Ultimate Guide: Build Your Kalshi Bitcoin Trading OpenClaw System Scale $100 → $5,000
Scale $100 → $5,000 | The AI Agent That Trades Prediction Markets While You Sleep
The new big thing isn’t OpenClaw — it’s the functionality. It’s the AI agent setup and how it runs autonomously for you.
This setup isn’t new. OpenClaw just made it more relevant in terms of what can be built with vibe coding, AI tools, and AI agents.
For me, I’ve been thinking about this idea for a long time — how to get AI agents to be like JARVIS and run things for me while I focus on the real, main work. I didn’t use OpenClaw, though. I built my own secure, local system. But this isn’t the article for that.
What this is about is the information and a few use cases you can build with OpenClaw — and ultimately with any AI agent system. That’s where the real opportunity is: making money in areas that were a little harder before, like stock trading, Forex, crypto trading, and the new wave of prediction markets.
I even had a client ask about this for scalping (there’s a free guide on this using n8n and Alpaca).
What we’re focusing on now is the newer, easier trend of setting up OpenClaw with prediction markets so you can potentially have the AI pay for itself — especially considering Claude Code costs and API usage fees.
With the way the economy is right now, I love focusing on entrepreneurial ways of finding side hustles and new income streams. Some people call this gambling. I see it differently — more like an investment. You invest time to build the system and some capital to test and start.
If it’s properly set up and planned, after that initial investment, it can begin generating income on its own.
AKA: Passive income.
So let’s jump into it.
What is Kalshi?
Kalshi is a federally regulated prediction market platform in the United States, overseen by the CFTC. You're not buying Bitcoin directly. You're buying contracts on what Bitcoin will do.
Here's an example of how it works in practice.
Say Bitcoin is trading at $64,800 right now. Kalshi has a market open: "Will BTC close above $65,000 at 11 AM EST?"
You can buy YES contracts at 43 cents each. If BTC closes above $65,000, each contract pays $1.00. If it doesn't, you lose your 43 cents.
That's it. Binary outcome. Every contract settles to either $1.00 or $0.00.
What makes this interesting — and profitable for automated systems — is that these markets open and close every hour. New BTC, ETH, SOL, and XRP markets go live constantly throughout the trading day. And the pricing on these contracts is set by humans who are manually checking prices and placing orders.
Humans are slow. AI agents are not.
What Is OpenClaw?
OpenClaw is an open-source AI agent framework that runs locally on your computer. Think of it like a personal AI that has its own memory, its own tools, and runs on a schedule without you touching it.
You don't write a full application. You write three files:
SOUL.md — who the agent is and how it thinks
AGENTS.md — what it does, what rules it follows, how it makes decisions
SKILL.md — what tools it can call and how to use them
OpenClaw reads those three files, connects to whatever APIs you point it at, and runs. It has a heartbeat — it wakes up on schedule, does its job, and writes what it learned into memory files for the next run.
No servers. No cloud infrastructure. No always-on WebSocket connections you have to maintain. Just your machine, three config files, and Kalshi's free public API.
The Edge Nobody Talks About
Here's what most people miss about these markets.
When Bitcoin drops $400 in 30 seconds on Coinbase, Kalshi's hourly contracts take 60 to 120 seconds to reprice. The people who set those prices are still navigating the UI, checking their phone, deciding what to do.
In that 90-second window, the YES contract for "BTC above $65,000" might still be priced at 52 cents — even though the real probability just dropped to 35 cents based on where Bitcoin actually is right now.
That gap between reality and what the market is pricing? That's the trade.
A person doing this manually will almost always miss it. By the time they open the app, find the market, and place the order — the gap is gone. Other traders have already corrected it.
An AI agent running every 15 minutes, comparing live Kalshi prices to what history says they should be worth — that agent catches the gap every time.
How Our Agent Actually Works
Here's the full loop in plain English:
Step 1 — Learn from history first
We have a 1-year dataset of Kalshi BTC market outcomes. Before the agent ever places a trade, it reads that dataset and writes what it finds into a MEMORY.md file. Things like:
"BTC YES markets priced between 45–50 cents with 20–40 minutes to close resolved YES 64% of the time."
That pattern lives in memory permanently. Every trade decision after that is informed by it.
Step 2 — Scan live markets every 15 minutes
The agent calls Kalshi's free public REST API — no authentication required for market data — and pulls all open BTC hourly markets. It filters to markets closing in 10 to 50 minutes. That's the sweet spot.
import requests
response = requests.get(
"https://api.elections.kalshi.com/trade-api/v2/markets",
params={"status": "open", "series_ticker": "KXBTC", "limit": 50}
)
markets = response.json()["markets"]No API key. Completely free. Just a GET request.
Step 3 — Compare live price to historical pattern
For each open market, the agent checks: what is the current YES price? What does memory say the historical resolution rate is for similar setups? If the gap between those two numbers is 7 cents or more — that's a signal.
