Analytical Framework Behind Garpdesk
Garpdesk combines three layers of AI-driven analysis — Fundamental, Technical, and Quantitative — to evaluate each stock from multiple perspectives. Each analysis type plays a specific role in identifying undervalued growth opportunities and determining the optimal timing for long-term investments.
Analysis Frequency
| Analysis Type | Frequency |
|---|---|
| Fundamental Analysis | Once per month |
| Technical Analysis | Daily, after market close |
| Quantitative Analysis | Every hour during active market sessions |
1. Fundamental Analysis
Objective
To evaluate each company's financial health, performance, and fair value. This serves as the foundation for subsequent technical and quantitative analyses.
Data Sources
Annual and quarterly reports, earnings releases, financial forecasts, public company data from financial portals and stock exchanges, as well as macroeconomic analyses and statistics.
What the AI reviews
- Income Statement: Revenue, Gross Profit, Operating Income, Net Income, EPS (Earnings Per Share)
- Balance Sheet: Assets, Liabilities, Equity, Debt-to-Equity Ratio
- Cash Flow Statement: Operating, Investing, and Financing Cash Flow, Free Cash Flow
- Financial Ratios: P/E, P/B, P/S, ROE, ROA, Dividend Yield
In essence, we calculate an estimated fair value for each stock — output serves as a key input for the next stage: Technical Analysis.
2. Technical Analysis
Objective
To forecast potential price movements based on historical market behavior — focusing on trend direction and timing for long-term entries. Unlike fundamental analysis, technical analysis doesn't ask why prices move, but how they move.
Data Sources
Market price and volume data obtained from financial and trading platforms.
What the AI monitors
- Price Charts: Candlestick, line, and volume-based charts
- AI-trained Indicators: Custom-developed algorithms that detect trend reversals
- Trading Volume: Used to confirm the strength of price movements
Garpdesk uses these inputs to identify transition points — where a stock shifts from a red (bearish) to a green (bullish) trend line — signaling the start of a recovery phase.
3. Quantitative Analysis
Objective
To apply statistical and mathematical models for decision-making and strategy validation.
What the AI analyzes
- Historical Data: Price movements, volumes, and volatility patterns
- Algorithms & Models: Regression, correlation, and advanced machine learning models
- Backtesting: Historical testing of strategy performance to verify predictive accuracy
By training on large datasets of historical market behavior, the AI continuously learns to detect high-probability "Buy" setups — algorithmic patterns that have historically preceded strong growth performance.
How It All Comes Together
- Fundamental analysis identifies what companies are worth watching.
- Technical analysis determines when market momentum turns favorable.
- Quantitative analysis validates why these signals are statistically significant.
Together, they form the backbone of the AI-powered GARP Rating — the system that helps investors find undervalued growth stocks and time their entries with precision.
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