Prepared by Chief | March 18, 2026
Fee Structures at Volume · All Market Categories · Discrete Edge Cases · Where the Alpha Is
Kalshi uses a probability-weighted formula:
Fee per contract = 0.07 × price × (1 − price)
This means fees are highest at 50/50 odds and drop toward zero at the extremes. For S&P 500 and Nasdaq markets, the multiplier is halved to 0.035.
| Contract Price | Fee per Contract | Cost on 100 Contracts | Effective Rate |
|---|---|---|---|
| $0.10 (90/10 odds) | $0.0063 | $0.63 | 0.63% |
| $0.25 (75/25) | $0.013 | $1.31 | 0.52% |
| $0.50 (50/50) | $0.0175 | $1.75 | 0.35% |
| $0.75 (75/25) | $0.013 | $1.31 | 0.17% |
| $0.90 (90/10) | $0.0063 | $0.63 | 0.07% |
Key insight: At high-probability contracts (near $0.90+), fees are almost negligible. This favors strategies where you're buying contracts you're highly confident about — like weather markets where your model says 90%+ but the market says 75%.
Maker fees: Generally lower or zero on most Kalshi markets. Using limit orders instead of market orders saves significantly — one experienced guide says this is the single most impactful thing new traders miss.
At volume example: 1,000 trades/month at $0.50 avg price, 10 contracts each = ~$175/month in fees. That's the cost of doing business — your edge needs to exceed this.
| Market Type | Taker Fee | Maker Fee | Notes |
|---|---|---|---|
| Polymarket Global (most markets) | 0% | 0% | 2% of net profits at withdrawal |
| Polymarket US (CFTC-regulated) | 0.10% | 0% | Flat taker fee on all trades |
| 15-minute crypto markets | Up to 3% | USDC rebates | Higher fees to deter HFT bots |
At volume: Polymarket Global's 2%-of-profits model is actually very expensive if you're profitable. On $10K in net profits, that's $200. Polymarket US at 0.10% is much cheaper — a $1,000 position costs $1.00 in fees. Plus ~$0.02–$0.05 in Polygon gas per transaction.
| Platform | Fee Model | Cost | Notes |
|---|---|---|---|
| Robinhood | Flat per-contract | $0.02/contract | Simple, predictable. New to prediction markets. |
| FanDuel Predicts | Payout-based | 2% of potential payout | Via CME-regulated exchange |
| DraftKings | Flat per-contract | $0.01 + exchange fee | Entering market |
| PredictIt | Profit + withdrawal | 10% of profits + 5% withdrawal | Most expensive — avoid |
For automated, high-frequency strategies: Kalshi with limit orders is the sweet spot. Maker fees are minimal, the API is clean, and you control exactly what price you trade at. At 50/50 contracts, you're paying ~1.75¢ per contract — your edge needs to consistently exceed that. At extreme prices (90/10), fees drop below 1¢. Always use limit orders — the fee difference and fill protection are massive at scale.
Here's the landscape of what you can actually trade, with honest assessments of where the edge is.
| Category | Weekly Volume | Share | Liquidity |
|---|---|---|---|
| Sports | $127.1M | 84% | Deep |
| Crypto | $15.9M | 10.5% | Good |
| Politics | $3.7M | 2.4% | Moderate |
| Economics | $1.7M | 1.1% | Moderate |
| Weather | ~$500K | <1% | Thin |
| Other (mentions, culture, science) | ~$1M | <1% | Thin |
What: Daily high temperature predictions for NYC (Central Park/KNYC), Chicago (Midway/KMDW), Miami (MIA/KMIA), Austin (Bergstrom/KAUS), Denver (DEN/KDEN), Houston (Hobby/KHOU), Philadelphia (PHL/KPHL). Six 2-degree brackets per day.
Resolution: NWS Daily Climatological Report from specific weather stations. Markets launch at 10 AM the previous day.
The Edge: Most traders check weather.com or Apple Weather. The real data sources are multiple weather models (GFS, HRRR, NAM, ECMWF), NWS MOS bias-corrected forecasts, and real-time station observations. Huge exploitable knowledge gap in how NWS stations report data — hourly vs 5-minute stations have different rounding/conversion quirks that create discrepancies between what you see on the time series and what the official high actually is.
Risks: Low liquidity limits position size (~$20-50 profit ceiling per city per day). Weather is inherently noisy — models diverge, microclimates matter.
