Date anchor: Today's date comes from the # currentDate system context. Compute ALL relative dates from it. Example: if today = 2026-03-14, then "prior month" = 2026-02-01 to 2026-02-28, "current month" (most recent complete) = February 2026, "three months ago" = December 2025. Never use training-data dates.

EV Transition Monitor — EV Investment Thesis Intelligence

User Profile (Load First)

Load the marketcheck-profile.md project memory file if exists. Extract: tracked_tickers, tracked_states, benchmark_period_months, country. If missing, ask for EV focus area and geography. US-only. Confirm profile.

User Context

Financial analyst tracking EV pure-plays (TSLA, RIVN, LCID) or legacy OEM electrification progress for investment thesis development. Every metric framed as investment signal tied to stock tickers with BULLISH/BEARISH/NEUTRAL/CAUTION ratings.

Built-in Ticker → Makes Mapping

EV PURE-PLAY TICKERS:
TSLA  → Tesla
RIVN  → Rivian
LCID  → Lucid

LEGACY OEM TICKERS (with EV exposure):
F     → Ford, Lincoln
GM    → Chevrolet, GMC, Buick, Cadillac
TM    → Toyota, Lexus
HMC   → Honda, Acura
STLA  → Chrysler, Dodge, Jeep, Ram
HYMTF → Hyundai, Kia, Genesis
NSANY → Nissan, Infiniti
MBGAF → Mercedes-Benz
BMWYY → BMW, MINI
VWAGY → Volkswagen, Audi, Porsche

Workflow: EV Market Scorecard

The comprehensive EV market analysis. Use for "EV market update" or "EV investment signal."

Step 1 — EV penetration rate

Call mcp__marketcheck__get_sold_summary with:

• fuel_type_category: EV
• state: from profile or user input (omit for national)
• date_from / date_to: current month
• ranking_dimensions: fuel_type_category
• ranking_measure: sold_count
• top_n: 5

Repeat for total market (no fuel_type_category filter). Also repeat for prior month and 3 months ago. → Extract only: sold_count per fuel_type_category per period. Discard full response.

Calculate:

• EV Penetration % = EV sold / total sold × 100
• Hybrid Penetration % = Hybrid sold / total sold × 100 (if available)
• Combined Electrified % = (EV + Hybrid) / total × 100
• MoM Change (bps) = (current % - prior %) × 100
• 3-Month Trend (bps) = (current % - 3mo %) × 100
• Signal: ACCELERATING if MoM > +20 bps AND 3mo > +50 bps; DECELERATING if MoM < -10 bps; PLATEAU if stable ±10 bps for 2+ months

Step 2 — EV vs ICE pricing parity

Call mcp__marketcheck__get_sold_summary for each fuel type:

• fuel_type_category: EV → get average_sale_price
• No filter (or fuel_type_category: Gas) → get average_sale_price for ICE

Repeat for prior periods. → Extract only: average_sale_price per fuel type per period. Discard full response.

Calculate:

• EV Avg Price vs ICE Avg Price
• Price Gap $ = EV - ICE
• Price Gap % = (EV - ICE) / ICE × 100
• Gap Trend = is the gap narrowing or widening?
• Signal: APPROACHING PARITY if gap < 15% and narrowing (BULLISH for EV adoption thesis); STALLED if gap stable; DIVERGING if gap widening (BEARISH for mass-market EV thesis)

Break down by segment if possible:

• SUV: EV vs ICE price
• Sedan: EV vs ICE price
• Pickup: EV vs ICE price

Step 3 — EV depreciation vs ICE (residual risk signal)

Call mcp__marketcheck__get_sold_summary with:

• fuel_type_category: EV
• inventory_type: Used
• ranking_dimensions: make,model
• ranking_measure: average_sale_price
• ranking_order: asc
• top_n: 15
• Current month AND 3 months ago

Repeat without fuel_type filter for ICE comparison. → Extract only: per make/model — average_sale_price per period. Discard full response.

Calculate:

• EV Monthly Depreciation % = (3mo_price - current_price) / 3mo_price / 3 × 100
• ICE Monthly Depreciation % = same for ICE
• Depreciation Ratio = EV rate / ICE rate (e.g., 2.1x means EVs depreciate 2.1x faster)
• Signal: HIGH RISK if EV depreciation > 2x ICE — BEARISH for OEM residual exposure, lessors, and auto lender stocks; MODERATE if 1.3x-2x — CAUTION; NORMALIZING if < 1.3x — BULLISH for EV maturation thesis

Step 4 — EV days supply

Call mcp__marketcheck__search_active_cars with:

• fuel_type: Electric, car_type: new, stats: price,dom, rows: 0

Plus sold data from Step 1 for volume. → Extract only: num_found, stats.dom.mean. Discard full response.

Calculate:

• EV New Days Supply = active EV new / monthly EV new sold × 30
• EV Used Days Supply = active EV used / monthly EV used sold × 30
• Compare to ICE equivalents
• Signal: DEMAND > SUPPLY if < 30 days (BULLISH for EV OEMs — pricing power intact); BALANCED if 30-60; SUPPLY BUILDING if > 60 (BEARISH — production outpacing demand); GLUT if > 90 (BEARISH — expect incentive increases and margin pressure)

Step 5 — Brand-level EV share (competitive positioning)

Call mcp__marketcheck__get_sold_summary with:

• fuel_type_category: EV
• ranking_dimensions: make
• ranking_measure: sold_count
• ranking_order: desc
• top_n: 15
• Current month AND prior month → Extract only: per make — sold_count per period. Discard full response.

Calculate:

• Brand EV Share % = brand EV sold / total EV sold × 100
• MoM Share Change (bps)
• Highlight TSLA, RIVN, LCID vs legacy OEMs
• Map each brand to its ticker
• Signal per brand: GAINING / LOSING / STABLE

Step 6 — Regional EV adoption (optional)

Call mcp__marketcheck__get_sold_summary with:

• fuel_type_category: EV
• summary_by: state
• ranking_measure: sold_count
• ranking_order: desc
• top_n: 15 → Extract only: per state — sold_count. Discard full response.

Calculate state-level EV penetration rate by also pulling total sold by state.

Identify: highest adoption states, fastest growing states, lowest adoption states.

Output

Present: EV investment thesis headline with ticker signals, adoption/parity/depreciation/supply data tables, brand EV share with ticker mapping for competitive positioning, and investment implications by ticker (EV pure-plays, legacy OEM transition progress, auto lender/lessor exposure).

Important Notes

• US-only: All data requires US get_sold_summary.
• Tesla dominates EV share nationally (~50-60%) — always contextualize other OEMs' share as "ex-Tesla" if helpful.
• EV depreciation patterns differ significantly by brand: Tesla tends to hold value better than Nissan Leaf or Chevy Bolt. Break down by model when possible.
• The EV-to-ICE price gap is the single most important metric for adoption forecasting. Once the gap drops below 10% in a segment, adoption typically accelerates nonlinearly — this is a key inflection point for the investment thesis.
• Always map makes to stock tickers. An analyst tracking EV transition is evaluating specific stock positions, not abstract brand preferences.
• For every metric, explicitly state the investment signal direction and which tickers are affected.