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.
What’s in it for Darwinex, Traders and Investors?
- 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.
- 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.
- 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.
- Can individual traders really compete with institutions?
- Serious Algo Trading – Advantages & Disadvantages
- The Quantitative Approach to Algorithmic R&D
You may also wish to read:
- DARWIN Filters: A Practical Alternative to Markowitz Portfolio Theory
- Hidden Markov Models & Regime Change: DARWINs vs. S&P500
- LVQ and Machine Learning for Algorithmic Traders – Part 1
- LVQ and Machine Learning for Algorithmic Traders – Part 2
- LVQ and Machine Learning for Algorithmic Traders – Part 3
- ZeroMQ – How To Interface Python/R with MetaTrader 4
- DO’s and DONT’s of MT4 Backtesting
- How To Identify Overfit Trading Strategies
- Currency Index Indicator for MetaTrader 4
- Constructing a Currency Portfolio in MetaTrader
- Setting up a DARWIN Data Science Environment
- Machine Learning on DARWIN Datasets (MLD-I)
- 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!
The Darwinex Team
Do you have what it takes? – Join the Darwinex Trader Movement!