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Quantitative Trader

Why Quantitative? – Serious Algorithmic Trading Series (Part 2)

Quantitative Trading

Quantitative Trading

In case you haven’t already, you may wish to read the first two posts of the Serious Algorithmic Trading Series, here and here.

Many individual traders will often hear the term “Quant” and may visualize either:

  1. A sharply dressed City trader working in a hedge fund, sat in front of his Bloomberg or Thomson Reuters Eikon terminals, or
  2. A stocky chap wearing a Batman t-shirt, with telescopes for specs (and possibly a doctorate in Nuclear Physics), sat in his basement trading room pouring over mathematical models, following a staple fast food diet.

For the avoidance of doubt: you do NOT need a university degree to become a Quantitative Trader (though having one does come in very handy, IF you were paying attention in class!)

Now, both these individuals above likely followed a similar academic (or independent learning) path to their current disposition.

It’s even possible that the City trader was once a frequent visitor to his own basement trading room, before being spotted by a financial recruiter and beamed up to the 39th floor.

This prompts a few questions:

Why do Quants become Quants in the first place?

Quantitative Trader

Quantitative Trader

There are numerous advantages to quantitative trading, advantages that not only reduce the financial impact of discretionary decision-making going wrong, but also help measure performance in a manner that permits efficient future learning.

For the purpose of distinction, it must be noted that unlike algorithmic trading that employs technical analysis, the quantitative approach to algorithmic trading involves primarily Bayesian Statistics and Time Series Analysis.

These are more effective methods of generating uncorrelated risk-adjusted returns, all too often ignored by the retail fundamental or technical analyst. Those who recognize their advantages, naturally progress down a path to becoming Quants.

Is Quantitative Trading reserved for just institutions?

In short, no it isn’t. Find out why in part 1 of this series here.

Why should this interest retail traders?

The world of quantitative trading extends far beyond that of technical moving average crossovers, candlestick patterns, martingales, grids, and off-the-shelf trading systems celebrating their latest and greatest “trading secrets” that resulted in their most sensationally overfitted backtests to date.

It does so because the markets are a much more complex phenomenon than we think, and undergo several micro-structure and regime changes frequently.

Darwinex - The Open Trader Exchange

Darwinex – The Open Trader Exchange

Note: To protect investors from overfitted systems, all strategies listed as DARWINS on the Darwin Exchange are analyzed for proficiency in 12 investment attributes. Investors therefore have the ability to immediately assess whether a strategy’s current equity curve is likely to persist in future or not, something standard backtest results cannot reveal.

Indeed one could go as far as to say that the market is a stochastic process in and of itself.

Generating uncorrelated risk-adjusted returns in as seemingly stochastic an environment as this, is therefore a mammoth task.

Why more retail traders haven’t then embraced quantitative methods (that enable the discovery and subsequent exploitation of inefficiencies in stochastic processes) can only be attributed to a lack of awareness and “approachable” learning resources.

We will aim to address this in Part 3 of this series, “The Quantitative Approach to Algorithmic Trading”, and all subsequent posts that fall in the same category.

Advantages of Quantitative Trading

  1. Alpha is an elusive beast.

    Alpha is difficult to discover, gradually loses its profitability as more capital is allocated to its exploitation, and inevitably transitions from a source of risk-adjusted profitability to a source of risk.

    Quantitative methods allow traders to not only discover and exploit alpha, but to do so in a manner that maximises its profitability horizon and minimizes risk of decay.

    Quantitative analysis also facilitates the creation of statistical testing frameworks that can forecast risk of alpha decay in advance.

    Note: DARWIN investors benefit from a Scalability/Capacity investment attribute  displayed on each asset listed on the Darwin Exchange. It is a score ranging from 1 to 10 that indicates how much capital the underlying strategy can support without decay. The higher the score, the more assets under management (AuM) it can attract.

  2. Statistically Robust vs. Regular Backtesting

    Quantitative traders can make use of statistical tools that add another layer of validity to backtests.

    In simple terms, if you’re currently examining a backtest and taking its results with a “grain of salt”, substitute the latter with some rigorous quantitative testing and you have a much more credible (or not) backtest on your hands.

  3. Quantitative Trading encourages thinking in “Portfolios”

    Common behaviours among a large segment of individual traders include deploying a single trading strategy on a single asset, or deploying the same strategy on a mix of assets, without careful consideration given to weighted allocation as would be the case in an institutional setting.

    In the former’s case, the trader has put all his/her eggs in one basket.

    In the latter’s case, the trader has employed logic that goes something like “if it’s a robust strategy, it will perform well in all markets, under all market conditions.”

