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GPT-6.1 Sol (effort default (no reasoning-effort override) · thinking on) on Backtest a moving-average crossover on a bundled synthetic price file

OGPT-6.1 Soldefault · thinking on · Codex CLI 0.163.0 (codex exec --json) · cloud · 2026-10-22 · by @bobsiopener

Trust 80%Fully autonomoussuccess · first try
Estimated cost
$0.12
Wall time
2m
Human step-ins
0
Outcome
Pass

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Summary

Exact prompt

# Backtest a moving-average crossover on a bundled synthetic price file `prices.csv` is a **synthetic** daily closing-price series (no real market data), and `input.json` gives the rule's parameters. Write `solve.py` (Python 3 standard library only) so that `python solve.py input.json answer.json` backtests the rule exactly as specified. This is an offline exercise: no data downloads, no broker, no trading. We re-run it on fresh files, so do not hard-code anything. ## Input `input.json`: `prices_file` (CSV path, relative to the working directory), `fast`, `slow` (window lengths, fast < slow), `cost_bps` (cost per position change, in basis points), `initial` (starting equity). The CSV has a header `date,close`; dates are ISO `YYYY-MM-DD`. ## Rules 1. Cleaning: rows can be out of order and a date can appear more than once. For a repeated date, the **last** row in the file wins. Then sort by date. Let c[0..n-1] be the closes; `days` = n. 2. SMA_w(t) = mean of c[t-w+1 .. t] (inclusive). Signal s[t] = 1 if SMA_fast(t) > SMA_slow(t) (strictly), else 0. For t < slow - 1, s[t] = 0. 3. Position: p[0] = 0 and p[t] = s[t-1] for t >= 1 (trade on the next day's close). 4. Equity starts at `initial`. For t = 1..n-1: e[t] = e[t-1] * (1 + p[t] * (c[t]/c[t-1] - 1)) * (1 - cost * |p[t] - p[t-1]|), with cost = cost_bps / 10000. The daily strategy return is e[t]/e[t-1] - 1. 5. `trades` = number of t in 1..n-1 with p[t] != p[t-1]. `exposure` = (sum of p[1..n-1]) / (n - 1). 6. `total_return` = e[n-1]/initial - 1; `cagr` = (e[n-1]/initial) ** (252 / (n - 1)) - 1; `max_drawdown` = the largest value of 1 - e[t]/max(e[0..t]) (0 or positive); `sharpe` = mean(daily returns) / population standard deviation (`statistics.pstdev`) of daily returns * sqrt(252), or 0 if the deviation is 0; `buy_hold_return` = c[n-1]/c[0] - 1. 7. Round `final_equity` (= e[n-1]) to 2 decimals and the other fractions to 6 decimals when writing. ## Output `answer.json` keys: `days`, `final_equity`, `total_return`, `trades`, `cagr`, `max_drawdown`, `sharpe`, `exposure`, `buy_hold_return`. ## Chart (required visual) Also write `chart.svg` in the working directory: a static, self-contained SVG (root element `<svg>`, no scripts, no external images, fonts, stylesheets or links) that can be rendered headless. It must contain `<polyline id="equity">` drawing strategy equity by trading day. ## Scoring The `chart_svg_static` check (one more check, counted in partial credit) passes only if `chart.svg` exists after the fixture run, parses, has root `<svg>`, contains the ids above with the stated tags, and has no `<script>`, `<img>`/`<image>`, `<link>`, `<iframe>`/`<object>`/`<embed>`, `on*` handlers, or `href`/`src` pointing anywhere except `#...` or `data:`. Its drawing is not graded. Hidden checks (partial credit = fraction passed; all must pass): `data_cleaning` (days and buy-and-hold return), `equity_and_trades`, `risk_metrics` on the given files; `fresh_run_1`, `fresh_run_2` (all keys on freshly generated files). Tolerances: `final_equity` within 0.011, fractions within 0.00001, `days` and `trades` exact. Each run has a 60-second limit.

