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name: geopolitical-market-impact description: Use when mapping geopolitical events to market impact, classifying war/sanctions/policy events, estimating impact magnitude and duration, matching historical precedents, or building event-driven trading signals from macro/political news

Geopolitical Market Impact

Overview

Geopolitical events create asymmetric, non-linear market shocks. The challenge is: (1) classifying events by type and severity, (2) estimating impact direction and magnitude from historical analogues, and (3) acting before the crowd prices it in — or avoiding false alarms.

Core principle: Markets often front-run geopolitical risk. The best entry is frequently on rumour, not confirmation. Study buy the rumour, sell the news carefully.

When to Use

• Classifying incoming geopolitical news for trading impact
• Searching for historical precedents to estimate price response
• Building event-driven trading signals from news feeds
• Estimating sanction/war/election impact on specific assets
• Assessing second-order effects (e.g., oil → airline stocks)

Event Classification Framework

from enum import Enum
from dataclasses import dataclass
from typing import Optional

class EventType(Enum):
    WAR_ESCALATION    = "war_escalation"
    WAR_DE_ESCALATION = "war_de_escalation"
    SANCTIONS_NEW     = "sanctions_new"
    SANCTIONS_LIFTED  = "sanctions_lifted"
    ELECTION_RESULT   = "election_result"
    POLICY_CHANGE     = "policy_change"      # rates, tariffs, regulation
    NATURAL_DISASTER  = "natural_disaster"
    SUPPLY_DISRUPTION = "supply_disruption"  # Suez, pipelines, ports
    COUP_INSTABILITY  = "coup_instability"
    DIPLOMATIC_CRISIS = "diplomatic_crisis"

class Severity(Enum):
    LOW    = 1   # localised, limited economic ties
    MEDIUM = 2   # regional spillover, moderate trade impact
    HIGH   = 3   # global supply chain, major economy involved
    EXTREME = 4  # nuclear risk, G7 direct involvement

@dataclass
class GeopoliticalEvent:
    event_type: EventType
    severity: Severity
    countries_involved: list[str]
    affected_commodities: list[str]   # e.g. ['crude_oil', 'wheat', 'natural_gas']
    affected_currencies: list[str]    # e.g. ['RUB', 'EUR', 'USD']
    description: str
    source_reliability: float         # 0-1, Reuters=0.9, Twitter=0.3

Asset Impact Matrix

# Historical directional impact by event type and asset class
# Values: typical directional bias (+ = up, - = down, ? = uncertain)
IMPACT_MATRIX = {
    EventType.WAR_ESCALATION: {
        'gold':          +1,    # safe haven
        'usd':           +1,    # safe haven flows
        'oil':           +1,    # supply disruption risk
        'natural_gas':   +1,
        'wheat':         +1,    # Russia/Ukraine = major exporters
        'equities':      -1,    # risk-off
        'btc':           -1,    # risk-off (initially)
        'bonds':         +1,    # safe haven
        'eur':           -1,    # if European theatre
    },
    EventType.SANCTIONS_NEW: {
        'affected_country_currency': -1,
        'oil':           +1,    # if sanctioned country is oil exporter
        'gold':          +1,
        'usd':           +1,
    },
    EventType.ELECTION_RESULT: {
        # Highly context-dependent — use historical_precedents() function
    },
    EventType.SUPPLY_DISRUPTION: {
        'affected_commodity': +1,
        'substitutes':   +1,
        'consumers':     -1,    # companies dependent on commodity
    },
}

def estimate_impact(event: GeopoliticalEvent, asset: str) -> dict:
    """Estimate directional impact and magnitude."""
    matrix = IMPACT_MATRIX.get(event.event_type, {})
    direction = matrix.get(asset, 0)

    # Severity multiplier
    magnitude_pct = {
        Severity.LOW: 0.5,
        Severity.MEDIUM: 2.0,
        Severity.HIGH: 5.0,
        Severity.EXTREME: 15.0,
    }[event.severity]

    return {
        'direction': direction,
        'estimated_move_pct': direction * magnitude_pct,
        'confidence': event.source_reliability,
        'note': 'Directional estimate only; timing and confirmation critical',
    }

Historical Precedent Database

HISTORICAL_PRECEDENTS = [
    {
        'event': 'Russia invades Ukraine (Feb 2022)',
        'type': EventType.WAR_ESCALATION,
        'impacts': {
            'crude_oil':    +28,   # % move in 3 months
            'wheat':        +60,
            'natural_gas':  +200,  # EU benchmark
            'gold':         +8,
            'eur_usd':      -6,
            'rub_usd':      -50,   # initial, then recovered on capital controls
            'spx':          -12,   # in 3 months
        },
        'time_to_peak': '3-6 weeks',
        'reversal': 'Partial within 3 months as supply routes adapted',
    },
    {
        'event': 'US-China tariff escalation (2018-2019)',
        'type': EventType.POLICY_CHANGE,
        'impacts': {
            'cny_usd':      -8,
            'spx':          -20,   # peak drawdown
            'soybeans':     -15,
            'gold':         +8,
        },
        'time_to_peak': '6-12 months',
    },
    {
        'event': 'Iran nuclear deal JCPOA exit (2018)',
        'type': EventType.SANCTIONS_NEW,
        'impacts': {
            'crude_oil':    +20,
            'irr_usd':      -70,   # Iranian rial
        },
        'time_to_peak': '6 months',
    },
    {
        'event': 'Brexit referendum (Jun 2016)',
        'type': EventType.ELECTION_RESULT,
        'impacts': {
            'gbp_usd':      -10,   # day after
            'ftse_100':     -8,    # day after (recovered quickly as GBP offset)
            'gold':         +5,
            'eurostoxx':    -8,
        },
        'time_to_peak': '1-2 days for initial shock',
    },
]

