They were never meant to see the light of day again.
But I dug them up. All\u00a0500.<\/p>\n
This is the story of what happens when ancient, forgotten trading code meets modern markets. No tweaks. No mercy. Just a raw experiment in truth\u200a\u2014\u200aa tale of curiosity, chaos, and maybe, redemption.<\/p>\n
This all started with a\u00a0backup.<\/p>\n
I wasn\u2019t looking for alpha. I was cleaning old project folders\u200a\u2014\u200athe digital equivalent of rummaging through your attic\u200a\u2014\u200aand I found a zip archive: strategies_old_backup_finalfinal.zip.<\/p>\n
Inside? Five hundred strategy files. Some named after vague ideas like ema_grind.py, others tagged with volatile memories of 2021: doge_spikecatcher.py, elon_moontrap.py. It was a chaotic, dusty graveyard of hand-crafted logic.<\/p>\n
Many of them I didn\u2019t even remember writing. Maybe I didn\u2019t. Maybe they were half-baked experiments from sleep-deprived weekends, or copied snippets modified beyond recognition.<\/p>\n
Most traders would toss this aside. But I had a question burning in my\u00a0head:<\/p>\n
\u201cWhat happens if you run them all straight into today\u2019s\u00a0market?\u201d<\/em><\/p>\n Not in theory. Not as a thought experiment. Actually run\u00a0them.<\/p>\n I decided to find\u00a0out.<\/p>\n The idea was simple: no optimization, no cherry-picking, no rewriting. I wanted to see what happens when you throw forgotten strategies into a modern battlefield and give them no second\u00a0chances.<\/p>\n Before running the full gauntlet, every strategy was initially tested on a narrower slice of data: June and July 2025<\/strong>. This gave me a controlled window to observe how different strategies behaved in a high-volatility, high-volume market\u00a0phase.<\/p>\n But unlike most filtering workflows, I didn\u2019t discard the weak ones early. Every single strategy\u200a\u2014\u200aeven the ones that flopped in the 2-month window\u200a\u2014\u200awere run across the full 4-month dataset spanning April to July\u00a02025.<\/strong><\/p>\n I wanted to see whether the June\u2013July behavior was representative or misleading. Were some strategies just unlucky in that window? Could others be overfitting to it? Running them across four months let me watch for consistency, robustness\u200a\u2014\u200aand dramatic reversals.<\/p>\n The 4-month window was the true benchmark<\/strong>. It\u2019s what I used for the hard cuts, the rankings, and ultimately to decide which ones went forward into live\u00a0testing.<\/p>\n Market pairs<\/strong>: 28 pairs (both long and\u00a0short)Time window<\/strong>: 4 months of real 2025\u00a0dataFees<\/strong>: 0.1% per\u00a0tradeNo hyperopt. No manual\u00a0tuning.<\/strong><\/p>\n This wasn\u2019t a performance competition. Most of these strategies had been written for entirely different market regimes. Some were clearly meant for 2020\u20132021 altcoin mania. Others looked like they belonged in slow-moving mean-reversion environments. No one was thinking about 2025\u2019s fragmented liquidity, algorithmic volatility bursts, or the high noise-to-signal ratio of today\u2019s\u00a0markets.<\/p>\n If a strategy could still breathe under modern conditions, that meant something.<\/p>\n So I built the backtest pipeline, cleaned up just enough to get things running, fed the scripts in, and hit \u201crun\u201d. The logs started\u00a0flying.<\/p>\n It felt like resurrecting ghosts.<\/p>\n Visualizing the chaos: Each dot represents one strategy\u2019s backtest result\u200a\u2014\u200atotal profit vs number of trades, colored by average drawdown. It\u2019s easy to spot who was living on the\u00a0edge.<\/em><\/p>\n The results came in\u00a0waves.<\/p>\n Some strategies didn\u2019t even make it to execution:<\/p>\n Broken logicDeprecated indicatorsSyntax errors that hadn\u2019t been touched since Python\u00a03.6<\/p>\n Those were culled immediately. No time for\u00a0fixes.<\/p>\n Then came the ones that technically ran\u200a\u2014\u200abut exposed themselves quickly:<\/p>\n Drawdown > 20%<\/strong>?\u00a0Deleted.Winrate < 50%<\/strong>?\u00a0Gone.Average profit < 0.05%<\/strong>? No\u00a0thanks.Less than 30 trades in 2 months<\/strong>? Inactive =\u00a0dead.<\/p>\n This first filter wiped out 432 strategies<\/strong>. That\u2019s over 85% gone without\u00a0mercy.<\/p>\n Some were close\u200a\u2014\u200awinrates just under threshold, or decent stats but too few trades. But rules were rules. The entire point was not to start rationalizing or making exceptions.<\/p>\n That left 68 survivors<\/strong>.<\/p>\n 68 ghosts that could still\u00a0fight.<\/p>\n Among the survivors were a few strategies with perfect\u00a0stats.<\/p>\n 100% winrateZero drawdownUnrealistic equity growth with no volatility<\/p>\n Too good to be true? These were tempting. The kind of strategies you\u2019d screenshot for Twitter clout. But I\u2019ve seen this movie\u00a0before.