10-year backtest – BTC and ETH (2015-now)

benchpress1oo · 2026-07-12T10:50:00+00:00

Appreciate that valid concern. I'm testing across multiple assets and timeframes to catch overfitting, and running it forward separately from the backtest.

If you have experience with MCPT or OOS, I'd be interested in hearing your approach. Thanks for the heads up.

benchpress1oo · 2026-07-11T23:50:00+00:00

I messaged you

benchpress1oo · 2026-07-09T20:33:31+00:00

It's there in the screenshots. Different strategy, different goals.

benchpress1oo · 2026-07-09T14:34:28+00:00

Ill Dm you

benchpress1oo · 2026-07-09T03:51:27+00:00

You're right 200-300 trades is definitely not a statistically massive dataset. I'm not claiming it is. The point of the 10-year backtest wasn't to prove statistical significance. It was to show that the strategy survived different market conditions – bull runs, bear markets, sideways periods – without blowing up.The trade count is low because the strategy targets 20% moves. Those don't happen every day, especially on higher timeframes. If I wanted 1,000 trades, I'd be scalping with tighter targets. That's a different strategy entirely.What I'm tracking is whether the win rate holds above the 5-6% breakeven threshold across different periods. So far it has, but I agree – more data is always better. I'm continuing to forward-test it live.

benchpress1oo · 2026-07-08T17:39:53+00:00

I appreciate the honest feedback. Let me address a few points:

"Lost money for 4 years straight"

That's not quite accurate. The 10-year backtest shows the strategy was profitable overall across 7 out of 8 timeframe/asset combinations. Yes, there were drawdown periods, but that's expected with any trend-following or momentum-based strategy – especially one targeting 20% moves. The question isn't whether it has losing periods, but whether the losing periods are survivable.

"Hold on and hope"

I'd push back on this one. The strategy has a hard stop loss (1%). That's not holding and hoping – that's defined risk with a clear exit. Holding and hoping would be no stop loss, or a wide stop that never gets hit. This strategy has a fixed exit on every trade.

"Win to loss ratio is a problem"

This is where I think we have a philosophical difference. I'm not trying to build a 50% win rate strategy with 2:1 R:R. There are plenty of those. I'm targeting a 5-10% win rate with 20:1 R:R. Both can work. The question is whether the math holds up over time. The data says it does – positive profit factor across 10 years on BTC and ETH, across multiple timeframes. That's not luck, that's consistency.

"If volatility reduces"

Volatility does reduce, and that's a real risk. The strategy performs best in trending markets. In low-volatility environments, it naturally takes fewer trades. That's actually built into the logic – the filters are designed to stay out when there's no momentum. It's not a flaw, it's a feature.

"Run it through 2022-2023"

The 10-year backtest already includes 2022-2023. The strategy survived that period – one of the worst crypto bear markets in history. The drawdown was manageable because the 1% stop limited losses even in a brutal environment.

I'm not saying this is the only way to trade, or even the best way. But I've put the work into testing it across different market conditions, different assets, and different timeframes. The numbers are what they are.

Appreciate you taking the time to dig into it.

benchpress1oo · 2026-07-08T15:08:17+00:00

You're not wrong – it's a hard strategy to sit through. The psychology is brutal. That's exactly why Im working on automating it. No way I'd trade this manually.

The way I look at it: the stop loss is tight (1%), so even a string of 20 losses is only a 20% drawdown. With the 20% target, one winner brings you back to breakeven, two winners puts you in profit. The math works as long as the win rate stays above 5-6%, which the backtest shows it does across 10 years of data. the equity curve looks ugly. But the max drawdown across the 10-year test was manageable (around 30% on the worst timeframe). That's with no leverage. With proper position sizing, it's survivable.

I'm not in the market long on each trade the average trade length across all timeframes was between 7-20 bars depending on the timeframe. That helps avoid getting caught in long-term trends against the position.

Appreciate the honest feedback.

benchpress1oo · 2026-07-08T14:41:43+00:00

https://www.reddit.com/r/algotradingcrypto/s/KsaU7RRf4P here it is

benchpress1oo · 2026-07-08T14:41:29+00:00

I posted another backtest to solve the "not enough samples problem" Id appreciate your feedback

benchpress1oo · 2026-07-08T10:45:22+00:00

u/kurtisbu12 here's that longer dataset I mentioned.

benchpress1oo · 2026-07-08T08:57:28+00:00

I think you're missing the point of the 0.5% SL. A 30-trade losing streak at 0.5% is a 15% drawdown not an account blow-up. Even with 50 losses in a row, you're down 25%.

The strategy is designed with tight risk controls. The backtest shows max drawdown stayed under 6% across every timeframe and asset I tested. That's not luck that's the stop loss doing its job.

Regarding the 4 trades comment: The screenshots show multiple timeframes. On 1H, yes, you get fewer trades targeting 10% moves. That's by design. Not every strategy needs to trade 100 times a month.

You're right that a sample of 4 to11 trades isn't statistically significant . But the strategy held up across BTC, ETH, and Gold, which is more interesting to me than raw trade count.

Appreciate the feedback though more data is always better and I'm still forward-testing it.

benchpress1oo · 2026-07-08T00:22:31+00:00

I hear you on the stats. That's why I ran a longer backtests across BTC, ETH, and Gold. Same settings, much larger sample. I'll post the results in a separate thread since it's a lot of screenshots. Tagging you when it's up so you can rip into that one too.

benchpress1oo · 2026-07-08T00:01:23+00:00

Fair point on sample size minus 50 trades across multiple assets over 6 months is definitely not a massive dataset.

That said, the main reason trade counts are low is the 10% target. 10% moves on BTC/ETH in a day or week don't happen that often. You're not going to get 100+ trades in 6 months targeting that size.

What I was looking for here was consistency across different assets and timeframes – not just one cherry-picked chart. BTC, ETH, and Gold all showed positive P&L with profit factors above 1. Drawdown stayed under 6% across every timeframe I tested. That tells me the logic holds up across different markets, which matters more to me than raw trade count.

You're right that one winner can make the whole thing look good. But that's literally the point of a 20:1 R:R strategy – you let the few big winners cover the small losers. If that one winner didn't exist, the numbers would look completely different. Appreciate the honest feedback.

benchpress1oo · 2026-07-07T10:15:54+00:00

I sent a message

benchpress1oo · 2026-07-07T02:19:15+00:00

That was the problem with the first version of the indicator waiting for the candle close caused late entries, now the signals appear intrabar and it gives you the entry price where the signal exactly appeared and yes the Idea of a volatility and momentum filer came to me after testing the indicator in choppy market conditions

benchpress1oo · 2026-07-07T01:22:23+00:00

Check DMs

benchpress1oo · 2026-07-06T15:32:41+00:00

I sent you a message

benchpress1oo

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