Methodology
Transparent approach to signals and forecasting
Data Sources
All forecasts are built on verified, public data sources:
- Energy & Commodities: Alpha Vantage, EIA (Energy Information Administration), FRED (Federal Reserve)
- Food Commodities: Trading Economics, World Bank, FAO (Food and Agriculture Organization)
- Safe Haven Assets: Yahoo Finance, central bank data feeds
- Market Indicators: Public APIs with daily updates
Data is collected daily and stored with full version history for audit purposes.
Signal Generation
Signals (bullish/neutral/bearish) are derived from multiple technical indicators:
Momentum Analysis
Compares recent price movement (7-day average) against the previous period (7-14 days back). Momentum scores above 0.6 indicate bullish conditions; below 0.4 indicates bearish.
Trend Detection
Uses moving average crossovers: when short-term averages (7 days) cross above long-term averages (14 days), it signals upward momentum. The reverse indicates downward pressure.
Volatility Assessment
Measures price standard deviation relative to mean (coefficient of variation). High volatility indicates unstable conditions; low volatility suggests consolidation.
Confidence Levels
- High: Clear directional momentum (>0.7 or <0.3) with aligned trend indicators
- Medium: Moderate momentum (0.4-0.6) or mixed signals
- Low: Insufficient historical data or conflicting indicators
Forecast Models
Price forecasts combine statistical methods with volatility adjustment:
Linear Regression Trend
Uses the past 30 days to establish a baseline trend line. Extends the trend forward to generate point predictions for 30 and 90 days ahead.
Volatility Bands
Calculates standard deviation of recent prices to establish confidence ranges. Bands widen for longer-term forecasts to reflect increased uncertainty.
Confidence Intervals
95% statistical intervals based on historical residual error. If recent predictions consistently fall within these ranges, confidence increases.
What This Approach Does NOT Do
- It does not predict black swan events (wars, natural disasters, sudden policy shifts)
- It does not incorporate sentiment analysis or news feeds (yet)
- It is not trained on machine learning models with thousands of variables
- It is not optimized for high-frequency trading signals
This system is designed for medium-term visibility (30-90 days) in markets sensitive to global instability. It prioritizes transparency over complexity.
Updates & Maintenance
Daily: Price and indicator updates
Weekly: Signal generation and forecast refresh
Monthly: Track record evaluation and methodology refinement
Open Questions
Forecasting is inherently uncertain. I track what works and what doesn't on the Track Record page, including misses and lessons learned.
Signal Glossary
Plain-language definitions for every term used across the dashboard.
Signal Type
- Bullish — Momentum and trend indicators both point upward. Price likely to rise in the near term.
- Bearish — Momentum and trend point downward. Price likely to fall.
- Neutral — Indicators are mixed or consolidating. No clear directional edge.
Confidence Level
- High — Momentum above 0.7 or below 0.3, with aligned trend and low noise. Strong directional signal.
- Medium — Moderate momentum (0.4–0.6) or mixed indicators. Direction is probable but not certain.
- Low — Insufficient historical data or conflicting indicators. Treat as directional awareness only.
Momentum Score
The ratio of the recent 7-day average price to the prior 7-day average. A score of 1.0 means no change. Above 0.6 signals bullish pressure building. Below 0.4 signals bearish pressure. The 0.4–0.6 range is the neutral consolidation zone.
Volatility
Coefficient of variation over 30 days — standard deviation divided by the mean price. High means price swings are large relative to the current price level. Low means the market is stable and consolidating. High volatility widens forecast confidence bands.
Forecast Range
The 95% statistical confidence interval around the forecast. There is a 95% probability the actual price will land within this range, based on historical residual error patterns. Ranges widen for 90-day forecasts to reflect greater uncertainty over longer horizons.