Duplicate dates (last wins) and swapped rows, next-day execution, strict crossover, cost per position change, population-deviation Sharpe and running-peak drawdown. 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).

These are synthetic test cases for comparing AI models, not financial advice.

Success criteria: 1. data_cleaning 2. equity_and_trades 3. risk_metrics 4. fresh_run_1 5. fresh_run_2 6. chart_svg_static

Setup notes: Use Case Scout batch9 case j11-sma-crossover-backtest (offline fixture). Prepared with batch9/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. Visual output (chart.svg) rendered offline to PNG with google-chrome headless, network blocked, 1280x800 at ~3 s after load. 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: 6/6. 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 9 (3 failed, recovered). Single run.

Estimated cost List-price estimate; not actually billed.

Output

Metrics

Estimated cost
$0.12
Wall time
2m
Human step-ins
0
Turns
1
Model usage
ModelInput tokOutput tokCost
OGPT-6.1 Solopenai/gpt-6.1-soleffort default (no reasoning-effort override) · thinking on184,0674,044$0.12

Reproduce this run

  1. Use the use case prompt on the use case page.
  2. 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.
  3. It counts as a success only if it meets every success criterion: 1. data_cleaning 2. equity_and_trades 3. risk_metrics 4. fresh_run_1 5. fresh_run_2 6. chart_svg_static
  4. 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

