Solver concentration

For each month, the share of positive-leg volume held by solver accounts that belongs to the largest solver (Top 1) and to the five largest (Top 5). The CSV also carries an HHI.

Solver concentration

Table

Share of solver-side volume. Solver = account facing 100+ distinct counterparties (heuristic).

Top 1Top 5

Source · pipeline/sql/71_solver_concentration.sql. Months under 28 cached days are gaps. · 2026-01-01 → 2026-10-01

32%

Latest (2026-10-01)

66%

Peak (2026-01-01)

25%

Low (2026-07-01)

43%

Mean of shown points

Points

How it is computed

Pair receipts with exactly two accounts (one user, one solver).
Mark an account as solver if it faced at least 100 distinct counterparties over the cache.
Rank solvers by monthly positive-leg volume and take the share of the total.

Read this before quoting it

The solver label is a heuristic, not an on-chain flag.
Volume attribution counts every positive leg, so the same swap contributes to both its user and solver.

The query

Download solver_concentration.csv

Runs in DuckDB over the local cache of BigQuery logs. Re-run with make metrics or python -m pipeline.metrics solver_concentration. Metric queries read the base tables built by 00_setup.sql.

pipeline/sql/71_solver_concentration.sql

1-- Share of user-received volume served by the top 1 / 3 / 5 solvers, per month.
2WITH v AS (
3  SELECT date_trunc('month', l.date) AS month, l.account_id, sum(l.usd_best) FILTER (WHERE l.amount_raw > 0) AS vol
4  FROM legs_usd l JOIN pair_receipts USING (receipt_id) JOIN account_roles r ON r.account_id = l.account_id
5  WHERE NOT l.failed AND r.role = 'solver' GROUP BY ALL),
6k AS (SELECT *, row_number() OVER (PARTITION BY month ORDER BY coalesce(vol, 0) DESC) AS rk FROM v)
7SELECT CAST(month AS DATE) AS month, (SELECT count(DISTINCT date) FROM legs WHERE date_trunc('month', date) = k.month) AS days_covered, count(*) AS active_solvers,
8       sum(vol) FILTER (WHERE rk = 1) / sum(vol) AS top1_share,
9       sum(vol) FILTER (WHERE rk <= 3) / sum(vol) AS top3_share,
10       sum(vol) FILTER (WHERE rk <= 5) / sum(vol) AS top5_share,
11       sum(power(vol, 2)) / power(sum(vol), 2) AS hhi
12FROM k GROUP BY k.month ORDER BY month;

pipeline/sql/00_setup.sql (base tables: legs, prices, roles)

