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Monte Carlo trade-order shuffling asks a narrow but useful question: if the same completed trades had happened in a different order, how different could the equity path—and its drawdown or recovery time—have been? It preserves the observed trade outcomes and changes only their sequence. That makes it a sequencing stress test, not proof that a strategy has an edge, a test of whether its trades were selected well, or a forecast of future performance.
Calling the exercise “falsifying” an equity curve is shorthand for challenging a favorable historical path: a smooth curve may depend on a lucky order. The shuffle cannot prove the strategy false or predict that it will fail.
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What trade-order shuffling tests—and what it does not
A backtest’s equity curve is one trajectory through the observed trades. Maximum drawdown, time spent below a prior peak, recovery duration, and whether a specified balance threshold is breached can all change when the same trade outcomes are reordered. Jesse’s documentation describes the basic sequence: collect trades, shuffle their order, rebuild the equity curve, calculate scenario metrics, and compare the results with the original path (Jesse Trade-Order Shuffling).
For fixed additive trade P&L, with position sizes and costs already included and held constant, the sum of all trade P&L is unchanged by order. The path between start and finish is not. A different order can therefore produce a deeper peak-to-trough loss or a longer recovery even when the ending balance is identical.
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This assumption matters. If position size is a fraction of current equity, if positions overlap, or if margin, stops, liquidation, or exposure duration affect outcomes, a vector of independent closed-trade P&Ls does not reproduce those mechanics. The simple method below is then only a simplified sequencing stress test; model the relevant position or bar-level state to assess those risks.
Prepare the trade data and define the question
Start with a chronological vector of completed, net trade outcomes. Include the fees and slippage treatment intended for the backtest. If a trade’s P&L must stay associated with its size or another position attribute, shuffle the trade records together—not each field independently. Decide what you mean by “risk” before running simulations: for example, dollar drawdown, percentage drawdown, longest time under water, or a specific balance or margin threshold.
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- Sequencing question: Given these observed trades, how sensitive is the equity path to their order?
- Edge question: Would a statistic still look unusual under a defensible no-edge null? A sequencing shuffle does not answer this by itself.
- Model question: Does the simulation represent the sizing, costs, overlap, and liquidation rules that matter to the strategy?
Keep the observed chronological result as the baseline. A simulated percentile is conditional on that particular set of trades: shuffling cannot add a worse trade, a new market regime, or a future slippage event.
Rebuild equity for each shuffled sequence in Python
The following illustrative NumPy pattern computes percentage maximum drawdown and the longest number of trades spent below a running peak. It assumes additive trade P&L, a fixed initial balance, and no path-dependent sizing or liquidation. It is an example implementation, not a tested trading model. The seed makes the random draws reproducible; the chosen 10,000 scenarios are an example setting, not a universal requirement.
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import numpy as np
def path_metrics(equity):
peaks = np.maximum.accumulate(equity)
# Percentage drawdown; assumes the running peaks are positive.
drawdowns = (peaks - equity) / peaks
max_drawdown = float(np.max(drawdowns))
# Longest consecutive run of trade steps below a prior peak.
underwater = equity < peaks
longest_underwater = current = 0
for is_underwater in underwater[1:]: # Ignore the initial balance.
if is_underwater:
current += 1
longest_underwater = max(longest_underwater, current)
else:
current = 0
return max_drawdown, longest_underwater
def shuffled_paths(trade_pnl, initial_equity=10_000.0,
n_sims=10_000, seed=7):
trade_pnl = np.asarray(trade_pnl, dtype=float)
if trade_pnl.ndim != 1 or trade_pnl.size == 0:
raise ValueError("trade_pnl must be a non-empty one-dimensional array")
if initial_equity <= 0:
raise ValueError("initial_equity must be positive for percentage drawdown")
if n_sims <= 0:
raise ValueError("n_sims must be positive")
rng = np.random.default_rng(seed)
observed_equity = initial_equity + np.r_[0.0, np.cumsum(trade_pnl)]
observed = path_metrics(observed_equity)
scenarios = np.empty((n_sims, 2), dtype=float)
for i in range(n_sims):
shuffled_pnl = rng.permutation(trade_pnl)
equity = initial_equity + np.r_[0.0, np.cumsum(shuffled_pnl)]
scenarios[i] = path_metrics(equity)
return observed, scenariosHere, maximum drawdown is a fraction of the running peak (multiply by 100 for percent), while underwater duration is measured in trade steps, not calendar days. If the strategy can reach zero or negative equity, percentage drawdown needs a different convention; do not treat this calculation as a margin or ruin model. To test a threshold, explicitly check each reconstructed path against that threshold and implement the strategy’s actual margin or liquidation rules.
