portfolio allocation Archives - Algorithmic and Mechanical Forex Strategies

2 Painful hits

February 13, 2017 by Shaun Overton 14 Comments

December and January were extremely unkind to me. I took a huge loss on December 9 that coincided with the Fed meeting and another big punch in January. In total, I went from a 28% profit to a ~4% net loss.

Deservedly, my inbox quickly flooded with comments and suggestions on the drawdown. The most common of those was to stop trading during news events.

So… why am I still trading during news events? There are a few answers to that question.

Curve fitting

It’s not like the strategy loses money on every single news event. It’s 100% true that news events like the Fed meeting can and badly hurt. Say that I’m determined to exclude news events in the future. I’d have to

Collect historical news event data
Create a second algorithm, which selects the news events that forbid and allow trading to continue
Test how the news algorithm interacts with Dominari
Repeat this many times until I’m happy with the final result

Due to the tiny number of news events that impact the markets like the December 9th announcement, my data set is miniature. The risk of overfitting to historical news events is huge.

Working with tiny amounts of data provides little in the way of long run confidence. Focusing my efforts elsewhere is far more likely to improve performance and requires much less work.

Too many trades

Too many trades sounds a bit naive, so let’s dig into what that means. Dominari trades a portfolio of 7 different instruments. All instruments cross with USD.

EURUSD
GBPUSD
USDCHF
AUDUSD
NZDUSD
USDJPY
USDCAD

Many subscribers correctly observed that the major losses occurred with trades open on all 7 pairs in the portfolio at the same time. A good predictor of trade performance is the number of trades open simultaneously.

1-3 trades seems to be consistently profitable
4-5 trades leads to biting my nails
6-7 trades is neutral to disastrous

Testing and confirming the max open trades rule was quick and easy. 5+ trades is very dangerous.

Accordingly, Dominari now exits all open trades if there are 5 or more trades open at any given time.

The next feature of Dominari will be a reversal strategy. Dominari was clearly prone to sudden equity changes if 5+ trades were open at the same time.

Make the losses work for us

An obvious counter strategy is to open trades in the opposite direction whenever Dominari would otherwise open too many trades. Testing the idea is very easy.

Coding a Dominari reversal strategy, however, would require a major reprogramming of the expert advisor’s code.

The number of trades per year would be miniscule. I doubt that it would average even 1 trade per month.

The idea is that Dominari can be the normal trading strategy. Whenever Dominari opens too many trades, the strategy then switches into reversal mode and trend trades with a simple trailing stop.

Switching direction should mostly reverse the negative trade skewness back in the positive direction. Almost all of the offending trades open at exactly the same time.

If the biggest losing trades opened at different times, there would be the risk of being too late to the party. All blowout trades opening at the same time means that the strategy can realistically reverse 100% of would-be losses into profits.

Sitting at the top of the docket are changes to Pilum. You can expect to hear about those soon so that I can incorporate Pilum into the Dominari signals. Once that and 2 other internal projects are finished, I’ll be able to dedicate the time required to fully implement the Dominari Reversal System.

Equity stop loss

Dominari uses emergency stop losses on all tickets. That is appropriate 99% of the time for individual trades. Those emergency losses reset once per hour in line with the concept of the TODS.

A little of the problem was bad luck. My stops came within a handful of pips of being triggered. Then they reset even further away, which made a bad problem worse.

When all trades move at the same time, then clearly the strategy could suffer extreme losses.

The first attempted solution after the Fed announcement was to add a portfolio level stop loss. The way that I wrote it also updated once per hour. When a second negative movement came in January, I stopped trying to be clever. It’s a flat, simple, stupid stop loss. If I lose more than 4% on all open trades, the entire Dominari portfolio goes flat.

I’m still trading Dominari

I still have my money trading the Dominari system; my confidence in the long term performance hasn’t changed, but it obviously requires safeguards. The max number of trades and the portfolio level stop loss will go a long way to limiting the impact of big moves in the future. AND, I should get the counter-strategy developed relatively soon to turn potential frowns upside down.

Lastly, many of you questioned why I’ve been so quiet. The honest answer is that I needed some time to process what happened. It’s easy to feel overwhelmed and discouraged when you get knocked down. I needed some time to process what happened.

