097: Derek Wong – How to Think About Strategies Like a Quant and Diversify Like a Boss

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Chat for more information. Tasty Trade Inc. is a registered broker dealer and member of FINRA, NFA, and SIPC. Podcast. What is happening team? Thank you so much for joining me. On this episode, I have our very first guest from China, Derek Wong. He is the director of systematic trading and options at a private fund in Shanghai.

Initially though, Derek got his start in the agricultural pits at the CBOT, then following on from this, he's worked at various quant shops in Chicago, South Korea and nowadays mainland China. After discussing Derek's backstory, we talk emerging markets, cultural differences of Chinese investors, convergent and divergent strategies, diversification, and some slightly unconventional ways of thinking about how you trade.

Derek also has some awesome insight for traders who are discretionary and what traders should focus on once reaching profitability. I thought Derek was a really incredible guest and I hope you get some value from listening to this episode. One last thing I do want to mention and that is Derek has very kindly offered to answer any trading questions that you might have. So if you've got a question that you want to ask, just go to chatwithtraders.com forward slash ninety-seven.

Scroll to the bottom of the page and write in the comments area for a response from Derek. Alright, that's all from me. Let's get into it. Derek, how's things? Good day for you? Yeah, it's actually uh quite a good day. I can definitely say that. So today we hit our yearly target for the fund, actually. So it was quite nice.

That's very cool. Nice to hear. Congratulations. Uh huh. Thank you. How do you go about setting those those yearly targets? Uh actually it's by our mandate and the uh CIO, so based on that we look at uh what departments, what teams are good can contribute what and then so we look to mix those teams together. So my team's target has been met. Other teams may have not been. So we're kind of like siloed out, if that makes any sense.

Okay. So what does that mean sort of going forward for the rest of the year? Obviously there's still what, like another two and a half months left? You can consider it like we are in kind of take profit mode if that makes any sense. So we've reduced our leverage, we've reduced our volatility, and we're trying to hover around this area for the remainder of the year.

Okay, so you're not like trying to push harder and really take it as far as you can, you're sort of being a little bit more conservative. Right, because how our sh products work is we do have some guaranteed products. that we need to make sure that we can deliver on that annualized return and then we also do just a profit split.

So I need to make sure that I can cover those costs and those r give that guaranteed returns to investors as well as, you know, have the other party who's uh doing profit split be satisfied as well. So right now we're kind of in that happy medium. I would like to kind of cruise out, basically window dress until for the rest of the year.

Okay, cool. Well, like I said, man, that's really good news. So congratulations. And I'm really excited to have you on the podcast because you're actually my first guest from China. But if I understand correctly, you haven't always lived in China. Is that right? That is right. So before I I'm born in the Chicago and I started my trading career in Chicago in the uh pits at the uh Chicago Board of Trade.

and I always had a Asian leaning so as I uh worked through different uh funds and uh then I leaned over to South Korea, did a little bit of work there and then eventually made my way to mainland China. Yeah, yeah. And we're gonna get right into that. But I mean, first of all, how did you actually become involved with trading? Because I know you were originally studying to be an engineer. So, you know, how did this all sort of play out?

Uh it's actually an interesting story or well rather I was picking up the scraps. So my brother was offered a job to work for a friend of his father down at the Bought a trade and he didn't want the job. So I ended up taking that job and I said, Hey, you're not gonna do it. I'm gonna do it. And that was it. I took the job. I I interviewed and you know, however that's gonna be, you know? And I just started, you know, clerking and running down in the pits.

As electronic really started to make that transition in the early two thousands, le Okay. So what was your what was your job specifically like as a clerk? Like what sort of things did that involve? What were you doing like each day when you showed up? Okay, so each day when we were basically filling institutional order flow.

And we had a couple of traders as well. So what we would do is we would look at what our clients wanted to trade in corn that was our major pit, uh soybeans as well, rough rice and the eggs. So we would look at you know the previous day activities, what was, you know, what orders were sitting in our book. What we looked at. look like uh to Phil. And uh, you know, we looked at seasonality, we were looking at, you know, what kind of volumes we're expecting and how we were gonna get that out there.

And then basically just, you know, filling, you know, fairly large orders for these people. for these institutions and commercials for hedging or, you know, trading purposes. And so we had One guy who had a big palm pilot, I guess you could think of it like that, or a big uh iPad type thing who could do electronics.

And we had a couple of people in each pit that would fill by hand. So we had a mix of electronic and um we'll say pit traders and we could work the or orders either side to to fill there. their orders coming in over the phone. Right. So if I understand this correctly, you were

You were filling large institutional orders. So pretty much they would say we want to buy X amount of whatever that might be. And then it was up to you to try and get them the best price on that order. Is that correct? That is correct. Okay. So how were you How did they measure that? How did they try how did they sort of gauge whether you were successful in getting them the best price or otherwise?

Well, they would they would measure us by how the market had moved. I mean, back then it was still pretty slow. Not a lot of people had as good of tick data as we have now. So we're looking at average prices during the day, the settlement price, because it might take us a couple of days to work the order or they might have certain conditions that needed to be met.

So those were different benchmarks. E every company would give different different criteria that they were looking to have executed against. And so we would look at that and say, Okay, how can we best

You know, suit their needs of the client. Okay. So how long were you working at the Chicago Board of Trade for? Uh for two years. Right. And then what happened from that point? Like where did you go from there and and why did you decide to leave? Right. So then I was that was still when I was quite young s in university. So after that I ended up going into the family business a little bit.

uh and then transferred back into to trading. So kind of a roundabout way. Uh I'm the oldest son. So in a Chinese family it is my duty to kind of take hold uh the helm of a of the family business And I tried to do that but it really wasn't my thing. So I ended up using those previous connections I had built from the floor And actually I went to school with quite a lot of traders. My friend's parents I didn't realize it until after

that they all were a lot of traders, a lot of people at the Border Trade, Board of Options, stuff like that. And so I kinda used that to lean into it. And that allowed me to use my engineering experience to start to do programming and uh analytics on the more quant side. So I s joined a small fund who had systematic trading strategies. Uh and so I that's how I got into the quant side because uh using that um engineering background. Does that make sense?

