In this study, you’ll learn what trading drawdown statistics looked like across 175 post-earnings momentum setups, including the average and median drawdown, how often trades reached 5%, 10%, 15%, and 20% adverse excursion, how frequently those trades recovered, and how different stop-loss levels affected simulated profitability. The goal is to identify where normal volatility appeared to end and where genuine trade failure began.
Drawdown is one of those trading statistics that looks simple until you start measuring it trade by trade.
A position moving 5% against you sounds bad. A 10% drawdown sounds considerably worse. But whether either number is actually excessive depends on what happens afterward.
For this study, I analyzed 175 completed post-earnings momentum setups from my trading dataset to determine how much adverse movement occurred after entry and, more importantly, how often those drawdowns eventually recovered.
The dataset tracks each setup from the close of the first hourly earnings candle through the end of the following trading session, including both maximum favorable excursion (MFE) and maximum adverse excursion (MAE).
The results suggest something important about trading risk:
Small and medium drawdowns were extremely common. Deep drawdowns were not.
And somewhere between a 10% and 15% adverse move, the probability of recovery changed dramatically.
How Much Drawdown Did 175 Trades Experience?
Maximum adverse excursion (MAE) was measured from the close of the first hourly earnings candle through the end of the following trading session.
Quick Answer: What does drawdown mean in trading?
Drawdown in trading is the decline a position experiences from your entry price before it either recovers or the trade ends. In my study of 175 post-earnings momentum setups, the average maximum drawdown was 5.62% and the median was 3.91%; 42.3% of trades moved at least 5% against the position, 21.7% reached a 10% drawdown, and only 6.9% reached 15% or more. Importantly, some trades recovered after 5% and even 10% drawdowns, showing that drawdown measures adverse price movement—not necessarily whether a trade will ultimately be a winner or loser.
Key Trading Drawdown Statistics
Trading Drawdown Statistics
Maximum adverse excursion across 175 completed post-earnings setups.
The first thing that stands out is the difference between the average and median. The average trade experienced a 5.62% maximum adverse move, while the median experienced only 3.91%.
That gap tells us that a relatively small number of ugly trades pulled the average higher.
In fact, 57.7% of all setups never experienced a 5% drawdown at all.
But that also means more than four out of every ten setups moved at least 5% against the trade at some point.
For a $1,000 position, the average maximum adverse move translates to approximately $56.19, while the median works out to about $39.10.
A 5% Drawdown Was Not Necessarily a Failed Trade
This may be the most practically useful finding in the study.
Of the 74 setups that experienced at least a 5% adverse move, 21 eventually recovered and finished the next session profitable in the original trade direction.
That means approximately 28.4% of trades crossing the 5% drawdown threshold were ultimately winners.
A 5% stop, therefore, would not simply eliminate losing trades.
It would also eliminate a meaningful group of trades that needed additional room before the expected post-earnings move developed.
A 5% Drawdown Did Not Always Mean the Trade Failed
Among trades that moved at least 5% against the position, a meaningful number still recovered by the next-day close.
5%+ drawdown
profitable
crossing 5% MAE
Individual examples throughout the dataset show exactly this type of behavior. RRGB, for example, experienced an 8.70% adverse excursion but eventually finished approximately 1.50% above its entry level.
This is one reason a tight stop can look sensible from a risk-management perspective while still damaging a strategy’s expectancy.
Normal volatility and strategy failure are not necessarily the same thing.
Even 10% Drawdowns Sometimes Recovered
At the 10% level, recoveries became much less common — but they still happened. Thirty-eight of the 175 setups or 21.71% experienced a maximum drawdown of at least 10%.
Seven of those ultimately finished profitable.
That represents an 18.4% recovery rate among trades that crossed 10% drawdown.
Recovery Was Still Possible — But Much Less Common
Once trades crossed a 10% adverse excursion, the odds of recovering dropped sharply.
10%+ drawdown
profitable
10%+ MAE
Despite moving more than 14% against the position, the trade eventually recovered to finish slightly profitable.
