G-Prophet Whitepaper: An Uncertainty Analysis and Review Tool for Multi-Market Research
G-Prophet combines market-state recognition, scenario-path modeling, trend filtering, technical indicators, market sentiment, and rolling validation into a repeatable research workflow.
Abstract
G-Prophet is not an auto-trading system and does not promise deterministic outcomes. Its core value is turning market data, model output, risk observations, and review records into a structured research workflow. The system uses prediction ranges, directional observations, model stability, risk levels, and historical validation to make each research judgment traceable.
Core Principles
Uncertainty, not certainty: outputs are research hypotheses, not action commands.
Market first, symbol second: identify market state before individual assets.
Ranges before points: focus on paths, volatility bands, and risk boundaries.
Continuous review: verify direction, deviation, and stability after predictions mature.
System Architecture
Data layer
Market quotes, OHLCV history, technical indicators, market breadth, sentiment data, and model-run records.
Modeling layer
G-Prophet 2026V1, G-Prophet2026V2, LSTM, Transformer, random forest, and ensemble models.
Analysis layer
Directional observation, model reference price, scenario range, stability, risk level, and multi-algorithm consensus.
Validation layer
Actual prices are backfilled after expiry to calculate direction agreement rate, price proximity, and market-level metrics.
Prediction Method
- 1
Market-state recognition: trend, range, breakout, pullback, and volatility expansion detection.
- 2
Scenario-path modeling: many possible paths form the model range.
- 3
Trend filtering: remove low-quality samples that do not match the current market structure.
- 4
Technical confirmation: RSI, MACD, Bollinger Bands, KDJ, and related indicators improve interpretability.
- 5
Rolling validation: matured predictions are verified and aggregated into in-product diagnostics.
Validation and Metrics
G-Prophet provides in-product diagnostics for direction agreement rate, price proximity, completed samples, market segmentation, and model-stability calibration. Direction agreement rate means the model-implied and actual up/down directions match. Price proximity measures distance between model reference and actual prices. Model stability describes internal consistency and is not the probability of being correct.
Developer and AI Agent Access
G-Prophet provides an external API, Python SDK, MCP Server, and AI Skills documentation. Developers can call prediction, market data, technical indicator, sentiment, stock analysis, multi-agent deep analysis, task status, and usage endpoints.
Boundaries and Risk
- Markets can be affected by news, liquidity, policy, macro conditions, and sentiment shocks.
- Historical direction agreement does not determine future performance.
- Model stability is not a deterministic outcome or empirical probability.
- Users should make independent decisions based on their own risk tolerance.
- All outputs are for research only and are not action instructions.
Important Notice
G-Prophet outputs are for research and reference only. They are not action instructions. Financial markets are uncertain; use independent judgment.