get('trained_at', 'unknown')}"
|
||||
|
||||
|
||||
def warm_up_in_background() -> None:
|
||||
"""Create the ONNX session on a daemon thread, once per process, so
|
||||
the first news cycle does not pay the 3–5 s load under
|
||||
NewsEngine._cache_lock. Idempotent; a later get_news_model() call
|
||||
simply waits on the lock if the thread is still loading."""
|
||||
global _warm_started
|
||||
if _warm_started or _bundle is not None or _failed:
|
||||
return
|
||||
with _lock:
|
||||
if _warm_started:
|
||||
return
|
||||
_warm_started = True
|
||||
threading.Thread(target=get_news_model, name="news-model-warmup",
|
||||
daemon=True).start()
|
||||
|
||||
|
||||
def reset_for_tests() -> None:
|
||||
"""Forget any loaded or failed state (test_all.py only)."""
|
||||
global _bundle, _failed, _warm_started
|
||||
with _lock:
|
||||
_bundle = None
|
||||
_failed = False
|
||||
_warm_started = False
|
||||
@@ -4,3 +4,7 @@ requests>=2.28
|
||||
twelvedata>=1.2.0
|
||||
python-telegram-bot>=20.0
|
||||
websockets>=12.0
|
||||
# news_model.py CPU runtime. Dev box (Python 3.12): 1.30.0 / 0.23.2; live box
|
||||
# (Python 3.9): 1.19.2 / 0.22.2 — identical scores on the 2026-09-27 bundle.
|
||||
onnxruntime>=1.19,<2
|
||||
tokenizers>=0.22,<1
|
||||
|
||||
+637
-3
@@ -26,7 +26,7 @@ sys.path.insert(0, os.path.dirname(__file__))
|
||||
import json
|
||||
import app_config
|
||||
for _k in ("TELEGRAM_TOKEN", "TELEGRAM_CHAT_ID", "TELEGRAM_CHANNEL_ID",
|
||||
"TWELVE_DATA_API_KEY", "DEEPSEEK_API_KEY"):
|
||||
"TWELVE_DATA_API_KEY", "DEEPSEEK_API_KEY", "NEWS_MODEL_MODE"):
|
||||
os.environ.pop(_k, None)
|
||||
# The suite must never write the developer's (or the live box's) real
|
||||
# databases either: the RSS fixture tests run the real archive hook and
|
||||
@@ -11436,8 +11436,10 @@ def _():
|
||||
|
||||
@test("news archive: shipped code imports no scikit-learn/scipy/lightgbm")
|
||||
def _():
|
||||
"""The corpus is trained on offline in venv/; anything the live signal
|
||||
path can reach must stay numpy-only (requirements.txt is the contract)."""
|
||||
"""The corpus is trained on offline (ML-NEWS); anything the live signal
|
||||
path can reach stays numpy-only plus the ONNX runtime that scores
|
||||
headlines (news_model.py: onnxruntime + tokenizers, imported lazily,
|
||||
Module 38). requirements.txt is the contract: no training stack."""
|
||||
banned = {"sklearn", "scipy", "lightgbm", "torch", "transformers", "cupy"}
|
||||
roots = [Path(".")] + [Path(p) for p in ("providers", "pair_signals")]
|
||||
offenders = []
|
||||
@@ -11460,6 +11462,8 @@ def _():
|
||||
req = open("requirements.txt").read()
|
||||
for name in banned:
|
||||
assert name not in req, f"{name} must not enter requirements.txt"
|
||||
for name in ("onnxruntime", "tokenizers"):
|
||||
assert name in req, f"{name} is the news model's CPU runtime"
|
||||
|
||||
|
||||
@test("news archive: DeepSeek's gold score is kept for every headline, gold keyword or not")
|
||||
@@ -15379,6 +15383,636 @@ if _GPU37:
|
||||
_gk37.BACKEND.release_device_memory()