I also needed time to double check the changes that I made to the portfolio were actually beneficial. It’s very easy to appease traders when they’re upset by rushing out features before they’re thoughtfully considered.

My money is on the line (I lost 2,000 euros between the two moves). What hurt my subscribers hurt me, too.

Working 8 days a week

December 6, 2016 by Shaun Overton 22 Comments

Reaching an all-time high in my equity curve means it’s time to buckle down and keep improving. My Dominari strategy has done very well over the past 7 months and especially this and last month.

Is the party going to continue?

I certainly expect so. Drawdowns are inevitable, but that’s part of trading. Short-term performance is exciting, but my ambitious goal is to turn my starting balance of €8,000 into €50,000 within the next 3 years. As of this writing, I’m at €9,323.

You’re probably wondering how a 16% profit leads me to extrapolate an annual return of nearly 100%. The answer is that I dramatically changed my leverage at the end of September… just in time for that ugly drawdown. If I was trading on my current leverage, the current live return would be around 40% (i.e., right on track to hit my goal).

What really counts is what I’ve really done. So far, I’m up €1,323 with another €40,677 to go by December 6, 2019.

The research for Dominari is effectively finished. It’s been slightly more than a year since I began researching the strategy. Although minor variations of Dominari popped up or came from traders copying my signals, none of them improved the long term outcomes.

One version that improved the risk profile was to trade with limit orders. The original blog post mentioned limit orders, but the variation placed them considerably further from the current market than what I tried previously. I also lacked a system for choosing settings appropriate to every pair, which I’ve more than likely resolved. The problem is that I have a million things on my to-do list and only 8 hours a day. You’ll see some of my top projects when you scroll down.

Pilum: The latest and greatest

Pilum is a strategy based on a statistical process that identifies momentum. One of the scary elements about most quantitative strategies is that most of them are mean-reverting. They buy when the price drops and sell when the price rises. The approach is favorable (i.e., profitable) in the long run, but it takes some psychological fortitude to trade.

Pilum is a major advancement because now I’ll have a strategy that should profit exactly when Dominari is most vulnerable to a drawdown.

The new strategy uses limit orders to enter the market. Something like 90% of these orders never execute. But when they do execute, I win 75% of the time. Additionally, my profile of winners to losers is very comfortable.

Most traders understand the ideas even if the statistical jargon is unfamiliar. Pilum’s biggest winner is larger than its biggest loss. The average winner is bigger than the average loser. And, it wins 77% of the time.

So far, I’ve done a sort of piecemeal backtest using R. When I finish the Quantilator (see below), I’ll redo the backtest in a fully fledged trading platform. More than likely, I’ll use QuantConnect to test the strategy level approach.

Trading platforms drive me crazy! The biggest problem that I have as a trader is continuously reallocating capital across my portfolio. MetaTrader, NinjaTrader and the likes implicitly assume that I want to trade some percentage of my account balance on every trade. Either that, or that I trade fixed lots.

Trading that way is extremely inefficient. I’m trying to trade 40+ currencies, so I need to be able to decide which ones need the money for trading and which ones don’t have signals. Then, among the ones that do have signals, I need to dish out their allocations proportionately. The allocations aren’t the same for each instrument. If you know of any good FX platforms that meet this requirement, then let me know in the comments section.

Testing Pilum on its own is important. More important than the performance of Pilum is how that performance interacts with Dominari. That means taking the daily equity values of each currency. Does Dominari lose when Pilum wins and vice versa? Should I allocate capital 50-50 between the strategies or does one strategy deserve the lion’s share of the portfolio? Is one strategy so good that it should get all of the money?

The main concern with portfolio allocation is how it relates to leverage. Dominari historically make 96% annual returns, inclusive of trading costs. But, it’s also trading with leverage of roughly 19:1. It’s possible for markets to rip over stops and create significant losses.

USDCHF lost 40% of its value in one hour in January 2015. Say that the scenario was even more extreme and that nobody could place a trade during that time at any price. That 40% move is multiplied by the 19x leverage used. That’s a theoretical 800% loss; more than the money in the account.

Everyone loves leverage because they think of profits. Leveraging losses is a lot less exciting.

