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Artificial Financial Intelligence
Time. Numbers. Statistical Phenomena.
Time. Numbers. Statistical Phenomena.
Everyone sees the same OHLCV candles. Not everyone sees the statistical regularities that appear, decay, and re‑emerge across instruments and timeframes.
We approach capital‑market time series as a mathematical, causal, statistical, and meta‑heuristic problem — not a financial one.
Reverse the logic: Statistical Phenomena first, explanation later.
Reverse the logic: Statistical Phenomena first, explanation later.
In traditional quantitative workflows, a researcher uses intuition and domain knowledge to validate a statistical pattern in the data. We reverse that logic. If the pattern existed and preceded its explanation, then it should be discovered directly — not reasoned into existence.
Most Statistics based Quantitative Strategies are Generic in nature and are Asset-Agnostic.
Most Statistics based Quantitative Strategies are Generic in nature and are Asset-Agnostic.
Momentum, dispersion, pressure, flow, Stat-Arb or order-book microstructure — these are statistical ideas, not financial ones.
If a phenomenon holds true on one asset, its variation likely holds on others — equities, FX, commodities, crypto.
e.g.: Illustrative phenomenon (dummy assets)
Event A : When Relative Strength Acceleration on Dummy Asset A exceeds +1.2σ and a Momentum Divergence metric on Dummy Asset B is above 0.3σ, a joint outcome follows within 12 minutes.
Observed 46 times in last 90 days • Same‑direction outcome in 38/46 cases → 82.6% consistency
Event B : When Relative Strength Acceleration on Dummy Asset y exceeds +1.7σ and a Momentum Divergence metric on Dummy Asset z is above 0.42σ, a joint outcome follows within 25 minutes.
Observed 30 times in last 90 days • Same‑direction outcome in 24/30 cases → 80% consistency
Numbers shown are illustrative to show how a variation of a strategy can be applied to other assets, differing only conditionally, while remaining same in Principle.
We go where your hypothesis would never.
Limitless Possibilities
Limitless Possibilities
In large-n systems, where statistical events occur frequently within short horizons, patterns often precede their explanations.
Traditional research waits for a hypothesis to be validated against such patterns; we invert that process.
If the phenomenon exists in the data with sufficient recurrence, its discovered first — and explained later.
This inversion allows us to treat every dataset as a mathematical object, independent of its label or origin.
Whether the search space is intra-asset (within equities, within crypto, within FX) or cross-asset (equities vs FX, crypto vs commodities), the treatment remains the same: a time series is a time series.
From this uniform lens, we surface statistical irregularities that domain expertise may overlook or find counter-intuitive — yet they remain only a quantitative hypothesis away from validation.
They are reproducible, falsifiable, and structurally sound.
This is statistical arbitrage in its absolute form:
we perform the statistics, and leave its arbitrage to you.
Always learning.
Always learning.
From 30 years to 30 seconds, Equities, FX & Commodities to Crypto — our systems continuously renew hypotheses across the universe. Discover. Discard. Rediscover.
MADE IN INDIA, WORKS ON WALL STREET.
MADE IN INDIA, WORKS ON WALL STREET.
While the world chases AGI (Artificial General Intelligence), we’re building something sharper, something that doesn't Generalize , something that speaks in Absolutes: AFI—Artificial Financial Intelligence.
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