Market Synthesis: AI Engine for Macro Imbalances

Built for investment teams that want earlier warning of market regime breaks, with reasoning they can audit.

Most large drawdowns do not come from small forecasting errors. They come from sudden shifts in policy, liquidity, correlation, or narrative.
Market Synthesis is designed to flag those shifts before they are obvious in price alone.

The core idea: Soros-style imbalance detection

The system is built around a simple market reality: when market narrative and structural reality diverge, price can move violently when that gap closes.

This is the same style of thinking behind classic macro dislocation trades:

Soros-style reflexivity cycle — Market Synthesis detects the gap between narrative and reality

Market Synthesis is designed to detect those high-tension setups early and score how actionable they are.

What this project does

Market Synthesis is a decision-support engine for macro and multi-asset teams.
Given an event context and relevant market narrative, it produces:

1. a directional view (long, short, neutral)
2. a confidence score (0-100)
3. a structured explanation of why that view was reached

The goal is not to replace PM judgment.
The goal is to improve speed, consistency, and challenge quality in portfolio decisions.

Why this is more than “one model, one opinion”

A single agent can sound convincing while missing the best counter-argument.
This framework forces internal disagreement before a conclusion is allowed.

Multi-stage orchestration flow (production logic)

8-agent adversarial orchestration — enrichment feeds real news into domain-specialized Prosecutor, Defender, and Expert agents

This creates practical advantages over single-agent output:

1. Real news enrichment: debates are informed by actual contemporaneous coverage, not thin descriptions.
2. Full domain specialization: all 3 debate agents (Prosecutor, Defender, Expert) use category-specific prompts — 7 × 3 = 21 specialized prompt templates.
3. Lower one-sided bias: every thesis is stress-tested before scoring.
4. Better calibration: confidence is corrected after adjudication, not guessed in one pass.
5. Deterministic guardrails: when the system’s own signals say “no trade,” direction is forced to neutral.

Why this matters for PMs, CIOs, and risk teams

1. Faster escalation: identifies events that deserve immediate committee attention.
2. Cleaner IC process: provides a structured prosecution-vs-defense brief in one artifact.
3. Cross-team alignment: discretionary and risk functions review the same rationale.
4. Portfolio protection: supports earlier hedging and exposure rebalancing.

Evidence snapshot

v38 full portfolio backtest (140 trades, 12 years, real news feeds, adversarial exit system, market-data-driven entry gates):

Per-category performance (v38)

Net of costs (IBKR fee model)

Reading the numbers: these are real P&L numbers from a full backtest against yfinance historical prices. For each signal, the system enters a position in the category-appropriate instrument via an LLM instrument selector with coherence gate. The adversarial exit system (same debate structure as entry) runs periodically until the narrative-reality gap closes. A volatility-based stop-loss (3-sigma of 5-day returns) is the only mechanical override.

Commodity is the standout category at 100% win rate across 46 trades. Geopolitical runs at 90% over 20 trades. These are classic Soros-style setups where structural supply/demand imbalance or policy impossibility creates a clear gap that the market is slow to price.

Out-of-sample holds up: the 42 trades from 2023 onward (unseen during development) maintain 81% win rate and +9.2% median, confirming the system generalizes beyond the training window.

Entry gates are market-data-driven, not hard-coded rules. Each gate uses observable market signals (bond momentum, FX levels, equity drawdowns) to filter trades where the move is already priced in. This makes the gates generalizable and auditable.

Full methodology: Methodology | Full findings: Findings | Exit system: Exit System

Domain expert routing

Events are classified into 7 specialized categories, each with a purpose-built expert:

Practical deployment model

Use as a decision-support layer, not autonomous execution:

1. weekly macro/risk review feed
2. watchlist prioritization
3. hedge scenario challenge
4. pre-trade thesis stress test

Read next

• For Funds (business case + user guide)
• Tradability (entry/exit algorithm)
• Findings (v38 evidence)
• Methodology (pipeline architecture)
• Exit System (adversarial exits)
• Technical summary

research project