Gemini 4 Argon (effort medium · thinking on) on Backtest a moving-average crossover on a bundled synthetic price file

GGemini 4 Argonmedium · thinking on · Antigravity CLI 1.4.8 (agy -p) · cloud · 2026-10-28 · by @bobsiopener

Trust 80%Fully autonomoussuccess · first tryCompare

Estimated cost: $0.09
Wall time: 2m
Human step-ins: 0
Outcome: Pass

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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. Inspired by https://news.ycombinator.com/item?id=42559744 Headless, offline run on a reproducible fixture. Cost shown is a list-price API-equivalent estimate (run on a Google account via Antigravity CLI, not billed per token).

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: batch9 case j11-sma-crossover-backtest (offline fixture). Prepared with prepare.sh on the shared box; only TASK.md + starting files went to the agent machine, hidden keys stayed on the box. Prompt = TASK.md verbatim. Flags: -p --model gemini-4-argon-medium --sandbox --dangerously-skip-permissions --disable-slash-commands --output-format stream-json, cwd=workdir. Scored on the box with the case's scorer. Single run, no retries, no human input. Timeout: 10-minute inactivity limit, no total time cap. Visual output (chart.svg) rendered offline to PNG with google-chrome headless, network blocked, 1280x800 at ~3 s after load. Requested model gemini-4-argon-medium (effort medium); priced at the base model row gemini-4-argon (pricing.json, https://ai.google.dev/gemini-api/docs/pricing). Score: 6/6. Cost is an ESTIMATE: measured tokens x pricing.json list price (Google account, nothing billed per token); output_tokens include thinking tokens. Tool calls 5 (0 failed). Single run.

Estimated cost List-price estimate; not actually billed.

Output

Gemini 4 Argon (effort medium · thinking on) on Backtest a moving-average crossover on a bundled synthetic price fileSuccess

Metrics

Estimated cost: $0.09
Wall time: 2m
Human step-ins: 0
Turns: 5

Model usage
Model	Input tok	Output tok	Cost
GGemini 4 Argongoogle/gemini-4-argoneffort medium · thinking on	72,548	4,132	$0.09

Reproduce this run

Use the use case prompt on the use case page.
Same setup: Gemini 4 Argon (google/gemini-4-argon, effort medium · thinking on) via Antigravity CLI 1.4.8 (agy -p), cloud.
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
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

