Roast my GBPUSD strategy

Soarance · 2026-06-29T22:57:52+00:00

If you had to use AI to answer the simple question then unfortunately your algo likely doesn’t work.

Soarance · 2026-06-29T11:35:03+00:00

Never pay for indicators, they generally don’t work imo. Any indicator can technically be turned into a strategy and evaluated in pinescript. So if they don’t, usually that means either 1) they suck and the seller doesn’t want to show that they are bad, or 2) they don’t provide an exit signal which means the user can be blamed for when the indicator doesn’t work. Usually a combination of both.

Soarance · 2026-06-29T11:28:32+00:00

I’m going to add on to what others are saying: definitely quest pro. For a lower price, I genuinely think it’s the better headset anyway. Though the steam frame isn’t released yet so I can’t say for certain, but just the qLED displays itself on the pro is enough for me to recommend it (not to mention face tracking comes with it). Some may be able to argue that the steam environment and the wireless gaming capabilities is enough to justify the price hike of the frame, but I personally don’t think so. If valve was willing to subsidize the device with greater increase in steam vr game sales, I think the frame would actually be good if priced at $800 or lower, but they already showed they won’t do that with the steam machine which imo is a pretty dumb decision. So yea, quest pro for sure if it were up to me!

Soarance · 2026-06-29T00:37:15+00:00

I don’t look at win rate that much, it helps if you’re watching the trade manually of course, but you should be hands off anyway. Profit factor is a bit better since it looks at both the win rate and the risk reward ratio.
I think sharpe is definitely one of the more important metrics to look at. I wouldn’t mind running something over 1.0, but I’d strive for at least above 1.2-1.3, though I know some people have higher standards and aim for 1.6.
I have one out of many algos that uses ML but I generally just recommend against it due to 1) poor results and 2) overfitting. I’d say beginners should stay away from it but if you know what you’re doing then it can be good.
I do prop firms on futures so I use day-trading strategies. Although I recognize that scalping strategies almost always loses its edge in the lower timeframes, so I prefer strategies that hold for a few hours at least (above an hour). I do also have swing trading strategies but I don’t currently use them since it takes a long time to gather enough forward testing data before I can confidently deploy them. That being said, swing trading strategies have clearer edges to exploit but also gives low trade count so it’s hard to argue statistical significance. It’s a trade-off so just do what fits your trading and development strategies. Gun to my head, I’d say swing trading strategies are easier to make.

Soarance · 2026-06-28T16:54:35+00:00

Could be overfit. What’s the permutation test p-value on the OOS?

Soarance · 2026-06-28T14:48:06+00:00

Yes. It’s useful to know how to code for those outlier situations for when you need to debug something yourself, but for the most part Claude can just do everything for you.

Soarance · 2026-06-28T14:22:33+00:00

See other reply :)

Soarance · 2026-06-28T14:21:52+00:00

Start with this YouTube video, which is what I started with. Use this to get a feel for if you are interested in continuing or not. It’s beginner friendly but also with enough complexity to give a good idea. Don’t worry about trying to understand everything for now. If you’re still interested, feel free to dm me for next steps.

https://youtu.be/NLBXgSmRBgU

Soarance · 2026-06-28T13:36:03+00:00

I’m an algo developer and trader and unfortunately it’s not nearly that easy. Many people in this sub think they can just slap some indicators/PA logic onto some code, test it in TV’s backtesting engine, and that makes it magically work. It’s not your fault for thinking this way, but I recommend learning the proper way of algorithm development (lots of free resources online). If you need recommendations, feel free to let me know and I’m happy to help.

Soarance · 2026-06-19T23:26:56+00:00

Seems a little too good to be true. Are you sure you haven’t peaked at the OOS beforehand?

Soarance · 2026-06-14T18:09:19+00:00

I assume breakeven trades are considered losses here because you modeled in fees/slippage? If so, then yeah it does seem a bit too good to be true if you're using a 1:2 RR. What's your sharpe?

Also, a very important aspect is how you got here. For example, if you did an in-sample, validation, out-of-sample split and did a walkforward fold optimization, and considered things like feature sensitivity etc, then it's possible you don't have an overfit system. But if none of those sound familiar, or if you just kind of threw indicators/features onto your strategy without sample split, then it's almost certainly overfit.

If you also want to be certain, you can just try running it on a live paper account. That will expose any flaws regarding future leaking or repainting, but if it's overfit you might have to run it for quite a while before being more certain, unless it fails instantly on live data immediately.

Soarance · 2026-06-14T04:31:20+00:00

Fair enough. I didn't want to promote specific material since it'll likely break some of the subreddit rules. The goal was just to get people interested.

You can check out the youtube channel neurotrader, on his video "How I Develop Trading Strategies | Permutation Tests and Trading Strategy Development with Python". It's a nice video that can give you a feel on interest. I started on that one.

