{"approach":"Audit exogenous common random numbers for fixed incumbent B and fixed simplify; no candidate or oracle built. At simulator commit b244530ee2a6247b5f930830eeca9dd950da0e7e, call serial in-process run_batch with defaults and 2000 steps separately per strategy and seed. Read-only subclass wrappers call original methods once and record their returned latent probability/score and generated retail orders. No source file in the simulator was modified.","tried":["Verified B UTF-8 SHA-256 against the required hash; verified simplify SHA-256.","Timed 20 paired seeds first; projected complete block below 30 minutes. Used all 1000 prescribed seeds.","Checked instrumented B against uninstrumented B on seeds 241000..241019, exact full-result equality.","Compared latent and retail traces and sampled regimes for B versus simplify on every measured seed.","Calculated sample SD (ddof=1) of per-seed B-minus-simplify total edge and estimated n=ceil((SD/0.25)^2); no finalist seeds used."],"next":[{"change":"Evaluate replacing B with the supplied simplify policy as a whole-policy simplification. This is the only concrete strategy change measured here; require independent finalist evaluation before any promotion.","edge_at_stake":{"simplify_minus_B":-2.2652883286786003,"se":0.5610380882114246,"retail_difference":-4.3972089077183005,"arbitrage_difference":2.1319205790265},"reason":"Common paths make this comparison meaningful. The policies bundle multiple changes, so this audit does not identify a causal single-rule improvement or justify tuning any particular parameter."}],"code":"import sys,json,hashlib,time,statistics,math\nfrom pathlib import Path\nfrom dataclasses import asdict\nsys.path.insert(0,str(Path(__file__).parent/'simulator'))\nfrom orderbook_pm_challenge import engine,runner\nfrom orderbook_pm_challenge.config import ChallengeConfig,ParameterVariance\nfrom orderbook_pm_challenge.loader import load_strategy_factory\nROOT=Path(__file__).parent\nEXPECTED='baef955ec10728839f7c3357016ec9eb0550e7ec689bbd5171b1cdd5c9e37273'\nassert hashlib.sha256((ROOT/'B.py').read_bytes()).hexdigest()==EXPECTED\nassert hashlib.sha256((ROOT/'simplify.py').read_bytes()).hexdigest()=='bd69d74807d70fc0acd7b35765b602bd05813b920c2ad706b10160baff45de22'\nfactories={k:load_strategy_factory(str(ROOT/(k+'.py'))) for k in ['B','simplify']}\nOriginalProcess=engine.JumpDiffusionScoreProcess\nOriginalRetail=engine.RetailFlow\nclass TraceProcess(OriginalProcess):\n    def __post_init__(self):\n        super().__post_init__();self.trace=[];traces['process']=self\n    def current_true_probability(self):\n        p=super().current_true_probability();self.trace.append((self.current_step,self.current_score,p));return p\nclass TraceRetail(OriginalRetail):\n    def __init__(self,*args,**kwargs):\n        super().__init__(*args,**kwargs);self.trace=[];traces['retail']=self\n    def generate_orders(self):\n        orders=super().generate_orders();self.trace.append([(o.side,o.notional) for o in orders]);return orders\ntraces={}\ndef run(k,s,instrument):\n    engine.JumpDiffusionScoreProcess=TraceProcess if instrument else OriginalProcess\n    engine.RetailFlow=TraceRetail if instrument else OriginalRetail\n    t=time.perf_counter()\n    r=runner.run_batch(factories[k],base_config=ChallengeConfig(),variance=ParameterVariance(),n_simulations=1,seed_start=s,workers=1).simulation_results[0]\n    seconds=time.perf_counter()-t\n    trace=None\n    if instrument:\n        trace={name:obj.trace for name,obj in traces.items()}\n        assert len(trace['process'])==2001 and len(trace['retail'])==2000,(s,k,r)\n    return asdict(r),seconds,trace\n\ndef main():\n    n=int(sys.argv[1]);start=int(sys.argv[2]) if len(sys.argv)>2 else 241000\n    out=ROOT/('runs-'+str(start)+'.jsonl')\n    with out.open('w') as f:\n        for s in range(start,start+n):\n            b,tb,bt=run('B',s,True);a,ta,at=run('simplify',s,True)\n            row={'seed':s,'B':b,'simplify':a,'seconds_B':tb,'seconds_simplify':ta,\n                 