Breaking Rules: Going Beyond Traditional Trend Following ft. Tillmann Sachs & Vishal Sharma

[00:00:07] Vishal

This kind of alpha that I’m getting from this strategy, it’s unique in its own way. So, there are multiple ways in which we are looking at it and, obviously, as a whole development of the program takes place, there has to be return drivers from multiple sides, multiple angles, multiple perspectives, without necessarily having commonality or anything like that, or even in the timeframe or even in the approach. And this particular stuff blends in very nicely with that program, which is a bit slow, looking at a longer-term picture. This is looking at a shorter-term picture and is completely different, as such.

[00:00:45] Intro

Imagine spending an hour with the world’s greatest traders. Imagine learning from their experiences, their successes and their failures. Imagine no more welcome to Top Traders Unplugged, the place where you can learn from the best hedge fund managers in the world so you can take your manager due diligence or investment career to the next level.

Before we begin today’s conversation, remember to keep two things in all the discussion we’ll have about investment performance is about the past and past performance does not guarantee or even infer anything about future performance. Also understand that there’s a significant risk of financial loss with all investment strategies and you need to request and understand the specific risks from the investment manager about their products before you make investment decisions. Here’s your host, veteran hedge fund manager Niels Kaastrup-Larsen.

[00:01:40] Niels

Welcome to another episode in the Open Interest series on Top Traders Unplugged, hosted by Moritz Siebert. In life as well as in trading maintaining a spirit of curiosity and open-mindedness is key and this is precisely what the Open Interest series is all about. Join Moritz as he engages in candid conversations with seasoned professionals from around the globe to uncover their insights, successes and failures, offering you a unique perspective on the investment landscape. So, with no further ado, please enjoy the conversation.

[00:02:16] Moritz

Hey everyone, it’s Moritz here and thank you for listening to episode number 12 of the Open Interest series on Top Traders Unplugged. Today I’ll be speaking with Tilman Sachs and Vishal Sharma from J8 Capital Management. J8 is a London based systematic hedge fund and their flagship, Global Absolute Return Strategy, is live since January 2015.

Tilman, Vishal and I will be speaking about what differentiates their Global Absolute Return strategy from other CTA and trend following funds and how they manage risk across a portfolio of independent quantitative strategies which includes FX carry, commodity term structure arbitrage, commodity inventory basis trading, and volatility term structure arbitrage. We’ll also speak about how one of their most recent strategies, the Short-Term Redwood Strategy, blends into their portfolio.

But before we start, let me give you some background on Tillman and Vishal. Tillman Co-founded J8 in 2012 and prior to that worked in the structured derivative space at Barclays, UBS, and AIG Financial Products. He studied civil engineering in Germany and then went on to complete a PhD at Nanyang Technological University in Singapore.

Vishal joined J8 in December 2020 and today is the head of trading and portfolio management. In a prior role, Vishal developed quantitative short-term trading models with AEON Asset Architecture and ARB Trading Group, both prop shops based in London. Vishal holds a Bachelor of Engineering from the Indian Institute of Technology in Kanpur.

Let me stop it here so we can move over to the meat of our conversation. Tilman and Vishal, welcome to Open Interest. It’s great to have both of you here.

[00:04:01] Tillman

Thank you very much for having us.

[00:04:05] Vishal

Thanks, Moritz.

[00:04:06] Moritz

You’re more than welcome.

Tilman, let’s start with you. Interestingly you are a civil engineer by education, so you’re focused on construction and building design and infrastructure planning, all that type of stuff. What got your interest in finance as opposed to construction? And why did you start J8 Capital Management?

[00:04:26] Tillman

Well, during my studies, my civil engineering studies, as you know there are several branches one can go into and I was very early drawn into the area of construction management and project financing. And within project financing I then found risk as the most exciting part.

During my PhD I developed the methodology to quantify political risks in infrastructure project financing and this eventually led me, through many multiple steps, to my first job in London. So that is really how the link happened from civil engineering to finance.

[00:05:10] Moritz

Okay, and what got you interested in a systematic and rules-based approach as opposed to, you know, trading fundamentally or discretionarily and making investment decisions that way?