    Unfortunately, this logic is prone to leading traders into intractable situations where the lack of an appropriate risk-adjusted allocation matrix may lead to unexplained underperformance.

    Construct DARWIN Portfolios

    Construct DARWIN Portfolios

    Quantitative analysis allows traders to construct portfolios of assets or strategies (or both), such as to maximize their aggregate profitability while minimizing their aggregate risk. This is simply not possible to achieve without quantitative methods being employed.

    Note: Investors on the Darwin Exchange can construct uncorrelated portfolios of DARWINS using our proprietary diagnostics toolkit. Twelve investment attributes (as of Darwinex Reloaded) allow investors to not only select DARWINS that match their criteria for investment, but also reduce overall portfolio risk through adequate diversification. As of Darwinex Reloaded, investors are even rewarded for diversifying their DARWIN portfolios, through the introduction of Diversification Rebates.

One of the most commonly voiced disadvantages of Quantitative Trading is that it’s a complex discipline to master.

So is learning to drive a car for the very first time. The right instructor can be the difference between a confident future driver and a jittery one.

Quantitative trading is no different. Over the course of these posts (that aim to make the complex simple), the best most approachable educational material will always be referenced for the reader’s benefit.

The fruits of a Quant’s labour are far sweeter than one can imagine. You will be well served by visiting the Darwin Exchange Leaderboard for inspiration.

The next post will begin our journey into the Quantitative Approach to Algorithmic Trading.

Trade safe,
The Darwinex Team

Do you have what it takes? – Join the Darwinex Trader Movement!

Darwinex - The Open Trader Exchange

Darwinex – The Open Trader Exchange

Advantages of Trading $DWC vs. Other Listed Assets

Serious Algorithmic Trading Series – Part 1

Can individual traders really compete with large institutions?

 

This post is the first of a four-part “Serious Algorithmic Trading” series, introduced here.

A trader will at one point or another, question how practical it is to compete with large institutions in the same markets.

After all, it is “big money” at the end of the day that moves these markets.

The reality is that institutions face several regulatory, technological, structural and capital constraints that individual traders don’t.

It’s these very constraints that also lead to institutional funds often exhibiting a degree of predictability that is visible to – and can potentially be exploited by – individual traders. This phenomenon will be discussed in more detail in post #3, “The Quantitative Approach to Algorithmic R&D”.

The answer to the question is therefore YES.

Let’s examine why in the contexts of Scalability & Market Impact, Risk Management, and Trading Technology.

Scalability & Market Impact

Individual traders can design and execute trading strategies that target small inefficiencies in the market.

Such inefficiencies often have capacities up to a fairly limited amount of capital before they lose their profitability.

This limited capital support usually ranges from a few hundred thousand to a few million dollars, and is therefore of little to no interest to institutional funds trading with a substantially larger capital base.

Note: For the benefit of the Darwinex community, a Scalability/Capacity investment attribute is displayed on each DARWIN asset listed on the Darwin Exchange. It is a score ranging from 1 to 10 that indicates how invest-able the underlying strategy is. The higher the score, the more assets under management (AuM) the strategy can support.

Furthermore, individual trader capitalization is much lower than that of institutions. This is an advantage as retail trading activity in highly liquid markets cannot therefore create any substantial market impact.

Risk Management

Risk Management

Risk Management

Without middle or compliance offices enforcing industry standards and regulatory oversight, individual traders have the option to model their own risk management techniques as they deem fit, promoting flexibility that can indeed contribute to generating excess returns.

However,

While this may certainly be an advantage for some experienced traders, it is a double-edged sword.

At no risk of being “overruled”, individual traders are at greater risk of exercising “nonoptimal” risk management decisions, often leading to negative outcomes, e.g. accounts blowing up due to excessive use of leverage or aggressive risk management that wouldn’t otherwise have been permitted in an institutional setting.

Note: The Darwin Exchange solves this problem for investors in DARWINs. Traders listing strategies on the Darwin Exchange never manage investor capital themselves.

Darwinex manages all DARWIN assets, and enforces its own risk management to deliver a fixed Value-at-Risk to investors, thereby insulating them from the underlying strategy’s specific risk profile. Traders simply license their intellectual property to Darwinex in exchange for 20% performance fees on any profits generated for investors.

In addition, a Risk Management investment attribute is displayed on all listed DARWIN assets. It is a score ranging from 1 to 10, indicating the ability of the underlying strategy to yield stable risk with consistent use of leverage. The higher the score, the more invest-able the strategy is.

Furthermore, with no enforcement of industry best practices and risk management oversight, individual traders often find themselves modeling risk at the execution level (e.g. stop losses and take profits), without much consideration given to risk at the portfolio level (e.g. mixture of assets or strategies deployed on the same account).