'same_process':bt['process']==at['process'],'same_retail':bt['retail']==at['retail'],\n                 'same_parameters':b['regime']==a['regime'],\n                 'retail_order_count':sum(len(x) for x in bt['retail']),\n                 'process_hash':hashlib.sha256(json.dumps(bt['process']).encode()).hexdigest(),\n                 'retail_hash':hashlib.sha256(json.dumps(bt['retail']).encode()).hexdigest()}\n            if s<241020:\n                plain,tp,_=run('B',s,False);row['plain_seconds']=tp;row['instrumentation_identical_full_result']=plain==b\n                row['plain_edges']={key:plain[key] for key in ['total_edge','retail_edge','arb_edge','failed']}\n            f.write(json.dumps(row)+'\\n');f.flush()\n            if (s-start+1)%10==0: print(s+1,round(tb+ta,3),flush=True)\nif __name__=='__main__': main()\n\n# Save the following section as summarize.py beside audit.py, B.py, simplify.py and simulator/.\nimport json,math,statistics,hashlib\nfrom pathlib import Path\nROOT=Path(__file__).parent\n\ndef summarize(rows):\n    rows=sorted(rows,key=lambda r:r['seed'])\n    assert [r['seed'] for r in rows]==list(range(241000,242000))\n    n=len(rows)\n    def stats(key):\n        data=[r[key] for r in rows] if key in ['B','simplify'] else [dict((f,r['B'][f]-r['simplify'][f]) for f in ['total_edge','retail_edge','arb_edge']) for r in rows]\n        return {'mean':statistics.mean(x['total_edge'] for x in data),\n                'retail_mean':statistics.mean(x['retail_edge'] for x in data),\n                'arb_mean':statistics.mean(x['arb_edge'] for x in data),\n                'se':statistics.stdev(x['total_edge'] for x in data)/math.sqrt(n),\n                'retail_se':statistics.stdev(x['retail_edge'] for x in data)/math.sqrt(n),\n                'arb_se':statistics.stdev(x['arb_edge'] for x in data)/math.sqrt(n),\n                'sd':statistics.stdev(x['total_edge'] for x in data)}\n    b=stats('B');a=stats('simplify');d=stats('difference')\n    required=math.ceil((d['sd']/0.25)**2)\n    unpaired_sd=math.sqrt(b['sd']**2+a['sd']**2)\n    bmean=b['mean'];amean=a['mean']\n    covariance=sum((r['B']['total_edge']-bmean)*(r['simplify']['total_edge']-amean) for r in rows)/(n-1)\n    correlation=covariance/(b['sd']*a['sd'])\n    validation=[r for r in rows if 'instrumentation_identical_full_result' in r]\n    assert len(validation)==20\n    assert all(r['instrumentation_identical_full_result'] for r in validation)\n    assert all(r['same_process'] and r['same_retail'] and r['same_parameters'] for r in rows)\n    failures=[{'seed':r['seed'],'strategy':k,'error':r[k]['error']} for r in rows for k in ['B','simplify'] if r[k]['failed']]\n    assert not failures\n    harness=(ROOT/'audit.py').read_text()\n    code=harness+'\\n# Save the following section as summarize.py beside audit.py, B.py, simplify.py and simulator/.\\n'+Path(__file__).read_text().split(\"if __name__\" + \" == '__main__':\")[0]\n    # Only the reusable summarize function is embedded; its source does not embed itself recursively.\n    evidence={'same_latent_path_seeds':sum(r['same_process'] for r in rows),\n        'same_retail_orders_seeds':sum(r['same_retail'] for r in rows),\n        'same_sampled_parameters_seeds':sum(r['same_parameters'] for r in rows),\n        'latent_state_probability_observations_per_strategy':n*2001,\n        'retail_step_lists_per_strategy':n*2000,\n        'retail_order_count_per_strategy':sum(r['retail_order_count'] for r in rows),\n        'latent_comparison':'Exact tuple equality of (step, score, true_probability), including the initial state and all 2000 steps.',\n        'retail_comparison':'Exact per-step equality of all generated (side, notional) orders in generation order, including empty lists; sell share quantities are deterministic from the identical probability and notional.',\n        'sampled_parameters_comparison':'Exact equality of every RegimeSummary field; all unvaried fields