GPT-6.1 Sol (effort default (no reasoning-effort override) · thinking on) on Limit order book matching engine with self-trade prevention, FOK and modify priority
OGPT-6.1 Soldefault · thinking on · Codex CLI 0.163.0 (codex exec --json) · cloud · 2026-10-22 · by @bobsiopener
- Estimated cost
- $0.06
- Wall time
- 1m
- Human step-ins
- 0
- Outcome
- Pass
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Summary
Exact prompt
# Limit order book matching engine `input.json` has `events` (a list, processed in order, index = position) and `bbo_every` k. Write `solve.py` (Python 3 standard library only); `python solve.py input.json answer.json`. Fresh inputs are generated, including one with **60,000 events that must finish within 20 seconds** (other runs: 30 s). Prices and quantities are integers (ticks/lots); all arithmetic is exact. ## Events - `{"op": "new", "id", "side": "B"|"S", "type": "LMT"|"MKT", "qty", "px" (LMT only), "tif": "GTC"|"IOC"|"FOK" (LMT only, default GTC), "owner" (default ""), "stp": "cancel_resting"|"cancel_incoming" (default cancel_resting), "post_only" (default false)}` - `{"op": "cancel", "id"}` - `{"op": "modify", "id", "qty"?, "px"?}` (a missing field keeps the current value; `qty` is the new *remaining* quantity) - anything else: reject `bad_op`. ## Rules 1. A reject is recorded as `{"event": index, "id": event's id or null, "reason"}` and the event has no other effect. 2. New-order validation, first failure wins: `bad_id` (id not a non-empty string), `duplicate_id` (id already used by ANY earlier new event, including rejected ones once they passed `bad_id`), `bad_side`, `bad_type`, `bad_qty` (not a positive integer; booleans and floats are invalid), `bad_price` (LMT only: not a positive integer), `bad_tif` (LMT only). For MKT orders `px` and `tif` are ignored and the order behaves as IOC. 3. Then, in order: a `post_only` LMT that would cross the opposite best is rejected `post_only_would_cross`; a MKT order with an empty opposite side is rejected `no_liquidity`; a FOK order is rejected `fok_unfilled` if the fill check (rule 8) fails. 4. Matching: an incoming order trades against the opposite side in price-time priority (best price first; within a price, earlier arrival first) while it has quantity left and the best opposite price is acceptable (buy: ask <= limit; sell: bid >= limit; MKT: any). Each trade executes at the **resting** order's price for min(both remaining). 5. Self-trade prevention (same `owner` on both sides), using the **incoming** order's `stp`: `cancel_resting` removes that resting order (no trade) and matching continues; `cancel_incoming` cancels the incoming order's remaining quantity at that point (trades already done stand; it never rests). 6. After matching, a GTC LMT remainder rests on the book (as a new arrival). IOC/FOK/MKT remainders are discarded. 7. Trades: `{"seq": 1, 2, ..., "buy_id", "sell_id", "px", "qty", "aggressor": side of the incoming order}`. 8. FOK fill check: walk the opposite resting orders in priority order over acceptable prices; skip orders of the same owner if `stp` is `cancel_resting`; **stop** at the first same-owner order if `stp` is `cancel_incoming`. Pass if the summed quantity of the walked other-owner orders >= qty. 9. `cancel`: if the id is not a live resting order, reject `unknown_order`; otherwise remove it. 10. `modify`: not live -> `unknown_order`. New qty must be an integer >= 0 and new px a positive integer, else `bad_modify`. qty 0 -> remove the order. Same price and new qty <= current remaining -> update in place, **keeping time priority**. Otherwise (price change or qty increase) the order **loses priority**: it is taken off the book and re-entered as an incoming GTC LMT with the same id, owner and stp (no post-only, no duplicate-id check, no FOK), which may trade (rule 4-5) and rests last in time at its price. 11. After every k-th event (event index i with (i+1) % k == 0), append `[best_bid, best_ask]` to `bbo` (null for an empty side). ## answer.json `{"trades": [...], "rejects": [...], "bbo": [...], "book": {"bids": [[px, total_remaining, order_count], ...], "asks": [...]}` (top 5 price levels, bids high to low, asks low to high) `, "positions": {owner: net bought minus sold}, "cash": {owner: sum of sell px*qty minus buy px*qty}` (owners that traded, sorted by name) `, "volume": total traded qty, "vwap": volume-weighted price rounded to 4 decimals or null, "resting_orders": live resting orders at the end}` ## Scoring Hidden checks (partial credit = fraction passed; all must pass): `validation_rejects` (the rule-2/9/10 reasons in order), `all_rejects`, `first_50_trades`, `all_trades`, `bbo_path`, `final_book_and_counts`, `positions_cash_vwap` on the given input; `fresh_run_1`, `fresh_run_2` (every key) on fresh inputs; `big_input_60k_events_20s` (every key, 20-second limit). vwap within 0.0001, everything else exact.
