Strategy
AI Custom strategy management
Text, Pine, TradingView, PDF, image, and video sources are structured into constrained IR and XAI with version diff, approval, PAPER verification, deletion control, and package export.
System architecture
This page is a technical structure overview of how NoahAI decomposes, verifies, and controls financial judgment. It is for understanding the end-to-end flow of judgment creation and verification, not individual algorithms or implementation detail.
NoahAI's core goal is not automation that replaces judgment but AI judgment infrastructure that structures, verifies, and explains financial judgment. We focus on operable trust (control, logging, explanation, verification), not short-term performance.
The architecture below shows the full flow from market/personal context input through judgment, guardrails (risk control), (optional) execution, logging/report, and feedback.
This page is not for explaining returns on specific assets or promoting automated trading. NoahAI technology is financial AI decision infrastructure built around judgment, risk control, logging, and verification; execution automation is always optional and contract-governed.
v3.9.1.0 Web UI internal integration candidate
Moving AI Custom and the trading engine into a Web UI operating layer
NoahAI now separates the legacy desktop UI from the runtime path and moves operation into React Web UI, an Electron shell, a UI-neutral Python sidecar, and strict Gateway contracts. The point is not cosmetic UI change; it is auditable AI Custom, settings, execution, shutdown, and update control.
Platform
Web UI and safe sidecar
Users run NoahAI.exe. The internal Python engine starts behind a loopback token and shutdown handshake. The sidecar no longer creates the trading runtime through main.py or CustomTkinter UI.
Safety
Regime, risk, and order validation
Source evidence, strategy version, market regime, risk budget, order rules, and ownership ledger are connected. Ambiguous, unsupported, or unverified actions pause with reasons instead of being invented.
Evaluation
AlphaArena and advanced analysis
Financial intelligence, allocation, risk, performance, life-finance, and AI summary views now have dedicated Web screens. AlphaArena is PAPER-only in this internal candidate; LIVE is fail-closed before external gates.
Technical flow
1
Source and strategy
Conversation, Pine, PDF, video, version, approval
2
Validation IR
Evidence, gaps, unsupported scope, regime, risk
3
Gateway contract
Permission, idempotency, audit, secret blocking
4
Headless engine
Venues, brokers, recorder, safe shutdown
5
Web UI
Charts, settings, strategy, logs, updater, manual
Current public boundary
v3.9.1.0 is an internal integration candidate with a Windows installer candidate and fixed SHA. Source checks and Web build pass, but public distribution waits for signing, clean install, upgrade from v3.9.0.10, updater, rollback, broker/exchange, and 24–72h PAPER E2E. v3.9.0.10 remains the legacy fallback baseline.
A core technology within the NoahAI financial wealth OS
Turn personal financial knowledge into a verifiable decision structure
AI Custom is not the whole of NoahAI. It is a core technology inside the NoahAI financial wealth OS, structuring and validating user knowledge while the broader platform connects market, asset, and personal financial context.
01 Structure knowledge
Organize books, documents, charts, and conversations as traceable sources and decision rules.
02 Validate
Check regimes, costs, risk, and missing conditions; prefer HOLD when evidence is insufficient.
03 Approve and control
Connect to optional execution only within user approval, institution permissions, guardrails, and stop conditions.
04 Record and review
Keep rationale, settings, and outcomes reviewable so later decisions can improve.
Personal financial context
Consider goals, assets, risk tolerance, and current circumstances—not one strategy alone.
Trustworthy operations
Separate judgment, execution, and records while preserving explanation, audit, and replay.
An accumulating environment
Build a financial decision environment that improves through validation and review, not a single signal.
AI Custom is one of NoahAI’s defining technologies, not the company’s entire purpose. NoahAI spans personal financial context, risk control, explanation, records, multiple assets, and institutional connectivity as AI financial decision infrastructure.
1
Market Data Layer
Market data input
We collect and standardize real-time market information: price, volume, volatility, order book. (News, filings, policy, etc. are extended in stages.)
2
Personal Context Layer
Personal financial context
We manage account/position state plus asset allocation, time horizon, risk tolerance, and behavior patterns. Judgment is organized in explainable form on top of this context.
3
Decision Layer
Agent judgment
We structure what to consider and why from collected data and personal context. When needed we present executable options; every judgment is logged and verifiable.
4
Risk & Guardrails
Risk control and guardrails
Conservative control rules (limits, halt conditions, max loss, prohibited rules, emergency stop) are applied first. The goal is controllability, not speed; we prevent abnormal behavior and excessive risk.