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Evidence log
{"type": "thread.started", "thread_id": "01a163f5-0f51-70e1-913b-d53c250ec54e"}
{"type": "turn.started"}
{"type": "item.completed", "item": {"id": "item_0", "type": "agent_message", "text": "I\u2019ll inspect the bundled files, implement the backtest and SVG chart, then verify the results locally."}}
{"type": "item.started", "item": {"id": "item_1", "type": "command_execution", "command": "/bin/bash -lc \"pwd && rg --files -g 'AGENTS.md' -g 'input.json' -g 'prices.csv' -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 'prices.csv' -g 'solve.py'\"", "aggregated_output": "/home/bob/usecase-runs/batch/out/gpt-6.1-sol/106_j11/work\ninput.json\nprices.csv\n", "exit_code": 0, "status": "completed"}}
{"type": "item.started", "item": {"id": "item_2", "type": "command_execution", "command": "/bin/bash -lc 'cat input.json && head -6 prices.csv && ls -la'", "aggregated_output": "", "exit_code": null, "status": "in_progress"}}
{"type": "item.completed", "item": {"id": "item_2", "type": "command_execution", "command": "/bin/bash -lc 'cat input.json && head -6 prices.csv && ls -la'", "aggregated_output": "{\n \"prices_file\": \"prices.csv\",\n \"fast\": 20,\n \"slow\": 50,\n \"cost_bps\": 10,\n \"initial\": 10000\n}date,close\n2023-01-02,99.26\n2023-01-03,101.84\n2023-01-04,102.13\n2023-01-05,102.55\n2023-01-06,104.28\ntotal 28\ndrwxr-xr-x 6 bob bob 4096 Oct 22 10:12 .\ndrwxrwxr-x 3 bob bob 4096 Oct 22 10:12 ..\ndr-xr-xr-x 2 bob bob   40 Oct 22 10:12 .agents\ndr-xr-xr-x 2 bob bob   40 Oct 22 10:12 .aws\ndr-xr-xr-x 2 bob bob   40 Oct 22 10:12 .codex\ndr-xr-xr-x 2 bob bob   40 Oct 22 10:12 .git\n-rw-r--r-- 1 bob bob 3140 Oct 22 10:08 TASK.md\n-rw-r--r-- 1 bob bob   94 Oct 22 10:08 input.json\n-rw-r--r-- 1 bob bob 8739 Oct 22 10:08 prices.csv\n", "exit_code": 0, "status": "completed"}}
{"type": "item.started", "item": {"id": "item_3", "type": "file_change", "changes": [{"path": "/home/bob/usecase-runs/batch/out/gpt-6.1-sol/106_j11/work/solve.py", "kind": "add"}], "status": "in_progress"}}
{"type": "item.completed", "item": {"id": "item_3", "type": "file_change", "changes": [{"path": "/home/bob/usecase-runs/batch/out/gpt-6.1-sol/106_j11/work/solve.py", "kind": "add"}], "status": "completed"}}
{"type": "item.started", "item": {"id": "item_4", "type": "command_execution", "command": "/bin/bash -lc 'python solve.py input.json answer.json && cat answer.json'", "aggregated_output": "", "exit_code": null, "status": "in_progress"}}
{"type": "item.completed", "item": {"id": "item_4", "type": "command_execution", "command": "/bin/bash -lc 'python solve.py input.json answer.json && cat answer.json'", "aggregated_output": "/bin/bash: line 1: python: command not found\n", "exit_code": 127, "status": "failed"}}
{"type": "item.started", "item": {"id": "item_5", "type": "command_execution", "command": "/bin/bash -lc \"python3 solve.py input.json answer.json && cat answer.json && python3 - <<'PY'\nimport csv, json, math, statistics, tempfile, os\nfrom pathlib import Path\nimport xml.etree.ElementTree as ET\nimport solve\ncfg=json.loads(Path('input.json').read_text())\nrows={}\nwith open(cfg['prices_file']) as f:\n for r in csv.DictReader(f): rows[r['date']]=float(r['close'])\nc=[rows[d] for d in sorted(rows)]\ns=[int(t>=cfg['slow']-1 and statistics.mean(c[t-cfg['fast']+1:t+1])>statistics.mean(c[t-cfg['slow']+1:t+1])) for t in range(len(c))]\np=[0]+s[:-1]; e=[cfg['initial']]\nfor t in range(1,len(c)): e.append(e[-1]*(1+p[t]*(c[t]/c[t-1]-1))*(1-cfg['cost_bps']/10000*abs(p[t]-p[t-1])))\nresult=json.loads(Path('answer.json').read_text())\nassert result['final_equity']==round(e[-1],2)\nassert result['trades']==sum(a\"'!