Risk & Exchange Issues In Algo Trading
Algorithmic trading comes with hazards such as software errors, delays, technical failures, slippage, liquidity concerns, and sudden market crashes (black swan events). Overfitting, subpar execution, and regulatory changes are other difficulties. Order rejections, data feed delays, API failures, and trading restrictions such as tick size limits and circuit breakers are all examples of exchange-related problems. High-frequency trading and fragmented liquidity introduce additional complexity. Traders use stop-loss procedures, backup infrastructure, stress testing, and real-time monitoring to reduce risks. It’s critical to adjust to shifting regulations, maintain consistent connectivity, and optimize for certain exchanges. Strong Risk Management and adaptable tactics are crucial for long-term success in algo trading.
Following are the Major Issues in Algorithmic Trading;
1.) Order Types:
One choice that an algorithmic trader must make is how and when to make use of the different exchange orders available. This choice usually falls into the realm of the execution system, but we will consider it here as it can greatly affect strategy backtest performance.
There are two types of order that can be carried out:
- Market orders
- Limit orders.
A market order executes a trade immediately, irrespective of available prices. Thus large trades executed as market orders will often get a mixture of prices as each subsequent limit order on the opposing side is filled. Market orders are considered aggressive orders since they will almost certainly be filled, albeit with a potentially unknown cost.
Limit orders provide a mechanism for the strategy to determine the worst price at which the trade will get executed, with the caveat that the trade may not get filled partially or fully. Limit orders are considered passive orders since they are often unfilled, but when they are a price is guaranteed. An individual exchange’s collection of limit orders is known as the limit order book, which is essentially a queue of buy and sell orders at certain sizes and prices.
When backtesting, it is essential to model the effects of using market or limit orders correctly. For high-frequency strategies in particular, backtests can significantly outperform live trading if the effects of market impact and the limit order book are not modelled accurately.
2.) Price Consolidation:
There are particular issues related to backtesting strategies when making use of daily data in the form of Open-High-Low-Close (OHLC) figures, especially for equities. Note that this is precisely the form of data given out by Yahoo Finance, which is a very common source of data for retail algorithmic traders!
Cheap or free datasets, while suffering from survivorship bias (which we have already discussed above), are also often composite price feeds from multiple exchanges. This means that the extreme points (i.e. the open, close, high and low) of the data are very susceptible to “outlying” values due to small orders at regional exchanges. Further, these values are also sometimes more likely to be tick-errors that have yet to be removed from the dataset.
This means that if your trading strategy makes extensive use of any of the OHLC points specifically, backtest performance can differ from live performance as orders might be routed to different exchanges depending upon your broker and your available access to liquidity. The only way to resolve these problems is to make use of higher frequency data or obtain data directly from an individual exchange itself, rather than a cheaper composite feed.
3.) Forex Trading and ECNs:
The backtesting of foreign exchange strategies is somewhat trickier to implement than that of equity strategies. Forex trading occurs across multiple venues and Electronic Communication Networks (ECN). The bid/ask prices achieved on one venue can differ substantially from those on another venue. One must be extremely careful to make use of pricing information from the particular venue you will be trading on in the backtest, as opposed to a consolidated feed from multiple venues, as this will be significantly more indicative of the prices you are likely to achieve going forward.
Another idiosyncrasy of the foreign exchange markets is that brokers themselves are not obligated to share trade prices/sizes with every trading participant, since this is their proprietary information[6]. Thus it is more appropriate to use bid-ask quotes in your backtests and to be extremely careful of the variation of transaction costs between brokers/venues.
4.) Shorting Constraints:
When carrying out short trades in the backtest it is necessary to be aware that some equities may not have been available (due to the lack of availability in that stock to borrow) or due to a market constraint, such as the US SEC banning the shorting of financial stocks during the 2008 market crisis.
This can severely inflate backtesting returns so be careful to include such short sale constraints within your backtests, or avoid shorting at all if you believe there are likely to be liquidity constraints in the instruments you trade.
Read Also; Biases in Algorithmic Trading Strategy