5
Execution Layer
(Optional) execution and automation
Within user settings and guardrails we automate repetitive work or provide executable options. Auto-execution is optional; default is judgment, logging, and explanation. Execution is atomic for consistency.
6
Logging & Report
Logging and reports
The full process (input, context, judgment, execution, result) is logged in a standard format and reports are generated for reproducibility and audit/traceability.
7
Feedback Loop
Feedback loop
Result analysis → policy improvement → next judgment. We aim for a judgment structure that accumulates experience, not fixed automation. Outcomes are accumulated at anonymized pattern level to improve system judgment policy over time and to improve safety and consistency.
External financial system integration
Current operation, integration, and extension:
Binance (standalone)
• python-binance–based standalone
• Binance Algo Order API (v3.8.9.9)
• TP/SL -2021 fix (v3.8.9.11)
• Advanced execution interface
CCXT integration
• Bybit, OKX, Bitget (futures)
• Upbit, Bithumb (spot)
• Unified decision support
• Per-exchange stats
Securities / ETF (extending)
• StockExchange interface (v3.8.9.11+)
• Domestic broker API integrated (operation/verification)
• ETF/equity UI complete (2026-01-18)
• Asset-class engine separation (crypto/securities)
Extension
• Overseas equities/futures (testing)
• Real estate (planned)
• Modular architecture for easy extension
Role of Analyst AI
NoahAI's Analyst AI is not a single model that makes judgment for you; it is a multi-module decision structure with separated roles to decompose, verify, and explain judgment from multiple angles.
Each module has distinct responsibilities (analysis, evaluation, risk control, verification) to minimize judgment bias and single points of failure and to present judgment in understandable form.
Core modules
The modules below are not a public API list; they are components of the decision infrastructure used inside NoahAI to perform financial judgment safely. Each module is designed for judgment, verification, and logging—not execution-first.
This set is designed as an Analyst AI structure with separated analysis, evaluation, risk, and verification roles to minimize bias and single points of failure.
1. analyzer.py – Market analysis and AI signal generation
- Technical indicators: RSI, MACD, Bollinger, SMA/EMA, ATR, volume
- Market regime: Volatility, trend strength, momentum, sentiment
- Signals: Technical + AI-enhanced signals
- Dynamic thresholds: Volatility thresholds by regime
- Confidence: Technical + AI data + sentiment (0.0–1.0)
2. evaluator.py – Asset selection
- Multi-dimensional score: Volatility (35%), trend (25%), volume (20%), frequency (10%), depth (5%), RSI (5%)
- AI evaluation: Asset evaluation using AI learning data
- Regime-based strategy: LOW/NORMAL/HIGH volatility
- Hybrid: Caching + fallback + hardcoding for stability and performance
- Optimization: Load 4min → near-instant; API dependency 100% → 30%
3. unified_trader.py – Orchestration and state
- Multi-exchange: CCXT-based integration
- Position monitoring: Per-exchange cycle (5–15s)
- Execution policy: User settings and guardrails (halt, limits, exceptions)
- Verification mode: Validate judgment and risk without live execution
4. ai_manager.py – AI learning and pattern
- Market analysis: Regime and dynamic settings
- Exit analysis: Exit timing optimization
- Pattern verification: Pre-entry pattern (k-NN)
- TP/SL analysis: Trade cause and improvement
- Reports: Daily/weekly/monthly
- Conversational AI: Natural-language assistant
5. recorder.py – Database and learning data
- Persistent trade logging
- AI learning data management
- Statistics and performance
6. alpha_arena_trader.py – Alpha Arena (research)
Alpha Arena is a research/verification-only environment fully separate from live decisions and user assets.
- LLM experiments: DeepSeek 3.1, Qwen 3 Max
- Benchmark: nof1.ai Alpha Arena verification
- Independent mode: Separate from main pipeline
- Scope: Compare and replay engine/policy under same conditions
Extension points
This architecture is modular and can extend as follows:
- Assets: Crypto → securities/ETF → real estate → everyday finance (transfers, checks, budgeting) on the same judgment–logging–verification structure.
- Channels: Text → voice (accessibility) and step-by-step guidance for seniors and the digitally excluded.
- Protection: Anomaly detection from context and behavior to mitigate fraud and phishing risk.
Related technical docs
Details of explanation, logging, and learning linked to this architecture are in the docs below.