meta-learning

name	meta-learning
description	Governs when and how the adaptive WTI analyst updates its strategy skill. Consult this before calling any strategy mutation tool. The process is deliberately conservative — it resists updating on individual surprises and requires pattern-level evidence before revising strategy.

Meta-learning: strategy update governance

The four learning layers

wti-strategy has four distinct layers, each with its own evidence bar and mutation tool. Work bottom-up: always start with an observation before opening a hypothesis, and always accumulate enough hypothesis outcomes before graduating to a calibration correction.

Layer	Tool	Evidence bar
Observations	record_observation	Pattern visible across ≥2 forecasts — not a single surprise
Hypotheses	open_hypothesis	One strong observation suggesting a durable pattern
Hypothesis outcomes	record_hypothesis_outcome	Each resolution relevant to an open hypothesis
Calibration corrections	graduate_hypothesis	Tool enforces threshold (currently 2 confirmations) — rejects if not met
Approach narrative	update_approach_narrative	Only when the calibration record reveals a structural insight

When to update

Engage the update process only when you have pattern-level evidence — not after a single surprising outcome. Appropriate triggers:

• A self-review or backtesting exercise spanning five or more origins reveals a systematic bias (e.g. intervals consistently too narrow in a specific vol regime, or a directional skew that persists across horizons).
• A user identifies a recurring pattern in your errors and you can verify it with code or data.
• You run a code-execution analysis on historical WTI data that reveals a durable relationship not currently captured in your strategy.

Do not update after a single resolution, even a large miss. Markets have noise; one bad forecast is not a signal.

How to update: the tool call sequence

Step 1 — Always: record an observation

record_observation(
    finding="<specific pattern, including regime, horizon, and direction>",
    linked_hypothesis="<hyp-id if this feeds an open hypothesis, else omit>"
)

This is always the right first step. It costs nothing and builds the audit record that governs future decisions.

Step 2 — If a durable pattern is suspected: open a hypothesis

open_hypothesis(
    claim="<testable claim about your forecasting behaviour>",
    initial_evidence="<the observation(s) that motivated this hypothesis>"
)

A hypothesis is a candidate calibration correction under active testing. State the claim in terms of a specific condition and a directional error — not a market opinion. The tool assigns an ID (e.g. hyp-001) and records the initial evidence as a linked observation automatically.

Step 3 — On each subsequent resolution: update hypothesis counts

record_hypothesis_outcome(
    hypothesis_id="hyp-001",
    outcome="confirmed"  # or "refuted"
)

Call this for any resolution where the outcome is directly relevant to an open hypothesis. A single refutation does not close the hypothesis — continue accumulating evidence. The tool returns the current counts and how many more confirmations are needed to graduate.

Step 4 — When the threshold is reached: graduate to calibration

graduate_hypothesis(
    hypothesis_id="hyp-001",
    condition="<the specific condition under which the correction applies>",
    adjustment="<the concrete adjustment to make when the condition is met>",
    horizon_scope="all"  # or "5bd", "10bd", "21bd", etc.
)

The tool enforces the confirmation threshold. If the hypothesis has not accumulated enough confirming outcomes, it will reject the call and state exactly how many more are needed. Do not attempt to work around this.

The tool automatically:

• Marks the hypothesis as confirmed
• Adds the calibration correction to wti-strategy
• Records a linked observation and version history entry

Step 5 — Rarely: update the approach narrative

update_approach_narrative(
    new_text="<complete replacement text for the Approach section>",
    rationale="<why the current narrative no longer captures the strategy>"
)

Only call this when the calibration record reveals a structural insight that the approach narrative no longer captures — for example, when multiple graduated corrections collectively suggest the relative weighting of evidence sources has shifted. The rationale argument is required and will be logged in the version history.

Do not call this during a live prediction task. Approach updates belong in self-review or resolution-handling invocations.

Guarding against over-learning

The greatest risk in a self-updating strategy is chasing noise. Before opening a hypothesis or proposing a graduation, ask:

• Is this pattern visible across multiple origins, or just one?
• Would this update have improved performance over the past ten forecasts, or only the most recent few?
• Am I reacting to a one-time market event (e.g. a geopolitical shock) rather than a durable forecasting flaw?

If uncertain, call record_observation without opening a hypothesis. Revisit after more evidence accumulates.

What NOT to update

• Do not open a hypothesis after a single resolution.
• Do not attempt to graduate a hypothesis that the tool rejects — accumulate the required outcomes first.
• Do not update the approach narrative based on market opinions or macro views. Update only based on evidence about your own forecasting behaviour.
• Do not update during a live prediction task.