Qwen3 Coder 480B Turbo (Venice) (thinking on) on Backtest a moving-average crossover on a bundled synthetic price file
QQwen3 Coder 480B Turbo (Venice)thinking on · venice-mini-agent v1.3.1 · cloud · 2026-10-24 · by @bobsiopener
- Estimated cost
- $0.02
- Wall time
- 25s
- Human step-ins
- 0
- Outcome
- Pass
Compare with other models on this task →
Rescored on 2026-10-28. Previous score: 95.0 / 100, outcome: success. 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. Original benchmark task written for usecaserank.com (not based on a specific public post). Headless, offline run on a reproducible fixture. Cost shown is a list-price estimate (measured tokens x the site's price table). Rescore note (Oct 23, 2026): re-scored from the saved work dir: the original scoring step failed to start (exit 127, scorer interpreter missing), so the first score was not a real result. Model output and measured data unchanged.
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 $0.05, 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. Harness: venice-mini-agent v1.3.1 via Venice OpenAI-compatible API (16000 max tokens/call, $0.05 run cap). Score: 6/6. 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
- $0.02
- Wall time
- 25s
- Human step-ins
- 0
- Turns
- 10
| Model | Input tok | Output tok | Cost |
|---|---|---|---|
| QQwen3 Coder 480B Turbo (Venice)venice/qwen3-coder-480b-a35b-instruct-turbothinking on | 117,646 | 2,128 | $0.02 |
Reproduce this run
- Use the use case prompt on the use case page.
- Same setup: Qwen3 Coder 480B Turbo (Venice) (venice/qwen3-coder-480b-a35b-instruct-turbo, 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": "Perfect! The implementation is complete and working correctly. Let me summarize what I've done:\n\n1. Created a Python script `solve.py` that:\n - Reads input parameters from a JSON file\n - Loads and cleans price data from a CSV file (handling duplicates by keeping the last entry for each date)\n - Calculates fast and slow simple moving averages\n - Generates trading signals based on SMA crossover\n - Backtests the strategy with proper position sizing and transaction costs\n - Calculates all required metrics (total return, CAGR, max drawdown, Sharpe ratio, etc.)\n - Outputs results to a JSON file\n - Creates a static SVG chart of the equity curve\n\n2. The script handles all the requirements:\n - Data cleaning (sorting by date, removing duplicates)\n - Proper moving average calculation\n - Correct signal and position generation\n - Accurate equity calculations with transaction costs\n - All required risk and performance metrics\n - Proper rounding of outputs\n - Static SVG chart generation with the required ID\n\n3. Verified the solution works correctly with the provided test data, producing:\n - An `answer.json` file with all required metrics\n - A `chart.svg` file that meets all the specification requirements\n\nThe solution uses only Python standard library components and follows all the instructions precisely.",
