# BTC traded notional on Nado for one day -- raw side ## Question What was the total traded notional, in USD, of the BTC perpetual on Nado on 2026-09-27 (UTC day)? One number. ## System prompt You are an agent assisting a quantitative researcher. Work the question end to end: find the data, compute, check your own numbers, and make the judgement calls a careful quant would make. You have a Bash tool; Python 3 with duckdb, pandas, pyarrow and numpy is at /tmp/peerfeed-a21/venv/bin/python. Work inside your current directory. When you are done, end with a section headed FINAL ANSWER that gives every number asked for and states each judgement call you made. The researcher has downloaded PeerFeed's parquet archive for the relevant venues and data types to /tmp/peerfeed-a21/archive, in the layout PeerFeed delivers it. That directory is your only source of market data; there is no API access. ## [tool call 1] Bash { "command": "cd /tmp/peerfeed-a21/archive; find . | head -100; find . -type f | wc -l; find . -name \"*.md\" -o -name \"*.txt\" -o -name \"*.json\" | head" } ## [tool result] . ./apex ./dydx ./paradex ./pacifica ./edgex ./extended ./hyperliquid ./nado ./apex/trades ./dydx/trades ./paradex/trades ./pacifica/trades ./pacifica/prices ./edgex/trades ./edgex/ticker ./extended/funding ./hyperliquid/trades ./hyperliquid/activeAssetCtx ./nado/funding_rate ./nado/trades ./extended/funding/parquet ./hyperliquid/trades/parquet ./hyperliquid/activeAssetCtx/parquet ./nado/funding_rate/parquet ./nado/trades/parquet ./apex/trades/parquet ./dydx/trades/parquet ./paradex/trades/parquet ./pacifica/trades/parquet ./pacifica/prices/parquet ./edgex/trades/parquet ./edgex/ticker/parquet ./extended/funding/parquet/2026 ./hyperliquid/trades/parquet/2026 ./hyperliquid/activeAssetCtx/parquet/2026 ./nado/funding_rate/parquet/2026 ./nado/trades/parquet/2026 ./apex/trades/parquet/2026 ./dydx/trades/parquet/2026 ./paradex/trades/parquet/2026 ./pacifica/trades/parquet/2026 ./pacifica/prices/parquet/2026 ./edgex/trades/parquet/2026 ./edgex/ticker/parquet/2026 ./extended/funding/parquet/2026/09 ./hyperliquid/trades/parquet/2026/09 ./hyperliquid/activeAssetCtx/parquet/2026/09 ./nado/funding_rate/parquet/2026/09 ./nado/trades/parquet/2026/09 ./apex/trades/parquet/2026/09 ./dydx/trades/parquet/2026/09 ./paradex/trades/parquet/2026/09 ./pacifica/trades/parquet/2026/09 ./pacifica/prices/parquet/2026/09 ./edgex/trades/parquet/2026/09 ./edgex/ticker/parquet/2026/09 ./extended/funding/parquet/2026/09/27 ./extended/funding/parquet/2026/09/22 ./extended/funding/parquet/2026/09/26 ./extended/funding/parquet/2026/09/20 ./extended/funding/parquet/2026/09/23 ./extended/funding/parquet/2026/09/24 ./extended/funding/parquet/2026/09/21 ./extended/funding/parquet/2026/09/28 ./extended/funding/parquet/2026/09/25 ./hyperliquid/trades/parquet/2026/09/27 ./hyperliquid/trades/parquet/2026/09/22 ./hyperliquid/trades/parquet/2026/09/26 ./hyperliquid/trades/parquet/2026/09/20 ./hyperliquid/trades/parquet/2026/09/23 ./hyperliquid/trades/parquet/2026/09/24 ./hyperliquid/trades/parquet/2026/09/21 ./hyperliquid/trades/parquet/2026/09/28 ./hyperliquid/trades/parquet/2026/09/25 ./hyperliquid/activeAssetCtx/parquet/2026/09/27 ./hyperliquid/activeAssetCtx/parquet/2026/09/22 ./hyperliquid/activeAssetCtx/parquet/2026/09/26 ./hyperliquid/activeAssetCtx/parquet/2026/09/20 ./hyperliquid/activeAssetCtx/parquet/2026/09/23 ./hyperliquid/activeAssetCtx/parquet/2026/09/24 ./hyperliquid/activeAssetCtx/parquet/2026/09/21 ./hyperliquid/activeAssetCtx/parquet/2026/09/28 ./hyperliquid/activeAssetCtx/parquet/2026/09/25 ./nado/funding_rate/parquet/2026/09/27 ./nado/funding_rate/parquet/2026/09/22 ./nado/funding_rate/parquet/2026/09/26 ./nado/funding_rate/parquet/2026/09/20 ./nado/funding_rate/parquet/2026/09/23 ./nado/funding_rate/parquet/2026/09/24 ./nado/funding_rate/parquet/2026/09/21 ./nado/funding_rate/parquet/2026/09/28 ./nado/funding_rate/parquet/2026/09/25 ./nado/trades/parquet/2026/09/27 ./nado/trades/parquet/2026/09/22 ./nado/trades/parquet/2026/09/26 ./nado/trades/parquet/2026/09/20 ./nado/trades/parquet/2026/09/23 ./nado/trades/parquet/2026/09/24 ./nado/trades/parquet/2026/09/21 1220 Shell cwd was reset to /tmp/peerfeed-a21/work-s2-raw ## [tool call 2] Bash { "command": "cd /tmp/peerfeed-a21/archive/nado/trades/parquet/2026/09; ls -R 26 27 28 | head -60" } ## [tool result] 26: 00-02.parquet 02-04.parquet 04-06.parquet 06-08.parquet 08-10.parquet 10-12.parquet 12-14.parquet 14-16.parquet 16-18.parquet 18-20.parquet 20-22.parquet 22-24.parquet 27: 00-02.parquet 02-04.parquet 04-06.parquet 06-08.parquet 08-10.parquet 10-12.parquet 12-14.parquet 14-16.parquet 16-18.parquet 18-20.parquet 20-22.parquet 22-24.parquet 28: 00-02.parquet 02-04.parquet 04-06.parquet 06-08.parquet 08-10.parquet 10-12.parquet 12-14.parquet 14-16.parquet 16-18.parquet 18-20.parquet 20-22.parquet 22-24.parquet Shell cwd was reset to /tmp/peerfeed-a21/work-s2-raw ## [tool call 3] Bash { "command": "cd /tmp/peerfeed-a21/archive/nado/trades/parquet/2026/09; /tmp/peerfeed-a21/venv/bin/python -c \"\nimport duckdb\nc=duckdb.connect()\nprint(c.sql(\\\"describe select * from read_parquet('27/*.parquet')\\\"))\nprint(c.sql(\\\"select * from read_parquet('27/*.parquet', filename=true) limit 5\\\").df().to_string())\nprint(c.sql(\\\"select symbol, count(*), min(ts), max(ts) from read_parquet('27/*.parquet') group by all\\\").df().to_string())\n\" 2>&1 | head -80" } ## [tool result] Traceback (most recent call last): File "", line 6, in _duckdb.BinderException: Binder Error: Referenced column "symbol" not found in FROM clause! Candidate bindings: "is_taker_buyer" ┌────────────────┬──────────────────────────┬─────────┬─────────┬─────────┬─────────┐ │ column_name │ column_type │ null │ key │ default │ extra │ │ varchar │ varchar │ varchar │ varchar │ varchar │ varchar │ ├────────────────┼──────────────────────────┼─────────┼─────────┼─────────┼─────────┤ │ type │ VARCHAR │ YES │ NULL │ NULL │ NULL │ │ timestamp │ TIMESTAMP WITH TIME ZONE │ YES │ NULL │ NULL │ NULL │ │ product_id │ BIGINT │ YES │ NULL │ NULL │ NULL │ │ price │ VARCHAR │ YES │ NULL │ NULL │ NULL │ │ taker_qty │ VARCHAR │ YES │ NULL │ NULL │ NULL │ │ maker_qty │ VARCHAR │ YES │ NULL │ NULL │ NULL │ │ is_taker_buyer │ BOOLEAN │ YES │ NULL │ NULL │ NULL │ │ recorded_at │ TIMESTAMP WITH TIME ZONE │ YES │ NULL │ NULL │ NULL │ └────────────────┴──────────────────────────┴─────────┴─────────┴─────────┴─────────┘ type timestamp product_id price taker_qty maker_qty is_taker_buyer recorded_at filename 0 trade 2026-09-27 01:59:05.439566+02:00 30 4409000000000000 95700000000000000000000 95700000000000000000000 False 2026-09-27 01:59:05.556000+02:00 27/00-02.parquet 1 trade 