GLM 4.7 (Venice) (thinking on) on Backtest a moving-average crossover on a bundled synthetic price file
ZGLM 4.7 (Venice)thinking on · venice-mini-agent v1.3.1 · cloud · 2026-10-25 · by @bobsiopener
- Estimated cost
- $0.04
- Wall time
- 2m
- Human step-ins
- 0
- Outcome
- Fail
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Superseded. This run was rescored on 2026-10-28 and replaced by a newer run. It no longer counts toward rankings, use-case tables or the data export; it is kept here for the record.
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. Inspired by https://news.ycombinator.com/item?id=42559744 Headless, offline run on a reproducible fixture. Cost shown is a list-price estimate (measured tokens x pricing.json list price). Run stopped early: run cost cap $0.0386 reached (cap set for this run: $0.0386). A run stopped at its cost cap counts as a fail. The scorer was run on what the agent had produced by then: 6/6.
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 batch 9 case j11-sma-crossover-backtest (offline fixture). Prepared with batch9/prepare.sh; hidden key/tests kept outside the agent's work dir. Prompt = TASK.md verbatim. Scored with the scout's scorer: `python score.py WORKDIR KEYDIR`. Tools: list_files/read_file/write_file/run_shell (venice-mini-agent v1.3), web disabled. Single run, no retries. Visual output (chart.svg) rendered offline to PNG with google-chrome headless, network blocked, 1280x800 at ~3 s after load. Harness: venice-mini-agent v1.3.1 (/workspace/usecase-runner/venice_agent2.py) via the Venice OpenAI-compatible API: same tool loop, system prompt and limits as the cases 02-25 Venice runs (max 25 turns, max_tokens 16000/call, temperature 0.2, Venice system prompt and web search off, per-run cost cap $0.0386, thinking on (model default)). Same task text, work-dir layout and scorer as the Claude runs of this case. Cost = measured per-call tokens x venice_prices.json list price; paid from the Venice DIEM daily allowance. Run ended early: run cost cap $0.0386 reached. Stopped at its cost cap $0.0386 ($0.042101 over 10 turns), so it counts as a fail. Its leftover output scored 6/6 on the scorer. Harness: venice-mini-agent v1.3.1 via Venice OpenAI-compatible API (16000 max tokens/call, $0.0386 run cap). Score: 6/6. Cost is an ESTIMATE: measured per-call tokens x pricing.json list price (paid from Venice DIEM allowance; Venice billing not itemized). input_tokens include cached reads. Single run.
Estimated cost List-price estimate; not actually billed.
Output
Metrics
- Estimated cost
- $0.04
- Wall time
- 2m
- Human step-ins
- 0
- Turns
- 10
| Model | Input tok | Output tok | Cost |
|---|---|---|---|
| ZGLM 4.7 (Venice)venice/zai-org-glm-4.7thinking on | 123,245 | 3,554 | $0.04 |
Reproduce this run
- Use the use case prompt on the use case page.
- Same setup: GLM 4.7 (Venice) (venice/zai-org-glm-4.7, thinking on) via
venice-mini-agent v1.3.1, 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.
Was this useful?
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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"content": "Now I'll implement the solution:",
"reasoning_content": "Now I understand the structure. The input.json specifies:\n- prices_file: \"prices.csv\"\n- fast: 20\n- slow: 50\n- cost_bps: 10\n- initial: 10000\n\nThe prices.csv has:\n- A header \"date,close\"\n- Some dates are out of order (e.g., 2023-01-17 comes before 2023-01-16)\n- Some dates are repeated (e.g., 2023-03-29 appears twice, 2023-05-23 appears twice, etc.)\n\nI need to:\n1. Read and clean the data (keep last row for duplicate dates, sort by date)\n2. Calculate SMA_fast and SMA_slow\n3. Generate signals (1 if fast > slow, else 0)\n4. Calculate positions (trade on next day's close)\n5. Calculate equity with costs\n6. Compute all metrics\n7. Write answer.json and chart.svg\n\nLet me write the solve.py file:",
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{
"turn": 2,
"tool": "write_file",
"args": {