Yeah, yeah, no it does. So I mean I'm just curious, what was the family business? Oh, we were uh import export and uh in beef and so it's still physical commodities, but the actual cash business. So we did have a a seat on the exchange that would do the hedging. So I was m I moved back to the the physical side of things. So I w it was same value chain for a producer, but I was the producer instead of the trader.

Okay. Okay. I'm with you. So tell us a bit about the quant fund that you moved into. What type of strategies were they trading there? And what was your role more specifically? Okay, so my role there was uh junior quant strategist, I guess would be the way to put it. Uh they were a multi strategy fund run by a ex uh Merrill Lynch. Guy. He had uh previously worked in Japan, launching a desk there, and came back to the States and was doing a stateside fund.

And so what we were doing was impli we had uh some PhDs and we were creating we had three hundred trading strategies and it was just a multi strategy and we were trying to see how we could fit that together

to trade the US and then still keep a leaning towards Asia, which is how it evolved into going into South Korea. That's crazy. Three hundred strategies. Is that is that normal? This is where I really learned how to manage Uh analyze individual strategies and then manage them into a single portfolio.

And so really that kind of developed into my philosophy that really got baked into me really early thinking about More than an individual strategy, more than just how any one strategy works with another strategy, but thinking of very like 30,000 foot view. of how to think about running a fund based on strategies, right?

Yeah, and I mean that's something I really want to pick your brain about as we get a little bit deeper into this, because I know that's something you're very big on is the multi strategy approach. Going into this quant fund, did your engineering background help you? Like to what extent was that uh beneficial?

Okay, so actually one of my final projects, this was almost like ten years ago, more than ten years ago, was doing self driving cars, the logic for self driving cars. So what we were doing was we were scanning the roads. And taking input from, you know, the white lines on the road to make sure a car could stay inside those lines and predict collisions.

So by having that understanding of logic and, you know, seeing, okay, if this situation, what happens and programmatically working out a lot of different edge cases that allowed me to transfer over quite well to the trading side of things because I understood, you know, this thing is gonna be automated. It's it's already running by itself. How do you make that run, you know, as well by itself that it can do? And if some unexpected situation comes up, like a unexpected collision

You know, how is it going to react in those cases? So when I went to the trading side, it's the same thing. So you have a system, right? It's going to automatically trade by itself when an edge case or high risk or high volatility point comes up. the system gonna react? How are is your autom automated car gonna react? And I think that it was quite a very easy leap for me to to go there and still to apply a scientific process and engineering problem solving mentality.

uh to the problem. Okay. And going into the quant fund, like previously you were at you were trading on the f or you were a clerk on the uh Chicago Board of Trade, you know, on the floor. Did it require much of a a mind shift from You know, doing things that way to actually working in a quant fund that was sort of juggling three hundred strategies. It was a bit of a shift, but I a lot of people in Chicago come from the floor. So a lot of people ha made that transition and then went upstairs.

So a lot of the analogies that I had are that basis of understanding, they could communicate it to me. I think it's a much different environment, say in London or some other place that might not have as uh a strong pit structure. So those guys, I think it would have been a much difficult, much more difficult transition. But because I was surrounded by guys or, you know, the upper management was

or had uh pit experience, um, they can make a lot of analogies. So it was really transcribing down what their strategies were or what they had felt worked over the years. And then adding this level of engineering on top of that or or statistics and mathematics built around what they already knew worked. So they already had a you know, several strategies they had been trading for ten, twenty years.

They wanted to make that programmatic and then use engineering and mathematical principles to verify or validate those strategies. Okay. Yeah, sure. That makes sense.

And you know, your role there as a junior quant strategist, what did what sort of things did that entail? Honestly, it's just a glorified intern. I I just watched a lot of the other guys um what their ideas were, watching their process debugging code because I had already k knew how to s code in C plus plus, so having a coding was definitely important. I also could understand the formulas, the mathematical formulas that they were using. And so just being a sponge, man.

I just learned as much as I could and figure out why were these things working. What was the analogy between the pit or the, you know, the market and what this trading strategy is trying to accomplish. And why is why is this phenomenon they're targeting something that we believe is profitable? Mm-hmm. Okay. So, you know, I'm not sure how long you stayed there for, but what brought you over to South Korea and China like? Um walk us through through that stage.

Right. So like I said, a lot of these guys well, actually the boss he ha who's he was in Japan first. So a lot of emerging markets. And I'm Chinese American and he was Japanese American and the original guy I was working for on the floor was Korean American. So he had a lot of Asian influence there. And there is a big difference between emerging markets. and we'll say mature markets.

But for trading strategies uh automatic or algorithmic trading strategies, you require a certain level of infrastructure to be in place. So earlier, you know, say eight nineties, early nineties, late eighties, Japan was that type of market. They had the technology infrastructure, they had that type of thing. You could go do that. Then South Korea came online.

They had electronic markets, they had futures, they had options. And then China would is the next progressive step because in emerging markets. there is a higher signal to noise ratio and a higher usually propensity for we'll say retail traders, less institutional, less HFT, less of well established players that have high amounts of infrastructure.

So you can reduce your competition and increase your profitability by being able to navigate inside of those markets with strategies and technology and process that you have already developed, say in the States.

