CASE 12
The Intrabar Fill That Never Happened
Open 100, high 110, low 90, close 105. Your target is 108 and stop is 95. The bar touched both. Which order filled first?
Validation verdict for intrabar fill ordering
When both protective and profit orders are reachable inside one historical bar, the result depends on intrabar path information or broker-emulator assumptions. OHLC endpoints alone may not identify the real sequence.
What the headline metric obscures about intrabar fill ordering
A backtest reports a single completed trade—winner or loser—so the fill appears factual. Yet the candle summary contains only four prices, not every tick or the exact path from open to high, low and close.
The ambiguity is especially dangerous for tight-stop strategies, bracket orders, scalps and systems that enter and exit on the same bar. A small number of favorable same-bar decisions can create most of the reported edge.
How intrabar fill ordering enters the backtest
OHLC omits path order
The high and low prove that two prices were reached, but not necessarily which came first after entry.
The emulator uses an assumed path
Historical order fills follow platform rules unless lower-timeframe detail is available and enabled.
Entry timing changes eligibility
An order created at bar close, open or after a fill has different earliest execution opportunities.
Recalculation can chain orders
Settings that recalculate after fills or process orders on close can generate several decisions within one bar.
Compact reconstruction of intrabar fill ordering
| Assumed path after entry | First level hit | Trade result | Net effect over 18 ambiguous bars | Verdict |
|---|---|---|---|---|
| Open → high → low → close | Target 108 | +1.6R | +28.8R | Optimistic path |
| Open → low → high → close | Stop 95 | −1.0R | −18.0R | Adverse path |
| Lower-TF reconstruction | Mixed | +0.2R avg | +3.6R | Evidence-based |
| Ambiguous bars excluded | — | — | +1.1R total strategy | Edge nearly gone |
The same higher-timeframe candles support opposite results. The correct response is not to choose the path you prefer, but to obtain lower-timeframe evidence where possible and stress every unresolved bar both ways.
The test that can overturn the intrabar fill ordering verdict
Flag every trade where entry, stop and target could coexist within one bar. Reconstruct with lower-timeframe data where supported; otherwise calculate best-case and worst-case paths.
What trade-list analysis can and cannot identify about intrabar fill ordering
Export-level red flags for intrabar fill ordering
- Many trades enter and exit on the same candle
- Both stop and target are inside one bar repeatedly
- Most profit comes from very tight brackets on coarse timeframes
- Bar Magnifier or lower-timeframe coverage changes the result sharply
- Order-processing settings are undocumented
What the export reveals about intrabar fill ordering
- Same-bar frequency and concentration of P&L in short-duration trades from timestamps
- Differences between default and lower-timeframe/backtest versions when both exports are supplied
- Best/adverse/exclusion stress scenarios for identified ambiguous trades
- Whether the strategy remains profitable after removing unresolved fills
What intrabar fill ordering still requires from settings, code, or market data
- A trade list without lower-timeframe prices cannot reconstruct the true intrabar path. It can only flag and stress the ambiguity.
- Even lower-timeframe bars are not tick data and may retain ambiguity. Live queue position, partial fills and liquidity remain outside a simple historical emulator.
Turn intrabar fill-order ambiguity into a falsifiable backtest diagnosis.
Case file 12/20 · OHLC-PATH · one failure mechanism, one falsifiable protocol
Research abstract: intrabar fill ordering
Case file 12/20 · OHLC-PATH · one failure mechanism, one falsifiable protocol
This article tests one central proposition: OHLC alone cannot identify O→H→L→C versus O→L→H→C, and silently choosing the optimistic ordering manufactures profit. The question is not merely whether the displayed net profit or win rate was arithmetically calculated. The deeper identification problem is whether we know what constitutes one observation, what information was available at the decision time, which assumptions are necessary for the profit to exist, and how much of the conclusion survives when those assumptions are perturbed. The research object is therefore not one performance table; it is the linked data-generation, fill-generation, estimation, selection, and capital-allocation process.
