MarketPilot is an open-source Python platform designed for market research, structural analysis, risk monitoring, and policy-aware experimentation in financial systems.
It supports researchers and engineering teams in building robust data pipelines, analytical models, and governance-aware workflows within a modular, production-oriented environment.
MarketPilot is intended for research, monitoring, diagnostics, and experimentation.
It does not provide investment advice, trading instructions, execution logic, or performance guarantees.
The platform must not be used to generate or present buy/sell signals, allocations, target prices, or return expectations.
MarketPilot builds on the analytical foundations of AlphaFusionNet and extends them toward agent-based reasoning, feedback analysis, and policy-constrained learning—with a strict focus on non-directional intelligence and oversight.
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Transparent & Traceable Analytics
End-to-end traceability from raw data to model outputs, enabling auditability and governance. -
Agent-Based Reasoning (Research-Oriented)
Modular analytical agents (e.g., analysis, risk, compliance, research) interact within a controlled reasoning graph to explore hypotheses and structural conditions. -
Multi-Layer Reflection (Conceptual)
Micro / meso / macro reflection layers designed to assess consistency, contradiction, and stability of analytical outputs. -
Risk-Aware Modeling Frameworks
Support for constraint-aware learning setups focused on exposure control, stability, and stress conditions, not execution. -
Scenario & Stress Analysis
Tools to study how analytical indicators and system behavior respond under hypothetical or historical stress regimes. -
Governance & Policy Tracking
Versioned policy artifacts, decision logs, and reproducible experiment metadata for supervisory review.
| Category | Description | Status |
|---|---|---|
| Data Layer | Data ingestion, cleaning, alignment (prices, news, fundamentals, macro) | Done |
| Feature Engineering | Statistical, structural, NLP-derived, and graph-based features | In Progress |
| Reasoning Graph | Typed analytical agents with guardrails and message passing | Planned |
| Reflection Layers | Micro / meso / macro analytical consistency checks | Planned |
| Constraint-Aware Learning | Policy- and constraint-driven learning setups | Planned |
| Evaluation Framework | Historical validation, stress testing, and robustness checks | Planned |
| Dashboards | Monitoring-oriented dashboards and analytical summaries | In Design |
| Automated Testing | Unit and integration tests for analytical components | In Progress |
Phase 1 — Data Foundations
Build resilient ingestion and alignment pipelines to generate consistent analytical panels.
Phase 2 — Reflection & Risk Awareness
Introduce multi-layer reflection mechanisms to evaluate analytical stability and contradiction.
Phase 3 — Constraint-Aware Learning
Develop learning workflows governed by explicit constraints and policy definitions.
Phase 4 — Evaluation & Stress Diagnostics
Implement validation and stress diagnostics for analytical robustness (non-performance-based).
Phase 5 — Monitoring & Research Dashboards
Deliver dashboards focused on system behavior, drift, and analytical consistency.
MIT — see LICENSE.
MarketPilot is inspired by prior research frameworks focused on financial analytics and AI-assisted reasoning:
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FinRL — Reinforcement learning research framework for financial environments
https://github.com/AI4Finance-Foundation/FinRL -
FinRobot — LLM-based financial research and analysis agents
https://github.com/AI4Finance-Foundation/FinRobot -
FinGPT — Financial language models for market and macro analysis
https://github.com/AI4Finance-Foundation/FinGPT -
TradingAgents — Multi-agent analytical research framework
https://github.com/TauricResearch/TradingAgents
MarketPilot is a research and market intelligence platform.
It does not provide financial advice, execution capabilities, or outcome predictions.
All use is at the user’s discretion and responsibility, subject to applicable regulations and internal governance.