Great! Let’s tackle (2) Multi-Agent Risk & Portfolio Management and (3) Noise, Bots & Source Credibility in depth.

(2) Multi-Agent Risk & Portfolio Management

a. Coordinating Multiple Agents for Trading Decisions

1. Conflict Resolution
   • Scenario: One agent sees negative sentiment and suggests shorting, while another agent’s fundamental or technical analysis suggests a buy.
   • Solution: A “manager” or “arbiter” agent (or layer) compares the risk/reward from each recommendation. It could do a weighted approach based on confidence scores, or it might decide to wait for more data before acting.
2. Position Sizing & Leverage
   • Instead of just “take a short position,” the system could decide how big that position should be.
   • Key Data: current portfolio exposure, historical volatility of the asset, agent confidence, and user-configured risk tolerance (e.g., never risk more than 2% of capital on a single trade).
3. Stop-Losses & Real-Time Adjustments
   • Agents can watch the market post-trade. If price action or sentiment changes drastically, they might tighten the stop-loss or exit early.
   • Implementation:
     • A “Risk Management Agent” polls trades every minute or hour, updates stop-loss levels if sentiment flips, and logs these changes in a shared knowledge base.
4. Portfolio-Level Considerations
   • If you already have too much exposure to a particular sector, an agent might veto or reduce further trades in that sector.
   • Example: If you’re 50% allocated to tech and a new tech short signal comes in, your manager might say, “We’re already heavily in tech; either reduce that position or skip this new trade.”
5. Agent Roles
   • Agent A (Sentiment/News): Scans social data, identifies signals.
   • Agent B (Fundamentals): Reads balance sheets or uses a model that weighs P/E ratios, earnings reports, etc.
   • Agent C (Risk Management): Oversees the entire portfolio, sets trade sizes, monitors open positions.
   • Manager/Orchestrator: Decides how to blend or prioritize Agent A’s signals vs. Agent B’s fundamental outlook, with Agent C’s guidance on risk.

b. Example Workflow

1. News Spike Detected:
   • Agent A: “Massive negative sentiment spike for $XYZ—short recommended.”
   • Agent B: Checks fundamentals: “But $XYZ has strong fundamentals; I give a mild bullish bias.”
   • Agent C: Reviews your existing portfolio (maybe you’re 30% allocated to $XYZ already).
2. Manager Decisions:
   • Manager weighs confidence scores: “Agent A has a 90% confidence in a short-term drop, Agent B is 60% bullish long-term.”
   • Could decide to do a short-term short with a tight stop-loss, or size it smaller to manage risk.
3. Execution & Monitoring:
   • Manager instructs the system to short a certain number of shares/contracts.
   • Agent C keeps watch; if sentiment recovers quickly, it might close the position early.

(3) Noise, Bots & Source Credibility

a. The Bot Problem

1. Fake News & Bot Amplification
   • Social media is saturated with automated accounts that spread misleading or manufactured hype.
   • If your system trades automatically on these signals, you can get whipsawed.
2. Bot Detection Techniques
   • Heuristics: e.g., accounts with extremely high posting frequency, very few followers, or strange follower-to-following ratios.
   • Social Graph Analysis: If multiple suspicious accounts retweet or comment on each other in closed loops, that may indicate a coordinated botnet.
   • ML Classifier: Train a model to spot bot-like behavior based on metadata (profile age, tweet frequency, engagement style).
3. Filtering Out Bot Traffic
   • Flag suspect accounts as “low credibility” or “junk.”
   • Adjust weighting of sentiment from these accounts so they have minimal influence on trading decisions.
   • Potentially ignore them entirely if they exceed a certain “bot suspicion score.”