[00:05:22] Tillman

Yeah, well, my first job after my PhD was to join AIG Financial Products or Banque AIG it was called back then in London. And the first product group I got myself heavily involved with was, back then it was called the Dow Jones AIG Commodity Index, which is today the Bloomberg Commodity Index.

And there I learned, really, everything around what are futures, how do futures work, how are they being traded, and most importantly how one can translate a systematic trading acumen, that is not discretionary but based on rules and long-term research and studies, how to translate that into a formulaic index or strategy approach.

And that’s really a modern treatment because, by being able to translate, let’s say, economic fundamental approaches in the commodities market, to translate those into a systematic strategy allows us to take out all the discretionary, emotional, behavioral, finance aspects that otherwise discretionary traders would be impacted by.

[00:06:42] Moritz

Absolutely. I couldn’t agree more. Vishal, what about you? You studied what is it, engineering in India. What got you interested in finance?

[00:06:53] Vishal

Yeah, I think that’s a very good question. Sometimes I also ask this question to myself as where and how did I land up in this particular stuff?

When I graduated from IIT, quantitative trading was a thing that I never even heard of. It was not in my plans, not even heard of, not even aware that there’s something like this that happens. But it was a stroke of luck, around 2004, 2005, being in the UK, I was looking at the ways of investing. I had some savings and was looking for some investing and that was the start of it. I started looking into it but with the proliferation of computing power available through the laptops, and availability of data, I started using my technical skills to understand it. How is it something more? Is it something less? What happens?

It was some very basic questions and I started reading more as such, and the curiosity just built upon, and on, and on, and on. And it just so happened that there was a big financial crisis in 2008, 2009 that just basically opened my eyes, and I saw the returns of the systematic managed futures. It so happened, at that time, I was working with the Visa Europe and the technical part of the things as such but then I switched over a carrier and joined AEON Asset Architecture.

Just a brief comment, like I think when you said in the introduction, AEON Asset Architecture was a hedge fund here in UK, not a prop shop as compared to ARB.

But so yeah, in 2009 I joined AEON Asset Architecture, and the primary focus of AEON was in short-term trading. That was my first job and that’s where I got initiated. Somehow, after that, it’s been just… I don’t know where to put the single stuff, how the things happen, but sometimes just things roll on, and on, and suddenly you find yourself in a different carrier. Fifteen years back, if I look at it, it’s been okay, not something that I had designed, thought, I don’t know.

[00:09:11] Moritz

Sure, you just stuck with it. It’s good.

Now Tillman, you have a strategy that’s, I think, live since 2015, I think is the date I mentioned, called the J8 Global Absolute Return Strategy. Maybe if you could give us some background on that strategy, how you developed it and how it differentiates from other CTA like strategies that I think would be really helpful.

[00:09:38] Tillman

Yeah. Well, The Global Absolute Return Strategy really comes from the idea that, as Fama and French say, there’s no free lunch in capital markets other than diversification. And while asset class diversification is quite well known in portfolio construction, such as the infamous 60/40 portfolio, what is less well known is that you can also diversify across model risk. And if you weight the signals according to the risk assigned to a signal, that you get an additional diversification boost.

So, what I try to achieve, or what we try to achieve in our methodology and construction is not to be only diversified across capital markets that are uncorrelated to itself, or have low correlation to itself, or to other markets, but also to be diversified across the various different return drivers and model components that we use in our construction of those strategies.

And what we resorted to is, instead of constructing one big monolith, let’s say, for instance, for example, a trend following strategy that then has a number of filters in to react to other market environments that may impact the trend following, instead of doing that (which many CTAs do), what we did is we decomposed all those different possible return drivers within the market, created a single strategy for each single return driver and risk component that we wanted to extract, and only after that combined all these different sub strategies and risk drivers in a risk weighted portfolio together, to then get a very balanced overall return profile.

So, just to give you an example, we have, in the commodity space, three independent strategies. One is a trend following strategy, the other one is a term structure arbitrage strategy, and the third one selects commodities based on inventory fundamentals which you observe on the curvature of the commodity curve.

And here you can already see, because we have three subs or three strategies, or three distinct strategies that extract different drivers in the financial markets on the same underlying markets, you already see that the signal that we generate for a single market is actually composed of three components. But instead of putting everything into one big monolith strategy where one may lose the oversight of where that actual return or drawdown come from, we decompose it and thereby enjoy the granularity to observe and to refine our strategies over time per risk driver. And this I think is a huge benefit over many other programs because we really try to not only understand but to distinctly extract these factors. And each single substrate that we construct, therefore, really consists of only one single degree of freedom, let’s say, complexity and simplicity, if you want to put it this way.