So I saw that pro them do that progression and so coming to China for me was just the continuation of that type of thinking. And I have done some quantitative research on this that you can look at a price series and s and see that there's a lot more dispersion in, say, China versus the US. And in China, the stock market is, I'm gonna say rough guess here, eighty percent retail, twenty percent institutional. While say in the States it's the absolute reverse.

So y if you can imagine walking into a market that's like that. You can understand why it's such a tempting offer. Yeah, yeah. Okay. So I mean that that's a really interesting answer and there's just a a few things I'd like to ask you around that. So I mean first of all, would you mind just explaining and just really clarifying what do you mean by emerging markets? Like what defines an emerging market? So I guess I'm not using a traditional like uh

the economist's view of an emerging market. I wanna say financially developing and the we'll say securitization of certain financial products. So there is becoming to be A so China is interesting because its economy is the second largest in the world. But their financial system has not caught up to say be the second most developed financial system in the stock market, in the futures market, in the options market. That is equivalent to that, right?

So you can look at many other countries that have a smaller economy but have it have a much stronger financial system. So it's not the economy part I'm looking at, it's at the financial system part. And so for there, say the development of new futures or m new exchanges or stock markets, things like that.

That's the kind of development I would like to see. So another example of this would probably be India would be another great example of this type of emerging financial market. They have a they have been a very strong emerging economy. for, you know, over a decade.

But I would say that their financial market and securitization of financial products has come to light probably in the last three years where it's quite good to trade, where they have developed enough infrastructure, where you have developed enough. liquidity where there is enough institutions in the market and enough trading in the market to make it worthwhile to trade and not completely bonkers. But not so much so where it's become like the US.

or Europe where everything is, you know, highly efficient, you know, high amounts of HFT, high amounts of say quant funds or, you know, investment banks or things of that nature.

Okay. And you also you brought up a point about signal to noise ratio. What does that refer to? Okay, so from we'll say uh engineering standpoint, we're on this call, right? So there is some noise in the background on this Skype call. But how much of the sound that is coming through your system is my voice and how much of that is white noise or lag from the internet or other ki types of latency or, you know, resistance coming from the wires of our, you know, different setups.

The greater the signal is, the clearer you will hear my voice. So any strategy is also trying to capture a signal, whether that's um, we'll say uh trend followings or a mean reversion or a carry trade or whatever, there's some signal you're looking to capture. How strong is that signal versus the background noise of what's going on in the market? And how easy are you able to discern one from the other? Mm. Right, right. That's a really good analogy. I like how you did that.

Very good. Now the eighty percent retail versus compared to twenty percent institutional in China, why is it that way? I mean, do most uh funds in China trade uh US markets? Like why is the ratio split like that? Well, no, they do not trade outside markets because of the R and B exchange. So that's they have a certain quota that they can every Chinese national can do per year, which is not very large. And they don't have

floating exchange rates. So that does put a crimp on how much they can trade abroad. So they're limited only to the domestic markets. Also, hedge funds, investment banks A lot of that stuff is quite new. So it's not also a lot of it is fraud. Or shadow banking or there's a lot of guaranteed products that's gonna get you, you know, twelve percent a year or something like that on on on leveraged properties or some crazy insane. Products. So a lot of people are savers.

They save the money and they manage it themselves. And so they're gonna go out and buy houses. They're going to invest it in the stock market. They're gonna invest it anywhere they can get a return, but they're not putting it in an institution like we would in the States with a 401k. you know, or put it into like a Vanguard ETF or, you know, SPY or something like that. They're out there investing in stocks themselves.

And there's a huge population doing that. So that really causes that percentage ratio. Okay, so probably a a good portion eighty percent of the retail a lot of them are are mainly investors and not necessarily active traders. Would that be a fair assumption? Oh no, they are active traders. They are ten times more active than than any American. Their average trade might be less than a week.

Their holding period three months is long term. If you told someone you h you held a three month trade in China, they were like, wow, you must have made a hundred percent on it or something. Like it must i that's not the type of thinking it's It's just a totally different type of thinking and culture and and background of the retail and and the institutional here in China. I mean it's quite a different perspective on things.

That's so crazy. So y you know that common thing that you hear floating about is ninety percent of traders fail. W how does that sit in China? Like do most people, like this eighty percent we were talking about here, uh most of them do fairly well or Okay, so In a bull market, everything is easy. China has been in quite the bull market for a very long time, so people are used to making money. After the stock market crash, I would say significant people took a hit.

So I just talked to a coworker of mine last week talking about this. Uh we we trade mostly futures, so I can give you a better perspective on that. We were discussing the differences between, you know, traders, both non professional and professional traders uh abroad ver versus here. And he said in China it's ninety five percent of people fail. Um for the retailer area. So actually it's a a higher percentage.

of people are failing here because a lot of them trade rumors. A lot of them have, you know, listen to um Gurus. Uh, on T V there are guys drawing charts and technical analysis, you know, telling your grandma how to take a trade in her retirement account. I mean, it's um swacky man. I I don't know. I I I cannot if you have never seen it, it's hard for me to explain what it's like. But it's definitely a lot of retirees probably got very much hurt m in the last year.

Yeah, it it's funny you mention that actually because I remember reading an article uh a little while ago now about I'm pretty sure it was in China and there was like a soap opera on TV and They mentioned a company and that company did something which would be interpreted as being good news and this was only in the soap opera, like it was all made up, it was just acting. And that company's stock, like

sword over the next like week after that episode came out or something like that. Like is that the sort of thing that happens on a regular basis? Absolutely. The virality of news in China is extremely fast. So we have WeChat. Uh for people who don't know it's like WhatsApp, but for mainland China and there are tons of investment groups. private ones, public ones, you know, whatever. Um, someone makes a recommendation who's got, you know, a high rating, you'll s you'll see thousands of people.

you know, buy into that, you know, definitely you'll have a volatility spike. Definitely you'll see the market move in a particular stock. I mean, it's no question. that anything they see on T V reality or not, I mean if th they pay these stars to wear, you know, some clothes or some hat, if it becomes fashionable

automatically in the next like two weeks everyone's gonna have one. I mean just how things spread like wildfire in China um just because of the interconnectedness, I think does, you know, play into that. And that definitely uh when you think about retailers doing this, it's definitely hits their psychology. I mean if you imagine how retailers think and you imagine how some of these people

have don't have a trading or financial background and they're out there trading their own money, they're gonna be hitting with these swings. So you compound the the huge amount of connectedness with trading psychology and you can imagine why the types of moves you see in the Chinese market exist. Mm.