|
||||
|
||||
|
||||
# ══════════════════════════════════════════════════════════════════════
|
||||
# MODULE 38: local news model (news_model.py, 2026-09-27)
|
||||
# The owner's ML-NEWS XAU transformer scores headlines locally. The
|
||||
# model has a gold head only, so the rules pinned here are about what
|
||||
# it may and may not write: xau_score and XAU's per-currency entry, never
|
||||
# USD; the DeepSeek cache is never touched; primary mode never archives
|
||||
# the model's own output (owner decision: newsdata.db stops growing once
|
||||
# the model scores, except the optional DeepSeek audit sample); mode off
|
||||
# is byte-for-byte today's path. The real bundle is exercised only when
|
||||
# NEWS_MODEL_DIR or the configured path exists (SKIP otherwise).
|
||||
# ══════════════════════════════════════════════════════════════════════
|
||||
|
||||
print("\n═══ Module 38: local news model ═══")
|
||||
|
||||
import ast as _ast38
|
||||
import dataclasses as _dc38
|
||||
import logging as _log38
|
||||
import sqlite3 as _sqlite38
|
||||
from datetime import datetime as _dt38, timezone as _tz38
|
||||
from pathlib import Path as _Path38
|
||||
import app_config as _ac38
|
||||
import llm_client as _lc38
|
||||
import news_archive as _na38
|
||||
import news_engine as _ne38
|
||||
import news_model as _nm38
|
||||
from config import NewsConfig as _NC38
|
||||
from news_engine import NewsEngine as _NE38, NewsSentiment as _NS38
|
||||
|
||||
_FAKE_BUNDLE38 = {"meta": {"trained_at": "2026-09-27T00:00:00+00:00",
|
||||
"relevance_threshold": 0.2}}
|
||||
|
||||
|
||||
def _h38(headline, currencies=("XAU", "USD"), score=0.3, by=None,
|
||||
source="keyword"):
|
||||
h = _NS38(headline=headline, source="https://feed.example/rss",
|
||||
timestamp=_dt38.now(_tz38.utc),
|
||||
currencies_mentioned=list(currencies), sentiment_score=score,
|
||||
keywords=["k"],
|
||||
sentiment_by_currency=dict(
|
||||
by if by is not None else {c: score for c in currencies}))
|
||||
h.score_source = source
|
||||
return h
|
||||
|
||||
|
||||
class _Model38:
|
||||
"""Swap news_model's bundle + scorer for a scripted fake; restore after.
|
||||
`results` is a dict applied to every headline or a callable(text)."""
|
||||
|
||||
def __init__(self, results=None, raise_exc=None, bundle=_FAKE_BUNDLE38):
|
||||
self.results = results if results is not None else \
|
||||
{"relevant": True, "score": 0.8}
|
||||
self.raise_exc = raise_exc
|
||||
self.bundle = bundle
|
||||
self.calls = []
|
||||
|
||||
def __enter__(self):
|
||||
self._saved = (_nm38.get_news_model, _nm38.score_batch,
|
||||
_nm38.warm_up_in_background)
|
||||
_nm38.get_news_model = lambda: self.bundle
|
||||
_nm38._bundle = self.bundle # teacher_tag() reads it directly
|
||||
_nm38.warm_up_in_background = lambda: None
|
||||
|
||||
def sb(bundle, texts):
|
||||
texts = list(texts)
|
||||
self.calls.append(texts)
|
||||
if self.raise_exc is not None:
|
||||
raise self.raise_exc
|
||||
if callable(self.results):
|
||||
return [self.results(t) for t in texts]
|
||||
return [dict(self.results) for _ in texts]
|
||||
_nm38.score_batch = sb
|
||||
return self
|
||||
|
||||
def __exit__(self, *exc):
|
||||
(_nm38.get_news_model, _nm38.score_batch,
|
||||
_nm38.warm_up_in_background) = self._saved
|
||||
_nm38.reset_for_tests()
|
||||
return False
|
||||
|
||||
|
||||
def _app38(mode="off", audit=0, path="/nonexistent/xau_transformer"):
|
||||
app = _dc38.replace(_TEST_APP)
|
||||
app.news_model_settings = {"mode": mode, "path": path,
|
||||
"deepseek_audit_per_day": audit}
|
||||
return app
|
||||
|
||||
|
||||
@test("news model: scores keyword rows only; USD keeps its keyword value")
|
||||
def _():
|
||||
"""Rule A + F. The model has a gold head: it may write xau_score and
|
||||
XAU's entry, never USD's (get_pair_sentiment forms XAU − USD), and it
|
||||
never overwrites a DeepSeek-scored row."""
|
||||
llm = _h38("Fed hikes, dollar jumps", score=0.4,
|
||||
by={"XAU": -0.7, "USD": 0.4}, source="llm")
|
||||
llm.xau_score = -0.7
|
||||
kw = _h38("Gold climbs as yields slip", by={"XAU": 0.3, "USD": -0.2})
|
||||
before = _dc38.asdict(llm)
|
||||
with _Model38({"relevant": True, "score": 0.81}):
|
||||
n = _NE38(_NC38())._apply_model_sentiment([llm, kw])
|
||||
assert n == 1, n
|
||||
assert _dc38.asdict(llm) == before, "an llm row is never touched"
|
||||
assert kw.score_source == "model", kw.score_source
|
||||
assert kw.xau_score == 0.81, kw.xau_score
|
||||
assert kw.sentiment_by_currency["XAU"] == 0.81, kw.sentiment_by_currency
|
||||
assert kw.sentiment_by_currency["USD"] == -0.2, \
|
||||
"USD must keep the keyword value"
|
||||
assert kw.sentiment_score == 0.3, \
|
||||
"a headline naming USD keeps its keyword overall score"
|
||||
|
||||
|
||||
@test("news model: overall score comes from the model only for [] or ['XAU']")
|
||||
def _():
|
||||
"""Rule B: the archive's `score` column is sentiment_score; for a
|
||||
gold-only headline it is the model's number, for a mixed headline it
|
||||
stays keyword so the row is honest about what the model scored."""
