"""
Executive Summary: Calculates the implied probability of a merger closing based on the current market spread, target price, offer price, and unaffected price.
"""
from __future__ import annotations
from datetime import datetime, timezone
from skills.utils import log_lesson
TOOL_META = {
"name": "merger_spread_implied_probability",
"tier": "free",
"description": "Calculates M&A deal closing probability from market spread, offer price, and unaffected downside.",
}
def merger_spread_implied_probability(target_ticker: str, offer_price: float, current_price: float, unaffected_price: float) -> dict:
"""Calculate the market-implied probability of an M&A transaction closing.
Args:
target_ticker: Ticker symbol of the target company
offer_price: The acquisition offer price per share
current_price: The current trading price of the target
unaffected_price: The estimated price if the deal breaks
Returns:
Dict with implied closing probability, gross spread, and downside risk.
"""
try:
ts = datetime.now(timezone.utc)
# Gross spread
if current_price >= offer_price:
implied_prob = 1.0
gross_spread = 0.0
elif current_price <= unaffected_price:
implied_prob = 0.0
gross_spread = (offer_price - current_price) / current_price
else:
# (Current - Unaffected) / (Offer - Unaffected)
implied_prob = (current_price - unaffected_price) / (offer_price - unaffected_price)
gross_spread = (offer_price - current_price) / current_price
# Downside risk
downside_risk = (current_price - unaffected_price) / current_price
return {
"status": "success",
"data": {
"target_ticker": target_ticker.upper(),
"offer_price": offer_price,
"current_price": current_price,
"unaffected_price": unaffected_price,
"implied_closing_probability_pct": round(implied_prob * 100, 2),
"gross_spread_pct": round(gross_spread * 100, 2),
"downside_risk_pct": round(downside_risk * 100, 2),
"market_sentiment": "High Confidence" if implied_prob > 0.8 else ("Skeptical" if implied_prob < 0.5 else "Neutral")
},
"timestamp": ts.isoformat()
}
except Exception as e:
log_lesson(f"merger_spread_implied_probability: {e}")
return {
"status": "error",
"error": str(e),
"timestamp": datetime.now(timezone.utc).isoformat()
}