Yeah, it's fascinating, isn't it? Yeah. Let's bring this forward to current times. Now, bring us up to speed with with what you're doing today. All right. So uh today I'm working at a fund called uh Fortrade Investment Management Co Limited. and we are um a investment arm of a Shanghai listed company and for us uh we are a private fund and I am the director of systematic and options trading.

So What I do on a daily basis is just trade a portfolio of algorithms that fit, like I said earlier today, our mandate. and our, you know, target and use a variety of systems and uh y control my portfolio in such a way to generate a product that will satisfy those investors' needs. Okay. So you mentioned earlier that you trade futures and you've just mentioned that you trade options as well. Are they the two products that you trade or do you also uh trade anything else?

No, those that's all we trade and actually in China there's only one options contract that exists. So and it's highly regulated. You can only have ten contracts on. So we are preparing for that, but like I said, developing market There are no options on commodity futures yet. It just does not exist. That is an innovation that will come out in the future. So preparing for that. um is what, you know, my department is and that's my official title. But

Just imagine that that there is none. So so the options that are available, what are they what's the underlying asset? It's uh it's on the stock market index. On the stock market index. Right. So how do you use those options? Like what purpose do they serve in your portfolio? I'll be totally honest with you. We don't trade them, it's just in my official title. Got it. Got it. Okay.

So when it comes out I can trade it, right? But uh until then. Right. At least it's in your title. Um so when is that expected to take place. Like is that happening sometime soon? That's a fantastic question because we have been waiting for it for quite a long time and it's all up to the government regulation. Everyone tells me it's coming soon or next year.

But until the C S R C which is the equivalent of the Chin uh Chinese SEC approves that and the party approves that. There there's no telling. I was actually down um I want to say about a month ago, a little bit more than a month ago. talking to the chairman of uh the Jongzhou Exchange and other exchange officials in Jalion to try to find out what the target is for this and, you know, it's still up in the air. Everyone is very excited saying it's gonna come through, but there was no

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Let's get stuck into the strategies part. Talk to me about what are the type of strategies that you're trading at the fund. What are your algorithms doing? Okay, so I like to think of trading strategies in two families. Divergent and convergent trading. Most people associate these two types with trend following and mean reversion, but I don't think that that's such a great definition. So I would prefer to use divergent and convergent. Majority of our strategies, our

Divergent strategies because as we know, China is highly volatile. It does have a lot of you know big moves, so we do look to capture that. And then backing that up with some diversification and the convergence strategies. Okay, so would you like to flesh that out a little bit more? Like why why do you refer to these strategies as convergent and divergent rather than trend following and mean reversion? Like is are they

Are they different in some way or do you just prefer those titles? No, I think the definition is much better. So let me see how I can put this in a very crystallized manner. So let's start with convergent. In convergent, we believe in something, and normally that something should occur. So in mean reversion, we believe that there is some mean or some true value, and when price is away from that. it should return. So we believe in this mean or that true value as

a state or a price that we can can hold. And when things are away from that, we believe it should approach to that. Another good example of convergent trading is value. investing, you believe that the fundamental pricing of a company is X. And if the share price is not representative of that fundamental value, then we should buy it until it approaches that fundamental value.

So that is something we believe in fundamentally, right? And this is something we have researched or or we have found and we expect the state of the market to maintain that relationship. Does that make sense? It does, yeah. So when you're talking about convergent and divergent strategies, the trend really has quite little to do with that, right? Right. So a trend we believe it's not gonna be normal. The state of the market is not

going to fit into our criteria. There is going to be some radical shift Right. That's gonna happen. So it we can imagine a trend as a radical shift away from where price was before, right? So that means that whatever that quote unquote fundamental idea that it was in convergent trading is no longer viable in the divergent trading, right? Because, you know, the price has

So that that is where we come to the difference. Another, I guess, characteristic of these two would be the distribution of the returns for both of these strategies. I think that's a much important more important thing to look at because they have opposite return distributions and usually you'll be able to classify what type of strategy you know that is by looking at that or even look at a a hedge fund or managers or traders.

um return distribution and figure out which one of these two he's leaning towards. Okay. Would you mind just dumbing that down for us a little bit about the return distribution between the two? Okay. So if I add up all the percent returns or the log returns I generate every day and I put that into a histogram on my Excel, it will draw me a picture. Right? And so we'll say that there is a line where my average is. So we put it on the Excel and we get the histogram.

If we have the tail on the right side, okay, we have most of our losses on the left side. So that means we'll have a big fat lump where we're losing more frequently, and we have a big right tail. Which would mean our profits will be much larger than our average losses, which is a characteristic of a trend falling. So that'd be a divergent. On the other side,

You know, if we had a convergence strategy, our, you know, big hump would be on the right side of zero, right? So we'd get a lot of winners a lot of the time. So we'd have a very high uh win rate, but our tail w you know, would be to the left and that means we would have a lot of la infrequent but large losses. Does that make sense? Yeah, no that does make sense. So I mean my I guess my next question would be how do your how your algorithms

programmed or your has strategies set up to detect when to trade a convergent or a divergent strategy? That's a good question. So first of all, we need to think top level and For the most part. When you make a fund, you're making a product. And each one of these strategies is an ingredient. Each individual strategy is an ingredient. We need to mix those together somehow, right? That mixing together, we use something called dynamic portfolio. And uh what this does is allows us to look at

families of strategies, right? So you might have some calendar carry trades, you might have we'll just say breakout trades or momentum trades, or you might have you know, statistical arbitrage or you'll you'll have all these different families of systems out there, all with these different parameters and we need to, you know, be the chef and combine these together in such a way that makes sense for our end goal, right?

