The Investor Agent: AI Portfolio Management With Live Data and Mechanical Risk Limits
A pre-built agent that pulls SEC filings and live prices, runs a four-voice debate, and produces sized trade proposals — enabled in two minutes via Telegram.
Dzianis Vashchuk
8 min read
Every OpenClaw tenant now ships with a pre-built Investor agent — a portfolio strategist that pulls live market data, runs a structured internal debate, and produces sized trade proposals with explicit risk limits. It replaces a manual workflow that most people handle with a spreadsheet, a few browser tabs, and vibes.
This post covers what the agent does, how it thinks, and how to activate it on your own Telegram bot in about two minutes.
The problem it solves
Retail investors and small fund operators face a specific tooling gap. They have access to the same data as institutions (EDGAR filings, Yahoo charts, news) but lack the structured process to turn raw data into a disciplined recommendation. The usual failure mode: you read a headline, check the price, feel conviction, and size the position by gut.
The Investor agent encodes a repeatable process: fetch data from authoritative sources, run a multi-perspective debate, apply risk limits mechanically, and produce a proposal the operator approves or rejects. No execution without explicit approval.
What the agent actually does
Real data, not memory
Every analysis starts with live data pulls — not LLM training knowledge. The agent runs bash curls to:
- SEC EDGAR — revenue, net income, EPS from 10-K XBRL filings (zero-padded CIK,
us-gaaptaxonomy) - Yahoo Finance — current price, 52-week range, SMA50/200, RSI14, annualized volatility
- Google News RSS — 2-3 real recent headlines with outlet attribution
If a data fetch fails, the agent says so and labels any fallback figure [TRAINING KNOWLEDGE — UNVERIFIED] with a confidence penalty. It never fabricates a price, filing accession, or headline.
Four-voice internal debate
Before any trade proposal, the agent runs a structured debate across four analytical lenses:
| Voice | Role | What it checks |
|---|---|---|
| Warren | Fundamental | EDGAR revenue/margin/FCF trends, balance sheet leverage, moat evidence |
| Linda | Technical | Price trend, support/resistance, RSI, volume, entry zone + stop level |
| Cathie | Sentiment | Recent news, narrative shifts, crowding signals, insider activity |
| Stanley | Macro | Regime classification: RISK_ON / NEUTRAL / DEFENSIVE / RISK_OFF |
The debate isn't cosmetic. Each voice must cite a specific metric ("RSI 68, up from 52 in 21 days on 1.4× average volume" — not "strong momentum"). Every bull case gets a bear challenger. Every signal gets an invalidation criterion.
Stanley's macro regime gates everything. If the regime is RISK_OFF, no new long positions are opened regardless of single-stock conviction.
Mechanical risk limits
The agent enforces hard limits at the proposal layer:
- Single position: ≤5% NAV
- Single sector after trade: ≤25% NAV
- Cash reserve: ≥15% NAV
- Daily P&L loss: >3% → halt
- High-water-mark drawdown >15% → flat-to-cash + human review
- Notional vs. volume: reject if notional > 10× median 20-day dollar volume
These aren't suggestions. The agent surfaces rejections verbatim and does not reason around them.
Approval gate
The agent proposes; you approve. Nothing executes without an explicit approve reply. Every proposal includes: ticker, direction, size as %NAV, entry, stop, time horizon, three invalidation criteria, confidence score (0-10) with limiting factor, thesis, and a bull/bear summary.
Paper trading is the default. Live execution requires connecting a broker (Alpaca, or similar). No broker connected → the agent delivers the backtested recommendation and stops.
Skills the agent has
| Skill | What it does |
|---|---|
hedge-fund-research | Universe screening (rank watchlist by momentum/pullback/RSI), EDGAR fundamentals, Yahoo technicals |
hedge-fund-trade | Paper portfolio execution (buy/sell with risk check), position sizing, NAV tracking |
| Browser (bundled) | Web access for Morningstar fair values, Seeking Alpha theses, FRED macro data |
google-workspace-cli | Google Drive/Gmail/Calendar — export spreadsheets, email portfolio summaries |
The agent also runs backtests on real historical data. Ask it to test a rule ("buy the dip when RSI < 30") and it runs the backtest first, reports CAGR / Sharpe / max drawdown vs buy-and-hold, and states plainly whether it beat passive investing.