|
||||
gold = _h38("Gold rallies", currencies=("XAU",), score=0.2)
|
||||
mixed = _h38("Fed hikes, gold falls", currencies=("USD", "XAU"), score=0.1)
|
||||
none = _h38("Stocks crater worldwide", currencies=(), score=0.0, by={})
|
||||
with _Model38({"relevant": True, "score": -0.55}):
|
||||
_NE38(_NC38())._apply_model_sentiment([gold, mixed, none])
|
||||
assert gold.sentiment_score == -0.55 and gold.sentiment_by_currency["XAU"] == -0.55
|
||||
assert mixed.sentiment_score == 0.1, mixed.sentiment_score
|
||||
assert mixed.sentiment_by_currency["XAU"] == -0.55, mixed.sentiment_by_currency
|
||||
assert mixed.sentiment_by_currency["USD"] == 0.1, mixed.sentiment_by_currency
|
||||
assert none.sentiment_score == -0.55 and none.sentiment_by_currency == {}, \
|
||||
"no per-currency entry is invented for a headline naming none"
|
||||
assert all(h.xau_score == -0.55 for h in (gold, mixed, none))
|
||||
assert all(h.score_source == "model" for h in (gold, mixed, none))
|
||||
|
||||
|
||||
@test("news model: a not-relevant headline shows 0.0 but archives its raw score")
|
||||
def _():
|
||||
"""Rule C + D. relevant=False can still carry |score| ~0.2 from the
|
||||
direction head; NEUTRAL_HEADLINE_THRESHOLD (0.1) would show 0.15 as
|
||||
"leaning bearish". The displayed value is zeroed; xau_score is not."""
|
||||
h = _h38("Copper slides on China data", currencies=("XAU",), score=0.05)
|
||||
with _Model38({"relevant": False, "score": -0.18}):
|
||||
_NE38(_NC38())._apply_model_sentiment([h])
|
||||
assert h.sentiment_by_currency["XAU"] == 0.0, h.sentiment_by_currency
|
||||
assert h.sentiment_score == 0.0, h.sentiment_score
|
||||
assert h.xau_score == -0.18, "training data stays raw"
|
||||
assert _ne38._is_neutral_headline(h), "neutral means neutral"
|
||||
|
||||
|
||||
@test("news model: never touches NewsEngine._llm_sent_cache")
|
||||
def _():
|
||||
"""Rule E: that cache decides what is re-sent to DeepSeek. A headline
|
||||
the model scored must still be eligible for DeepSeek in fallback mode
|
||||
and for the audit sample."""
|
||||
saved = _NE38._llm_sent_cache
|
||||
_NE38._llm_sent_cache = {"deadbeef": (0.1, 0.2)}
|
||||
try:
|
||||
with _Model38():
|
||||
_NE38(_NC38())._apply_model_sentiment(
|
||||
[_h38("Gold up"), _h38("Gold down")])
|
||||
assert _NE38._llm_sent_cache == {"deadbeef": (0.1, 0.2)}, \
|
||||
_NE38._llm_sent_cache
|
||||
finally:
|
||||
_NE38._llm_sent_cache = saved
|
||||
|
||||
|
||||
@test("news model: a scoring failure or a missing model leaves keyword scores standing")
|
||||
def _():
|
||||
rows = [_h38("Gold up"), _h38("Gold down")]
|
||||
with _Model38(raise_exc=RuntimeError("ORT exploded")):
|
||||
n = _NE38(_NC38())._apply_model_sentiment(rows)
|
||||
assert n == 0 and all(h.score_source == "keyword" for h in rows)
|
||||
assert all(h.xau_score is None for h in rows)
|
||||
with _Model38(bundle=None) as m:
|
||||
n = _NE38(_NC38())._apply_model_sentiment(rows)
|
||||
assert n == 0 and m.calls == [], "no scoring call without a bundle"
|
||||
assert all(h.score_source == "keyword" for h in rows)
|
||||
# a length mismatch is a failure too, not a silent misalignment
|
||||
saved = _nm38.score_batch
|
||||
with _Model38() as m:
|
||||
_nm38.score_batch = lambda b, texts: [{"relevant": True, "score": 0.5}]
|
||||
n = _NE38(_NC38())._apply_model_sentiment(rows)
|
||||
assert n == 0 and all(h.score_source == "keyword" for h in rows)
|
||||
assert _nm38.score_batch is saved
|
||||
|
||||
|
||||
@test("news model: primary mode with no model degrades to DeepSeek, not to keywords")
|
||||
def _():
|
||||
saved_cache = _NE38._llm_sent_cache
|
||||
_NE38._llm_sent_cache = {}
|
||||
_lc38._client = _StubLLM({})
|
||||
try:
|
||||
rows = [_h38("Gold up"), _h38("Stocks fall", currencies=())]
|
||||
ne = _NE38(_NC38())
|
||||
ne._llm_headline_scores = lambda texts: [(0.4, 0.6)] * len(texts)
|
||||
with _Model38(bundle=None):
|
||||
out = ne._enrich_headline_scores(rows, "primary")
|
||||
assert out == [], "no audit copies on the DeepSeek path"
|
||||
