AI Trading Research

AI Trading Research Agent

When to Use

• User wants an AI agent to do market research and present trade ideas — but user decides what to actually trade
• Building cron-driven market briefs, watchlist scanners, or trade journaling systems
• Evaluating free data APIs (Finnhub, Alpha Vantage, Alpaca) for trading agent use
• Designing the human-in-the-loop pattern: agent proposes, human disposes

Core Architecture

YOU (decide entry/exit/position size)
  ↑
AGENT (research + signals + tracking)
  ├── Cron: Pre-market scan (30 min before open)
  ├── Cron: Intraday alerts (price levels, news)
  ├── Cron: End-of-day journal + P&L
  └── On-demand: "What do you think about X?"

The Five Pillars

1. Pre-market research — news scan, economic calendar, earnings, sentiment
2. Screening — filter watchlist by technical criteria (MA, volume, RSI, etc.)
3. Signal generation — specific trade ideas with entry, stop, target, and reasoning
4. Trade journaling — log every idea, entry/exit, P&L, and reasoning
5. Post-trade analysis — review what worked, what didn't, adjust

Human-in-the-Loop Rules (Hard Constraints)

• Agent never places trades. Always present ideas as proposals.
• Always include: ticker, direction (long/short), entry level, stop loss, target, and 1-sentence reasoning.
• User must explicitly confirm before any trade is recorded as "entered."
• Track closed trades with actual P&L vs projected P&L.
• Agent's track record is always visible — no survivorship bias.

Data Sources (Free Tier)

See references/data-sources.md for detailed API comparison, rate limits, and setup.

Quick reference:

• Finnhub (60 calls/min free) — real-time quotes, news, earnings, economic calendar. Best all-rounder.
• Alpaca (paper trading free) — paper trading API + market data. Commission-free. Use paper keys for development.
• Alpha Vantage (25 calls/day free) — historical data, technical indicators. Good for backtesting.
• Yahoo Finance (unofficial) — backup for quotes and basic data.

Cron Pattern for Market Briefs

Three standard schedules for a US equities-focused trading agent:

For crypto (24/7): run every 4-6 hours instead of market-hours-based.

Bait vs Real (What to Ignore on X)

Red flags (probably bait):

• "I gave AI $150 and it made $XX,XXx" — no risk management shown, survivorship bias
• "Fully autonomous trader" — nobody serious runs fully autonomous without kill switches
• "Just connect ChatGPT to your broker" — that's gambling, not a strategy
• No mention of position sizing, stop losses, or track record over 50+ trades

Green flags (probably real):

• Starts with paper trading, shows actual track record
• Has explicit risk rules (max position size, stop losses, etc.)
• Shows the full workflow: data → analysis → decision → journal
• Acknowledges the human makes final decisions

Integration with Multi-Agent Discord

If the user runs a multi-agent Discord setup (reference: project-workspace skill), the trading researcher fits as:

• A dedicated profile (e.g., trader profile with its own Discord bot)
• Home channel like #trading-ideas or #market-briefs
• Cron jobs deliver scheduled briefs to the channel
• User replies in-thread to confirm/decline trade ideas
• Writer agent can format trade reviews; Analyst agent can compute stats

Pitfalls

• Don't over-automate early. Start with morning brief cron only, add others after 2 weeks of use.
• Free API limits are real. Finnhub allows 60 calls/min — if scanning 50+ tickers, batch or sequence calls.
• Paper trade first. Even if the user plans real trades, run the agent on paper for 30+ trades to validate the workflow.
• Journal or die. The agent is useless without tracking which ideas played out. Make the journal cron non-negotiable.
• Timezone matters. Cron times should be in ET for US markets, UTC for crypto. Label all cron schedules with timezone.