The Overnight Costs Your Backtest Never Paid

For third-party reproduction, fix the estimand as “net holding-period P&L including financing, borrow, roll, holding days, and holiday treatment.” 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 “a position-by-holding-day-by-cost-type accrual record” 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 expectancy after carry, cost by holding duration, crisis-rate sensitivity, covariance with price loss, and break-even holding days 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 unmodeled overnight holding costs, daily swap, triple-charge days, holiday roll, variable borrow fees, and futures roll differentials 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. historical daily swap, borrow availability, broker triple-charge rules, and futures-roll execution requires additional evidence. Mark each causal link as observed, bounded by assumption, or externally unverified. This prevents daily swap, triple-charge days, holiday roll, variable borrow fees, and futures roll differentials 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 build a position-by-day-by-cost ledger and accrue directional swap, financing, borrow fees, triple-charge dates, holidays, and roll differences. Reconcile total and row-level differences by sign, date, symbol, and order type. If discrepancies concentrate in the exact state associated with unmodeled overnight holding costs, treat that concentration as a primary finding rather than dismissing it as rounding.

Report expectancy after carry, cost by holding duration, crisis-rate sensitivity, covariance with price loss, and break-even holding days 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 daily blocks that preserve long holding periods and persistent financing or borrow states 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 expectancy after carry, cost by holding duration, crisis-rate sensitivity, covariance with price loss, and break-even holding days, the rejection-side tail mass, and the rate at which the verdict changes sign.

Interrogate the mechanism “daily swap, triple-charge days, holiday roll, variable borrow fees, and futures roll differentials” 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 unmodeled overnight holding costs. Allocate spread, slippage, financing, borrow, roll, conversion, rounding, and rejected orders to the relevant unit. Recompute expectancy after carry, cost by holding duration, crisis-rate sensitivity, covariance with price loss, and break-even holding days 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 unmodeled overnight holding costs 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 unmodeled overnight holding costs is concentrated in one trend, volatility, liquidity, rate, or session state. Define regimes prospectively or on training data only. Report statewise expectancy after carry, cost by holding duration, crisis-rate sensitivity, covariance with price loss, and break-even holding days, occupancy, transition probabilities, and costs, then reweight the mixture to adverse but realistic future compositions.

For the overnight carrying costs 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 “base financing, crisis financing, holding duration, and placement of triple-charge days” one axis at a time before creating a joint sensitivity surface. Add the negative control “permute cost rates within the same period while preserving holding days to test whether positions concentrate specifically on expensive dates.” 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 build a position-by-day-by-cost ledger and accrue directional swap, financing, borrow fees, triple-charge dates, holidays, and roll differences, 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 “net expectancy clears the threshold under base, upper-quantile, and crisis carry costs and joint price-plus-carry loss remains absorbable.” 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, “base financing, crisis financing, holding duration, and placement of triple-charge days,” 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 holding days, daily cost rate, triple-charge exposure, borrow fee, and joint deterioration with price loss 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 unmodeled overnight holding costs 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 “zero-cost assumption → overstated long-hold profit → ignore financing and borrow stress → profitable headline → loss after real carry,” and monitor holding days, daily cost rate, triple-charge exposure, borrow fee, and joint deterioration with price loss prospectively without turning a historical pass into a promise of future profit.

The estimand “net holding-period P&L including financing, borrow, roll, holding days, and holiday treatment” is identified only within the columns present in the TradingView export and the stated assumptions. If historical daily swap, borrow availability, broker triple-charge rules, and futures-roll execution cannot be observed, report bounds rather than a false point estimate.

Past estimates of unmodeled overnight holding costs need not belong to the same population after changes in rules, participants, volatility, costs, or data specifications. Track holding days, daily cost rate, triple-charge exposure, borrow fee, and joint deterioration with price loss in rolling and regime-specific windows.

For overnight cost, 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 net expectancy clears the threshold under base, upper-quantile, and crisis carry costs and joint price-plus-carry loss remains absorbable, 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 daily swap, triple-charge days, holiday roll, variable borrow fees, and futures roll differentials may improve the result. Compare no deletion, conservative imputation, and worst-case imputation, and display how expectancy after carry, cost by holding duration, crisis-rate sensitivity, covariance with price loss, and break-even holding days changes.

Run the control “permute cost rates within the same period while preserving holding days to test whether positions concentrate specifically on expensive dates.” If the control performs similarly, suspect processing rules or common market drift before attributing performance to the strategy.

After a provisional pass, log holding days, daily cost rate, triple-charge exposure, borrow fee, and joint deterioration with price loss 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 “daily swap, triple-charge days, holiday roll, variable borrow fees, and futures roll differentials.” Give lower-tail outcomes, failed scenarios, and unresolved mismatches the same visual prominence as favorable results; test count is not proof of correctness.