One misplaced priority (modify), STP mode, or FOK-walk rule shifts every later trade and BBO; a list-based book times out on 60k events. Original benchmark task written for usecaserank.com. Headless, offline run on a reproducible fixture. Cost shown is a list-price API-equivalent estimate (run on a ChatGPT subscription, not billed).
Success criteria: 1. validation_rejects 2. all_rejects 3. first_50_trades 4. all_trades 5. bbo_path 6. final_book_and_counts 7. positions_cash_vwap 8. fresh_run_1 9. fresh_run_2 10. big_input_60k_events_20s
Setup notes: Use Case Scout batch12 case m02-order-book-matching (offline fixture). Prepared with batch12/prepare.sh on the shared box; only TASK.md + starting files were copied to the agent machine, hidden key/tests stayed on the box. Prompt = TASK.md verbatim on stdin. Flags: --json -s workspace-write --skip-git-repo-check --ephemeral --ignore-user-config --ignore-rules -C <workdir>. Scored on the box with the scout's scorer: `python score.py WORKDIR KEYDIR`. Single run, no retries. User-level AGENTS.md (846 bytes, sha256 83ec4fe50da8) may have been in context; --ignore-user-config does not document excluding it. Serving model not confirmed: requested via -m; the event log does not echo the serving model id. Price source is secondary (pricing.json verification='secondary', https://www.actuia.com/en/news/ai-api-pricing-in-october-2026-050-to-50-for-output/), not an official OpenAI price page. Score: 10/10. Cost is an ESTIMATE: measured tokens x pricing.json list price (ChatGPT plan, nothing billed); input_tokens include cached reads; output_tokens include reasoning tokens. Tool calls 3 (1 failed, recovered). Single run.
Estimated cost List-price estimate; not actually billed.
Metrics
- Estimated cost
- $0.06
- Wall time
- 1m
- Human step-ins
- 0
- Turns
- 1
| Model | Input tok | Output tok | Cost |
|---|---|---|---|
| OGPT-6.1 Solopenai/gpt-6.1-soleffort default (no reasoning-effort override) · thinking on | 71,421 | 2,877 | $0.06 |
Reproduce this run
- Use the use case prompt on the use case page.
- Same setup: GPT-6.1 Sol (openai/gpt-6.1-sol, effort default (no reasoning-effort override) · thinking on) via
Codex CLI 0.163.0 (codex exec --json), cloud. - It counts as a success only if it meets every success criterion: 1. validation_rejects 2. all_rejects 3. first_50_trades 4. all_trades 5. bbo_path 6. final_book_and_counts 7. positions_cash_vwap 8. fresh_run_1 9. fresh_run_2 10. big_input_60k_events_20s
- Log model, tokens, wall time, human step-ins and the evidence log, then submit it as a reproduction. A different model? Submit a head-to-head rerun.
How runs are scored: Methodology.
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Automated checks
Trust 80% comes from these checks (how Trust % works).
