THE 3 FORMULAS BEHIND POLYMARKET TRADING BOTS
> i tried to actually understand what the author was pointing to in those two pages
after digging into the formulas, this is basically what they mean
> Page 1 - how Polymarket prices outcomes (LMSR):
C(q) = b · ln(Σ e^(qᵢ/b))
> the probability you see in the market is basically a softmax:
pᵢ = e^(qᵢ/b) / Σ e^(qⱼ/b)
> which is funny because it's literally the same math used in neural networks
so in a way the market itself acts like a classifier that aggregates beliefs
> Page 2 - how the trading bot decides when to act
the key formula is surprisingly simple:
EV = p̂·(1−p) − (1−p̂)·p = p̂ − p
where:
p̂ = your estimated probability
p = market probability
if your estimate is higher than the market price → you have edge → trade
> then the system keeps updating beliefs using Bayesian updates:
log P(H|D) = log P(H) + Σ log P(Dₖ|H) − log Z
so the loop is basically:
1. Estimate the real probability
2. Compare it to the market price
3. If EV > 0 → place trade
4. Update the estimate when new data arrives
> repeat
After looking at the latency table in the screenshot, it seems the real edge isn’t the formulas themselves
> it’s execution speed
their cycle shows ~828ms from data → update → trade
> so honestly if you drop these formulas into an AI agent (@claudeai or @openclaw), it already has a pretty clear blueprint for what kind of system to build
if I have misunderstood something, please write in the comments
Claude Bot found a strategy that makes +$180k a week
trades:
$472 -> $53,158
$3,444 -> $112,248
$1,247 -> $100,276
Сlaude analyzed the best polymarket traders and found everything they have in common on one page
you need to understand simple arbitrage and spread rules to make money on prediction markets
example trader
profile: https://t.co/vgaOhfA8YT
prediction markets has long stopped being gambling it's formulas and simple math