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{"event": "step_update", "step_update": {"conversation_id": "915a321d-a0a7-489c-9219-00c95c7e20a0", "step_index": 7, "state": "ACTIVE", "step_type": "tool", "tool_name": "run_command", "tool_info": {"name": "run_command", "parameters": {"CommandLine": "python3 solve.py input.json answer.json && cat answer.json && head -n 15 chart.svg"}}}}
{"event": "step_update", "step_update": {"conversation_id": "915a321d-a0a7-489c-9219-00c95c7e20a0", "step_index": 7, "state": "DONE", "step_type": "tool", "tool_name": "run_command", "duration_seconds": 1.3435441510000001, "tool_info": {"name": "run_command", "parameters": {"CommandLine": "python3 solve.py input.json answer.json && cat answer.json && head -n 15 chart.svg"}, "output": "Command exited with code 0.\n\nCommand output:\n{\r\n  \"days\": 509,\r\n  \"final_equity\": 8589.77,\r\n  \"total_return\": -0.141023,\r\n  \"trades\": 9,\r\n  \"cagr\": -0.072635,\r\n  \"max_drawdown\": 0.294082,\r\n  \"sharpe\": -0.450763,\r\n  \"exposure\": 0.356299,\r\n  \"buy_hold_return\": -0.260528\r\n}\r\n<svg xmlns=\"http://www.w3.org/2000/svg\" viewBox=\"0 0 800 400\" width=\"800\" height=\"400\">\r\n  <rect x=\"0\" y=\"0\" width=\"800\" height=\"400\" fill=\"#ffffff\" />\r\n  <line x1=\"60\" y1=\"30\" x2=\"60\" y2=\"360\" stroke=\"#333333\" stroke-width=\"1\" />\r\n  <line x1=\"60\" y1=\"360\" 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704.31,230.67 705.71,225.24 707.11,236.62 708.50,252.42 709.90,271.38 711.30,294.14 712.70,301.22 714.09,285.29 715.49,296.29 716.89,302.86 718.29,322.45 719.69,328.77 721.08,341.92 722.48,356.97 723.88,360.00 725.28,349.89 726.67,350.69 728.07,350.69 729.47,350.69 730.87,350.69 732.26,350.69 733.66,350.69 735.06,350.69 736.46,350.69 737.85,350.69 739.25,350.69 740.65,350.69 742.05,350.69 743.44,350.69 744.84,350.69 746.24,350.69 747.64,350.69 749.04,350.69 750.43,350.69 751.83,350.69 753.23,350.69 754.63,350.69 756.02,350.69 757.42,350.69 758.82,350.69 760.22,350.69 761.61,350.69 763.01,350.69 764.41,350.69 765.81,350.69 767.20,350.69 768.60,350.69 770.00,338.86\" />\r\n</svg>\r\n"}}}
{"event": "step_update", "step_update": {"conversation_id": "915a321d-a0a7-489c-9219-00c95c7e20a0", "step_index": 8, "state": "DONE", "step_type": "agent_response", "duration_seconds": 23.221369552, "usage": {"input_tokens": 7758, "output_tokens": 185, "thinking_tokens": 61, "cache_read_tokens": 12966, "total_tokens": 7943}}}
{"event": "step_update", "step_update": {"conversation_id": "915a321d-a0a7-489c-9219-00c95c7e20a0", "step_index": 9, "state": "ACTIVE", "step_type": "tool", "tool_name": "run_command", "tool_info": {"name": "run_command", "parameters": {"CommandLine": "python3 -c '\nimport xml.etree.ElementTree as ET\ntree = ET.parse(\"chart.svg\")\nroot = tree.getroot()\nassert root.tag.endswith(\"svg\")\neq = root.find(\".//*[@id=\\\"equity\\\"]\")\nassert eq is not None and eq.tag.endswith(\"polyline\")\nprint(\"SVG check passed!\")\n'"}}}}
{"event": "step_update", "step_update": {"conversation_id": "915a321d-a0a7-489c-9219-00c95c7e20a0", "step_index": 9, "state": "DONE", "step_type": "tool", "tool_name": "run_command", "duration_seconds": 1.856344806, "tool_info": {"name": "run_command", "parameters": {"CommandLine": "python3 -c '\nimport xml.etree.ElementTree as ET\ntree = ET.parse(\"chart.svg\")\nroot = tree.getroot()\nassert root.tag.endswith(\"svg\")\neq = root.find(\".//*[@id=\\\"equity\\\"]\")\nassert eq is not None and eq.tag.endswith(\"polyline\")\nprint(\"SVG check passed!\")\n'"}, "output": "Command exited with code 0.\n\nCommand output:\nSVG check passed!\r\n"}}}
{"event": "step_update", "step_update": {"conversation_id": "915a321d-a0a7-489c-9219-00c95c7e20a0", "step_index": 10, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "Implemented [solve.py](file:///home"}}
{"event": "step_update", "step_update": {"conversation_id": "915a321d-a0a7-489c-9219-00c95c7e20a0", "step_index": 10, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "/bob/usecase-runs/b2930/out-poll-2026-1"}}
{"event": "step_update", "step_update": {"conversation_id": "915a321d-a0a7-489c-9219-00c95c7e20a0", "step_index": 10, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "0-27/agy/gemini-4-argon-medium/j11/work/solve.py"}}
{"event": "step_update", "step_update": {"conversation_id": "915a321d-a0a7-489c-9219-00c95c7e20a0", "step_index": 10, "state": "ACTIVE", "step_type": "agent_response", "text_delta": ") using only the Python 3 standard library and v"}}
{"event": "step_update", "step_update": {"conversation_id": "915a321d-a0a7-489c-9219-00c95c7e20a0", "step_index": 10, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "erified it on [input.jso"}}
{"event": "step_update", "step_update": {"conversation_id": "915a321d-a0a7-489c-9219-00c95c7e20a0", "step_index": 10, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "n](file:///h"}}
{"event": "step_update", "step_update": {"conversation_id": "915a321d-a0a7-489c-9219-00c95c7e20a0", "step_index": 10, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "ome/bob/usecase"}}
{"event": "step_update", "step_update": {"conversation_id": "915a321d-a0a7-489c-9219-00c95c7e20a0", "step_index": 10, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "-runs/b2930/out-poll-2026-10-27/"}}
{"event": "step_update", "step_update": {"conversation_id": "915a321d-a0a7-489c-9219-00c95c7e20a0", "step_index": 10, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "agy/gemini-4-argon-medium/j11/work/i"}}
{"event": "step_update", "step_update": {"conversation_id": "915a321d-a0a7-489c-9219-00c95c7e20a0", "step_index": 10, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "nput.json) and [prices."}}
{"event": "step_update", "step_update": {"conversation_id": "915a321d-a0a7-489c-9219-00c95c7e20a0", "step_index": 10, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "csv](file:///home/bob/usecase-runs/b2930/out-pol"}}