Note: Darwinex addresses this problem for investors in DARWINs. Our Investor Platform enables investors to see the correlation and diversification benefit of selecting any mix of DARWIN assets, before making any investment decisions.

Trading Technology

Financial Trading Technology

Financial Trading Technology

Institutional traders do not enjoy the same flexibility as retail traders, in terms of their choice of technology for trading and strategy development.

Retail traders can choose from a large selection of servers, hardware, trading platforms, programming languages and toolkits, without corporate IT policy or a predetermined list of “permitted systems” affecting their technology preferences.

The only disadvantage that accompanies this flexibility though, is that it can get quite expensive (relative to a retail trader’s available funding) to purchase hardware, software and server subscriptions. These costs must therefore be paid for by traders themselves, whereas in institutions they would most likely be mitigated by management fees charged.

In the next post, we will discuss the core advantages and disadvantages of algorithmic/quantitative trading.

Trade safe,
The Darwinex Team

Do you have what it takes? – Join the Darwinex Trader Movement!

Darwinex - The Open Trader Exchange

Darwinex – The Open Trader Exchange

Serious Algorithmic Trading

Serious Algorithmic Trading Series – Introduction

You’ll learn how algorithmic trading differs substantially from “automated trading using technical analysis”. The two are in fact entirely different disciplines, as this series will demonstrate.

You’ll also learn that contrary to popular opinion, algorithmic trading is not synonymous with “automated trading”, though a large percentage of algorithmic trading systems today are indeed automated. We’ll shed more light on this as we go along.

Each part of this series will be written in a manner that allows us to write additional “series of posts” under each “part” down the line, thus creating a pathway of sorts for traders to dig deeper and expand their knowledge base as they progress through this content.

What’s inspired this algorithmic trading series?

The Darwin Exchange has observed a steady rise in the number of listed DARWINs producing competitive risk-adjusted returns over time, where the underlying strategies employ techniques found in institutional algorithmic trading circles, e.g. in Quant hedge funds and prop desks.

Some of these DARWINs experience risk-adjusted returns that demonstrably outperform those of discretionary strategies consistently.

This is a VERY welcome development, and we hope to help it along by publishing content that helps more traders start in the right place in their quantitative trading journey.

 

Darwinex - The Open Trader Exchange

Darwinex – The Open Trader Exchange

What’s in it for Darwinex, Traders and Investors?

  1. Darwinex pairs traders with competitive risk-adjusted returns, with savvy investors looking for competitive risk-adjusted returns. Therefore, the more strategies there are on the Darwin Exchange – that offer competitive risk-adjusted returns – the greater the number of savvy investors who will be motivated to back them, leading to potentially greater AuM for traders and hence the potential for greater performance fees.
  2. Since traders get 20% performance fees on a high watermark basis, their interests are aligned with those of Darwinex Investors, i.e. both need to experience profitability to survive. Robust algorithmic trading techniques generate robust risk-adjusted alpha – this series will therefore identify a clear academic and practical path for traders to follow in their hunt for algorithmic alpha.
  3. It’s in Darwinex’ interests to provide both traders and investors with the necessary tools and resources to aid successful trading and investing respectively.

The three constituent parts of this series are defined below. Future posts will then focus on each part individually.

These are:

  1. Can individual traders really compete with institutions?
  2. Serious Algo Trading – Advantages & Disadvantages
  3. The Quantitative Approach to Algorithmic R&D

 

You may also wish to read:

  1. DARWIN Filters: A Practical Alternative to Markowitz Portfolio Theory
  2. Hidden Markov Models & Regime Change: DARWINs vs. S&P500
  3. LVQ and Machine Learning for Algorithmic Traders – Part 1
  4. LVQ and Machine Learning for Algorithmic Traders – Part 2
  5. LVQ and Machine Learning for Algorithmic Traders – Part 3
  6. ZeroMQ – How To Interface Python/R with MetaTrader 4
  7. DO’s and DONT’s of MT4 Backtesting
  8. How To Identify Overfit Trading Strategies
  9. Currency Index Indicator for MetaTrader 4
  10. Constructing a Currency Portfolio in MetaTrader
  11. Setting up a DARWIN Data Science Environment
  12. Machine Learning on DARWIN Datasets (MLD-I)
  13. Working with DARWIN Time Series Data in R (MLD-II)

Kindly bookmark this page as we’ll be posting updates here as and when new content is published.

Lastly, please do share this post using the buttons on this page, with anyone who may find this series useful!

Trade safe,
The Darwinex Team

Do you have what it takes? – Join the Darwinex Trader Movement!

Darwinex - The Open Trader Exchange