Trade log disclosure principles

Last updated: December 2025

Why we only publish part of the picture

NoahAI Labs does not publish full trade logs. For the reasons below, we anonymize at the pattern level and publish selectively:

Privacy: To protect personal data and account identifiers.
Security: To reduce exposure when trading patterns could be used against users.
No hype: To prevent cherry-picked trades from being used as marketing claims.
Operations-first: The goal is to document structure and patterns—not individual fills.

Anonymization standards

Published trade logs are anonymized as follows:

Accounts: Remove user accounts, wallet addresses, and other direct identifiers.
Notional size: Use ratios or pattern aggregates instead of absolute amounts.
Timing: Aggregate to time bands or patterns instead of exact timestamps.
Instruments: Emphasize asset type and pattern rather than specific instrument detail.

Anonymized data is used only for validating operating structure and pattern learning—and is processed so individual users or specific trades cannot be traced.

No performance hype

Important principle

Trade logs are not used to exaggerate performance or to fuel marketing narratives.

We do not emphasize return figures.
We do not generalize a period’s outcome into a universal claim.
We do not solicit investment or guarantee returns.
Trade logs are published only for operational records and verification.

What transparency means here

We publish trade logs to:

Operational verification: Show that the system runs and guardrails behave as designed.
Pattern learning: Learn risk signals and improvements from anonymized pattern units.
Reproducibility: Keep records in a form that can be reproduced under the same conditions.
Trust without hype: Share fact-based operational records without exaggeration.

Trade logs are operational records—not marketing tools. We build trust through transparency while prioritizing privacy and security.

Sample trade log

Below is an anonymized sample. Real logs remove personal data completely and are published at the pattern level.

[Trade log sample — anonymized]

Date: 2025-01-XX
Exchange: [redacted]
Symbol: BTCUSDT
Side: LONG
Entry price: $XX,XXX
Entry time: XX:XX:XX
Exit price: $XX,XXX
Exit time: XX:XX:XX
Return: +X.XX%
Holding time: Xh XXm

[Decision rationale]
- Technical: RSI oversold, MACD golden cross
- Regime: Uptrend
- Risk assessment: Low
- Position size: Conservative

[Execution]
- Entry: market order
- TP/SL: auto-placed
- Monitoring: live position tracking
- Exit: auto close on TP

[Risk management]
- Guardrails triggered: none
- Anomalies: none
- Loss limit: not reached

Production logs follow this shape, with personal data removed and pattern-level anonymization before any publication.

Pattern examples

Example 1: Uptrend pattern

A case study of successful trade patterns in an uptrend.

Market regime: Uptrend, low volatility
Entry conditions: RSI oversold, MACD golden cross
Position size: Conservative (low risk)
Outcome: TP hit, profit taken
Learning: Conservative entries work in uptrends

Example 2: Downtrend pattern

A case where risk management worked in a downtrend.

Market regime: Downtrend, high volatility
Entry conditions: Technical buy signal
Position size: Very conservative (volatility-aware)
Outcome: SL hit, loss contained
Learning: Conservative sizing matters in downtrends

Example 3: Guardrails triggered

A case where guardrails prevented large losses.

Market regime: Sharp move, volatility spike
Detection: Volatility spike detected
Response: Automatic position reduction
Outcome: Large loss avoided
Learning: Why guardrails matter

How we analyze outcomes

1. No exaggerated returns

NoahAI Labs does not exaggerate or guarantee returns.

We publish only what actually happened in production.
No selective disclosure (not “only winners”).
Past results do not imply future performance.
We are not providing investment advice.

2. Operations-focused metrics

Analysis emphasizes operational metrics—not headline returns.

Stability: Did the system run reliably?
Guardrails: Did safeguards behave as intended?
Logging: Were trades recorded consistently?
Risk management: Was risk managed as designed?

3. Transparency

Where we publish logs, we aim for honest transparency.

Include losing trades, not only wins.
Publish guardrail activations when relevant.
Share learning and improvement signals.
Document changes when processes evolve.

What we publish

We are still building collection and anonymization pipelines. When production data meets our anonymization standards, we will publish at the pattern level.

Published logs are treated as durable records; when we revise content, we document what changed.