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# Introduction to Algorithmic Trading

4.7

Algorithmic trading uses a computer program that follows a defined set of instructions to place a trade. The trade, in theory, can generate profits at a speed and frequency that is impossible for a human trader.

The defined sets of instructions are based on timing, price, quantity, or any mathematical model. Apart from profit opportunities for the trader, algo-trading renders markets more liquid and trading more systematic by ruling out the impact of human emotions on trading activities.

## Algorithmic Trading in Practice

Suppose a trader follows these simple trade criteria:

Buy 50 shares of a stock when its 50-day moving average goes above the 200-day moving average. Sell shares of the stock when its 50-day moving average goes below the 200-day moving average.

Using these two simple instructions, a computer program will automatically monitor the stock price and place the buy and sell orders when the defined conditions are met. The trader no longer needs to monitor live prices and graphs or put in the orders manually. The algorithmic trading system does this automatically by correctly identifying the trading opportunity.

### Algorithmic Trading Strategies

Any strategy for algorithmic trading requires an identified opportunity that is profitable in terms of improved earnings or cost reduction. The following are common trading strategies used in algo-trading:

• Trend-following Strategies

The most common algorithmic trading strategies follow trends in moving averages, channel breakouts, price level movements, and related technical indicators. These are the easiest and simplest strategies to implement through algorithmic trading because these strategies do not involve making any predictions or price forecasts. Trades are initiated based on the occurrence of desirable trends, which are easy and straightforward to implement through algorithms without getting into the complexity of predictive analysis. Using 50- and 200-day moving averages are a popular trend-following strategy.

• Arbitrage Opportunities

Buying a dual-listed stock at a lower price in one market and simultaneously selling it at a higher price in another market offers the price differential as risk-free profit or arbitrage. The same operation can be replicated for stocks vs. futures instruments as price differentials do exist from time to time. Implementing an algorithm to identify such price differentials and placing the orders efficiently allows profitable opportunities.

• Index Fund Rebalancing

Index funds have defined periods of rebalancing to bring their holdings to par with their respective benchmark indices. This creates profitable opportunities for algorithmic traders, who capitalize on expected trades that offer 20 to 80 basis points profits depending on the number of stocks in the index fund just before index fund rebalancing. Such trades are initiated via algorithmic trading systems for timely execution and the best prices.

• Mathematical Model-based Strategies

Proven mathematical models, like the delta-neutral trading strategy, allow trading on a combination of options and the underlying security. (Delta neutral is a portfolio strategy consisting of multiple positions with offsetting positive and negative deltas—a ratio comparing the change in the price of an asset, usually marketable security, to the corresponding change in the price of its derivative—so that the overall delta of the assets in question totals zero.)

• Trading Range (Mean Reversion)

The mean reversion strategy is based on the concept that the high and low prices of an asset are a temporary phenomenon that reverts to their mean value (average value) periodically. Identifying and defining a price range and implementing an algorithm based on it allows trades to be placed automatically when the price of an asset breaks in and out of its defined range.

• Volume-weighted Average Price (VWAP)

The volume-weighted average price strategy breaks up a large order and releases dynamically determined smaller chunks of the order to the market using stock-specific historical volume profiles. The aim is to execute the order close to the volume-weighted average price (VWAP).

• Time Weighted Average Price (TWAP)

Time-weighted average price strategy breaks up a large order and releases dynamically determined smaller chunks of the order to the market using evenly divided time slots between a start and end time. The aim is to execute the order close to the average price between the start and end times thereby minimizing market impact.

• Percentage of Volume (POV)

Until the trade order is fully filled, this algorithm continues sending partial orders according to the defined participation ratio and according to the volume traded in the markets. The related “steps strategy” sends orders at a user-defined percentage of market volumes and increases or decreases this participation rate when the stock price reaches user-defined levels.

• Implementation Shortfall

The implementation shortfall strategy aims at minimizing the execution cost of an order by trading off the real-time market, thereby saving on the cost of the order and benefiting from the opportunity cost of delayed execution. The strategy will increase the targeted participation rate when the stock price moves favorably and decrease it when the stock price moves adversely.

• Beyond the Usual Trading Algorithms

There are a few special classes of algorithms that attempt to identify “happenings” on the other side. These “sniffing algorithms”—used, for example, by a sell-side market maker—have the built-in intelligence to identify the existence of any algorithms on the buy-side of a large order. Such detection through algorithms will help the market maker identify large order opportunities and enable them to benefit by filling the orders at a higher price. This is sometimes identified as high-tech front-running.

### Technical Requirements for Algorithmic Trading

Implementing the algorithm using a computer program is the final component of algorithmic trading, accompanied by backtesting (trying out the algorithm on historical periods of past stock-market performance to see if using it would have been profitable). The challenge is to transform the identified strategy into an integrated computerized process that has access to a trading account for placing orders. The following are the requirements for algorithmic trading:

• Computer-programming knowledge to program the required trading strategy hired programmers, or pre-made trading software.
• Network connectivity and access to trading platforms to place orders.
• Access to market data feeds that will be monitored by the algorithm for opportunities to place orders.
• The ability and infrastructure to backtest the system once it is built before it goes live on real markets.
• Available historical data for backtesting depending on the complexity of rules implemented in the algorithm.

1. Minimize market impact

A large trade can potentially change the market price. Such a trade is known as a distortionary trade because it distorts the market price. In order to avoid such a situation, traders usually open large positions that may move the market in steps.

A trading algorithm can solve the problem by buying shares and instantly checking if the purchase has had any impact on the market price. It can significantly reduce both the number of transactions needed to complete the trade and also the time taken to complete the trade.

1. Ensures rules-based decision-making

Traders and investors often get swayed by sentiment and emotion and disregard their trading strategies. For example, in the lead-up to the 2008 Global Financial Crisis, financial markets showed signs that a crisis was on the horizon. However, a lot of investors ignored the signs because they were caught up in the “bull market frenzy” of the mid-2000s and didn’t think that a crisis was possible. Algorithms solve the problem by ensuring that all trades adhere to a predetermined set of rules.

#### Wrapping up

Now that we understand the nitty-gritty of Algorithmic trading, let’s learn the practical of Algorithmic trading in the next chapter.

#### A quick recap

• Algorithmic trading is the use of process- and rules-based algorithms to employ strategies for executing trades.
• It has grown significantly in popularity since the early 1980s and is used by institutional investors and large trading firms for a variety of purposes.
• While it provides advantages, such as faster execution time and reduced costs, algorithmic trading can also exacerbate the market's negative tendencies by causing flash crashes and immediate loss of liquidity.