cutebear

a pre-market analysis system that runs before every weekday open. thirty-one instruments are reduced to ten derived signals in code, the model reads verified numbers instead of doing arithmetic on raw prices, and the next day it grades its own call against what the market did. predictions are easy to demo and hard to keep honest. a market does not care what you claimed.

open cutebear → the method behind it

informational only, and not financial advice.

what it is

a pre-market read on the nasdaq-100, with self-scoring

runs

9:14am ET weekdays, sixteen minutes before the open

grades itself

6:30pm ET, against the actual closing direction

evidence per run

24 news feeds, an economic calendar, and 31 market instruments

model

gpt-5.6-luna, reading signals computed before the prompt is built

how a morning run works

gather evidence in parallel

24 news feeds are fetched concurrently across four tiers: major financial wires, market-specific desks, macro and central-bank coverage, and specialist sources. an economic calendar is pulled and each release tagged against forecast as a beat, a miss, or in line.

compute the signals before the model sees anything

31 instruments are fetched in parallel and turned into ten derived signals in code, not by the model, among them volatility term structure, tail-hedging skew, leveraged-ETF flow ratios, relative volume, market breadth, index crowding, credit risk appetite, and position against the 200-day average. the model receives verified numbers rather than raw prices to do arithmetic on.

one read, on numbers it did not have to derive

the evidence pack goes to a single model, gpt-5.6-luna, with the arithmetic already done. it is asked for judgment, not calculation: which signals dominate, what the market is not pricing in, and the strongest argument against its own conclusion.

tie the confidence label to the signals

each call is labelled low, medium, or high against a fixed rule: high means three or more of the precomputed signals point the same way, low means they contradict each other. the label is anchored to that signal set rather than to how certain the model happens to sound.

degrade honestly

if the model fails or returns something that will not parse, the run publishes nothing and records which step failed. the dashboard then says it has no call for the session rather than showing yesterday's. a missing answer that says so beats a confident answer that quietly lost its inputs.

grade the call

every prediction is stored with the trading day it refers to, then graded after the close against actual direction and kept on a permanent record with a running letter grade. predictions that are not yet resolved are marked pending, not quietly dropped.

the running accuracy is not on this page, it is in the system, computed live from graded history and sitting at the top of the log tab. open it and read it off the same record we do. a hit rate retyped onto a marketing page is a claim; the log is evidence.

what it demonstrates

this is a small system, and that is the point. it shows the practices we bring to much larger client work in a form you can inspect in one sitting.

the same harness every run

identical evidence shape, schedule, and output schema on every run, so calls stay comparable across months instead of being one-off demos. the same reason an eval holds its harness fixed.

confidence defined against a rule

the label follows a stated rule about how many precomputed signals agree, rather than the model's own sense of certainty. a definition you can check beats a number you cannot.

deterministic work stays in code

every number that can be computed is computed before the prompt is built. models are used for judgment, not arithmetic.

a scoreboard it cannot argue with

predictions are graded automatically against reality on a fixed schedule. the same discipline as holding out an eval set instead of admiring the training loss.

under the hood

php json file store parallel http (curl_multi) gpt-5.6-luna scheduled cron pipeline 30s quote cache intraday charting

interface

five views: synopsis, full analysis, the news digest it read, the raw signal table, and a graded history log

live ticker

17 symbols with change windows from one hour to ninety days, refreshed on a short cache

access

open to read; the run that writes it is the only thing that can change it

want a system that keeps score on itself?

ensembles, judge harnesses, and evaluation loops for your domain, built the same way.

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