assert all(h.score_source == "llm" for h in rows), \
|
||||
[h.score_source for h in rows]
|
||||
assert rows[0].xau_score == 0.6
|
||||
finally:
|
||||
_NE38._llm_sent_cache = saved_cache
|
||||
_lc38._client = None
|
||||
|
||||
|
||||
@test("news model: primary mode never calls DeepSeek for headlines; off mode never calls the model")
|
||||
def _():
|
||||
saved_cache = _NE38._llm_sent_cache
|
||||
saved_app = _ac38.get_app_config()
|
||||
_NE38._llm_sent_cache = {}
|
||||
_lc38._client = _StubLLM({})
|
||||
llm_calls = []
|
||||
try:
|
||||
ne = _NE38(_NC38())
|
||||
|
||||
def scores(texts):
|
||||
llm_calls.append(list(texts))
|
||||
return [(0.4, 0.6)] * len(texts)
|
||||
ne._llm_headline_scores = scores
|
||||
_ac38.set_app_config(_app38("primary", audit=0))
|
||||
rows = [_h38("Gold up"), _h38("Gold down")]
|
||||
with _Model38({"relevant": True, "score": 0.9}) as m:
|
||||
out = ne._enrich_headline_scores(rows, "primary")
|
||||
assert out == [] and llm_calls == [], llm_calls
|
||||
assert m.calls == [["Gold up", "Gold down"]], m.calls
|
||||
assert all(h.score_source == "model" for h in rows)
|
||||
|
||||
_ac38.set_app_config(saved_app)
|
||||
rows = [_h38("Gold up"), _h38("Gold down")]
|
||||
with _Model38() as m:
|
||||
ne._enrich_headline_scores(rows, "off")
|
||||
assert m.calls == [], "off mode is today's path exactly"
|
||||
assert llm_calls == [["Gold up", "Gold down"]], llm_calls
|
||||
assert all(h.score_source == "llm" for h in rows)
|
||||
finally:
|
||||
_ac38.set_app_config(saved_app)
|
||||
_NE38._llm_sent_cache = saved_cache
|
||||
_lc38._client = None
|
||||
|
||||
|
||||
@test("news model: fallback mode scores only what DeepSeek left keyword-scored")
|
||||
def _():
|
||||
saved_cache = _NE38._llm_sent_cache
|
||||
_NE38._llm_sent_cache = {}
|
||||
_lc38._client = _StubLLM({})
|
||||
try:
|
||||
ne = _NE38(_NC38())
|
||||
ne._llm_headline_scores = lambda texts: [(0.4, 0.6)] * len(texts)
|
||||
rows = [_h38("Gold up"), _h38("Gold down")]
|
||||
with _Model38() as m:
|
||||
ne._enrich_headline_scores(rows, "fallback")
|
||||
assert m.calls == [], "DeepSeek scored everything; nothing left"
|
||||
assert all(h.score_source == "llm" for h in rows)
|
||||
# DeepSeek fails → the model fills in, and the headlines stay OUT
|
||||
# of the DeepSeek cache so the next window retries them
|
||||
ne._llm_headline_scores = lambda texts: None
|
||||
rows = [_h38("Silver up"), _h38("Silver down")]
|
||||
with _Model38({"relevant": True, "score": -0.3}) as m:
|
||||
ne._enrich_headline_scores(rows, "fallback")
|
||||
assert m.calls == [["Silver up", "Silver down"]], m.calls
|
||||
assert all(h.score_source == "model" for h in rows)
|
||||
assert len(_NE38._llm_sent_cache) == 2, "only the two DeepSeek scored"
|
||||
finally:
|
||||
_NE38._llm_sent_cache = saved_cache
|
||||
_lc38._client = None
|
||||
|
||||
|
||||
@test("news model: the DeepSeek audit sample is capped per UTC day and archive-only")
|
||||
def _():
|
||||
saved_cache = _NE38._llm_sent_cache
|
||||
saved_app = _ac38.get_app_config()
|
||||
saved_day = (_NE38._audit_day, _NE38._audit_sent_today)
|
||||
_NE38._llm_sent_cache = {}
|
||||
_NE38._audit_day, _NE38._audit_sent_today = "", 0
|
||||
_lc38._client = _StubLLM({})
|
||||
sent = []
|
||||
try:
|
||||
ne = _NE38(_NC38())
|
||||
|
||||
def scores(texts):
|
||||
sent.append(list(texts))
|
||||
return [(0.4, -0.6)] * len(texts)
|
||||
ne._llm_headline_scores = scores
|
||||
rows = [_h38(f"Gold headline {i}") for i in range(10)]
|
||||
_ac38.set_app_config(_app38("primary", audit=3))
|
||||
with _Model38({"relevant": True, "score": 0.7}):
|
||||
copies = ne._enrich_headline_scores(rows, "primary")
|
||||
assert [len(b) for b in sent] == [3], sent
|
||||
assert len(copies) == 3, len(copies)
|
||||
assert all(c.score_source == "llm" and c.xau_score == -0.6
|
||||
and c.sentiment_by_currency["XAU"] == -0.6
|
||||
and c.sentiment_by_currency["USD"] == 0.4 for c in copies)
|
||||
assert all(h.score_source == "model" and h.xau_score == 0.7
|
||||
for h in rows), "the displayed objects keep the model's numbers"