=b for a,b in zip(p,p[1:]))\nroot=ET.parse('\"'chart.svg').getroot()\nassert root.tag=='{http://www.w3.org/2000/svg}svg'\nassert root.find(\\\".//{http://www.w3.org/2000/svg}polyline[@id='equity']\\\") is not None\nfor node in root.iter():\n assert node.tag.split('}')[-1] not in {'script','img','image','link','iframe','object','embed'}\n assert not any(k.startswith('on') or k in {'href','src'} for k in node.attrib)\nwith tempfile.TemporaryDirectory() as d:\n path=Path(d)/'prices.csv'\n path.write_text('date,close\\\\n2024-01-03,3\\\\n2024-01-01,1\\\\n2024-01-02,9\\\\n2024-01-02,2\\\\n2024-01-04,2\\\\n2024-01-05,1\\\\n')\n r,_=solve.backtest(dict(prices_file=str(path),fast=1,slow=2,cost_bps=100,initial=100))\n assert r['days']==5 and r['trades']==2 and r['exposure']==0.5 and r['final_equity']==65.34, r\nprint('Independent equity/trade calculation, duplicate-date fixture, and SVG checks passed.')\nPY\"", "aggregated_output": "", "exit_code": null, "status": "in_progress"}}
{"type": "item.completed", "item": {"id": "item_5", "type": "command_execution", "command": "/bin/bash -lc \"python3 solve.py input.json answer.json && cat answer.json && python3 - <<'PY'\nimport csv, json, math, statistics, tempfile, os\nfrom pathlib import Path\nimport xml.etree.ElementTree as ET\nimport solve\ncfg=json.loads(Path('input.json').read_text())\nrows={}\nwith open(cfg['prices_file']) as f:\n for r in csv.DictReader(f): rows[r['date']]=float(r['close'])\nc=[rows[d] for d in sorted(rows)]\ns=[int(t>=cfg['slow']-1 and statistics.mean(c[t-cfg['fast']+1:t+1])>statistics.mean(c[t-cfg['slow']+1:t+1])) for t in range(len(c))]\np=[0]+s[:-1]; e=[cfg['initial']]\nfor t in range(1,len(c)): e.append(e[-1]*(1+p[t]*(c[t]/c[t-1]-1))*(1-cfg['cost_bps']/10000*abs(p[t]-p[t-1])))\nresult=json.loads(Path('answer.json').read_text())\nassert result['final_equity']==round(e[-1],2)\nassert result['trades']==sum(a\"'!=b for a,b in zip(p,p[1:]))\nroot=ET.parse('\"'chart.svg').getroot()\nassert root.tag=='{http://www.w3.org/2000/svg}svg'\nassert root.find(\\\".//{http://www.w3.org/2000/svg}polyline[@id='equity']\\\") is not None\nfor node in root.iter():\n assert node.tag.split('}')[-1] not in {'script','img','image','link','iframe','object','embed'}\n assert not any(k.startswith('on') or k in {'href','src'} for k in node.attrib)\nwith tempfile.TemporaryDirectory() as d:\n path=Path(d)/'prices.csv'\n path.write_text('date,close\\\\n2024-01-03,3\\\\n2024-01-01,1\\\\n2024-01-02,9\\\\n2024-01-02,2\\\\n2024-01-04,2\\\\n2024-01-05,1\\\\n')\n r,_=solve.backtest(dict(prices_file=str(path),fast=1,slow=2,cost_bps=100,initial=100))\n assert r['days']==5 and r['trades']==2 and r['exposure']==0.5 and r['final_equity']==65.34, r\nprint('Independent equity/trade calculation, duplicate-date fixture, and SVG checks passed.')\nPY\"", "aggregated_output": "{\n  \"days\": 509,\n  \"final_equity\": 8589.77,\n  \"total_return\": -0.141023,\n  \"trades\": 9,\n  \"cagr\": -0.072635,\n  \"max_drawdown\": 0.294082,\n  \"sharpe\": -0.450763,\n  \"exposure\": 0.356299,\n  \"buy_hold_return\": -0.260528\n}\nTraceback (most recent call last):\n  File \"<stdin>\", line 14, in <module>\nAssertionError\n", "exit_code": 1, "status": "failed"}}
