12 pre-registered crypto strategies, 0 survived costs + placebo tests. Where do retail traders actually find an edge?

[u/worldrecchicken]

I've spent the last weeks building a strict research pipeline for BTC/ETH spot trading (EUR pairs). Everything has been tested on historical data and in paper trading, with no real money yet. My methodology is probably stricter than most hobby setups. The result: nothing survives. I'd like to hear from people who got further, or who hit the same wall.

My setup (the rules I hold myself to)

• Criteria are written down and committed to git before each test and never changed afterwards.
• Costs are always included: 0.25%/side in early tests, now 0.10%/side (taker), plus slippage.
• Strict walk-forward. The result has to hold per year and per market regime.
• Placebo test: 1,000 block-bootstrapped fake price series. A strategy only counts as "proven" at p < 0.01.
• Bar: beat buy-and-hold after costs and have a smaller max drawdown, with 99% intervals and a Bonferroni-style correction for the number of attempts.
• A held-out "vault" dataset (Bitstamp 2018–Mar 2019) that nobody has looked at. Each strategy gets exactly one shot at it.
• A hard cap of 13 attempts in total. If nothing works by then, my conclusion is "not feasible at these costs".
• No LLMs in the trading decision. They have seen the future in their training data.

What I tested (all failed)

#	Strategy	Result
1–3	Time-series momentum, 24h/72h/168h lookback, vol-scaled, daily rebalance	Failed (168h closest: ETH p = 0.013)
4	Intraday momentum around the US open (14–15 UTC signal, hold until 21 UTC)	Failed
5–7	Online learning over these "experts" (Hedge, 1/N mix, Fixed Share)	Failed (Sharpe below buy-and-hold)
8	Momentum variant with a different volatility estimator	Failed
9	5-min breakout (close > 4h high)	≈ placebo, −0.55%/trade after costs
10	5-min dip-buy (1h drop > 2σ)	≈ placebo, −0.55%/trade
11	Panic-day buying	≈ placebo (p 0.47–0.48)
12	Slow trend filter on a broad coin basket	Sharpe 0.55 vs buy-and-hold 0.60. Smaller drawdown (77% vs 88%), but p = 0.57 and only 3/7 years positive

Other findings:

• 0 of 240 flexible-exit variants were positive, and 0 of 270 "conditions" from a trade journal were positive. The exit isn't the problem. The entry has no edge.
• Paper trading confirmed this. A dip rule went 10/10 winners in one night, but that was one single recovery move, not 10 independent trades. The backtest over 7 years says it's placebo-level.
• After FOMC decisions crypto volatility roughly doubles in the following hour (BTC 0.66% → 1.25%), but the direction is unknown, so there's nothing tradeable.
• Lead-lag between exchanges: the lead I measured is ≤ 0.06%, far below the cost of a round trip.
• With ~0.2–0.5% friction per round trip, even a near-perfect predictor at horizons under 1 hour earns almost nothing.

Where I'm stuck: my real questions

1. Source of edge. For a retail trader without colocation or speed, what edge in crypto spot is documented to survive 0.1–0.2% costs? Is it structural (liquidation cascades, funding imbalances, forced selling, order book imbalance) rather than price patterns?
2. Is my bar too strict, or the right one? p < 0.01 against block-bootstrap placebos, plus beating buy-and-hold on Sharpe and drawdown. How do serious shops set this bar for single-asset strategies?
3. Beating buy-and-hold in crypto. BTC/ETH buy-and-hold has a huge drift. Should a timing strategy be benchmarked against risk-adjusted buy-and-hold, or against cash?
4. Regime dependence. Many strategies seem to work in one regime only. Is it legitimate to run a regime-switching approach, or is that overfitting in disguise?
5. Paper vs live. How far did your paper results drift from live (fills, slippage, latency)?
6. Has anyone gotten past this point? Not "my backtest is great", but sustained live profit after costs on BTC/ETH spot. What was the edge, and how long did it last?

What's running now: paper trading 24/7 on real exchange data, with 42 strategies in a "sandbox" that each have a pre-set required number of trades and a deadline. I'm also collecting order book, liquidation and lead-lag data for months before forming new hypotheses.

I'm happy to share the methodology. Honest stories about what didn't work for you are especially welcome. That's the rarest information online.

[u/DoctorFuu]

Because from what it sounds you are just using past price patterns (i.e. technical analysis). Technical analysis is a pseudo-science and doesn't work.

You find an edge by knowing a real-world event that should be reflected in the price but isn't known enough for other people to catch up on it. Then you need to use statistical analysis to find if the effect is observed in the price history. Then if it exists you need an extra analysis to find if there are signals thzt allow you to react in time to trade. The signals probably aren't in the price patterns but in an external source related to the real-world event you're trading. You can use technical analysis for position timing, I guess, if it makes you happy. It probably won't change your outcome by much anyways as long as you have a replicable way of entering positions.

[u/worldrecchicken → u/DoctorFuu]

Fair, and my results kind of agree with you, all 12 were price-only and all 12 died. I've started logging news, scheduled events (FOMC etc.), liquidations and order book data for a few months before testing anything new. Problem so far: after FOMC vol doubles but direction is random. Any example of the kind of real-world event you mean, that's slow enough to still trade on for a retail setup?

[u/selfintersection]

Surely everything you've tried and will try has already been implemented by pros and is already baked into the prices. The winning strategy is to accept that there is no hope for success on a retail setup.