27:I[335117,["/_next/static/immutable/chunks/1cj4ko_jrqeik.js","/_next/static/immutable/chunks/2eg50opuj-71a.js","/_next/static/immutable/chunks/0gnoy8s9rvklr.js","/_next/static/immutable/chunks/1pp7w2nguzt04.js","/_next/static/immutable/chunks/31t_xjz38-n6o.js","/_next/static/immutable/chunks/2bn9z758k16c5.js","/_next/static/immutable/chunks/0r9ga7d-v1hnd.js","/_next/static/immutable/chunks/2ai-scozzlw1b.js","/_next/static/immutable/chunks/2wy6kyi9anavr.js","/_next/static/immutable/chunks/1qd4q4booj7ia.js","/_next/static/immutable/chunks/42s3gl78jovr3.js","/_next/static/immutable/chunks/3nhkfgljn-29h.js","/_next/static/immutable/chunks/3zlf3s0jm-sv4.js","/_next/static/immutable/chunks/1qtc80h7i17zq.js","/_next/static/immutable/chunks/2vhyphwigip4t.js","/_next/static/immutable/chunks/176oqyv098-42.js","/_next/static/immutable/chunks/2kyx4c9s0_d3o.js","/_next/static/immutable/chunks/0ybivi-z4nnz-.js","/_next/static/immutable/chunks/06-36guf2s-4h.js","/_next/static/immutable/chunks/2uydhhqvrx71j.js","/_next/static/immutable/chunks/04wd27qaufbg9.js","/_next/static/immutable/chunks/1soeneurmqkwk.js","/_next/static/immutable/chunks/0rkq3ln9m44a1.js","/_next/static/immutable/chunks/0onel87fia260.js","/_next/static/immutable/chunks/3x4wyz8tqgl2c.js","/_next/static/immutable/chunks/3vuk0a6j3ye14.js"],"DatasetDetailBrowser"] 28:T1b86,You are given a 30-minute Natural Gas CFD CSV staged at: - /app/input/natgas_M30_201201030000_202402092330-873db394-1071-47f5-a168-bc41ff5b01fb.csv Your job is to recreate a complete, self-contained strategy research + backtesting deliverable set (code + reports). You must begin with **market-structure + indicator diagnostics** to understand the data (pivot points, key levels, S/R, RSI, candles, ATR, moving averages), then design and backtest multiple strategy angles, sweep parameters, and produce at least one strategy whose average monthly return is at least 30% (>= 0.30) on the available history using a realistic, forward-only backtest (no lookahead bias). Long-only or long/short is allowed. Important guidance: - Start with a **diagnostic analysis notebook-style workflow** (but deliver it as markdown + code, not a notebook). - Prior successful work suggests **volatility breakout + volatility regime filter** can work well; include it as one of the evaluated families. - You may test up to ~400 total configurations across all strategies (keep runtime reasonable). - In addition to global performance, emphasize **recent robustness**: the best strategy should show strong performance in the **last 12 months of data** (see requirements below). --- ## Hard requirements (must create these files under /app) ### 0) Diagnostics-first analysis Create: - **/app/eda_levels_indicators.md** This must contain your initial analysis of the dataset, specifically: 1) Data overview: time range, bar count, missingness, any oddities. 2) **Pivot points & key levels**: - Calculate daily pivots (Classic floor pivots: P, R1/R2/R3, S1/S2/S3) from prior day H/L/C. - Show how often price reacts near pivots (basic counts/heatmaps are fine). 3) **Important price levels / support & resistance**: - Provide at least 2 level-finding approaches (e.g., rolling swing highs/lows, volume-at-price proxy using bar counts, clustering of highs/lows, fractals). - Summarize the most significant levels observed. 4) **RSI(14) + RSI moving average crossover**: - Compute RSI(14) and an RSI moving average (e.g., SMA 7–21 on RSI). - Summarize regimes (e.g., overbought/oversold frequency, crossover distribution). 