Analyst layer
Named agents such as Buffett, Graham, Burry, Lynch, Ackman, Cathie Wood, and Druckenmiller provide style-specific interpretations.
Malisasa Quant
Malisasa Quant is the multi-agent research and portfolio engine behind future Malisasa beta products. It combines analyst-style signals, risk overlays, portfolio decisions, and backtesting into one system-level workflow.
Beta engine
Multi-agent orchestration
Research and risk overlays
Public access not open yet
Contact for roadmap updates
This system is presented for educational product transparency only. It is not investment advice or an offer of managed trading.
Current engine footprint
- Multiple named investor-style agents plus technical, sentiment, fundamentals, and valuation specialists.
- Portfolio manager and risk manager stages on top of analyst outputs.
- Data ingestion for prices, company news, insider trades, and financial line items.
- Backtesting support with long and short portfolio mechanics.
Architecture
What the engine is designed to do
The codebase already points to a layered workflow: fetch data, run multiple analysts, aggregate reasoning, apply risk controls, then produce a portfolio-oriented decision surface.
Cross-check layer
Dedicated technical, sentiment, fundamentals, and valuation agents can supplement or challenge style-based opinions.
Decision layer
A portfolio manager and risk manager sit above the analyst outputs to shape position direction, sizing, and trade quality.
Data and Simulation
How it extends beyond a simple plugin
Malisasa Quant is not only a UI idea. The repository already includes supporting utilities that make it useful as a research system rather than a single prompt wrapper.
Market and company data
The engine fetches price history, financial statements, company news, and insider-trade data, with caching and fallback logic in the data layer.
Backtesting workflow
A backtester module supports long and short positions, cash tracking, margin handling, exposure measurement, and portfolio-value history.
Telemetry and progress
The backend includes event logging, progress updates, and request tracing so the workflow can be observed as a real product service.
Relationship
How this connects to the beta add-in
AIHF Addin is the browser-facing product shell. Malisasa Quant is the deeper research engine that can power richer future workflows once the product surface is ready.
AIHF Addin
Focuses on the user-facing add-in flow: sign-in, ticker entry, model selection, and receiving one structured result.
Malisasa Quant
Focuses on the engine: multi-agent signals, portfolio logic, risk constraints, and possible future research workflows.
Current status
Both remain beta concepts under the Malisasa brand, with no public install or public managed-trading offer published here.