Computing Implied Yield from Kalshi Tickers, Step by Step

Implied yield is the annualized return a Kalshi binary contract pays if held to resolution, expressed as the unit a fixed-income trader would recognize. The formula is two lines of math and forty lines of code, including the edge cases. This guide walks through both.

The Formula

IY = (1 / p) ^ (365 / τ) − 1

Where p is the current YES price (between 0 and 1) and τ is the days remaining until the market resolves. The formula gives you a fractional return — multiply by 100 if you want a percentage.

That is the whole math. The work is in pulling p and τ cleanly from the Kalshi API and handling the failure modes.

Step 1 — Pull the Market

Hit the Kalshi REST endpoint:

GET https://api.elections.kalshi.com/trade-api/v2/markets/{ticker}

You get back a market object with the fields you need. The two we care about:

• yes_bid, yes_ask, or last_price — these are integer cents (0 to 100)
• close_ts — Unix epoch seconds at which the market resolves

Pick one of the price fields based on what you want IY to represent. If you want IY for the side you can actually buy at, use yes_ask and divide by 100. If you want a midpoint estimate, average yes_bid and yes_ask and divide by 100. The midpoint version is what most dashboards display.

Step 2 — Compute τ

τ is just close_ts − now converted from seconds to days:

τ_days = (close_ts − now_unix_seconds) / 86400

Be careful with the rounding here. You want a float, not an integer. A market closing in 30 hours has τ = 1.25, not τ = 1, and the difference matters when IY is exponentiating.

Step 3 — TypeScript Implementation

interface KalshiMarket {
  ticker: string
  yes_bid: number  // cents, 0–100
  yes_ask: number  // cents, 0–100
  last_price: number  // cents, 0–100
  close_ts: number  // unix seconds
}

export function computeImpliedYield(market: KalshiMarket, opts: { side: 'mid' | 'ask' | 'last' } = { side: 'mid' }): number | null {
  const now = Date.now() / 1000
  const tauDays = (market.close_ts - now) / 86400

  // Edge case 1 — market is already past resolution time
  if (tauDays <= 0) return null

  // Edge case 2 — market is so close to expiry that IY explodes
  if (tauDays < 0.25) return null  // less than 6 hours; meaningless

  let priceCents: number
  switch (opts.side) {
    case 'ask': priceCents = market.yes_ask; break
    case 'last': priceCents = market.last_price; break
    case 'mid': priceCents = (market.yes_bid + market.yes_ask) / 2; break
  }
  const p = priceCents / 100

  // Edge case 3 — pinned to either resolution
  if (p <= 0 || p >= 1) return null

  return Math.pow(1 / p, 365 / tauDays) - 1
}

This returns a fractional return. Multiply by 100 to display as a percent.

Step 4 — Python Implementation

The Python version is the same shape:

import time
from typing import Literal, Optional

def compute_implied_yield(
    market: dict,
    side: Literal['mid', 'ask', 'last'] = 'mid',
) -> Optional[float]:
    now = time.time()
    tau_days = (market['close_ts'] - now) / 86400

    if tau_days <= 0:
        return None
    if tau_days < 0.25:  # less than 6 hours
        return None

    if side == 'ask':
        price_cents = market['yes_ask']
    elif side == 'last':
        price_cents = market['last_price']
    else:  # mid
        price_cents = (market['yes_bid'] + market['yes_ask']) / 2

    p = price_cents / 100
    if p <= 0 or p >= 1:
        return None

    return (1 / p) ** (365 / tau_days) - 1

Step 5 — Edge Cases You Will Hit

There are three failure modes that crash a naive implementation. All three are handled in the code above. The reasons matter:

1. τ ≤ 0 — market is past resolution time but not yet settled. This happens routinely when Kalshi has not yet pushed the resolution event but the close_ts has already passed. Treat IY as undefined; the market should be excluded from any scan.

2. τ < 0.25 days — market is in the final hours. Mathematically the formula still works, but the output is a number with twelve digits and zero economic meaning. Pinning the cutoff at 6 hours is a convention I picked because it cleanly separates "still tradable for a thesis" from "settlement noise."

3. p ≤ 0 or p ≥ 1 — market is pinned. A market trading at 100 cents has zero remaining yield. A market trading at 0 cents has infinite yield (and zero practical interest). Both are meaningless from an IY perspective and should be excluded.

A fourth case worth noting: very wide bid-ask spreads. IY based on a midpoint when the bid is 12¢ and the ask is 38¢ is fiction — there is no real trade at that midpoint. If you care about executable IY, always pull from yes_ask instead of mid.

Step 6 — Batch Wrapper for sf scan

Once the per-market function works, you can vectorize it across the full Kalshi universe:

async function scanByImpliedYield(minIY: number = 0.5): Promise<Array<{ ticker: string; iy: number }>> {
  const allMarkets = await fetchAllActiveKalshiMarkets()
  const results = []
  for (const m of allMarkets) {
    const iy = computeImpliedYield(m, { side: 'ask' })
    if (iy !== null && iy >= minIY) {
      results.push({ ticker: m.ticker, iy })
    }
  }
  return results.sort((a, b) => b.iy - a.iy)
}

Run this against a 4000-market universe and you get a sorted list of "tradable IY > 50% annualized" — which is the practical input to a yield-driven scan strategy. Hook it into the SimpleFunctions CLI as sf scan --by-iy desc and you have a one-line command for finding bond-adjacent contracts that are paying enough to compete with the rest of your fixed-income stack.

The Habit Worth Building

Compute IY on every Kalshi contract you look at, even when you do not plan to trade it on yield grounds. The number gives you a sanity check that raw probability cannot: a 32-cent contract with 9 days remaining is not a casual position, no matter what your mid-price intuition says. The IY formula is the alarm bell that tells you when your intuition has missed the time axis.