How many scenarios should you run, and how should you read them?
Jesse recommends at least 1,000 scenarios for its trade-order-shuffling procedure (Jesse documentation). Treat that as the software’s recommendation, not a universal adequacy threshold. With 1,000 draws, the most extreme tail percentiles are based on few observations; more draws reduce Monte Carlo sampling noise but do not fix an unrepresentative trade sample, an incorrect model, or a flawed null.
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Report the observed statistic beside selected simulated percentiles, and state the number of scenarios and random seed. For drawdown, larger values are worse: an observed drawdown in the unusually low end of the shuffled distribution suggests the historical sequence was relatively favorable among rearrangements of these trades. A high upper tail shows how much worse the same set could look under other orders. Neither result says how often those orders occur in the market or what future trades will do.
Jesse’s research API gives a Python example using num_scenarios=1000 and returning original and scenario metrics including return, drawdown, volatility, Sharpe, and Calmar (Jesse Monte Carlo Analysis). The number is an API example, not evidence that 1,000 scenarios are statistically sufficient for every use.
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Why a shuffle is not automatically a permutation test of edge
A randomization test is meaningful only when its randomization represents a defensible null hypothesis for the statistic being tested. Shuffling order is useful for path-dependent measures, but it does not change order-independent statistics computed from the same fixed return vector. For example, shuffling fixed returns leaves a Sortino ratio unchanged. As Ushana Kevin Iorkumbul writes, “This means that shuffling the order of the returns does not change the Sortino Ratio at all, and a permutation test built on order-shuffling would produce a constant null distribution that tests nothing.” (MQL5 article, 30 July 2026.)
To test an edge claim, define a no-edge null that fits how the strategy generates trades, then randomize the data in a way that destroys the directional effect under that null and recompute the statistic. Random sign flips are one example discussed in the MQL5 article, but that is not a universally valid null for every strategy. A shuffle-based distribution of drawdowns is best described as a conditional sequencing distribution, not an edge-test p-value.
How this differs from other robustness checks
| Method | What changes | Question it can address | Key limitation |
|---|---|---|---|
| Trade-order shuffle | Order of the observed trade outcomes; the set of outcomes is held fixed. | How sensitive are sequence-dependent equity measures to ordering? | Does not create new trades or market conditions; simple closed-trade shuffles may omit sizing, overlap, and liquidation mechanics. |
| Permutation or sign randomization for an edge test | Labels, signs, or another feature according to a specified null. | Is a chosen statistic unusual under that no-edge null? | The null and randomization must be justified for the strategy; order shuffling alone may leave the statistic unchanged. |
| Bootstrap | Resampled observations, usually with replacement. | How sensitive are estimates to sampling variation in the observed trades? | Changes the sample composition and answers a different question from reordering a fixed set. |
| Market-data or candle perturbation | Market paths or input data, followed by rerunning the strategy. | How robust is behavior to altered market conditions? | Requires rerunning the strategy and a justified perturbation model; it is not a completed-trade reorder. |
Jesse’s Monte Carlo documentation distinguishes scenario analysis that can include market-data perturbations from trade-order shuffling (Jesse Monte Carlo Analysis).
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1Fix the driver behind crashes, sound loss and screen glitches2Repair Windows errors before they cause bigger problems3Scan for outdated or missing drivers - takes under a minuteWhen a Monte Carlo p-value is appropriate
If a genuine randomization test has a defensible null and b of m randomly drawn permutations are at least as extreme as the observed statistic, do not report a p-value of zero when b is zero. Phipson and Smyth explain the finite-sample correction commonly written as (b + 1) / (m + 1) (Phipson and Smyth, “Permutation P-values Should Never Be Zero”). Specify whether the test is one-sided or two-sided, which direction counts as extreme, whether the original arrangement is included in the reference set, and how ties are handled. This correction does not turn a descriptive trade-order stress distribution into a valid test of edge.
What to pair with a sequencing stress test
A favorable result under one shuffle analysis does not establish that the strategy will remain profitable. Evaluate it alongside untouched out-of-sample or walk-forward results, realistic transaction costs, survivorship-aware data, and an account of how many strategy variants were tried. Trade-order shuffling checks how one observed set of trades behaves under rearrangement; it cannot remove overfitting or stand in for testing on new data.
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