So how we do that is we go through the three-dimensional diversification model. that I I have developed. Okay, so would you mind explaining that model maybe a little bit more? Right. So we know that diversification is really important. Everyone knows it's the only free lunch on Wall Street, right? So let's take that as uh a fundamental building block of you know what we're trying to do. So how are all the different ways that we can diversify ourselves?

We can diversify self across strategies, right? That's one dimension. We can diversify self across time frames or parameter sets, right? And we can diversify ourselves across markets. So looking at those three dimensions of diversification. We can pick the optimal set of strategies and strategy families to make up our

Total basket. I think it might be helpful if you could walk us through your your strategy workflow, right? Like From how new strategies go from idea through to implementation. Would you be able to step us through that part of it? From idea to implementation. Okay, let's see how to break this down. So first obviously we're gonna have brainstorming ideation, right? So the most important part there is we are hypothesis based

Models, not data mining. So we a lot of us here have trading experience or we have we have other teams to lean on for ideation, people who specialize in different areas or came from the physical. We can talk about you know, um some idea they have, right? And from that idea we start to do research. We will start to say, okay, there is something we want to capture, say that's a trend. Okay. We know trends exist in this market.

If they exist, how can we go about capturing them? And so that's where the modeling takes place. Then we want to say, okay, we ha we have modeled it. How well? Is that model capturing that phenomenon? So how good is this trading strategy good at capturing that trend? Right? Once we have implemented that, then we wanna say, okay. a lot of From the retail side of things.

Everyone's looking for the perfect parameter, right? Everyone's looking for the optimizing on the best parameter set and saying, hey, you know, this, you know. EMA uh twenty-nine and you know that, you know, Bollinger Band at 32 or you know, whatever, find this best strategies, best parameter. I I don't believe in that. Because I think if you're capturing a specific phenomenon

That phenomenon is also it's fluid. It's not stationary. So it's you're gonna have different phases. So there might be short term trends, there might be medium term trends, there might be long term trends. If I'm good at capturing trends, I should also be good at capturing trends over different time frames. Or it should this idea should be good at capturing them across these timeframes. So I'm gonna throw cast a larger net, right? I'm gonna throw a big net out there and I'm gonna say, Okay

How does this, you know, trend following strategy follow and so I would make a family of them. I'm make a family of all these different parameter sets. And then I'm gonna treat that family as one one unit, we'll say trend following strategy unit. So I might have very fast trends all the way to very slow trends and then I'll be looking at all of them and see how to mix them. you know, over different periods. Right. So I'm rebalancing them. I'm looking at at, you know, rebalancing my portfolio.

using different mixes of these things. Because at that point Each one of these things is now no longer a strategy. They're all a return time series. They're all an instrument. I can now invest in any of this net this this family of strategies as if it's a stock or as this it's a future. I'll say, hey, this one looks good. I'm going to put money into it. I just went along the strategy because I've invested money into it. Right?

So that's how I'm looking at that. Once I've come up with whatever my portfolio is of these strategies. Then we've done our, you know, back testing with our in house software. Then we're looking at, you know, simulation trading. you know, implementing that, doing some, you know, exchange based small money trading, looking to see what any edge cases, what do we need to do, and then ramping that up to full on investments.

Okay, so right at the beginning of that answer you said that you focus on hypothesis based strategies over data mining strategies. Would you mind just explaining the difference between those two? Sure. So a hypothesis based strategy development means I'm coming from a research or a human idea. Like I said, I I believe trends exist, so how could I go about catching trends? Or I believe value investing works.

Or, you know, companies can be fairly valued. So I'm gonna go about researching that. Data mining would be, okay, I've got, you know, 200 terabytes of of tick data for, you know, a hundred different markets, I'm gonna run as much regressions and machine learning algorithms, you know, support vector machines, whatever you want to run on it, and say, okay, now I have some model that looks like it's gonna work.

You know. So one is coming from a much more human analysis experiential research side, and one is a lot more data crunching, and I would say much more opaque. because if I start doing all these optimizations, you know, machine learning algorithms it becomes less and less clear what the underlying logic or what the underlying

phenomenon I'm trying to capture, it just looks like something works. Something is working. So that is the differential. Yeah. No, that's a good explanation. And and do you see Like g you know, listening to your answer there, it kinda sounds like you're not a fan of the data mining approach. Do you feel as though it can work and some people do make it work, or do you feel as though it's it's dangerous territory?

No, I I do believe it works. I have used it before, but there is like a voodoo line. Okay. There is a line where you start to do too much of it, it just becomes magic voodoo. where the computer is spitting out some glorious answer. For me, it's always about unraveling it. I personally, as a human being, as a you know, a a scientist, engineer, researcher, I want to understand what it is we're doing.

Once it becomes so opaque that I can't unravel the logic, I can't unravel what is going on, or I can't easily do it, and it becomes, you know, overly complicated and unintuitive, I get uncomfortable. So I would say Up to like supervised learning is probably okay. Classification is probably okay. But once you start to go like random forest, deep learning, you know, the kind of reinforcement learning where where it gets very, very deep.

And you can't, you know, there's things that are going back into the system and it's evolving so much that you can't take a step back and open the box. gets to that point where I cannot open the box, that's where I call it voodoo magic, you know.

