Confirm Sector Rotation Signals Through Price, Valuation, and Estimate Support

Sector rotation is easy to identify after prices have already moved. It is harder to determine whether that movement is supported by fundamentals.

A sector may outperform because earnings expectations are improving. It may also rally because valuations are expanding, macro conditions are shifting, or short-term sentiment is directing capital toward a particular theme. Price performance shows where leadership may be forming, but it does not explain whether that leadership has sufficient confirmation behind it.

This is why sector rotation analysis needs more than a performance table. Research teams need a structured way to compare sector price momentum with valuation context and near-term estimate support. When all three signals move in the same direction, the rotation case becomes easier to defend. When price strength appears without estimate support or alongside stretched valuation, the move may require closer review.

This article builds a sector rotation confirmation model using Financial Modeling Prep data through Claude MCP. The model reviews sector performance, compares valuation against historical context, checks whether representative-company estimates provide near-term support, and classifies each sector using neutral research labels such as Confirmed rotation, Momentum without confirmation, Valuation-stretched leadership, Deteriorating sector, or Review required.

The goal is not to predict sector rotation or make allocation decisions. It is to help analysts test whether current sector movement is supported by price, valuation, and estimate evidence. This is not an institutional ownership, 13F, or fund-flow analysis. The focus is sector momentum confirmation rather than ownership flows.

Key Takeaways

• Sector rotation should not be confirmed through price performance alone.
• Claude MCP can compare sector momentum with valuation context and estimate support when the periods, inputs, and classification rules are clearly defined.
• Estimate evidence must be labelled carefully, especially when representative companies are used instead of full sector-level coverage.
• Sectors with conflicting, incomplete, or unreliable signals should be marked Review required rather than forced into a clean classification.
• The output is designed to prioritize research follow-up, not provide sector-allocation recommendations.

FMP APIs Used for the Analysis

Sector rotation confirmation needs more than one market signal. Price performance shows which sectors are moving, while valuation and estimate data help test whether that movement has fundamental support.

For this analysis, Claude can use the following Financial Modeling Prep datasets through MCP:

Analytical role	FMP dataset
Sector price momentum	Sector Performance Snapshot
Broader market context	Market Performance Data
Valuation context	Historical Sector PE Ratios
Estimate support	Analyst Financial Estimates
Optional breadth check	Biggest Gainers
Sector identification and mapping	Available Sectors API and Company Profile Data API

The example uses four editorial sector labels: Technology, Financials, Energy, and Health Care. During data retrieval, Claude maps Financials to FMP's Financial Services label and Health Care to Healthcare. Technology and Energy retain the same labels. This mapping keeps the prompt, retrieved data, and final output consistent.

The core confirmation layers are sector performance, historical valuation, and estimate support. These datasets connect price momentum with valuation context and near-term earnings expectations.

Biggest Gainers is optional supporting context only. It may help identify whether short-term strength is spread across several companies or concentrated in a few stocks, but it is not required for the final classification. Because it does not appear in the example output, it should not be treated as a core confirmation layer.

Estimate data also requires careful handling. When estimates are available only at the company level, Claude should identify the representative company used for each sector. The output must not imply complete sector-level estimate coverage when the evidence comes from a single company.

The objective is not to retrieve every available market dataset. It is to use a focused data layer that tests whether sector movement is supported by price, valuation, and estimate evidence.

Data Quality Comes Before Rotation Classification

Sector rotation analysis depends on consistent definitions and aligned time windows. If performance, valuation, and estimate data use different periods or sector groupings, the model may confuse timing noise with a genuine rotation signal.

The first check is sector consistency. The sector labels used in performance data should match the classifications used for valuation and estimate analysis. When FMP returns different naming conventions, the prompt should show the mapping clearly and use one label set throughout the output.

The second check is time-window alignment. A 30-trading-day sector move should not be compared loosely with valuation data from an unrelated period or estimates from a different fiscal basis. The signals do not need to update at the same frequency, but the period used for each layer must be stated clearly.

The third check is estimate coverage. Analyst estimates are generally company-level inputs, while performance and valuation may be available at the sector level. In this example, one large-cap company is used as a representative estimate input for each sector.

This provides a useful indication of near-term estimate support, but it does not measure estimate breadth across the entire sector. A single representative company should not be presented as full sector-level estimate breadth.

