Predict & Profit Blog

How Python's or operator on an xarray DataArray raises a silent ValueError that took down my AIGEFS integration before it ever ran a real trade.

How to know your bot is still running at 3am without buying a monitoring SaaS: a heartbeat file, a lightweight HTTP endpoint, and a cron job that emails you when things go quiet.

How the Econ Bot's regime-change auto-close works, why I almost didn't ship it, and the testing path that made me confident enough to turn it on.

My full git push to production workflow for solo Python projects: no Docker, no Kubernetes, no Jenkins, just SSH and a shell script that actually works.

How Kalshi winnings get taxed, what the 1099 threshold actually means, why you need your own records, and what changes when you trade through an LLC.

Real pricing, real tradeoffs, and the exact setup I use to run two trading bots and a blog on a $6/month VPS.

The exact Postgres setup, schemas, and backup commands running the Weather Bot and Econ Bot on a $6 RackNerd VPS.

The psychological cost of knowing the right answer and watching the wrong one get implemented anyway, for three decades.

Real numbers from the first few months of predictandprofit.io: hosting costs, Gumroad fees, time invested, first sale, and what marketing actually moved the needle.

The moment a buyer emails you at 11pm with a setup question is the moment your side project becomes a product, whether you are ready or not.

NOAA's AI-enhanced forecast system went operational in December 2025, and the accuracy numbers are significant enough to change how an ensemble-based trading bot should weight its sources.

The logging configuration that actually helps you reconstruct what your bot did after a bad trade, with real code from the Predict & Profit bots.

Predict & Profit v2.2 is the release where the bots became more honest with themselves: submitted orders are no longer treated as fills, NO-side weather edges are supported, failed data sources are visible, and the dashboard was rebuilt for real operation…

A real post-mortem on the Econ Bot's April 2026 CPI positions: how the Cleveland Fed nowcast performed, which strikes resolved correctly, and where the signal missed.

Thirty years of institutional knowledge, revoked by a screen share and an HR script, and what that moment teaches you about building things you actually own.

Forced distribution reviews guarantee that 10% of your team fails regardless of absolute performance, and I watched it hollow out one of the best engineering groups I've ever been part of.

Corporate language is engineered to make inaction sound like progress, and after 30 years of it I built something that speaks plainly.

After 30 years in software you hit a ceiling that nobody talks about in the job posting.

Most algo trading tutorials show you how to place a trade.

The Open-Meteo ensemble API gives you access to 31 or more GFS members per forecast cycle at zero cost.

I spent years on corporate on-call rotations, getting paged at 3 AM for pipelines I did not write, on infrastructure I did not own.

The first version of the bot had no real filters.

Every Friday afternoon, corporate engineers across the country write status updates that nobody reads.

For years I sat in sprint planning sessions estimating story points on features nobody would use.

A trade signal tells you what to buy.

For most of my career I waited 12 months to find out what my work was worth.

What does it actually cost to run a 62-member ensemble weather bot on Kalshi, and what does it actually return?

Raw Finnhub news sentiment scores are noisy and time-agnostic.

The Cleveland Fed publishes a daily CPI nowcast that is, on most days, the single most accurate near-term inflation signal available to the public.

After 30 years of watching enterprise software collapse under the weight of accumulated requirements, I built a bot with exactly one mandate: find edge, execute trade, log result.

Running your bot in a terminal is not a deployment.

For 30 years I let other people decide if I was performing.

TCP connections to Alpaca's streaming API drop quietly.

Running two automated trading bots on the same server sounds simple until both try to write a trade record at the same millisecond.

Return-to-office mandates are corporate theater dressed up as productivity.

A technical walkthrough of the Predict & Profit macroeconomic trading engine: BLS, BEA, FRED, Zillow rent proxies, Cleveland Fed nowcast comparison, and final Kalshi order-book edge calculation.

A bad dictionary key that returns an empty list instead of raising an exception is more dangerous than a broken trading algorithm.

How I wired the Kronos candlestick foundation model into an Alpaca crypto bot, why it works better as a confirmation layer, and what that taught me about Predict & Profit automation.

Implement Kalshi API RSA-PSS signing in Python with private-key loading, signed request headers, timestamp handling, and the failure cases that cause 401 errors.

I spent 30 years building systems for corporations.

Corporate engineering is a trade where you exchange your sharpest hours for a salary and someone else's roadmap.

30 years of knowing the VP's architecture is wrong and saying nothing because you need the paycheck.

Every trading system loses.

As Kalshi prediction markets mature, simple arbitrage is gone.

Zero commission on IB ForecastTrader sounds great until you look at market depth.

I am 60, a full-time Senior Data Engineer, and I launched a Kalshi trading bot product in 30 days.

A single weather model will lie to you.

Most algo traders overtrade.

Real first-month numbers from the Predict & Profit Kalshi weather bot: trades placed, win rate, P&L, losses, lessons, and why public tracking matters.

Institutional market makers do not use JSON APIs for weather data.