Setting a stop-loss is one of the recurring challenges in trading. A stop placed too close to the entry can be triggered by ordinary price fluctuations, even if the market subsequently moves in the expected direction. A wider stop, on the other hand, increases the potential loss and requires a smaller position\u00a0size.<\/p>\n
While working on CryptoVigil, we decided to investigate this problem from a different perspective: how often does the market reach different stop-loss levels, regardless of the quality of the trading signal?<\/strong> We wanted to understand how stop-hit probability depends on the cryptocurrency, its volatility, trade direction, and entry\u00a0timing.<\/p>\n To answer these questions, we analyzed nearly three years of Binance Futures market data and simulated 9.4 million trades. The study revealed several consistent patterns that may be useful when developing and adjusting risk management strategies.<\/p>\n We used historical data from all available cryptocurrencies on Binance Futures between January 2024 and October 2026, including coins that were subsequently delisted.<\/p>\n At the beginning of every hour, we simulated two positions: one long and one short. For each position, we checked whether the price reached a stop-loss placed at a specified distance from the entry. In total, the dataset included approximately 9.4 million hypothetical trades<\/strong>.<\/p>\n The study examined fixed-percentage stops starting at 1%, as well as stop distances calculated using ATR (Average True Range). Stop-hit probabilities were measured over several time horizons, including one hour, four hours, and 24\u00a0hours.<\/p>\n We deliberately avoided using signals from any particular trading strategy. This approach allowed us to examine how frequently the market reaches a given price level without introducing the additional variable of entry\u00a0quality.<\/p>\n However, these movements should not all be considered random market noise. The dataset also includes directional moves and genuine trends. The results should therefore be interpreted as baseline probabilities of a stop being reached, rather than measures of any particular strategy\u2019s performance.<\/p>\n Our first objective was to measure how frequently fixed-percentage stop-losses are triggered across different cryptocurrencies.<\/p>\n The differences were substantial. For example, a stop-loss placed 5% away from the entry was reached within 24 hours in approximately 10% of simulations on BTC. For altcoins, the figure ranged from 29% to\u00a036%.<\/p>\n In other words, with the same stop distance, altcoin positions were approximately three times more likely to be stopped\u00a0out.<\/p>\n The difference was particularly noticeable with tighter stops. A 2% stop was reached in approximately 40% of cases on BTC and ETH, compared with 64\u201368% on altcoins.<\/p>\n These results help explain why universal stop-loss settings are poorly suited to different cryptocurrencies. The same percentage distance may be relatively wide for BTC but represent an ordinary daily movement for a volatile\u00a0altcoin.<\/p>\n When choosing a stop-loss, traders therefore need to consider not only the maximum acceptable loss but also the typical price behavior of the instrument.<\/p>\n Next, we examined what happens when stop distances are measured relative to each cryptocurrency\u2019s volatility rather than as fixed percentages.<\/p>\n For this comparison, we used ATR, which measures the average price range over a specified period. In our study, daily ATR was calculated using the preceding 14\u00a0days.<\/p>\n This produced one of the most interesting findings of the entire\u00a0study.<\/p>\n With fixed-percentage stops, hit probabilities varied significantly between groups of cryptocurrencies. However, once we measured stop distances in ATR, those differences almost disappeared.<\/p>\n A stop placed one daily ATR away from the entry was reached within 24 hours in approximately 11% of cases<\/strong>, whether we examined BTC, large altcoins, or smaller cryptocurrencies.<\/p>\n The left chart shows substantial differences between cryptocurrency groups. In the right chart, the lines almost completely overlap.<\/p>\n We also compared the results separately for 2024, 2025, and 2026. The same general relationship remained consistent throughout the study\u00a0period.<\/p>\n Another interesting observation was that the most popular altcoins were sometimes more volatile than smaller cryptocurrencies. High trading activity does not necessarily mean that an asset experiences smaller price fluctuations.<\/p>\n From a practical perspective, this makes ATR a more consistent reference for choosing stop distances. Instead of applying the same 3% or 5% stop to every coin, traders can account for the actual volatility of each instrument.