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"result": "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\n2023-01-09,105.64\n2023-01-10,107.50\n2023-01-11,109.43\n2023-01-12,107.34\n2023-01-13,109.73\n2023-01-17,112.86\n2023-01-16,110.63\n2023-01-18,111.85\n2023-01-19,111.45\n2023-01-20,110.49\n2023-01-23,108.09\n2023-01-24,105.70\n2023-01-25,104.86\n2023-01-26,103.02\n2023-01-27,101.93\n2023-01-30,100.99\n2023-01-31,100.15\n2023-02-01,101.43\n2023-02-02,100.30\n2023-02-03,99.77\n2023-02-06,98.81\n2023-02-07,99.70\n2023-02-08,100.51\n2023-02-09,98.33\n2023-02-10,99.59\n2023-02-13,98.86\n2023-02-14,97.26\n2023-02-15,97.37\n2023-02-16,99.69\n2023-02-17,98.07\n2023-02-20,97.55\n2023-02-21,95.29\n2023-02-22,94.59\n2023-02-23,93.01\n2023-02-24,92.72\n2023-02-27,91.65\n2023-02-28,90.29\n2023-03-01,90.68\n2023-03-02,91.55\n2023-03-03,90.22\n2023-03-06,87.43\n2023-03-07,86.81\n2023-03-08,86.29\n2023-03-09,86.81\n2023-03-10,86.98\n2023-03-13,86.95\n2023-03-14,87.79\n2023-03-15,87.93\n2023-03-16,86.47\n2023-03-17,87.18\n2023-03-20,85.30\n2023-03-21,83.73\n2023-03-22,83.50\n2023-03-23,83.98\n2023-03-24,83.22\n2023-03-27,81.58\n2023-03-28,80.15\n2023-03-29,84.49\n2023-03-29,78.96\n2023-03-30,80.23\n2023-03-31,81.73\n2023-04-03,81.92\n2023-04-04,81.91\n2023-04-05,81.01\n2023-04-06,81.31\n2023-04-07,82.20\n2023-04-10,82.55\n2023-04-11,83.99\n2023-04-12,84.46\n2023-04-13,84.20\n2023-04-14,84.45\n2023-04-17,83.78\n2023-04-18,81.66\n2023-04-19,80.59\n2023-04-20,81.77\n2023-04-21,80.78\n2023-04-24,81.49\n2023-04-25,81.14\n2023-04-26,79.63\n2023-04-27,83.14\n2023-04-28,82.48\n2023-05-01,83.61\n2023-05-02,83.99\n2023-05-03,85.03\n2023-05-04,84.93\n2023-05-05,84.50\n2023-05-08,85.03\n2023-05-09,83.24\n2023-05-10,82.67\n2023-05-11,82.46\n2023-05-12,81.84\n2023-05-15,80.22\n2023-05-16,79.92\n2023-05-17,77.77\n2023-05-18,76.47\n2023-05-19,75.57\n2023-05-22,76.40\n2023-05-23,80.50\n2023-05-23,75.23\n2023-05-24,74.54\n2023-05-25,75.82\n2023-05-26,76.47\n2023-05-29,77.36\n2023-05-30,78.32\n2023-05-31,77.36\n2023-06-01,77.78\n2023-06-02,77.96\n2023-06-05,78.35\n2023-06-06,80.10\n2023-06-07,79.28\n2023-06-08,80.57\n2023-06-09,79.92\n2023-06-12,81.01\n2023-06-13,81.50\n2023-06-14,80.65\n2023-06-15,79.47\n2023-06-16,79.67\n2023-06-19,78.77\n2023-06-20,76.60\n2023-06-21,75.14\n2023-06-22,76.81\n2023-06-23,76.16\n2023-06-26,77.15\n2023-06-27,76.49\n2023-06-28,75.86\n2023-06-29,75.09\n2023-06-30,74.65\n2023-07-03,73.27\n2023-07-04,72.65\n2023-07-05,72.39\n2023-07-06,71.13\n2023-07-07,72.07\n2023-07-10,71.85\n2023-07-11,73.34\n2023-07-12,72.81\n2023-07-13,70.95\n2023-07-14,70.89\n2023-07-17,72.32\n2023-07-18,71.61\n2023-07-19,70.74\n2023-07-20,71.85\n2023-07-21,71.79\n2023-07-24,70.30\n2023-07-25,72.03\n2023-07-26,71.91\n2023-07-27,70.68\n2023-07-28,68.12\n2023-07-31,67.58\n2023-08-01,67.12\n2023-08-02,67.11\n2023-08-03,66.21\n2023-08-04,65.08\n2023-08-07,64.31\n2023-08-08,61.84\n2023-08-09,63.51\n2023-08-10,64.14\n2023-08-11,63.26\n2023-08-14,62.80\n2023-08-15,62.41\n2023-08-16,62.87\n2023-08-17,61.10\n2023-08-18,61.45\n2023-08-21,60.94\n2023-08-22,61.28\n2023-08-23,62.18\n2023-08-24,62.33\n2023-08-25,63.15\n2023-08-28,62.29\n2023-08-29,62.14\n2023-08"
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"content": "Now I'll create the Python script to perform the backtest according to the specifications:\n\n",