So, if you could earn 96% annual returns and only use 5:1 leverage, that is exponentially superior to earning 96% on 19:1 leverage. I need to compare the returns of Pilum to Dominari per unit of risk. That allows me to do cool things like

Minimize the negative variance of the returns
Increase absolute return
Dynamically increase/decrease strategy allocations if I find patterns in their interactions

It’s a lower tech way of averaging strategies, like the litte guy’s version of what Numerai is doing… except that I have to create all of the strategies myself.

Quantilator

I spent the last few months sending surveys to segments of my subscribers asking how I can better serve you. Articles about indicators are overwhelmingly my most popular content. The trouble with that content is that I can’t complete the research fast enough to keep up.

The most valuable thing I’ve learned from the developing algorithms for the past 11 years is my process of deciding whether or not an indicator offers predictive value.

Say that you’re interested in gaps: do gaps predict future returns? What I normally do is code a gap indicator in R, implement my analysis methodology and give a verdict. My methodology is kind of like a system for building systems. Using my approach usually takes an hour for every new idea that comes along.

An hour is pretty short. An hour is REALLY short compared to when I didn’t have a research methodology. I used to waste months on junk strategies.

With Quantilator, I’ll be able to analyze anything in under 5 minutes. I’ll only have to follow 3 steps:

Run a script in MT4 to export price data and indicator data
Upload the exported data to Quantilator
Analyze the results

This tool will be 100% free. I’m planning to go through the most popular indicators in MetaTrader to analyze whether or not they offer an edge. I’m building a library of small edges that can be combined into powerful strategies like Dominari and Pilum. And, in the spirit of open source, I plan to make that library available to you for free.

I’m writing this tool in Django, which is a Python framework for displaying web content. The initial version will do the analysis. I’m hoping to use this as an education tool. A bit of momentum can justify make it a fully fledged library with sample data, indicators and training videos and more.

Quantilator’s name comes from a key concept in my system analysis methodology; I break data into quantiles. These quantiles calculate average market returns for a given period of time. The quant in Quantilator refers to quantiles, but I really like the implied double entendre of making you a quant. After all, that is what I’m doing for you!

Update: The Quantilator is now freely available at http://q.onestepremoved.com/

The Big Switch

February 1, 2016 by Shaun Overton 60 Comments

I moved all of my trading funds into Dominari this month.

I’ve been talking about this system ever since I start live demo testing back in November. Needless to say, I’ve been extremely satisfied with the live results.

My initial live account started trading on January 4 with a starting balance of €1,000 at Pepperstone. Once I saw that the live trades matched my expectations, I quickly kicked that account balance up to a total of €10,000.

And because I want to test the effect of broker selection, I threw another $5,000 in an FXCM account. The Pepperstone account contains the bulk of the money and runs the MT4 version of the strategy. The FXCM version uses Seer, which has been more of a pain to get running smoothly, though I can say that it’s still my favorite platform for testing ideas.

The cost non-problem

The equity curve of the Dominari without trading costs from 2013-2015.

My biggest concern about launching the strategy live was trading costs. Some back of the envelope math suggested that everything would be ok. Live demo testing indicated that it would be ok. But you never really know until you start trading live.

Through the month of January, I’ve consistently monitored the commissions relative to the profit. I fluctuates up and down with the trading account, but I estimate that the spread commission costs are approximately 20-25% of the profit. That’s a relatively high percentage, although it’s nowhere near as bad as it could be given the extreme trading frequency.

Dominari is a high-frequency strategy that averages about 49 trades per day on 28 currency pairs. Everything happens so fast in the account that I’m hard pressed to remember any individual trades. Dominari executed more than 900 trades in the month of January alone. It’s dizzying watching the equity fluctuate up and down. The important thing is that the trend moves from the lower left to the upper right.

QB Pro?

It’s not dead. I still believe it’s a great strategy and totally worthy of your trading. In fact, both Dominari and QB Pro depend critically on one of my favorite indicators, the SB Score.

The reason I got into algorithmic trading is that it emotionally separates me from the responsibility for the outcome. If I have a losing month, it’s just the strategy. There’s not much to do about that.

When there’s an element of discretion, it’s difficult to separate the random component. Sometimes you win, sometimes you lose, but you generally expect to make money. When there’s discretion in an algorithmic strategy, it’s very difficult to know whether losses are my fault or simple bad luck.