This is where the distinction between drawdown tolerance and unlimited risk becomes important.
The data does not suggest that every trade should simply be allowed to run indefinitely.
Instead, it suggests that this particular momentum strategy naturally experiences more volatility than a 5% or even 10% stop comfortably accommodates.
The 15% Level Was Different
Then something interesting happened. Only 12 of the 175 setups — 6.9% of the sample — experienced a drawdown of 15% or greater.
And unlike the trades crossing 5% and 10%, none of those 12 trades recovered to finish as an EOD winner.
That creates a surprisingly clear dividing line in this dataset.
Below 15%, there were numerous examples of ugly trades recovering. Beyond 15%, there weren’t any.
15% Was Where Recoveries Disappeared
Deep drawdowns were rare — but once a trade crossed 15% adverse excursion, the outcome changed dramatically.
15%+ drawdown
as EOD winners
15%+ MAE
A 5% drawdown was often noise. A 10% drawdown was dangerous but still recoverable. At 15%, recovery disappeared entirely in this sample.
The worst example was MDB, which experienced a 23.28% maximum adverse excursion and eventually finished approximately 16.68% against the original long position.
Other deep-drawdown setups showed similar behavior. HPE reached a 16.25% adverse excursion before closing 11.87% against the trade, while CRDO reached 19.84% adverse excursion and ultimately closed substantially against the original short direction.
In other words:
A 5% drawdown was often noise. A 10% drawdown was dangerous but recoverable. A 15% drawdown was rare and, in this sample, consistently associated with losing trades.
Larger Earnings Moves Produced Larger Drawdowns
The size of the initial earnings move also mattered.
When I separated the setups according to the absolute size of the first hourly earnings candle, average MAE increased almost continuously:
Larger Earnings Moves Came With Larger Drawdowns
Average maximum adverse excursion increased steadily as the initial first-hour earnings move became larger.
Only 6.3% of the sub-5% setups experienced a drawdown of at least 5%. Among setups making a 20%+ initial move, that jumped to 60.9%. And 43.5% of the 20%+ setups experienced at least a 10% adverse excursion.
That makes intuitive sense.
The more violently a stock moves immediately after earnings, the more violently it can retrace afterward.
The dataset includes several examples of enormous initial earnings candles followed by equally substantial reversals. Some produced strong continuation, while others completely failed.
The practical takeaway is that a larger initial move does not necessarily mean a cleaner momentum trade.
In fact, my data suggests the opposite when it comes to drawdown.
What Happened When I Simulated Different Stop Losses?
I also ran a simple stop-loss simulation using a fixed $1,000 position on every setup.
The assumption was straightforward: if MAE crossed the stop level, the trade was exited at that percentage loss. Otherwise, the position was held passively until the next-day EOD exit.
This does not account for gaps, slippage or imperfect fills, so it should be treated as a backtest rather than a guarantee of executable performance.
How Different Stop Losses Changed Simulated Profit
Each setup used a fixed $1,000 position. Trades were stopped only if MAE crossed the selected threshold; otherwise they were held to next-day EOD.
The 5% stop reduced simulated profit by approximately 28.4% compared with the unrestricted passive EOD strategy.
A 10% stop reduced it by approximately 24.2%. The 15% catastrophic stop, however, reduced total simulated profit by only 2.8%.
That’s an interesting tradeoff.
The 15% stop did not technically maximize historical profit. Passive holding still produced the highest result.
But a 15% catastrophic stop preserved approximately 97% of the passive strategy’s simulated profit while putting a defined ceiling on individual trade risk.
For my strategy, that’s arguably a much more important distinction than simply asking which stop produced the largest backtested number.
What These Trading Drawdown Statistics Suggest
The biggest lesson from these 175 post-earnings setups is that drawdown needs context. A trade moving against you does not automatically mean the original setup failed.
In this dataset, 42.3% of trades experienced at least a 5% adverse move, and more than one-quarter of those trades later recovered to become winners.
Even some 10%+ drawdowns recovered.
