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.


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