come from the same default ChallengeConfig.',\n        'instrumentation_validation':{'seeds':[241000,241019],'n':20,'identical_full_results':20,\n        'comparison':'Exact equality of the complete asdict(SimulationResult), including total_edge, retail_edge, arb_edge, failed, inventory, cash, fill counts and settlement.',\n        'edges':[{'seed':r['seed'],**r['plain_edges']} for r in validation]},\n        'per_seed_difference_order':'B minus simplify total_edge, one value for each seed 241000 through 241999 inclusive',\n        'per_seed_incumbent_total_edges':[r['B']['total_edge'] for r in rows],\n        'per_seed_differences':[r['B']['total_edge']-r['simplify']['total_edge'] for r in rows],\n        'largest_absolute_difference_seeds':[{'seed':r['seed'],'B_minus_simplify':r['B']['total_edge']-r['simplify']['total_edge']} for r in sorted(rows,key=lambda r:abs(r['B']['total_edge']-r['simplify']['total_edge']),reverse=True)[:5]],\n        'trace_hashes_sha256':hashlib.sha256(json.dumps([[r['seed'],r['process_hash'],r['retail_hash']] for r in rows],separators=(',',':')).encode()).hexdigest()}\n    return {\n      'approach':'Audit exogenous common random numbers for fixed incumbent B and fixed simplify; no candidate or oracle built. At simulator commit b244530ee2a6247b5f930830eeca9dd950da0e7e, call serial in-process run_batch with defaults and 2000 steps separately per strategy and seed. Read-only subclass wrappers call original methods once and record their returned latent probability/score and generated retail orders. No source file in the simulator was modified.',\n      'tried':['Verified B UTF-8 SHA-256 against the required hash; verified simplify SHA-256.','Timed 20 paired seeds first; projected complete block below 30 minutes. Used all 1000 prescribed seeds.','Checked instrumented B against uninstrumented B on seeds 241000..241019, exact full-result equality.','Compared latent and retail traces and sampled regimes for B versus simplify on every measured seed.','Calculated sample SD (ddof=1) of per-seed B-minus-simplify total edge and estimated n=ceil((SD/0.25)^2); no finalist seeds used.'],\n      'next':[{'change':'Evaluate replacing B with the supplied simplify policy as a whole-policy simplification. This is the only concrete strategy change measured here; require independent finalist evaluation before any promotion.',\n               'edge_at_stake':{'simplify_minus_B':-d['mean'],'se':d['se'],'retail_difference':-d['retail_mean'],'arbitrage_difference':-d['arb_mean']},\n               'reason':'Common paths make this comparison meaningful. The policies bundle multiple changes, so this audit does not identify a causal single-rule improvement or justify tuning any particular parameter.'}],\n      'code':code,'code_sha256':hashlib.sha256(code.encode()).hexdigest(),\n      'incumbent_code_sha256':'baef955ec10728839f7c3357016ec9eb0550e7ec689bbd5171b1cdd5c9e37273',\n      'alternative_code_sha256':'bd69d74807d70fc0acd7b35765b602bd05813b920c2ad706b10160baff45de22',\n      'tuning':None,'seeds':{'start':241000,'end_inclusive':241999,'role':'fixed diagnostic evaluation; no tuning or finalist seeds'},'n':n,'complete':True,\n      'incumbent':{k:b[k] for k in ['mean','retail_mean','arb_mean','se']},\n      'findings':[{'name':k,'count':n,**v} for k,v in [('B',b),('simplify',a),('B_minus_simplify_paired_difference',d)]],\n      'paired':None,'pairing_audit':{'direction':'B minus simplify','per_seed_difference_sd':d['sd'],'paired_mean_diff':d['mean'],'paired_se':d['se'],\n        'target_se':0.25,'estimated_total_seeds_for_target_se':required,'additional_seeds_beyond_measured':max(0,required-n),\n        'formula':'ceil((sample_standard_deviation / 0.25)**2)',\n        'precision_note':'Estimated sample requirement for independent seed pairs with the same distribution; not a guaranteed SE or permission to use more seeds. Large per-seed differences can make the variance estimate unstable. No extra seeds were run.',\n        'unpaired_difference_sd_estimate':unpaired_sd,'unpaired_se_at_measured_n_per_strategy':unpaired_sd/math.sqrt(n),\n        'unpaired_estimated_seeds_per_strategy_for_target_se':math.ceil((unpaired_sd/.25)**2),\n        'paired_variance_fraction_of_unpaired':d['sd']**2/unpaired_sd**2,'total_edge_correlation':correlation,\n        'scope':'Identical exogenous market path and submitted retail flow. Fills, order-book evolution, competitor replenishments, cash and inventory depend on the strategy.'