Polymarket Geopolitics Sentiment Reversal Trader

Trades mean reversion on geopolitical markets pushed to probability extremes by breaking news. Markets at >92% or <8% with long time horizons systematically over-react and revert. Combines overreaction bias detection with staleness factor (days-to-resolution) for conviction sizing.

Geopolitics Sentiment Reversal Trader

This is a template. The default signal is purely price-based with a staleness (days-to-resolution) multiplier -- no external API required. The skill discovers geopolitical markets at probability extremes, identifies likely overreactions using time-horizon analysis, and trades mean reversion. The skill handles all the plumbing (market discovery, geopolitics filtering, trade execution, safeguards). Your agent provides the alpha.

Strategy Overview

Geopolitical prediction markets are uniquely susceptible to overreaction. When breaking news hits -- a military strike, a sanctions announcement, a ceasefire collapse -- retail traders rush to reprice markets. The result is a predictable pattern: markets overshoot to probability extremes (>92% or <8%) and then revert 30-50% of the move within 24-48 hours.

This skill systematically identifies and trades these overreactions by combining two signals:

1. Probability extremes: Markets pushed beyond the reversal zones (>92% or <8%) where overreaction is statistically most likely.
2. Staleness factor: Markets with long time horizons (30-180 days) at extreme prices are far more likely to be overreactions than markets with 2-3 days to resolution where the extreme price may reflect genuine resolution information.

Edge Thesis: Behavioral Finance Overreaction Bias

The overreaction hypothesis (De Bondt & Thaler, 1985) documents that markets systematically overshoot in response to dramatic news and subsequently revert. In geopolitical prediction markets, this bias is amplified by three mechanisms:

1. Availability heuristic under fear Breaking geopolitical news (airstrikes, troop movements) activates fear responses. Retail traders anchor to the most dramatic possible outcome and price accordingly. A single airstrike pushes "Will there be a full-scale invasion?" from 40% to 93% -- even though the base rate for escalation from single strikes to full invasion is historically low.

2. Asymmetric attention The news that pushes a market to 95% gets wall-to-wall coverage. The slow de-escalation that brings it back to 70% happens quietly over days. Retail attention is front-loaded on the shock, creating a predictable overshoot-and-decay pattern.

3. Time-horizon mispricing A market at 92% with 180 days to resolve embeds enormous uncertainty that retail ignores in the heat of the moment. The staleness factor captures this: longer horizons mean more room for reversion, more intervening events, and more time for the initial overreaction to wash out.

Signal Logic

Step 1 -- Market Discovery

Keyword sweep across geopolitical topics: war, ceasefire, military, strike, Iran, Israel, Gaza, Lebanon, sanctions, nuclear, troops, conflict, attack, bomb, invasion, Hezbollah, Hamas.

A regex filter then confirms the market question is genuinely geopolitical (avoids false positives from keywords appearing in non-geopolitical contexts).

Step 2 -- Signal Gates

• Spread <= MAX_SPREAD (wide spreads eat the reversal edge)
• Days to resolution >= MIN_DAYS (standard minimum)
• Days to resolution >= MIN_DAYS_FOR_REVERSAL (need enough runway for reversion)

Step 3 -- Signal Direction

Condition	Trade	Rationale
p >= REVERSAL_ZONE_HIGH (92%) and p >= NO_THRESHOLD	Buy NO	Market is "too certain" -- expect reversion DOWN
p <= REVERSAL_ZONE_LOW (8%) and p <= YES_THRESHOLD	Buy YES	Market is "too pessimistic" -- expect reversion UP
Between zones	Skip	Not at an overreaction extreme

Step 4 -- Staleness Factor

The staleness factor is the key innovation. It scales from 0.0 to 1.0 based on days to resolution:

Days to resolution	Staleness factor	Interpretation
7 (minimum)	0.0	Near-term -- extreme price may be correct
30	0.28	Some room for reversion
60	0.64	Substantial overreaction likely
90+	1.00	Maximum -- extreme is almost certainly an overreaction

Formula: staleness = min(1.0, (days - MIN_DAYS_FOR_REVERSAL) / (90 - MIN_DAYS_FOR_REVERSAL))