That limitation must remain visible in the output. The raw evidence table should identify the company and fiscal periods used. The final classification table should describe the signal as representative-company estimate support, and the confidence and supporting rationale should acknowledge when the conclusion relies on only one company.

For example, improving Microsoft estimates may provide supporting evidence for Technology, but they do not establish that estimates are improving across all technology companies. A broader conclusion would require additional constituents or direct sector-level estimate evidence.

These checks keep the classifications grounded. Before assigning labels such as Confirmed rotation, Momentum without confirmation, or Deteriorating sector, Claude should confirm that sector definitions, periods, valuation data, and estimate coverage are suitable for comparison. When the evidence is incomplete, conflicting, or too narrow, the sector should be marked Review required rather than forced into a clean rotation label.

Building the Sector Rotation Confirmation Model

Once the data inputs are aligned, the model can test whether sector movement is supported by more than price performance.

The analysis uses three confirmation layers.

Price momentum

The first layer identifies whether a sector is generating positive performance or clearly outperforming the broader market over the selected window. Price strength is the starting signal, but it is not enough to confirm rotation on its own.

Valuation context

The second layer compares the sector's current valuation with its recent historical range. This helps distinguish leadership supported by reasonable valuation from a move driven mainly by multiple expansion.

A sharp change in sector PE should not automatically be interpreted as genuine expansion or compression. Composition changes, unprofitable constituents, or data discontinuities may require analyst review.

Estimate support

The third layer evaluates whether near-term earnings expectations provide support for the price signal.

When true time-stamped revision history is available, the model can assess whether consensus estimates have moved upward or downward. When the data contains only current estimates for different fiscal years, the output should describe the result as estimate direction or representative-company estimate support, not as an estimate revision.

A representative company can provide useful supporting evidence, but it does not establish full sector-level estimate breadth.

The model classifies each sector using the following neutral research labels:

Rotation Label	Directional Criteria
Confirmed rotation	Sector performance is positive or clearly above the broader-market benchmark, valuation is not clearly stretched versus historical context, and estimate support is improving or stable with enough data quality and coverage to support a sector-level conclusion.
Momentum without confirmation	Sector performance is positive or above the benchmark, but estimate support is weak, unavailable, too narrow, or not yet visible.
Valuation-stretched leadership	Sector performance is positive or above the benchmark, but valuation expansion appears to be contributing more than estimate improvement.
Deteriorating sector	Sector performance is weak or below the benchmark, and valuation context or estimate support also indicates deterioration.
Review required	Sector definitions, time windows, valuation data, estimate coverage, representative-company mapping, or other inputs are incomplete, conflicting, or unreliable.

A sector should not receive a Confirmed rotation classification solely because estimates for one representative company are stable or improving. The estimate layer must provide enough coverage and data quality to support a broader sector-level conclusion.

These classifications are not allocation instructions. They help analysts distinguish between confirmed, incomplete, stretched, deteriorating, and unreliable sector signals before deciding where deeper research is required.

Accessing FMP Data Through Claude MCP

Financial Modeling Prep supports access through the FMP MCP server, allowing Claude to retrieve FMP datasets directly through the Model Context Protocol.

Before configuring the connector, create an FMP account and obtain an API key. The FMP Quickstart guide explains where to find the key in the FMP dashboard.

In Claude, the setup follows this path:

Settings → Connectors → Add custom connector

After adding the FMP MCP connector and API key, Claude can retrieve sector performance, historical sector valuation, market performance, financial estimates, and supporting company-level data during the analysis.

Treat the API key as a private credential. Do not include the actual key in shared prompts, screenshots, public notebooks, GitHub repositories, published articles, or example connector configurations. Use a placeholder such as YOUR_FMP_API_KEY in any material intended for public or team-wide distribution. If a key is exposed, replace it through the FMP dashboard before continuing to use the connector.

Before running the complete sector review, validate the setup using one sector and one dataset. For example, ask Claude to retrieve the latest sector performance snapshot or historical sector PE data for Technology.

This small test confirms that:

• the MCP connector and API key are working;
• the expected sector label is returned;
• the reporting period is clearly identified;
• the required metric fields are available.

For the full analysis, Claude uses the connected FMP datasets to compare sector price momentum, valuation context, and estimate support. The analyst still controls the review logic: the prompt defines the sector list, time windows, evidence limitations, classification labels, and follow-up actions.