- Run date is not in the future : passed
- Model is in the dated pricing table : passed
- Cost recomputed from tokens matches : passed
- Cached tokens are consistent : passed
- Output throughput is plausible : passed
- Autonomy matches step-ins : passed
- Step-ins do not exceed turns : passed
- Retries agree with first-try result : passed
- Evidence log SHA-256 matches : passed
- Evidence log is substantial : passed
Evidence log
{"type": "thread.started", "thread_id": "01a16405-3652-7ae3-a5dd-4fc2a5c9342e"}
{"type": "turn.started"}
{"type": "item.completed", "item": {"id": "item_0", "type": "agent_message", "text": "I\u2019ll implement price-time matching with indexed price levels, then check the edge cases and performance on 60,000 events.\n"}}
{"type": "item.started", "item": {"id": "item_1", "type": "command_execution", "command": "/bin/bash -lc \"pwd && rg --files -g 'AGENTS.md' -g 'input.json' -g 'solve.py'\"", "aggregated_output": "", "exit_code": null, "status": "in_progress"}}
{"type": "item.completed", "item": {"id": "item_1", "type": "command_execution", "command": "/bin/bash -lc \"pwd && rg --files -g 'AGENTS.md' -g 'input.json' -g 'solve.py'\"", "aggregated_output": "/home/bob/usecase-runs/batch/out/gpt-6.1-sol/121_m02/work\ninput.json\n", "exit_code": 0, "status": "completed"}}
{"type": "item.started", "item": {"id": "item_2", "type": "command_execution", "command": "/bin/bash -lc \"cat > solve.py <<'PY'\nimport sys,json,heapq\nfrom collections import OrderedDict\nfrom decimal import Decimal, localcontext, ROUND_HALF_UP\n\nclass Level:\n __slots__=('orders','total','owners')\n def __init__(self):\n self.orders=OrderedDict(); self.total=0; self.owners={}\n\nclass Engine:\n def __init__(self):\n self.levels={'B':{},'S':{}}; self.heaps={'B':[],'S':[]}\n self.live={}; self.used=set(); self.trades=[]; self.rejects=[]; self.bbo=[]\n self.positions={}; self.cash={}; self.volume=0; self.notional=0\n def best(self,side):\n h=self.heaps[side]; levels=self.levels[side]\n while h and ( -h[0] if side=='B' else h[0]) not in levels: heapq.heappop(h)\n return (-h[0] if side=='B' else h[0]) if h else None\n def adjust(self,o,delta):\n level=self.levels[o['side']][o['px']]; level.total+=delta\n owner=o['owner']; n=level.owners.get(owner,0)+delta\n if n: level.owners[owner]=n\n else: level.owners.pop(owner,None)\n def remove(self,o):\n side=o['side']; px=o['px']; level=self.levels[side][px]\n self.adjust(o,-o['qty']); del level.orders[o['id']]; del self.live[o['id']]\n if not level.orders: del self.levels[side][px]\n def rest(self,o):\n side=o['side']; px=o['px']; levels=self.levels[side]\n if px not in levels:\n levels[px]=Level(); heapq.heappush(self.heaps[side],-px if side=='B' else px)\n levels[px].orders[o['id']]=o; self.live[o['id']]=o; self.adjust(o,o['qty'])\n @staticmethod\n def acceptable(o,px):\n return o['type']=='MKT' or (px<=o['px'] if o['side']=='B' else px>=o['px'])\n def can_fill(self,o):\n side='S' if o['side']=='B' else 'B'; needed=o['qty']; owner=o['owner']\n levels=self.levels[side]\n for px in sorted(levels,reverse=side=='B'):\n if not self.acceptable(o,px): break\n level=levels[px]\n if o['stp']\"'!