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"content": "#!/usr/bin/env python3\nimport json\nimport csv\nimport statistics\nimport math\n\ndef main():\n # Read input\n with open(sys.argv[1], 'r') as f:\n input_data = json.load(f)\n \n prices_file = input_data['prices_file']\n fast = input_data['fast']\n slow = input_data['slow']\n cost_bps = input_data['cost_bps']\n initial = input_data['initial']\n \n # Read and clean price data\n price_dict = {}\n with open(prices_file, 'r') as f:\n reader = csv.DictReader(f)\n for row in reader:\n date = row['date']\n close = float(row['close'])\n # Last row wins for duplicate dates\n price_dict[date] = close\n \n # Sort by date\n sorted_dates = sorted(price_dict.keys())\n closes = [price_dict[date] for date in sorted_dates]\n n = len(closes)\n days = n\n \n # Calculate SMA\n def sma(t, w):\n if t < w - 1:\n return None\n return sum(closes[t-w+1:t+1]) / w\n \n # Generate signals\n signals = []\n for t in range(n):\n if t < slow - 1:\n signals.append(0)\n else:\n fast_sma = sma(t, fast)\n slow_sma = sma(t, slow)\n if fast_sma > slow_sma:\n signals.append(1)\n else:\n signals.append(0)\n \n # Calculate positions (trade on next day's close)\n positions = [0] * n\n for t in range(1, n):\n positions[t] = signals[t-1]\n \n # Calculate equity\n cost = cost_bps / 10000.0\n equity = [initial] * n\n daily_returns = []\n \n for t in range(1, n):\n price_return = closes[t] / closes[t-1] - 1\n position_change = abs(positions[t] - positions[t-1])\n equity[t] = equity[t-1] * (1 + positions[t] * price_return) * (1 - cost * position_change)\n daily_returns.append(equity[t] / equity[t-1] - 1)\n \n # Calculate metrics\n trades = sum(1 for t in range(1, n) if positions[t] != positions[t-1])\n exposure = sum(positions[1:]) / (n - 1) if n > 1 else 0\n \n final_equity = equity[-1]\n total_return = final_equity / initial - 1\n \n if n > 1:\n cagr = (final_equity / initial) ** (252 / (n - 1)) - 1\n else:\n cagr = 0\n \n # Max drawdown\n max_drawdown = 0\n peak = equity[0]\n for e in equity:\n if e > peak:\n peak = e\n dd = 1 - e / peak\n if dd > max_drawdown:\n max_drawdown = dd\n \n # Sharpe ratio\n if len(daily_returns) > 0:\n mean_return = statistics.mean(daily_returns)\n try:\n stdev = statistics.pstdev(daily_returns)\n if stdev == 0:\n sharpe = 0\n else:\n sharpe = mean_return / stdev * math.sqrt(252)\n except statistics.StatisticsError:\n sharpe = 0\n else:\n sharpe = 0\n \n # Buy and hold return\n buy_hold_return = closes[-1] / closes[0] - 1 if n > 0 else 0\n \n # Write output\n output = {\n 'days': days,\n 'final_equity': round(final_equity, 2),\n 'total_return': round(total_return, 6),\n 'trades': trades,\n 'cagr': round(cagr, 6),\n 'max_drawdown': round(max_drawdown, 6),\n 'sharpe': round(sharpe, 6),\n 'exposure': round(exposure, 6),\n 'buy_hold_return': round(buy_hold_return, 6)\n }\n \n with open(sys.argv[2], 'w') as f:\n json.dump(output, f, indent=2)\n \n # Create chart.svg\n create_chart(equity)\n\ndef create_chart(equity):\n width = 800\n height = 400\n padding = 40\n \n min_eq = min(equity)\n max_eq = max(equity)\n range_eq = max_eq - min_eq if max_eq != min_eq else 1\n \n # Scale to fit\n points = []\n for i, e in enumerate(equity):\n x = padding + (i / (len(equity) - 1)) * (width - 2 * padding) if len(equity) > 1 else width / 2\n y = height - padding - ((e - min_eq) / range_eq) * (height - 2 * padding)\n points.append(f\"{x:.2f},{y:.2f}\")\n \n polyline_points = \" \".join(points)\n \n svg = f'''<?xml version=\"1.0\" encoding=\"UTF-8\"?>\n<svg xmlns=\"http://www.w3.org/2000/svg\" width=\"{width}\" height=\"{height}\" viewBox=\"0 0 {width} {height}\">\n <rect width=\"100%\" height=\"100%\" fill=\"white\"/>\n <polyline id=\"equity\" points=\"{polyline_points}\" fill=\"none\" stroke=\"blue\" stroke-width=\"2\"/>\n</svg>'''\n \n with open('chart.svg', 'w') as f:\n f.write(svg)\n\nif __name__ == '__main__':\n import sys\n main()\n"
},
"result": "wrote 4425 chars"
},
{
"turn": 3,
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{
"turn": 3,
"tool": "write_file",
"args": {