Okay. Very good. Also in your answer there, you talked about you want to know how well is this model capturing the trend or the phenomena. So how do you measure that? How do you know how well that's working? Right. So actually I generate Simulated data. I start with simulation data. And we'll just keep going with the trend following method because I think everyone's comfortable with it and um I you know I I'm a big fan of trend following. So I can generate

a synthetic data that has a certain amount of treatment and a certain amount of noise. So I can set a signal to noise ratio and I can see what different parameters you know, this trading strategy and how much noise can it take? How much trend does it require? And that way I can see, okay, in this, you know, laboratory world, in this, you know, clean room world, it can handle this.

And then I can say, okay, I want to take synthetic data based on market data. And I can say, okay, d is this still capturing what I believe it should capture? Which one of these, you know, is catching, you know, trends, are the trends existing, you know, is the correlation between these two things high? And if so, then it is capturing something. And then we can take it to to real data. And start the background because uh Chinese historical data is quite limited. Some of our contracts.

have less than two years of data. So I've gotta be extremely, extremely careful on how much data I use. So we developed these synthetic methods and it actually gave us ability to control things in a much more precisely tuned manner than just back testing on live market data because that is always gonna have some component of noise and that noise will be changing and you can't identify it. But when I put in exactly, you know, twenty percent white noise, I know it's exactly twenty percent.

I know exactly how much it it's catching and I know exactly how much that's a affecting my system's ability to capture that we'll say perfect, you know sign wave trend or that perfect triangular wave trend. Um, those are the two ty types of waves that we use to to to check for trend following. Either, you know, a triangle wave is like um

very sharp at the top and then down in a sidebade would be more smooth. So I can create different types of trends, different types of noise, different types of these types of things and see how they're all performing in a very you know, clean and and controllable environment. Okay. So two questions. What is synthetic data and why do you not just use goes straight to historical price data. I know you mentioned there that there's In China, there's a

Only two years for some contracts that you'd want to test on. So you've got to be very careful that that doesn't straight away become in sample data. But I feel as though there's probably another reason for this as well. Right. So synthetic data is data you generate yourself. So I will generate a random, you know, time series or price chart, just, you know, use a random number generator in Excel. That is synthetic data. You just generated

you know, ten years of data using a random number generator. What we have done is used that in such a way that allows us to control certain parameters. So it's much like before you, you know, have any drug, right, you know, released from a pharmaceutical company. You're gonna have different testing methods. You're gonna go through these different regimes. You're gonna wanna have to have very controlled environment.

I want to do that with my trading strategy. I want to control the environment that it's released into. I don't want to just throw it straight into to market environment because there might be some confounding factors in the market environment that affect the strategy, but I'm not able to unravel you know, why that's occurring.

But in the laboratory environment where I can generate these synthetic trends and synthetic noise or synthetic mean reversion, I can control everything. So I can see, okay, this much, you know, additional noise reduces the profit of this Trend following strategy by this much. But in the market date if I say it's two thousand eight to two thousand nine or two thousand nine to two thousand ten or any given year.

that's what I can change, but I don't know how much noise was added, how much trend each year had specifically. Right? And each market is gonna be affected by different factors each year. So it's not as easy to determine the exact, you know, cause and effect. that I can discover using synthetic data. Okay.

It's very interesting. I'm completely new to this idea of synthetic data. I've never heard about that sort of approach or or read about it before. So that's certainly something I'm gonna be looking into a little deeper.

How many strategies are you trading today at the fund? You know, you you spoke a little earlier about how the first quant fund you worked at was juggling between three hundred strategies. What's it look like at your fund? Yes, actually we have we have quite a f uh much smaller number of of strategies here.

We just started this year, so we're only looking, you know, at about a handful of strategies, but we're we're actually in in development right now to launch a brand new family of of strategies coming out. So it's uh it's a little bit less. Okay, well that makes sense. You're only new. So I really wanna dig into this a little more about mixing strategies together. So Let's just start with the basics. What are the benefits of trading a mixture of strategies?

Okay, I I want to preface this with a concept that I think is really, really important. Once a strategy is made, do not think of it as a strategy anymore. Think of it as uh time series or something you can invest in. It it is now a security, a stock, um

a future or whatever you're trading. And that that P N L line, that is the price line that you're gonna be trading. W when you consider the P and L's or the return series of these strategies in that manner, it makes much more sense to use diversification because if I'm gonna diversify stocks and they have their price lines, why shouldn't I diversify strategies if they also have price slides. So I think

A lot of people treat their strategy, their whatever that P and L is is like something sacred. It's different than the market. No, it is a market. It is It it it's generating a PL or or a price line that you either want to be invested in or you don't want to be invested in or somewhere in between. And how is investing in that going along that strategy? Going to help the performance

that I'm trying to achieve. So is investing in this strategy going to help me reach my overall objective? Is that going to uh what whatever that objective may be? And if so You know, can I mix more of them together to, you know, reduce my drawdown, decrease my volatility, you know, decrease the amount of internal correlation I have, decrease the amount of any effect.

any large market shock is going to give me. I mean there's tons of benefits and it really increases your your return, risk-adjusted return. So for us, it's not about absolute return. It's about risk adjusted return because if you can increase your risk adjusted return, I can always add more capital or more leverage. So as long as I can keep can manage that.

It becomes a uh one of the best tools you can do uh to to easily increase your risk adjusted returns. Okay then. And what are the most important considerations when combining strategies? Well, I would say correlation is probably the most important between mixing strategies. Also conceptually, why are you mixing them? Don't just

you know, use some, you know, portfolio optimizer and just finding specific values. I mean, we do do that, but you wanna have some logic to it. So like I said, we divide our strategies into different families. We are looking to bring these families in because they contribute

in different ways, right? I don't wanna just, you know, have all of one type and none of another. I want to say or I want to say for the Chinese market, I want to have a lot more divergent strategies than I have convergent strategies because the Chinese market is a divergent market. So I can start with this kind of logical step.