The primary estimand is an identified interval of fill P&L that remains valid when the within-bar price order is unknown. The observation unit is defined as an OHLC bar touching multiple order prices and the set of feasible within-bar paths. Without this definition, split fills, duplicated signals, common events, synthetic prices, or timestamp conversions can be double-counted as independent evidence. A larger row count does not necessarily contain more independent information. An academically defensible analysis fixes the relationship between the observation unit and the estimand before it reports sample size, standard error, or statistical confidence.
The principal sensitivity axes are optimistic, pessimistic, random-bridge, and lower-timeframe replay assumptions. The hidden state is the order of high and low, same-bar target/stop competition, and queue position. In particular, one long-wick bar touching both target and stop can determine the sign of total P&L. Means and medians alone are incapable of describing that mechanism, so the analysis combines central estimates with lower quantiles, expected shortfall, sign stability, boundary-hitting frequency, and contribution concentration. The objective is not to find one pessimistic number, but to map the full region in which the original conclusion changes sign or ceases to be economically usable.
The conclusion does not attempt to prove that a backtest is good. It separates the component that remains after attempted falsification from the component that disappears when assumptions are reconstructed. The governing decision principle is to enumerate ambiguous bars and report optimistic/pessimistic bounds plus lower-timeframe results instead of treating one fill as fact. This is not trading advice; it is a research procedure for measuring how much evidentiary weight a TradingView trade export can carry. Liquidity not present in the file, broker-specific rules, future regimes, outages, and gaps require separate evidence, and statistical survival never guarantees future profit.
The numerical values illustrate the method for intrabar fill-order ambiguity; they are not a real strategy, client record, or forecast.
Hypotheses and identification target for intrabar fill ordering
an identified interval of fill P&L that remains valid when the within-bar price order is unknown
H₀ for intrabar fill ordering: The reported performance is not materially dependent on the suspected failure mechanism and survives reasonable perturbations.
H₁ for intrabar fill ordering: The reported performance depends materially on the suspected failure mechanism and deteriorates after reconstruction, perturbation, or dependence-aware resampling.
an identified interval of fill P&L that remains valid when the within-bar price order is unknown
an OHLC bar touching multiple order prices and the set of feasible within-bar paths
the order of high and low, same-bar target/stop competition, and queue position
optimistic, pessimistic, random-bridge, and lower-timeframe replay assumptions
Formal estimands for intrabar fill ordering
Definitions precede inference.
Ω_b={p∈C([0,1],[L,H]):p(0)=O,p(1)=C,min p=L,max p=H}Continuous intrabar price paths that begin at O, end at C, touch H and L, and are consistent with the same OHLC bar.Π_b⁻=inf_{p∈Ω_b}Π_b(p), Π_b⁺=sup_{p∈Ω_b}Π_b(p)Lower and upper P&L bounds when the intrabar path is not identified; infimum and supremum avoid assuming the extrema are attained.A=n_ambiguous/n_fillsFraction of fills whose order-hit sequence cannot be identified from OHLC alone.The primary estimand is an identified interval of fill P&L that remains valid when the within-bar price order is unknown. The observation unit is defined as an OHLC bar touching multiple order prices and the set of feasible within-bar paths. Without this definition, split fills, duplicated signals, common events, synthetic prices, or timestamp conversions can be double-counted as independent evidence. A larger row count does not necessarily contain more independent information. An academically defensible analysis fixes the relationship between the observation unit and the estimand before it reports sample size, standard error, or statistical confidence.
The principal sensitivity axes are optimistic, pessimistic, random-bridge, and lower-timeframe replay assumptions. The hidden state is the order of high and low, same-bar target/stop competition, and queue position. In particular, one long-wick bar touching both target and stop can determine the sign of total P&L. Means and medians alone are incapable of describing that mechanism, so the analysis combines central estimates with lower quantiles, expected shortfall, sign stability, boundary-hitting frequency, and contribution concentration. The objective is not to find one pessimistic number, but to map the full region in which the original conclusion changes sign or ceases to be economically usable.