But 15% was different. Only 6.9% of the entire sample reached that level, and not one of those trades ultimately finished profitable.
That doesn’t prove that 15% is a universally optimal stop loss. Another strategy, market environment or dataset could produce entirely different results.
But for my post-earnings momentum strategy, the evidence increasingly suggests that tight stops can interfere with normal trade volatility, while a much wider catastrophic stop may provide a better balance between allowing trades to develop and protecting against genuine failures.
And perhaps the most important drawdown statistic isn’t the worst loss.
It’s determining where normal volatility appears to end and actual strategy failure begins.
If you want to go deeper:
- Explore the Trading Statistics Hub to understand how different sectors behave across market cycles
- Study real setups inside the Trade Reviews section
- Learn the framework behind high-probability setups in the Post-Earnings Momentum Strategy
This is how you turn raw market data into repeatable trading edge.
More Trading Statistics…
Trading Drawdown Statistics FAQ
What are trading drawdown statistics?
Trading drawdown statistics measure how far a trade, strategy, or portfolio moves against you before recovering or reaching its final outcome. Common drawdown metrics include maximum drawdown, median drawdown, average drawdown, and maximum adverse excursion (MAE).
In this study of 175 completed post-earnings momentum setups, I used MAE to measure the largest adverse move from the entry point through the following trading session.
What was the average trading drawdown in this study?
The average maximum drawdown was 5.62%, while the median drawdown was 3.91%.
The difference between the two suggests that a relatively small number of severe drawdowns pulled the average higher.
How common is a 5% drawdown in trading?
In my dataset, 42.3% of trades experienced a drawdown of at least 5%, meaning 57.7% never crossed that threshold.
Importantly, a 5% drawdown did not necessarily mean the trade failed. Of the 74 trades that reached 5%+ drawdown, 21 later recovered to finish profitable, a recovery rate of 28.4%.
Can a trade recover after a 10% drawdown?
Yes, although recovery became considerably less common.
Thirty-eight trades in the study experienced at least a 10% drawdown, and seven eventually recovered to finish profitable, producing an 18.4% recovery rate.
This suggests that a 10% adverse move was dangerous, but it was not automatically equivalent to a failed setup.
What happened after a 15% trading drawdown?
This was one of the clearest findings in my trading drawdown statistics.
Only 12 of 175 trades, or 6.9%, experienced a drawdown of 15% or greater — and none of those 12 recovered to finish as an EOD winner.
That made 15% a particularly interesting risk threshold within this specific post-earnings momentum dataset.
What was the worst drawdown in the study?
The largest maximum adverse excursion was 23.28%, recorded by MDB. The trade ultimately finished approximately 16.68% against the original long position.
Severe drawdowns like this were uncommon, with only 2.3% of completed setups reaching a 20%+ adverse excursion.
Do larger earnings moves produce larger drawdowns?
They did in this dataset.
Average drawdown increased from 1.61% for first-hour moves below 5% to 3.92% for 5–10% moves, 6.72% for 10–20% moves, and 9.34% for moves above 20%.
That suggests unusually large post-earnings moves may also carry significantly greater retracement risk.
What stop loss performed best in the drawdown backtest?
A passive next-day EOD exit with no stop generated the highest simulated profit at $4,938 using fixed $1,000 positions.
However, the 15% catastrophic stop produced $4,799.30, preserving roughly 97% of the passive strategy’s simulated profit while still establishing a defined maximum-loss threshold.
In comparison, the 5% and 10% stops produced substantially lower simulated profits.
What is the main takeaway from these trading drawdown statistics?
The biggest takeaway is that normal trading volatility and strategy failure are not necessarily the same thing.
In this dataset, 5% drawdowns were common and frequently recoverable. Even some 10% drawdowns recovered. But once adverse movement reached 15%, recoveries disappeared entirely within the sample.
That does not make 15% a universally optimal stop loss. It does suggest that traders should use strategy-specific trading drawdown statistics rather than choosing stop-loss levels arbitrarily.
Leave a Reply