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I tested 7 crypto trading strategies against 24 months of data – none worked

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#crypto#trading#finance
I tested 7 crypto trading strategies against 24 months of data – none worked
TL;DR · WeSearch summary

A recent analysis tested seven crypto trading strategies over a 24-month period, revealing that none were profitable. The study highlighted specific patterns that led to losses across all strategies, emphasizing the challenges faced by retail traders. The findings serve as a cautionary tale for those considering similar trading approaches.

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Original publisherGitHub
Canonical URLhttps://github.com/francisx1999/crypto-trading-bot-postmortem
Publication timeSat, 30 May 2026 14:15:14 +0000
Retrieval time2026-05-30T14:29:38.588Z
Last seen2026-05-30T14:29:38.588Z
Headline sourcePublisher (no WeSearch rewrite)
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Excerpt methodFirst ~120 words (~800 chars) of extracted publisher body, fair-use limited.
SummaryWeSearch · cerebras-chat (WeSearch summarizer)
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Publisher visitYes — open original
Substitutes article?No — link-out required for full text

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Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.

Opening excerpt (first ~120 words) tap to expand

I Tested 7 Crypto Trading Strategies Against 24 Months of Real Data. None of Them Worked. Here's the Pattern. by Francis Oyakhire I spent the last six weeks building, deploying, and stress-testing a retail crypto trading bot. Two exchanges, seven distinct strategies, 100 hyperopt epochs, and a 7.3 GB historical dataset later, here's what I have to show for it: every single strategy I tested lost money over the same 24-month window. This isn't a clickbait hook. The numbers are at the bottom of the post, with the actual freqtrade backtest receipts. What I want to share isn't "I failed" — it's the specific pattern that killed multiple strategy families in identical ways, and the capital math that means even success would barely matter at the scale most retail traders can afford to risk.

Excerpt limited to ~120 words for fair-use compliance. The full article is at GitHub.

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