Tradability Analysis: Entry, Hold, Exit

Market Synthesis is validated not just on detection accuracy but on tradability — can a fund manager actually profit from these signals?

This page documents the complete trading algorithm derived from backtesting 303 signals (2014-2026) against real market data via yfinance, using an adversarial LLM-based exit system.

Key Finding: Adversarial Exit Beats Mechanical Rules

The adversarial exit system uses a weekly LLM check: “is the narrative-reality gap still open?” It holds winners longer (gap still exploitable) and cuts losers earlier (gap closed or reversed), producing better returns with dramatically lower tail risk.

Per-Category Results (303 Signals Backtested)

Commodity and currency peg are the strongest performers — classic Soros-style trades where structural supply/demand or reserve depletion creates clear mispricings.

Entry Timing Algorithm

Entry timing is category-specific. Fast-repricing events need immediate entry; slow-building events benefit from confirmation.

Category-specific entry timing — derived from backtest of 4 strategies on each category

Entry Rules by Category

What Does NOT Help for Entry Timing

Tested and rejected:

• Volume spikes: Only 33% correlation with optimal entry. Volume peaks on day 1 (headline splash), not at the drawdown trough.
• VIX reversal: Marginal improvement for sovereign_debt only. Not worth the complexity.
• RSI / oversold signals: Insufficient data, unreliable on macro events.

Position Management Algorithm

Position management: time-based exit with headline-driven partial profit-taking

Category Parameters

Adversarial Exit System

The exit system is an LLM-based weekly check that asks: “is the narrative-reality gap that justified this trade still open?”

Adversarial exit: LLM weekly check replaces mechanical time-based exits

How it works

Each week, the exit detector receives:

1. The original signal (thesis, category, direction, gap summary)
2. Current news headlines from that week
3. Current price data for the instrument

It then decides whether the gap is still exploitable. This produces two key behaviors:

• Holds winners longer — if a commodity supply disruption is still unresolved, the system keeps the position open past the mechanical hold period
• Cuts losers earlier — if a central bank successfully defends, the system exits before the stop-loss is hit

Exit verdict distribution (303 signals)

Why adversarial exit beats mechanical rules

Mechanical exits (fixed hold period + stop-loss) fail in two ways:

1. Exit too early on winners where the thesis is still playing out (commodity supply disruptions can last months)
2. Exit too late on losers where the gap has already closed (central bank successfully defended)

The LLM exit check solves both: it understands whether the thesis is still valid, not just whether price moved.

Instrument Selection

The system uses a 3-layer instrument selector:

1. Disk cache — previously validated ticker assignments (living map, updated as results come in)
2. Static registry — category × region → default instruments (e.g., sovereign_debt + Europe → EUFN, TLT)
3. LLM fallback — Sonnet adjudicates the best tradeable expression for novel signals, with regression testing before cache update

Category instruments (top performers)

Implementation

The adversarial exit backtest is implemented in tests/backtest_adversarial_exit.py:

# Run the full backtest
python -u tests/backtest_adversarial_exit.py --workers 5 --label v10

# Key components:
# - Adversarial exit detector: detectors/adversarial_exit_detector.py
# - Instrument selector: shared/instrument_selector.py (3-layer with cache)
# - Signal store: shared/signal_store.py (635 signals, append-only)

The entry timing algorithm is implemented in shared/entry_timing.py:

from shared.entry_timing import get_entry_recommendation

rec = get_entry_recommendation(
    category="sovereign_debt",
    score=85,
    direction="short",
    pre_move_pct=0.0,
)

# Returns: EntryRecommendation(
#   entry_type="momentum_confirm",
#   momentum_days=2,
#   stop_loss_pct=-6.0,
#   position_size=0.75,
#   hold_days=23,
#   peak_day=25,
# )

All metrics derived from feed-driven backtest on real news data (2014-2026), validated against yfinance market prices. 303 signals backtested with adversarial LLM exit system. No hand-crafted test events.