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About Us

Story

The founders of Manuka Capital have decades experience in software engineering, fintech, financial market analysis, security valuation and investment banking. This led to building, deploying and maintaining technology platforms on behalf of enterprise systems for blue chip national and multinational companies.

Based on past experience and avidly following new technology breakthroughs the founders seek to harness the benefit of technology on areas where powerful yet fallible human abilities used to provide the edge in portfolio management. Combining experience and ongoing technology research opened up to us the new field of deep learning and genetic algorithms that swam across a vast amount of data hunting for learnings that lead to discovering an edge that delivers a positive alpha from price series data.

Trading today in world markets is approx. 70% algorithm driven and Manuka Capital driver is to capture these resources and totally automate the selection of algorithms based on real time market performance, using a robust methodology to select, deploy and deprecate under-performing systems as managed.

Over the past 5-10 years there have been significant changes in the way financial markets are traded. 90% of hedge funds still fail to beat the buy and hold strategy, Manuka Capital offer a vehicle to participate in this market alongside those that perform with the niche funds that beat the market averages.

Manuka Capital is not only about beating the market average, but it performs in a way that is robust and consistent. The platform that manages the range of algorithms mitigates a significant exposure to drawdowns and other volatilities. Volatility that may be due to technical, financial or geographical risk that these funds are exposed to on a daily basis.

Trading Rails develops superior semi-automated trading systems based on scientific, mathematical and statistical methods and by employing a proprietary approach. We offer these trading systems to clients in order to provide them with alternative investment opportunities that are uncorrelated to all other financial assets or asset classes.

Unlike most system vendors, we can provide you with third party audited, real life trading results. Our algorithms, especially the portfolios we offer, have a proven history of being consistently profitable. We have been trading automated strategies for several years now in our own accounts and are very satisfied with their performance.

Our Business Model

  • Private closed fund
  • We invest our own money alongside investors
  • We aim to outperform 24% absolute return every 12 months
  • We aim to limit drawdowns swiftly and allow successful trades to run
  • We are not a retail fund
  • We limit investment to only assets we understand well, cap the amount of investors, as well as cap the amount of capital invested
  • We provide a leading T1 award-winning portal to understand our performance
  • We keep things calm in your portfolio
  • We don’t guess, predict or forecast the market
  • We let machines, applying historical data and trends do the heavy lifting

Our Portfolio

The primary objective with all investment portfolios is to realise alpha (positive returns), and inherently they’re exposed to different elements of risk. Be that market risk, price, volatility and correlation, interest rates and naturally foreign exchange rates. Withstanding those elements they are also exposures to counterparty defaults, credit risk, this can bring on market liquidity issues, particularly if long-term investment periods are part of the product. Over time, market liquidity can dry and become particularly acute when there’s increasing demands on fund redemptions. Finally, investments can be executed incorrectly due to process errors and calculation or human mistakes, operational risks. The objective of risk management is measuring and making all these risks quantifiable such that can be controlled better. We also go a long way to mitigating this with diversity across the portfolios, not just in asset classes (commodities) but also using differing variables, be that time, execution processes or variations of algorithmic models.