It is common, as a beginning algorithmic trader practising at retail level, to question whether it is still possible to compete with the large institutional quant funds. In this article I would like to argue that due to the nature of the institutional regulatory environment, the organisational structure and a need to maintain investor relations, that funds suffer from certain disadvantages that do not concern retail algorithmic traders.
The capital and regulatory constraints imposed on funds lead to certain predictable behaviours, which are able to be exploited by a retail trader. “Big money” moves the markets, and as such one can dream up many strategies to take advantage of such movements. We will discuss some of these strategies in future articles. At this stage I would like to highlight the comparative advantages enjoyed by the algorithmic trader over many larger funds.
There are many ways in which a retail algo trader can compete with a fund on their trading process alone, but there are also some disadvantages:
Capacity - A retail trader has greater freedom to play in smaller markets. They can generate significant returns in these spaces, even while institutional funds can’t.
Crowding the trade - Funds suffer from “technology transfer”, as staff turnover can be high. Non-Disclosure Agreements and Non-Compete Agreements mitigate the issue, but it still leads to many quant funds “chasing the same trade”. Whimsical investor sentiment and the “next hot thing” exacerbate the issue. Retail traders are not constrained to follow the same strategies and so can remain uncorrelated to the larger funds.
Market impact - When playing in highly liquid, non-OTC markets, the low capital base of retail accounts reduces market impact substantially.
Leverage - A retail trader, depending upon their legal setup, is constrained by margin/leverage regulations. Private investment funds do not suffer from the same disadvantage, although they are equally constrained from a risk management perspective.
Liquidity - Having access to a prime brokerage is out of reach of the average retail algo trader. They have to “make do” with a retail brokerage such as Interactive Brokers. Hence there is reduced access to liquidity in certain instruments. Trade order-routing is also less clear and is one way in which strategy performance can diverge from backtests.
Client news flow - Potentially the most important disadvantage for the retail trader is lack of access to client news flow from their prime brokerage or credit-providing institution. Retail traders have to make use of non-traditional sources such as meet-up groups, blogs, forums and open-access financial journals.
Retail algo traders often take a different approach to risk management than the larger quant funds. It is often advantageous to be “small and nimble” in the context of risk.
Crucially, there is no risk management budget imposed on the trader beyond that which they impose themselves, nor is there a compliance or risk management department enforcing oversight. This allows the retail trader to deploy custom or preferred risk modelling methodologies, without the need to follow “industry standards” (an implicit investor requirement).
However, the alternative argument is that this flexibility can lead to retail traders to becoming “sloppy” with risk management. Risk concerns may be built-in to the backtest and execution process, without external consideration given to portfolio risk as a whole. Although “deep thought” might be applied to the alpha model (strategy), risk management might not achieve a similar level of consideration.
Investor Relations Outside investors are the key difference between retail shops and large funds. This drives all manner of incentives for the larger fund - issues which the retail trader need not concern themselves with:
One area where the retail trader is at a significant advantage is in the choice of technology stack for the trading system. Not only can the trader pick the “best tools for the job” as they see fit, but there are no concerns about legacy systems integration or firm-wide IT policies. Newer languages such as Python or R now possess packages to construct an end-to-end backtesting, execution, risk and portfolio management system with far fewer lines-of-code (LOC) than may be needed in a more verbose language such as C++.
However, this flexibility comes at a price. One either has to build the stack themselves or outsource all or part of it to vendors. This is expensive in terms of time, capital or both. Further, a trader must debug all aspects of the trading system - a long and potentially painstaking process. All desktop research machines and any co-located servers must be paid for directly out of trading profits as there are no management fees to cover expenses.
In conclusion, it can be seen that retail traders possess significant comparative advantages over the larger quant funds. Potentially, there are many ways in which these advantages can be exploited. Later articles will discuss some strategies that make use of these differences.