Auto Fin Plugin

中文

Auto Fin fetches a rolling window of CLS telegraph news (24 hours by default), selects items related to configured topics, searches ReMe for useful historical context, and writes one Chinese Markdown report with validated wikilinks. Current news and topic selection stay in runtime memory; only the final report becomes durable memory. This directory is an independent Python distribution. Its single reme.plugins entry point exposes a plugin.yaml containing the three 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]>=0.4.1.8"
reme plugins install reme-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

To add Auto Fin to another application instead, select that config explicitly, for example:

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

Pipeline

CLS public telegraph endpoint (rolling 24 hours)
        ↓
normalize and deduplicate in RuntimeContext
        ↓
topic Agent selects real news IDs in bounded batches
        ↓
research Agent uses memory_search + read on historical memory
        ↓
validate historical wikilinks in code
        ↓
daily/YYYY-MM-DD/auto_fin.md

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 receives batches of current news and returns only related news_id values. Code ignores unknown IDs and deduplicates repeated IDs, then preserves the source-news order. If nothing is relevant, the job succeeds as a skip without writing or sending a report.

auto_fin_merge_step receives only selected current news. It exposes memory_search and read, instructs the Agent to search no later than yesterday, 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.

Same-day reruns use the existing report as context and replace it with the revised result. The final write is atomic and refreshes the daily index. No JSONL, intermediate Markdown, or structured Agent output is written.

Parameters

The three plugin cron Jobs start with the application and run daily at 09:30, 11:30, and 18:00 in Asia/Shanghai.

Output

.reme/daily/YYYY-MM-DD/auto_fin.md

The report includes a title, description, current CLS evidence, historical analysis, contextual wikilinks, and a fixed non-investment disclaimer. Network errors and invalid Agent output fail explicitly; 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.