2026-09-27 01:59:05.442381+02:00 30 4409000000000000 34300000000000000000000 34300000000000000000000 False 2026-09-27 01:59:05.559000+02:00 27/00-02.parquet 2 trade 2026-09-27 01:59:14.980349+02:00 36 4847700000000000000 81000000000000000000 81000000000000000000 True 2026-09-27 01:59:15.098000+02:00 27/00-02.parquet 3 trade 2026-09-27 01:59:16.299113+02:00 16 92560000000000000000 6000000000000000000 6000000000000000000 True 2026-09-27 01:59:16.421000+02:00 27/00-02.parquet 4 trade 2026-09-27 01:59:16.299113+02:00 16 92560000000000000000 4800000000000000000 4800000000000000000 True 2026-09-27 01:59:16.421000+02:00 27/00-02.parquet Shell cwd was reset to /tmp/peerfeed-a21/work-s2-raw ## [agent thinking] ## [tool call 4] Bash { "command": "cd /tmp/peerfeed-a21/archive/nado; ls; ls funding_rate/parquet/2026/09/27 | head -3; /tmp/peerfeed-a21/venv/bin/python -c \"\nimport duckdb\nc=duckdb.connect(); c.sql(\\\"set timezone='UTC'\\\")\nprint(c.sql(\\\"select product_id, count(*), median(price::double/1e18) p, min(timestamp), max(timestamp) from read_parquet('trades/parquet/2026/09/27/*.parquet') group by all order by p desc\\\").df().to_string())\nprint(c.sql(\\\"select filename, min(timestamp), max(timestamp), count(*) from read_parquet('trades/parquet/2026/09/2[678]/*.parquet', filename=true) group by all order by 1\\\").df().to_string())\nprint(c.sql(\\\"describe select * from read_parquet('funding_rate/parquet/2026/09/27/*.parquet')\\\"))\n\"" } ## [tool result] funding_rate trades 00-02.parquet 02-04.parquet 04-06.parquet product_id count_star() p min("timestamp") max("timestamp") 0 1 27 84636.000000 2026-09-27 01:50:38.739155+00:00 2026-09-27 22:14:13.579310+00:00 1 2 23344 84557.000000 2026-09-26 23:59:17.693162+00:00 2026-09-27 23:58:51.574092+00:00 2 28 827 4279.300000 2026-09-27 00:15:03.747070+00:00 2026-09-27 23:56:14.340202+00:00 3 159 2 4271.550000 2026-09-27 14:13:21.055333+00:00 2026-09-27 23:42:55.145509+00:00 4 3 3 2714.800000 2026-09-27 06:29:08.322200+00:00 2026-09-27 22:01:43.353765+00:00 5 4 23234 2694.700000 2026-09-26 23:59:46.493266+00:00 2026-09-27 23:58:48.762694+00:00 6 138 283 1777.500000 2026-09-27 00:13:47.249276+00:00 2026-09-27 23:45:04.116052+00:00 7 18 9282 1640.860000 2026-09-26 23:59:46.523030+00:00 2026-09-27 23:58:48.768314+00:00 8 136 382 1092.615000 2026-09-27 01:11:09.336188+00:00 2026-09-27 23:51:00.831901+00:00 9 14 1041 777.280000 2026-09-26 23:59:46.523543+00:00 2026-09-27 23:58:10.124402+00:00 10 117 39 774.980000 2026-09-27 07:19:17.422015+00:00 2026-09-27 21:46:11.926955+00:00 11 100 405 770.890000 2026-09-27 00:44:31.057151+00:00 2026-09-27 23:56:57.002435+00:00 12 149 14 751.740000 2026-09-27 05:30:05.543027+00:00 2026-09-27 22:20:56.483923+00:00 13 115 8 749.615000 2026-09-27 01:31:59.270305+00:00 2026-09-27 19:47:20.635544+00:00 14 108 326 749.000000 2026-09-27 00:17:26.593342+00:00 2026-09-27 23:48:47.357643+00:00 15 98 688 745.080000 2026-09-27 00:02:30.165728+00:00 2026-09-27 23:53:30.663143+00:00 16 126 31 634.720000 2026-09-27 01:24:13.585790+00:00 2026-09-27 22:35:07.957920+00:00 17 130 8 565.695000 2026-09-27 00:56:52.339224+00:00 2026-09-27 21:46:08.973961+00:00 18 34 92 550.405000 2026-09-27 00:06:18.662012+00:00 2026-09-27 23:48:17.128848+00:00 19 110 4 518.500000 2026-09-27 08:45:07.664088+00:00 2026-09-27 14:12:04.885433+00:00 20 155 5 372.840000 2026-09-27 