Running the Sector Rotation Analysis

This example uses four sectors with different economic drivers:

• Technology
• Financials
• Energy
• Health Care

The article uses these editorial labels consistently. Where FMP uses different sector names, Claude applies the following mapping:

Editorial Label	FMP Label
Financials	Financial Services
Health Care	Healthcare

Technology and Energy use the same labels in both the article and FMP data.

The example output states the exact run date, performance window, benchmark, and sector-performance basis. Because FMP did not explicitly identify the weighting methodology in this run, the sector series is labelled provider-defined. Comparisons with the capitalization-weighted S&P 500 should therefore be treated as directional rather than strictly like-for-like.

Use the following prompt in Claude after connecting the FMP MCP server:

Use Financial Modeling Prep data through MCP to perform a compact sector rotation confirmation review. The run should finish quickly and use no more than 12 FMP MCP tool calls. Do not print tool-search progress, endpoint discovery notes, or intermediate calculations. If a required field is unavailable, retry no more than once. After one failed retry, mark the field as Unavailable and continue. Do not search for substitute endpoints, additional companies, or alternative datasets. EDITORIAL SECTORS AND FMP MAPPING Use exactly these four sectors: - Technology → FMP label: Technology → Representative company: MSFT - Financials → FMP label: Financial Services → Representative company: JPM - Energy → FMP label: Energy → Representative company: XOM - Health Care → FMP label: Healthcare → Representative company: LLY Use the editorial labels in both final tables. OBJECTIVE Evaluate whether recent sector movement is supported by: 1. Price performance 2. Valuation context 3. Representative-company estimate support This is a research-prioritization review. Do not provide allocation or trading recommendations. DATA SCOPE Retrieve only: 1. Sector performance for the most recent 30 completed trading days 2. S&P 500 price return over the same start and end dates 3. Current sector PE 4. The closest available sector PE observation approximately six months earlier 5. Annual EPS estimates for the representative company for the current and next fiscal year Do not retrieve: - Biggest Gainers - complete historical PE series - 12-month PE reference points - quarterly or TTM estimates - news, filings, or company profiles - additional representative companies - full sector constituent lists RUN METADATA Before the tables, provide one line in this exact format: Run date: YYYY-MM-DD | Performance window: YYYY-MM-DD to YYYY-MM-DD | Sector performance basis: [equal-weighted, cap-weighted, provider-defined, or unknown] | Benchmark: S&P 500 | Benchmark return: X.XX% If the weighting method is not explicitly returned by FMP, label it Provider-defined or Unknown. Do not spend additional tool calls investigating the methodology. ESTIMATE RULES Use annual estimates only. Compare the representative company's current-fiscal-year EPS estimate with its next-fiscal-year EPS estimate. Do not call this an estimate revision unless time-stamped prior consensus or explicit revision history is available. Otherwise label it: Representative-company estimate support — [ticker]: Improving, Stable, Weakening, or Unavailable A single representative company is not full sector-level estimate breadth. State this limitation in both tables. CLASSIFICATION RULES Use only: - Confirmed rotation - Momentum without confirmation - Valuation-stretched leadership - Deteriorating sector - Review required Confirmed rotation: Use only when performance is positive or clearly above the benchmark, valuation is not clearly stretched, and estimate evidence has enough breadth and quality to support a sector-level conclusion. Do not assign Confirmed rotation solely from one representative company. Momentum without confirmation: Use when performance is positive or above the benchmark, but estimate evidence is representative-only, unavailable, or too narrow. Valuation-stretched leadership: Use when performance is positive or above the benchmark and current PE is materially above the six-month reference point without sufficient estimate support. Deteriorating sector: Use when performance is negative or below the benchmark and representative-company estimate support is weakening, provided the valuation data is reliable. Review required: Use when performance, benchmark, valuation, estimate periods, or data quality are missing, conflicting, or unreliable. CONFIDENCE Use only High, Medium, or Low. Confidence cannot be High when the estimate layer uses only one representative company. OUTPUT Return exactly two markdown tables with exactly four rows each. TABLE 1: RAW EVIDENCE Use exactly these columns: | Sector | Performance Window | Sector Performance | Benchmark Difference | Current PE / Six-Month PE | Representative Estimate Input | Estimate Support | Data Limitation | Requirements: - Show exact dates. - Label whether the sector is above or below the benchmark and by how much. - Identify the representative ticker and fiscal years. - State “Not full sector estimate breadth” for representative-company inputs. - Do not add extra rows or tables. TABLE 2: ROTATION CLASSIFICATION Use exactly these columns: | Sector | Price Signal | Valuation Context | Estimate Support | Rotation Classification | Confidence | Supporting Rationale | Analyst Follow-Up | Requirements: - Use the same four-sector order. - Keep each rationale below 35 words. - Keep each follow-up below 20 words. - State when estimate support is representative-company evidence only. - Do not rank sectors. Do not use buy, sell, hold, rotate in, rotate out, overweight, underweight, outperform, or underperform as recommendations or classification labels.

