Field notes from
the automation studio.

Nvidia funds OpenAI, OpenAI buys Nvidia chips, the stock lifts on the revenue. A walk through the AI circular revenue loop and what probably survives.

Stop loss explained — what it actually is, the four legitimate placement methods, and the mistakes that have nothing to do with strategy.

Demo account, paper trading, trading simulator — same product, three labels. What they actually are, what they teach you, and the four ways every demo lies.

Prop firms have become a normal route into trading capital for retail-scale traders, with all the complications that brings. An honest look at the model, the pass rate ranges, the patterns we see in passers and failers, and the math that decides whether the structure is fair.

The retail temptation around Fed days, NFP, and CPI prints is to trade the spike. The pro move is to use macro events as context for restraint, and to model the second-order effects across asset classes. A framework, not a signal set.

Prediction markets sit awkwardly between trading and gambling, and 2026 is the year retail finally has to take them seriously as a category. A walk through what they are, how the math works, where the regulatory complexity bites, and why most retail content frames them wrong.

An honest forecast of where retail trading is heading over the next five years — what AI changes, what regulation reshapes, what costs do, and what the next generation of retail traders will need that the current generation never had.

A structured 30-day plan for using a trading demo account the way it was meant to be used — observation first, journal second, paper trades last. The exact week-by-week cadence we run with Tradoki cohorts, and why rushing the demo phase is the single most common mistake.

There are a handful of trading myths that survive every cycle, every market regime, every educational fad. The data on each of them is unflattering. Here is the short list.

Most EU retail traders who think they are trading forex are trading forex-via-CFD — and the two are not the same product. A walk through the instrument, the ESMA leverage regime, how Germany, Austria and Switzerland differ, and when CFDs add value.

An honest, comprehensive starting point for anyone considering active trading in 2026 — what to expect, what not to believe, what to learn first, and what the data on retail outcomes actually says.

Most retail traders pick instruments by accident — the asset their broker pushes, the asset they saw on social. This pillar guide walks through the eight-question asset selection framework Tradoki teaches, across forex, crypto, stocks, indices, commodities, and REITs.

Trading psychology gets sold as breathwork, affirmations, and books that are mostly anecdote. The actual problem is mechanical. Here is what I think the pop-science version misses.

Most traders practise by trading. That is not deliberate practice — that is just paying for tuition. Here is the ninety-day plan Tradoki cohorts use after week eight to actually build skill.

Most trading journals fail because they capture the wrong fields. Here is the exact template Tradoki cohorts use, the four post-mortem questions that matter, and why screenshots without context are noise.

Risk of ruin is the math underneath every trading career. Most retail traders have never sat with it. This is the long-form guide we use inside Tradoki to make the math unavoidable.

Solo trading courses have low completion rates. The cohort model — fixed start date, fixed peers, fixed end — is how Tradoki gets students to the finish line. Here is the design and the data behind it.

What the eight weeks of the Tradoki curriculum actually contain — week by week, with the assignments, the live sessions, and what we have changed since cohort one.

Selling signals is the easiest revenue model in trading education. We chose not to. Here is the long-form reasoning, and why I think the choice is the most important one we have made.

The ten-month story of why Tradoki exists, what we tried first, what we threw away, and the specific decisions that shaped the company we ended up running.

AI live-trading bots blow up for the same reason they look attractive: they remove the human checkpoint that survives regime change. Here is what we have observed, and the narrower set of AI uses that hold up.

The right way to use a frontier model in trading work is as a fast, well-read junior analyst — not as a principal making calls. The framing changes which prompts you write, which outputs you trust, and where the model's value actually lives.

The 'AI trading signals' market sells subscriptions to systems that have no live edge. The data we have collected over twelve months is unflattering enough that I will say it directly.

AI assistants are good at producing Pine Script that compiles. They are bad at producing Pine Script that does what you meant. Here is the workflow we use to keep that gap from costing real money.

AI is genuinely useful for traders in narrow, specific ways and dangerous in others. An honest mid-decade audit of what the current generation of models actually does, where it fails, and how to use it without being used by it.

Trading the initial spike on a high-impact economic print is one of the most reliable ways to lose money fast. Here is how the news-fade pattern actually forms and why retail keeps walking into it.

Volume profile is a useful map of where price has done business. It is also a discipline that has been turned into a cult. Here is how I actually use it without pretending it is magic.

Multi-timeframe top-down analysis fails when it confirms a hunch instead of disqualifying a trade. The three-timeframe framework we use to fix that.

Liquidity sweeps are not the market hunting your stop. They are a structural feature of how price discovers resting orders — and why retail keeps walking in.

Mean reversion still works in 2026, but only inside narrow regimes. The framework we teach, and the failure modes we see retail traders repeat.