Probability Assessment

If the block is absent or incomplete, call polymarket_search using the most relevant signal-category search phrases (see default signal maps below).

Default signal weights by asset type:

Step 2b — Extract price threshold markets for chart (asset price queries only)

If the query is about an asset price (e.g. "Will BTC be higher?", "Gold price forecast"):

After gathering Polymarket markets, identify all markets that mention a specific dollar price level in their question text — patterns like:

• "Will BTC exceed $70,000?"
• "Will Gold (GC) settle at >$6,200 in June?"
• "Will BTC reach $80K by end of March?"

For each such market, record {price, probability} where:

• price = the dollar level as a number (e.g. 70000, 6200, 80000)
• probability = the YES probability (0–1) — treat these as upper-tail (P(asset price > this level))

PRE-FLIGHT CHECK — you MUST complete this before writing any output:

1. Count the distinct price levels you have from Polymarket.
2. If you have 2 or more distinct price levels, CALL price_distribution_chart NOW — before writing the Signal Evidence section. This is mandatory, not optional. Pass the {price, probability}[] array and the current asset price.
3. Embed the chart output verbatim in the Signal Evidence section under Polymarket, immediately before the summary table.
4. If you have fewer than 2 price levels, note "No chart: fewer than 2 price thresholds available" in the Polymarket evidence section and continue.

⚠️ Do NOT skip price_distribution_chart when you have ≥2 price levels. The chart is the most informative visual in the entire output — it shows the crowd-implied price distribution at a glance. Missing it is a quality failure.

Step 2c — Markov distribution (asset price queries only)

If the query is about an asset price AND you have ≥2 Polymarket price thresholds:

After calling price_distribution_chart (Step 2b), call markov_distribution to enrich the distribution with regime-aware Markov interpolation and 90% Monte Carlo confidence intervals.

1. Gather inputs (you likely already have these from prior steps):

   • ticker — the asset symbol (e.g. NVDA, BTC-USD, SPY). For commodities, use the liquid ETF: gold → GLD, silver → SLV, oil → USO.
   • horizon — forecast horizon in trading days (convert calendar days: 30 calendar ≈ 21 trading)
   • historicalPrices — 60–90 days of daily close prices from get_market_data (oldest first)
   • polymarketMarkets — the raw Polymarket market objects (question + probability + volume)
   • sentiment (optional) — bullish/bearish from social_sentiment if already gathered

2. Call markov_distribution with these inputs. The tool will:

   • Classify each day into a regime state (bull/bear/high_vol_bull/high_vol_bear/sideways)
   • Estimate a 5×5 Markov transition matrix with Dirichlet smoothing
   • Detect structural breaks (regime shifts mid-window) and widen CI when detected
   • Blend Markov log-normal estimates with bias-corrected Polymarket anchors
   • Return P(price > X) for 20+ levels with [5th, 95th] percentile CI bounds

3. Embed the output in the Signal Evidence section after the price_distribution_chart. Highlight the regime state, mixing-time weight, and any warnings (sparse states, structural break, cross-platform divergence).

4. Report in the Signal Evidence section:

   • Current regime state (e.g. "bull", "high_vol_bear")
   • Mixing-time weight (e.g. "42% Markov / 58% Polymarket anchors")
   • R²_OS if available (positive = Markov adds predictive value over mean)
   • Any ⚠️ warnings from metadata

⚠️ Do NOT skip markov_distribution when you have ≥2 price thresholds and historical prices. It provides crucial uncertainty quantification (CI bounds) that price_distribution_chart alone cannot produce. Missing it is a quality failure for price distribution queries.

Step 3 — Gather remaining signals

Collect as many of these as are relevant and available. Each becomes a LogOddsSignal with a probability [0,1] and its category weight from the table above.

3a. Social sentiment

Call social_sentiment or x_search with the asset ticker or event keyword. Convert the bullish/bearish ratio to a probability:

• 70% bullish posts → probability ≈ 0.70
• Assign weight: 0.15 (social sentiment is a noisy signal)

3b. Analyst consensus

• Use get_financials to retrieve EPS estimates or analyst ratings.
• If analysts forecast a beat by ≥5%: probability ≈ 0.72
• If estimates are flat: probability ≈ 0.50
• If estimates revised down: probability ≈ 0.30
• Assign weight: 0.25

3c. Historical base rate

• What fraction of similar events has happened historically? (e.g. NVDA beats EPS ~80% of recent quarters → probability = 0.80)
• Use web_search for company earnings history if not in memory.
• Assign weight: 0.20

Step 4 — Compute weighted log-odds combination

Apply the formula to each signal you have (drop absent signals, re-normalise remaining weights to sum to 1.0):

log_odds(p) = ln(p / (1 − p))          [clamp p to 0.001–0.999]

combined_log_odds = Σ wᵢ × log_odds(pᵢ)

p_combined = 1 / (1 + exp(−combined_log_odds))

σ = √(Σ wᵢ × (log_odds(pᵢ) − combined_log_odds)²)
lower = 1 / (1 + exp(−(combined_log_odds − σ)))
upper = 1 / (1 + exp(−(combined_log_odds + σ)))

Flag divergence (⚠️) when σ > 0.3.