Example Claude Output Structure and Signal Interpretation

This example reflects one MCP run using the stated window and available data. Figures and classifications will change as market data, valuation data, and estimates update.

Run date: July 20, 2026
Performance window: June 8-July 17, 2026
Benchmark: S&P 500 price return of +0.70% over the same window
Sector performance basis: Provider-defined FMP sector performance; weighting methodology was not independently verified in this run

The output first presents the evidence used for the review. This keeps the sector performance, valuation inputs, representative-company estimates, and data limitations visible before Claude assigns a classification.

Table 1: Raw Evidence Layer

Sector	Performance Window	Sector Performance	Benchmark Difference	Current PE / Six-Month PE	Representative Estimate Input	Estimate Support	Data Limitation
Technology	2026-06-08 to 2026-07-17	-5.83%	Below by 6.53 pp	46.73 / Unavailable	MSFT FY2027 EPS 19.46 → FY2028 EPS 22.78	Improving	Not full sector estimate breadth; six-month PE unavailable after one restricted-access retry
Financials	2026-06-08 to 2026-07-17	-2.70%	Below by 3.40 pp	20.04 / Unavailable	JPM FY2026 EPS 24.31 → FY2027 EPS 24.83	Stable	Not full sector estimate breadth; six-month PE unavailable after one restricted-access retry
Energy	2026-06-08 to 2026-07-17	-13.35%	Below by 14.05 pp	17.26 / Unavailable	XOM FY2026 EPS 10.99 → FY2027 EPS 10.44	Weakening	Not full sector estimate breadth; six-month PE unavailable after one restricted-access retry
Health Care	2026-06-08 to 2026-07-17	-0.37%	Below by 1.07 pp	23.06 / Unavailable	LLY: Unavailable after one restricted-access retry	Unavailable	Not full sector estimate breadth; both six-month PE and representative estimate data are unavailable

The second table shows how Claude translated the evidence into research classifications. Each rationale remains tied to the available data, while the follow-up column identifies the minimum evidence needed for reassessment.

Table 2: Final Classification Layer

Sector	Price Signal	Valuation Context	Estimate Support	Rotation Classification	Confidence	Supporting Rationale	Analyst Follow-Up
Technology	Below benchmark (-5.83% vs. +0.70%)	Current PE 46.73; six-month reference unavailable	Improving (MSFT only; not full sector breadth)	Review required	Medium	Price declined below the benchmark while MSFT estimates improved, but the missing six-month PE reference prevents a reliable valuation assessment.	Obtain the six-month sector PE reference before confirming the classification.
Financials	Below benchmark (-2.70% vs. +0.70%)	Current PE 20.04; six-month reference unavailable	Stable (JPM only; not full sector breadth)	Review required	Medium	The sector declined modestly below the benchmark while JPM estimates remained stable, but missing six-month PE data blocks a complete valuation assessment.	Source the six-month sector PE data to complete the valuation comparison.
Energy	Below benchmark (-13.35% vs. +0.70%)	Current PE 17.26; six-month reference unavailable	Weakening (XOM only; not full sector breadth)	Review required	Medium	The sharp decline below the benchmark aligns with weakening XOM estimates, but missing six-month PE data prevents confirmation of broader valuation deterioration.	Prioritize retrieval of the six-month PE reference given the size of the decline.
Health Care	Below benchmark (-0.37% vs. +0.70%)	Current PE 23.06; six-month reference unavailable	Unavailable (LLY data restricted)	Review required	Low	The sector was slightly below the benchmark, but no historical valuation reference or representative-company estimate data was available.	Restore LLY estimate access and obtain the six-month PE reference before reassessment.

Interpreting the Signals

In this MCP run, Claude did not force any sector into a clean rotation label. All four sectors were marked Review required because the six-month PE reference was unavailable, leaving the valuation layer incomplete.