pipeline/sql/00_setup.sql

1-- Base tables. Inputs: raw parquet cache ({RAW}) plus the `assets` and `prices` tables registered by metrics.py
2-- from data/reference/. Nothing here is aggregated; every metric file below builds on these.
3
4-- forward-filled daily price per key (calendar x key), so a missing day reuses the last known price
5CREATE OR REPLACE TABLE prices_filled AS
6WITH cal AS (SELECT CAST(d AS DATE) AS date FROM generate_series(DATE '2024-12-31', DATE '2026-10-30', INTERVAL 1 DAY) t(d)),
7     keys AS (SELECT DISTINCT price_key FROM prices)
8SELECT k.price_key, c.date,
9       last_value(p.price IGNORE NULLS) OVER (PARTITION BY k.price_key ORDER BY c.date) AS price
10FROM keys k CROSS JOIN cal c LEFT JOIN prices p ON p.price_key = k.price_key AND p.date = c.date;
11
12-- one row per (token_diff entry, asset); entry_id identifies one token_diff entry (one account's side of a swap)
13CREATE OR REPLACE TABLE legs AS
14SELECT entry_id, CAST(ts AS DATE) AS date, ts, failed, intent_hash, account_id, receipt_id,
15       e.key AS asset, TRY_CAST(e.value AS DOUBLE) AS amount_raw
16FROM (SELECT row_number() OVER () AS entry_id, *,
17             unnest(map_entries(CAST(diff::JSON AS MAP(VARCHAR, VARCHAR)))) AS e
18      FROM read_parquet('{RAW}/swaps/*.parquet'));
19
20-- usd: priced from the price table. usd_imputed: for an entry with exactly ONE unpriced asset, the value implied
21-- by value conservation (minus the sum of the priced legs of the same entry), only if its sign matches the leg.
22-- usd_best = usd, else usd_imputed. Imputed values are always reported separately, never silently mixed in.
23CREATE OR REPLACE TABLE legs_usd AS
24WITH base AS (
25  SELECT l.*, a.symbol, a.origin_chain,
26         l.amount_raw / pow(10, a.decimals) AS qty,
27         CASE WHEN a.price_key = 'USD' THEN 1.0 ELSE p.price END AS price,
28         l.amount_raw / pow(10, a.decimals) * CASE WHEN a.price_key = 'USD' THEN 1.0 ELSE p.price END AS usd
29  FROM legs l
30  LEFT JOIN assets a ON a.asset = l.asset
31  LEFT JOIN prices_filled p ON p.price_key = a.price_key AND p.date = l.date),
32e AS (
33  SELECT *, sum(usd) OVER (PARTITION BY entry_id) AS priced_sum,
34         count(*) FILTER (WHERE usd IS NULL) OVER (PARTITION BY entry_id) AS n_unpriced
35  FROM base)
36SELECT * EXCLUDE (priced_sum, n_unpriced),
37       CASE WHEN usd IS NULL AND n_unpriced = 1 AND priced_sum IS NOT NULL
38                 AND sign(-priced_sum) = sign(amount_raw) THEN -priced_sum END AS usd_imputed,
39       coalesce(usd, CASE WHEN usd IS NULL AND n_unpriced = 1 AND priced_sum IS NOT NULL
40                               AND sign(-priced_sum) = sign(amount_raw) THEN -priced_sum END) AS usd_best
41FROM e;
42
43-- fees_collected per entry, priced the same way
44CREATE OR REPLACE TABLE fees_usd AS
45SELECT CAST(f.ts AS DATE) AS date, f.failed, f.e.key AS asset,
46       TRY_CAST(f.e.value AS DOUBLE) / pow(10, a.decimals) *
47         CASE WHEN a.price_key = 'USD' THEN 1.0 ELSE p.price END AS usd
48FROM (SELECT ts, failed, unnest(map_entries(CAST(fees::JSON AS MAP(VARCHAR, VARCHAR)))) AS e
49      FROM read_parquet('{RAW}/swaps/*.parquet') WHERE fees <> '{}') f
50LEFT JOIN assets a ON a.asset = f.e.key
51LEFT JOIN prices_filled p ON p.price_key = a.price_key AND p.date = CAST(f.ts AS DATE);
52
53-- NEP-245 mint/burn (deposits, withdrawals, refunds); token_ids and amounts are parallel arrays
54CREATE OR REPLACE TABLE mt_usd AS
55SELECT CAST(m.ts AS DATE) AS date, m.failed, m.event, m.memo, m.owner_id, m.asset,
56       TRY_CAST(m.amt AS DOUBLE) / pow(10, a.decimals) *
57         CASE WHEN a.price_key = 'USD' THEN 1.0 ELSE p.price END AS usd,
58       a.origin_chain, a.symbol, a.asset IS NULL AS unknown_asset
59FROM (SELECT ts, failed, event, memo, owner_id,
60             unnest(from_json(token_ids::JSON, '["VARCHAR"]')) AS asset,
61             unnest(from_json(amounts::JSON, '["VARCHAR"]')) AS amt
62      FROM read_parquet('{RAW}/mt/*.parquet')) m
63LEFT JOIN assets a ON a.asset = m.asset
64LEFT JOIN prices_filled p ON p.price_key = a.price_key AND p.date = CAST(m.ts AS DATE)
65WHERE m.asset LIKE 'nep141:%' OR m.asset LIKE 'nep245:%';
66
67
68-- Swap pairing: a receipt whose successful entries belong to exactly two accounts is one user<->solver swap.
69CREATE OR REPLACE TABLE pair_receipts AS
70SELECT receipt_id, min(account_id) AS a, max(account_id) AS b
71FROM (SELECT DISTINCT receipt_id, account_id FROM legs WHERE NOT failed)
72GROUP BY receipt_id HAVING count(*) = 2;
73
74-- Solver heuristic (documented in METHODOLOGY): an account is a solver if it faced >= 100 distinct counterparties
75-- across the window. Users face a handful of solvers; solvers face thousands of users. Not an on-chain label.
76CREATE OR REPLACE TABLE account_roles AS
77WITH sides AS (SELECT a AS account_id, b AS cp FROM pair_receipts UNION ALL SELECT b, a FROM pair_receipts)
78SELECT account_id, count(DISTINCT cp) AS n_counterparties, count(*) AS n_swaps,
79       CASE WHEN count(DISTINCT cp) >= 100 THEN 'solver' ELSE 'user' END AS role
80FROM sides GROUP BY account_id;