Step 5 — Output structured assessment

The output must follow this exact order: evidence first, then the summary table, then interpretation.

5a — Signal Evidence (show the raw data, always first)

For every signal you used, list the exact data points you read. Do not summarise or paraphrase — show the actual question text, percentage, and where it came from. This is the most important part of the output.

**Signal Evidence**

Polymarket (crowd)
  • "Will BTC exceed $70K by March 30?" → 3.7% YES  ($350K volume)
  • "Will BTC stay above $60K through March?" → 99.7% YES  ($280K volume)
  • Implied range: $60–70K with ~4% chance of upside breakout

ETF / Product flows (weight 30%)
  • IBIT net flow last 7 days: −$120M (net outflow)
  • Spot BTC ETF combined AUM: −2.1% WoW
  • Interpretation: institutional flows are bearish short-term

Fed / Rates (weight 20%)
  • "Fed cuts before July 2026?" → 15% YES  (Polymarket)
  • 10Y yield: 4.32% (Reuters, Mar 28)
  • Interpretation: no rate tailwind expected in 30-day window

Recession risk (weight 15%)
  • "US recession by EOY 2026?" → 36% YES  (Polymarket)
  • Near-term 30-day recession probability: ~5% (baseline)
  • Interpretation: macro risk elevated but not imminent

Social sentiment (weight 15%)
  • Reddit r/Bitcoin: 42% bullish / 58% bearish  (150 posts, 24h)
  • Fear & Greed Index: 9/100  (Extreme Fear)
  • Interpretation: contrarian signal — Extreme Fear has historically marked local lows

5b — Summary table (after evidence)

📊 Probability Assessment: [Event question]

| Signal                  | Probability | Weight |
|-------------------------|-------------|--------|
| Polymarket (crowd)      |          4% |    35% |
| ETF/Product flows       |         25% |    30% |
| Fed rates               |         45% |    20% |
| Recession risk          |         40% |    15% |
|-------------------------|-------------|--------|
| **Combined (log-odds)** | **24% ±6pp**|        |

*Signals are [consistent / ⚠️ divergent — treat with caution].*

5c — Interpretation (after table)

One short paragraph:

• What the combined probability means in plain language
• The single most informative signal and why (reference the evidence above)
• Any data gaps (missing signals, thin Polymarket liquidity)
• Suggested action framing

Step 6 — Bear case (required)

Apply Munger's inversion: state what would have to be true for the bull scenario to fail. Always include this block after the interpretation:

**Bear case**: [1–2 sentences describing the primary scenario that invalidates
the thesis]. If [specific trigger — e.g. "Bitcoin breaks below $58K support"],
this assessment would revise down to ~X%. Watch: [one concrete indicator to
monitor — e.g. "weekly close below $60K", "SEC enforcement action", "Fed
surprise hike"].

Keep it concise (3 sentences max). Do not simply restate the low probability as the bear case — identify the mechanism (catalyst + path) that makes it play out.

Constraints

• Never report a probability below 1% or above 99%.
• If only one signal is available, report it with a ±15pp uncertainty band and note that it is a single-source estimate.
• Do not fabricate Polymarket probabilities — only use values returned by polymarket_search or the pre-injected 🎯 block.
• Polymarket probabilities are market-implied, not guaranteed outcomes. Always include a disclaimer at the end.

Trajectory Mode (Short-Horizon Forecasts)

When the user asks for day-by-day price movement (e.g., "What will AAPL do over the next 7 days?"), use the markov_distribution tool with trajectory: true:

markov_distribution({
  ticker: "AAPL",
  horizon: 7,
  historicalPrices: [...],
  polymarketMarkets: [],
  trajectory: true,
  trajectoryDays: 7
})

This returns a day-by-day table with expected price, 90% CI, P(up), and cumulative return for each day. Present the trajectory table alongside your probability assessment for a complete short-term outlook.

When to use trajectory mode:

• User asks for day-by-day or multi-day outlook
• Short horizons (1–14 days) where the path matters, not just the endpoint
• Comparing how uncertainty grows over the forecast window