|
||||
assert copies[0] is not rows[0]
|
||||
assert len(_NE38._llm_sent_cache) == 3, "audited once, never twice"
|
||||
# same day, second cycle: the cap is spent
|
||||
more = [_h38(f"Gold headline {i}") for i in range(10, 20)]
|
||||
with _Model38({"relevant": True, "score": 0.7}):
|
||||
copies2 = ne._enrich_headline_scores(more, "primary")
|
||||
assert copies2 == [] and len(sent) == 1, (copies2, sent)
|
||||
# cap 0 (the shipped value): no call at all
|
||||
_NE38._audit_day, _NE38._audit_sent_today = "", 0
|
||||
_ac38.set_app_config(_app38("primary", audit=0))
|
||||
with _Model38({"relevant": True, "score": 0.7}):
|
||||
copies3 = ne._enrich_headline_scores(
|
||||
[_h38("Gold headline 99")], "primary")
|
||||
assert copies3 == [] and len(sent) == 1
|
||||
finally:
|
||||
_ac38.set_app_config(saved_app)
|
||||
_NE38._llm_sent_cache = saved_cache
|
||||
_NE38._audit_day, _NE38._audit_sent_today = saved_day
|
||||
_lc38._client = None
|
||||
|
||||
|
||||
def _cycle38(mode, audit, model_results, llm_scores):
|
||||
"""Run one fake RSS cycle under `mode` and return (returned headlines,
|
||||
archived rows by headline). Restores every piece of shared state."""
|
||||
gold = "Gold surges as Fed signals rate cuts"
|
||||
stocks = "Stocks crater worldwide as banking crisis fears spread"
|
||||
saved_health = _NE38._source_health
|
||||
saved_cache = _NE38._llm_sent_cache
|
||||
saved_app = _ac38.get_app_config()
|
||||
saved_env = os.environ.get("NEWS_ARCHIVE_PATH")
|
||||
saved_archives = dict(_na38._archives)
|
||||
saved_day = (_NE38._audit_day, _NE38._audit_sent_today)
|
||||
_na38._archives.clear()
|
||||
_NE38._source_health = {}
|
||||
_NE38._llm_sent_cache = {}
|
||||
_NE38._audit_day, _NE38._audit_sent_today = "", 0
|
||||
_lc38._client = _StubLLM({})
|
||||
_lc38._client._model = "deepseek-v4-flash" # the archive's teacher_model
|
||||
with tempfile.TemporaryDirectory() as d:
|
||||
path = os.path.join(d, "newsdata.db")
|
||||
os.environ["NEWS_ARCHIVE_PATH"] = path
|
||||
stamp = b"<pubDate>" + _m19_pubdate(1) + b"</pubDate>"
|
||||
rss = (b"<rss><channel>"
|
||||
b"<item><title>" + gold.encode() + b"</title>" + stamp + b"</item>"
|
||||
b"<item><title>" + stocks.encode() + b"</title>" + stamp + b"</item>"
|
||||
b"</channel></rss>")
|
||||
|
||||
class _Sess:
|
||||
def get(self, url, timeout=None):
|
||||
return type("R", (), {"status_code": 200, "content": rss})()
|
||||
|
||||
try:
|
||||
cfg = _NC38()
|
||||
cfg.rss_feeds = ["https://feed.example/rss"]
|
||||
ne = _NE38(cfg)
|
||||
ne._session = _Sess()
|
||||
ne._llm_headline_scores = llm_scores
|
||||
_ac38.set_app_config(_app38(mode, audit=audit))
|
||||
with _Model38(model_results):
|
||||
heads = ne._fetch_news_sentiment_raw()
|
||||
rows = {}
|
||||
if os.path.exists(path):
|
||||
con = _sqlite38.connect(f"file:{path}?mode=ro", uri=True)
|
||||
try:
|
||||
rows = {r[0]: r for r in con.execute(
|
||||
"SELECT headline, score_source, score, xau_score,"
|
||||
" teacher_model, teacher_prompt_sha1 FROM headlines")}
|
||||
finally:
|
||||
con.close()
|
||||
return heads, rows
|
||||
finally:
|
||||
_ac38.set_app_config(saved_app)
|
||||
_NE38._source_health = saved_health
|
||||
_NE38._llm_sent_cache = saved_cache
|
||||
_NE38._audit_day, _NE38._audit_sent_today = saved_day
|
||||
_lc38._client = None
|
||||
for arch in _na38._archives.values():
|
||||
arch.close()
|
||||
_na38._archives.clear()
|
||||
_na38._archives.update(saved_archives)
|
||||
if saved_env is None:
|
||||
os.environ.pop("NEWS_ARCHIVE_PATH", None)
|
||||
else:
|
||||
os.environ["NEWS_ARCHIVE_PATH"] = saved_env
|
||||
|
||||
|
||||
@test("news model: primary mode archives nothing but the audit rows; off and fallback archive as before")
|
||||
def _():
|
||||
"""Owner decision 2026-09-27: newsdata.db stops growing once the
|
||||
local model scores. Its output is not training data; only DeepSeek
|
||||
audit copies (llm) are stored. The platform still receives the same
|
||||
currency-matched headlines in every mode."""