5) **Candlestick patterns**: - Detect a small set of patterns (at least 5) such as: bullish/bearish engulfing, hammer/shooting star, doji, morning/evening star (simplified rules OK). - Provide counts and any naive forward-return conditioning (e.g., average next N bars return). 6) **ATR & volatility**: - Compute ATR(14) and show volatility regimes (quantiles or rolling z-score). 7) **Moving averages**: - Compute multiple SMA/EMA (e.g., 20/50/100/200) and summarize trend regime statistics. You may generate figures (saved under /app/plots) to support the EDA. ### 1) /app/strategy_angles.md - List at least 5 distinct strategy ideas/angles. - These must be informed by your diagnostics (above). - For each angle: brief rationale and the parameters you will sweep. ### 2) /app/backtest.py - Loads the CSV and constructs OHLCV series (if the CSV already contains OHLCV, use it directly). - Implements a realistic backtest with **no lookahead bias** (signals generated from past data only; execution at next bar open or close, but must be consistent and documented). - Includes transaction costs and slippage assumptions (state them in code and in /app/report.md). - Must be deterministic (set seeds if any randomness is used). - Must support: a) running a parameter sweep over multiple strategies b) a reproducible best run via: `python /app/backtest.py --best` - The `--best` run must regenerate /app/best_strategy.json and be consistent with /app/results.csv. - Must also save an equity curve image for each tried configuration under /app/plots. **Additional recent-performance constraint (last 12 months):** - For every configuration, compute **last-12-month** metrics (ending at the dataset end): - last12_avg_monthly_return - last12_positive_months (count of months with positive return) - The best strategy selected for /app/best_strategy.json must satisfy: - avg_monthly_return >= 0.30 (global, full history) - AND last12_positive_months == 12 (profits every month in the last 1 year) ### 3) /app/results.csv One row per tried configuration including at least these columns: - strategy_name, params_json, start_date, end_date, trades, win_rate, profit_factor, max_drawdown, annual_return, avg_monthly_return And additionally include: - last12_avg_monthly_return, last12_positive_months ### 4) /app/best_strategy.json JSON describing the best found strategy/config and key metrics. Must include: - strategy_name, params (object) - start_date, end_date - trades, win_rate, profit_factor, max_drawdown - annual_return, avg_monthly_return - last12_avg_monthly_return, last12_positive_months Constraints for best: - avg_monthly_return must be >= 0.30 - last12_positive_months must be exactly 12 ### 5) /app/report.md - Summarize the diagnostics-first research, assumptions, strategy iterations, and present the best strategy with metrics. - Explicitly mention transaction cost + slippage assumptions and the execution model. - Include a ranked section that lists the **top 10 configurations** from best to worst. - Sorting: primarily avg_monthly_return desc; tie-breakers: last12_avg_monthly_return desc; then max_drawdown asc. - For each configuration in the top 10: include full metrics **including last12 metrics** and embed its equity curve image via markdown. ### 6) /app/report_all_ranked.md - Ranked list of **all tried configurations** from best to worst (same sorting as above), embedding each configuration’s equity curve image. ### 7) /app/strategies_summary.md - Compact markdown summary tables comparing tried strategies/configurations (derived from /app/results.csv) and highlighting the best. ### 8) Pine Script outputs