The Regime That Generated Almost All Your Profits

First, fix the estimand as “state-conditional expectancy and aggregate performance under a future mixture of market regimes.” 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 trade or time interval carrying a predeclared regime label” 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 statewise expectancy, positive-profit concentration, occupancy, transition matrix, and performance under alternative future regime mixtures 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 profit concentration in one market regime, occupancy of trend, range, and stress states, state transitions, and ex-post regime classification 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. future regime occupancy, structural change in state definitions, and crisis execution costs requires additional evidence. Mark each causal link as observed, bounded by assumption, or externally unverified. This prevents occupancy of trend, range, and stress states, state transitions, and ex-post regime classification 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 classify trades with a regime rule fixed without looking at profit, then reconstruct aggregate performance from statewise expectancy, cost, occupancy, and transition probabilities. Reconcile total and row-level differences by sign, date, symbol, and order type. If discrepancies concentrate in the exact state associated with profit concentration in one market regime, treat that concentration as a primary finding rather than dismissing it as rounding.

Report statewise expectancy, positive-profit concentration, occupancy, transition matrix, and performance under alternative future regime mixtures 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 that preserve regime duration and transitions 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 statewise expectancy, positive-profit concentration, occupancy, transition matrix, and performance under alternative future regime mixtures, the rejection-side tail mass, and the rate at which the verdict changes sign.

Interrogate the mechanism “occupancy of trend, range, and stress states, state transitions, and ex-post regime classification” 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 profit concentration in one market regime. Allocate spread, slippage, financing, borrow, roll, conversion, rounding, and rejected orders to the relevant unit. Recompute statewise expectancy, positive-profit concentration, occupancy, transition matrix, and performance under alternative future regime mixtures 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 profit concentration in one market regime 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 profit concentration in one market regime is concentrated in one trend, volatility, liquidity, rate, or session state. Define regimes prospectively or on training data only. Report statewise statewise expectancy, positive-profit concentration, occupancy, transition matrix, and performance under alternative future regime mixtures, occupancy, transition probabilities, and costs, then reweight the mixture to adverse but realistic future compositions.

For the regime profit concentration 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 “state definition, transition probabilities, occupancy weights, and state-specific costs” one axis at a time before creating a joint sensitivity surface. Add the negative control “freeze regime labels outside the training period and compare with any ex-post profitable classification.” 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 classify trades with a regime rule fixed without looking at profit, then reconstruct aggregate performance from statewise expectancy, cost, occupancy, and transition probabilities, 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 “no major regime carries an unacceptable loss and aggregate expectancy clears the threshold under adverse but realistic regime mixtures.” 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, “state definition, transition probabilities, occupancy weights, and state-specific costs,” 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 statewise expectancy, profit contribution, occupancy, transition probability, and classification confidence 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 profit concentration in one market regime 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 “one prolonged regime → concentrated profit → dilution in the full-sample average → illusion of universality → loss after transition,” and monitor statewise expectancy, profit contribution, occupancy, transition probability, and classification confidence prospectively without turning a historical pass into a promise of future profit.

The estimand “state-conditional expectancy and aggregate performance under a future mixture of market regimes” is identified only within the columns present in the TradingView export and the stated assumptions. If future regime occupancy, structural change in state definitions, and crisis execution costs cannot be observed, report bounds rather than a false point estimate.

Past estimates of profit concentration in one market regime need not belong to the same population after changes in rules, participants, volatility, costs, or data specifications. Track statewise expectancy, profit contribution, occupancy, transition probability, and classification confidence in rolling and regime-specific windows.

For regime concentration, 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 no major regime carries an unacceptable loss and aggregate expectancy clears the threshold under adverse but realistic regime mixtures, 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 occupancy of trend, range, and stress states, state transitions, and ex-post regime classification may improve the result. Compare no deletion, conservative imputation, and worst-case imputation, and display how statewise expectancy, positive-profit concentration, occupancy, transition matrix, and performance under alternative future regime mixtures changes.

Run the control “freeze regime labels outside the training period and compare with any ex-post profitable classification.” If the control performs similarly, suspect processing rules or common market drift before attributing performance to the strategy.

After a provisional pass, log statewise expectancy, profit contribution, occupancy, transition probability, and classification confidence 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 “occupancy of trend, range, and stress states, state transitions, and ex-post regime classification.” Give lower-tail outcomes, failed scenarios, and unresolved mismatches the same visual prominence as favorable results; test count is not proof of correctness.