<\/p>\n A wider stop must still be accompanied by a proportionally smaller position size if the goal is to maintain the same monetary\u00a0risk.<\/p>\n We also wanted to understand how frequently the price returns to a favorable level after a stop-loss has already been triggered.<\/p>\n To investigate this, we examined cases where the stop was reached but, by the end of the day, the price had returned to the favorable side of the original\u00a0entry.<\/p>\n For altcoins with a 2% stop, this occurred in approximately one out of three cases<\/strong>. In other words, after a long position was stopped out, the price subsequently recovered above the original entry\u00a0level.<\/p>\n For BTC with the same 2% stop, the frequency was approximately one in six. For altcoins with a wider 5% stop, it was around one in six to\u00a0eight.<\/p>\n These results demonstrate that triggering a stop-loss does not always coincide with a sustained move against the original position.<\/p>\n Of course, a subsequent price recovery does not prove that the initial trade was correct. However, using very tight stops creates a substantial probability that a position will be closed during an intermediate price movement.<\/p>\n For trading strategies, this suggests that stop distance should be evaluated separately from signal quality. If a strategy regularly produces such situations, one possible area for further research is comparing different stop distances while adjusting position sizes to maintain consistent risk.<\/p>\n We also compared stop-hit probabilities for long and short positions.<\/p>\n Among smaller altcoins, we identified a noticeable asymmetry: sharp upward price movements occurred more frequently than comparable downward\u00a0moves.<\/p>\n Specifically, price increases of 15% or more within 24 hours occurred approximately 1.6 times as often<\/strong> as declines of the same magnitude.<\/p>\n This difference was also reflected in the results for short positions.<\/p>\n With a 15% stop, the probability of being stopped out was 4.3% for short positions and 2.7% for long positions. At a 10% stop distance, the corresponding figures were 9.5% and\u00a07.6%.<\/p>\n This means that, with identical stop distances, short positions on smaller altcoins were more likely to encounter substantial adverse price movements.<\/p>\n For strategies focused on short selling, this is an important characteristic of the market. Even during a sustained downtrend, individual cryptocurrencies can experience sudden and significant short-term pumps.<\/p>\n The findings suggest that the risks of short positions should be evaluated separately, particularly when trading highly volatile\u00a0assets.<\/p>\n The next factor we examined was entry\u00a0timing.<\/p>\n We compared how frequently a stop-loss placed 2% away from the entry was triggered within the following four hours, depending on the time of\u00a0day.<\/p>\n The highest probabilities occurred between approximately 11:00 and 15:00 UTC, when trading activity increases around the beginning of the US market\u00a0session.<\/p>\n For example, positions opened at 13:00 UTC reached their stops in approximately 37% of cases. For positions opened at 20:00 UTC, the figure was around\u00a025%.<\/p>\n That represents almost a 1.5-fold difference in stop-hit frequency based solely on entry timing, with the stop distance held constant.<\/p>\n The result illustrates that intraday volatility is not evenly distributed. During more active trading hours, the probability of reaching a nearby stop increases.<\/p>\n This does not mean that trading during these periods should be avoided. Higher activity can also create opportunities for strategies designed to capture strong directional moves. However, when evaluating risk, traders should account for the increased probability of short-term adverse price movements.<\/p>\n The final factor we investigated was the price movement immediately preceding an\u00a0entry.<\/p>\n We wanted to understand how stop-hit probability changes when a position is opened right after an unusually large hourly candle, compared with a relatively quiet market\u00a0period.<\/p>\n The results showed a strong relationship between the size of the previous candle and the probability of reaching a stop during the following hour.<\/p>\n After a calm candle, a stop placed approximately one typical hourly range away was reached in about one out of eleven cases. When the previous candle was three times its usual size, that probability increased to roughly one out of\u00a0three.<\/p>\n A similar relationship appeared when using a fixed 2%\u00a0stop.