How to enable it
The Investor agent is pre-registered on every OpenClaw tenant but not active by default. To give it its own Telegram bot (so you can message it directly), you need a bot token from Telegram's BotFather.
Step 1: Create a Telegram bot token
- Open Telegram and search for
@BotFather - Send
/newbot - Choose a display name (e.g., "My Investor")
- Choose a username (must end in
bot, e.g.,MyInvestorBot) - BotFather replies with a token like
7123456789:AAF...— copy it
Step 2: Ask Claw to enable the Investor agent
Message your main OpenClaw bot (@OpenClawBoxBot or your default Claw agent) in Telegram:
Enable the investor agent on a new Telegram bot.
Bot token: 7123456789:AAFxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx
Claw will update your gateway config: add a new Telegram account with that token, bind the investor agent to it, and restart the gateway. Within 10-20 seconds your new bot is live.
Step 3: Message your Investor bot
Open Telegram, search for your bot's username, and send it a message:
Is COST a buy at current levels? It's down from highs.
The agent will pull live COST data from EDGAR and Yahoo, run the four-voice debate, and deliver a sized recommendation with entry/stop/invalidation — or tell you why it's not a trade right now.
What the agent won't do
- No execution without approval. Every trade requires your explicit
approve. - No fabricated data. If it can't fetch a number, it says so.
- No financial advice. Every trade-related reply ends with a mandatory disclaimer. The agent is a structured research tool, not a licensed advisor.
- No MNPI. If a request is based on non-public information, the agent refuses and explains why.
- No overriding risk limits. If your portfolio is at the sector cap, the agent won't help you rationalize adding more exposure.
Design trade-offs
Why emulation instead of real sub-agents? Default OpenClaw tenants don't have specialist sub-agents registered. The agent could refuse to work without them, or it can emulate the analytical lenses internally. We chose emulation-first — it works on every tenant out of the box. If you provision real specialist models (the openclaw/hf-* series), the agent automatically delegates to them instead of emulating.
Why 10KB instead of 30KB? OpenClaw's gateway truncates agent instructions at ~12KB when injecting them into the model's context window. The original 30KB playbook was silently cut off at the two-thirds mark — losing critical sections on risk management, backtesting rules, and the approval gate. The consolidated version keeps all the substance in 10.4KB. Nothing useful was removed; dense paragraphs replaced verbose lists.
Why not a fine-tuned model? Fine-tuning locks you to one model provider and makes iteration slow. The instruction-based approach works with any model the gateway offers (GPT-5.1, Claude Opus, DeepSeek) and updates in seconds by editing a text file.
What this looks like in practice
A typical interaction:
You: "NVDA dipped 8% this week. Worth adding?"
Investor: Pulls live price ($128.45, 52wk high $153.13, drawdown -16.1%, RSI 38, SMA200 $131.20). Runs EDGAR (Q4 rev $39.3B, NI $22.1B). Fetches 3 recent headlines.
Debate: Warren notes margins expanding + data center backlog. Linda flags price below SMA200 — technically weak until reclaim. Cathie sees crowded long positioning + 3 negative headlines. Stanley classifies regime NEUTRAL (not RISK_OFF).
Proposal: Buy 3% NAV at $127-129 (below SMA200), stop at $118 (-7%), horizon 60 days. Confidence 6/10 — limited by technical weakness and crowded positioning. Invalidation: (1) fails to reclaim SMA200 within 15 sessions, (2) next earnings miss, (3) regime shifts to RISK_OFF.
That's the entire pipeline in one reply: data → debate → risk check → sized proposal → wait for your approval.
The Investor agent ships on every OpenClaw tenant today. Create a bot token, tell Claw to wire it up, and you have a disciplined research desk in your Telegram. It won't make you rich — but it will make your process repeatable and auditable.