So, we use a lot of simple underlying construction modules to assemble an overall complex product, but the single parts that go in are very distinct in various discreet from all the return drivers that we try to extract. And on the on the parameterization side are also put on a very simple footing.

[00:13:53] Moritz

Couple of follow up questions Tillman, on that. On your website which is publicly available, so we’re not disclosing any secrets here, I can see that there’s a total of eight strategies that you’re trading: four trend following strategies, and then there’s four arbitrage or carry based strategies.

On these trend following systems it says commodity trend following, currency trend following, bond trend following, cross-asset short-term momentum. Are all of these different systems in terms of design? Would you trade the commodity trend following system in the same way that you’re trading the currency trend following system, or do you specifically adjust the rules or the parameters for the currency trend following system to work differently than for commodities or bonds?

[00:14:38] Tillman

Well, the underlying logic for the trend following systems in commodities, currencies, and bonds is the same across all three. But then for each single market, so, for example for the euro FX market, or the oil market, or gold, or the government bond market, let’s say the 10-year US treasury, or the jump 10-year US treasury, each single market has their own parameterization. So, we look at each market individually, and we determine the market specific parameter individually, but the over overall underlying methodology and how we calculate the trend following signal, that is the same for all trend following models.

Now, you mentioned four trend following strategies. I just mentioned three. That is because the three trend following strategies, one is currencies and bonds which are the original, let’s say, long-term trend following strategies that were included in our program from the start. We recently added the cross-asset short-term momentum strategy, which is Vishal’s strategy.

And the beauty of this strategy is that because it’s so short-term it kicks in with returns and with a hedge component where our long-term strategies may suffer drawdowns during reversal periods. But I leave it to Vishal, later, to elaborate on his system and how that works in an exact practice.

[00:16:25] Moritz

Absolutely, I’m looking forward to that. Before we get there, notably absent from your trading universe are equity index futures or equities in general. Why is that?

[00:16:37] Tillman

Well, what we found is that equity indices are, per se, a very different animal than let’s say the single commodity underlying. So, for example, if you use or if you look at the liquid futures on equity indices such as the S&P 500 or the Euro stocks or DAX or NASDAQ or Nikkei, Nikkei 225, what you see with all these indices is that, A, they’re composed of a number of underlying that go into the index, that’s clear. But they also do have a survival bias within the index. So, only those companies that do well over a certain period of time, over a certain threshold, those companies are included and companies that don’t do well drop out of the indices.

So, you do have a survival bias within those indices which distorts, let’s say, the long-term nature of a trend following approach where you try to go through the entire economic cycle of an underlying. So, let’s say oil or gold, you can go through an entire economic cycle with long and short signals. Here the signals are forcefully truncated, in that sense, by excluding or including companies. So, the survival bias is one of the aspects why we don’t trade it.

The second aspect is that most of these companies, or probably almost all of these companies encoded in these indices, they do somewhat rely on a financing structure which is reflected in interest rates. And we do trade interest rate futures in the bond markets. These companies, they rely somewhat on international trade. So, on FX we have, already, FX strategies in our portfolio. And they rely somewhat on input, be it energy or be it a raw material commodity, to produce something of one shape or the other. Most strategies, most companies rely on commodity input to function and unless, of course, they’re a service company.

But why we don’t do equities? It’s because we say, okay, we want to avoid redundancy effects, or residence effects, by trading equities on top that will just be triggered by movements in the underlying markets which we trade already anyways. So, that’s another reason why we exclude equities.

So, the two first reasons that I mentioned are, essentially, the survival bias inherent. The second is that we already trade all the other input factors that go into equities anyway. And the third factor could be, one could argue, that once a stock is issued by a company, it is a 100% speculative instrument and it does not really serve any more economic function to the firm, itself, other than paying dividends to the shareholders and raising the initial capital. So, it’s a 100% speculative instrument which is simply traded on the market speculatively. Whereas a commodity, for example, is a real input material into production, into the economic life cycle. So, there’s clear demand and supply.

