Building the agentic trading operating system, in public.
Numbers over adjectives. Long answers, short ledes. Notes from the team shipping multi-LLM consensus, MCP for traders, and the on-chain stack.
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llms.txt →Kalshi API Tutorial: Build Your First Market Monitor
Build a read-only Kalshi market monitor before adding orders. This tutorial retrieves a small set of open markets, reads dollar-denominated quotes, preserves missing values, and saves the market rules with a timestamp. The Python example uses the standard library and needs no API key for these public market reads.
Polymarket Copy Trading Bots: How to Choose
A Polymarket copy trading bot watches another trader and submits separate orders for your account. Your execution price, size and timing can differ from theirs. Start by comparing controls, data quality and actual fills, then test whether the trades you want to follow can be copied under your own limits.
Polymarket API Guide: Read Markets, Prices and Place Orders
The Polymarket API is two services: the Gamma API for market and event data, and the CLOB API for order books, prices and orders. Reading needs no key. Placing an order needs a wallet signature and API credentials derived from that wallet. Here is how each part works, with a request you can run now.
Best Paper Trading Platforms in 2026: Simulators Compared
The best paper trading platform depends on what you are rehearsing. Webull and thinkorswim simulate manual stock trading, TradingView adds paper fills to its charts, Investopedia turns it into a game, and NickAI runs automated strategies with $100k of virtual money before they touch a real account.
Paper Trading vs Backtesting: When to Use Each
Backtesting replays a strategy against history; paper trading runs it live with virtual money. Backtests answer whether the logic ever worked, paper trading answers whether it works now, on real fills and timing. The sequence that avoids both failure modes: build, backtest, paper trade, then commit small real size.
AI Paper Trading: How to Test Any Strategy with Virtual Money
AI paper trading tests an automated workflow with virtual funds. Review the inputs, decision rules, simulated orders, and execution logs before considering a live connection. Here is a practical NickAI walkthrough.
AI Stock Trading Bot: Build and Test an Alpaca Workflow
An AI stock trading bot combines market data, decision rules, and a brokerage connection. Build an inactive NickAI workflow, connect an Alpaca paper account, and inspect its behavior before enabling a schedule.
Best AI Trading Bots in 2026: The Honest Comparison
The best AI trading bot depends on what you want it to do. Pionex ships free grid bots on its own exchange, 3Commas and Cryptohopper run preset strategies across major exchanges, and NickAI builds the strategy for you from a plain English description, across stocks, crypto, and prediction markets.
ChatGPT Trading Bot: What It Can Do and How to Build One
ChatGPT alone cannot place trades. A ChatGPT trading bot wraps a model with market data, decision rules and an execution connection to your exchange or broker. This guide shows what a chat model can and cannot do, compares three ways to build one, and walks through five steps you can test before any live order.
NickAI vs Cryptohopper: Which Fits How You Trade in 2026
Cryptohopper is an established cloud bot platform for crypto: indicator-based strategies, a marketplace, and paper trading. NickAI is an agentic trading platform: you describe the strategy in plain English and Nick builds and runs it across stocks, crypto, and prediction markets. Which fits depends on how you trade.
Introducing the New Nick: A Complete Trading Agent
The new Nick is a complete trading agent. Whatever you want to do in markets, a quick question, a one-off trade, a scheduled automation, or a full quant strategy, you now do it by talking to Nick. Your funds stay in your own exchange or wallet the whole way.
Can You Compete With AI Agents in Prediction Markets? (2026)
You cannot beat AI agents in prediction markets by trading manually: they read news faster, price niche events better, and never sleep. But you can compete by running your own agent. The edge in 2026 is not human-vs-AI; it is whose AI, on which venue, with what information. This is the honest assessment of where humans still win, where agents dominate, and how to level the field.