Current YES price: 46 cents. Historical resolution rate for this setup: 64%. Gap: 18 cents.
That's a strong signal. The market is underpricing YES by 18 cents relative to what history says it should be worth.
Step 4 — Check liquidity before touching anything
Before any trade, the agent checks the orderbook:
orderbook = requests.get(
f"https://api.elections.kalshi.com/trade-api/v2/markets/{ticker}/orderbook"
).json()
yes_bid = orderbook["orderbook"]["yes"][0][0]
no_bid = orderbook["orderbook"]["no"][0][0]
book_sum = yes_bid + no_bid
# Normal market: sum is 97–99 cents
# Mispriced market: sum drops below 95 cents
if book_sum < 95:
# Liquidity was pulled — one side is cheap. Buy it.
passIf the YES and NO bids don't add up to close to 100 cents, liquidity was pulled from one side. That's another signal — and sometimes an even stronger one than the historical gap.
Step 5 — Risk check, then execute
Every trade goes through an automated risk check before execution. No exceptions.
The gap must be at least 7 cents
At least 20 contracts are available on the entry side
Market must close in 10–50 minutes
Max 5% of portfolio per trade
Max 40% of portfolio deployed at once
If all checks pass, the agent places a limit order via the Kalshi API. It waits 90 seconds. If the order hasn't filled, it cancels and moves on.
(You can also change these parameters to better suit your needs)
Step 6 — Write everything to memory
After every trade resolves — win or loss — the agent writes what happened to its memory files. Patterns that win three times in a row get promoted to MEMORY.md confirmed setups. Patterns that lose twice get flagged and avoided.
The agent gets smarter every single day because it never forgets anything.
The Memory System
This is the part that makes OpenClaw genuinely different from just running a script.
OpenClaw maintains two memory layers:
MEMORY.md is long-term memory. Curated. Only the most important patterns live here. The agent reads this at the start of every session, so it always knows what's been working and what hasn't.
memory/YYYY-MM-DD.md is the daily journal. Every trade is logged with full context. What the signal was, what the outcome was, and what the agent learned.
Over weeks and months, these compounds form a system that knows the Kalshi BTC markets better than any human trader who isn't watching full-time.
## Confirmed Patterns
- YES markets priced 44–48¢, 25–35 min to close, book sum > 97¢ → WIN rate: 71% (tracked over 23 trades)
## Patterns to Avoid
- YES markets priced 44–48¢ when book depth < 30 contracts → fills poorly, avoid thin books
## Best Trade Windows
- 9AM–11AM EST hourly markets consistently show the widest gaps at openWhat You Need to Get Started
On the technical side, this is lighter than you think:
A Kalshi account (free to sign up at kalshi.com)
An API key from your Kalshi account settings
OpenClaw installed on your machine
Python with three packages: requests, pandas, python-dotenv
Our 5-file agent workspace dropped into your OpenClaw folder
That's it. No servers. No cloud accounts. No subscription fees beyond your Kalshi account.
The Kalshi API for reading market data is completely free and requires no authentication. You only need the API key when you're ready to place actual trades.
The Files We Built
The full system is 4 files:
SOUL.md — defines the agent's identity. Patient, disciplined, focused on learning from every trade. Its core belief: every loss is data, write it all down.
AGENTS.md — the full strategy document. How to read the historical data, how to scan live markets, how to spot a gap, how to check liquidity, the complete 7-step trading workflow, and critically — the memory rules that tell the agent exactly what to write and when.
SKILL.md — every tool the agent can call, with exact commands and API endpoints. load_history.py, scan_markets.py, get_market_data.py, risk_check.py, place_trade.py, get_portfolio.py, daily_report.py.
MEMORY.md — the structured starter file for long-term memory. Pre-built with the right sections: BTC Historical Patterns, Confirmed Patterns, Patterns to Avoid, Best Trade Windows, Current Stats. The agent fills these in as it runs.
And a readme lol,
Ready to Build This?
This system is available exclusively to our community.
Get the full 5-file agent workspace file for $49.00 in our SHOP
Read ourFree article on using Polymarket with OpenClaw.
Join the community to get access, ask questions, and share what you're finding → buymeacoffee.com/streets2entrepreneurs
We'll be expanding this to ETH, SOL, and XRP markets next, and adding multi-agent mode so multiple assets run in parallel. If you're already in the community, watch the announcements channel.
Start with PAPER_MODE=true. Learn how the agent thinks. Watch it find signals. When you're confident in what it's doing, then go live.
The edge is real. The question is whether you move fast enough to use it.
Need private setup help? Book a free AI consultation - https://cal.com/bookme-daniel/ai
This is not financial advice. Trading on Kalshi involves real risk. Always start with paper trading. Past performance of any pattern or strategy does not guarantee future results. Kalshi is regulated by the CFTC.
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