Illustrative recomputation design for intrabar fill ordering
For the intrabar ordering reconstruction, table values are illustrative calculations used to expose a verdict reversal; they are not a user’s observed TradingView result.
| ID | Recomputation layer | Operation | Comparison | Diagnostic purpose |
|---|---|---|---|---|
| S0 | Reported result | Restate the Strategy Tester aggregate | Base | Apparent conclusion |
| S1 | Unit reconstruction | an OHLC bar touching multiple order prices and the set of feasible within-bar paths | Reassess count and dependence | Information correction |
| S2 | Independent recomputation | Rebuild price, size, cost, and currency row by row | Separate reconciliation error | Measurement validity |
| S3 | Local stress | optimistic, pessimistic, random-bridge, and lower-timeframe replay assumptions | Perturb one factor only | Causal sensitivity |
| S4 | Tail injection | one long-wick bar touching both target and stop can determine the sign of total P&L | Recompute lower quantiles and boundary hits | Capital preservation |
| S5 | Dependence-aware resampling | Generate paths across several block lengths | Intervals and sign stability | Estimation uncertainty |
| S6 | Selection adjustment | Log search, OOS review, and exclusions | Correct maximum-selection bias | Generalization |
| S7 | Full gate | enumerate ambiguous bars and report optimistic/pessimistic bounds plus lower-timeframe results instead of treating one fill as fact | Compare with predeclared thresholds | Pass / hold / reject |
The illustrative recomputation for intrabar fill ordering changes one processing layer at a time, then combines only predeclared layers. S0 is never treated as ground truth; it is the statement to be audited. S1 and S2 ask whether the exported unit and arithmetic are coherent. S3 and S4 identify local sensitivity and tail failure. S5 changes the uncertainty model rather than the trade list. S6 adjusts for the search that preceded publication. S7 applies the same gate to every version. This order prevents an adverse result from being explained away by simultaneously changing several assumptions.
In the intrabar ordering figures, color and position encode diagnostic sensitivity only; they do not represent statistical significance or future P&L.
Diagnostic figures specific to intrabar fill ordering
Four separate visual tests; no decorative chart reuse.
Multi-layer audit questions for intrabar fill ordering
A result is only as strong as its weakest unresolved layer.
An independent verifier should, fix the estimand as “an identified interval of fill P&L that remains valid when the within-bar price order is unknown.” Do not substitute net profit, win rate, or a visually smooth curve for that target. Declare the horizon, account currency, included frictions, and operating-stop boundary before calculation. Any post-result change creates a new hypothesis and version, preventing the question from being selected after the answer is known.
Reconstruct the observation unit as “an OHLC bar touching multiple order prices and the set of feasible within-bar paths” before treating rows as independent evidence. Report raw rows, parent trades, decisions, event clusters, and the denominator used for each average or standard error. Recompute ambiguous-fill rate, pessimistic bound, optimistic bound, identification-interval width, and lower-timeframe replay difference under more than one defensible aggregation rule so that a larger export is not mistaken for a larger information set.
Preserve the hash of the TradingView export and the symbol, timeframe, session, timezone, order-processing settings, costs, account currency, and Pine version. For intrabar fill-order ambiguity, the order of high and low, same-bar target/stop competition, and queue position directly affects reproducibility. Keep immutable source, normalized, and analysis layers separate, with every join, deletion, imputation, and conversion recorded in a transformation ledger.
The export identifies only what can be rebuilt from recorded time, price, quantity, and P&L. tick sequence, order book, queue position, and completeness of lower-timeframe history requires additional evidence. Mark each causal link as observed, bounded by assumption, or externally unverified. This prevents the order of high and low, same-bar target/stop competition, and queue position from being presented as a confirmed fact when the available data support only an interval or conditional conclusion.
Do not adopt the platform summary as ground truth. Independently define the set of intrabar paths consistent with OHLC and compute lower and upper P&L bounds across possible target, stop, and limit hit orders. Reconcile total and row-level differences by sign, date, symbol, and order type. If discrepancies concentrate in the exact state associated with intrabar fill-order ambiguity, treat that concentration as a primary finding rather than dismissing it as rounding.
Report ambiguous-fill rate, pessimistic bound, optimistic bound, identification-interval width, and lower-timeframe replay difference with intervals or resampling distributions, not point estimates alone. Match the uncertainty method to sample size, skewness, heavy tails, censoring, and selection history. If normal, quantile, and dependence-aware methods disagree on the sign, classify the edge as unidentified and show the minimum detectable effect and lower decision bound.
