GPT Alpha Orchestrator

The Four-Layer Stack

1. Extraction: narrative labels, guidance deltas, entity links, event objects
2. Ranking: convert raw extractions into comparable scores
3. Gating: require confirmation from price, liquidity, volatility, and crowding
4. Portfolio: size positions under gross, net, sector, and single-name limits

Most amateur "AI trading" workflows stop at extraction. That is the easiest layer and the least economically important one. The difficult part is deciding when signals are comparable, when they conflict, and when they should be ignored entirely.

Signal Blend

Composite Alpha Score =
    0.30 * NarrativeRegime
  + 0.30 * GuidanceDelta
  + 0.20 * KnowledgeGraphImpact
  + 0.20 * EventLinkageScore

Those weights are placeholders. In practice you would shrink them, re-estimate them walk-forward, and cap the contribution of any single module. Institutional systems are skeptical by design. If a new module looks amazing out of sample, they usually cut its weight first rather than raise it.

Why Blending Helps

• Narrative models are broad but slow
• Guidance models are timely but seasonal
• Knowledge-graph models find second-order effects but can be stale
• Event models are fast but decay quickly

The blend works because the weaknesses differ. That is exactly what you want in a multi-signal portfolio.

Gating Rules

• No trade if liquidity is too poor for your size
• No trade if text signal conflicts with price and breadth confirmation
• Half size when realized volatility is in the top decile
• Half size if multiple modules point the same way for the same crowded theme
• Kill signal after repeated schema failures or unstable extraction output

Gating is what prevents a research engine from becoming a turnover machine. A good alpha signal with bad gating becomes a mediocre portfolio.

Portfolio Construction

For retail and small professional accounts, the clean implementation is usually a small long/short basket plus liquid index or sector overlays.

Universe:
    liquid U.S. equities + sector ETFs + index ETFs

Portfolio:
    5-10 longs
    5-10 shorts
    sector caps
    single-name caps
    index hedge for beta neutrality when needed

Rebalance:
    daily for event and earnings signals
    weekly for narrative and graph signals

Conflict Resolution

If one module is bullish a name and another is bearish, do not force a trade. Reduce conviction or move to a relative-value expression. The orchestrator exists to turn disagreement into disciplined inaction when necessary.

Human-in-the-Loop Governance

This is non-negotiable:

• Version prompts and models
• Store evidence spans for every output
• Replay historical events when prompts change
• Review low-confidence or high-impact trades manually
• Track live versus expected turnover and slippage
• Track module-by-module PnL, hit rate, and decay

If you cannot answer why a signal fired, which prompt version produced it, and how it performed historically under the same rules, then the system is not investable.

Kill-Switches

Disable module if:
    live hit rate falls below floor for 20 trades
    extraction schema error rate spikes
    average confidence collapses
    turnover doubles with no alpha gain
    output becomes highly sensitive to minor prompt edits

Institutional systems are brutal about deallocation. A module does not deserve capital because it once backtested well. It deserves capital only while it is stable, interpretable, and still economically alive.

Portfolio Reality

Do not build an HFT fantasy around GPT. Build something robust enough to survive bad prompts, crowded headlines, and dull regimes. The realistic implementation is daily or weekly, highly liquid, and strongly risk constrained.