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MR

Marcus Rivera

Head of Quantitative Strategy

Marcus Rivera spent seven years in quantitative trading at Jane Street and Citadel Securities before pivoting full-time to prediction markets in 2024. He holds an MS in Financial Engineering from Columbia University and a BS in Mathematics from UC Berkeley. At Bot for Kalshi, Marcus leads strategy research — developing, backtesting, and deploying quantitative models across weather, economic, sports, and political markets. His work focuses on market microstructure, optimal execution, and systematic edge detection in event contract markets. When he's not staring at orderbooks, he's rock climbing in the Gunks or arguing about optimal Kelly fraction at dinner parties.

Articles by Marcus

Trading Economic Indicators on Kalshi (2026)
Strategies

Trading Economic Indicators on Kalshi (2026)

How to trade Kalshi economic markets — CPI, jobs reports, GDP, and Fed rate decisions. Nowcasting models, timing strategies, and the data that matters.

· 12 min read
How to Backtest a Kalshi Trading Bot Using Historical Market Data
Trading Bots

How to Backtest a Kalshi Trading Bot Using Historical Market Data

A practical, step-by-step guide to backtesting a Kalshi trading bot with real historical market data — covering data sourcing, simulation logic, slippage modeling, and interpreting results without fooling yourself.

· 9 min read
Kelly Criterion for Kalshi: Optimal Position Sizing
Strategies

Kelly Criterion for Kalshi: Optimal Position Sizing

How to use the Kelly criterion to optimally size your Kalshi trades. The math behind position sizing, practical adjustments, and why most traders should use half-Kelly.

· 11 min read
Kalshi Arbitrage: What's Real and What's a Myth (We Built the Scanner)
Strategies

Kalshi Arbitrage: What's Real and What's a Myth (We Built the Scanner)

We built real Kalshi arbitrage scanners and put the actual production fee formula in the math: the single-market myth, the one real edge, and why the fee dome means there's no free money on a regulated venue.

· 16 min read
Kalshi Market Making: A Liquidity Provider's Guide
Strategies

Kalshi Market Making: A Liquidity Provider's Guide

How to run a market making strategy on Kalshi — posting two-sided quotes, managing inventory risk, and earning the spread in prediction markets.

· 13 min read
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Strategies

Why Most Kalshi “Signals” Are Noise: A Real Order-Book Reality Check

We scanned Kalshi's live public order book. Most markets are too wide and thin to trade, single-market arbitrage is structurally impossible, and the market list is mostly auto-generated. What actually survives the filter.

· 10 min read
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Trading Bots

Kalshi Copy Trading in 2026: What Actually Works (Honest Guide)

Can you copy trade on Kalshi? We checked Kalshi's own docs, the services (Duel.trade, kalshitradingbot), and scanned 2,000 real trades. The honest guide — plus the rules-based way that works.

· 16 min read
How a Kalshi Bot Lost 369 Trades in a Row (And What We Learned)
Trading Bots

How a Kalshi Bot Lost 369 Trades in a Row (And What We Learned)

We built a Kalshi bot for cheap-contract asymmetric upside. It lost 369 paper trades in a row. Forensic breakdown of three structural flaws.

· 12 min read
Kalshi Weather Trading: Temperature Market Guide
Strategies

Kalshi Weather Trading: Temperature Market Guide

An honest, data-driven guide to Kalshi weather (temperature) markets: how forecast models (GFS, ECMWF, Open-Meteo) price daily-high contracts, why most 'edge' is model-vs-market disagreement, and how to automate it properly.

· 14 min read
7 Proven Kalshi Trading Strategies (2026)
Strategies

7 Proven Kalshi Trading Strategies (2026)

Weather events, sports edges, arbitrage, market making, and more — 7 proven strategies with entry rules, position sizing, and real Kalshi P&L examples.

· 20 min read Essential Guide