Do not narrow uncertainty with an IID shuffle alone. Resample time blocks containing ambiguous bars and validation segments with lower-timeframe data using several fixed block lengths and stationary bootstrap. Preserve random seed, repetition count, wrap rule, and missing-data treatment. For each block specification, report the distribution of ambiguous-fill rate, pessimistic bound, optimistic bound, identification-interval width, and lower-timeframe replay difference, the rejection-side tail mass, and the rate at which the verdict changes sign.
Interrogate the mechanism “the order of high and low, same-bar target/stop competition, and queue position” with lower quantiles, expected shortfall, influence, cluster length, and boundary-hitting measures. Historical maximum loss is not a loss cap. Define several absorbing or operating boundaries—capital, margin, mandate drawdown, and recovery time—and record which boundary fails first under each stress.
A flat commission deduction is not an execution model for intrabar fill-order ambiguity. Allocate spread, slippage, financing, borrow, roll, conversion, rounding, and rejected orders to the relevant unit. Recompute ambiguous-fill rate, pessimistic bound, optimistic bound, identification-interval width, and lower-timeframe replay difference under base, upper-quantile, and crisis states while preserving the possibility that costs and losses worsen together.
Count the complete population of periods, symbols, timeframes, parameters, exits, filters, and metrics that were tried. Do not detach the attractive result for intrabar fill-order ambiguity from rejected candidates, interim changes, or repeated validation reviews. Where appropriate, use PBO, SPA, and a Deflated Sharpe Ratio, and treat an unrecorded trial count as a material audit limitation.
Test whether intrabar fill-order ambiguity is concentrated in one trend, volatility, liquidity, rate, or session state. Define regimes prospectively or on training data only. Report statewise ambiguous-fill rate, pessimistic bound, optimistic bound, identification-interval width, and lower-timeframe replay difference, occupancy, transition probabilities, and costs, then reweight the mixture to adverse but realistic future compositions.
For the intrabar fill ordering case, the same trade set can follow different capital paths under another inception date, order, initial balance, rounding rule, or stop condition. Separate fixed quantity, fixed R, and percentage sizing, then use circular shifts and block orderings to recompute drawdown, recovery, and boundary hits. Equal terminal P&L does not imply equal path risk.
Perturb “optimistic, pessimistic, random-bridge, and lower-timeframe replay assumptions” one axis at a time before creating a joint sensitivity surface. Add the negative control “process every ambiguous bar under both pessimistic and optimistic hit order and test whether the verdict agrees at both bounds.” Predefine the grid and crisis rule so that neither the most favorable nor the most damaging cell is selected after inspection. Save the slope, curvature, and exact point where the decision boundary is crossed.
Have a second implementation define the set of intrabar paths consistent with OHLC and compute lower and upper P&L bounds across possible target, stop, and limit hit orders, then compare critical row-level outputs. Regression fixtures should include empty files, duplicate timestamps, extreme costs, reverse ordering, missing values, and boundary cases. Agreement between implementations is insufficient if they share the same bad input, so separate data construction and review roles where feasible.
Predeclare the decision rule. This case passes only if “the pessimistic bound clears the expectancy threshold, or at minimum the identification interval does not cross the decision boundary.” Near a boundary, disclose interval width and economic materiality rather than a binary badge. If only one favorable block length, cost state, or implementation passes, classify the result as assumption-sensitive rather than robust.
The evidence ledger must store the input hash, code version, settings, exclusions, “optimistic, pessimistic, random-bridge, and lower-timeframe replay assumptions,” block lengths, random seed, repetition count, and every scenario output. Keep exploratory and confirmatory results in separate namespaces and retain failed trials. When new TradingView data arrive, create a new version and track ambiguous-bar rate, identification width, optimistic-fill contribution, and lower-timeframe coverage rather than overwriting the old result.
Translate statistical changes into capital consequences. A shift in expectancy, lower quantile, recovery time, or boundary risk caused by intrabar fill-order ambiguity should be mapped to trade count, capital, margin, and continuation. A small per-trade difference can compound under high turnover, while a rare loss can be decisive near an absorbing boundary.