for the **top 10** configurations - One Pine Script v5 file per top-10 configuration under: /app/pinescript - Name files with a stable filename (e.g., rank_01_.pine) - Must implement the same signal logic and execution assumptions as closely as Pine allows (document any unavoidable differences). --- ## Plots requirement (equity curves) - Save one equity curve image per tried configuration under: /app/plots - Use compressed images (prefer PNG with reasonable DPI or JPEG with quality ~70-85). - Keep each image reasonably small (e.g., <= ~200KB if possible) while remaining readable. - In reports, embed images using relative links like: `![](plots/)` ## Constraints - Do NOT use the network. - Do NOT modify the input CSV. - Keep runtime reasonable (optimize sweeps; vectorize when possible). ## Success criteria - /app/best_strategy.json must report avg_monthly_return >= 0.30 - /app/best_strategy.json must report last12_positive_months == 12 - The verifier will run: `python /app/backtest.py --best` and will check that the produced /app/best_strategy.json matches the criteria and that deliverables exist.29:T1dbe,#!/bin/bash set -euo pipefail mkdir -p /logs/verifier fail() { echo "$1" >&2 echo 0 > /logs/verifier/reward.txt exit 0 } # 1) Check required files exist req_files=( /app/strategy_angles.md /app/backtest.py /app/results.csv /app/best_strategy.json /app/report.md /app/report_all_ranked.md /app/strategies_summary.md ) for f in "${req_files[@]}"; do [[ -f "$f" ]] || fail "Missing required file: $f" done # 2) Run reproducible best python /app/backtest.py --best || fail "backtest.py --best failed" # 3) Validate best_strategy.json structure + threshold python - <<'PY' || exit 0 import json, os, re, sys import pandas as pd best_path = '/app/best_strategy.json' try: with open(best_path, 'r') as f: best = json.load(f) except Exception as e: print(f'Failed to load {best_path}: {e}', file=sys.stderr) sys.exit(2) required = [ 'strategy_name','params','start_date','end_date','trades','win_rate', 'profit_factor','max_drawdown','annual_return','avg_monthly_return' ] missing = [k for k in required if k not in best] if missing: print('Missing keys in best_strategy.json:', missing, file=sys.stderr) sys.exit(3) try: amr = float(best['avg_monthly_return']) except Exception: print('avg_monthly_return not numeric', file=sys.stderr) sys.exit(4) if amr < 0.30: print(f'avg_monthly_return too low: {amr}', file=sys.stderr) sys.exit(5) # 4) results.csv sanity df = pd.read_csv('/app/results.csv') if len(df) < 10: print('results.csv seems too small (need multiple configs tried, including top 10).', file=sys.stderr) sys.exit(6) need_cols = {'strategy_name','params_json','start_date','end_date','trades','win_rate','profit_factor','max_drawdown','annual_return','avg_monthly_return'} if not need_cols.issubset(set(df.columns)): print('results.csv missing required columns:', sorted(list(need_cols - set(df.columns))), file=sys.stderr) sys.exit(7) # Hard cap at ~400 if len(df) > 420: print(f'results.csv too large ({len(df)} rows) - expected ~<=400 configs', file=sys.stderr) sys.exit(10) # Ensure best is present among sweep results name = best['strategy_name'] subset = df[df['strategy_name'] == name] if subset.empty: print('best strategy_name not found in results.csv', file=sys.stderr) sys.exit(8) if float(df['avg_monthly_return'].max()) < 0.30: print('No configuration in results.csv meets avg_monthly_return >= 0.30', file=sys.stderr) sys.exit(9) # 5) report files embed plots for rp in ['/app/report.md','/app/report_all_ranked.md']: try: txt = open(rp,'r',encoding='utf-8').read() except