02:13:33.980177+00:00 2026-09-27 20:49:56.247125+00:00 21 114 15 372.770000 2026-09-27 01:28:56.493256+00:00 2026-09-27 13:13:02.380960+00:00 22 128 13 354.700000 2026-09-27 02:52:38.426337+00:00 2026-09-27 15:36:01.378945+00:00 23 147 21 345.100000 2026-09-27 04:08:31.820713+00:00 2026-09-27 23:50:46.417930+00:00 24 106 74 343.875000 2026-09-27 00:57:22.360956+00:00 2026-09-27 23:48:50.921753+00:00 25 143 10 341.375000 2026-09-27 03:19:57.804147+00:00 2026-09-27 23:27:22.684674+00:00 26 102 6 340.310000 2026-09-27 02:58:18.260311+00:00 2026-09-27 21:45:23.845275+00:00 27 68 439 334.990000 2026-09-27 00:20:15.201709+00:00 2026-09-27 23:58:10.167706+00:00 28 32 3721 326.760000 2026-09-27 00:02:05.489202+00:00 2026-09-27 23:58:10.007772+00:00 29 134 11 263.270000 2026-09-27 00:58:52.678116+00:00 2026-09-27 07:53:31.368241+00:00 30 104 41 250.140000 2026-09-27 09:35:09.522074+00:00 2026-09-27 22:42:39.516887+00:00 31 174 12 238.975000 2026-09-27 02:16:34.760448+00:00 2026-09-27 23:32:14.604146+00:00 32 153 10 225.470000 2026-09-27 01:32:42.151709+00:00 2026-09-27 23:48:33.994829+00:00 33 112 255 224.990000 2026-09-27 01:08:27.325084+00:00 2026-09-27 22:27:25.268976+00:00 34 182 6 192.700000 2026-09-27 07:25:38.413803+00:00 2026-09-27 22:00:36.507058+00:00 35 166 87 161.520000 2026-09-27 01:28:42.417966+00:00 2026-09-27 23:35:49.707704+00:00 36 26 336 154.630000 2026-09-27 00:03:04.946448+00:00 2026-09-27 23:58:09.917080+00:00 37 140 268 148.820000 2026-09-27 00:11:01.436299+00:00 2026-09-27 22:57:06.631347+00:00 38 132 190 124.660000 2026-09-27 00:22:49.736182+00:00 2026-09-27 23:37:29.412886+00:00 39 8 21707 122.110000 2026-09-26 23:59:43.201974+00:00 2026-09-27 23:58:54.338519+00:00 40 90 611 93.691000 2026-09-27 00:01:15.565276+00:00 2026-09-27 23:51:32.785041+00:00 41 16 29488 92.197000 2026-09-26 23:59:16.299113+00:00 2026-09-27 23:58:57.869154+00:00 42 170 32 89.415000 2026-09-27 01:59:21.395929+00:00 2026-09-27 23:27:37.535356+00:00 43 168 2 80.865000 2026-09-27 13:45:50.616784+00:00 2026-09-27 13:45:50.616784+00:00 44 76 405 71.117000 2026-09-27 00:06:29.917755+00:00 2026-09-27 23:58:10.082207+00:00 45 88 646 64.158000 2026-09-27 00:22:12.992107+00:00 2026-09-27 23:57:38.239064+00:00 46 124 139 30.058000 2026-09-27 00:09:03.964223+00:00 2026-09-27 23:58:47.314164+00:00 47 74 231 14.141000 2026-09-27 00:05:01.528149+00:00 2026-09-27 23:58:16.404971+00:00 48 64 285 10.981000 2026-09-27 01:01:04.406740+00:00 2026-09-27 23:47:46.256086+00:00 49 46 5946 9.836050 2026-09-26 23:59:46.486142+00:00 2026-09-27 23:58:32.733925+00:00 50 164 5 8.102000 2026-09-27 12:04:41.383352+00:00 2026-09-27 12:27:12.723838+00:00 51 78 5891 5.342800 2026-09-27 00:00:30.409426+00:00 2026-09-27 23:58:46.394094+00:00 52 36 4711 4.715300 2026-09-26 23:59:14.980349+00:00 2026-09-27 23:58:10.578899+00:00 53 58 440 1.630150 2026-09-27 00:38:31.371713+00:00 2026-09-27 22:49:08.455328+00:00 54 122 322 1.625750 2026-09-27 00:01:28.247643+00:00 2026-09-27 23:49:02.160301+00:00 55 10 2310 1.524150 2026-09-26 23:59:46.503977+00:00 2026-09-27 23:58:11.538116+00:00 56 24 2468 1.269100 2026-09-27 00:01:32.304974+00:00 2026-09-27 23:58:10.188108+00:00 57 92 9 1.138090 2026-09-27 07:40:04.893045+00:00 2026-09-27 21:42:33.052297+00:00 58 5 10 1.000300 2026-09-27 08:15:50.925317+00:00 2026-09-27 22:25:46.097039+00:00 59 48 234 0.728965 2026-09-27 