Separate hypothesis design, implementation, independent recalculation, and approval where practical. Stop automatically on material reconciliation error, unresolved missing data, non-reproducibility, or a predeclared threshold breach. Audit the chain “OHLC-only observation → assumed intrabar order → optimistic fill → overstated P&L → reversal under tick or lower-timeframe replay,” and monitor ambiguous-bar rate, identification width, optimistic-fill contribution, and lower-timeframe coverage prospectively without turning a historical pass into a promise of future profit.
Falsification protocol for intrabar fill ordering
enumerate ambiguous bars and report optimistic/pessimistic bounds plus lower-timeframe results instead of treating one fill as fact
Freeze the TradingView source for the intrabar fill ordering audit
Store the export without alteration and record its hash, export time, strategy, symbol, timeframe, and settings. Preserve every column relevant to intrabar fill-order ambiguity; deletions and imputations belong only in derived tables.
Reconstruct the observation unit for intrabar fill ordering
Aggregate rows into “an OHLC bar touching multiple order prices and the set of feasible within-bar paths,” and report raw rows, parent trades, events, and independent clusters. Recompute the critical result under another defensible aggregation.
Independently recompute the displayed intrabar fill ordering result
Independently define the set of intrabar paths consistent with OHLC and compute lower and upper P&L bounds across possible target, stop, and limit hit orders. Reconcile row-level and aggregate outputs with Strategy Tester and preserve where discrepancies concentrate.
Isolate the intrabar fill ordering mechanism
Treat intrabar fill-order ambiguity as the principal mechanism and move “optimistic, pessimistic, random-bridge, and lower-timeframe replay assumptions” one axis at a time while holding other settings fixed.
Map the operating boundary for intrabar fill ordering
Combine the primary and interacting axes on a predeclared grid and recompute ambiguous-fill rate, pessimistic bound, optimistic bound, identification-interval width, and lower-timeframe replay difference. Record the width and connectivity of the acceptable region and every boundary crossing.
Resample the dependence structure relevant to intrabar fill ordering
Use time blocks containing ambiguous bars and validation segments with lower-timeframe data with several fixed block lengths and stationary bootstrap. Save every random seed, repetition count, and block specification.
Inspect influence points and operating boundaries for intrabar fill ordering
For the intrabar ordering influence test, remove the largest contributor, top-k contributors, selected periods, and relevant regimes in sequence; then recompute lower-tail measures and the operating boundary.
Apply negative controls and conservative bounds to intrabar fill ordering
Process every ambiguous bar under both pessimistic and optimistic hit order and test whether the verdict agrees at both bounds. Bound tick sequence, order book, queue position, and completeness of lower-timeframe history as unobserved factors rather than elevating the optimistic value into the final answer.
Apply the predeclared gate to intrabar fill ordering
Do not move the threshold after seeing results. Compare with “the pessimistic bound clears the expectancy threshold, or at minimum the identification interval does not cross the decision boundary,” and distinguish pass, hold, and reject. Any unresolved material mismatch causes a hold.
Save a reproducible evidence package for intrabar fill ordering
Bundle the source, transformation ledger, formulas, figures, all scenarios, failure logs, and code version for rerun in another environment. Prospectively monitor ambiguous-bar rate, identification width, optimistic-fill contribution, and lower-timeframe coverage.
Decision gate for intrabar fill ordering
Reject the story before trusting the curve.
How to read the intrabar fill ordering figures and equations
The figures for intrabar fill ordering use illustrative recomputations constructed to expose this specific failure mode. Do not infer statistical significance from line position or color alone; first verify the estimand, units, denominator, censoring rule, and cost sign defined by the equations. A sensitivity surface is not a causal estimate. It shows how a conclusion changes only within the stated assumptions. Resampling should compare an IID shuffle with stationary and block bootstrap procedures across several block lengths so that loss clustering and regime persistence are not silently destroyed. Store the random seed, iteration count, block length, bandwidth, and missing-data treatment, and claim reproducibility only after an independent implementation reproduces the same aggregates.
This case passes only if “the pessimistic bound clears the expectancy threshold, or at minimum the identification interval does not cross the decision boundary” across reconstructed values, local perturbations, joint sensitivity, dependence-preserving resampling, and the negative control, with no material sign reversal or unresolved reconciliation error. A pass is limited evidence against the stated failure mode, not certification of future profit.