|
||||
gold = "Gold surges as Fed signals rate cuts"
|
||||
stocks = "Stocks crater worldwide as banking crisis fears spread"
|
||||
model = {"relevant": True, "score": 0.9}
|
||||
llm = lambda texts: [(0.5, 0.6)] * len(texts)
|
||||
|
||||
heads, rows = _cycle38("primary", 0, model, llm)
|
||||
assert [h.headline for h in heads] == [gold], heads
|
||||
assert heads[0].score_source == "model" and heads[0].xau_score == 0.9
|
||||
assert rows == {}, f"primary mode must not archive: {rows}"
|
||||
|
||||
heads, rows = _cycle38("primary", 25, model, llm)
|
||||
assert set(rows) == {gold, stocks}, rows
|
||||
assert all(r[1] == "llm" and r[3] == 0.6 and r[4] == "deepseek-v4-flash"
|
||||
and r[5] == _ne38._LLM_SENTIMENT_PROMPT_SHA1
|
||||
for r in rows.values()), rows
|
||||
assert heads[0].score_source == "model" and heads[0].xau_score == 0.9, \
|
||||
"the audit never changes what is displayed"
|
||||
|
||||
heads, rows = _cycle38("off", 0, model, llm)
|
||||
assert set(rows) == {gold, stocks} and all(r[1] == "llm" for r in rows.values())
|
||||
assert heads[0].score_source == "llm"
|
||||
|
||||
heads, rows = _cycle38("fallback", 0, model, lambda texts: None)
|
||||
assert set(rows) == {gold, stocks}, rows
|
||||
assert all(r[1] == "model" and r[3] == 0.9 for r in rows.values()), rows
|
||||
assert all(r[4] == "xau_transformer@2026-09-27T00:00:00+00:00"
|
||||
for r in rows.values()), "model rows carry the bundle tag"
|
||||
assert all(r[5] == _ne38._LLM_SENTIMENT_PROMPT_SHA1 for r in rows.values()), \
|
||||
"and the DeepSeek prompt the model was distilled from"
|
||||
|
||||
|
||||
@test("news model: config block is validated; env NEWS_MODEL_MODE wins; absent = off")
|
||||
def _():
|
||||
saved_app = _ac38.get_app_config()
|
||||
try:
|
||||
base = copy.deepcopy(_FULL_CFG)
|
||||
base["news_model"] = {"path": "/models/xau", "threads": 4,
|
||||
"mode": "primary", "deepseek_audit_per_day": 0}
|
||||
app = load_app_config(_write_json(base))
|
||||
assert _ac38.news_model_mode(app) == "primary"
|
||||
assert _ac38.news_model_path(app) == "/models/xau"
|
||||
assert _ac38.news_model_threads(app) == 4
|
||||
assert _ac38.news_model_audit_per_day(app) == 0
|
||||
for bad in ({"mode": "banana", "path": "/m"},
|
||||
{"path": "/m", "threds": 4},
|
||||
{"mode": "primary"}, # no path
|
||||
{"mode": "primary", "path": ""},
|
||||
{"mode": "off", "threads": 0},
|
||||
{"mode": "off", "threads": True},
|
||||
{"mode": "off", "deepseek_audit_per_day": -1},
|
||||
{"mode": "off", "deepseek_audit_per_day": 2.5}):
|
||||
payload = copy.deepcopy(_FULL_CFG)
|
||||
payload["news_model"] = bad
|
||||
try:
|
||||
load_app_config(_write_json(payload))
|
||||
except AppConfigError:
|
||||
continue
|
||||
raise AssertionError(f"accepted {bad}")
|
||||
# absent section → off, defaults
|
||||
plain = load_app_config(_write_json(copy.deepcopy(_FULL_CFG)))
|
||||
assert plain.news_model_settings == {}
|
||||
assert _ac38.news_model_mode(plain) == "off"
|
||||
assert _ac38.news_model_path(plain) is None
|
||||
assert _ac38.news_model_threads(plain) == 4
|
||||
assert _ac38.news_model_audit_per_day(plain) == 0
|
||||
# a relative path resolves against the repo root
|
||||
rel = _dc38.replace(_TEST_APP)