},\n      'evidence':evidence,'failures':failures,\n      'seconds_per_simulation':sum(r['seconds_B']+r['seconds_simplify'] for r in rows)/(2*n),\n      'timing':{'first_20_pair_seconds':sum(r['seconds_B']+r['seconds_simplify'] for r in validation),\n                'first_20_projected_1000_pair_minutes':sum(r['seconds_B']+r['seconds_simplify'] for r in validation)/20*1000/60,\n                'measured_B_seconds':sum(r['seconds_B'] for r in rows),'measured_simplify_seconds':sum(r['seconds_simplify'] for r in rows),\n                'uninstrumented_validation_seconds':sum(r['plain_seconds'] for r in validation)},\n      'reproduction':{'inputs':'Fetch the two artifact JSON code fields into UTF-8 B.py and simplify.py; checkout the pinned simulator into simulator/. Python standard library only; no installed dependencies.',\n                      'runs':['python3 audit.py 20','python3 audit.py 980 241020'],\n                      'summary':'Load JSONL rows from both runs, call summarize(rows). The code field concatenates audit.py and summarize.py with a boundary comment; split it there.',\n                      'sources':'URLs, quotes and artifact hashes are in sources.json. Findings and trace equality counts are generated measurements, not external-source claims.'}}\n\n","code_sha256":"7fad38c97ed542a44543ce3f66a5255c8e836f3ff38caf078ce7be8af8375363","incumbent_code_sha256":"baef955ec10728839f7c3357016ec9eb0550e7ec689bbd5171b1cdd5c9e37273","alternative_code_sha256":"bd69d74807d70fc0acd7b35765b602bd05813b920c2ad706b10160baff45de22","tuning":null,"seeds":{"start":241000,"end_inclusive":241999,"role":"fixed diagnostic evaluation; no tuning or finalist seeds"},"n":1000,"complete":true,"incumbent":{"mean":91.336495610556,"retail_mean":116.0825007424162,"arb_mean":-24.7460051318298,"se":4.668341125789028},"findings":[{"name":"B","count":1000,"mean":91.336495610556,"retail_mean":116.0825007424162,"arb_mean":-24.7460051318298,"se":4.668341125789028,"retail_se":4.977582578319656,"arb_se":1.53428792694876,"sd":147.62590852127946},{"name":"simplify","count":1000,"mean":89.0712072818774,"retail_mean":111.6852918346979,"arb_mean":-22.6140845528033,"se":4.676957939617574,"retail_se":4.841474402454461,"arb_se":1.7030058097134533,"sd":147.89839609999785},{"name":"B_minus_simplify_paired_difference","count":1000,"mean":2.2652883286786003,"retail_mean":4.3972089077183005,"arb_mean":-2.1319205790265,"se":0.5610380882114246,"retail_se":0.42995785792518687,"arb_se":0.43522622280411516,"sd":17.741582128545648}],"paired":null,"pairing_audit":{"direction":"B minus simplify","per_seed_difference_sd":17.741582128545648,"paired_mean_diff":2.2652883286786003,"paired_se":0.5610380882114246,"target_se":0.25,"estimated_total_seeds_for_target_se":5037,"additional_seeds_beyond_measured":4037,"formula":"ceil((sample_standard_deviation / 0.25)**2)","precision_note":"Estimated sample requirement for independent seed pairs with the same distribution; not a guaranteed SE or permission to use more seeds. Large per-seed differences can make the variance estimate unstable. No extra seeds were run.","unpaired_difference_sd_estimate":208.96732863221712,"unpaired_se_at_measured_n_per_strategy":6.608127150387244,"unpaired_estimated_seeds_per_strategy_for_target_se":698678,"paired_variance_fraction_of_unpaired":0.007208217959934034,"total_edge_correlation":0.9927934701298826,"scope":"Identical exogenous market path and submitted retail flow. Fills, order-book evolution, competitor replenishments, cash and inventory depend on the strategy."