00:05:00.233673+00:00 2026-09-27 23:58:10.131623+00:00 60 188 36 0.591035 2026-09-27 04:43:32.908247+00:00 2026-09-27 23:30:31.332240+00:00 61 142 324 0.564820 2026-09-27 03:43:14.690712+00:00 2026-09-27 22:54:49.137187+00:00 62 80 559 0.544360 2026-09-27 00:12:05.488790+00:00 2026-09-27 23:57:09.582362+00:00 63 86 835 0.344980 2026-09-26 23:59:46.506507+00:00 2026-09-27 23:58:09.874156+00:00 64 42 60 0.289565 2026-09-27 02:27:55.898198+00:00 2026-09-27 22:47:46.746383+00:00 65 72 4439 0.274060 2026-09-27 00:08:45.629988+00:00 2026-09-27 23:58:52.955181+00:00 66 172 45 0.270420 2026-09-27 03:52:50.067265+00:00 2026-09-27 22:13:53.168955+00:00 67 22 1950 0.194815 2026-09-26 23:59:39.984994+00:00 2026-09-27 23:58:45.105115+00:00 68 186 245 0.174700 2026-09-27 00:18:31.084997+00:00 2026-09-27 23:54:09.546339+00:00 69 50 2259 0.107970 2026-09-26 23:59:32.728887+00:00 2026-09-27 23:58:48.774579+00:00 70 52 474 0.097160 2026-09-27 00:57:38.479961+00:00 2026-09-27 23:58:10.151198+00:00 71 82 241 0.080190 2026-09-27 00:47:16.810371+00:00 2026-09-27 23:49:47.893276+00:00 72 54 36 0.058405 2026-09-27 08:02:42.646842+00:00 2026-09-27 22:34:00.636406+00:00 73 158 38 0.046925 2026-09-27 00:38:10.989490+00:00 2026-09-27 21:48:50.149643+00:00 74 120 73 0.044760 2026-09-27 00:02:58.629008+00:00 2026-09-27 22:35:49.143004+00:00 75 20 2147 0.027215 2026-09-27 00:02:24.043062+00:00 2026-09-27 23:58:41.825092+00:00 76 40 1486 0.010105 2026-09-27 00:02:21.752752+00:00 2026-09-27 23:54:54.808801+00:00 77 30 7737 0.004949 2026-09-26 23:59:05.439566+00:00 2026-09-27 23:58:09.889874+00:00 78 38 227 0.004377 2026-09-27 00:08:42.274018+00:00 2026-09-27 23:58:10.075388+00:00 79 56 100 0.003701 2026-09-27 00:30:40.603386+00:00 2026-09-27 23:35:28.420114+00:00 filename min("timestamp") max("timestamp") count_star() 0 trades/parquet/2026/09/26/00-02.parquet 2026-09-25 23:59:00.749086+00:00 2026-09-26 01:58:56.987000+00:00 10673 1 trades/parquet/2026/09/26/02-04.parquet 2026-09-26 01:59:06.302017+00:00 2026-09-26 03:58:51.252580+00:00 7856 2 trades/parquet/2026/09/26/04-06.parquet 2026-09-26 03:59:03.945256+00:00 2026-09-26 05:58:49.449982+00:00 6935 3 trades/parquet/2026/09/26/06-08.parquet 2026-09-26 05:59:01.571980+00:00 2026-09-26 07:58:59.020084+00:00 6362 4 trades/parquet/2026/09/26/08-10.parquet 2026-09-26 07:59:00.948064+00:00 2026-09-26 09:58:59.712293+00:00 9179 5 trades/parquet/2026/09/26/10-12.parquet 2026-09-26 09:59:00.258966+00:00 2026-09-26 11:58:53.550362+00:00 8442 6 trades/parquet/2026/09/26/12-14.parquet 2026-09-26 11:58:59.980117+00:00 2026-09-26 13:58:59.130267+00:00 9782 7 trades/parquet/2026/09/26/14-16.parquet 2026-09-26 13:58:58.858288+00:00 2026-09-26 15:58:58.199722+00:00 9434 8 trades/parquet/2026/09/26/16-18.parquet 2026-09-26 15:59:00.835813+00:00 2026-09-26 17:58:58.412280+00:00 8752 9 trades/parquet/2026/09/26/18-20.parquet 2026-09-26 17:59:01.443315+00:00 2026-09-26 19:58:56.730303+00:00 7155 10 trades/parquet/2026/09/26/20-22.parquet 2026-09-26 19:59:01.340030+00:00 2026-09-26 21:58:59.067218+00:00 13975 11 trades/parquet/2026/09/26/22-24.parquet 2026-09-26 21:59:01.385579+00:00 2026-09-26 23:58:50.942018+00:00 8287 12 trades/parquet/2026/09/27/00-02.parquet 2026-09-26 23:59:05.439566+00:00 2026-09-27 01:58:53.672498+00:00 8254 13 trades/parquet/2026/09/27/02-04.parquet 2026-09-27 01:59:07.169001+00:00 2026-09-27 