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"arguments": "{\"path\": \"solve.py\", \"content\": \"import json\\nimport csv\\nimport statistics\\nimport sys\\nfrom datetime import datetime\\nfrom collections import OrderedDict\\n\\n\\ndef read_prices(filename):\\n \\\"\\\"\\\"Read prices from CSV file and handle duplicates\\\"\\\"\\\"\\n prices_dict = {}\\n \\n with open(filename, 'r') as f:\\n reader = csv.reader(f)\\n next(reader) # Skip header\\n for row in reader:\\n date_str, close_str = row\\n date = datetime.strptime(date_str, '%Y-%m-%d')\\n close = float(close_str)\\n prices_dict[date] = close\\n \\n # Sort by date\\n sorted_prices = OrderedDict(sorted(prices_dict.items()))\\n dates = list(sorted_prices.keys())\\n closes = list(sorted_prices.values())\\n \\n return dates, closes\\n\\n\\ndef calculate_sma(closes, window):\\n \\\"\\\"\\\"Calculate Simple Moving Average\\\"\\\"\\\"\\n sma = [0.0] * len(closes)\\n for i in range(window - 1, len(closes)):\\n sma[i] = sum(closes[i - window + 1:i + 1]) / window\\n return sma\\n\\n\\ndef backtest_strategy(dates, closes, fast_window, slow_window, cost_bps, initial_equity):\\n \\\"\\\"\\\"Backtest the moving average crossover strategy\\\"\\\"\\\"\\n n = len(closes)\\n \\n # Calculate SMAs\\n sma_fast = calculate_sma(closes, fast_window)\\n sma_slow = calculate_sma(closes, slow_window)\\n \\n # Generate signals\\n signals = [0] * n\\n for t in range(slow_window - 1, n):\\n if sma_fast[t] > sma_slow[t]:\\n signals[t] = 1\\n \\n # Calculate positions\\n positions = [0] * n\\n for t in range(1, n):\\n positions[t] = signals[t-1]\\n \\n # Calculate equity curve\\n equity = [initial_equity] * n\\n daily_returns = [0.0] * (n - 1)\\n cost = cost_bps / 10000.0\\n \\n for t in range(1, n):\\n price_return = closes[t] / closes[t-1] - 1\\n transaction_cost = cost * abs(positions[t] - positions[t-1])\\n equity[t] = equity[t-1] * (1 + positions[t] * price_return) * (1 - transaction_cost)\\n daily_returns[t-1] = equity[t] / equity[t-1] - 1\\n \\n # Calculate metrics\\n days = n\\n final_equity = round(equity[-1], 2)\\n total_return = round(equity[-1] / initial_equity - 1, 6)\\n \\n # Count trades\\n trades = 0\\n for t in range(1, n):\\n if positions[t] != positions[t-1]:\\n trades += 1\\n \\n # Calculate exposure\\n exposure = round(sum(positions[1:]) / (n - 1), 6)\\n \\n # Calculate CAGR\\n cagr = round((equity[-1] / initial_equity) ** (252 / (n - 1)) - 1, 6)\\n \\n # Calculate max drawdown\\n peak = equity[0]\\n max_dd = 0.0\\n for e in equity:\\n if e > peak:\\n peak = e\\n dd = 1 - e / peak\\n if dd > max_dd:\\n max_dd = dd\\n max_drawdown = round(max_dd, 6)\\n \\n # Calculate Sharpe ratio\\n if len(daily_returns) > 0 and statistics.pstdev(daily_returns) != 0:\\n sharpe = round(statistics.mean(daily_returns) / statistics.pstdev(daily_returns) * (252 ** 0.5), 6)\\n else:\\n sharpe = 0.0\\n \\n # Calculate buy and hold return\\n buy_hold_return = round(closes[-1] / closes[0] - 1, 6)\\n \\n return {\\n \\\"days\\\": days,\\n \\\"final_equity\\\": final_equity,\\n \\\"total_return\\\": total_return,\\n \\\"trades\\\": trades,\\n \\\"cagr\\\": cagr,\\n \\\"max_drawdown\\\": max_drawdown,\\n \\\"sharpe\\\": sharpe,\\n \\\"exposure\\\": exposure,\\n \\\"buy_hold_return\\\": buy_hold_return\\n }, equity\\n\\n\\ndef create_chart_svg(dates, equity, filename):\\n \\\"\\\"\\\"Create SVG chart of equity curve\\\"\\\"\\\"\\n n = len(equity)\\n \\n # Chart dimensions\\n width = 800\\n height = 400\\n margin = 50\\n \\n # Find min/max values for scaling\\n min_equity = min(equity)\\n max_equity = max(equity)\\n range_equity = max_equity - min_equity or 1 # Avoid division by zero\\n \\n # Scale factors\\n x_scale = (width - 2 * margin) / (n - 1) if n > 1 else 1\\n y_scale = (height - 2 * margin) / range_equity\\n \\n # Create points for polyline\\n points = []\\n for i in range(n):\\n x = margin + i * x_scale\\n y = height - margin - (equity[i] - min_equity) * y_scale\\n points.append(f\\\"{x},{y}\\\")\\n \\n polyline_points = \\\" \\\".join(points)\\n \\n # Create SVG content\\n svg_content = f'''<svg width=\\\"{width}\\\" height=\\\"{height}\\\" xmlns=\\\"http://www.w3.org/2000/svg\\\">\\n <polyline id=\\\"equity\\\" fill=\\\"none\\\" stroke=\\\"blue\\\" stroke-width=\\\"2\\\" points=\\\"{polyline_points}\\\" />\\n</svg>'''\\n \\n with open(filename, 'w') as f:\\n f.write(svg_content)\\n\\n\\ndef main():\\n # Read input parameters\\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 \\n with open(input_file, 'r') as f:\\n params = json.load(f)\\n \\n # Extract parameters\\n prices_file = params['prices_file']\\n fast_window = params['fast']\\n slow_window = params['slow']\\n cost_bps = params['cost_bps']\\n initial_equity = params['initial']\\n \\n # Read and process prices\\n dates, closes = read_prices(prices_file)\\n \\n # Run backtest\\n results, equity_curve = backtest_strategy(\\n dates, closes, fast_window, slow_window, cost_bps, initial_equity\\n )\\n \\n # Write results to JSON file\\n with open(output_file, 'w') as f:\\n json.dump(results, f, indent=2)\\n \\n # Create chart\\n create_chart_svg(dates, equity_curve, 'chart.svg')\\n\\n\\nif __name__ == \\\"__main__\\\":\\n main()\"}"
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},
{
"turn": 3,
"tool": "write_file",
"args": {
"path": "solve.py",
"content": "import json\nimport csv\nimport statistics\nimport sys\nfrom datetime import datetime\nfrom collections import OrderedDict\n\n\ndef read_prices(filename):\n \"\"\"Read prices from CSV file and handle duplicates\"\"\"\n prices_dict = {}\n \n with open(filename, 'r') as f:\n reader = csv.reader(f)\n next(reader) # Skip header\n for row in reader:\n date_str, close_str = row\n date = datetime.strptime(date_str, '%Y-%m-%d')\n close = float(close_str)\n prices_dict[date] = close\n \n # Sort by date\n sorted_prices = OrderedDict(sorted(prices_dict.items()))\n dates = list(sorted_prices.keys())\n closes = list(sorted_prices.values())\n \n return dates, closes\n\n\ndef calculate_sma(closes, window):\n \"\"\"Calculate Simple Moving Average\"\"\"\n sma = [0.0] * len(closes)\n for i in range(window - 1, len(closes)):\n sma[i] = sum(closes[i - window + 1:i + 1]) / window\n return sma\n\n\ndef backtest_strategy(dates, closes, fast_window, slow_window, cost_bps, initial_equity):\n \"\"\"Backtest the moving average crossover strategy\"\"\"\n n = len(closes)\n \n # Calculate SMAs\n sma_fast = calculate_sma(closes, fast_window)\n sma_slow = calculate_sma(closes, slow_window)\n \n # Generate signals\n signals = [0] * n\n for t in range(slow_window - 1, n):\n if sma_fast[t] > sma_slow[t]:\n signals[t] = 1\n \n # Calculate positions\n positions = [0] * n\n for t in range(1, n):\n positions[t] = signals[t-1]\n \n # Calculate equity curve\n