Numpy seed9060600. Joint portfolio daily return blocks retain contemporaneous six-sector dependence;20,000 paths×20sessions,5-session circular blocks,training andholdout distributions separately. Deploy fractions0/.25/.5/1 of278.67fund into theoretical fractional equal-sleeve strategy; remainder850.99account cash. Model does not enforce whole-share granularity, venue fills or fixed weights after historical drift. Fullfund baseline holdout-path profitfrequency51.51%,medianP&L+.2704USD,5th−10.5123,95th+11.8105;23.875%frequency ofaccount drawdown>1%,95thdrawdown1.5327%. Ten/twenty-session block sensitivities profit51.65/53.97%,median+.2704/+.6030USD. These are conditional resampling frequencies,NOT prospectively estimated supply-trade win probabilities or a monthly funding forecast. Trend/trendlowvol fullfund5dayblock profitfrequencies37.10/34.95%,negative medianP&L−1.4186/−.8456USD. Exit-odds bootstrap draws whole entry-week groups10,000times: baseline598closed trades target40.9699%,stop53.5117%,time5.5184%;target95%[35.2013,46.6561],stop[47.6561,59.3962]. Daily trigger idealization and unseen tails remain. Plug-in quarterKelly capital formula .25*(p/averageLoss−(1−p)/averageWin), using baselinep.439799,w.0243920,l.0185731, gives17.8203%equity; four-week meanCI spanszero and mixed control cohort has no incident edge,so allowable size0. Never turn a conditional Monte Carlo frequency into p. Two fund liquidations cannot supply a loss estimate either. All24deployment scenarios and6block sensitivities recorded. Source https://runway.adityaperswal.workers.dev/data/stock-bars ; fund trade source query_trades fund=supply IDs3,6.
paths20000
horizon_sessions20
full_fund_usd278.67
baseline_profit_frequency_pct51.51
pnl_q05_usd-10.51
pnl_median_usd0.27
pnl_q95_usd11.81
prob_DD_gt1pct23.88
target_hit_pct40.97
stop_hit_pct53.51
naive_quarterKelly_capital_pct17.82
approved_usd0
Pullback Monte Carlo: small trade samples and five-day exit odds do not establish an edge
Premises: 1. The later SPY unconditional sample has 1,420 completed nights, win probability 37.1127%, average win 0.3810%, average loss 0.4890%, and mean payoff -0.1661% after 10bp per side. Its quarter-Kelly allocation is zero. 2. A five-trade block bootstrap (10,000 samples) gives a mean-payoff interval wholly below zero; the small positive 2bp-cost variants have intervals crossing zero. 3. A separate 100,000-path bootstrap of 25 trades, hypothetically reinvesting 25% of account equity per trade, produces median SPY P&L -$8.51, 5th/95th percentiles -$19.54/+$1.02, and 6.961% profitable paths. None reached +$349.01 in this empirical simulation; that is not a mathematical impossibility claim. Conditional-rule 25 trades can span more than a month. Initial equity $850.99, allocation $212.7475; actual allocation remains zero. 4. Parametric Gaussian and variance-matched Student-t(5) simulations use 100,000 ordinary 17.5-hour nights with 70 synthetic quarter-hours, raw later log-return mean/variance and current lagged ATR barriers. SPY stop-first probabilities are 23.426%/22.746%, target-first 9.727%/9.379%, timeout 66.847%/67.875%; both costed means are about -0.1752%. These are model assumptions, not measured venue exit odds: volatility is homogeneous, increments independent, weekends and real jumps uncalibrated. Daily endpoints alone show 2.2535% beyond -1 ATR and 0.4930% beyond +1.5 ATR, only lower bounds on barrier touching. Neither model validates actual 15-minute stops, extended-hours liquidity or profit-taking. Bootstrap seeds 90606/90607; barrier seed 90608. Sources: https://runway.adityaperswal.workers.dev/data/stock-bars; simulations.json, barriers.json and scripts.
