Raising the Bar for Future Research
An expanded methodology for future ML robustness testing — behavior under shifted, noisy or missing data, and across market regimes — was adopted as standing research policy.
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Development History
A curated, chronological record of early and pre-launch milestones through 2026-08-14 — from the project's earliest working prototype onward. It is not a log of every task or audit; it is the shape of how the system was actually built. For everything since, use the News selector.
Runtime
Trade History, on Request
A Telegram trade-history reporting feature was deployed to production, letting users request automatic and on-demand summaries of observed virtual trading activity.
ResearchHow the Layer Combines Model Outputs, Decided Carefully
A leakage-safe, independently audited procedure was adopted for selecting how multiple model outputs are combined into a single directional ranking.
AuditA Negative Result, and What Came Next
A full independent-evaluation phase closed after methodological audit, finding the evidence not yet sufficient for Production status — and a new, tighter, bounded research programme began immediately.
ResearchComparing Candidates, Not Picking Favorites
A structured, multi-phase model-comparison programme evaluated several candidate approaches side by side under frozen rules, closing without granting any of them Production status by default.
ResearchOne Methodology, Not Several
A canonical, leakage-audited dataset and training methodology was unified across model families, replacing several parallel, slightly inconsistent approaches.
ResearchA Full Model Family, Trained and Accepted
A full family of model architectures was trained, validated and accepted end to end on the second reference market.
AuditA Second Market, the Same Discipline
A full research/audit cycle closed on a second reference market, clearing it for a full model-training cycle under the same evaluation discipline as the first.
ProductOpening the Doors: Public Beta
Tantoryn AI opened its Telegram-facing public beta: the first audience-facing observation stage, with real-money order execution explicitly disabled.
EngineeringA New Home for the Runtime
Infrastructure was migrated to a new production server, completing with zero data loss and improved network stability.
MilestoneOne Candidate, Start to Finish
The first fully automated proof that a live market candidate could move through generation, ML scoring, risk approval, and virtual trade open and close without manual intervention.
RuntimeThe First Overnight Run
The system completed its first overnight live-paper run — roughly fifteen hours and hundreds of automated cycles — proving basic runtime resilience under real network conditions.
ResearchChoosing What Stays In
After comparative testing, the production candidate-family set was finalized, and at least one experimental family was retired from production based on the evidence.
MilestonePipeline Connected, End to End
The Candidate Generator, ML evaluation and Risk/Lifecycle stages were connected into one pipeline and validated out-of-sample on a reference market for the first time.
AuditThe First Audit Gate
The first full data-quality audit gate closed on a reference market dataset, correcting multi-timeframe merge logic and label definitions before further ML work proceeded.
EngineeringA Backtest Engine Honest About Costs
A paper-trading runtime and a realistic backtest engine were built, modeling transaction costs, slippage and execution delay instead of assuming frictionless fills.
EngineeringRebuilding the Market-Data Foundation
The market-data pipeline was rebuilt and expanded to include liquidity, macro and on-chain sources, and the first baseline ML training cycle ran on a reference market.
ResearchFirst ML Models, and a Lesson About Labels
The first machine-learning model stack was assembled — and an early flaw in how training labels were defined was found and corrected before it could mislead later work.
MilestoneThe First Line of Code
Tantoryn AI's earliest working system: a basic data pipeline and trading-bot framework covering data collection, risk management, asset filtering and reporting.