Saki
59 lines
2.6 KiB
Markdown
59 lines
2.6 KiB
Markdown
# Financial Screener & Personal Finance Assistant
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## Project Overview
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This project is a modular, rule-based financial analysis engine designed to evaluate assets and manage personal investment portfolios. It separates data acquisition, strategy configuration, and evaluation logic to provide actionable investment insights.
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---
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## Architecture Structure
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### 1. Data Pipeline (`/src/data/`)
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- **Fetcher:** Handles API communication (e.g., Yahoo Finance).
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- **Mapper:** Normalizes disparate API responses into a unified flat object structure.
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- **Asset Models (`/models/`):** Defines common properties for `Stock`, `Etf`, and `Bond`.
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### 2. Logic & Configuration (`/src/config/` & `/src/utils/`)
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- **`ScoringConfig.js`:** Houses all thresholds, gates, and weights.
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- **`RuleMerger.js`:** Dynamically applies sector-specific overrides to base rules.
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### 3. Evaluation & Personal Assistant (`/src/engine/` & `/src/assistant/`)
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- **`ScoringEngine.js`:** Orchestrates evaluation, applying market context and sector overrides.
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- **`PortfolioManager.js` (NEW):** Tracks individual holdings, cost basis, and performance metrics.
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- **`AdvisorModule.js` (NEW):** Provides personalized suggestions based on screening results and portfolio health.
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- **`EventMonitor.js` (NEW):** Tracks calendar events (Earnings Calls) to trigger alerts.
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---
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## Data Flow Diagram
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## Future Enhancements
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### Phase 1: Core Engine & Soft Scoring
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- **Soft Scoring System:** Transition from "Hard Gates" to a weighted point-based system.
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- **Market Context Integration:** Automate the `marketContext` parameter by fetching real-time 10Y Treasury Yields.
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### Phase 2: Personal Finance Features
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- **Personal Portfolio Tracking:** Implement a `PortfolioManager` to track custom user holdings, monitor unrealized P&L, and calculate weightings relative to total assets.
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- **Automated Financial Coaching:** Develop an `AdvisorModule` that analyzes the portfolio and provides suggestions (e.g., "Reduce exposure to High-Debt REITs," or "Rebalance to increase Technology allocation").
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- **Earnings Call Notification System:** \* Integrate an earnings calendar API.
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- Implement a polling or webhook service to monitor for upcoming calls.
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- Add a notification service (Email, Push, or CLI log) to alert the user 24 hours prior to a scheduled earnings call.
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### Phase 3: Infrastructure & Intelligence
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- **Caching Layer:** Use local JSON caching to reduce API overhead.
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- **Sentiment Analysis:** Integrate news-scrapers to weight "Buy" signals based on recent headlines.
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- **Backtesting Module:** Run historical simulations to test strategy performance.
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---
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_Maintained by: AI Collaborator_
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