mt-data-mcp/docs/forecast/UNCERTAINTY.md

Uncertainty & Confidence Intervals

Forecasts are estimates, not guarantees. Understanding uncertainty helps you size positions appropriately and avoid overconfidence.

Related:

• FORECAST.md — Price forecasting
• BARRIER_FUNCTIONS.md — TP/SL probability analysis
• GLOSSARY.md — Term definitions

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Types of Uncertainty

Model Confidence Intervals

Intervals derived from the forecasting model's assumptions (e.g., normal distribution of errors).

Limitation: Financial data often violates these assumptions (fat tails, regime changes), so intervals may be too narrow.

Conformal Intervals

Intervals calibrated from historical forecast errors. No distributional assumptions—just empirical coverage.

Advantage: More realistic bounds based on actual performance.

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Model Confidence Intervals

Request intervals with --ci-alpha:

python cli.py forecast_generate EURUSD --timeframe H1 --horizon 12 \
  --model theta --ci-alpha 0.1 --json

Parameters:

• --ci-alpha 0.1 → 90% confidence interval
• --ci-alpha 0.05 → 95% confidence interval

Output includes:

{
  "forecast": [...],
  "lower": [...],
  "upper": [...]
}

Interpretation:

• "If model assumptions hold, the true value will fall between lower and upper about 90% (or 95%) of the time."

Caution: Financial markets have fat tails. Model CIs often underestimate extreme moves.

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Conformal Intervals (forecast_conformal_intervals)

Conformal prediction calibrates intervals from rolling backtest residuals, making no distributional assumptions.

How It Works

1. Run a rolling-origin backtest on historical data
2. Collect actual forecast errors at each horizon step
3. Use error quantiles to set interval width for new forecasts

Usage

python cli.py forecast_conformal_intervals EURUSD --timeframe H1 \
  --method theta --horizon 12 --steps 25 --spacing 10 --alpha 0.1 --json

Parameters:

Parameter	Description	Default
--method	Forecasting method	theta
--horizon	Forecast horizon	12
--steps	Number of backtest points	25
--spacing	Bars between backtest points	10
--alpha	Miscoverage rate (0.1 = 90% interval)	0.1

Output

{
  "forecast": [1.1755, 1.1756, ...],
  "lower_price": [1.1740, 1.1738, ...],
  "upper_price": [1.1770, 1.1774, ...],
  "conformal_residual_quantiles": [0.0005, 0.0008, ...]
}

Interpretation:

• lower_price / upper_price: Empirically calibrated bounds
• "Based on the last 25 forecasts, the actual price stayed within these bounds ~90% of the time."

When to Use

• When you don't trust model-based intervals
• For trading decisions where reliability matters
• When historical data shows fat tails or regime changes

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Triple-Barrier Labeling (labels_triple_barrier)

A different approach to uncertainty: instead of predicting where price goes, label what happened historically.

Concept

For each historical bar, ask: "Within the next N bars, did price hit the take-profit level, stop-loss level, or neither?"

Labels:

• +1 (Win): TP hit first
• -1 (Loss): SL hit first
• 0 (Neutral): Neither hit within horizon

Usage

python cli.py labels_triple_barrier EURUSD --timeframe H1 --horizon 12 \
  --tp-pct 0.5 --sl-pct 0.3 --output compact --json

Parameters:

Parameter	Description
--horizon	Maximum bars to wait
--tp-pct	Take-profit distance (% of price)
--sl-pct	Stop-loss distance (% of price)
--tp-pips / --sl-pips	Alternative: distance in pips

Output

{
  "entries": ["2025-12-18 17:00", "2025-12-18 18:00", ...],
  "labels": [1, -1, 0, 0, 1, ...],
  "holding_bars": [5, 3, 12, 12, 8, ...],
  "summary": {
    "counts": {"pos": 45, "neg": 32, "neut": 123}
  }
}

Interpretation:

• Label distribution shows historical win/loss rates for these barrier levels
• Use this to evaluate signal quality or train ML models

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Practical Applications

Conservative Position Sizing

Use conformal intervals instead of model CIs:

# Get conformal intervals
python cli.py forecast_conformal_intervals EURUSD --horizon 12 --alpha 0.1

# Use lower_price as stop-loss floor
# Size position so max loss (if lower_price is hit) is within risk budget

Validating Signal Quality

Use triple-barrier labels to evaluate entry signals:

# Label historical entry points
python cli.py labels_triple_barrier EURUSD --horizon 12 --tp-pct 0.5 --sl-pct 0.3

# Check win rate: counts.pos / (counts.pos + counts.neg)
# If win rate < 50%, signal needs improvement

Comparing Forecast Methods

Backtest with conformal intervals to compare reliability:

# Method A
python cli.py forecast_conformal_intervals EURUSD --method theta --horizon 12 --steps 50

# Method B
python cli.py forecast_conformal_intervals EURUSD --method sf_autoarima --horizon 12 --steps 50

# Compare interval widths—narrower = more precise (if coverage is similar)

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Quick Reference

Task	Command
Model CI (90%)	python cli.py forecast_generate EURUSD --model theta --ci-alpha 0.1
Conformal intervals	python cli.py forecast_conformal_intervals EURUSD --method theta --horizon 12
Triple-barrier labels	python cli.py labels_triple_barrier EURUSD --horizon 12 --tp-pct 0.5 --sl-pct 0.3

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See Also

• FORECAST.md — Price forecasting
• BARRIER_FUNCTIONS.md — TP/SL probability analysis
• GLOSSARY.md — Term definitions