{"type": "item.started", "item": {"id": "item_6", "type": "command_execution", "command": "/bin/bash -lc \"python3 - <<'PY'\nimport solve,json,csv,statistics\nfrom pathlib import Path\ncfg=json.loads(Path('input.json').read_text()); rows={}\nwith open('prices.csv') as f:\n for r in csv.DictReader(f):rows[r['date']]=float(r['close'])\nc=[rows[d] for d in sorted(rows)];s=[int(t>=49 and statistics.mean(c[t-19:t+1])>statistics.mean(c[t-49:t+1])) for t in range(len(c))];p=[0]+s[:-1];e=10000\nfor t in range(1,len(c)):e*= (1+p[t]*(c[t]/c[t-1]-1))*(1-.001*abs(p[t]-p[t-1]))\nprint(e,sum(a\"'!=b for a,b in zip(p,p[1:])))\nPY'", "aggregated_output": "", "exit_code": null, "status": "in_progress"}}
{"type": "item.completed", "item": {"id": "item_6", "type": "command_execution", "command": "/bin/bash -lc \"python3 - <<'PY'\nimport solve,json,csv,statistics\nfrom pathlib import Path\ncfg=json.loads(Path('input.json').read_text()); rows={}\nwith open('prices.csv') as f:\n for r in csv.DictReader(f):rows[r['date']]=float(r['close'])\nc=[rows[d] for d in sorted(rows)];s=[int(t>=49 and statistics.mean(c[t-19:t+1])>statistics.mean(c[t-49:t+1])) for t in range(len(c))];p=[0]+s[:-1];e=10000\nfor t in range(1,len(c)):e*= (1+p[t]*(c[t]/c[t-1]-1))*(1-.001*abs(p[t]-p[t-1]))\nprint(e,sum(a\"'!=b for a,b in zip(p,p[1:])))\nPY'", "aggregated_output": "8691.844721850734 9\n", "exit_code": 0, "status": "completed"}}
{"type": "item.started", "item": {"id": "item_7", "type": "command_execution", "command": "/bin/bash -lc \"python3 - <<'PY'\nimport csv,statistics\nfrom decimal import Decimal\nr={}\nwith open('prices.csv') as f:\n for x in csv.DictReader(f):r[x['date']]=Decimal(x['close'])\nc=[r[d] for d in sorted(r)]\nfor t in range(49,len(c)):\n a=sum(c[t-19:t+1])*50>sum(c[t-49:t+1])*20\n b=statistics.mean(map(float,c[t-19:t+1]))>statistics.mean(map(float,c[t-49:t+1]))\n if a\"'!=b:print(t,a,b,sum(c[t-19:t+1])/20,sum(c[t-49:t+1])/50)\nPY'", "aggregated_output": "", "exit_code": null, "status": "in_progress"}}
{"type": "item.completed", "item": {"id": "item_7", "type": "command_execution", "command": "/bin/bash -lc \"python3 - <<'PY'\nimport csv,statistics\nfrom decimal import Decimal\nr={}\nwith open('prices.csv') as f:\n for x in csv.DictReader(f):r[x['date']]=Decimal(x['close'])\nc=[r[d] for d in sorted(r)]\nfor t in range(49,len(c)):\n a=sum(c[t-19:t+1])*50>sum(c[t-49:t+1])*20\n b=statistics.mean(map(float,c[t-19:t+1]))>statistics.mean(map(float,c[t-49:t+1]))\n if a\"'!=b:print(t,a,b,sum(c[t-19:t+1])/20,sum(c[t-49:t+1])/50)\nPY'", "aggregated_output": "279 False True 62.614 62.614\n", "exit_code": 0, "status": "completed"}}
{"type": "item.started", "item": {"id": "item_8", "type": "command_execution", "command": "/bin/bash -lc \"python3 - <<'PY'\nimport csv\nr={}\nwith open('prices.csv') as f:\n for x in csv.DictReader(f):r[x['date']]=float(x['close'])\nc=[r[d] for d in sorted(r)]\nprint(sum(c[260:280])/20,sum(c[230:280])/50)\nPY\"", "aggregated_output": "", "exit_code": null, "status": "in_progress"}}
