saas-short-trader

Category: Research & Analysis | Uploader: serenorg | Downloads: 0 | Version: v1.0（Latest）

Alpaca-branded SaaS short trader with MCP-native execution: scores AI disruption risk, builds capped short baskets, and tracks paper/live PnL in SerenDB.

Changelog: Source: GitHub https://github.com/serenorg/seren-skills

Directory Structure

Current level: alpaca/saas-short-trader/

📁 scripts/
- 📄 dry_run_checklist.md 2.1 KB
- 📄 dry_run_prompt.txt 1.4 KB
- 📄 mcp_native_runbook.md 1.1 KB
- 📄 run_agent_server.py 6.5 KB
- 📄 self_learning.py 25.8 KB
- 📄 self_learning_schema.sql 3.9 KB
- 📄 seren_client.py 2.6 KB
- 📄 serendb_bootstrap.py 6.6 KB
- 📄 serendb_schema.sql 5.3 KB
- 📄 serendb_storage.py 13.9 KB
- 📄 setup_cron.py 6.6 KB
- 📄 setup_serendb.py 1.5 KB
- 📄 strategy_engine.py 41.1 KB
📄 .env.example 318 B
📄 .gitignore 42 B
📄 config.example.json 670 B
📄 README.md 3.2 KB
📄 requirements.txt 40 B
📄 SKILL.md 4.5 KB

SKILL.md

---
name: saas-short-trader
description: "Alpaca-branded SaaS short trader with MCP-native execution: scores AI disruption risk, builds capped short baskets, and tracks paper/live PnL in SerenDB."
---

# Alpaca SaaS Short Trader

Autonomous strategy agent for shorting SaaS names under AI-driven multiple compression.

Default backend is MCP-native:
- Data collection via `mcp__seren-mcp__call_publisher`
- Storage and PnL via `mcp__seren-mcp__run_sql` / `mcp__seren-mcp__run_sql_transaction`
- Project/database lifecycle via `mcp__seren-mcp__list_*` / `create_*`

Legacy Python/API scripts remain available as fallback, not default.

## What This Skill Provides

- MCP-native 30-name SaaS universe scoring and ranking
- MCP-native 8-name capped short basket construction
- Hedged short watchlist and catalyst notes
- Paper / paper-sim / live execution modes
- SerenDB persistence for runs, orders, marks, and PnL
- Self-learning champion/challenger loop with promotion gates
- seren-cron setup for continuous automation

## Runtime Files

- `scripts/dry_run_prompt.txt` - single copy/paste MCP-native run prompt (default)
- `scripts/dry_run_checklist.md` - MCP-native readiness checklist
- `scripts/mcp_native_runbook.md` - canonical MCP execution contract
- `scripts/strategy_engine.py` - core scan/monitor/post-close engine
- `scripts/serendb_storage.py` - persistence layer
- `scripts/seren_client.py` - publisher gateway client
- `scripts/self_learning.py` - learning loop
- `scripts/run_agent_server.py` - authenticated webhook runner for seren-cron
- `scripts/setup_cron.py` - create/update cron jobs
- `scripts/setup_serendb.py` - apply base + learning schemas
## Execution Modes

- `paper` - plan and store paper orders
- `paper-sim` - simulate fills/PnL only (default)
- `live` - real broker execution path (requires explicit user approval)

## MCP-Native Workflow (Default)

1. Resolve target database with MCP:
   - project: `alpaca-short-trader`
   - database: `alpaca_short_bot`
2. Ensure `serendb_schema.sql` and `self_learning_schema.sql` are applied via MCP SQL.
3. Query publishers via MCP:
   - `alpaca`
   - `sec-filings-intelligence`
   - `google-trends`
   - `perplexity` (fallback: `exa`)
4. Score exactly 30 names and cap planned shorts at 8.
5. Persist run, candidates, order events, position marks, and daily PnL to SerenDB.
6. Persist learning snapshots, labels, policy assignment/events.
7. Return selected names, feed status, and PnL summary.

Use `scripts/dry_run_prompt.txt` for one-copy/paste execution.

## Continuous Schedule (Recommended ET)

- Scan: `15 8 * * 1-5` (08:15 ET)
- Monitor: `15 10-15 * * 1-5` (hourly, 10:15-15:15 ET)
- Post-close: `20 16 * * 1-5` (16:20 ET)
- Label update: `35 16 * * 1-5` (MCP SQL upsert)
- Retrain: `30 9 * * 6`
- Promotion check: `0 7 * * 1`

## Legacy Python Fallback (Optional)

```bash
cd alpaca/saas-short-trader
python3 -m pip install -r requirements.txt
cp .env.example .env
cp config.example.json config.json
python3 scripts/setup_serendb.py --api-key "$SEREN_API_KEY"
```

## Legacy Run Once (Optional)

```bash
python3 scripts/strategy_engine.py --api-key "$SEREN_API_KEY" --run-type scan --mode paper-sim
python3 scripts/strategy_engine.py --api-key "$SEREN_API_KEY" --run-type monitor --mode paper-sim
python3 scripts/strategy_engine.py --api-key "$SEREN_API_KEY" --run-type post-close --mode paper-sim
python3 scripts/self_learning.py --api-key "$SEREN_API_KEY" --action full --mode paper-sim
```

## Legacy Continuous Runner (Optional, seren-cron)

1. Start runner:

```bash
SEREN_API_KEY="$SEREN_API_KEY" SAAS_SHORT_TRADER_WEBHOOK_SECRET="$SAAS_SHORT_TRADER_WEBHOOK_SECRET" \
python3 scripts/run_agent_server.py --host 0.0.0.0 --port 8787
```

2. Create cron jobs:

```bash
python3 scripts/setup_cron.py \
  --runner-url "https://YOUR_PUBLIC_RUNNER_URL" \
  --webhook-secret "$SAAS_SHORT_TRADER_WEBHOOK_SECRET"
```

## Safety Notes

- Live trading is never auto-enabled.
- Strategy enforces max 8 names and exposure caps.
- If required data feeds fail and strict mode is enabled, run is blocked and persisted as blocked.
- Prefer MCP-native execution in constrained/runtime-sandboxed environments.

## Disclaimer

This skill trades real financial instruments including equities and short positions. Use at your own risk. Short selling carries unlimited loss potential — losses can exceed your initial investment. Past performance does not guarantee future results. This skill does not constitute financial, investment, or tax advice. Only risk capital you can afford to lose. Consult a licensed financial advisor before trading.

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