QB Pro depends on the manual portfolio selection. Not surprisingly, I heavily favor Dominari because the portfolio selection is static. I can say with my hand over my heart that Dominari is a black box, fully algorithmic strategy.

I’m still updating the portfolio over at Seer Hub and will continue making the selections for clients. For clients that are in the managed account at Pepperstone, I switched the strategy in the middle of the month. I feel responsible as the manager to give clients the best possible performance. And since that’s where I’m placing ~$16,000 of my own money, I feel a fiduciary duty to do the same for my customers. Dominari is where I believe the best opportunity lies.

How you can get Dominari

I plan to offer Dominari as trading signals to anyone with a MetaTrader account within the next month or so. A lot of hard work has gone into developing the strategy. And while I’m confident to the tune of $16,000 of my own money, I want to be even more certain before I release Dominari to a wider audience.

What do you think of the results so far? Leave your thoughts in the comments area below.

Trading Platform Limitations

October 18, 2015 by Shaun Overton 6 Comments

This post was authored by Ben Fulloon, a respected trader and subscriber to OneStepRemoved.

I developed an awesome strategy with a drawdown ratio of 13.67. Sounds amazing, right? Too bad that my trading platform overstated the results by more than double!

It’s important to learn about both your brokers and platforms limitations. Sometimes these intricacies only become apparent through time and experience. It’s so frustrating when your trading platform doesn’t function or report results as expected.

In this article I’ll point out two limitations of NinjaTrader 7, one bad limitation and one which can actually turn out surprisingly better for the trader in certain situations. However, this is more to do with the broker I’m using and not the platform itself.

NinjaTrader is definitely not the only platform that has limitations: MetaTrader, TradeStation, X-Trader, Matlab, etc. all have limitations for quantitative finance.

I’ll just be writing about NinjaTrader in this article to keep it fairly short and easy to read. I am also not intending to make out NinjaTrader as being a bad platform either. But, there are definitely some improvements that could be made to make it a lot easier and more convenient for quantitative traders to develop and trade strategies.

The first quirk relates to the broker I’m using. Specifically, it’s the day trade margins that I care about. These day trade margins end 15 minutes before the close of the session. For instance the ES (Emini S&P500) has a day trade margin of $500, which ends at 4:00pm CT that then reverts back to the full trading margin of $5060 before the session closes at 4:15pm CT. (Times stated are correct at time of Writing, The ES now closes at 4:00pm CT and the Day Trade margin ends at 3:45pm CT)

I’ll show you a screenshot of the results of a day trading strategy that I developed. This strategy trades the ES, NQ (Emini Nasdaq 100) and the YM (Emini Dow) all at the same time. The easiest way to exit on close with NinjaTrader is setting “Exit on Close” to true which will then exit on the close of the session.

According to the results the strategy makes a total of $332,771.60 with a maximum drawdown of $25,912.27 since 2008 to now. This is a drawdown ratio of 12.84. That’s oustanding!

The issue is… and you knew there’d be a problem… is that the strategy exits at 4:15pm CT. Day trading margin ends at 4:00pm CT. The strategy is therefore highly likely to get a margin call with a small account size.

It makes sense to tweak the strategy to make best use of the day trading margin. Ninjatrader offers a custom session template, which in this case I made end at 4:00pm CT. The results of the custom session template is as follows.

The exact same strategy applied to the same instruments to avoid a margin call makes $335,819.30 with a maximum drawdown of $24,560.51. This is a drawdown ratio of 13.67.

I didn’t change the strategy with the goal of improving the drawdown ratio AND the profit. But hey, I’ll take it. Finding a limitation in the platform can actually benefit you in some situations.

This strategy is based on trading 3 different instruments. The ES, the NQ and the YM. The problem is that I backtested it using an instrument list in NinjaTrader. What this means is they’re all tested separately. NinjaTrader then combines the test results for you as a total result like the results of the screenshots above.

Here’s what it looks like when you test them as an instrument list. This shows the different profits and drawdowns of the individual instruments.

Now at first glance it reads that the trader would have made $335,819.30 with a maximum drawdown of $24,560.51 if they traded all three instruments together. Don’t you agree?