All of these arguments, put together, convinced us to not include equity indices in the long-term trend following space because we found that those moves are well respected or well thought of in the other markets that we already traded. That is not to say that trading equity indices does not make sense if you trade them differently.

And again, I want to point to Vishal, who will come in later, who introduced equity indices in a very different fashion where it makes perfect sense to trade them that way. But in our long-term, slow momentum, or trend following approach, we did not find any added value by adding equity indices to our platform.

[00:21:22] Moritz

Interesting, okay, so you have the equities excluded at the level of the Global Absolute Return Strategy. But we’ll come to Vishal’s shorter-term strategy. And my understanding is you’re trading them there, in a different setup.

Staying with that portfolio which consists of several moving parts and several strategies that span across trend following but also FX carry, I think you’re trading VIX, there’s commodity term structure arbitrage, how do you put all of that together and how do you manage risk around that?

[00:21:58] Tillman

Yeah, so just so briefly, maybe allude to the other four strategies that go in there. They’re essentially arbitrage strategies, either arbitrage of time or arbitrage of essence.

So, with the term structure arbitrage, we simply arbitrage the longer end of the curve versus the short end of the curve. But we also close out the short end when we see that the curve would go straight up and be otherwise unsustainable.

So, there’s a natural risk management system built into there, similar to the volatility arbitrage trade which runs on the VIX futures and on the V stocks futures, where we also arbitrage the long end as well as the short end. Essentially, it’s to benefit and to profit from the role-yield differentials of the futures along the term structure. And then you can say, okay, here we arbitrage time.

When it comes to carry trade in currencies and the inventory picker in commodities, there we arbitrage between asset classes. So, carry trade is classic, right? You borrow in the low interest rate currency, and you invest in the high interest rate currency. Here we do that over the US dollar consideration and we reverse the trades when we observe a selloff in the market based on short-term realized volatility.

And the final strategy in commodities inventory is that we simply observe the markets based on the curvature of the underlying commodities and try to deduce whether they’re an over or under supply, and then we go long the undersupplied one, and short the oversupplied one. Because you would expect oversupplied markets and prices to fall, and undersupplied market prices to increase.

[00:23:49] Moritz

Sorry to interrupt real quick, Tillman, where do you get the inventory data from?

[00:23:54] Tillman

So, we don’t use inventory data per se, we use the price data along the contract term structure of the futures curve. So, we look, for example, let’s say where’s the price of the December future versus the price of the September future. And then we see which one is higher, which one is lower, which may indicate that you are, rather than in an undersupply situation, it’s an oversupply situation. That’s how we read indirectly the state of inventory. Because if we would use fundamental inventory data, that is often delayed and then also subject to corrections. So, it’s not really precise data.

[00:24:44] Moritz

So essentially, it’s a basket of, shall we say, it’s risk premia strategies. You have commodity curve carry, you’re favoring back predation versus contango, these type of things. You have FX carry. One strategy that is interesting in that space, or in that mix, is your vol term structure arbitrage system, which I presume has something to do with the VIX and or V stocks index?

Is that specifically a strategy that’s looking to be short volatility or short, you know, expected variance; essentially looking to realize or make money off of the equity risk premium or would you also go long the VIX index occasionally?

[00:25:27] Tillman

Well, that strategy is purely long/short on the term structure, and it does not go long the VIX index, which can be, in its own right, a very dicey proposition. Because if you look long-term and, let’s say, rolling VIX futures time series, if you look, let’s say, at the front months or nearby rolling VIX future time series, you will observe easily that you have annualized losses of around 50%, 60% per annum, which are purely rolling losses. So, it makes perfect sense to try to arbitrage that out through a short position.

But then if you go short the front month of volatility and then you see a 300% volatility spike, it can completely be a catastrophic event to your entire portfolio. So, you have to hedge that risk and to manage that risk. And we are fully aware of that risk and, and our systems are designed to mitigate it.

[00:26:34] Moritz

When you put all of these systems together, how do you allocate risk among them? Do they all have a target volatility, or like a target value at risk, or something along those lines?

[00:26:48] Tillman

Yeah, that’s a very important question. I mean there’s several ways how one can construct a portfolio. What we have found is if we simply take the inverse of the realized volatility of each of the systems and then weight it proportionately, so, essentially equal risk weighted asset allocation. What we found is that this gives us the greatest stability of the overall return portfolio.