03:58:40.758136+00:00 6690 14 trades/parquet/2026/09/27/04-06.parquet 2026-09-27 03:59:09.373979+00:00 2026-09-27 05:58:58.871403+00:00 10706 15 trades/parquet/2026/09/27/06-08.parquet 2026-09-27 05:59:02.868691+00:00 2026-09-27 07:58:59.612933+00:00 12319 16 trades/parquet/2026/09/27/08-10.parquet 2026-09-27 07:59:00.962456+00:00 2026-09-27 09:58:58.792089+00:00 15388 17 trades/parquet/2026/09/27/10-12.parquet 2026-09-27 09:59:00.451170+00:00 2026-09-27 11:58:58.047776+00:00 10082 18 trades/parquet/2026/09/27/12-14.parquet 2026-09-27 11:59:00.348953+00:00 2026-09-27 13:58:59.384051+00:00 15736 19 trades/parquet/2026/09/27/14-16.parquet 2026-09-27 13:59:01.440085+00:00 2026-09-27 15:58:58.830121+00:00 21528 20 trades/parquet/2026/09/27/16-18.parquet 2026-09-27 15:58:59.971406+00:00 2026-09-27 17:58:57.180069+00:00 13238 21 trades/parquet/2026/09/27/18-20.parquet 2026-09-27 17:59:00.505711+00:00 2026-09-27 19:58:57.198830+00:00 14723 22 trades/parquet/2026/09/27/20-22.parquet 2026-09-27 19:59:01.528094+00:00 2026-09-27 21:58:58.846464+00:00 15545 23 trades/parquet/2026/09/27/22-24.parquet 2026-09-27 21:59:00.966032+00:00 2026-09-27 23:58:57.869154+00:00 20554 24 trades/parquet/2026/09/28/00-02.parquet 2026-09-27 23:59:02.775753+00:00 2026-09-28 01:58:59.488710+00:00 31397 25 trades/parquet/2026/09/28/02-04.parquet 2026-09-28 01:59:00.502658+00:00 2026-09-28 03:58:58.293667+00:00 26639 26 trades/parquet/2026/09/28/04-06.parquet 2026-09-28 03:59:00.555786+00:00 2026-09-28 05:58:58.178183+00:00 20886 27 trades/parquet/2026/09/28/06-08.parquet 2026-09-28 05:59:00.665253+00:00 2026-09-28 07:58:59.155675+00:00 21692 28 trades/parquet/2026/09/28/08-10.parquet 2026-09-28 07:59:02.995794+00:00 2026-09-28 09:58:57.716032+00:00 21877 29 trades/parquet/2026/09/28/10-12.parquet 2026-09-28 09:59:02.473279+00:00 2026-09-28 11:58:56.876935+00:00 16529 30 trades/parquet/2026/09/28/12-14.parquet 2026-09-28 11:59:00.529343+00:00 2026-09-28 13:58:59.371994+00:00 27356 31 trades/parquet/2026/09/28/14-16.parquet 2026-09-28 13:59:02.388738+00:00 2026-09-28 15:58:59.753695+00:00 34000 32 trades/parquet/2026/09/28/16-18.parquet 2026-09-28 15:58:59.877988+00:00 2026-09-28 17:58:57.573304+00:00 27870 33 trades/parquet/2026/09/28/18-20.parquet 2026-09-28 17:59:02.136948+00:00 2026-09-28 19:58:59.365514+00:00 25594 34 trades/parquet/2026/09/28/20-22.parquet 2026-09-28 19:59:00.476963+00:00 2026-09-28 21:58:58.373273+00:00 18102 35 trades/parquet/2026/09/28/22-24.parquet 2026-09-28 21:58:59.885796+00:00 2026-09-28 23:58:53.783206+00:00 14157 ┌──────────────────┬──────────────────────────┬─────────┬─────────┬─────────┬─────────┐ │ column_name │ column_type │ null │ key │ default │ extra │ │ varchar │ varchar │ varchar │ varchar │ varchar │ varchar │ ├──────────────────┼──────────────────────────┼─────────┼─────────┼─────────┼─────────┤ │ type │ VARCHAR │ YES │ NULL │ NULL │ NULL │ │ timestamp │ TIMESTAMP WITH TIME ZONE │ YES │ NULL │ NULL │ NULL │ │ product_id │ BIGINT │ YES │ NULL │ NULL │ NULL │ │ funding_rate_x18 │ BIGINT │ YES │ NULL │ NULL │ NULL │ │ update_time │ BIGINT │ YES │ NULL │ NULL │ NULL │ │ recorded_at │ TIMESTAMP WITH TIME ZONE │ YES │ NULL │ NULL │ NULL │ └──────────────────┴──────────────────────────┴─────────┴─────────┴─────────┴─────────┘ Shell cwd was reset to /tmp/peerfeed-a21/work-s2-raw ## [agent thinking] ## [tool call 