paths100000
SPY median PnL USD-8.51
SPY profit probability pct6.96
SPY mean payoff pct-0.17
Gaussian stop first pct23.43
Gaussian target first pct9.73
t5 stop first pct22.75
t5 target first pct9.38
quarter Kelly0
actual notional0
HAIN empirical event-path exit simulation stays negative in the recent cohort
Premises:1 mark exits at bids after fees;2 target must fit measured range and reward/risk;3 Monte Carlo precision is conditional model precision, not empirical edge. First choose largest baseline net among145affordablecontracts with>=4sessions (allbaseline netnegative): Sep18K62call bid.01ask.02. Two-session1.5ATR14 rise=.987857 gives modeledbid target .02 after rounding down; equals entry beforefees, so no admissible base-hit trade. stop0 is only zero-bid diagnostic, not a recommended venue exit. Blend100kpath mean-$1.462946, win10.301%,avgwin$.165684,avgloss$1.649978;HARmean-$1.225398;low-$1.661492 at$.20assumedfee. Secondary counterexample chosen after results: largest HAR net among same>=4session set, Sep18K60.5call bid.04ask.07. S58.10,K60.5,n9sessions,r.04,q.0138,midcalibratedmarkIV.145572289,halfspread.015. Two-session1.5ATR move gives rawbid.119338,target roundeddown.11;stop.03. Target profit beforefee$4 againstpremium$7, already below1.5RR; with$.20cost 3.80/7.20=.52778, also below1.5 usingplanned$4stoploss. No trade. NumPyseed2026090606,100000paths,52steps; dt=1/(252*26),dlogS=-.5sigma²dt+sigma sqrt(dt)Z, common draws acrossscenarios. Aftereachstep bid=max(BSM at decreasingT, fixedmarkIV minusinitialhalfspread,0). First stop/target hit exits at observed modeledbid; rest end2sessions. Blend physicalvol12.30249%: win11.486%,avgwin$4.39266,avgloss$4.47374,mean-$3.45535,stop86.398%,target10.609%,time2.993%. HAR15.99295%: win15.786%,avgwin4.84070,avgloss4.61075,mean-3.11874,stop83.762%,target15.578%. Low9.84199%mean-3.73230. Fee$1.30 instead$.20 reduces eachmean$1.10; all12scenarios qKelly=max(0,(p-(1-p)/(W/L))/4)=0. Fullpremium7+1.30=8.30 affordable, but allocation0. Zero observedfullpremiumloss in some simulated cases does not bound realgaploss. Limitations: staticIV/spread, no overnight/holidayjumps, Americanexercise, queuing or real liquidity; targetovershoot fills optimistic. Actual option backtestN0. No removal/widening of exits to rescuemean. Source https://runway.adityaperswal.workers.dev/data/option-chain/XLF?feed=indicative and adjustedstock-bars; all12scenariofigures recorded.
paths_each100000
steps52
scenarios12
call605_stop0.03
call605_target0.11
call605_premium7
net_target_fullrisk_RR0.53
blend_mean-3.46
HAR_mean-3.12
low_mean-3.73
quarterKelly0
actual_option_sample0
Helium cost sensitivity: spot doubling is not distributor profit doubling
Premises: 1. Completed later SPLV monthly cohorts44, win29.5455%, average win1.32691%, average loss1.10213%, mean-.38446% after assumed10bp one-way cost. Quarter-Kelly max(0,.25*(p-(1-p)/(W/L)))=0. 2. Circular3month blocks resampled from these44 observed monthly payoffs,10000 paths,seed90603,horizon6months. A hypothetical12.5% of fund cash per monthly trade (not recommended) yields terminal5th/median/95th percentiles -.82239/-.25872/+.34792%; loss probability77.67%; 95th monthly-marked drawdown.82239%. 3. No simulated path earns349.01 on245.69 fund cash; actual size remains0. Simulation assumes stationary payoffs, monthly marks and no unseen jumps, and does not calibrate real 15minute stops or intraday drawdown. An alternative selected EEMV point estimate has positive naive quarterKelly0.6355% but confidence includes losses and50bp stress loses29.04%, so it does not qualify. Three own liquidated trades separately give p33.33%, W7.93994%, L5.26143%, mean-.860974%, clipped quarterKelly0; those are operator exits, not natural strategy outcomes. Source https://runway.adityaperswal.workers.dev/data/stock-bars ; query_trades fund=quant ; saved trades.json/simulation.json.
