If we had to list one question that tops the list of FAQ since admitting live investors to the platform – it’s that.
Which DARWINs maximise long term return?
Is it the ones with the best return to date? Or the ones that had the lowest drawdown within each risk level?
Better still – how about those with the best ratio of historical return to historical drawdown?
Our neutral (DARWIN Exchange) stance
We’re a crowd-sourced marketplace.
Crowd-sourcing is about harnessing everyone’s talent for everyone’s benefit…. which implies that no-one is cleverer than the combination of the crowd at something. Rather it’s the sum of aligned, collective effort that delivers the best results.
If that’s our ethos – wouldn’t we contradict it by claiming that independent traders can beat Wall Street – and then lectured on how best to pick them?
Yes we would, and we won’t do that: we’re sure that Darwinex investors will be cleverer than Darwinex employees at picking DARWINs.
Which is the best news Darwinex could get, and the reason why we’ll stay neutral on this… lecturing people defeats the purpose of Darwinex.
Having said that, right now we operate with inside information as there’s tools cooking that we prototype but investors can’t yet use – which is why we make this one exception!
Is it risk / return ratios?
Good investments exhibit favorable risk / return ratios. But not every investment with favorable past risk / return ratios is smart.
To understand why, consider two alternatives, each with an amazing risk/return ratio last year:
- The best trader in the world (assume you knew for sure that he was the best trader) – he’s a good investment!
- The best simulation out of 10000 random simulations randomly trading foreign exchange for a year
Repeat: imagine you put 10.000 monkeys to trade 1 year ago. Every morning for the last year, each monkey pushed a button, which triggered a (random!) trade up or down the EUR/USD.
How much would you bet BET that the best out of the 10.000 monkeys achieved mind-boggling risk/return (Sharpe, Sortino, etc.) last year? (I personally would bet everything I have!).
Would you take advice from last year’s lucky monkey on how to trade next year?
Right – a rational investor shouldn’t. If you want to expand on this – here’s a nice book.
How about skill?
Which brings us to – how to tell the best trader in the world from last year’s luckiest monkey?
A hard one to answer indeed! Facts will tell whether our approach is right or wrong – but here’s our thinking.
We don’t look at the result (WHAT risk/return ratio?), because both a (lucky) monkey and a great trader can achieve great risk/return in a year.
Rather, our algorithms analyse the process (HOW did the strategy achieve profits?). We strongly believe that a trader would pass the following set of questions, but a monkey would fail. Were your profits:
- Over a statistically significant number of independent trading decisions?
- With stable trade and strategy level risk?
- Through a consistent pattern in trade length and profitability?
- Beating trades taken slightly earlier or later than yours (did the market consistently start going your way when you opened trades, and did it go against you after you left?)
- Beating an army of random monkeys taking the same risk as you? (note – the question is not absolute, which is what risk/return tracks, but relative to the random monkeys!)
The algorithms answer with a 0-10 grade for 1) Experience, 2) Risk Management, 3) Consistency, 4) Timing and 5) Performance. (10 is good, 0 is bad).
Is this the right way? Time will tell.
Common sense suggests that acing both the process (how you trade) and the result (what risk/return you make) you got is a more demanding filter than just risk/return. Obviously, an UBER lucky monkey can still ace both (thus the score penalty on strategies with low experience), but unlike the risk/reward test, the odds in this test are stacked against the lucky monkey.
So is risk/return meaningless?
Different strategies are better suited to certain market conditions than to others. First to know this are DARWIN providers: some operate different DARWINs and dynamically allocate capital across them. Sometimes it’s not the DARWIN, but the market conditions that’s wrong. If a strategy pointed consistently upwards and now it points consistently downwards, perhaps the market inefficiency it was targeting is either temporarily or permanently gone.
Which is why it pays to also track all strategies by a DARWIN provider.
Some DARWIN providers list several strategies with high scores across the board. At any point in time, some win and some don’t. Some even blend strategies combining several individual DARWINs in a single blend-DARWIN where providers personally mix to their preference.
Note that this still comes back to skill: it’s a lot easier to produce a second skill based strategy once you’ve done your first. Which is exactly where the monkey fails!
Any more hints?
Yes. Our research suggests that consistency pays: a well rounded strategy obtaining high scores in each of the 6 criteria is a LOT more likely to profit in the future than one with low or unevenly distributed DARWIN investment attribute scores.
Given the choice between 1 month stellar performance and high DARWIN Investment Atributes (Darwinia) we choose skill. This is why DARWINs with high grades rack up Darwinia prizes even on bad performing months. We pay for skill because we know that skill pays in the long run in a way that luck does not. We could be right or wrong, but we put our walk where our talk is.
Why this post – if you’re neutral?
Isn’t it contradictory to say we’re a neutral venue and then start giving advice on how to pick DARWINs?
Yes it is.
We’re working on tools empowering DARWIN investors to come up with their own theories and/0r d0 their own research. Once we release them, this post will disappear. But until we’ve given you enough information to beat us, we sincerely our head-start coming up with the tools helps your DARWIN investing!