- The estimand and observation unit were fixed before outcomes were reviewed
- For intrabar ordering, any material disagreement between reported and independently recomputed values must be resolved or explicitly explained.
- The intrabar ordering claim passes this gate only when its acceptable stress region is broad and connected rather than one isolated favorable island.
- The sign of the intrabar ordering estimate must remain stable across defensible block lengths, saved seeds, and reasonable interval methods.
- For intrabar fill ordering, economic margin remains after deleting the largest and top-five contributors and key regimes
- For intrabar fill ordering, conservative cost, fill, and capital-boundary scenarios remain inside the stopping mandate
Limitations, external validity, and reproducibility of the intrabar fill ordering audit
Every inference has a boundary.
The first limitation is that a trade export does not contain the complete market state. If order-book depth, queue position, network latency, rejected orders, broker liquidity, or realized financing history is absent, an identified interval of fill P&L that remains valid when the within-bar price order is unknown remains model-mediated. Model outputs should be displayed as scenario ranges and must not be formatted as though they were directly observed facts.
A second limitation specific to the intrabar fill ordering analysis is structural change. A long historical sample does not guarantee a common population when market rules, participants, volatility, rates, spreads, data construction, or Pine execution semantics change. Do not increase nominal sample size by indiscriminately pooling old periods. Estimate rolling and regime-conditioned behavior and test parameter stability around detected changes.
A third limitation specific to the intrabar fill ordering analysis is reuse of the diagnostic battery. Applying these tests repeatedly to the same data and editing the strategy until it passes turns the diagnostic process itself into another optimizer. Every post-test edit starts a new model version and requires untouched or prospective evidence. A test chosen after reading the outcome belongs to exploration and cannot be counted as independent confirmation.
A fourth limitation for the intrabar fill ordering analysis is the distinction between statistical survival and operational suitability. Behavioral tolerance, locked capital, tax, regulation, outages, account terms, order-size limits, market-order restrictions, and liquidity discontinuities cannot be resolved from a CSV alone. The lab is a diagnostic for discovering hidden failure risk earlier; it is not investment advice, a performance warranty, or a guarantee of bounded loss. User-specific constraints remain a separate decision layer.
The estimand “an identified interval of fill P&L that remains valid when the within-bar price order is unknown” is identified only within the columns present in the TradingView export and the stated assumptions. If tick sequence, order book, queue position, and completeness of lower-timeframe history cannot be observed, report bounds rather than a false point estimate.
Past estimates of intrabar fill-order ambiguity need not belong to the same population after changes in rules, participants, volatility, costs, or data specifications. Track ambiguous-bar rate, identification width, optimistic-fill contribution, and lower-timeframe coverage in rolling and regime-specific windows.
For intrabar ordering, repeatedly applying the same diagnostic battery and editing until it passes turns verification into another optimizer. Every post-audit change therefore creates a new model version and requires untouched evidence.
Even if the pessimistic bound clears the expectancy threshold, or at minimum the identification interval does not cross the decision boundary, the analysis does not establish tax, regulatory, behavioral, liquidity, order-size, or systems suitability. Separate statistical diagnosis from live-operating approval.
Deleting observations related to the order of high and low, same-bar target/stop competition, and queue position may improve the result. Compare no deletion, conservative imputation, and worst-case imputation, and display how ambiguous-fill rate, pessimistic bound, optimistic bound, identification-interval width, and lower-timeframe replay difference changes.
Run the control “process every ambiguous bar under both pessimistic and optimistic hit order and test whether the verdict agrees at both bounds.” If the control performs similarly, suspect processing rules or common market drift before attributing performance to the strategy.
After a provisional pass, log ambiguous-bar rate, identification width, optimistic-fill contribution, and lower-timeframe coverage sequentially and stop on persistent departures from the predeclared predictive range. Diagnose implementation drift before reoptimizing history.
Multiple methods can agree because they share the same bad input or the same mechanism “the order of high and low, same-bar target/stop competition, and queue position.” Give lower-tail outcomes, failed scenarios, and unresolved mismatches the same visual prominence as favorable results; test count is not proof of correctness.
Independent and adversarial findings for intrabar fill ordering
The intrabar ordering case has a separate review line for formulas, chart encodings, data definitions, and falsifiability so agreement on one layer cannot mask failure on another.