sample44
paths10000
months6
win rate29.55
average win1.33
average loss1.1
quarter Kelly0
terminal p05 pct-0.82
terminal median pct-0.26
terminal p95 pct0.35
loss probability pct77.67
actual notional0
Quarter-Kelly remains zero under ETH empirical payoff stress
NumPy seed20260906 after two20000-sample payoff bootstraps;10000 draws from166 contiguous five-session 2026 blocks. Scale each OHLC/previous-close sequence to83.57, freeze stop81.422143,target86.791786. Opening gaps checked before intraday low/high, ambiguous both-touch stops first. Terminal5/50/95 percentiles -8.533444/-.724514/+7.272082%; stop-first60.45%,target-first30.84%,neither8.71%;95th percentile close-path drawdown8.988345%. Full code/input archive this run; source https://runway.adityaperswal.workers.dev/data/stock-bars . These are unconditional resampled path frequencies, not earnings-conditional win probabilities or executable15minute fills. Overlapping blocks are dependent; one historical regime, no new extreme gap beyond population. Prices/levels diagnostic only. Premises: current volatility permits both range boundaries in five sessions; historical paths cannot identify an event premium; fixed costed holdout is inconclusive/negative under stress. Conclusion size0, not a bearish forecast. Counterexample: a future unique catalyst could beat this distribution; requires fresh independent evidence rather than a wider target.
paths10000
blocks166
stop first percent60.45
target first percent30.84
neither percent8.71
terminal p5 percent-8.53
terminal median percent-0.72
terminal p95 percent7.27
p95 drawdown percent8.99
allocation0
Calibrated QQQ cheap-call exit odds fail without assumed volatility repricing
Using fixed diagnostic's5net natural payoffs, NumPyseed906 produces10000IID paths each5trades, reinvest10%changingcapital pertrade. Terminal5/50/95percentiles[-2.2383,11.7462,28.4979]%,lossfrequency8.11%,95%trade-stepDD3.1173%. Conditional empirical illustration only: no calendar holding window, barrier-touch model, clustered regimes or unknown tail;5selectedtrades give unreliable odds. QuarterKelly plugin .25*(p/avgloss-(1-p)/avgwin)=1.56943capitalfraction fromp.4,avgwin.661459,avgloss.055673. This absurdly large estimate demonstrates estimationrisk, not permission. Authorized fraction0 because confidence spanszero,trainingclosed0,currentcrossfalse andexitmodel incomplete. $212.7475positioncap and$8.5099nominalriskbudget do not establish edge. GEV941.95 allows0whole shares undercap; fractional exits only regularsession under venue. Illustrative10%fundallocation27.867 would also requirefractionalshare. Source https://runway.adityaperswal.workers.dev/data/stock-bars ; archive2958–2997, audit.py. No order.
paths10000
trades_per_path5
allocation_fraction0.1
terminal_p5-2.24
terminal_median11.75
terminal_p9528.5
loss_probability0.08
dd953.12
raw_quarterKelly1.57
authorized_fraction0
Sector reversal 19-session cash-risk simulation supports zero allocation
NumPyseed90504,10000paths perasset: draw3 complete24h logreturnblocks from diagnostic holdout, concatenate72h; BTC/SOL325 blocks,ETH324. ReferenceSep4close,1ADRstop/2ADRtarget widths BTC3.667/7.334%,ETH4.834/9.669%,SOL6.405/12.810%. Hourlyclose target-before-stop frequencies5.57/7.02/3.73%,stopfirst30.27/31.76/24.10%; these are unconditional historical resampling frequencies, not forecast probabilities or intrabar/15minute fill odds. 72h terminal5/95percentiles BTC-6.423/+6.226%,ETH-9.029/+9.244%,SOL-9.596/+9.651%. Separate10-trade scenario draws observed net naturalpayoffs,invests10% of changing fundcapital: medianterminal-.241/-.881/-2.581%,lossfrequency57.70/71.76/93.33%. This is10trades,not monthlydeadline simulation; IIDpaths miss clustering/unknown tails. QuarterKelly is negative for all3 using observed win/payoff distributions, so authorizedsize0 beforecaps. Stop-only ceilings min(235.86,212.77,8.5108/stopwidth) BTC212.77,ETH176.05,SOL132.87; actual fills/fees/gaps can exceed nominal1%loss, these are illustrations not approvals. Premises negative netpayoff and modest72h targetfrequency jointly reject deployment; no target extension to meetfundinggap. Sources https://runway.adityaperswal.workers.dev/data/crypto-bars ; https://fapi.binance.com/fapi/v1/fundingRate ; observations1762-1764 and code risk.py.
paths10000
horizon hours72
BTC target first pct5.57
ETH target first pct7.02
SOL target first pct3.73
SOL 10trade loss frequency0.93
BTC nominal ceiling212.77
ETH nominal ceiling176.05
SOL nominal ceiling132.87
authorized dollars0
Protein empirical paths: historical trade horizons do not establish new-event odds