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These examples walk through actual NAV calculations using the midprice-v1 methodology. Each example uses realistic numbers and can be replicated with a spreadsheet and public market data from Polymarket.
Every number below is computed using 8-decimal-place precision with round-half-up, matching the production system exactly. If you follow along in a spreadsheet, your results should match within rounding tolerance.

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Example 1: Equal-Weighted Index with 5 Active Markets

A macro-themed series tracking 5 economic prediction markets, all actively trading with two-sided order books. Series Composition:
#MarketBest BidBest AskMidpriceRaw Weight
1Fed cuts by June0.820.830.82500.20
2CPI below 3% by Q20.710.720.71500.20
3Unemployment above 4.5%0.350.370.36000.20
410Y yield below 4%0.440.460.45000.20
5GDP growth above 2%0.580.590.58500.20
1

Compute midprices

For each market, average the best bid and best ask:
Market 1: (0.82 + 0.83) / 2 = 0.82500000
Market 2: (0.71 + 0.72) / 2 = 0.71500000
Market 3: (0.35 + 0.37) / 2 = 0.36000000
Market 4: (0.44 + 0.46) / 2 = 0.45000000
Market 5: (0.58 + 0.59) / 2 = 0.58500000
Each midprice represents the market’s implied probability for that event occurring.
2

Normalize weights

Sum of raw weights = 5 x 0.20 = 1.00
Since the raw weights already sum to 1.00, normalized weights equal the raw weights:
w'_1 = w'_2 = w'_3 = w'_4 = w'_5 = 0.20000000
3

Compute Raw NAV

Multiply each normalized weight by its market’s midprice and sum:
raw_nav = (0.20 x 0.82500000)
        + (0.20 x 0.71500000)
        + (0.20 x 0.36000000)
        + (0.20 x 0.45000000)
        + (0.20 x 0.58500000)

        = 0.16500000
        + 0.14300000
        + 0.07200000
        + 0.09000000
        + 0.11700000

        = 0.58700000
4

Compute Index Level

Assume the series launched with an inception Raw NAV of 0.55000000:
index_level = 100 x (0.58700000 / 0.55000000)
            = 100 x 1.06727273
            = 106.72727273
The index has gained approximately 6.7% from inception.
Interpretation: The weighted-average implied probability across all 5 markets is about 58.7%. The highest-confidence market is “Fed cuts by June” at 82.5%, while the lowest is “Unemployment above 4.5%” at 36%. The index is up ~6.7% from inception, meaning the aggregate probability of these events has increased since the series launched.
To verify this yourself, look up the current best bid and best ask for each market on Polymarket, compute the midprices, and plug them into the formula. Your result should match the published Raw NAV within the 8th decimal place.

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Example 2: Index with Resolved Markets

A 4-market series where two markets have already settled — one won, one lost. Series Composition:
#MarketStatusTracked OutcomePrice SourcePrice UsedRaw Weight
1Fed cuts in JanuaryResolvedYES (Won)Settlement1.000.25
2CPI below 2% in Q1ResolvedYES (Lost)Settlement0.000.25
3Recession by mid-yearActiveYESMidprice0.180.25
4Core PCE below 2.5%ActiveYESMidprice0.620.25
1

Apply resolution prices

  • Market 1: The tracked outcome (YES) won — settlement price = $1.00
  • Market 2: The tracked outcome (YES) lost — settlement price = $0.00
  • Markets 3 and 4 remain active and use live midprices from the order book
2

Normalize weights

All four markets participate in the computation regardless of resolution status.
Sum of raw weights = 4 x 0.25 = 1.00
Normalized weight = 0.25 for each market
3

Compute Raw NAV

raw_nav = (0.25 x 1.00)     Market 1: resolved winner
        + (0.25 x 0.00)     Market 2: resolved loser
        + (0.25 x 0.18)     Market 3: active
        + (0.25 x 0.62)     Market 4: active

        = 0.25000000
        + 0.00000000
        + 0.04500000
        + 0.15500000

        = 0.45000000
4

Interpret the result

The Raw NAV of 0.45 reflects the mix of settled outcomes and remaining active markets:
  • The winning market (#1) contributes its full weight (0.25) to the NAV
  • The losing market (#2) contributes nothing (0.00)
  • The two active markets contribute based on their current implied probabilities
Resolved markets remain in the index with their settlement prices. They are not removed. This means a losing market at $0.00 permanently drags the index down by its weighted share, while a winning market at $1.00 permanently supports it. This is by design — the index tracks the full history of outcomes.
What if both resolved markets had won? 
raw_nav = (0.25 x 1.00) + (0.25 x 1.00) + (0.25 x 0.18) + (0.25 x 0.62)
        = 0.25 + 0.25 + 0.045 + 0.155
        = 0.70
The NAV would be 0.70 instead of 0.45 — a significant difference driven entirely by the resolution outcomes.