{"type": "item.completed", "item": {"id": "item_8", "type": "command_execution", "command": "/bin/bash -lc \"python3 - <<'PY'\nimport csv\nr={}\nwith open('prices.csv') as f:\n for x in csv.DictReader(f):r[x['date']]=float(x['close'])\nc=[r[d] for d in sorted(r)]\nprint(sum(c[260:280])/20,sum(c[230:280])/50)\nPY\"", "aggregated_output": "62.614 62.614\n", "exit_code": 0, "status": "completed"}}
{"type": "item.completed", "item": {"id": "item_9", "type": "agent_message", "text": "The bundled data includes a day when the two averages are exactly equal. The implementation uses decimal arithmetic for that strict comparison so rounding noise cannot trigger a trade. I\u2019m checking the results and SVG structure now."}}
{"type": "item.started", "item": {"id": "item_10", "type": "command_execution", "command": "/bin/bash -lc \"python3 - <<'PY'\nimport csv,json,tempfile\nfrom pathlib import Path\nimport xml.etree.ElementTree as ET\nimport solve\ncfg=json.loads(Path('input.json').read_text()); rows={}\nwith open(cfg['prices_file']) as f:\n for r in csv.DictReader(f):rows[r['date']]=float(r['close'])\nc=[rows[d] for d in sorted(rows)]; s=[0]*len(c)\nfor t in range(cfg['slow']-1,len(c)):s[t]=int(sum(c[t-cfg['fast']+1:t+1])/cfg['fast']>sum(c[t-cfg['slow']+1:t+1])/cfg['slow'])\np=[0]+s[:-1];e=[cfg['initial']]\nfor t in range(1,len(c)):e.append(e[-1]*(1+p[t]*(c[t]/c[t-1]-1))*(1-cfg['cost_bps']/10000*abs(p[t]-p[t-1])))\nr=json.loads(Path('answer.json').read_text());assert r['final_equity']==round(e[-1],2)\nassert r['trades']==sum(a\"'!=b for a,b in zip(p,p[1:]))\nroot=ET.parse('\"'chart.svg').getroot();assert root.tag=='{http://www.w3.org/2000/svg}svg'\nassert root.find(\\\".//{http://www.w3.org/2000/svg}polyline[@id='equity']\\\") is not None\nfor node in root.iter():\n assert node.tag.split('}')[-1] not in {'script','img','image','link','iframe','object','embed'}\n assert not any(k.startswith('on') or k in {'href','src'} for k in node.attrib)\nwith tempfile.TemporaryDirectory() as d:\n path=Path(d)/'prices.csv';path.write_text('date,close\\\\n2024-01-03,3\\\\n2024-01-01,1\\\\n2024-01-02,9\\\\n2024-01-02,2\\\\n2024-01-04,2\\\\n2024-01-05,1\\\\n')\n r,_=solve.backtest(dict(prices_file=str(path),fast=1,slow=2,cost_bps=100,initial=100))\n assert r['days']==5 and r['trades']==2 and r['exposure']==0.5 and r['final_equity']==65.34,r\nprint('Independent equity/trade calculation, duplicate-date fixture, and static SVG checks passed.')\nPY\"", "aggregated_output": "", "exit_code": null, "status": "in_progress"}}
{"type": "item.completed", "item": {"id": "item_10", "type": "command_execution", "command": "/bin/bash -lc \"python3 - <<'PY'\nimport csv,json,tempfile\nfrom pathlib import Path\nimport xml.etree.ElementTree as ET\nimport solve\ncfg=json.loads(Path('input.json').read_text()); rows={}\nwith open(cfg['prices_file']) as f:\n for r in csv.DictReader(f):rows[r['date']]=float(r['close'])\nc=[rows[d] for d in sorted(rows)]; s=[0]*len(c)\nfor t in range(cfg['slow']-1,len(c)):s[t]=int(sum(c[t-cfg['fast']+1:t+1])/cfg['fast']>sum(c[t-cfg['slow']+1:t+1])/cfg['slow'])\np=[0]+s[:-1];e=[cfg['initial']]\nfor t in range(1,len(c)):e.append(e[-1]*(1+p[t]*(c[t]/c[t-1]-1))*(1-cfg['cost_bps']/10000*abs(p[t]-p[t-1])))\nr=json.loads(Path('answer.json').read_text());assert r['final_equity']==round(e[-1],2)\nassert r['trades']==sum(a\"'!=b for a,b in zip(p,p[1:]))\nroot=ET.parse('\"'chart.svg').getroot();assert root.tag=='{http://www.w3.org/2000/svg}svg'\nassert root.find(\\\".