I want to say have some objective criteria that I want to, you know, optimize against, whether that, you know, increasing risk adjusted returns, decreasing my drawdown, you know, incre you know, increasing my sharp ratio, what whatever that may be. and take my correlation into account. So I want to say, okay, if I mix these two together these together, what is the optimal mix of these types of strategies or these strategy families?

that will, you know, hit whatever that target is, which is usually, you know, risk adjusted returns. Okay. So when you talk about correlation as a is an important factor to consider uh when combining strategies, do you want correlation between your strategies or do you want them to be uncorrelated as much as possible?

Uncorrelated as much as possible. Just like you would want your stock portfolio to be uncor as as low correlation as possible, you want your system strategies to be as uncorrelated as possible. You also want the that will probably also lead to their drawdowns occurring at different times so then they can neutralize each other much better. Right. Exactly. Yes. Yes. I know you said that once you sort of deploy a strategy, you like to think about it as not being a strategy.

With that being said though, when do you decide when to actually add new and retire older strategies? Is that something you do uh in regard to the retiring older strategies? Yes, absolutely. So we do evaluations every quarter. And we also take that time to reoptimize our weights or do, you know, new back checking, uh backtesting and checking with the simulation and the real trades. And there's all looking at is this portfolio what we want to continue with?

Is it working? And we that is all automated. So we have our criteria. It's just re optimizing, distributing new weights uh to these strategies on a quarterly basis. Now, with retiring old strategies, if they no longer fit that criteria or they're no longer giving us good risk-adjusted returns. their weights will be reduced and if they lose a lot of money or they lose start losing money, the optimal allocation will be zero, right? So eventually it will retire.

But there's another part I wanna overlay on that there's a risk overlay or a supervisory overlay. Uh, where we're monitoring that on a daily basis, where we use uh statistical process control to see if these strategies are continuing to act. as they should. So one is a performance based, one is seeing is this doing the job it's supposed to be doing. So the strategy can be turned off from either of those situations. Okay.

That's that's a really good answer. And what software programs and languages, et cetera, are you using to do all of this? Like everything we've sort of been discussing over the last uh you know half an hour when we've been talking about the actual strategy part of it. What are you using? So we mostly use R, Python, C. Okay. So it's all sort of stuff that you've custom developed? Yes.

Okay. So you don't use any sort or very little third party software? I don't use any like third party retail software, but we do leverage a lot of open source. I mean there's a lot of great open source out there in the R community like Quant Performance Analytics and in you know Python. They've got

you know, um Quantopian they've got the the the zipline back to we don't use that, but I'm just for example there's a lot of different great packages out there. So actually we do the use the the Anaconda sci pie Python stack. And that's a great open source resource for us. that allows us to to build on that and then all of our other infrastructure backtesting, risk control, uh execution is all tied up in in our own uh in house software. Right.

So I mean that sounds like a lot of work. You know, like how much time has gone into setting all of this up, like setting up your infrastructure there in regard to your programs? Like how long did that all take? Was that just like six months of solid coding before you even started uh trading anything? Like significant time. I would say like a team of coders is gonna take you to need six months. Just sort of

Taking this down a level, I don't know if that's the right way to to to frame it. And I'm not really sure the best way to frame this question, but you know, some of the things we've talked about here, I think to some people are gonna seem like they're really advanced.

Are these really advanced subjects? I mean, I don't want people to think, Oh, this guy's got an engineer degree, you know, this is well beyond me. I don't have an engineer degree. How advanced is this sort of stuff we're talking about, or is it something that you think could be picked up?

you know, if someone actually sort of committed to it and wanted to put in a bit of time and effort, it's something that could be learned. Okay, so a hundred percent it could be picked up. And I wanna say that because I have done some trader education. So I have a group here in Beijing called the um Beijing Investors and Traders Group, and I give free lectures. Okay, on trading. How to think about trading, how to trade, how to do it for the retail. A hundred percent can be picked up.

Each individual concept I've discussed today is not complicated in and of itself. Talking about trend following is not complicated. Diversification is not complicated, right? Looking at, you know, your strategy and saying, okay, how can I make some data to put into it that would be the most hundred percent perfect data it's ever seen. And then from that add some random noise to it so I can see like push it to the limit.

I definitely you can do that in Excel. It's how it has been put together that's where the complexity gets added in. So if you break them up into components and you say, okay, I just want to focus on how to do hypothesis based systems building. So I need to come up with an idea and I need to see how good it it is at capturing that idea. That's you know one block. I wanna practice, you know, building diversified portfolios. Well

I said, okay, um there's a ton of books on that. I I I recommend, you know, active portfolio management. Uh that's probably the h holy bible of, you know, d diversify diversification. So I I'm a big fan of that book and when you when you look at that and and port it's portfolio building, you say, Okay, how can I do this? And you can even look on simple websites like how to use stocks and practice just doing it with stocks. I remember your strategy

is no different in t than than any other time series. So if I have a return series of stocks or I have a return series of my strategies, I can practice doing it that way. So just break it down to the more fundamental building blocks and then assemble them together. I think I have given um Just too many of the blocks combined together. I I've shown you the dish.

But I haven't shown you each ingredient. And so if I just teach you or someone teaches you each ingredient individually, you'll understand how and how to mix them together to create that final dish. Yeah, I think that's what was said. That's a great answer.

And, you know, a lot of things we've talked about here have been very kind of Quantitative based, I guess you could say. If there's just one thing from this conversation which you think discretionary traders should take away from this, what would that be? Systematize yourself. Use research to back up your hypothesis. All Discretionary traders have a hypothesis. They believe that there is something that they see or something that they understand that.

