Mistral Small 3.2 24B Instruct (Venice) (thinking on) on Backtest a moving-average crossover on a bundled synthetic price file
MMistral Small 3.2 24B Instruct (Venice)thinking on · venice-mini-agent v1.3.1 · cloud · 2026-10-25 · by @bobsiopener
- Estimated cost
- $0.0084
- Wall time
- 27s
- Human step-ins
- 0
- Outcome
- Fail
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Rescored on 2026-10-28. Previous score: 22.5 / 100, outcome: fail. See the previous run.
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 the site's price table).
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 the case's setup script; hidden key/tests kept outside the agent's work dir. Prompt = TASK.md verbatim. Scored automatically with the scout's scorer. 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 via the Venice OpenAI-compatible API: same tool loop, system prompt and limits as the cases 02-25 Venice runs (max 25 turns, max 16,000 output tokens per call, temperature 0.2, Venice system prompt and web search off, per-run cost cap $0.0156, 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 list price; paid from the Venice DIEM daily allowance. first_try_success corrected to false on 2026-10-25: a harness bug (the cost controller's finalize step did not tie it to success) had set it true on failed runs; scores and measurements unchanged. Harness: venice-mini-agent v1.3.1 via Venice OpenAI-compatible API (16000 max tokens/call, $0.0156 run cap). Score: 2/6. Cost is an ESTIMATE: measured per-call tokens x the site's list-price table (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
- $0.0084
- Wall time
- 27s
- Human step-ins
- 0
- Turns
- 9
| Model | Input tok | Output tok | Cost |
|---|---|---|---|
| MMistral Small 3.2 24B Instruct (Venice)venice/mistral-small-3-2-24b-instructthinking on | 86,203 | 1,423 | $0.0084 |
Reproduce this run
- Use the use case prompt on the use case page.
- Same setup: Mistral Small 3.2 24B Instruct (Venice) (venice/mistral-small-3-2-24b-instruct, 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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"final_message": "The backtest has been completed and the results are as follows:\n\n- **Days**: 509\n- **Final Equity**: 7315.27\n- **Total Return**: -0.268473\n- **Trades**: 11\n- **CAGR**: -0.143653\n- **Max Drawdown**: 0.294082\n- **Sharpe Ratio**: -0.939252\n- **Exposure**: 0.415354\n- **Buy and Hold Return**: -0.260528\n\nThe chart has been generated and saved as `chart.svg`.",
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"arguments": "{\"path\": \"./solve.py\", \"content\": \"import json\\nimport statistics\\nfrom collections import defaultdict\\n\\ndef backtest(input_file, output_file):\\n # Read input parameters\\n with open(input_file) as f:\\n params = json.load(f)\\n\\n prices_file = params['prices_file']\\n fast = params['fast']\\n slow = params['slow']\\n cost_bps = params['cost_bps']\\n initial = params['initial']\\n\\n # Read and clean price data\\n price_data = defaultdict(list)\\n with open(prices_file) as f:\\n next(f) # Skip header\\n for line in f:\\n date, close = line.strip().split(',')\\n price_data[date].append(float(close))\\n\\n # Take the last close price for each date\\n closes = [price_data[date][-1] for date in sorted(price_data.keys())]\\n days = len(closes)\\n\\n # Calculate SMAs\\n sma_fast = [sum(closes[i - fast + 1:i + 1]) / fast if i >= fast - 1 else 0 for i in range(days)]\\n sma_slow = [sum(closes[i - slow + 1:i + 1]) / slow if i >= slow - 1 else 0 for i in range(days)]\\n\\n # Calculate signals\\n signals = [1 if sma_fast[i] > sma_slow[i] else 0 for i in range(days)]\\n\\n # Calculate positions\\n positions = [0] * days\\n for i in range(1, days):\\n positions[i] = signals[i - 1]\\n\\n # Calculate equity\\n equity = [initial] * days\\n cost = cost_bps / 10000\\n for i in range(1, days):\\n equity[i] = equity[i - 1] * (1 + positions[i] * (closes[i] / closes[i - 1] - 1)) * (1 - cost * abs(positions[i] - positions[i - 1]))\\n\\n # Calculate metrics\\n trades = sum(1 for i in range(1, days) if positions[i] != positions[i - 1])\\n exposure = sum(positions[1:days]) / (days - 1)\\n total_return = equity[days - 1] / initial - 1\\n cagr = (equity[days - 1] / initial) ** (252 / (days - 1)) - 1\\n max_drawdown = max(0, *(1 - equity[t] / max(equity[0:t + 1]) for t in range(days)))\\n daily_returns = [(equity[i] / equity[i - 1] - 1) for i in range(1, days)]\\n sharpe = (statistics.mean(daily_returns) / statistics.pstdev(daily_returns) * (252 ** 0.5)) if statistics.pstdev(daily_returns) != 0 else 0\\n buy_hold_return = closes[days - 1] / closes[0] - 1\\n\\n # Prepare output\\n output = {\\n 'days': days,\\n 'final_equity': round(equity[days - 1], 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 # Write output\\n with open(output_file, 'w') as f:\\n json.dump(output, f, indent=4)\\n\\n # Generate chart SVG\\n generate_chart_svg(closes, equity, 'chart.svg')\\n\\ndef generate_chart_svg(closes, equity, output_file):\\n svg_content = f\\\"\\\"\\\"<svg width=\\\"800\\\" height=\\\"600\\\" xmlns=\\\"http://www.w3.org/2000/svg\\\">\\n <polyline id=\\\"equity\\\" points='{' '.join([f\\\"{i},{600 - int(equity[i] / max(equity) * 500)}\\\" for i in range(len(equity))])}' fill=\\\"none\\\" stroke=\\\"blue\\\" stroke-width=\\\"2\\\"/>\\n </svg>\\\"\\\"\\\"\\n with open(output_file, 'w') as f:\\n f.write(svg_content)\\n\\nif __name__ == '__main__':\\n import sys\\n if len(sys.argv) != 3:\\n print(\\\"Usage: python solve.py input.json answer.json\\\")\\n sys.exit(1)\\n \\n input_file = sys.argv[1]\\n output_file = sys.argv[2]\\n backtest(input_file, output_file)\"}"
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