Step 5 -- Conviction Sizing

For NO (high extreme reversion):

text

raw_conviction = (p - REVERSAL_ZONE_HIGH) / (1 - REVERSAL_ZONE_HIGH)
conviction = staleness * raw_conviction
size = max(MIN_TRADE, round(conviction * MAX_POSITION, 2))

For YES (low extreme reversion):

text

raw_conviction = (REVERSAL_ZONE_LOW - p) / REVERSAL_ZONE_LOW
conviction = staleness * raw_conviction
size = max(MIN_TRADE, round(conviction * MAX_POSITION, 2))

How Sizing Works

With defaults (REVERSAL_ZONE_HIGH=92%, REVERSAL_ZONE_LOW=8%, MIN_TRADE=$5, MAX_POSITION=$40):

High extreme reversion (buy NO):

Market price p	Days left	Staleness	Raw conviction	Final conviction	Size
93%	14d	0.08	12.5%	1.0%	$5 (floor)
95%	30d	0.28	37.5%	10.5%	$5 (floor)
95%	90d	1.00	37.5%	37.5%	$15
98%	60d	0.64	75.0%	48.0%	$19
98%	120d	1.00	75.0%	75.0%	$30

Low extreme reversion (buy YES):

Market price p	Days left	Staleness	Raw conviction	Final conviction	Size
7%	14d	0.08	12.5%	1.0%	$5 (floor)
5%	30d	0.28	37.5%	10.5%	$5 (floor)
3%	90d	1.00	62.5%	62.5%	$25
1%	120d	1.00	87.5%	87.5%	$35

Remix Ideas

• GDELT event velocity: Wire in GDELT's real-time event stream to measure the rate of new events. A single spike followed by silence is the classic overreaction pattern. Sustained event velocity means the market move may be justified -- reduce conviction.
• Twitter/X sentiment decay: Track sentiment volume on geopolitical hashtags. If sentiment peaked 12-24h ago and is declining, the overreaction is already unwinding -- increase conviction for the reversal trade.
• Historical reversion rate by event type: Airstrikes revert faster than sanctions announcements. Build a lookup table of event-type-specific reversion rates to weight conviction.
• Cross-market correlation: If multiple geopolitical markets spike simultaneously on the same news event, the overreaction is likely correlated. Trade the most extreme one and skip the rest to avoid correlated risk.
• Volatility regime detection: During periods of sustained geopolitical escalation (war phases), extreme prices may be correct. Add a regime filter that reduces conviction when multiple markets have been at extremes for >7 days (sustained crisis vs. flash overreaction).

Safety & Execution Mode

The skill defaults to paper trading (venue="sim"). Real trades only with --live flag.

Scenario	Mode	Financial risk
python trader.py	Paper (sim)	None
Cron / automaton	Paper (sim)	None
python trader.py --live	Live (polymarket)	Real USDC

autostart: false and cron: null -- nothing runs automatically until you configure it in Simmer UI.

Required Credentials

Variable	Required	Notes
SIMMER_API_KEY	Yes	Trading authority. Treat as high-value credential.

Tunables (Risk Parameters)

All declared as tunables in clawhub.json and adjustable from the Simmer UI.

Variable	Default	Purpose
SIMMER_MAX_POSITION	40	Max USDC per trade (reached at 100% conviction)
SIMMER_MIN_VOLUME	15000	Min market volume filter
SIMMER_MAX_SPREAD	0.08	Max bid-ask spread (8%)
SIMMER_MIN_DAYS	3	Min days until resolution (standard gate)
SIMMER_MAX_POSITIONS	6	Max concurrent open positions
SIMMER_MIN_TRADE	5	Floor for any trade (min USDC regardless of conviction)
SIMMER_YES_THRESHOLD	0.38	Standard YES gate -- only buy YES below this
SIMMER_NO_THRESHOLD	0.62	Standard NO gate -- only buy NO above this
SIMMER_REVERSAL_ZONE_HIGH	0.92	Above this probability, market is in overreaction zone (high)
SIMMER_REVERSAL_ZONE_LOW	0.08	Below this probability, market is in overreaction zone (low)
SIMMER_MIN_DAYS_REVERSAL	7	Min days to resolution for reversal trades (need runway for reversion)

Dependency

simmer-sdk by Simmer Markets (SpartanLabsXyz)

• PyPI: https://pypi.org/project/simmer-sdk/
• GitHub: https://github.com/SpartanLabsXyz/simmer-sdk