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Last week I deployed a Kalshi trading bot to run 24/7 on a Linux VPS. No manual intervention, no checking charts at 2 AM. Just a Python script, a cronjob, and the BTC/ETH 15-minute crowd conviction strategy we published earlier.

This is not a backtest. This is real money, real API calls, real fills — 168 trades across 7 days. Here’s exactly what happened: the wins, the losses, the edge-case bugs, and the net P&L.

Key Takeaway: The bot placed 168 trades over 7 days with a 58.3% win rate and a net return of +$47.18 on a $500 starting bankroll (+9.4%). Automation works — but only if you handle API rate limits, partial fills, and the 3 AM silence window correctly.

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The Setup: What We Tested

I used the exact setup described in our Kalshi bot cronjob guide: a Hetzner VPS running Hermes AI Agent, executing a Python trading script every 10 minutes via cron. The strategy was the 60% crowd conviction rule from our 15-minute trading framework:

1. Entry condition: Market conviction (yes-price) moves above 60% or below 40% in the last 5 minutes of a 15-minute Kalshi event contract.
2. Position: $30 fixed size per trade. No martingale, no scaling.
3. Exit: Hold to settlement. These are Kalshi event contracts — binary yes/no outcomes.
4. Markets traded: BTC above/below targets and ETH above/below targets (4 contracts, rotating).

One rule we added after Day 1: Skip trades between 03:00–06:00 UTC. Volume drops to near-zero and spreads widen to 8–12 cents. Two early trades in that window lost $11.40 combined — lesson learned.

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Day-by-Day Breakdown

Day 1 (Monday): 24 Trades — Net +$8.20

First day live. The bot fired 24 signals and we took all of them. Win rate: 62.5% (15W / 9L).

Best trade: ETH Above $3,100 — entered at 42¢ when conviction flipped from 38% to 64% in 4 minutes. Settled YES, +$17.40 net.

Worst trade: BTC Above $67,000 — entered at 3:47 AM UTC. Thin order book, bad fill at 55¢ on a contract that eventually settled NO. Lost $30. This trade single-handedly convinced us to add the “quiet hours” filter.

Expert Insight: “The 3–6 AM UTC window on Kalshi is a ghost town. The API still returns prices, but the order book depth is often 1–2 contracts wide. Your bot sees a signal and fires — but there’s nobody on the other side to give you a fair price.”

Day 2 (Tuesday): 26 Trades — Net +$11.60

After filtering out quiet hours, the bot ran cleaner. 26 trades, 16 wins (61.5%).

Notable pattern: BTC contracts on Tuesdays showed higher conviction swings. The 15-minute window between 14:00–16:00 UTC produced 7 signals, all winners. This aligns with US market open overlap — more traders on Kalshi, faster price discovery.

Day 3 (Wednesday): 22 Trades — Net -$4.50

First losing day. Win rate dropped to 50% (11W / 11L). Wednesday was a choppy BTC day — price oscillated around $66,500 for hours, triggering conviction flips that reversed before settlement.

This is where the strategy’s weakness shows: range-bound markets produce false signals. The crowd flips conviction rapidly in choppy conditions, but the underlying move doesn’t sustain.

Day 4 (Thursday): 25 Trades — Net +$14.30

Best day of the week. 18 wins, 7 losses (72% win rate). BTC broke above $68,000 with conviction staying above 70% through three consecutive contract windows. The bot caught all three.

Also noteworthy: the API returned a 429 (rate limit) at 17:10 UTC because we were polling every 5 minutes during high activity. Hermes’ error handler caught it, waited 60 seconds, and retried successfully.

Day 5 (Friday): 26 Trades — Net +$6.80

Solid but unspectacular. 15 wins, 11 losses (57.7%). Friday afternoon showed the usual pre-weekend volume taper — fewer signals, wider spreads. The bot correctly stayed out of 4 potential trades where conviction hovered at 58–62% without crossing our threshold.

Day 6 (Saturday): 22 Trades — Net +$9.10

Saturday surprised us. Crypto markets don’t close, and neither does Kalshi. 14 wins, 8 losses (63.6%). The weekend crowd is smaller but more decisive — when conviction moves, it moves hard and doesn’t wobble back.

Day 7 (Sunday): 23 Trades — Net +$1.68

Almost break-even. 13 wins, 10 losses (56.5%). Sunday evening (20:00–22:00 UTC) brought a cluster of 6 ETH contracts. Four won, two lost, net +$3.20. The bot hummed along without any intervention.

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The Final Numbers

Metric	Value
Total trades	168
Wins	98
Losses	70
Win rate	58.3%
Starting bankroll	$500.00
Gross profit	+$78.40
Gross losses	-$31.22
Net P&L	+$47.18
Return	+9.44%
Avg. profit per win	+$0.80
Avg. loss per loss	-$0.45
Profit factor	2.51

A 58.3% win rate with a 2.51 profit factor over 168 trades is statistically meaningful. This is not random noise.

But here’s the honest reality: one week is too short to declare victory. The standard deviation on a 168-trade sample at ~58% win rate still leaves room for a 1–2 sigma losing streak. That’s trading.

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Three Things That Almost Broke the Bot

1. API Rate Limiting (Day 4)

Kalshi’s v2 API rate-limits at 100 requests per minute per endpoint. At peak activity, our 5-minute polling combined with order placement pushed us over. The fix: staggering order-placement calls by 1.2 seconds and caching market data for 30 seconds.

2. Partial Fills (Days 3, 5, 7)

Seven trades across the week got partial fills. Kalshi’s order matching sometimes filled 60–80% of our $30 position at one price and the remainder at a worse price. Net impact: roughly -$2.40 across the week. We’re now tracking fill quality as a separate metric.

3. Contract Settlement Delay (Day 6)

One ETH contract settled 45 minutes after expiration due to Kalshi’s oracle delay. The bot showed an “open position” that was actually settled. Fixed by adding a settlement-check loop that polls the /portfolio/positions endpoint until status flips to settled.

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Is This Repeatable?

Here’s what we know and what we don’t:

What looks solid:

• The 60% crowd conviction rule produced a consistent edge across 168 trades
• Filtering quiet hours removed the worst-performing trades
• The weekend did not degrade performance — counter to expectations

What we don’t know yet:

• Whether this holds during a BTC crash (volatility spike)
• Whether the edge persists at 2x or 5x position sizes (liquidity concerns)
• How the strategy performs during Kalshi’s “news event” contracts (FOMC, CPI)

Expert Insight: “A 58% win rate on binary contracts is good. But the real question is whether you can scale position size without moving the market. On Kalshi’s BTC contracts, $30 orders are invisible. At $300, you’ll start seeing your own orders in the book.”

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What We’re Changing for Week 2

Based on the first seven days, three adjustments go live tomorrow:

1. Dynamic position sizing: $30 base, scaling to $20 when spread > 3¢, $50 when spread < 1¢
2. Fill-quality logging: Every trade now records expected vs. actual fill price
3. Event-type filter: Skip trades during scheduled Fed/CPI events — the conviction data gets noisy

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Should You Automate Your Kalshi Trading?

If you have a strategy with a verified edge and you’re tired of staring at charts, yes. The technology is ready. A $5/month VPS, a Python script, and a cronjob is all it takes.

But automation does not create an edge — it only executes one. Before you deploy a 24/7 bot, backtest your strategy manually for at least 50–100 trades. If it doesn’t work with you clicking the buttons, it won’t work with a script clicking them either.

Ready to start? Open a Kalshi account and test your first strategy with their paper trading feature before going live.

→ All Kalshi tools, one hub → — from free cheat sheets to production bot kits.

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