Machine Learning in Reports

How Grade My Investments leverages ML.NET to enhance financial reports with predictive analytics, forecasting, and intelligent insights.

ML PIPELINE

rendering diagram…

flowchart TB
    A[(Historical<br/>Financial Data)]:::data --> B[Data Preparation<br/>Normalize - Validate]:::svc
    B --> C[Feature Engineering<br/>Time Series]:::svc
    C --> D{Model<br/>Selection}:::app
    D --> E[SSA Forecasting<br/>Revenue - Debt - Cash Flow]:::app
    D --> F[IID Spike Detection<br/>Anomaly Alerts]:::app
    D --> G[Multi-class Classification<br/>Bullish - Neutral - Bearish]:::app
    D --> H[Health Score<br/>4-factor 0-100]:::app
    E --> I[4-Quarter<br/>Forecast]:::svc
    F --> J[Anomaly<br/>Alerts]:::svc
    G --> K[Sentiment<br/>Forecast]:::svc
    H --> L[Health<br/>Score]:::svc
    I --> M[(Excel Report<br/>JSON Output)]:::data
    J --> M
    K --> M
    L --> M
    classDef app fill:#1f2630,stroke:#C0894E,color:#F3EFE8;
    classDef svc fill:#241c14,stroke:#E2B583,color:#F3EFE8;
    classDef data fill:#22201a,stroke:#C0894E,color:#EBD3AE;
    classDef ext fill:#1b1f27,stroke:#5A616B,color:#C4C0B8;
    classDef sec fill:#1d2733,stroke:#5cc8e0,color:#eafaff;

ML.NET pipeline: historical financial data flows through four model types to produce forecast, anomaly, health, and sentiment outputs embedded in Excel and JSON reports.

ML.NET-Powered Report Enhancement

Intelligent Reports: Grade My Investments' reports go beyond traditional data presentation by incorporating machine learning predictions, forecasts, and anomaly detection directly into Excel and JSON outputs.

Enhanced Report Features

Future Revenue Predictions: 4-quarter ahead revenue forecasts with confidence intervals
Cash Flow Projections: Free cash flow trend analysis and predictions
Financial Health Scoring: Automated 0-100 health scores based on multiple metrics
Anomaly Alerts: Automatic detection of unusual trading patterns or financial metrics

ML Integration Workflow

Data Preparation Historical financial data is normalized and structured for ML processing

Model Training ML.NET trains forecasting models on-the-fly for each symbol

Prediction Generation Models generate forecasts with confidence intervals

Report Integration Predictions are seamlessly embedded into Excel worksheets and JSON

Excel Report ML Features

Enhanced Excel Worksheets

ML.NET predictions are integrated directly into Excel reports, providing users with actionable forecasts alongside traditional financial data:

New Worksheet Sections

Revenue Forecast Tab: Quarterly predictions with charts
Health Score Summary: Financial health ratings and explanations
Sentiment Forecast Tab: Market sentiment predictions and trend analysis
Anomaly Alerts: Highlighted unusual patterns
Confidence Intervals: Statistical reliability indicators

Visual Enhancements

Forecast Charts: Line graphs showing predicted trends
Confidence Bands: Visual uncertainty ranges
Health Score Gauges: Dashboard-style score visualizations
Color-Coded Alerts: Risk level indicators

Sample Excel Output with ML Features

Quarter	Historical Revenue	ML Forecast	Confidence	Health Score
Q3 2025	$89.5B	-	-	85/100
Q4 2025	$92.1B	-	-	87/100
Q1 2026	-	$95.2B	85%	88/100
Q2 2026	-	$98.1B	82%	89/100
Q3 2026	-	$101.5B	78%	90/100

Yellow rows indicate ML.NET predictions. Confidence decreases for longer-term forecasts.

Sample Sentiment Forecasting Tab

Period	Predicted Sentiment	Intensity Score	Confidence	Risk Level	Key Factors
Week 1	Bullish 📈	78/100	85%	Low	Strong momentum, RSI: 65
Week 2	Bullish 📈	74/100	82%	Low	Volume spike, positive MA
Week 3	Neutral ➡️	45/100	78%	Medium	Mixed signals, volatility
Week 4	Bearish 📉	25/100	75%	High	Resistance level, overbought

Sentiment forecasting uses technical indicators, price patterns, and volume analysis to predict market sentiment trends.

JSON Output with ML Data

Machine Learning Data Structure

JSON reports include structured ML predictions, making the data easily consumable by other applications:

{
  "symbol": "AAPL",
  "reportDate": "2026-04-01",
  "machineLearning": {
    "forecasts": {
      "revenue": {
        "algorithm": "SSA",
        "horizon": 4,
        "predictions": [
          {
            "quarter": "Q1 2026",
            "value": 95200000000,
            "confidence": 0.85,
            "upperBound": 98100000000,
            "lowerBound": 92300000000
          },
          {
            "quarter": "Q2 2026",
            "value": 98100000000,
            "confidence": 0.82,
            "upperBound": 102500000000,
            "lowerBound": 93700000000
          }
        ]
      },
      "cashFlow": {
        "algorithm": "SSA",
        "predictions": [...],
        "trend": "increasing"
      }
    },
    "anomalies": [
      {
        "type": "VolumeSpike",
        "date": "2026-03-28",
        "severity": "Medium",
        "description": "Trading volume 3.2x above 30-day average"
      }
    ],
    "healthScore": {
      "overall": 87,
      "components": {
        "revenueGrowth": 22,
        "debtToEquity": 21,
        "cashFlow": 23,
        "profitability": 21
      },
      "risk": "Low"
    }
  }
}

ML Model Types

Forecasting Models

Singular Spectrum Analysis (SSA)

Primary algorithm for revenue, debt, cash flow, and equity forecasting

Accuracy: 85-92%

Simple Trend Fallback

Fallback when SSA fails due to data constraints

Accuracy: 78-88%

Spike Detection (IID)

For anomaly detection in financial ratios

Detection rate: 95%

Multi-class Classification

For sentiment forecasting (Bullish/Neutral/Bearish)

Accuracy: 72-80%

Anomaly Detection

Detection Categories

Volume Spikes Critical
Price Volatility High
Ratio Outliers Medium
Seasonal Deviations Low

Health Scoring

Scoring Components

Revenue Growth

25%

Debt-to-Equity

25%

Cash Flow

25%

Profitability

25%

Score Interpretation

80-100: Excellent

60-79: Good

0-59: Poor

Performance Metrics

1000+

Stocks processed per minute

85%

Average forecast accuracy

95%

Anomaly detection rate

Quarters forecast horizon

Technical Implementation

ML.NET Integration

The ML.NET framework is integrated into both the Report Generator Alpha and PyEasy console applications through the MLForecastingService class, which implements the IMLForecastingService interface:

Key Implementation Features

Real-time Training: Models are trained on-demand for each symbol
SSA Forecasting: Singular Spectrum Analysis with simple trend fallback
Minimum Data: 4 quarters of historical data required per symbol
Memory Efficiency: Models are disposed after prediction to optimize memory
Parallel Processing: Multiple stocks can be analyzed simultaneously
Error Handling: Graceful fallback when SSA fails or insufficient data
Configurable Horizons: Forecast periods can be adjusted per use case (default: 4 quarters)

Data Pipeline

Historical Data Extraction

↓

Data Cleaning & Validation

↓

Feature Engineering

↓

ML Model Training

↓

Prediction Generation

↓

Report Integration