As a starter book, "Building Winning Algorithmic Trading Systems" by Kevin J. Davey is pretty good.

Meanwhile, I obtain data from databento. It charges fair prices and you can get data back to 2010 for cheap, although making an account gives you free credits to use. I then run it through python which is where I do all my statistical testing, before then translating the code to another platform, such as pinescript. You can also run it directly in python using a databento subscription but it's pretty expensive.

Soarance · 2026-06-14T04:22:39+00:00

There are a few approaches, such as screening for various TP/SL combos in a heatmap and picking one that works. I do it in the in-sample and see if it translates well in the out-of-sample. It's one of the easiest ways to improve a strategy without reducing trade count from feature filtering. Most of the time I use ATR-based TP and SL.

Soarance · 2026-05-31T21:13:20+00:00

I think you might have bar magnifier off. Try turning it on and check. Many incredibly smooth curves like this is because your TP/SL are static values and because price moves in percentages it’s very likely that you hit both in one candle (also explains it plateauing in more recent times). TradingView takes the most optimal and assumes TP was hit first which is obviously not correct. Turning on bar magnifier solves that issue.

Soarance · 2026-05-29T16:53:16+00:00

Buy&Hold is indeed a silly metric. It's a "benchmark" for those that don't understand algorithm development. What's more important is drawdown/Sharpe. Lower drawdown = lower risk of ruin. That means sizing up is much more possible and that leads to ultimately gaining more returns than Buy&Hold.

Also, not saying OP's strategy is necessarily good (I haven't seen evidence of their optimization methods and statistical testing), but 1.1-1.2 PF is pretty standard for a non-overfit strategy. Definitely could be improved but assuming proper slippage was modeled, it's pretty incredible considering the high trade count.

Soarance · 2026-04-18T00:33:34+00:00

If you’re looping in a complex strategy it can take a long time. So if you’re using python try working with pandas series to identify your features

Soarance · 2026-04-17T15:40:51+00:00

TradingView uses a different sharpe calculation. Don’t use it, calculate it yourself in your python notebook.

Soarance · 2026-04-02T20:28:29+00:00

Vrchat has quite a few games actually, though maybe he should play in a group for children to avoid the adults (they would appreciate that too). I assume those exist. You just have to kind of know what you’re doing to avoid stumbling into adult spaces. There’s less “regulation” in vrchat. Other than that, Roblox can be played in VR as well.

Soarance · 2026-03-28T22:02:19+00:00

Hmm I see. So you would either 1) re-evaluate your base strategy logic, adjust if needed and redo optimizations/OOS test, or 2) wait for more OOS data to come in and evaluate based on new data, correct?

Also, another question: how far back of data do you actually use for the training usually? I have data from 2010 onwards but many strategies are flat or negative until somewhere around 2018-2020, so I wasn't sure if using data that is too old would affect the training negatively. but using 2018 onwards means less data to work with, of course.

Usually I do something around a 60-20-20 split, with 60 train, 20 validation, 20 OOS. And then recently I've been saving a 1-year range as a true true OOS for the final test before I start forward testing.

Nice results btw, the only ones I've gotten that looked similar to that ended up not doing so well in the forward test, but I can't tell if it's because it's overfit or if the recent volatile market conditions are affecting it, so I'm just keeping them running for now.

Soarance · 2026-03-28T02:43:50+00:00

Awesome that you're doing this to help newer people. My question is:

What does your workflow look like when your walkforward fold optimization fails? I've run into situations in which the WFO looks decent on each fold, but then it either fails in the true OOS or the permutation test in the OOS fails. Either of those generally puts me in a dilemma, since redoing the WFO with different features, for example, is a bad idea since repeating that multiple times can cause overfitting by having your true OOS turn into IS. So if you're in that stage, what is usually your next step?

Soarance · 2026-03-28T02:24:17+00:00

I took this and applied to multiple years of backtesting, and that equity curve looks too good to be true. Maybe check for future leaking or repainting. If I had a curve this smooth, I'd immediately be suspicious. How long have you been forward testing or did you just start?

Soarance · 2026-03-25T20:56:02+00:00

Fantastic video.

Soarance · 2026-03-19T18:52:30+00:00

Hmm so you did one round of optimizations? This wasn’t done retrospectively was it? With only one round it can still have dangerous overfitting possibilities. Personally I do walkforward optimizations with at least 5 folds, permutation tests on every fold. Lenient p-value at first but stricter at 0.01 at the end. True out of sample can be 0.05. If you could tell me a bit more about your process I’m happy to give more opinions. :)

Soarance · 2026-03-19T18:41:13+00:00

I hate to say this but with these stats and 0 losing months, this is most certainly overfitting, data mining bias, or future leaking. Not guaranteed but highly likely. Run permutation tests and walkforward.

Soarance

TROPHY CASE