5] Bash { "command": "cd /tmp/peerfeed-a21/archive/nado; /tmp/peerfeed-a21/venv/bin/python -c \"\nimport duckdb\nc=duckdb.connect(); c.sql(\\\"set timezone='UTC'\\\")\nprint(c.sql(\\\"select distinct product_id from read_parquet('funding_rate/parquet/2026/09/27/*.parquet') order by 1\\\").df().product_id.tolist())\nc.sql(\\\"create view t as select *, filename f from read_parquet('trades/parquet/2026/09/2[678]/*.parquet', filename=true) where product_id=2 and timestamp>='2026-09-27' and timestamp<'2026-09-28'\\\")\nprint(c.sql(\\\"select count(*), count(distinct (timestamp,price,taker_qty,maker_qty,is_taker_buyer)), count(distinct (timestamp,price,taker_qty,maker_qty,is_taker_buyer,recorded_at)), sum((taker_qty<>maker_qty)::int), count(distinct type) from t\\\").df().to_string())\nprint(c.sql(\\\"select f, count(*) from t group by 1 order by 1\\\").df().to_string())\nprint(c.sql(\\\"select min(price::double/1e18), max(price::double/1e18), min(taker_qty::double/1e18), max(taker_qty::double/1e18), sum(taker_qty::double/1e18) from t\\\").df().to_string())\n\"" } ## [tool result] [2, 4, 8, 10, 14, 16, 18, 20, 22, 24, 26, 30, 32, 34, 36, 38, 40, 42, 46, 48, 50, 52, 54, 56, 58, 60, 62, 64, 66, 68, 70, 72, 74, 76, 78, 80, 82, 84, 86, 88, 90, 92, 94, 96, 98, 100, 102, 104, 106, 108, 110, 112, 114, 120, 122, 126, 128, 130, 132, 134, 136, 138, 140, 142, 158, 164, 166, 170, 172, 174, 176, 178, 180, 182, 184, 186, 188] count_star() count(DISTINCT main."row"("timestamp", price, taker_qty, maker_qty, is_taker_buyer)) count(DISTINCT main."row"("timestamp", price, taker_qty, maker_qty, is_taker_buyer, recorded_at)) sum(CAST((taker_qty != maker_qty) AS INTEGER)) count(DISTINCT "type") 0 23319 23319 23319 0.0 1 f count_star() 0 trades/parquet/2026/09/27/00-02.parquet 1129 1 trades/parquet/2026/09/27/02-04.parquet 1181 2 trades/parquet/2026/09/27/04-06.parquet 1273 3 trades/parquet/2026/09/27/06-08.parquet 1603 4 trades/parquet/2026/09/27/08-10.parquet 2332 5 trades/parquet/2026/09/27/10-12.parquet 1929 6 trades/parquet/2026/09/27/12-14.parquet 2400 7 trades/parquet/2026/09/27/14-16.parquet 3100 8 trades/parquet/2026/09/27/16-18.parquet 1940 9 trades/parquet/2026/09/27/18-20.parquet 1613 10 trades/parquet/2026/09/27/20-22.parquet 1653 11 trades/parquet/2026/09/27/22-24.parquet 3156 12 trades/parquet/2026/09/28/00-02.parquet 10 min((CAST(price AS "DOUBLE") / 1e+18)) max((CAST(price AS "DOUBLE") / 1e+18)) min((CAST(taker_qty AS "DOUBLE") / 1e+18)) max((CAST(taker_qty AS "DOUBLE") / 1e+18)) sum((CAST(taker_qty AS "DOUBLE") / 1e+18)) 0 84081.0 85125.0 0.00005 1.07675 646.8776 Shell cwd was reset to /tmp/peerfeed-a21/work-s2-raw ## [agent thinking] ## [agent] Found BTC-PERP (product_id 2) with 23,319 unique trades on the UTC day. Next I'll check for data gaps and compute the notional exactly. ## [tool call 6] Bash { "command": "cd /tmp/peerfeed-a21/archive/nado; /tmp/peerfeed-a21/venv/bin/python -c \"\nimport duckdb\nfrom decimal import Decimal, getcontext\ngetcontext().prec=60\nc=duckdb.connect(); c.sql(\\\"set timezone='UTC'\\\")\nc.sql(\\\"create view a as select * from read_parquet('trades/parquet/2026/09/2[678]/*.parquet') \\\")\nprint(c.sql(\\\"select timestamp, g from (select timestamp, timestamp - lag(timestamp) over (order by timestamp) g from a where timestamp>='2026-09-26 23:00' and timestamp<'2026-09-28 01:00') order by g