So, we do not use correlations or, let’s say, short-term good performance to do over or underweight, to put in tilts, to over and underweight certain assets. We don’t do that. We simply go by the realized volatility because we found that the realized volatility between assets is fairly stable over time.

Whereas of course correlations or short-term outperformance of certain strategies to then overweight them, that can be quite short-term or a shorter-term niche and unstable. And in order to avoid oversized drawdowns, we don’t do that.

And, as you can imagine, with having all these different strategies in the portfolio, you already have a super diversified return stream and the return and the volatility gets lower, and lower, and lower. But the beauty of trading futures is that futures are unfunded. So, you can very easily scale the futures or the exposure to a target volatility. So, what we try to achieve is we scale the exposure of our futures to achieve a realized target volatility of 10%. And that actually has worked very well over the last 10 years. I think you have a realized volatility of 10.26%. So, it’s very close to it. You’re never perfect on it, but it’s a target volatility.

So, just to put this into perspective, so for example, if an investor says okay, I want to invest 10 million into the strategy, then we would trade the strategy as if it was 10 million on allocation. But how we go to it is we look in the internal exposure calculation to get to the start of volatility of 10%, which is probably, for example, 300. So, that means that the 10 million will be traded as 30 million in terms of allocation market exposure in the market. And we found that over the period of time we just need to use between, let’s say, 6% and 10%, sometimes 12% margin for equity. So, let’s say, 10% margin for equity. So, we can trade this 10 million portfolio with a 1 million cash allocation to the margins.

And so that means that with 1 million cash you actually control 30 million market exposure. That is how our risk management then works in practice.

[0:29:59] Moritz

Absolutely. Now maybe the final point on the Global Absolute Return Strategy. By what you’ve described, it’s a multi-strat setup. Would you, yourself, put J8 into the kind of like CTA box and see CTAs as your peers? Or would you rather compare yourselves to say multi-strat pot funds like a multi-strat quantitative hedge fund? Because in a way that’s what you do.

You don’t have a puristic trend following set up. You’re trading a total of eight strategies across different styles, holding periods, long/short arbitrage focused, trend focused. Is the best benchmark for you guys a multi-strat hedge fund?

[00:30:48] Tillman

That’s also a tricky question. When we embarked on our survey, back in 2012 when it started, I conducted three global surveys, empirical surveys, about what the whole CTA industry is all about. And I created the questionnaire and sent this to brokers, to other CTA managers, to consultants and to investors and asked, in their opinion, what do they think CTAs are all about. And lo and behold, we collected about 120 replies over these three years.

And if you put the most common answer of these replies, the most common answer what they say it’s all about, if you just throw this into a very simplistic index model, you will see that this index model, broadly, to let’s say 80% of its returns, mimic or are similar to popular CTA benchmark indices such as the SocGen CTA index or the BTOP50 index out from Barclay Hedge.

What this really educated us on is that, broadly speaking, the most CTAs or what is perceived as a CTA universe is essentially a highly diversified portfolio trading on a 12 month momentum which is risk weighted, broadly speaking. So, if you ask, are we a CTA in the traditional sense? I would say we are 50% traditional CTA, 50%, let’s say, alternative risk premium. So, CTA plus, I would call it CTA plus.

And if you are multi-strat, yes, we are also multi-strat, but you could also say that we are heavily commodity focused with other asset classes joining it. So, it’s very difficult for me to put us into a fixed bucket directly.

[00:33:04] Moritz

Now Vishal, I guess one of the pluses is you with the recent addition of your short-term systematic system which, as we’ve learned from Tillman, does trade equities. If you could, give us some background on what it does and maybe together with Tillman, why you’ve decided to integrate it into the Global Absolute Return Strategy.

[00:33:28] Vishal

Yeah, if you go back, if I look back at how I started my trading carrier and people that I was associated with, I learned quite a lot on the short-term trading side of things. So, this strategy is like a culmination of lot of learnings that I gained over the years and my own experiences of trading.

And this strategy is primarily focused on short-term. The short-term defined as anything from like few hours to few days. So, that’s what we are talking about. We are not talking about months or anything like that. So, a major part of the trades, currently, are closed within the day, or maybe the next day. Very few of them are being held over like multiple days, which can be like three to five days. But that’s the kind of a horizon we are talking about.