The formula audit checks numerator, denominator, sign, unit, domain, and every conditioning assumption as one system. The material caution for this case is: The set Ω_b of paths consistent with one OHLC bar cannot recover the actual tick path. Define P&L bounds with infimum and supremum rather than assuming extrema are attained. The continuous-path premise is itself insufficient in a gapping market and requires separate jump bounds. A correct symbolic expression can still calculate the wrong quantity when a column, currency, time unit, or fee sign is misdefined, so those mappings are part of the mathematical audit.
The figure audit assigns distinct jobs: Figure 1 diagnoses intrabar fill-order ambiguity; Figure 2 maps joint sensitivity; Figure 3 shows the dependence-preserving distribution of P&L bound under ambiguous fills; Figure 4 traces causal propagation. Color denotes distance to a predeclared gate, not probability or observed performance. Axis units, zero, quantiles, censoring, and bounds must agree with captions and tables. A smooth SVG line is explanatory geometry, not evidence of estimation precision.
The adversarial test does not cherry-pick one hostile scenario. It uses the negative control “process every ambiguous bar under both pessimistic and optimistic hit order and test whether the verdict agrees at both bounds,” resamples time blocks containing ambiguous bars and validation segments with lower-timeframe data at several block lengths, and bounds tick sequence, order book, queue position, and completeness of lower-timeframe history as unobserved factors. Repetitions, seeds, exclusions, block specifications, and plotting range are frozen before results so the implementer cannot tune the audit after seeing the answer.
The independent conclusion is restricted to whether “the pessimistic bound clears the expectancy threshold, or at minimum the identification interval does not cross the decision boundary.” It does not certify a good strategy or future profit. Any material reconciliation error, formula-domain violation, table-figure contradiction, sign reversal across defensible block lengths, or failure to outperform the negative control produces hold or reject. Prospectively, monitor ambiguous-bar rate, identification width, optimistic-fill contribution, and lower-timeframe coverage.
Methodological references for intrabar fill ordering
Primary methods and official platform documentation.
- TradingView Pine Script® documentation: Strategies.
- TradingView Pine Script® documentation: Other timeframes and data.
- TradingView Pine Script® documentation: Chart information.
- Efron, B. (1979). Bootstrap Methods: Another Look at the Jackknife. Annals of Statistics.
- Politis, D. N. & Romano, J. P. (1994). The Stationary Bootstrap. JASA.
- Newey, W. K. & West, K. D. (1987). A Simple, Positive Semi-definite, Heteroskedasticity and Autocorrelation Consistent Covariance Matrix. Econometrica.
- Lo, A. W. (2002). The Statistics of Sharpe Ratios. Financial Analysts Journal.
- White, H. (2000). A Reality Check for Data Snooping. Econometrica.
References for the intrabar fill ordering case provide methodological context; they do not validate the synthetic numbers in this article or certify any backtest result. TradingView documentation is used for platform semantics, while statistical papers motivate uncertainty and selection controls.
Frequently asked questions about intrabar fill ordering
Does Bar Magnifier guarantee real fills?
It can use lower-timeframe historical detail for more precise simulation, but it is still a model with data-availability and granularity limits.
Are market orders affected?
Yes, especially around order creation timing, gaps and same-bar recalculation. The exact issue depends on the strategy settings and order types.
What if only a few bars are ambiguous?
Measure their profit contribution. One or two bars can still control the entire result.
Can a backtest exposed to intrabar fill ordering be trusted?
Do not judge the intrabar ordering case from a finished equity curve alone. Use the TradingView trade list to inspect the mechanism-specific concentration, path, cost, timing, and dependence evidence shown on this page.
Important limitations for the intrabar fill ordering analysis
This article provides educational, descriptive analysis of constructed backtest failure examples. It is not investment advice, a buy or sell signal, a forecast or a promise of performance. Backtest results depend on data, code, broker-emulator assumptions, costs, sizing and market structure. TradingView is a trademark of TradingView, Inc.; SG Group is independent and does not claim endorsement or sponsorship by TradingView.
Counterpart: 実際には起きていない約定｜同一バー内の注文順序が作る幻