Here\u2019s What Survived After 48 Hours, what failed, and what melted down before the dust even\u00a0settled.<\/p>\n
Okay, full disclosure: I cheated a\u00a0little.<\/p>\n
Alongside the 61 resurrected strategies, I also included two of my newest strats<\/strong>\u200a\u2014\u200aones I\u2019ve already been running in separate live environments. I wanted to see how they\u2019d behave when dropped into the same chaos as the\u00a0others.<\/p>\n Not as benchmarks. Just as curiosity.<\/p>\n Will they rise above the noise? Or melt down like the\u00a0rest?<\/p>\n I honestly can\u2019t wait to see how they stack up against the backtest relics. Let the unfair comparison begin.\u00a0;D<\/p>\n If you missed Part 1, here\u2019s the TL;DR: I found 500 dusty trading strategies buried in an old backup, ran them through 2025 market data, and culled them down to 61 viable candidates. No tweaks. No curve fitting. Just brutal honesty. Most didn\u2019t even deserve a footnote\u200a\u2014\u200abut a few had\u00a0teeth.<\/p>\n From those 61 strategies, I launched 118 live bots\u200a\u2014\u200aeach tied only to the pairs they showed promise on during backtests. Some strategies ran twice (EUR and USDC), all deployed into isolated containers with $10 stake per trade. Everything was split, monitored, and timestamped.<\/p>\n The goal? See what happens when you unleash forgotten logic into a live, volatile battlefield. Let the market judge. And 48 hours later, I got my verdict\u200a\u2014\u200aone bot at a\u00a0time.<\/p>\n If you haven\u2019t read part one you can start here: https:\/\/medium.com\/coinmonks\/buried-alpha-500-forgotten-strategies-vs-2025-markets-afd7ab310df8<\/a><\/p>\n The bots went live at 17:00 UTC. No sandbox. No simulation. Internal infrastructure only. Full exposure.<\/p>\n The first few hours were eerily quiet. A few test trades here and there, minimal drawdown, log output as expected. It was almost boring\u200a\u2014\u200aand I knew that meant trouble was\u00a0coming.<\/p>\n After the first 48 hours, 106 bots<\/strong> were still standing. That\u00a0means:<\/p>\n 2 were shut down manually after catastrophic live\u00a0behavior<\/em>10 were terminated due to runtime instability, broken logic, or critical exceptions<\/p>\n Another 8 showed smaller issues\u200a\u2014\u200ainconsistent signals, stale orders, or laggy execution\u200a\u2014\u200abut I patched those in-place without needing a redeploy.<\/p>\n The biggest surprise? The bots that died hardest weren\u2019t always the worst in backtest. A few decent ones tanked immediately. Others held on despite ugly setups. Reality doesn\u2019t care about your simulated winrate.<\/p>\n So far, no critical infrastructure issues. But I knew that wouldn\u2019t last. And one particular strategy\u2026 didn\u2019t just fail. It made a\u00a0scene.<\/p>\n He ruled in backtests. In live markets, he lasted 11\u00bd hours. RIP Backtest\u00a0King.<\/em><\/p>\n This one had it all on paper. In backtests, it looked untouchable:<\/p>\n 80%+ winrate0.4% avg profit per\u00a0tradeSmooth equity curve, minimal\u00a0drawdown<\/p>\n It was top of the shortlist. The undisputed backtest\u00a0king.<\/p>\n And then it went\u00a0live.<\/p>\n Within the first 90 minutes, it was already hemorrhaging. No obvious bugs. No wild volatility. Just\u2026 bad decision after bad decision.<\/p>\n It didn\u2019t blow up with flair. It didn\u2019t implode due to some clever trap. It just quietly drained capital in the dumbest possible\u00a0way.<\/p>\n I pulled the plug manually. And I did it fast. Watching it continue would\u2019ve been like rubbernecking at a car crash I\u00a0caused.<\/p>\n A strong reminder: backtests lie<\/strong>. Especially when they look\u00a0perfect.<\/p>\n The rest of the battlefield wasn\u2019t quiet either. If the first 12 hours were calm, the next 36 were full-blown operational chaos.