And this strategy is focused on the big futures, the futures which have good liquidity, which are easy to get in and get out of because of the short-term nature, that we should be able to trade it with sufficient liquidity.

And the base of the strategy is all about exploiting those extreme movements, or not even extreme. We’re talking in terms of like breakouts being measured in terms of volatility, being measured in terms of prevailing trends, any kind of big impact, those supposedly big movements measured in terms of the whole context of the market and reacting to that in a short-term basis. We are able to get there quickly and in a way that we are sampling the market every three minutes to five minutes.

So, that’s, in a very short summary, you would say that okay, we are sampling this market on a three to five minute basis. We understand what’s the current context in terms of trend, volatility, positioning as to how it is. If there is any big movement, we would be looking to get onto that movement. Obviously, the trade size and other things are all computed, have their own computations as such.

But yeah, that’s a brief summary of the short-term strategy. It is applicable to all kinds of asset classes in our simulations. And currently also we trade practically all asset classes as such. The equities which the main program doesn’t trade. But we, in the short-term, we definitely trade it. Commodities, currencies, bonds, we’re trading across these asset classes as such.

And the particular question of, how does it fit into this the GARS as such, that’s very interesting. That’s where myself and Tillman, we started talking about it, and overall we did a lot of simulations. What we realized was that this kind of alpha that I’m getting from this strategy, it’s unique in its own way.

So before even addition to the curve what we have been looking at is this correlation of the short-term strategy to any of the CTA indices available across the board, whether to S&P individually or to other industry benchmarks, is quite low. In fact, some of them are quite a bit negative also, and we do not find any kind of strong correlation with any of these indices.

And this also held true when we added this particular short-term strategy to the GARS in our simulation. And we found that it just enhanced the whole risk adjusted return. So, there are multiple ways in which we are looking at it and obviously, as a whole development of the program takes place, there has to be return drivers from multiple sides, multiple angles, multiple perspectives without necessarily having commonality or anything like that or even in the timeframe or even in the approach. And this particular stuff blends in very nicely with that program, which is a bit slow, looking at a longer-term picture. This is looking at a short-term picture. Completely different as such. Yeah.

[00:37:41] Moritz

By the way listeners, I think we should mention when Vishal says or he mentioned GARS, that’s the Global Absolute Return Strategy. And I think what I omitted to mention is that the short-term system that you’ve been just speaking about is called the Redwood Strategy.

[00:37:55] Vishal

Redwood or RWS as we say.

[00:37:57:00] Moritz

Okay, let’s maybe stick with Redwood. What is Redwood’s correlation to the Short-Term Traders Index, the SocGen Short Term Traders Index?

[00:38:08] Vishal

Yes, we ran it and we found practically nothing. It’s like very close to zero. I mean those numbers are pretty low as such.

[00:38:17] Moritz

Are you trading using intraday data or is it based on daily bars and you may exit or enter a position on the next bar?

[00:38:27] Vishal

Yeah, it’s completely intraday, as I said. Like we are sampling the market every three to five minutes. Different systems sample at three minutes, some sample at five minutes. So, practically everything to five minute computations are done and the output is generated. That doesn’t necessarily mean that trade has to be taken, but we are completely in tune with the market in that sampling period.

[00:38:51] Moritz

You’re trading long and short, I guess, both sides?

[00:38:55] Vishal

Yes, yes.

[00:38:56] Moritz

Is most of it based on exploiting or continuing with a trend that you have observed, or is it more around counter trend and mean reversion trading?

[00:39:08] Vishal

Yes. So again, this mean reversion, counter trend to trend, I mean it all has to be in the context. The longer-term trend may be like going up. When I’m looking at the market at this particular horizon, it may be coming down. So, now a longer-term horizon trading strategy may say that I may be going with the mean reversion. But actually, in my context it’s like the prevailing trend that’s there.

But yeah, so my trend is defined in a slightly different way than the long-term trend or as to how. And that’s one of the beauties of this particular strategy is it has its own context. Context is not just about the trend, the volatility that is currently there in the market and all these things need to be taken into account.

[00:39:55] Moritz

Okay. How many markets do you trade there, Vishal?

