Every RSS feed, Twitter handle, Telegram channel, financial indicator,
and whale-tracker source the bot ingests gets a measured expectancy
delta on real trade outcomes. With-vs-without paired-trade win rates,
sample sizes, and a verdict — computed nightly by the source-ablation
cron. No other AI prediction product publishes per-source attribution.
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Status
Methodology validated, sample sizes still accumulating.
At the current paired-trade count, no individual source has crossed the
Wilson-CI statistical-significance threshold for a likely real
verdict yet. The list below shows directional contributions and is
recomputed nightly — bookmark this page; it gets stronger with
every settled trade. (For comparison: most "AI trading" projects
publish zero per-source attribution at any sample size.)
How to read this
Effect (pp): change in win-rate (percentage points) when
the source IS present versus when it ISN'T.
Expectancy Δ (USD): change in average P&L per trade.
Verdict:likely real means
the Wilson-CI bands don't overlap (statistically meaningful effect);
no clear effect means signal is
present but inconclusive at current sample size;
insufficient data means there
aren't enough paired trades yet. Sources reported as
negative contributions are candidates for pruning;
positive contributions are candidates for amplification.
Source / layer
Variant
n (with)
n (without)
Effect (pp)
Expectancy Δ ($)
Verdict
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Continuous-feature monotonicity
Binary "had whale yes/no" hides the difference between 1 whale-trade
and 30. This panel groups trades by item-count bucket per source-type
and reports the Spearman rank correlation between bucket index and
win rate. monotone_increasing = volume genuinely matters
(real signal that scales). monotone_decreasing = volume
actively hurts (suspicious). non_monotone = presence was a
red herring; volume is noise. Each ρ carries a 95% confidence
interval; ≠0 marks correlations
whose interval excludes zero, and an underpowered
badge flags sources with too few paired trades to trust the number yet.