='\"'cancel_incoming':\n needed-=level.total-level.owners.get(owner,0)\n elif owner not in level.owners:\n needed-=level.total\n else:\n for resting in level.orders.values():\n if resting['owner']==owner: return False\n needed-=resting['qty']\n if needed<=0: return True\n if needed<=0: return True\n return False\n def match(self,o):\n other='S' if o['side']=='B' else 'B'\n while o['qty']:\n px=self.best(other)\n if px is None or not self.acceptable(o,px): break\n r=next(iter(self.levels[other][px].orders.values()))\n if r['owner']==o['owner']:\n if o['stp']=='cancel_incoming': return\n self.remove(r); continue\n qty=min(o['qty'],r['qty']); buy,sell=(o,r) if o['side']=='B' else (r,o)\n self.trades.append({'seq':len(self.trades)+1,'buy_id':buy['id'],'sell_id':sell['id'],'px':px,'qty':qty,'aggressor':o['side']})\n value=px*qty; self.volume+=qty; self.notional+=value\n for order,sign in ((buy,1),(sell,-1)):\n owner=order['owner']; self.positions[owner]=self.positions.get(owner,0)+sign*qty\n self.cash[owner]=self.cash.get(owner,0)-sign*value\n o['qty']-=qty\n if r['qty']==qty: self.remove(r)\n else: self.adjust(r,-qty); r['qty']-=qty\n if o['qty'] and o['type']=='LMT' and o['tif']=='GTC': self.rest(o)\n def run(self,data):\n integer=lambda v: type(v) is int\n for i,e in enumerate(data['events']):\n oid=e.get('id'); op=e.get('op'); reason=None\n if op=='new':\n if not isinstance(oid,str) or not oid: reason='bad_id'\n elif oid in self.used: reason='duplicate_id'\n else:\n self.used.add(oid)\n if e.get('side') not in ('B','S'): reason='bad_side'\n elif e.get('type') not in ('LMT','MKT'): reason='bad_type'\n elif not integer(e.get('qty')) or e['qty']<=0: reason='bad_qty'\n elif e['type']=='LMT' and (not integer(e.get('px')) or e['px']<=0): reason='bad_price'\n elif e['type']=='LMT' and e.get('tif','GTC') not in ('GTC','IOC','FOK'): reason='bad_tif'\n if reason is None:\n o={'id':oid,'side':e['side'],'type':e['type'],'qty':e['qty'],'px':e.get('px'),'tif':e.get('tif','GTC') if e['type']=='LMT' else 'IOC','owner':e.get('owner',''),'stp':e.get('stp','cancel_resting')}\n best=self.best('S' if o['side']=='B' else 'B')\n if e.get('post_only',False) and o['type']=='LMT' and best is not None and self.acceptable(o,best): reason='post_only_would_cross'\n elif o['type']=='MKT' and best is None: reason='no_liquidity'\n elif o['tif']=='FOK' and not self.can_fill(o): reason='fok_unfilled'\n else: self.match(o)\n elif op in ('cancel','modify'):\n o=self.live.get(oid) if isinstance(oid,str) else None\n if o is None: reason='unknown_order'\n elif op=='cancel': self.remove(o)\n else:\n qty=e.get('qty',o['qty']); px=e.get('px',o['px'])\n if not integer(qty) or qty<0 or not integer(px) or px<=0: reason='bad_modify'\n elif qty==0: self.remove(o)\n elif px==o['px'] and qty<=o['qty']:\n self.adjust(o,qty-o['qty']); o['qty']=qty\n else:\n self.remove(o); o.update(qty=qty,px=px,tif='GTC'); self.match(o)\n else: reason='bad_op'\n if reason: self.rejects.append({'event':i,'id':oid,'reason':reason})\n if (i+1)%data['bbo_every']==0: self.bbo.append([self.best('B'),self.best('S')])\n book={}\n for side,key in (('B','bids'),('S','asks')):\n levels=self.levels[side]\n book[key]=[[px,levels[px].total,len(levels[px].orders)] for px in sorted(levels,reverse=side=='B')[:5]]\n vwap=None\n if self.volume:\n with localcontext() as ctx:\n ctx.prec=max(40,len(str(self.notional))+10)\n vwap=float((Decimal(self.notional)/Decimal(self.volume)).quantize(Decimal('0.0001'),rounding=ROUND_HALF_UP))\n return {'trades':self.trades,'rejects':self.rejects,'bbo':self.bbo,'book':book,'positions':dict(sorted(self.positions.items())),'cash':dict(sorted(self.cash.items())),'volume':self.volume,'vwap':vwap,'resting_orders':len(self.live)}\n\nif __name__=='__main__':\n with open(sys.argv[1]) as f: data=json.load(f)\n result=Engine().run(data)\n with open(sys.argv[2],'w') as f: json.dump(result,f,separators=(',',':'))\nPY\npython solve.py input.json answer.json\"", "aggregated_output": "", "exit_code": null, "status": "in_progress"}}
{"type": "item.completed", "item": {"id": "item_2", "type": "command_execution", "command": "/bin/bash -lc \"cat > solve.py <<'PY'\nimport sys,json,heapq\nfrom collections import OrderedDict\nfrom decimal import Decimal, localcontext, ROUND_HALF_UP\n\nclass Level:\n __slots__=('orders','total','owners')\n def __init__(self):\n self.orders=OrderedDict(); self.total=0; self.owners={}\n\nclass Engine:\n def __init__(self):\n self.levels={'B':{},'S':{}}; self.heaps={'B':[],'S':[]}\n self.live={}; self.used=set(); self.trades=[]; self.rejects=[]; self.bbo=[]\n self.positions={}; self.cash={}; self.volume=0; self.notional=0\n def best(self,side):\n h=self.heaps[side]; levels=self.levels[side]\n while h and ( -h[0] if side=='B' else h[0]) not in levels: heapq.heappop(h)\n return (-h[0] if side=='B' else h[0]) if h else None\n def adjust(self,o,delta):\n level=self.levels[o['side']][o['px']]; level.total+=delta\n owner=o['owner']; n=level.owners.get(owner,0)+delta\n if n: level.owners[owner]=n\n else: level.owners.pop(owner,None)\n def remove(self,o):\n side=o['side']; px=o['px']; level=self.levels[side][px]\n self.adjust(o,-o['qty']); del level.orders[o['id']]; del self.live[o['id']]\n if not level.orders: del self.levels[side][px]\n def rest(self,o):\n side=o['side']; px=o['px']; levels=self.levels[side]\n if px not in levels:\n levels[px]=Level(); heapq.heappush(self.heaps[side],-px if side=='B' else px)\n levels[px].orders[o['id']]=o; self.live[o['id']]=o; self.adjust(o,o['qty'])\n @staticmethod\n def acceptable(o,px):\n return o['type']=='MKT' or (px<=o['px'] if o['side']=='B' else px>=o['px'])\n def can_fill(self,o):\n side='S' if o['side']=='B' else 'B'; needed=o['qty']; owner=o['owner']\n levels=self.levels[side]\n for px in sorted(levels,reverse=side=='B'):\n if not self.acceptable(o,px): break\n level=levels[px]\n if o['stp']\"'!='\"'cancel_incoming':\n needed-=level.total-level.owners.get(owner,0)\n elif owner not in level.owners:\n needed-=level.total\n else:\n for resting in level.orders.values():\n if resting['owner']==owner: return False\n needed-=resting['qty']\n if needed<=0: return True\n if needed<=0: return True\n return False\n def match(self,o):\n other='S' if o['side']=='B' else 'B'\n while o['qty']:\n px=self.best(other)\n if px is None or not self.acceptable(o,px): break\n r=next(iter(self.levels[other][px].orders.values()))\n if r['owner']==o['owner']:\n if o['stp']=='cancel_incoming': return\n self.remove(r); continue\n qty=min(o['qty'],r['qty']); buy,sell=(o,r) if o['side']=='B' else (r,o)\n self.trades.append({'seq':len(self.trades)+1,'buy_id':buy['id'],'sell_id':sell['id'],'px':px,'qty':qty,'aggressor':o['side']})\n value=px*qty; self.volume+=qty; self.notional+=value\n for order,sign in ((buy,1),(sell,-1)):\n