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"content": "#!/usr/bin/env python3\nimport json\nimport csv\nimport statistics\nimport math\nimport sys\n\ndef main():\n # Read input\n with open(sys.argv[1], 'r') as f:\n input_data = json.load(f)\n \n prices_file = input_data['prices_file']\n fast = input_data['fast']\n slow = input_data['slow']\n cost_bps = input_data['cost_bps']\n initial = input_data['initial']\n \n # Read and clean price data\n price_dict = {}\n with open(prices_file, 'r') as f:\n reader = csv.DictReader(f)\n for row in reader:\n date = row['date']\n close = float(row['close'])\n # Last row wins for duplicate dates\n price_dict[date] = close\n \n # Sort by date\n sorted_dates = sorted(price_dict.keys())\n closes = [price_dict[date] for date in sorted_dates]\n n = len(closes)\n days = n\n \n # Calculate SMA\n def sma(t, w):\n if t < w - 1:\n return None\n return sum(closes[t-w+1:t+1]) / w\n \n # Generate signals\n signals = []\n for t in range(n):\n if t < slow - 1:\n signals.append(0)\n else:\n fast_sma = sma(t, fast)\n slow_sma = sma(t, slow)\n if fast_sma > slow_sma:\n signals.append(1)\n else:\n signals.append(0)\n \n # Calculate positions (trade on next day's close)\n positions = [0] * n\n for t in range(1, n):\n positions[t] = signals[t-1]\n \n # Calculate equity\n cost = cost_bps / 10000.0\n equity = [initial] * n\n daily_returns = []\n \n for t in range(1, n):\n price_return = closes[t] / closes[t-1] - 1\n position_change = abs(positions[t] - positions[t-1])\n equity[t] = equity[t-1] * (1 + positions[t] * price_return) * (1 - cost * position_change)\n daily_returns.append(equity[t] / equity[t-1] - 1)\n \n # Calculate metrics\n trades = sum(1 for t in range(1, n) if positions[t] != positions[t-1])\n exposure = sum(positions[1:]) / (n - 1) if n > 1 else 0\n \n final_equity = equity[-1]\n total_return = final_equity / initial - 1\n \n if n > 1:\n cagr = (final_equity / initial) ** (252 / (n - 1)) - 1\n else:\n cagr = 0\n \n # Max drawdown\n max_drawdown = 0\n peak = equity[0]\n for e in equity:\n if e > peak:\n peak = e\n dd = 1 - e / peak\n if dd > max_drawdown:\n max_drawdown = dd\n \n # Sharpe ratio\n if len(daily_returns) > 0:\n mean_return = statistics.mean(daily_returns)\n try:\n stdev = statistics.pstdev(daily_returns)\n if stdev == 0:\n sharpe = 0\n else:\n sharpe = mean_return / stdev * math.sqrt(252)\n except statistics.StatisticsError:\n sharpe = 0\n else:\n sharpe = 0\n \n # Buy and hold return\n buy_hold_return = closes[-1] / closes[0] - 1 if n > 0 else 0\n \n # Write output\n output = {\n 'days': days,\n 'final_equity': round(final_equity, 2),\n 'total_return': round(total_return, 6),\n 'trades': trades,\n 'cagr': round(cagr, 6),\n 'max_drawdown': round(max_drawdown, 6),\n 'sharpe': round(sharpe, 6),\n 'exposure': round(exposure, 6),\n 'buy_hold_return': round(buy_hold_return, 6)\n }\n \n with open(sys.argv[2], 'w') as f:\n json.dump(output, f, indent=2)\n \n # Create chart.svg\n create_chart(equity)\n\ndef create_chart(equity):\n width = 800\n height = 400\n padding = 40\n \n min_eq = min(equity)\n max_eq = max(equity)\n range_eq = max_eq - min_eq if max_eq != min_eq else 1\n \n # Scale to fit\n points = []\n for i, e in enumerate(equity):\n x = padding + (i / (len(equity) - 1)) * (width - 2 * padding) if len(equity) > 1 else width / 2\n y = height - padding - ((e - min_eq) / range_eq) * (height - 2 * padding)\n points.append(f\"{x:.2f},{y:.2f}\")\n \n polyline_points = \" \".join(points)\n \n svg = f'''<?xml version=\"1.0\" encoding=\"UTF-8\"?>\n<svg xmlns=\"http://www.w3.org/2000/svg\" width=\"{width}\" height=\"{height}\" viewBox=\"0 0 {width} {height}\">\n <rect width=\"100%\" height=\"100%\" fill=\"white\"/>\n <polyline id=\"equity\" points=\"{polyline_points}\" fill=\"none\" stroke=\"blue\" stroke-width=\"2\"/>\n</svg>'''\n \n with open('chart.svg', 'w') as f:\n f.write(svg)\n\nif __name__ == '__main__':\n main()\n"
},
"result": "wrote 4421 chars"
},
{
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"enable_x_search": false,
"web_search_citations": [],
"return_search_results_as_documents": false
}
}
}
]
}
]