},"evidence":{"same_latent_path_seeds":1000,"same_retail_orders_seeds":1000,"same_sampled_parameters_seeds":1000,"latent_state_probability_observations_per_strategy":2001000,"retail_step_lists_per_strategy":2000000,"retail_order_count_per_strategy":506557,"latent_comparison":"Exact tuple equality of (step, score, true_probability), including the initial state and all 2000 steps.","retail_comparison":"Exact per-step equality of all generated (side, notional) orders in generation order, including empty lists; sell share quantities are deterministic from the identical probability and notional.","sampled_parameters_comparison":"Exact equality of every RegimeSummary field; all unvaried fields come from the same default ChallengeConfig.","instrumentation_validation":{"seeds":[241000,241019],"n":20,"identical_full_results":20,"comparison":"Exact equality of the complete asdict(SimulationResult), including total_edge, retail_edge, arb_edge, failed, inventory, cash, fill counts and settlement.","edges":[{"seed":241000,"total_edge":-10.7973070827,"retail_edge":101.7388460194,"arb_edge":-112.5361531023,"failed":false},{"seed":241001,"total_edge":-0.1441668873,"retail_edge":0.0260162005,"arb_edge":-0.1701830878,"failed":false},{"seed":241002,"total_edge":313.5569819948,"retail_edge":372.0214878517,"arb_edge":-58.4645058563,"failed":false},{"seed":241003,"total_edge":25.5316760857,"retail_edge":34.9495156485,"arb_edge":-9.4178395613,"failed":false},{"seed":241004,"total_edge":112.4574959535,"retail_edge":130.524944338,"arb_edge":-18.0674483852,"failed":false},{"seed":241005,"total_edge":27.5636390894,"retail_edge":40.0755063534,"arb_edge":-12.5118672652,"failed":false},{"seed":241006,"total_edge":-4.3270299401,"retail_edge":3.170790101,"arb_edge":-7.497820041,"failed":false},{"seed":241007,"total_edge":-110.0945429555,"retail_edge":81.4883736121,"arb_edge":-191.5829165673,"failed":false},{"seed":241008,"total_edge":24.3069047679,"retail_edge":41.8735178308,"arb_edge":-17.5666130633,"failed":false},{"seed":241009,"total_edge":85.6048493433,"retail_edge":104.3260605188,"arb_edge":-18.7212111749,"failed":false},{"seed":241010,"total_edge":18.1396328672,"retail_edge":23.9042622997,"arb_edge":-5.7646294316,"failed":false},{"seed":241011,"total_edge":0.8951497839,"retail_edge":22.5976832428,"arb_edge":-21.7025334587,"failed":false},{"seed":241012,"total_edge":128.5429922697,"retail_edge":154.0546967,"arb_edge":-25.511704431,"failed":false},{"seed":241013,"total_edge":41.4678490326,"retail_edge":54.3468029948,"arb_edge":-12.8789539632,"failed":false},{"seed":241014,"total_edge":14.8799002183,"retail_edge":35.1480719626,"arb_edge":-20.2681717437,"failed":false},{"seed":241015,"total_edge":14.9979782496,"retail_edge":17.7884086122,"arb_edge":-2.7904303626,"failed":false},{"seed":241016,"total_edge":5.1672574166,"retail_edge":18.7305717276,"arb_edge":-13.5633143111,"failed":false},{"seed":241017,"total_edge":12.8915334444,"retail_edge":27.4611359089,"arb_edge":-14.5696024656,"failed":false},{"seed":241018,"total_edge":9.9648414706,"retail_edge":63.0610685031,"arb_edge":-53.0962270325,"failed":false},{"seed":241019,"total_edge":17.1800680212,"retail_edge":21.2563867647,"arb_edge":-4.0763187438,"failed":false}]},"per_seed_difference_order":"B minus simplify total_edge, one value for each seed 241000 through 241999 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the two artifact JSON code fields into UTF-8 B.py and simplify.py; checkout the pinned simulator into simulator/. Python standard library only; no installed dependencies.","runs":["python3 audit.py 20","python3 audit.py 980 241020"],"summary":"Load JSONL rows from both runs, call summarize(rows). The code field concatenates audit.py and summarize.py with a boundary comment; split it there.","sources":"URLs, quotes and artifact hashes are in sources.json. Findings and trace equality counts are generated measurements, not external-source claims."}}