<\/p>\n IP bans<\/strong> started hitting around the 12\u201316 hour mark. And yes\u200a\u2014\u200aI may have accidentally DDoS\u2019ed\u00a0Binance.<\/p>\n Turns out \u201ca few\u201d<\/em> of the older strategies were still hammering Binance\u2019s REST endpoints with zero rate-limiting. Just raw, repeated requests\u200a\u2014\u200alike it was still\u00a02019.<\/p>\n They weren\u2019t malicious. Just dumb. And entirely my fault for not filtering them out sooner. I added basic rate-limiting to those bots and made a mental note: if any of them make it to the winners\u2019 circle, they\u2019ll need a full refactor before going anywhere near production.<\/p>\n The logs from that window were honestly hilarious in hindsight\u200a\u2014\u200abut also a useful reminder of what happens when you let nostalgia-code run\u00a0wild.<\/p>\n Lesson learned: don\u2019t let nostalgic code hit production without a\u00a0leash.<\/p>\n Memory leaks<\/strong> emerged in 4 containers. I suspect legacy loggers holding references or poorly scoped buffers. Fixed live, but noted for full teardown\u00a0later.System limits<\/strong> bit back. Again. Hit the wall on fs.inotify.max_user_instances. Quick sysctl patch fixed it, but it served as another reminder why I usually stage experiments\u200a\u2014\u200aand why I didn\u2019t this time. Sometimes eagerness works in your favor. This wasn\u2019t a subtle rollout\u200a\u2014\u200ait was full-on bot\u00a0war.Telegram notifications<\/strong> nearly melted my soul. 118 bots pushing alerts = noise hell. Especially when several were pinging for failed signals or trade flaps. I muted most of them, rerouted core signals to Slack, and reminded myself never to skip proper alert filtering again.<\/p>\n I didn\u2019t use my standard monitoring stack because I didn\u2019t want cross-contamination. No AI parsing, no news correlation, no external filters. Just clean signals. Pure chaos. It had to be\u00a0honest.<\/p>\n The infrastructure held. Barely. But it\u00a0held.<\/p>\n And that\u2019s exactly what I\u00a0wanted.<\/p>\n Here\u2019s what the raw numbers say after 48\u00a0hours:<\/p>\n Total strategies active<\/strong>:\u00a0106Strategies that placed trades<\/strong>:\u00a068Strategies with no trades<\/strong>: 30\u00a0(~28.3%)Strategies with net profit<\/strong>: \ud83d\udcca Total trades executed<\/strong>: 168 These early results don\u2019t prove much. But they\u00a0whisper.<\/p>\n (And no, the Backtest Hero isn\u2019t in the stats. He got benched before he could hurt anyone\u00a0else.)<\/p>\n More than 95% of all bots either made profit or broke even. Only 4 lost\u00a0money.<\/p>\n Some bots didn\u2019t just survive\u200a\u2014\u200athey\u00a0thrived.<\/p>\n A handful showed surprisingly stable execution, steady ROI, and higher-than-expected winrates. A couple even found themselves in profit territory early. Not much\u200a\u2014\u200abut enough to raise eyebrows.<\/p>\n Interestingly, a few of the most profitable bots had just 1\u20133 trades\u200a\u2014\u200apotentially signaling slow but confident setups. Others like one \u201canonymous contender\u201d ran 10 trades with 100% winrate and is still\u00a0ticking.<\/p>\n Were they lucky? Probably. But consistency over 48 hours counts for something.<\/p>\n That said\u200a\u2014\u200athe first 48 hours were anything but clean. Between IP bans, signal noise and infrastructure chaos, this was far from an ideal test environment.<\/p>\n So no, nothing is final yet. But some bots have earned the right to be watched\u00a0closely.<\/p>\n A full breakdown of winrate, PnL and trade count will follow in Part\u00a03.<\/p>\n What do you learn when 12 bots die, 8 misbehave, and one king eats dirt in 11\u00a0hours?<\/p>\n You learn that backtesting is seductive. You learn that chaos is always just below the surface. And you remember that trading bots don\u2019t need to explode to fail\u200a\u2014\u200athey can just quietly drain your\u00a0capital.