<\/p>\n I reviewed the code manually. One had a time-shifted signal\u200a\u2014\u200ausing future candles to decide present trades. Another used RSI but failed to align with price data, essentially giving a lagged mirror of the market. One literally had candle[-2] used in a forward\u00a0signal.<\/p>\n Bugs. Biases. Classic lookahead logic.<\/p>\n Seven of the 68 got cut here. One was so broken it was probably just a placeholder from a scrapped\u00a0project.<\/p>\n Final count: 61 real survivors<\/strong>.<\/p>\n Now came the real test: deployment.<\/p>\n Backtests are still theory. Real money doesn\u2019t care about backtest\u00a0curves.<\/p>\n I took the 61 clean strategies and matched each with only the pairs where it had performed well<\/strong> in backtests. No generalists. If a strategy only worked on LTC\/USDC and failed everywhere else\u200a\u2014\u200athat\u2019s what it\u00a0got.<\/p>\n Some strategies had strong performance on both EUR and USDC pairs. For those, I split deployments: one container for EUR-pairs, one for\u00a0USDC.<\/p>\n That brought us to 118 bots\u00a0total<\/strong>.<\/p>\n Each bot ran in its own isolated container. No shared memory, no shared risk. No cross-contamination.<\/p>\n $10 stake per trade.<\/strong> Normally I launch experiments like this in my hosted cluster environment\u200a\u2014\u200aisolated, redundant, clean.<\/p>\n But I was\u00a0eager.<\/p>\n This time, I pushed it all into my internal test nodes. No orchestration. No fallback. Just raw signal on bare\u00a0metal.<\/p>\n Local machines. Local\u00a0chaos.<\/p>\n Time to test durability.<\/p>\n My infrastructure was custom-built for this kind of parallel deployment, but even then, the pressure was real. CPU spikes, memory bottlenecks, noisy logs. I had to bump file descriptor limits and patch OS-level configs like fs.inotify.max_user_instances. IP bans started creeping in from Binance\u200a\u2014\u200atoo many bots, too many requests.<\/p>\n It was glorious.<\/p>\n More on that in Part\u00a02.<\/p>\n This isn\u2019t an alpha leak. This is a stress test. Trading Twitter is full of perfect equity curves, clean performance tables, and polished stories. But I wanted to see what actually<\/em> survives when you remove the polish, kill the curve-fitting, and drop every edge-case excuse.<\/p>\n It turns out: very\u00a0little.<\/p>\n Out of 500, I got 61. This experiment isn\u2019t done. The data\u2019s just starting to come in. More on that\u00a0next.<\/p>\n Welcome to\u00a02025.<\/p>\n Here\u2019s what\u2019s coming in the next\u00a0post:<\/p>\n Exact live results from the first 48\u00a0hoursThe \u201cbacktest king\u201d that went 2 wins \/ 23\u00a0lossesTechnical overhead and scaling\u00a0chaosWhich bots looked promising from Day\u00a01<\/p>\n The market doesn\u2019t care about your models. Stay tuned.<\/p>\n \ud83e\udde0 Follow the journey in real-time on Twitter: @swaphunt<\/a><\/p>\n \ud83d\udce1 I post live signals hourly from a parser monitoring the market\u2019s nervous system. No hype, just triggers. My bots scan 24\/7 and execute\u00a0live.<\/p>\n Buried Alpha: 500 Forgotten Strategies vs. 2025 Markets<\/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":" They were never meant to see the light of day again.But I dug them up. All\u00a0500. This is the story of what happens when ancient, forgotten trading code meets modern markets. No tweaks. No mercy. Just a raw experiment in truth\u200a\u2014\u200aa tale of curiosity, chaos, and maybe, redemption. Chapter 1: The Discovery This all started […]<\/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-86612","post","type-post","status-publish","format-standard","hentry","category-interesting"],"_links":{"self":[{"href":"https:\/\/mycryptomania.com\/index.php?rest_route=\/wp\/v2\/posts\/86612"}],"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=86612"}],"version-history":[{"count":0,"href":"https:\/\/mycryptomania.com\/index.php?rest_route=\/wp\/v2\/posts\/86612\/revisions"}],"wp:attachment":[{"href":"https:\/\/mycryptomania.com\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=86612"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/mycryptomania.com\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=86612"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/mycryptomania.com\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=86612"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}Chapter 2: The\u00a0Setup<\/h3>\n
The Rules:<\/h3>\n
It was a survival test. Brutal and\u00a0binary.<\/p>\nChapter 3: The First\u00a0Cull<\/h3>\n
Chapter 4: The False\u00a0Gods<\/h3>\n
Always.<\/p>\nChapter 5: From Survivors to Live\u00a0Bots<\/h3>\n
Small enough to be safe. Big enough to feel it if it blew\u00a0up.<\/p>\nChapter 6: What This Really\u00a0Is<\/h3>\n
This isn\u2019t some slick performance brag.<\/p>\n
A reality\u00a0check.<\/p>\n
And those still had to survive\u00a0reality.<\/p>\n
And as expected, one of the most promising backtests already cratered in live\u00a0trading.<\/p>\nPart 2 Preview: What Comes\u00a0Next<\/h3>\n
It only rewards what\u2019s still\u00a0alive<\/em>.<\/p>\n