|
||||
rel.news_model_settings = {"path": "models/x", "mode": "off"}
|
||||
assert _ac38.news_model_path(rel) == os.path.join(
|
||||
os.path.dirname(os.path.abspath(_ac38.__file__)), "models/x")
|
||||
# env override beats config.json; an invalid value falls back
|
||||
with patch.dict(os.environ, {"NEWS_MODEL_MODE": "off"}):
|
||||
assert _ac38.news_model_mode(app) == "off"
|
||||
with patch.dict(os.environ, {"NEWS_MODEL_MODE": "Fallback"}):
|
||||
assert _ac38.news_model_mode(app) == "fallback"
|
||||
with patch.dict(os.environ, {"NEWS_MODEL_MODE": "banana"}):
|
||||
assert _ac38.news_model_mode(app) == "primary"
|
||||
# the suite's hermetic config never loads the model
|
||||
assert _ac38.news_model_mode() == "off"
|
||||
assert "news_model" in _ac38._KNOWN_TOP
|
||||
finally:
|
||||
_ac38.set_app_config(saved_app)
|
||||
|
||||
|
||||
@test("news model: archive rows carry the model tag; an llm row is never downgraded by one")
|
||||
def _():
|
||||
row = _na38.build_row(_m32_head(score=0.5, source="model"),
|
||||
teacher_model="deepseek-v4-flash",
|
||||
teacher_prompt_sha1="p1",
|
||||
model_teacher="xau_transformer@T")
|
||||
assert row["teacher_model"] == "xau_transformer@T", row
|
||||
assert row["teacher_prompt_sha1"] == "p1", "the distillation prompt"
|
||||
row = _na38.build_row(_m32_head(score=0.5, source="llm"),
|
||||
teacher_model="deepseek-v4-flash",
|
||||
teacher_prompt_sha1="p1",
|
||||
model_teacher="xau_transformer@T")
|
||||
assert row["teacher_model"] == "deepseek-v4-flash", row
|
||||
row = _na38.build_row(_m32_head(score=0.5, source="keyword"),
|
||||
teacher_model="deepseek-v4-flash",
|
||||
teacher_prompt_sha1="p1",
|
||||
model_teacher="xau_transformer@T")
|
||||
assert row["teacher_model"] is None and row["teacher_prompt_sha1"] is None
|
||||
with tempfile.TemporaryDirectory() as d:
|
||||
path = os.path.join(d, "newsdata.db")
|
||||
arch = _na38.NewsArchive(path)
|
||||
try:
|
||||
arch.store([_na38.build_row(_m32_head(score=0.3, source="model"),
|
||||
teacher_prompt_sha1="p1",
|
||||
model_teacher="xau_transformer@T")])
|
||||
r = _m32_rows(path)["Gold surges as Fed signals rate cuts"]
|
||||
assert r[2] == "model" and r[5] == "xau_transformer@T", r
|
||||
arch.store([_na38.build_row(_m32_head(score=-0.7, source="llm"),
|
||||
teacher_model="deepseek-v4-flash",
|
||||
teacher_prompt_sha1="p1")])
|
||||
r = _m32_rows(path)["Gold surges as Fed signals rate cuts"]
|
||||
assert r[1] == -0.7 and r[2] == "llm" and r[5] == "deepseek-v4-flash", \
|
||||
"llm upgrades model"
|
||||
arch.store([_na38.build_row(_m32_head(score=0.3, source="model"),
|
||||
model_teacher="xau_transformer@T2")])
|
||||
r = _m32_rows(path)["Gold surges as Fed signals rate cuts"]
|
||||
assert r[1] == -0.7 and r[2] == "llm" and r[5] == "deepseek-v4-flash", \
|
||||
"model never downgrades llm"
|
||||
assert r[3] == 3, "but re-seeing it still counts"
|
||||
finally:
|
||||
arch.close()
|
||||
|
||||
|
||||
@test("news model: news_model.py imports onnxruntime/tokenizers lazily; requirements list them")
|
||||
def _():
|
||||
"""`import news_engine` must succeed on a box without the runtime;
|
||||
the bot then logs one warning and keeps the DeepSeek/keyword path."""
