Auto Fin Plugin

中文

Auto Fin fetches a rolling window of CLS telegraph news (24 hours by default), groups items by configured topics, researches each topic against ReMe history into its own note, and merges those notes into one dated brief whose validated wikilinks point back at every note. Current news and topic selection stay in runtime memory; only the notes and the brief become durable memory. This directory is an independent Python distribution. Its single reme.plugins entry point exposes a plugin.yaml containing the four Step backends and their Job configuration under application_defaults. Enable the installed plugin explicitly through plugins=["auto-fin"].

Auto Fin has no reliable market-price feed. It does not calculate returns, targets, or entry points and is not investment advice.

Quick start

1. Install ReMe and Auto Fin

python -m pip install "reme-ai[core]>=0.4.1.12"
reme plugins install plugins/auto-fin

2. Configure the model environment

Configure the LLM environment variables as described in the ReMe model-configuration guide. Other compatible models and providers can also be used.

3. Start ReMe with the plugin

reme start plugins='["auto-fin"]'

With no explicit config, ReMe loads default.yaml and adds the plugin to that service.

From another terminal, call the running HTTP service through ReMe's CLI client:

reme auto_fin topics="黄金,AI,存储芯片"

Or call its HTTP endpoint directly:

curl -s http://127.0.0.1:2333/auto_fin \
  -H 'Content-Type: application/json' \
  -d '{"topics":"黄金,AI,存储芯片"}'

The HTTP service also exposes the same Job as the auto_fin MCP tool at /mcp. The default topics are 黄金,机器人,半导体; an empty value also uses these defaults.

To host the application with both JSON and MCP access:

reme start plugins='["auto-fin"]' \
  service.backend=http

Custom application configs must provide agent_wrapper.default, a file_store.default with an enabled tag index, and the search, list_tags, frontmatter_read, and frontmatter_update Jobs used by Auto Fin and automatic tagging.

Pipeline

CLS public telegraph endpoint (rolling 24 hours)
        ↓
normalize and deduplicate in RuntimeContext
        ↓
topic Agent returns topic-to-news-ID mappings in prompt-sized batches
        ↓
one research Agent per topic examines its latest 20 articles, searches history up to three times,
and writes one note per topic with validated historical wikilinks
        ↓
digest Agent merges the notes into today's brief; code appends a link back to every note
        ↓
generate memory tags; the background file watcher refreshes indexes

auto_fin_data_step signs and paginates the same endpoint used by the CLS website. It starts at the decision time and stops only after covering the exact preceding 24 hours. Requests are rate-limited and retried; malformed records and records outside the window are discarded.

auto_fin_topic_step batches current news under a 100,000-character full-prompt limit and returns related news_id values for each topic. Code ignores unknown IDs and deduplicates repeated IDs, then preserves source-news order. One article may belong to multiple topics. If nothing is relevant, the job succeeds as a skip without writing a report.

auto_fin_research_step researches each nonempty topic with its latest 20 articles and writes one note per topic. It exposes only search, enforces a three-call search budget per topic, and keeps current CLS IDs, times, and titles as plain evidence. The prompt limits wikilinks to historical Markdown actually used by the Agent; the code-level boundary independently keeps only existing, workspace-relative Markdown targets. Missing, absolute, escaping, backslash, and self-referential targets are degraded to their readable aliases.

auto_fin_digest_step merges those notes into today's brief with no tools of its own: the notes already carry the research. Code appends a ## 主题详解 section linking back to every note, refreshes the daily index, and publishes the brief path for downstream steps. Both steps stop early when the topic step found no relevant news.

Same-day reruns reuse the note a topic already produced, feeding its body back to the research Agent and replacing it in place; the brief is replaced the same way. Writes are atomic. The workflow then runs auto_tag_step to update the generated files' memory-tag frontmatter; the normal background file watcher observes those source-file changes and refreshes derived indexes. No JSONL, intermediate Markdown, or structured Agent output is written.

Parameters

The plugin cron Job starts with the application and runs daily at 09:00 in the application timezone, which defaults to Asia/Shanghai. The rolling window uses timestamps and may cross calendar days. Report completion depends on news volume and model latency.

Output

.reme/daily/YYYY-MM-DD/<topic>.md                # one per topic that had relevant news
.reme/daily/YYYY-MM-DD/主题新闻观察（YYYY-MM-DD）.md  # today's merged brief, linking back to every note

File names come from the configured topics and the run date rather than the Agent's title: an Agent title is free text and can outgrow the filesystem limit for one name component. Names are sanitized, byte-truncated, and disambiguated; the Agent title is kept in the title frontmatter field. Every file carries kind frontmatter (auto-fin-topic or auto-fin-digest), so a rerun finds and replaces the notes it produced instead of duplicating them. Each file includes a title, description, current CLS evidence, historical analysis, contextual wikilinks, and a fixed non-investment disclaimer; the brief ends with a ## 主题详解 list linking to the topic notes. A fetch failure fails the run explicitly, and so does a run in which every topic fails; a single failing topic only logs a warning and the remaining topics continue. No relevant current news is a successful skip.

Validation

python -m pytest plugins/auto-fin -v

Unit tests mock the CLS and Agent boundaries and do not contact external services.