Exception as e: print(f'Failed to read {rp}: {e}', file=sys.stderr) sys.exit(11) if 'plots/' not in txt: print(f'{rp} does not appear to reference plots/ images', file=sys.stderr) sys.exit(12) # 6) Check /app/plots exists and has some images plots_dir = '/app/plots' if not os.path.isdir(plots_dir): print('Missing /app/plots directory', file=sys.stderr) sys.exit(13) imgs = [p for p in os.listdir(plots_dir) if p.lower().endswith(('.png','.jpg','.jpeg'))] if len(imgs) < 10: print('Expected at least 10 equity curve images in /app/plots', file=sys.stderr) sys.exit(14) # Basic size check: keep images reasonably small max_bytes = 900_000 for fn in imgs[:50]: sz = os.path.getsize(os.path.join(plots_dir, fn)) if sz > max_bytes: print(f'Plot {fn} too large: {sz} bytes (max {max_bytes})', file=sys.stderr) sys.exit(15) # 7) Pine Script for top 10 (heuristics, no compilation) ps_dir = '/app/pinescript' if not os.path.isdir(ps_dir): print('Missing /app/pinescript directory', file=sys.stderr) sys.exit(16) ps = sorted([p for p in os.listdir(ps_dir) if p.lower().endswith(('.pine','.pinescript','.txt'))]) if len(ps) < 10: print('Expected at least 10 Pine Script files in /app/pinescript for top 10 configs', file=sys.stderr) sys.exit(17) # Helper: remove comments (best-effort) def strip_comments(s: str) -> str: out = [] i = 0 in_block = False in_line = False while i < len(s): if not in_block and not in_line and s[i:i+2] == '/*': in_block = True i += 2 continue if in_block and s[i:i+2] == '*/': in_block = False i += 2 continue if not in_block and not in_line and s[i:i+2] == '//': in_line = True i += 2 continue if in_line and s[i] == '\n': in_line = False out.append('\n') i += 1 continue if not in_block and not in_line: out.append(s[i]) i += 1 return ''.join(out) # Basic parens balance def has_balanced_parens(s: str) -> bool: bal = 0 for ch in s: if ch == '(': bal += 1 elif ch == ')': bal -= 1 if bal < 0: return False return bal == 0 # Basic quote balance for double-quoted strings def has_balanced_quotes(s: str) -> bool: in_str = False i = 0 while i < len(s): ch = s[i] if ch == '"': # count preceding backslashes bs = 0 j = i - 1 while j >= 0 and s[j] == "\\": bs += 1 j -= 1 if bs % 2 == 0: in_str = not in_str i += 1 return not in_str for fn in ps[:10]: path = os.path.join(ps_dir, fn) content = open(path,'r',encoding='utf-8',errors='ignore').read() if '//@version=5' not in content: print(f'PineScript {fn} missing //@version=5 header', file=sys.stderr) sys.exit(18) # Must be a strategy, not indicator if re.search(r'\bstrategy\s*\(', content) is None: print(f'PineScript {fn} must declare strategy(...)', file=sys.stderr) sys.exit(19) # Must have at least one entry if ('strategy.entry' not in content) and ('strategy.order' not in content): print(f'PineScript {fn} missing strategy.entry/strategy.order', file=sys.stderr) sys.exit(21) # Must have an exit/close if ('strategy.exit' not in content) and ('strategy.close' not in content): print(f'PineScript {fn} missing strategy.exit/strategy.close', file=sys.stderr) sys.exit(22) # Forbid legacy security(...) if re.search(r'\bsecurity\s*\(', content): print(f'PineScript {fn} uses security(...). Please avoid it for this task.', file=sys.stderr) sys.exit(23) # request.security(...) is allowed only if lookahead is explicitly OFF and ON is not used if re.search(r'\brequest\.security\s*\(', content): if 'barmerge.lookahead_on' in content: print(f'PineScript {fn} uses barmerge.lookahead_on (disallowed).', file=sys.stderr) sys.exit(24) if 'barmerge.lookahead_off' not in content: print(f'PineScript {fn} uses request.security(...) but does not specify barmerge.lookahead_off.', file=sys.stderr) sys.exit(25) # Heuristic syntax checks on comment-stripped text stripped = strip_comments(content) if not has_balanced_parens(stripped): print(f'PineScript {fn} appears to have unbalanced parentheses (likely syntax error)', file=sys.stderr) sys.exit(20) if not has_balanced_quotes(stripped): print(f'PineScript {fn} appears to have unbalanced quotes (likely syntax error)', file=sys.stderr) sys.exit(26) print('OK') PY # If we got here, reward full echo 1 > /logs/verifier/reward.txt14:["$","$L6",null,{"className":"min-h-0 flex-1","useViewportContract":true,"contractContentClassName":"min-h-full","children":["$","div",null,{"className":"mx-auto flex min-h-full w-full max-w-screen-xl flex-1 flex-col gap-6 px-4 pb-4 pt-[calc(3.5rem+1rem)] xl:px-6 xl:pb-6 xl:pt-[calc(3.5rem+1.5rem)]","children":["$","$L27",null,{"dataset":{"kind":"catalog","id":"492638c5-d6cc-4754-ab63-a284927de41b","name":"Improved natgas-strategy-backtest-rebuild","description":"Recreate 30-min natgas CFD strategy research, parameter sweeps, and a reproducible backtest producing best_strategy artifacts.","ref":"492638c5-d6cc-4754-ab63-a284927de41b","sourceType":"custom","visibility":"public","version":null,"canonicalRef":null,"taskCount":1,"runHref":"/workflows/new?dataset_id=492638c5-d6cc-4754-ab63-a284927de41b","ownerId":"0187daa7-138f-4709-b8a5-da2ee634d5b1"},"initialTasks":{"tasks":[{"id":"e14f12a7-67eb-471f-8938-94ee8cd54575","dataset_id":"492638c5-d6cc-4754-ab63-a284927de41b","task_id":"natgas-strategy-backtest","instruction":"$28","verifier_script":"$29","test_files":{},"setup_commands":[],"metadata":{"domain":"quant.backtesting","offline":true,"timeframe":"30m","instrument":"natgas_cfd","_harbor_task_files":{"artifacts":[],"input_files":{},"input_file_refs":[{"path":"natgas_M30_201201030000_202402092330-873db394-1071-47f5-a168-bc41ff5b01fb.csv","filename":"natgas_M30_201201030000_202402092330-873db394-1071-47f5-a168-bc41ff5b01fb.csv","size_bytes":767544,"storage_key":"org-ee853ebb-6325-4bff-a4a8-4ff1789175a8/harbor-task-files/datasets/492638c5-d6cc-4754-ab63-a284927de41b/natgas-strategy-backtest/natgas_M30_201201030000_202402092330-873db394-1071-47f5-a168-bc41ff5b01fb-08be0524-c329-45a3-b90a-47b5ded0fd5a.csv","content_type":"text/csv"}]}},"created_at":"2026-07-01T16:10:19.313Z","updated_at":"2026-07-01T16:10:19.313Z","artifacts":[]}],"total":1},"tasksLoadError":null,"stats":{"dataset_id":"492638c5-d6cc-4754-ab63-a284927de41b","primary_metric_key":"mean_reward","generated_at":"2026-10-19T09:34:10.669Z","summary":{"run_count":0,"public_run_count":0,"participant_count":0,"organization_count":0,"best_score":null,"average_score":null,"last_completed_at":null},"leaderboard":[],"task_stats":[],"provider_stats":[],"score_series":[],"lineage":{"dataset_id":"492638c5-d6cc-4754-ab63-a284927de41b","user_id":"0187daa7-138f-4709-b8a5-da2ee634d5b1","visibility":"public","name":"Improved natgas-strategy-backtest-rebuild","version":null,"canonical_ref":null,"derived_from_dataset_id":null,"derived_from_name":null,"derived_from_version":null,"derived_from_canonical_ref":null,"root_dataset_id":"492638c5-d6cc-4754-ab63-a284927de41b","root_dataset_name":"Improved natgas-strategy-backtest-rebuild","root_dataset_version":null,"task_fingerprint":null,"metric_fingerprint":null,"primary_metric_key":"mean_reward"}},"statsLoadError":null,"existingWorkflows":[],"workflowsLoadError":null,"canEditDataset":false,"publicReadOnly":true}]}]}]

Improved natgas-strategy-backtest-rebuild

Performance