Here’s What Survived After 48 Hours, what failed, and what melted down before the dust even settled.
Chapter 1: Where We Left Off
Okay, full disclosure: I cheated a little.
Alongside the 61 resurrected strategies, I also included two of my newest strats — ones I’ve already been running in separate live environments. I wanted to see how they’d behave when dropped into the same chaos as the others.
Not as benchmarks. Just as curiosity.
Will they rise above the noise? Or melt down like the rest?
I honestly can’t wait to see how they stack up against the backtest relics. Let the unfair comparison begin. ;D
If you missed Part 1, here’s 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’t even deserve a footnote — but a few had teeth.
From those 61 strategies, I launched 118 live bots — each 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.
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 — one bot at a time.
If you haven’t read part one you can start here: https://medium.com/coinmonks/buried-alpha-500-forgotten-strategies-vs-2025-markets-afd7ab310df8
Chapter 2: Into the Fire
The bots went live at 17:00 UTC. No sandbox. No simulation. Internal infrastructure only. Full exposure.
The first few hours were eerily quiet. A few test trades here and there, minimal drawdown, log output as expected. It was almost boring — and I knew that meant trouble was coming.
After the first 48 hours, 106 bots were still standing. That means:
2 were shut down manually after catastrophic live behavior10 were terminated due to runtime instability, broken logic, or critical exceptions
Another 8 showed smaller issues — inconsistent signals, stale orders, or laggy execution — but I patched those in-place without needing a redeploy.
The biggest surprise? The bots that died hardest weren’t always the worst in backtest. A few decent ones tanked immediately. Others held on despite ugly setups. Reality doesn’t care about your simulated winrate.
So far, no critical infrastructure issues. But I knew that wouldn’t last. And one particular strategy… didn’t just fail. It made a scene.
Chapter 3: The Fall of the Backtest King
He ruled in backtests. In live markets, he lasted 11½ hours. RIP Backtest King.
This one had it all on paper. In backtests, it looked untouchable:
80%+ winrate0.4% avg profit per tradeSmooth equity curve, minimal drawdown
It was top of the shortlist. The undisputed backtest king.
And then it went live.
Within the first 90 minutes, it was already hemorrhaging.
By 11½ hours, it had opened 25 trades and won just 2.
No obvious bugs. No wild volatility. Just… bad decision after bad decision.
It didn’t blow up with flair. It didn’t implode due to some clever trap. It just quietly drained capital in the dumbest possible way.
I pulled the plug manually. And I did it fast. Watching it continue would’ve been like rubbernecking at a car crash I caused.
A strong reminder: backtests lie. Especially when they look perfect.
Chapter 4: Bugs, Bans, and Botrot
The rest of the battlefield wasn’t quiet either. If the first 12 hours were calm, the next 36 were full-blown operational chaos.
IP bans started hitting around the 12–16 hour mark. And yes — I may have accidentally DDoS’ed Binance.
Turns out “a few” of the older strategies were still hammering Binance’s REST endpoints with zero rate-limiting. Just raw, repeated requests — like it was still 2019.
They weren’t 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’ circle, they’ll need a full refactor before going anywhere near production.
The logs from that window were honestly hilarious in hindsight — but also a useful reminder of what happens when you let nostalgia-code run wild.
Lesson learned: don’t let nostalgic code hit production without a leash.
Memory leaks emerged in 4 containers. I suspect legacy loggers holding references or poorly scoped buffers. Fixed live, but noted for full teardown later.System limits 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 — and why I didn’t this time. Sometimes eagerness works in your favor. This wasn’t a subtle rollout — it was full-on bot war.Telegram notifications 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.
I didn’t use my standard monitoring stack because I didn’t want cross-contamination. No AI parsing, no news correlation, no external filters. Just clean signals. Pure chaos. It had to be honest.
The infrastructure held. Barely. But it held.
And that’s exactly what I wanted.
Chapter 5: The Unexpected Survivors
Here’s what the raw numbers say after 48 hours:
Total strategies active: 106Strategies that placed trades: 68Strategies with no trades: 30 (~28.3%)Strategies with net profit:
USDC: 31
EUR: 32Strategies with net loss: 4 (~3.8%)
📊 Total trades executed: 168
💵 USDC: 86 trades → $15.81 profit → 1.84% ROI
💶 EUR: 82 trades → €10.65 profit → 1.30% ROI
These early results don’t prove much. But they whisper.
(And no, the Backtest Hero isn’t in the stats. He got benched before he could hurt anyone else.)
More than 95% of all bots either made profit or broke even. Only 4 lost money.
Some bots didn’t just survive — they thrived.
A handful showed surprisingly stable execution, steady ROI, and higher-than-expected winrates. A couple even found themselves in profit territory early. Not much — but enough to raise eyebrows.
Interestingly, a few of the most profitable bots had just 1–3 trades — potentially signaling slow but confident setups. Others like one “anonymous contender” ran 10 trades with 100% winrate and is still ticking.
Were they lucky? Probably. But consistency over 48 hours counts for something.
That said — the first 48 hours were anything but clean. Between IP bans, signal noise and infrastructure chaos, this was far from an ideal test environment.
So no, nothing is final yet. But some bots have earned the right to be watched closely.
A full breakdown of winrate, PnL and trade count will follow in Part 3.
Chapter 6: Lessons from the Fire
What do you learn when 12 bots die, 8 misbehave, and one king eats dirt in 11 hours?
You learn that backtesting is seductive. You learn that chaos is always just below the surface. And you remember that trading bots don’t need to explode to fail — they can just quietly drain your capital.
But more than that, you learn where the gaps are in your own stack:
You learn which metrics actually matter.You learn what happens when 118 bots scream at once.You learn what breaks first — and how fast it breaks.
You realize that reliability beats intelligence. That logs lie less than dashboards. That slow, consistent bots outperform flashy, volatile ones — at least early on.
You also get confirmation that containers are the right choice. Isolation saved me from cascading failures. One dying bot didn’t pull down others. That matters.
And finally, you remember why this isn’t a simulation. Because nothing about the last 48 hours was theoretical.
Fast feedback loops — even when painful — are what separate toy projects from real systems.
Chapter 7: What Comes After 48 Hours?
The plan now is simple:
Keep all 106 remaining bots running into Day 7Monitor 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
And maybe… just maybe… adjust stake sizes.
Final Thoughts: See You After 7 Days
This was the warm-up. The fire test.
Next up: full-week survival.
Who grows? Who stagnates?
And who gets deleted for good?
Part 3 drops after the initial 7-day cycle — and maybe even deeper into a 28-day test window, depending on how things unfold.
Follow @SwapHunt for live updates and early data.
Let’s see who’s still breathing by then.
I Accidentally DDoS’ed Binance with 118 Crypto Bots. was originally published in Coinmonks on Medium, where people are continuing the conversation by highlighting and responding to this story.