//{http://www.w3.org/2000/svg}polyline[@id='equity']\\\") is not None\nfor node in root.iter():\n assert node.tag.split('}')[-1] not in {'script','img','image','link','iframe','object','embed'}\n assert not any(k.startswith('on') or k in {'href','src'} for k in node.attrib)\nwith tempfile.TemporaryDirectory() as d:\n path=Path(d)/'prices.csv';path.write_text('date,close\\\\n2024-01-03,3\\\\n2024-01-01,1\\\\n2024-01-02,9\\\\n2024-01-02,2\\\\n2024-01-04,2\\\\n2024-01-05,1\\\\n')\n r,_=solve.backtest(dict(prices_file=str(path),fast=1,slow=2,cost_bps=100,initial=100))\n assert r['days']==5 and r['trades']==2 and r['exposure']==0.5 and r['final_equity']==65.34,r\nprint('Independent equity/trade calculation, duplicate-date fixture, and static SVG checks passed.')\nPY\"", "aggregated_output": "Traceback (most recent call last):\n  File \"<stdin>\", line 22, in <module>\nAssertionError: {'days': 5, 'final_equity': 98.01, 'total_return': -0.0199, 'trades': 2, 'cagr': -0.718139, 'max_drawdown': 0.34, 'sharpe': 1.924395, 'exposure': 0.5, 'buy_hold_return': 0.0}\n", "exit_code": 1, "status": "failed"}}
{"type": "item.started", "item": {"id": "item_11", "type": "command_execution", "command": "/bin/bash -lc \"python3 - <<'PY'\nimport tempfile\nfrom pathlib import Path\nimport solve\nwith tempfile.TemporaryDirectory() as d:\n p=Path(d)/'prices.csv';p.write_text('date,close\\\\n2024-01-03,3\\\\n2024-01-01,1\\\\n2024-01-02,9\\\\n2024-01-02,2\\\\n2024-01-04,2\\\\n2024-01-05,1\\\\n')\n r,_=solve.backtest(dict(prices_file=str(p),fast=1,slow=2,cost_bps=100,initial=100))\n assert (r['days'],r['trades'],r['exposure'],r['final_equity'])==(5,2,0.5,98.01)\nprint('Duplicate-date and position-delay fixture passed; bundled equity/trades and SVG checks also passed.')\nPY\"", "aggregated_output": "", "exit_code": null, "status": "in_progress"}}
{"type": "item.completed", "item": {"id": "item_11", "type": "command_execution", "command": "/bin/bash -lc \"python3 - <<'PY'\nimport tempfile\nfrom pathlib import Path\nimport solve\nwith tempfile.TemporaryDirectory() as d:\n p=Path(d)/'prices.csv';p.write_text('date,close\\\\n2024-01-03,3\\\\n2024-01-01,1\\\\n2024-01-02,9\\\\n2024-01-02,2\\\\n2024-01-04,2\\\\n2024-01-05,1\\\\n')\n r,_=solve.backtest(dict(prices_file=str(p),fast=1,slow=2,cost_bps=100,initial=100))\n assert (r['days'],r['trades'],r['exposure'],r['final_equity'])==(5,2,0.5,98.01)\nprint('Duplicate-date and position-delay fixture passed; bundled equity/trades and SVG checks also passed.')\nPY\"", "aggregated_output": "Duplicate-date and position-delay fixture passed; bundled equity/trades and SVG checks also passed.\n", "exit_code": 0, "status": "completed"}}
{"type": "item.completed", "item": {"id": "item_12", "type": "agent_message", "text": "Implemented [solve.py](solve.py) and generated [answer.json](answer.json) and [chart.svg](chart.svg).\n\nVerified equity, trade counts, duplicate-date handling, position delay, and SVG structure. Final equity: **8,589.77**.\n\nRun with `python3 solve.py input.json answer.json` (`python` is unavailable here)."}}
{"type": "turn.completed", "usage": {"input_tokens": 184067, "cached_input_tokens": 149376, "cache_write_input_tokens": 0, "output_tokens": 4044, "reasoning_output_tokens": 237}}

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