[00:39:58] Vishal

Currently there are 12 futures that we trade.

[00:40:01] Moritz

Okay, okay, 12. And how many trades, on average, would you get in a given day? How active is the strategy?

[00:40:09] Vishal

The strategy is okay, I would say like we are talking about like 700 round trips per million per month kind of stuff.

[00:40:19] Moritz

Per month? Not per year but per month, 700 per month?

[00:40:22] Vishal

Yeah, something like that.

[00:40:24] Moritz

So about… So, 8,500 or something like that per year?

[00:40:29] Vishal

Yeah, per year on a million basis, on per million.

[00:40:33] Moritz

Okay, Okay. So, in terms of transaction costs, slippage, bid offer spreads paid, et cetera, I’m sure you’ve done the numbers. If you say, okay, I’m down how many percent before the year even starts because this is my expected transaction fee drag, cost drag, commission drag.

[00:40:52] Vishal

Yeah, so commission drags, I mean we have noticed that we are not like… In percentage terms, if you’re looking at it (I don’t have the exact numbers for you at this present moment), but it’s not anything substantial to say that the strategy is impacted by slightly varying transaction costs, and that we have multiple brokers with which we actually currently execute.

[00:41:17] Moritz

Interesting. Right, so how does it perform in terms of like risk adjusted returns when you compare it to the other systems you run? Is it a strong outperformer in the sense that it has higher Sharpe ratio or a better MAR ratio, lower drawdowns? What would you say?

[00:41:37] Vishal

Yeah, it has a higher Sharpe ratio. Returns wise, it’s on the par or slightly better. But comparably, the Sharpe ratio risk adjusted return is quite good. So, that’s one of the big things. And that’s quite evident from the fact that, if you go short-term, you should be able to react to market changes early as such. But the real challenge in the short-term stuff is to avoid whiplashes because there’s a lot of noise in the short-term space and that’s where the challenge lies.

[00:42:10] Moritz

Okay, so I’m just, I mean this is interesting. I’m so interested in the short-term trading systems because of the fact that they can be so diversifying to all the others.

[00:42:23] Vishal

Absolutely, yes, that’s the key thing. Once, if you start to go deeper into the stuff, each market presents their own nuances, and it presents this whole list of opportunities as such.

[00:42:37] Moritz

Yeah, yeah. I was just… So, I have a little like back of the envelope calculation here. If we’re just, you know, very generously saying that there is an execution commission of 75 cents, and there’s always exchange fees, whatever, like 50 cents and clearing fees 50 cents. So, let’s stay very competitive. But you always have some, unless you guys have massive HFT infrastructure or exchange memberships. You know, market impact is a thing and there is a bid offer spread and there is slippage. So, let’s say, per contract, it’s also generously five bucks. So, if we say it costs you about six dollars to trade a contract, would you say that’s fair enough? If it includes slippage and bid offer?

[00:43:19] Vishal

Yeah, $6, if it includes bid and offer. Yeah, that would be okay, yeah.

[00:43:26] Moritz

Then when we do these 8,000 round turns per million, per year, you’re down about 11%, 10.8% to be exact, in terms of expected execution and implementation costs. So, that is the hurdle you have to overcome which is kind of like the genesis for my question. The risk adjusted returns and all of that, they need to be very, very high. It needs to be an extremely good system in terms of accuracy and all of that to make back that drag, and then some.

[00:43:58] Vishal

Yep, you’re absolutely right. And that’s where the whole thing comes up that you will have to, or we will have to control like maybe how much. We do not want super sensitive systems to say that, okay, we are going into the market as such, how we control the exposure? And the biggest challenge, as you rightly pointed out, is if you can easily do over-trading in the short-term space, and you do not want to get into that space.

[00:44:27] Moritz

Yeah.

What are the fail saves and all the kind of like tech and infrastructure controls that you’re running around the system given that you’re now sampling, did you say every three minutes or five minutes? I think.

[00:44:39] Vishal

Yeah. Different markets sample three to five minutes.

[00:44:43] Moritz

So, it’s essentially a 24-hour a day process that needs to run reliably. How do you make sure it does? How do you support that and yeah, what do you do if it doesn’t work? I guess every once in a while there is a glitch. I mean nothing’s ever 100% perfect.