Our fit: Excellent starter — Perfect for learning the platform, building data pipelines, and testing automated execution. Won't get rich but will build the infrastructure for everything else.
What: CPI (MoM, YoY), Core CPI, PCE inflation, unemployment rate, nonfarm payrolls, GDP growth, Fed rate decisions, probability of recession. Federal Reserve validated Kalshi as "just as good if not better" than traditional forecasting in a February 2026 paper.
The Edge: These resolve against official BLS/BEA data. Leading indicators (regional Fed surveys, PMI, weekly jobless claims, commodity prices, wage data) all come out before the official number. If you build a model that weights these inputs, you can form probability estimates days or weeks ahead. As new data drops, you update and trade the drift.
Sweet spot: CPI and jobs reports have the most liquidity. Fed rate decisions are heavily traded but also heavily efficient. The edge is in the less-watched releases (Core CPI, GDP components, PCE).
Our fit: Strong — Government data pipelines are your literal day job. BLS API is free. The modeling is straightforward time series + leading indicators.
What: NFL, NBA, MLB, NHL, college basketball, soccer (25+ leagues), tennis. Game outcomes, spreads, totals.
The Edge (if any): One builder (EventEdge, posted yesterday on Reddit) has a real-time model across 9+ sports with 2,324 trades at 56.7% win rate and 6.5¢ average edge. Uses Kelly criterion sizing. But the experienced Kalshi guide literally says: "Avoid trading sports game markets. They have the highest volume and deepest liquidity, making them extremely efficient and very hard to beat."
Reality check: Sportsbooks have been pricing these for decades. Kalshi sports markets are arbitraged against traditional books. You're competing with DraftKings, FanDuel, and professional bettors who have proprietary injury data, real-time tracking, and decades of models. Casual bettors subsidize this market.
Our fit: Low priority — Unless you have a specific domain advantage (e.g., you follow a niche sport obsessively), this is the hardest category to beat. High volume but razor-thin edges eaten by professionals.
What: 5-minute and 15-minute BTC/ETH/SOL price prediction contracts. "Will Bitcoin be higher or lower in 15 minutes?" ~$70M/day in volume. Now over half of all crypto prediction trading.
The Edge: Latency arbitrage between Polymarket prices and Binance/Coinbase real-time feeds. When Polymarket's price lags behind exchange prices by even milliseconds, bots buy the mispricing. One wallet turned $300 into $400K in a month doing this.
Reality check: This is HFT territory. Sub-100ms execution, dedicated RPC nodes, co-located servers. Polymarket now charges up to 3% taker fees on these specifically to deter bots. The barrier to entry is high and the space is already saturated with sophisticated quant bots.
Our fit: Not our game — Requires speed infrastructure we don't have and a pure quant edge in crypto price prediction.
What: "Will Trump say 'Biden' in his next speech?" "How many times will X be mentioned?" These are event-driven markets tied to specific public appearances or broadcasts.
The Edge: One experienced trader says mention markets are his favorite niche. The edge is in parsing speech patterns, reading prepared remarks leaked ahead of time, and understanding the context of specific events. Very human-judgment dependent.
Unique risk: Resting limit orders get adversely filled — you get filled only when the word IS said (bad for your NO position). Must monitor during live events.
Our fit: Interesting side play — LLM analysis of speech transcripts and patterns could provide systematic edge. Low capital deployment but fun and learnable.
What: Elections, legislation passage, government shutdown duration, SCOTUS decisions, cabinet confirmations, international events. Polymarket's bread and butter.
The Edge: AI-powered news repricing. When news breaks, markets take 30 seconds to 5 minutes to adjust. LLM ensemble models can process news, cross-reference sources, and calculate updated probabilities faster than the crowd. This is Strategy 2 from Part 1.
Our fit: Strong — This is pure LLM + news pipeline work. No domain-specific data needed, just faster information processing.
What: Weekly average TSA passenger counts. Binary contracts at various thresholds (e.g., above 2.3M, above 2.35M, above 2.4M).
The Edge: TSA publishes daily checkpoint data publicly. Seasonal patterns are strong (holidays, summer travel). Time series modeling with basic statistics (rolling averages, holiday adjustments) can beat the market. Jacob Ferraiolo built and documented an entire bot for this.