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Example 3: Staleness — Missing Price Data

What happens when the system cannot fetch a current price for one of the underlying markets. Scenario: A 4-market series where Market #3’s price fetch fails after three retries.
#MarketCurrent PricePrevious PriceStatus
1Market A0.72 (live)0.70Fresh
2Market B0.55 (live)0.53Fresh
3Market Cfetch failed0.41Stale (using fallback)
4Market D0.88 (live)0.87Fresh
All markets have equal weight (0.25 each).
1

Apply fallback for Market #3

The system attempted to fetch Market C’s order book three times with exponential backoff. All attempts failed (e.g., API timeout or network error).Fallback rule: Use the last successfully fetched price. Market C’s previous midprice was 0.41, so that value is used.
2

Compute Raw NAV

raw_nav = (0.25 x 0.72)   Market A: fresh
        + (0.25 x 0.55)   Market B: fresh
        + (0.25 x 0.41)   Market C: stale (fallback)
        + (0.25 x 0.88)   Market D: fresh

        = 0.18000000
        + 0.13750000
        + 0.10250000
        + 0.22000000

        = 0.64000000
3

Flag the computation as stale

Because Market C used a fallback price, the entire computation is marked as stale. This flag is visible to investors.If Market C’s actual price had moved (say, from 0.41 to 0.48), the true NAV would be:
true_raw_nav = (0.25 x 0.72) + (0.25 x 0.55) + (0.25 x 0.48) + (0.25 x 0.88)
             = 0.65750000
The difference (0.6575 vs 0.6400) is 0.0175, or about 2.7% — entirely attributable to the stale data.
A stale NAV should be interpreted with caution. The published value may be higher or lower than reality, depending on how the affected market’s price has moved since the last successful fetch. Once the price feed recovers, subsequent computations will use fresh data and the staleness flag will clear.
When does staleness trigger?
ConditionResult
API timeout or network failureStale — uses last known price
One-sided order book (only bids, no asks)Stale — midprice cannot be computed
Market data unavailable for all marketsComputation fails entirely
Market is resolvedNever stale — settlement prices are definitive

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Example 4: Tracking Index Level Over Time

How the Index Level tracks performance from inception to present, and how it relates to Raw NAV. Scenario: A series launches with an inception Raw NAV of 0.4200. Over six weeks, market prices shift:
DateRaw NAVIndex Level CalculationIndex LevelChange
Week 0 (inception)0.42000000100 x (0.4200 / 0.4200)100.00—
Week 10.43500000100 x (0.4350 / 0.4200)103.57+3.57%
Week 20.39800000100 x (0.3980 / 0.4200)94.76-5.24%
Week 30.41000000100 x (0.4100 / 0.4200)97.62-2.38%
Week 40.45000000100 x (0.4500 / 0.4200)107.14+7.14%
Week 50.44200000100 x (0.4420 / 0.4200)105.24+5.24%
Week 60.47600000100 x (0.4760 / 0.4200)113.33+13.33%
1

Index Level at inception

At launch, the Index Level is always 100.00 — this is the base value.
index_level = 100 x (0.42000000 / 0.42000000) = 100.00000000
2

Index Level after a gain (Week 1)

Raw NAV increased from 0.4200 to 0.4350 (underlying probabilities rose):
index_level = 100 x (0.43500000 / 0.42000000)
            = 100 x 1.03571429
            = 103.57142857
The index is up 3.57% from inception.
3

Index Level after a decline (Week 2)

Raw NAV dropped to 0.3980 (some underlying probabilities fell):
index_level = 100 x (0.39800000 / 0.42000000)
            = 100 x 0.94761905
            = 94.76190476
The index is down 5.24% from inception. Note: the drawdown from Week 1 to Week 2 is even larger — from 103.57 to 94.76, a decline of 8.5% week-over-week.
4

Full recovery and beyond (Week 6)

By Week 6, Raw NAV has risen to 0.4760:
index_level = 100 x (0.47600000 / 0.42000000)
            = 100 x 1.13333333
            = 113.33333333
The index is up 13.33% from inception. An investor who held from launch has seen the index swing from 94.76 (Week 2 low) to 113.33 (Week 6).
Key properties of the Index Level:
PropertyExplanation
Always starts at 100The base value is set at inception
Proportional to Raw NAVA 10% increase in Raw NAV = a 10% increase in Index Level
Inception NAV is fixedThe denominator never changes — it is locked at the first computation
Cannot go below 0Because Raw NAV is bounded between 0 and 1, and inception is positive
Cannot exceed ~238Because Raw NAV maxes out at 1.0 (in the inception_raw_nav = 0.42 example above)
The Index Level tracks theoretical index performance. Actual investor returns depend on NAV per Share, which accounts for real holdings, uninvested cash, and fees. See NAV Per Share for details on this distinction.

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Formula Reference

For quick reference, all formulas used in these examples:
FormulaExpression
Midprice(best_bid + best_ask) / 2
Weight normalizationw'_i = w_i / sum(w)
Raw NAVsum(w'_i x price_i) for all markets
Index Level100 x (raw_nav / inception_raw_nav)
NAV per Share(position_value + custody_cash - accrued_fees) / shares_outstanding

NAV Methodology

Full methodology specification with verification guide.

NAV Per Share

How the published NAV translates to your share value.