|
||||
tree = _ast38.parse(_Path38("news_model.py").read_text(encoding="utf-8"))
|
||||
top = set()
|
||||
for node in tree.body:
|
||||
if isinstance(node, _ast38.Import):
|
||||
top.update(a.name.split(".")[0] for a in node.names)
|
||||
elif isinstance(node, _ast38.ImportFrom):
|
||||
top.add((node.module or "").split(".")[0])
|
||||
assert not top & {"onnxruntime", "tokenizers"}, top
|
||||
assert "numpy" in top
|
||||
lazy = set()
|
||||
for node in _ast38.walk(tree):
|
||||
if isinstance(node, (_ast38.Import, _ast38.ImportFrom)):
|
||||
names = ([a.name for a in node.names] if isinstance(node, _ast38.Import)
|
||||
else [node.module or ""])
|
||||
lazy.update(n.split(".")[0] for n in names)
|
||||
assert {"onnxruntime", "tokenizers"} <= lazy, "load_model imports them"
|
||||
req = open("requirements.txt").read()
|
||||
assert "onnxruntime" in req and "tokenizers" in req
|
||||
for word in ("torch", "transformers"):
|
||||
assert word not in req
|
||||
|
||||
|
||||
@test("news model: get_news_model never raises, fails once and remembers it")
|
||||
def _():
|
||||
saved_app = _ac38.get_app_config()
|
||||
_nm38.reset_for_tests()
|
||||
log = _log38.getLogger("news_model")
|
||||
records = []
|
||||
|
||||
class _H(_log38.Handler):
|
||||
def emit(self, r):
|
||||
records.append(r)
|
||||
h = _H()
|
||||
log.addHandler(h)
|
||||
try:
|
||||
_ac38.set_app_config(_app38("primary", path="/nonexistent/xau_transformer"))
|
||||
assert _nm38.get_news_model() is None
|
||||
assert _nm38.get_news_model() is None
|
||||
assert _nm38.teacher_tag() is None
|
||||
warns = [r for r in records if r.levelno == _log38.WARNING
|
||||
and "news model unavailable" in r.getMessage()]
|
||||
assert len(warns) == 1, "one warning, no retry spam"
|
||||
# path not configured is the same outcome
|
||||
_nm38.reset_for_tests()
|
||||
_ac38.set_app_config(_app38("off", path=""))
|
||||
_ac38.get_app_config().news_model_settings = {"mode": "primary"}
|
||||
assert _nm38.get_news_model() is None
|
||||
# warm-up is idempotent and never raises
|
||||
_nm38.reset_for_tests()
|
||||
_nm38.warm_up_in_background()
|
||||
_nm38.warm_up_in_background()
|
||||
assert _nm38._warm_started
|
||||
# a NewsEngine built in mode off never starts it
|
||||
_nm38.reset_for_tests()
|
||||
_ac38.set_app_config(saved_app)
|
||||
_NE38(_NC38())
|
||||
assert not _nm38._warm_started
|
||||
finally:
|
||||
log.removeHandler(h)
|
||||
_nm38.reset_for_tests()
|
||||
_ac38.set_app_config(saved_app)
|
||||
|
||||
|
||||
def _bundle_dir38():
|
||||
d = os.environ.get("NEWS_MODEL_DIR")
|
||||
if not d:
|
||||
try:
|
||||
d = _ac38.news_model_path(_ac38.load_app_config())
|
||||
except Exception:
|
||||
d = None
|
||||
if d and os.path.isfile(os.path.join(d, "meta.json")):
|
||||
return d
|
||||
return None
|
||||
|
||||
|
||||
@test("news model: the real bundle loads, labels the golden pair and stays under 50 ms/headline")
|
||||
def _():
|
||||
d = _bundle_dir38()
|
||||
if d is None:
|
||||
print(" SKIP: no bundle (set NEWS_MODEL_DIR or news_model.path)")
|
||||
return
|
||||
b = _nm38.load_model(d, threads=4)
|
||||
up = _nm38.score_news(b, "Fed cuts rates, signals more easing")
|
||||
off = _nm38.score_news(b, "Red Lobster endless shrimp losses")
|
||||
assert up["label"] == "bullish" and up["score"] > 0.5, up
|
||||
assert off["label"] == "neutral" and not off["relevant"], off
|
||||
assert abs(off["score"]) < _nm38.RELEVANT_MAX_IRRELEVANT, off
|
||||
batch = _nm38.score_batch(b, ["Gold jumps as dollar slides",
|
||||
"Fed hikes 50bp, dollar surges"])
|
||||
assert batch[0]["label"] == "bullish" and batch[1]["label"] == "bearish", batch
|
||||
t = time.time()
|
||||
for _ in range(20):
|
||||
_nm38.score_news(b, "Gold jumps as dollar slides")
|
||||
assert time.time() - t < 1.0, "20 single calls must take under a second"
|
||||
# the singleton path through app_config
|
||||
saved_app = _ac38.get_app_config()
|
||||
_nm38.reset_for_tests()
|
||||
try:
|
||||
_ac38.set_app_config(_app38("primary", path=d))
|
||||
loaded = _nm38.get_news_model()
|
||||
assert loaded is not None and loaded["meta"]["relevance_threshold"] == 0.2
|
||||
assert _nm38.get_news_model() is loaded, "loaded once"
|
||||
tag = _nm38.teacher_tag()
|
||||
assert tag and tag.startswith("xau_transformer@2026-"), tag
|
||||
# end to end through the engine: a real headline, real numbers
|
||||
h = _h38("Gold jumps as dollar slides", currencies=("XAU", "USD"),
|
||||
by={"XAU": 0.1, "USD": -0.1})
|
||||
n = _NE38(_NC38())._apply_model_sentiment([h])
|
||||
assert n == 1 and h.score_source == "model"
|
||||
assert h.sentiment_by_currency["XAU"] > 0.5 and h.xau_score > 0.5, h
|
||||
assert h.sentiment_by_currency["USD"] == -0.1
|
||||
finally:
|
||||
_nm38.reset_for_tests()
|
||||
_ac38.set_app_config(saved_app)
|
||||
|
||||
|
||||
|
||||
# ══════════════════════════════════════════════════════════════════════
|
||||
# SUMMARY
|
||||
# ══════════════════════════════════════════════════════════════════════
|
||||
|
||||
Reference in New Issue
Block a user