equity = [initial_equity] * n\n daily_returns = [0.0] * (n - 1)\n cost = cost_bps / 10000.0\n \n for t in range(1, n):\n price_return = closes[t] / closes[t-1] - 1\n transaction_cost = cost * abs(positions[t] - positions[t-1])\n equity[t] = equity[t-1] * (1 + positions[t] * price_return) * (1 - transaction_cost)\n daily_returns[t-1] = equity[t] / equity[t-1] - 1\n \n # Calculate metrics\n days = n\n final_equity = round(equity[-1], 2)\n total_return = round(equity[-1] / initial_equity - 1, 6)\n \n # Count trades\n trades = 0\n for t in range(1, n):\n if positions[t] != positions[t-1]:\n trades += 1\n \n # Calculate exposure\n exposure = round(sum(positions[1:]) / (n - 1), 6)\n \n # Calculate CAGR\n cagr = round((equity[-1] / initial_equity) ** (252 / (n - 1)) - 1, 6)\n \n # Calculate max drawdown\n peak = equity[0]\n max_dd = 0.0\n for e in equity:\n if e > peak:\n peak = e\n dd = 1 - e / peak\n if dd > max_dd:\n max_dd = dd\n max_drawdown = round(max_dd, 6)\n \n # Calculate Sharpe ratio\n if len(daily_returns) > 0 and statistics.pstdev(daily_returns) != 0:\n sharpe = round(statistics.mean(daily_returns) / statistics.pstdev(daily_returns) * (252 ** 0.5), 6)\n else:\n sharpe = 0.0\n \n # Calculate buy and hold return\n buy_hold_return = round(closes[-1] / closes[0] - 1, 6)\n \n return {\n \"days\": days,\n \"final_equity\": final_equity,\n \"total_return\": total_return,\n \"trades\": trades,\n \"cagr\": cagr,\n \"max_drawdown\": max_drawdown,\n \"sharpe\": sharpe,\n \"exposure\": exposure,\n \"buy_hold_return\": buy_hold_return\n }, equity\n\n\ndef create_chart_svg(dates, equity, filename):\n \"\"\"Create SVG chart of equity curve\"\"\"\n n = len(equity)\n \n # Chart dimensions\n width = 800\n height = 400\n margin = 50\n \n # Find min/max values for scaling\n min_equity = min(equity)\n max_equity = max(equity)\n range_equity = max_equity - min_equity or 1 # Avoid division by zero\n \n # Scale factors\n x_scale = (width - 2 * margin) / (n - 1) if n > 1 else 1\n y_scale = (height - 2 * margin) / range_equity\n \n # Create points for polyline\n points = []\n for i in range(n):\n x = margin + i * x_scale\n y = height - margin - (equity[i] - min_equity) * y_scale\n points.append(f\"{x},{y}\")\n \n polyline_points = \" \".join(points)\n \n # Create SVG content\n svg_content = f'''<svg width=\"{width}\" height=\"{height}\" xmlns=\"http://www.w3.org/2000/svg\">\n <polyline id=\"equity\" fill=\"none\" stroke=\"blue\" stroke-width=\"2\" points=\"{polyline_points}\" />\n</svg>'''\n \n with open(filename, 'w') as f:\n f.write(svg_content)\n\n\ndef main():\n # Read input parameters\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 \n with open(input_file, 'r') as f:\n params = json.load(f)\n \n # Extract parameters\n prices_file = params['prices_file']\n fast_window = params['fast']\n slow_window = params['slow']\n cost_bps = params['cost_bps']\n initial_equity = params['initial']\n \n # Read and process prices\n dates, closes = read_prices(prices_file)\n \n # Run backtest\n results, equity_curve = backtest_strategy(\n dates, closes, fast_window, slow_window, cost_bps, initial_equity\n )\n \n # Write results to JSON file\n with open(output_file, 'w') as f:\n json.dump(results, f, indent=2)\n \n # Create chart\n create_chart_svg(dates, equity_curve, 'chart.svg')\n\n\nif __name__ == \"__main__\":\n main()"