<\/p>\n But more than that, you learn where the gaps are in your own\u00a0stack:<\/p>\n You learn which metrics actually\u00a0matter.You learn what happens when 118 bots scream at\u00a0once.You learn what breaks first\u200a\u2014\u200aand how fast it\u00a0breaks.<\/p>\n You realize that reliability beats intelligence. That logs lie less than dashboards. That slow, consistent bots outperform flashy, volatile ones\u200a\u2014\u200aat least early\u00a0on.<\/p>\n You also get confirmation that containers are the right choice. Isolation saved me from cascading failures. One dying bot didn\u2019t pull down others. That\u00a0matters.<\/p>\n And finally, you remember why this isn\u2019t a simulation. Because nothing about the last 48 hours was theoretical.<\/p>\n Fast feedback loops\u200a\u2014\u200aeven when painful\u200a\u2014\u200aare what separate toy projects from real\u00a0systems.<\/p>\n The plan now is\u00a0simple:<\/p>\n Keep all 106 remaining bots running into Day\u00a07Monitor equity, trade velocity and volatility responseBegin tagging strategies based on risk profile, slippage sensitivity, and consistencyPrep automated blacklisting logic in n8n for low-performance clusters<\/p>\n And maybe\u2026 just maybe\u2026 adjust stake\u00a0sizes.<\/p>\n This was the warm-up. The fire\u00a0test.<\/p>\n Next up: full-week survival. Part 3 drops after the initial 7-day cycle\u200a\u2014\u200aand maybe even deeper into a 28-day test window, depending on how things\u00a0unfold.<\/p>\n Follow @SwapHunt<\/a> for live updates and early\u00a0data.<\/p>\n Let\u2019s see who\u2019s still breathing by\u00a0then.<\/p>\n I Accidentally DDoS\u2019ed Binance with 118 Crypto Bots.<\/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":" Here\u2019s What Survived After 48 Hours, what failed, and what melted down before the dust even\u00a0settled. Chapter 1: Where We Left\u00a0Off Okay, full disclosure: I cheated a\u00a0little. Alongside the 61 resurrected strategies, I also included two of my newest strats\u200a\u2014\u200aones I\u2019ve already been running in separate live environments. I wanted to see how they\u2019d behave […]<\/p>\n","protected":false},"author":0,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[2],"tags":[],"class_list":["post-87457","post","type-post","status-publish","format-standard","hentry","category-interesting"],"_links":{"self":[{"href":"https:\/\/mycryptomania.com\/index.php?rest_route=\/wp\/v2\/posts\/87457"}],"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=87457"}],"version-history":[{"count":0,"href":"https:\/\/mycryptomania.com\/index.php?rest_route=\/wp\/v2\/posts\/87457\/revisions"}],"wp:attachment":[{"href":"https:\/\/mycryptomania.com\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=87457"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/mycryptomania.com\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=87457"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/mycryptomania.com\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=87457"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}Chapter 2: Into the\u00a0Fire<\/h3>\n
Chapter 3: The Fall of the Backtest\u00a0King<\/h3>\n
By 11\u00bd hours, it had opened 25 trades<\/strong> and won just\u00a02<\/strong>.<\/p>\nChapter 4: Bugs, Bans, and\u00a0Botrot<\/h3>\n
Chapter 5: The Unexpected Survivors<\/h3>\n
USDC: 31
EUR:\u00a032Strategies with net loss<\/strong>: 4\u00a0(~3.8%)<\/p>\n
\ud83d\udcb5 USDC<\/strong>: 86 trades \u2192 $15.81 profit \u2192 1.84% ROI<\/strong>
\ud83d\udcb6 EUR<\/strong>: 82 trades \u2192 \u20ac10.65 profit \u2192 1.30%\u00a0ROI<\/strong><\/p>\nChapter 6: Lessons from the\u00a0Fire<\/h3>\n
Chapter 7: What Comes After 48\u00a0Hours?<\/h3>\n
Final Thoughts: See You After 7\u00a0Days<\/h3>\n
Who grows? Who stagnates?
And who gets deleted for\u00a0good?<\/p>\n