def find_precedents(event: GeopoliticalEvent, top_n: int = 3) -> list[dict]:
    """Match current event to historical precedents by type."""
    matches = [p for p in HISTORICAL_PRECEDENTS
               if p['type'] == event.event_type]
    return matches[:top_n]

Second-Order Effects

# Commodity → sector impact chains
SECOND_ORDER_CHAINS = {
    'crude_oil_up': {
        'airlines':       -1,   # fuel costs
        'shipping':       -1,
        'petrochemicals': +1,   # feedstock price up → margin down for consumers
        'oil_majors':     +1,
        'ev_sector':      +1,   # accelerated adoption
        'defense':        +1,   # increased military spending
    },
    'natural_gas_up': {
        'utilities':      -1,   # margin compression
        'lng_exporters':  +1,   # US LNG benefits
        'fertilisers':    -1,   # gas = feedstock for nitrogen fertilisers
        'food_producers': -1,
    },
    'wheat_up': {
        'food_companies': -1,
        'agri_tech':      +1,
        'emerging_markets': -1, # food import dependent nations: Egypt, Lebanon
    },
}

Timing Model

def geopolitical_signal_timing(event_severity: Severity) -> dict:
    """
    Markets typically price geopolitical events in phases.
    Returns suggested trade timing parameters.
    """
    timing_map = {
        Severity.LOW: {
            'initial_spike_duration': '1-4 hours',
            'peak_impact': '1-3 days',
            'mean_reversion': '1-2 weeks',
            'action': 'Wait for initial spike, fade if no escalation',
        },
        Severity.MEDIUM: {
            'initial_spike_duration': '4-24 hours',
            'peak_impact': '1-2 weeks',
            'mean_reversion': '4-8 weeks',
            'action': 'Trade with trend during peak, watch for plateau',
        },
        Severity.HIGH: {
            'initial_spike_duration': '1-3 days',
            'peak_impact': '4-12 weeks',
            'mean_reversion': '3-6 months',
            'action': 'Structural position; manage with trailing stops',
        },
        Severity.EXTREME: {
            'initial_spike_duration': '3-7 days',
            'peak_impact': 'Months to years',
            'mean_reversion': 'Uncertain',
            'action': 'Risk-off immediately; reassess when situation stabilises',
        },
    }
    return timing_map[event_severity]

Quick Reference — Reliable Safe Havens

Asset	Behaviour	Notes
Gold	Rises on uncertainty	Most reliable safe haven
USD	Rises on global crisis	Loses safe haven in US-specific events
CHF	Rises on European crisis	Swiss franc haven
JPY	Rises initially, may fall if Japan involved	Carry unwind amplifies
US Treasuries	Rises (yield falls)	Unless US fiscal crisis is the event
BTC	Inconsistent	Sometimes safe haven, often risk-off sells off
Oil	Rises on supply events	Falls on demand destruction events

Common Mistakes

1. Chasing the initial spike — first reaction is often wrong; wait for second-day confirmation
2. Ignoring resolution probability — a "warning shot" sanctions vs. full embargo = very different impact
3. Forgetting sanctions evasion — Russia/Iran showed sanctions rarely achieve stated economic goals
4. Linear extrapolation — market prices in a lot quickly; impact often peaks before situation resolves
5. Missing second-order effects — the wheat price impact on Egyptian pound is often more tradeable than wheat itself
6. Source reliability — Telegram/Twitter geopolitical news has 50%+ false alarm rate; weight accordingly

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Anti-Patterns

Anti-Pattern	Why It Fails	Correct Approach
Chasing the initial market reaction spike	First reaction to geopolitical events is wrong 40-60% of the time; driven by panic, not analysis	Wait for second-day confirmation; the initial spike often fully reverses within 24-48 hours
Linear extrapolation of event impact	Markets price in information quickly; impact often peaks before the situation reaches its worst point	Model impact as a decaying curve; most of the price move happens in the first 1-3 sessions
Treating all sanctions as economically devastating	Russia/Iran sanctions showed that evasion routes emerge quickly; actual economic impact often far less than projected	Assess enforcement capability and evasion probability; weight historical compliance rates for similar sanctions
Ignoring second-order effects for more tradeable opportunities	Direct impact on obvious assets is crowded; second-order effects are often more profitable	Map the supply chain: a Middle East conflict affects oil, but the Egyptian pound via wheat prices may be more tradeable
Using social media as primary geopolitical intelligence	Telegram/Twitter geopolitical news has 50%+ false alarm rate; reacting to every rumour destroys capital	Weight established sources (Reuters, AP, official government statements) heavily; treat social media as early warning only