The problem is that this is incorrect. NinjaTrader doesn’t actually combine the results like you’d think. The trader still would have made roughly that money. However, all the statistics aren’t quite correct.

To show this I recreated the exact same strategy however it will trade the ES, NQ and YM all at the same time instead of trading them separately like it does by default. These are the results when you program it into a multi-instrument strategy

It makes $335,915.30 which is roughly the same amount, but it has a maximum drawdown of $59,937.60 instead of the $24,560.51 it originally looked like it would be. This makes it a drawdown ratio of 5.60, which is a lot worse than the original 13.67.

If the trader decided to trade based upon the maximum drawdown of $24,560.51, they may get a nasty shock when the drawdown turns out to be twice as bad as they were expecting.

Incorrect calculations on such an important metric could jeopardize an account. You might assume that you can get away with half of the equity that’s actually required to trade the strategy. Oops?!?

The misleading statistics in NinjaTrader makes this strategy look really nice. But when the drawdown is more than double what it appeared that it would have been originally, you might get a nasty shock.

This is why it’s important to learn both your platforms and brokers limitations as early as possible. You don’t want to learn these limitations the hard way.

In a few weeks time, I’ll reveal a simple way to create multi-instrument strategies which show more accurate metrics. Stay tuned for my next article in the series.

Integrating Structural Diversification Into Your System

October 15, 2013 by Andrew Selby Leave a Comment

One of the easiest things to overlook when constructing a system is which markets you intend to trade and what percentage of capital you should allocate to each of those markets. The guys at Darwin Investment Strategies are experts in this department. They have recently gone back to basics and are working on a series of posts that break the idea of diversification down into easy to digest portions.

Which markets to trade and how much capital to allocate to each market is often overlooked during system design.

Their most recent article addresses the subject from the very beginning and discusses why diversification needs to go beyond the typical 60%/40% equities to bonds that is the industry standard. They argue that the 60/40 approach is only productive in one of the four possible economic regimes.

Generally we can say that the global economy is defined by four regimes, each of which combines a growth vector with an inflation vector. A period of accelerating growth in combination with rising inflation might be termed an ‘inflationary boom’, while a combination of accelerating growth simultaneous with falling inflation might be represent a ‘disinflationary boom’. On the other side of the axis, a period of slowing growth combined with rising inflation is often termed ‘stagflation’, and finally we might call a period of decelerating growth concurrent with falling inflation a ‘deflationary bust’.

After defining each of the four possible economic regimes, they move forward by placing each possible asset class on the two-dimensional chart where it performs best. Then, they begin to address how they should weight a portfolio across each of the four quadrants so that it is able to perform regardless of which economic regime is present.

To the extent that we are able to assign a relative probability to each regime over our rebalance horizon, we can bias our allocations in favour of some regimes over others. Unfortunately, it is very difficult to know with any confidence which regime we are actually in at any point in time, and much harder still to predict when the regime will change, and the direction of transition. If we were to assume that each of the four regimes is equally likely at any point in time, we might be wise to allocate 25% to each quadrant.

They also address the concern of whether to allocate 25% of the entire capital or not, but decide to keep things simple for this post.

To keep things simple, as a first approximation we will assume that we want to equally distribute capital across the four quadrants in order to ensure the portfolio is equally resilient to all four major states of the world via structural diversification.

They decide to split the capital equally across each of the four quadrants. Then they split each quadrants capital equally across each of the different assets in that quadrant. They proceed to discuss the pros and cons of this type of portfolio.

The advantage of the structural diversification framework outlined in this article is that it relies on an understanding of the various drivers of asset returns that is consistent with most financial and economic theory. These are excellent qualities so long as assets behave as they theoretically should, and the universe is coherent and thoughtfully diversified. However, managers are often faced with noisy, incoherent universes that have dramatically unbalanced exposures that would seriously impair the effectiveness of this simple approach. Further, risk characteristics and correlations can change quite dramatically through time, and a static allocation like this policy portfolio is unable to respond to these changes. As a result, this portfolio is vulnerable to extreme shocks.

They conclude the piece by reminding us that this portfolio would not be an ideal solution, but it does provide us with an interesting model to guide our thinking about this topic. Putting this level of thought into the markets that our systems trade could go a long way towards producing consistent returns in all types of economic regimes.