The price evidence was also consistently weak. Every sector finished below the S&P 500's +0.70% return over the same window. Energy recorded the largest gap at 14.05 percentage points below the benchmark, while Health Care was closest to the benchmark with a decline of 0.37%.

The representative-company estimate layer produced different signals. Microsoft showed improving forward EPS direction, JPM was broadly stable, and XOM showed weakening estimates. These inputs add context, but they represent one company per sector rather than full sector-level estimate breadth.

Technology and Financials therefore present conflicting evidence: recent sector performance was negative, while their representative-company estimates were improving or stable. Without the historical PE reference, the model could not determine whether current valuation offered support, showed stretch, or reflected a broader change in sector earnings.

Energy had the clearest negative alignment between price and representative-company estimates. However, the missing historical PE reference prevented Claude from confirming whether the current valuation also supported a Deteriorating sector classification.

Health Care had the smallest price decline, but its evidence was the least complete. Both the historical PE comparison and the LLY estimate input were unavailable, resulting in Low confidence and a direct request for additional data.

This is the main value of the framework. It does not treat price movement, a current PE value, or one company's estimate direction as sufficient on its own. A stronger rotation signal needs price momentum, valuation context, and estimate support to point in a consistent direction. When one layer is missing or conflicts with another, the model surfaces the limitation and directs the analyst toward the evidence needed next.

Screenshots may remain as visual proof that the output was generated through Claude MCP, but these text tables should be treated as the accessible and authoritative version of the example output.

Where Sector Rotation Confirmation Needs Analyst Review

This framework helps analysts test whether sector movement is supported by price, valuation, and estimate evidence. It should not be treated as a sector-rotation prediction model.

Claude can compare recent performance with valuation context and representative-company estimate support. It can also highlight cases where the evidence conflicts or remains incomplete. However, several conditions still require analyst judgment.

Review Trigger	Why It Matters
Price performance is positive but estimate support is unavailable	The sector may have momentum, but earnings confirmation remains incomplete
Price performance is weak while valuation or estimates appear supportive	Fundamentals may be improving before price confirms the signal
Valuation changes sharply without a clear earnings driver	The change may reflect price movement, earnings changes, sector composition, or a data artifact
Estimate data comes from one representative company	The result does not measure estimate breadth across the full sector
Sector labels differ across datasets	Performance, valuation, and estimate inputs may not be directly comparable
Performance data is influenced by micro-cap or low-price constituents	The result may not represent investable, capitalization-weighted sector exposure
Macro, policy, liquidity, or interest-rate conditions dominate price movement	Important drivers may sit outside the company and sector data used in the model
Sector strength is concentrated in only a few companies	The movement may be company-specific rather than broad-based

These triggers do not necessarily mean that a sector signal is incorrect. They identify where more evidence is needed before analysts rely on the classification.

For example, positive price performance with unavailable estimate support should not be labelled Confirmed rotation too quickly. It may be better classified as Momentum without confirmation until broader estimate evidence becomes available. Similarly, improving representative-company estimates with weak sector performance may warrant Review required because the price layer has not confirmed the fundamental signal.

The strongest use case for this framework is research prioritization. Strategy teams can identify sectors that need further investigation. Portfolio teams can compare existing sector exposure against the confirmation labels, while risk teams can monitor where price movement appears unsupported by valuation or estimate data.

The output directs analyst attention; it does not replace market judgment.

From Sector Momentum to Rotation Confirmation

Sector rotation analysis becomes more useful when price movement is tested against supporting evidence.

A sector can lead for many reasons. It may be benefiting from improving earnings expectations, valuation support, macro sensitivity, or short-term sentiment. Price performance shows where leadership is forming, but valuation and estimate data help analysts judge whether that leadership has confirmation behind it.

With FMP connected inside Claude through the MCP server, this review becomes easier to repeat. Claude can retrieve sector performance, historical valuation context, analyst estimate data, and supporting market signals in one structured pass. Teams that want to run this type of sector rotation confirmation review across broader coverage lists can review the available FMP pricing plans based on their data coverage, API usage, and research needs.

The classification should be read as a research label for follow-up review, not as a buy, sell, rotate-in, rotate-out, outperform, or underperform recommendation.

The final value is not predicting sector rotation. It is creating a repeatable way to test whether sector movement is supported by price momentum, valuation context, and estimate support before analysts decide where deeper review is needed.