Will have an effect on the market, whether that's a technical trader looking at a chart pattern or a value investor, you know, doing a fundamental or, you know, some guy trading, you know, an agricultural calendar spread or something like that, discretionarily. There is a reason why. You want to take that trade. Look at other examples of that trade, distill it to its most first principles. And back test that somehow.

Excel by hand. By hand is iffy because you can discretionarily leave out trades that, you know, but just try to take a look at it. Try to take a look at other examples of that and think about what could go wrong. What are the situations, like I said, edge cases where you might not be

able to capture that and then structure yourself, be disciplined and say, okay, this is what I believe. This is why I want to take this trade. So I'm going to execute this trade in this way every time. Reduce your own volatility. Reduce your performance randomness. Now when you say systematize yourself, you're not telling discretionary traders to become systematic traders, right? No, I mean w if you say, Okay, I I trade only head and shoulders or I'm a head and shoulders trader

Okay, define what a head and shoulders is. Look at all examples of this predefined head and shoulders that you are committed to trading. Look at all the statistics of this head and shoulders and say, Okay, this is what it is. I want to trade Generate rules for yourself to say, okay, I want to do on this situation, I like to do this. If something goes wrong.

What what am I gonna do? Right? I wanna look at you know, not just something goes right, I wanna go look wrong and I wanna say, Okay, I wanna build yourself a process. I guess that's the the the key point. What whatever your trading or how you came to this the decision to take that trade is irrelevant.

You should have a process once you have decided to put that position on and execute that process the same way because if you don't execute it the same way, you have added personal volatility, performance. randomness into your system. So when you or into your trading performance. So when you look at your, I'm sure everyone's using Excel or a journal or you're looking at a broker statement, it's harder to find out where you want to improve.

Right. So you want to improve yourself in in a step by step manner. It's much easier to evaluate what to improve if you've, you know, got a process and then you can change, you know, whatever is in that process and and move forward. I think it it makes it much easier. To see clear growth in where you've come from because you've made these changes, and it's a clear indicator whether the change is working or not.

And I think those are the two most important things for any trader to look at to see how their journey is going and to have a very clear picture of You know, is what I'm changing working and am I getting better at doing this job? That's a really awesome answer, man. I like that.

Um, I'm gonna squeeze in one more question before we wrap things up here. And this is a question that came up in the Chat with Traders Facebook group, which you're of course a member of, and that's how we met.

You answered this question in the group and I I think you found the question to be quite interesting and that question was what should traders focus on once becoming profitable? So how would you answer that? Well, I think a lot of retailers and a lot of people in the group think that becoming profitable is the end result, right? That is where you like once that happens, you're like, yes, I've made it. You know, that that that's um where I want to be. But It's only the start.

Um your journey. You can only really call yourself not to be offensive, you know a trader w when you can consistently make money at it, you know, I d I don't wanna be uh too much of a dick, but if you're not, you're still a student of the trade, we'll say. Well you're a student of the craft. And and then a after that you're not an apprentice anymore. you know, you you you have evolved into someone who has understands it.

So from that point there's so many more steps to go. You're not the perfect trader yet. You're not even close. You have just stepped into the world of consistently profitable traders. You know, the world uh where, you know, people do this for a living, people trade, you know, you know, millions and billions of dollars. You you have just, you know, got an entree into that circle. So you can improve yourself, improve your strategy, right? Improve how you're you're managing your strategies, right?

Look at maybe, you know, getting some assets under management, right? Look at expanding yourself. Leverage yourself. You have you now have a skill, right? Don't just look at yourself and say, hey, I'm only gonna trade my money. If if you have a skill, don't do it for free. Go try to find other people who who require that skill from you. And Just think about how you can continually improve. You you are not the optimal trader just yet. I don't think you ever become the optimal trader. So

What are your strengths? Make those stronger. What are your weaknesses? Either go around them or nullify them and have a continuous self-improvement. And as you continually self-improve, as traders, we are rewarded. And th through that reward it becomes a fantastic process where you get to feel good that you would have improved yourself.

And then the the world and the market verifies that you have made that improvement by giving you that reward. And so now you get to go into a virtuous cycle of becoming the best trader that you can be.

Very good. I feel like I'm repeating myself, but that was uh that was a really cool answer. Derek, this has been a lot of fun, man. I've really enjoyed having you on. I think we should do it again sometime. But uh where can listeners go to find out more about you? Do you have a website? Are you on Twitter? I'm not sure if you have either.

I honestly don't have any of that. Uh I'm in China and, you know, Twitter and all that kind of stuff is blocked. I I cannot because I'm in a fund, I cannot solicit uh externally, so Yeah. Find me Chatwith Traders. Yeah. Well you are in the Chat with Traders Facebook group. So if anyone wants to join the Facebook group, of course it's totally free. ChatwithTraders dot com forward slash Facebook will redirect you.

straight to the Facebook page, just hit the green join button and I'll let you in. Derek, would you be open to answering Maybe some of the listeners' questions in the comments uh on the Chat With Traders website. I probably should have run this past you before we got on the call, but would you be open to that?

Sure. If anyone wants to post any questions or comments or anything like that, I'd you know, definitely, you know, check the website or the Facebook group and uh drop me a message and I'll do my best to to answer it as best I can. Excellent. So guys, just to summarise, either you can have leave questions for Derek in the Facebook group.

chatwithtraders.com forward slash Facebook or in the comments section uh below this episode on the website. So you can get to that by going to chatwithtraders.com forward slash Facebook uh the episode number. I'm not quite sure what that episode number will be right now as we're recording this, but you'll see it. All right, Derek, well once again man, thank you very much. I I truly do appreciate it. It's been a lot of fun. No, it was great. Yeah, I had a great time.

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