owner=order['owner']; self.positions[owner]=self.positions.get(owner,0)+sign*qty\n self.cash[owner]=self.cash.get(owner,0)-sign*value\n o['qty']-=qty\n if r['qty']==qty: self.remove(r)\n else: self.adjust(r,-qty); r['qty']-=qty\n if o['qty'] and o['type']=='LMT' and o['tif']=='GTC': self.rest(o)\n def run(self,data):\n integer=lambda v: type(v) is int\n for i,e in enumerate(data['events']):\n oid=e.get('id'); op=e.get('op'); reason=None\n if op=='new':\n if not isinstance(oid,str) or not oid: reason='bad_id'\n elif oid in self.used: reason='duplicate_id'\n else:\n self.used.add(oid)\n if e.get('side') not in ('B','S'): reason='bad_side'\n elif e.get('type') not in ('LMT','MKT'): reason='bad_type'\n elif not integer(e.get('qty')) or e['qty']<=0: reason='bad_qty'\n elif e['type']=='LMT' and (not integer(e.get('px')) or e['px']<=0): reason='bad_price'\n elif e['type']=='LMT' and e.get('tif','GTC') not in ('GTC','IOC','FOK'): reason='bad_tif'\n if reason is None:\n o={'id':oid,'side':e['side'],'type':e['type'],'qty':e['qty'],'px':e.get('px'),'tif':e.get('tif','GTC') if e['type']=='LMT' else 'IOC','owner':e.get('owner',''),'stp':e.get('stp','cancel_resting')}\n best=self.best('S' if o['side']=='B' else 'B')\n if e.get('post_only',False) and o['type']=='LMT' and best is not None and self.acceptable(o,best): reason='post_only_would_cross'\n elif o['type']=='MKT' and best is None: reason='no_liquidity'\n elif o['tif']=='FOK' and not self.can_fill(o): reason='fok_unfilled'\n else: self.match(o)\n elif op in ('cancel','modify'):\n o=self.live.get(oid) if isinstance(oid,str) else None\n if o is None: reason='unknown_order'\n elif op=='cancel': self.remove(o)\n else:\n qty=e.get('qty',o['qty']); px=e.get('px',o['px'])\n if not integer(qty) or qty<0 or not integer(px) or px<=0: reason='bad_modify'\n elif qty==0: self.remove(o)\n elif px==o['px'] and qty<=o['qty']:\n self.adjust(o,qty-o['qty']); o['qty']=qty\n else:\n self.remove(o); o.update(qty=qty,px=px,tif='GTC'); self.match(o)\n else: reason='bad_op'\n if reason: self.rejects.append({'event':i,'id':oid,'reason':reason})\n if (i+1)%data['bbo_every']==0: self.bbo.append([self.best('B'),self.best('S')])\n book={}\n for side,key in (('B','bids'),('S','asks')):\n levels=self.levels[side]\n book[key]=[[px,levels[px].total,len(levels[px].orders)] for px in sorted(levels,reverse=side=='B')[:5]]\n vwap=None\n if self.volume:\n with localcontext() as ctx:\n ctx.prec=max(40,len(str(self.notional))+10)\n vwap=float((Decimal(self.notional)/Decimal(self.volume)).quantize(Decimal('0.0001'),rounding=ROUND_HALF_UP))\n return {'trades':self.trades,'rejects':self.rejects,'bbo':self.bbo,'book':book,'positions':dict(sorted(self.positions.items())),'cash':dict(sorted(self.cash.items())),'volume':self.volume,'vwap':vwap,'resting_orders':len(self.live)}\n\nif __name__=='__main__':\n with open(sys.argv[1]) as f: data=json.load(f)\n result=Engine().run(data)\n with open(sys.argv[2],'w') as f: json.dump(result,f,separators=(',',':'))\nPY\npython solve.py input.json answer.json\"", "aggregated_output": "/bin/bash: line 125: python: command not found\n", "exit_code": 127, "status": "failed"}}