desc limit 5\\\").df())\nprint(c.sql(\\\"select timestamp, g from (select timestamp, timestamp - lag(timestamp) over (order by timestamp) g from a where product_id=2 and timestamp>='2026-09-26 23:00' and timestamp<'2026-09-28 01:00') order by g desc limit 5\\\").df())\nrows=c.sql(\\\"select price, taker_qty from a where product_id=2 and timestamp>='2026-09-27' and timestamp<'2026-09-28'\\\").fetchall()\ns=sum(Decimal(p)*Decimal(q) for p,q in rows)/Decimal(10)**36\nprint(len(rows), s)\n\"" } ## [tool result] timestamp g 0 2026-09-28 00:34:50.055165+00:00 0 days 00:00:33.677916 1 2026-09-27 14:49:01.826109+00:00 0 days 00:00:31.937851 2 2026-09-28 00:32:49.220961+00:00 0 days 00:00:30.293450 3 2026-09-27 05:27:45.420929+00:00 0 days 00:00:30.190052 4 2026-09-28 00:31:48.910806+00:00 0 days 00:00:30.019749 timestamp g 0 2026-09-27 04:28:20.330181+00:00 0 days 00:04:40.870002 1 2026-09-27 00:29:15.366165+00:00 0 days 00:04:10.410434 2 2026-09-27 00:19:20.934586+00:00 0 days 00:03:33.065243 3 2026-09-27 03:59:26.747041+00:00 0 days 00:03:31.823538 4 2026-09-27 05:24:20.906199+00:00 0 days 00:03:27.737291 23319 54699332.87545 Shell cwd was reset to /tmp/peerfeed-a21/work-s2-raw ## [agent thinking] ## [agent] BTC perpetual trades on Nado came to about **$54.70M** in notional on 2026‑09‑27 (UTC), counting each trade once. I used the trades data in `nado/trades/parquet`. For every BTC‑PERP trade with a timestamp from 2026‑09‑27 00:00:00 up to (not including) 2026‑09‑28 00:00:00 UTC, I multiplied price by quantity and added them up. There were 23,319 trades and 646.88 BTC traded. I did the arithmetic in exact decimal. Checks I ran: - **File boundaries:** each file starts about a minute before its label (the "00‑02" file begins at 23:59). So the 27th's files include 25 trades from the 26th, and 10 trades from the 27th sit in the 28th's 00‑02 file. I read the 26th to 28th and filtered on the trade timestamps, not on file names. - **Duplicates:** there are no duplicate rows. - **Gaps:** there are no holes in the data. Across all Nado markets the longest silence around the day is 34 seconds. For BTC‑PERP alone the longest is 4.7 minutes overnight, which looks like quiet trading. - **Sanity:** prices ran from 84,081 to 85,125, which is plausible. Judgement calls: 1. **Which market:** I took product_id 2 as the BTC perpetual. It trades around $84.6k, has funding‑rate data, and fits Nado's pattern of odd IDs for spot and even IDs for perps. Product 1 (27 trades) looks like BTC spot and is left out. 2. **Units:** prices and quantities are stored as whole numbers scaled by 10^18, so I divided by 10^18. 3. **Counting each trade once:** the taker and maker quantities are identical on every row, so each trade counts once. If you want both sides counted, double the figure: $109,398,665.75. 4. **USD vs USDT0:** Nado prices are quoted in USDT0, which I treated as equal to USD. ## FINAL ANSWER **$54,699,332.88** total traded notional for Nado BTC‑PERP (product_id 2) on 2026‑09‑27 UTC, counting each trade once (23,319 trades, 646.8776 BTC). Judgement calls: product_id 2 is the BTC perpetual and product 1 is excluded; the day is set by trade timestamps, not file names; values are scaled down by 10^18; each trade counts once (both sides would be $109,398,665.75); USDT0 is taken as USD. ## [run ended] subtype=success turns=7 duration_ms=36079