When Point Value and Currency Conversion Make the Backtest P&L Wrong

Under adversarial review, fix the estimand as “account-currency P&L reconciled with dimensionally consistent contract multipliers, point values, and FX conversion.” 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 “price difference, quantity, contract specification, and conversion timestamp for every fill leg” 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 row-level reconciliation error, multiplier error, conversion-direction difference, conversion-time difference, accumulated rounding, and dimensional-check failures 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 contract-unit and currency-conversion mismatch, instrument multipliers, pip/tick value, inverse currencies, timestamped FX rates, and quanto terms 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. exchange specification changes, broker-specific multipliers, quanto contracts, and the source of FX conversion rates requires additional evidence. Mark each causal link as observed, bounded by assumption, or externally unverified. This prevents instrument multipliers, pip/tick value, inverse currencies, timestamped FX rates, and quanto terms 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 recompute account-currency P&L for every fill leg with explicit direction, quantity, price change, contract multiplier, tick value, FX direction, and conversion time. Reconcile total and row-level differences by sign, date, symbol, and order type. If discrepancies concentrate in the exact state associated with contract-unit and currency-conversion mismatch, treat that concentration as a primary finding rather than dismissing it as rounding.

Report row-level reconciliation error, multiplier error, conversion-direction difference, conversion-time difference, accumulated rounding, and dimensional-check failures 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 asset blocks that preserve contract specification and conversion currency 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 row-level reconciliation error, multiplier error, conversion-direction difference, conversion-time difference, accumulated rounding, and dimensional-check failures, the rejection-side tail mass, and the rate at which the verdict changes sign.

Interrogate the mechanism “instrument multipliers, pip/tick value, inverse currencies, timestamped FX rates, and quanto terms” 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 contract-unit and currency-conversion mismatch. Allocate spread, slippage, financing, borrow, roll, conversion, rounding, and rejected orders to the relevant unit. Recompute row-level reconciliation error, multiplier error, conversion-direction difference, conversion-time difference, accumulated rounding, and dimensional-check failures 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 contract-unit and currency-conversion mismatch 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 contract-unit and currency-conversion mismatch is concentrated in one trend, volatility, liquidity, rate, or session state. Define regimes prospectively or on training data only. Report statewise row-level reconciliation error, multiplier error, conversion-direction difference, conversion-time difference, accumulated rounding, and dimensional-check failures, occupancy, transition probabilities, and costs, then reweight the mixture to adverse but realistic future compositions.

For the point value and currency conversion 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 “multiplier substitutions, conversion direction, entry/exit FX timing, and rounding” one axis at a time before creating a joint sensitivity surface. Add the negative control “run fixed fixtures with known price differences, quantities, and FX rates and compare the engine with hand-calculated values.” 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 recompute account-currency P&L for every fill leg with explicit direction, quantity, price change, contract multiplier, tick value, FX direction, and conversion time, 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 “every row is dimensionally consistent, reconciliation error is within tolerance, and defensible conversion times do not change the performance sign.” 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, “multiplier substitutions, conversion direction, entry/exit FX timing, and rounding,” 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 symbol-level reconciliation error, specification version, FX rate, reciprocal use, and rounding residual 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 contract-unit and currency-conversion mismatch 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 “unit mismatch → multiplicative row-level P&L error → accumulation in totals → wrong risk assessment → mismatch with the live account,” and monitor symbol-level reconciliation error, specification version, FX rate, reciprocal use, and rounding residual prospectively without turning a historical pass into a promise of future profit.

The estimand “account-currency P&L reconciled with dimensionally consistent contract multipliers, point values, and FX conversion” is identified only within the columns present in the TradingView export and the stated assumptions. If exchange specification changes, broker-specific multipliers, quanto contracts, and the source of FX conversion rates cannot be observed, report bounds rather than a false point estimate.

Past estimates of contract-unit and currency-conversion mismatch need not belong to the same population after changes in rules, participants, volatility, costs, or data specifications. Track symbol-level reconciliation error, specification version, FX rate, reciprocal use, and rounding residual in rolling and regime-specific windows.

For currency conversion, 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 every row is dimensionally consistent, reconciliation error is within tolerance, and defensible conversion times do not change the performance sign, 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 instrument multipliers, pip/tick value, inverse currencies, timestamped FX rates, and quanto terms may improve the result. Compare no deletion, conservative imputation, and worst-case imputation, and display how row-level reconciliation error, multiplier error, conversion-direction difference, conversion-time difference, accumulated rounding, and dimensional-check failures changes.

Run the control “run fixed fixtures with known price differences, quantities, and FX rates and compare the engine with hand-calculated values.” If the control performs similarly, suspect processing rules or common market drift before attributing performance to the strategy.

After a provisional pass, log symbol-level reconciliation error, specification version, FX rate, reciprocal use, and rounding residual 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 “instrument multipliers, pip/tick value, inverse currencies, timestamped FX rates, and quanto terms.” Give lower-tail outcomes, failed scenarios, and unresolved mismatches the same visual prominence as favorable results; test count is not proof of correctness.