All trades were made by myself. No help, if that is what you mean.

Discretionary means that you decide where to enter and exit basic to current market condition so every transaction will be different.

Yes. Though the method is consistent

no - definitely not, on the basis of your figures so far!

i would want an expectancy ratio (if using that parameter) of *at least twice* that proportion (1.25 **minimum**) before even thinking about it

but with only 90 trades and a win rate just above 40% your sample-size is *nowhere near* statistically significant, anyway, and this is perhaps the main point (in that it’s objective, not opinion-based)

Ok, in that case, one month sample is too short, your strategy didn’t “see” other market conditions and you don’t know how will be your react to this change but you decide about live trading.,

Did you write that correctly 1.25 or 0.25?

I was considering doing testing on more months. How many do you think are enough?

How many months do you recommend I do?

You should cover all market types and volatility, howu h time do you need ? It is depends from market

it’s not about a “number of months”! it’s partly about the number of *trades*, and mostly about achieving statistical significance over a range of varying market conditions

yes, i wrote it correctly - anything below 1.0 is loss-making

1.25 is twice as profitable as 1.12

1.12 means that you expect to get a return of $112 for every $100 you lay out; 1.25 means that you expect to get a return of $125 for every $100 laid out, so that’s twice as profitable

* again*, i suggest to you, as i did here, that you are misleading yourself by using a less-than-optimal metric:

If you exclude the 1 in front you basically make $12 profit for every $100 you lay out with expectancy ratio of 0.12. Are we saying the same thing?

yes, sorry, i had not seen that you said “0.12” instead of “1.12”

1.12 means **+** 0.12

Babypips language is (as so often!) slightly different from that used in the rest of the trading world. Elsewhere, a figure less than 1.0 refers to a loss!

anyway, the answer to your question is still a big “**NO**”: your expectancy is **way** too low and your figures *nowhere near* statistically significant, sorry!

Can you point me in the right direction? An expectancy ratio above 0.25 is ideal. How much data would I need for it to be statistically significant?

i think so:

that’s *inversely proportional* to how close to 50% your win-rate is

with a win-rate of 50%, 100 will be a good enough sample-size, in principle

but statistically speaking, 60/40 is nowhere near 50/50 and will need more than 300 samples to be even reasonably “significant”

but even more important than this, is the point Greg made, above, about different types of market conditions …

**example**: if you’re looking at a trend-following method, and the period over which you look (for however many days/weeks/months) was a trending market, then the results will be good whether you look at 30 or 300 or 3,000 or 30,000 trades, but if you then try to use the system and the market stops trending and starts ranging (as they so often do!) then you will still probably have a disaster, regardless of the number of trades you checked up on

“robustness” is not about numbers alone - it has qualitative considerations as well as quantitative …

… just to make it even more difficult than it already is!

Thanks for the book recommendation. I am a fond reader. So in essence if I get my win rate up to 50% or better then 100 trades will be significant statistically.

oh, i think so - yes: and increasing your win-rate from about 40% to 50% has huge other benefits, too

it will (presumably?) increase your expectancy a lot from its current rather meager 1.12/+0.12 (that’s really important!), and also make the losing runs **far** easier to handle when you trade it (though you’ll still need to allow for some losing patches, of course)

Actually I hit a losing streak the past 2-3 days. I remember my ROI being 16%. Anyway. Thanks for the advice flamingo.

sorry guys, but statistical significant sample depends on degree of freedom in general, more complicate strategy = higher DoF = more trades to get statistical significant sample

Trading with no intrinsic value as for gold, silver and cryptos is only worth what billions of fundamental market traders would pay for it. Totally differing from trading FX currency pairs.

Thus, IMO, enjoy the ride. (ETR).

Hmmm. I’d agree with flamingo that I’d give it more time. But also it might help if you share the strategy you’re using just so that people can also make suggestions to further improve it? If only you don’t mind, of course!