},
"result": "wrote 5343 chars"
},
{
"turn": 4,
"response": {
"service_tier": "default",
"id": "chatcmpl-RDvDQKKE5JPKD7whLe1lkwH9",
"object": "chat.completion",
"created": 1792802399,
"model": "qwen3-coder-480b-a35b-instruct-turbo",
"choices": [
{
"index": 0,
"message": {
"role": "assistant",
"content": "Now let's run the script to test it:\n\n",
"reasoning_content": null,
"name": null,
"tool_calls": [
{
"id": "call_e253518acd366ada",
"type": "function",
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}
}
},
{
"turn": 4,
"tool": "run_shell",
"args": {
"command": "python solve.py input.json answer.json"
},
"result": "exit=127\nstdout:\n\nstderr:\nbash: line 1: python: command not found\n"
},
{
"turn": 5,
"response": {
"service_tier": "default",
"id": "chatcmpl-RIgUJhhQGcxbv7kDMfEezVhi",
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"model": "qwen3-coder-480b-a35b-instruct-turbo",
"choices": [
{
"index": 0,
"message": {
"role": "assistant",
"content": "Let me check what Python command is available:\n\n",
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"name": null,
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"id": "call_837494e9bb919023",
"type": "function",
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}
}
},
{
"turn": 5,
"tool": "run_shell",
"args": {
"command": "which python3"
},
"result": "exit=0\nstdout:\n/usr/bin/python3\n\nstderr:\n"
},
{
"turn": 6,
"response": {
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"choices": [
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"index": 0,
"message": {
"role": "assistant",
"content": "Let me try running with python3:\n\n",
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{
"id": "call_8a00f909e9802ee4",
"type": "function",
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},
{
"turn": 6,
"tool": "run_shell",
"args": {
"command": "python3 solve.py input.json answer.json"
},
"result": "exit=0\nstdout:\n\nstderr:\n"
},
{
"turn": 7,
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"choices": [
{
"index": 0,
"message": {
"role": "assistant",
"content": "Great! Now let's check the output files:\n\n",
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"tool_calls": [
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"id": "call_5db416e4e3d8ac84",
"type": "function",
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},
{
"turn": 7,
"tool": "read_file",
"args": {
"path": "answer.json"
},
"result": "{\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}"
},
{
"turn": 8,
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"choices": [
{
"index": 0,
"message": {
"role": "assistant",
"content": "Let's also check if the chart was created:\n\n",
"reasoning_content": null,
"name": null,
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{
"id": "call_f05803c8cecbc42f",
"type": "function",
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},
{
"turn": 8,
"tool": "run_shell",
"args": {
"command": "ls -l chart.svg"
},
"result": "exit=0\nstdout:\n-rw-r--r-- 1 box box 18714 Oct 23 20:40 chart.svg\n\nstderr:\n"
},
{
"turn": 9,
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},
{
"turn": 9,
"tool": "read_file",
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