[00:45:03] Vishal

I think that’s the other dimension that is part of anything that you’d start to do like short-term trading. There are no off hours or there’s nothing like okay, you can take a step back. So yes, for this particular thing like we have built our own in-house infrastructure for the fixed execution. We are, all of the systems, they’re currently completely automated.

The signals are generated, and they are fed into the fixed infrastructure where they’re completely… They’re executed automatically, like algorithmically as such. And as far as the monitoring of the systems is concerned, there is a whole mechanism of producing alerts and there is the desk, the operations desk. We also have an operations desk in India that we currently like have the whole monitoring being done from there. Also, from the UK like we have the whole thing here.

So yeah, we have monitoring in place. We have built the in-house infrastructure, we know the code like what has been there. Yeah, so, from that perspective, yes, this is a kind of an overhead for having the complete end-to end-automation.

[00:46:16] Moritz

What, what language are you guys coding in, multiple or do you have one favorite?

[00:46:21] Vishal

Yeah, so, it’s multiple. It’s a hybrid: part Python, Julia, and Java. So, there are parts which are in Python, there are parts which are in Julia, there are parts which are in Java.

[00:46:34] Moritz

Okay. Tillman, what would you say in terms of like next steps near term, maybe longer-term strategic outlooks for J8? Are you on the lookout for more of these diversifying very different types of systematic strategies for you to integrate into the global alternative or Global Absolute Return Strategy? What’s the future direction that you foresee for your company?

[00:47:01] Tillman

At J8 Capital management, we have really two pillars of business. One is our investment management business where the Redwood strategy and the GARS strategy in all its variants play a key role. And we also have a regulatory hosting business where we provide a regulatory umbrella to other managers and financial service providers.

So, we are growing both sides of the business with overlaps, such that if you see, for instance, a trader that is under our regulatory umbrella and we get to know him really well, and we think it makes sense for us to integrate him into our system, we may give that trader an allocation. But we are not actively out, like a multi-strategy program, to always try to source the next best trading idea for integration because we very much do our own research and I’m very much convinced that what we have is actually quite good in its own right. But, if it just so happens that we come across a trader that offers something that we have not considered or that we cannot do ourselves, then certainly there’s maybe a space.

I mean, as I said from the very start, our objective is with the, say, Global Absolute Return Strategy, is to provide the investor with access to the asset class systematic in a broader, diversified and all encompassing, inclusive, holistic way. And if you can improve that, the better. So that’s really where it goes.

Where we see the company growing is, at the moment we are around 100 million AUM. If we take in our other mandates, together we run roughly 150, close to, which we expand soon more. And our objective is that, while we do at the moment primarily manage accounts, our objective is to launch a fund vehicle that is open to global professional investors. And for that purpose, we are also still looking for the initial money to get it off the ground, but promising talks are in the pipeline.

[00:49:38] Moritz

Great. Well, best of luck for both the future fund and getting these capital raising talks completed successfully.

Maybe this is a good ending point to wrap it up. We’ve heard about your outlook. Thank you, Vishal, for describing to us the benefits of the Redwood Short Term Trading System. I think we’ve learned, in quite some detail, the workings of the Global Absolute Return Strategy, and about your firm.

So, Tillman and Vishal, thank you both for joining me on the podcast today. It was a real pleasure to speak with you and I’m sure our listeners will find our conversation both interesting and valuable. At least that is the hope.

And last but not least, a quick note to our listeners, as usual and as you know, we’ll include the most important takeaways of today’s discussion in our show notes. Should you have any questions, be that for me, for Vishal, for Tillman, please go ahead and just send us an email to info@toptradersunplugged.com. We are always very happy to respond. So, thank you for listening. And until next time on Open Interest.

[00:50:43] Vishal & Tillman

Thank you Moritz.

[00:50:45] Ending

Thanks for listening to Top Traders Unplugged. If you feel you learned something of value from today’s episode, the best way to stay updated is to go on over to iTunes and subscribe to the show so that you’ll be sure to get all the new episodes as they’re released. We have some amazing guests lined up for you and to ensure our show continues to grow, please leave us an honest rating and review in iTunes. It only takes a minute, and it’s the best way to show us you love the podcast. We’ll see you next time on Top Traders Unplugged.