{"event": "step_update", "step_update": {"conversation_id": "915a321d-a0a7-489c-9219-00c95c7e20a0", "step_index": 10, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "l-2"}}
{"event": "step_update", "step_update": {"conversation_id": "915a321d-a0a7-489c-9219-00c95c7e20a0", "step_index": 10, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "026-10-27/agy/gemini-4-argon-medium/"}}
{"event": "step_update", "step_update": {"conversation_id": "915a321d-a0a7-489c-9219-00c95c7e20a0", "step_index": 10, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "j11/"}}
{"event": "step_update", "step_update": {"conversation_id": "915a321d-a0a7-489c-9219-00c95c7e20a0", "step_index": 10, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "work/prices.csv), producing [answer."}}
{"event": "step_update", "step_update": {"conversation_id": "915a321d-a0a7-489c-9219-00c95c7e20a0", "step_index": 10, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "json](file:///home/bob/u"}}
{"event": "step_update", "step_update": {"conversation_id": "915a321d-a0a7-489c-9219-00c95c7e20a0", "step_index": 10, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "secase-runs/b"}}
{"event": "step_update", "step_update": {"conversation_id": "915a321d-a0a7-489c-9219-00c95c7e20a0", "step_index": 10, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "2930/out-poll-2026-10-27/agy/gemini-"}}
{"event": "step_update", "step_update": {"conversation_id": "915a321d-a0a7-489c-9219-00c95c7e20a0", "step_index": 10, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "4-argon-medium/j11/work/answer.json) and [chart."}}
{"event": "step_update", "step_update": {"conversation_id": "915a321d-a0a7-489c-9219-00c95c7e20a0", "step_index": 10, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "svg](file:///"}}
{"event": "step_update", "step_update": {"conversation_id": "915a321d-a0a7-489c-9219-00c95c7e20a0", "step_index": 10, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "home/bob/usecase-runs/b2930/out-poll-2026-"}}
{"event": "step_update", "step_update": {"conversation_id": "915a321d-a0a7-489c-9219-00c95c7e20a0", "step_index": 10, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "10-27/agy/gemini-4-argon-medium/j11/work/chart."}}
{"event": "step_update", "step_update": {"conversation_id": "915a321d-a0a7-489c-9219-00c95c7e20a0", "step_index": 10, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "svg).\n\n### Summary of Results (`answer.json`)\n- "}}
{"event": "step_update", "step_update": {"conversation_id": "915a321d-a0a7-489c-9219-00c95c7e20a0", "step_index": 10, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "**`days`**: `509`\n- **`f"}}
{"event": "step_update", "step_update": {"conversation_id": "915a321d-a0a7-489c-9219-00c95c7e20a0", "step_index": 10, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "inal"}}
{"event": "step_update", "step_update": {"conversation_id": "915a321d-a0a7-489c-9219-00c95c7e20a0", "step_index": 10, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "_equity`**: `8589.77`\n- **`total_return`**: `-0."}}
{"event": "step_update", "step_update": {"conversation_id": "915a321d-a0a7-489c-9219-00c95c7e20a0", "step_index": 10, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "14"}}
{"event": "step_update", "step_update": {"conversation_id": "915a321d-a0a7-489c-9219-00c95c7e20a0", "step_index": 10, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "1023`\n- **`trades`**: `9`\n- **`cagr`**: `-0.07"}}
{"event": "step_update", "step_update": {"conversation_id": "915a321d-a0a7-489c-9219-00c95c7e20a0", "step_index": 10, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "2635`\n- **`max_drawdown`**: `0.294082`\n"}}
{"event": "step_update", "step_update": {"conversation_id": "915a321d-a0a7-489c-9219-00c95c7e20a0", "step_index": 10, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "- **`sharpe`**: `-0.450763`\n- **`exp"}}
{"event": "step_update", "step_update": {"conversation_id": "915a321d-a0a7-489c-9219-00c95c7e20a0", "step_index": 10, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "osure`**: `0.3"}}
{"event": "step_update", "step_update": {"conversation_id": "915a321d-a0a7-489c-9219-00c95c7e20a0", "step_index": 10, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "56299`\n- **`buy_hold_return`**: `-0.260528"}}
{"event": "step_update", "step_update": {"conversation_id": "915a321d-a0a7-489c-9219-00c95c7e20a0", "step_index": 10, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "`"}}
{"event": "step_update", "step_update": {"conversation_id": "915a321d-a0a7-489c-9219-00c95c7e20a0", "step_index": 10, "state": "DONE", "step_type": "agent_response", "text_delta": "\n", "duration_seconds": 11.049979579, "usage": {"input_tokens": 2328, "output_tokens": 468, "thinking_tokens": 18, "cache_read_tokens": 18675, "total_tokens": 2796}}}
{"event": "result", "result": {"conversation_id": "915a321d-a0a7-489c-9219-00c95c7e20a0", "status": "SUCCESS", "response": "Implemented [solve.py](file:///home/bob/usecase-runs/b2930/out-poll-2026-10-27/agy/gemini-4-argon-medium/j11/work/solve.py) using only the Python 3 standard library and verified it on [input.json](file:///home/bob/usecase-runs/b2930/out-poll-2026-10-27/agy/gemini-4-argon-medium/j11/work/input.json) and [prices.csv](file:///home/bob/usecase-runs/b2930/out-poll-2026-10-27/agy/gemini-4-argon-medium/j11/work/prices.csv), producing [answer.json](file:///home/bob/usecase-runs/b2930/out-poll-2026-10-27/agy/gemini-4-argon-medium/j11/work/answer.json) and [chart.svg](file:///home/bob/usecase-runs/b2930/out-poll-2026-10-27/agy/gemini-4-argon-medium/j11/work/chart.svg).\n\n### Summary of Results (`answer.json`)\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", "duration_seconds": 103.469177275, "num_turns": 1, "usage": {"input_tokens": 24230, "output_tokens": 4132, "thinking_tokens": 1369, "cache_read_tokens": 48318, "total_tokens": 28362}}}