<\/p>\n Importantly, this effect was strongest immediately after a significant price movement. When the observation period was extended to 24 hours, the differences became substantially smaller.<\/p>\n This suggests that the market tends to maintain elevated short-term volatility for some time after a major price\u00a0impulse.<\/p>\n The finding is particularly relevant to breakout trading. A large candle may indicate the beginning of a directional move, but it also increases the likelihood that a nearby stop will be reached in the following hours.<\/p>\n For entries following significant price movements, it may therefore be useful to evaluate stop-hit probabilities separately. Possible approaches include waiting for volatility to settle or using a wider stop with a proportionally smaller position.<\/p>\n Our analysis of 9.4 million hypothetical trades showed that stop-hit probability depends on several factors that are often considered independently.<\/p>\n The most important is the volatility of the individual cryptocurrency. Applying the same percentage stop to BTC and altcoins produces substantially different results. Measuring stop distances in ATR makes their behavior much more comparable across instruments.<\/p>\n Trade direction, entry timing, and recent price movement also have a measurable influence. Particularly notable were the greater exposure of short positions on smaller altcoins to sudden upward moves and the increased frequency of stop-outs following large hourly\u00a0candles.<\/p>\n There are important limitations to consider when interpreting these findings. Because the simulations used entries without trading signals, the resulting probabilities do not represent the profitability or success rate of any specific strategy. Trading fees and slippage were also excluded from the calculations.<\/p>\n The main conclusion is not that stop-losses should simply be placed further away from the entry.<\/strong> A wider stop is reached less frequently, but it also increases the potential loss per unit of position\u00a0size.<\/p>\n Effective risk management requires selecting a stop distance that reflects the instrument\u2019s volatility, the conditions surrounding the entry, and the amount of capital at risk. Our findings provide a quantitative foundation for making those decisions and for conducting further research into trading strategies.<\/p>\n Why Stop-Loss Orders Get Hit: Findings from 9.4 Million Simulated Trades<\/a> was originally published in Coinmonks<\/a> on Medium, where people are continuing the conversation by highlighting and responding to this story.<\/p>","protected":false},"excerpt":{"rendered":" Setting a stop-loss is one of the recurring challenges in trading. A stop placed too close to the entry can be triggered by ordinary price fluctuations, even if the market subsequently moves in the expected direction. A wider stop, on the other hand, increases the potential loss and requires a smaller position\u00a0size. While working on […]<\/p>\n","protected":false},"author":0,"featured_media":249734,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[2],"tags":[],"class_list":["post-249733","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-interesting"],"_links":{"self":[{"href":"https:\/\/mycryptomania.com\/index.php?rest_route=\/wp\/v2\/posts\/249733"}],"collection":[{"href":"https:\/\/mycryptomania.com\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/mycryptomania.com\/index.php?rest_route=\/wp\/v2\/types\/post"}],"replies":[{"embeddable":true,"href":"https:\/\/mycryptomania.com\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=249733"}],"version-history":[{"count":0,"href":"https:\/\/mycryptomania.com\/index.php?rest_route=\/wp\/v2\/posts\/249733\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/mycryptomania.com\/index.php?rest_route=\/wp\/v2\/media\/249734"}],"wp:attachment":[{"href":"https:\/\/mycryptomania.com\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=249733"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/mycryptomania.com\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=249733"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/mycryptomania.com\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=249733"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}Research Methodology<\/h3>\n
Finding 1. The Same Percentage Stop Does Not Mean the Same Risk Across Different Coins<\/h3>\n
Finding 2. Using ATR Makes Stop-Hit Probabilities Much More Consistent Across\u00a0Coins<\/h3>\n
Finding 3. Prices Often Recover After Triggering a Stop-Loss<\/h3>\n
Finding 4. Short Positions on Small Altcoins Face Greater Exposure to Sudden\u00a0Pumps<\/h3>\n
Finding 5. Stop-Hit Probability Depends on the Time of\u00a0Entry<\/h3>\n
Finding 6. Tight Stops Are Much More Likely to Be Triggered After Large\u00a0Candles<\/h3>\n
Key Takeaways<\/h3>\n