Reality check: ~$20/week profit ceiling due to liquidity. But it's a perfect learning market — low stakes, well-documented, clean data source.
Our fit: Good for learning — Already has a tutorial series. Minimal capital at risk. Great for testing the full pipeline end-to-end.
What: Same event priced differently on Kalshi and Polymarket. Buy YES on one, NO on the other. Lock in risk-free profit if total cost < $1.00.
The Edge: One Reddit user built and open-sourced a bot for this. Works best on Fed rate decisions, political events, and economic indicators that are listed on both platforms. The edge exists because the platforms have different user bases with different biases.
Catches: Requires accounts and capital on both platforms. Settlement timing differs. Polymarket requires crypto (USDC), Kalshi uses USD. Execution risk: you might get filled on one side but not the other. Opportunities are shrinking as more bots compete.
Our fit: Phase 3 play — Requires accounts on both platforms and enough capital to make the thin margins worthwhile. But it's genuinely lower-risk than directional bets.
From the experienced Kalshi guide: "Sometimes the edge is hidden in how the market resolves. People underestimate how often misunderstandings of edge-case resolution lead to massive mispricing." Reading the actual contract rules — especially for weird edge cases (overtime rules, tiebreakers, data source specifics) — reveals mispricing that most traders miss because they didn't read the fine print.
Weather markets resolve using NWS station data. The 5-minute stations round temperatures to whole Fahrenheit, convert to Celsius, and then the NWS converts back — introducing rounding errors of up to ±1°F. If you understand which station is reporting and how the rounding works, you can infer the true temperature better than the displayed NWS values suggest. This is a genuine information asymmetry.
During live speeches, mention markets move in real-time. If you're watching/listening and can process faster than the crowd, you can trade mid-speech. But: resting limit orders get adversely filled (only filled when bad for you). The smart play is to trade BEFORE the event based on analysis of prepared remarks, past speech patterns, and event context.
Related markets sometimes have logical inconsistencies. Example: "Will CPI be above 3%?" at 85% AND "Will the Fed raise rates?" at 50%. If CPI above 3% nearly guarantees a rate hike historically, one of these is mispriced. Scanning for conditional relationships between related markets is a systematic strategy bots can automate.
As events approach resolution, markets sometimes become very efficient on the "likely" side but misprice the "unlikely" side. Buying cheap long-shot contracts in weather or mentions markets — when you have data suggesting the long shot is more likely than the 5% the market implies — can yield outsized returns. The EventEdge sports bot specifically looks for these "cheap YES contracts in underdog scenarios."
Putting it all together — ranked by our realistic competitive edge:
| # | Category | Our Edge | Liquidity | Profit Potential | Complexity | Verdict |
|---|---|---|---|---|---|---|
| 1 | Economic Indicators | Strong | Moderate | Medium-High | Medium | ⭐ Best risk-adjusted opportunity |
| 2 | Political/News Repricing | Strong | Good | Medium-High | Medium | ⭐ Best for LLM-powered approach |
| 3 | Weather | Strong | Thin | Low-Medium | Low | 🎓 Best learning ground |
| 4 | Cross-Platform Arb | Medium | Varies | Medium | High | 📈 Phase 2-3 expansion |
| 5 | TSA / Niche Data | Strong | Very Thin | Low | Low | 🎓 Tutorial exists, good starter |
| 6 | Mention Markets | Medium | Thin | Low-Medium | Low | 🎲 Fun side play |
| 7 | Sports | Low | Deep | Medium if edge exists | Very High | ⚠️ Hardest to beat |
| 8 | Crypto Short-Term | Low | Deep | High if fast enough | Very High | 🚫 HFT territory, not for us |
Rather than going all-in on one category, the smartest play is a multi-category portfolio that combines:
This diversifies across resolution timelines (daily, weekly, monthly, event-driven), data sources, and risk profiles. The bot infrastructure is largely shared — data ingestion, probability modeling, Kalshi API execution, logging/tracking.
Report prepared by Chief | Prediction Market Opportunity Map | March 18, 2026
Sources: DeFi Rate, Kalshi Help Center, Reddit r/Kalshi, Reddit r/SaaS (EventEdge), Kalshi Blog, CoinDesk, crypto.news, Polymarket Docs, GWU CFTC Research
This report is for research and planning purposes. Not financial advice. Prediction market trading involves real risk of loss.