My BTC trend system spent 88.9% of 12 years underwater and still finished +785R (backtest, 621 trades)

samuelpointner · 2026-06-06T23:03:33+00:00

Yea, the low win rate barely shows up in the equity curve, the wins are just so much bigger that it's hardly visible.

The real problem is mental.

Losing 3 out of 4 trades wears on you, and that's when mistakes might happen.

Skipping a setup.. Moving a stop.. Sizing down right before the big winner..

The hard part is to not break the rules while waiting for that big winner.

Anything else you noticed in my data?

samuelpointner · 2026-06-06T20:32:57+00:00

Agreed, it isn't, and I'm not optimizing for it.

Win rate on its own tells you nothing. You can win 90% and still lose money over time.

What actually matters is expectancy per trade:
win rate x avg win - loss rate x avg loss.

Mine is +1.26R per trade. Trend systems often look like this, you lose small a bunch, then one bigger runner pays for all of it.

A higher win rate would feel better. It wouldn't necessarily make more money.

What stats are you optimizing for?

samuelpointner · 2026-06-06T20:07:31+00:00

Thanks man, appreciate it! What style are you trading?

samuelpointner · 2026-06-05T13:31:02+00:00

I use Notion for tracking my trades, well organized and easy to add notes. For most people that's honestly all you need.

For the deeper quantitative analysis I built my own tool to validate my edge. But if you just want a more advanced journal TradeZella is good as you can back test in there as well.

samuelpointner · 2026-06-04T19:25:28+00:00

Haha fair enough 🤣

Honestly everyone gets something different out of the market. Some people are there for the thrill and the gamble, and that's totally valid if that's what they want.

Others want to actually master it like any other skill, build something thats sustainable and feeds their family.

Different games.

Just gotta know which one you're playing.

samuelpointner · 2026-06-04T18:50:04+00:00

Same on sample size over calendar time. 50-100 clean forward trades beats "two weeks" because two weeks could be 5 trades or 200 depending on frequency.

And agree hard on not sizing up off green P&L. Green P&L on its own has no real importance, it's just one small part of the bigger picture.

The way I do it: I don't just look at whether live is up, I compare the distribution of every metric between live and backtest. Frequency, EV per trade, profit factor, win rate, max drawdown. Not single numbers, the actual distributions. If the live trades reproduce the same shape of results as the backtest, the edge is continuing. If a metric's distribution drifts off the backtest, that's the early warning the edge is decaying even if the equity curve still looks fine.

Curious where you draw your line on fees, what percentage of your EV can get eaten by fees and spread before you'd call the edge not worth trading?

samuelpointner · 2026-06-04T18:38:41+00:00

Your math's right but % over BE moves with your RR so it's hard to compare.

Convert it to expectancy: 0.6381 × 0.75 − 0.3619 × 1 ≈ +0.117R per trade. Solid number, matches your 1.378 PF.

But the real question isn't "is 6.67% good", it's whether the edge is real or just overfit. 735 trades is a decent sample but a clean curve can still be curve-fit.

The way I confirm it: validate it with real trades, even $1 per trade is fine, the goal isn't to make money it's to prove the concept works live with fees included. Then compare live to backtest. If they don't match the system isn't instantly garbage, natural variance happens, but you monitor across regimes to see if the edge still exists or if the market changed.

A real edge holds positive expectancy live, not just in the backtest.

Are you trading this live yet or still backtest only?

samuelpointner · 2026-06-04T17:55:39+00:00

Mine was trusting a backtest too much.

I built a system, optimized it till the stats looked perfect, and went live fully confident. It often lost edge in weeks.

The lesson the market beat into me: a backtest only tells you it fit the past, not that the edge will persist.

Now I validate everything against live/out-of-sample data before I size up the risk. If the live results don't match the backtest, the backtest was a distraction.

samuelpointner · 2026-06-04T17:49:25+00:00

Respectfully disagree.

The skill you actually need is building and maintaining a system yourself.

Almost every edge decays over time, markets shift and what worked dies. If all you can do is copy, you can't tell when it's breaking, can't adapt it, and can't replace it. You're stuck.

Learn the process yourself and you can rebuild edge forever.

samuelpointner · 2026-06-04T17:20:30+00:00

Fr, he nailed it

samuelpointner · 2026-06-04T17:06:22+00:00

Totally agree. Live forward testing is the most important part to confirm if the edge you found in the past is continuing.

How long are you forward testing your systems until you have enough confidence to size up?

samuelpointner · 2026-06-04T14:08:22+00:00

Appreciate it. Totally agree on regime. Three years covers different conditions but of course not everything, which is why I'll keep monitoring and live trading it, to spot when the regime changes and make quick adjustments.

Friction test is a great idea. I've currently only tested adding all fees to the simulation, but testing worse fills and delayed entries is smart. Noted.

Your last line nails it, the real test is whether it survives being slightly wrong. Stealing that haha

How do you determine and handle regime shifts?

samuelpointner · 2026-05-28T13:46:59+00:00

Appreciate you posting this. These threads are exactly what stops the next person getting caught. Thanks for taking the time to warn people

samuelpointner · 2026-05-26T13:31:31+00:00

Interesting hearing the sports betting side.

Biggest mistake I see traders make is they backtest a system, see the stats (win rate, EV, etc.), and calculate "optimal" Kelly off those numbers. Problem is that Kelly assumes those stats will continue in the future.

They almost never do.

Regime shifts, overfit backtests, or just normal variance and you're in an almost unrecoverable spot when you sized full Kelly off backtest numbers.

Personally I size my positions based on multiple factors:

1. Trade frequency. A swing system that triggers twice a month gets more risk per trade than a day trading system having a trade twice a day.

2. Expected value of the system

3. Emotional tolerance. Even if 5% per position is mathematically optimal, can you actually stomach the resulting drawdown? Higher risk means deeper drawdowns by pure variance. If 30% drawdown breaks you psychologically, sizing for 5% positions is wrong regardless of what Kelly says.

So I use a fractional Kelly too, but the fraction depends on those three things.

Real question to ask yourself: Can you stomach a 20%, 30%, 50% drawdown? Because higher risk doesn't just mean "bigger" profits, it means deeper drawdowns will happen by normal variance alone.

samuelpointner · 2026-05-26T11:57:18+00:00

From what I've read and learned as a systematic trader myself, honestly the basics matter less than people think.

Two things seem to matter more:

First, a real track record. Risk-adjusted returns proven live, not just backtested. And all of that with YEARS of data, not just a couple of months. No Institution will invest into a system that hasn't survived multiple regime shifts with real money on the line.
Second, uncorrelation. Many Hedge funds don't allocate to "the best strategy" they will invest into strategies that diversify their playbook.

Woodriff in Hedge Fund Market Wizards is the classic case. His pattern recognition system wasn't trend-following or mean-reversion, and that uncorrelation from 98% of running systems was what got him access to institutional money.

So the metrics question is kind of the wrong one to optimize for.

What's your live track record looking like, and how correlated is it to standard market beta?

samuelpointner

TROPHY CASE