{"type": "item.started", "item": {"id": "item_3", "type": "command_execution", "command": "/bin/bash -lc \"python3 solve.py input.json answer.json && python3 - <<'PY'\nfrom solve import Engine\nimport time\n\ndef new(i,side,qty,px,owner='',**kw): return dict(op='new',id=i,side=side,type='LMT',qty=qty,px=px,owner=owner,**kw)\nr=Engine().run({'bbo_every':1,'events':[new('a','S',5,100,'a'),new('b','S',5,100,'b'),dict(op='modify',id='a',qty=3),new('c','B',4,100,'c')]})\nassert [(t['sell_id'],t['qty']) for t in r['trades']]==[('a',3),('b',1)]\nassert r['book']['asks']==[[100,4,1]]\nr=Engine().run({'bbo_every':1,'events':[new('a','S',5,100,'a'),new('b','S',5,101,'b'),new('c','B',6,101,'a',tif='FOK'),new('d','B',5,101,'a',tif='FOK')]})\nassert r['rejects']==[{'event':2,'id':'c','reason':'fok_unfilled'}]\nassert r['trades'][0]['sell_id']=='b' and r['resting_orders']==0\nr=Engine().run({'bbo_every':1,'events':[new('a','S',5,100,'a'),new('b','S',5,101,'b'),new('c','B',5,101,'a',tif='FOK',stp='cancel_incoming')]})\nassert len(r['rejects'])==1 and r['resting_orders']==2\nes=[new(str(i),'B' if i%2==0 else 'S',1,100,'a' if i%2==0 else 'b') for i in range(60000)]\nt=time.monotonic(); r=Engine().run({'events':es,'bbo_every':1}); elapsed=time.monotonic()-t\nassert r['volume']==30000 and r['resting_orders']==0\nprint('Edge cases passed; 60,000 events: %.3f seconds'%elapsed)\nPY\"", "aggregated_output": "", "exit_code": null, "status": "in_progress"}}
{"type": "item.completed", "item": {"id": "item_3", "type": "command_execution", "command": "/bin/bash -lc \"python3 solve.py input.json answer.json && python3 - <<'PY'\nfrom solve import Engine\nimport time\n\ndef new(i,side,qty,px,owner='',**kw): return dict(op='new',id=i,side=side,type='LMT',qty=qty,px=px,owner=owner,**kw)\nr=Engine().run({'bbo_every':1,'events':[new('a','S',5,100,'a'),new('b','S',5,100,'b'),dict(op='modify',id='a',qty=3),new('c','B',4,100,'c')]})\nassert [(t['sell_id'],t['qty']) for t in r['trades']]==[('a',3),('b',1)]\nassert r['book']['asks']==[[100,4,1]]\nr=Engine().run({'bbo_every':1,'events':[new('a','S',5,100,'a'),new('b','S',5,101,'b'),new('c','B',6,101,'a',tif='FOK'),new('d','B',5,101,'a',tif='FOK')]})\nassert r['rejects']==[{'event':2,'id':'c','reason':'fok_unfilled'}]\nassert r['trades'][0]['sell_id']=='b' and r['resting_orders']==0\nr=Engine().run({'bbo_every':1,'events':[new('a','S',5,100,'a'),new('b','S',5,101,'b'),new('c','B',5,101,'a',tif='FOK',stp='cancel_incoming')]})\nassert len(r['rejects'])==1 and r['resting_orders']==2\nes=[new(str(i),'B' if i%2==0 else 'S',1,100,'a' if i%2==0 else 'b') for i in range(60000)]\nt=time.monotonic(); r=Engine().run({'events':es,'bbo_every':1}); elapsed=time.monotonic()-t\nassert r['volume']==30000 and r['resting_orders']==0\nprint('Edge cases passed; 60,000 events: %.3f seconds'%elapsed)\nPY\"", "aggregated_output": "Edge cases passed; 60,000 events: 0.186 seconds\n", "exit_code": 0, "status": "completed"}}
{"type": "item.completed", "item": {"id": "item_4", "type": "agent_message", "text": "Created [solve.py](/home/bob/usecase-runs/batch/out/gpt-6.1-sol/121_m02/work/solve.py) and generated `answer.json`.\n\nValidated priority changes and FOK/STP cases. A synthetic 60,000-event run completed in 0.186 seconds.\n\n```bash\npython3 solve.py input.json answer.json\n```\n\nThis environment provides `python3`, but no `python` alias."}}
{"type": "turn.completed", "usage": {"input_tokens": 71421, "cached_input_tokens": 57344, "cache_write_input_tokens": 0, "output_tokens": 2877, "reasoning_output_tokens": 146}}