Transaction Cost In Algo Trading

Transaction Cost In Algo Trading💰

One of the most prevalent beginner mistakes when implementing trading models is to neglect (or grossly underestimate) the effects of transaction costs on a strategy. Though it is often assumed that transaction costs only reflect broker commissions, there are in fact many other ways that costs can be accrued on a trading model. The three main types of costs that must be considered include:

1.) Commission:

The most direct form of transaction costs incurred by an algorithmic trading strategy are commissions and fees. All strategies require some form of access to an exchange, either directly or through a brokerage intermediary (“the broker”). These services incur an incremental cost with each trade, known as commission.

Brokers generally provide many services, although quantitative algorithms only really make use of the exchange infrastructure. Hence brokerage commissions are often small on per trade basis. Brokers also charge fees, which are costs incurred to clear and settle trades. Further to this are taxes imposed by regional or national governments. For instance, in the UK there is a stamp duty to pay on equities transactions. Since commissions, fees and taxes are generally fixed, they are relatively straightforward to implement in a Backtest engine (see below).

2.) Slippage:

Slippage is the difference in price achieved between the time when a trading system decides to transact and the time when a transaction is actually carried out at an exchange. Slippage is a considerable component of transaction costs and can make the difference between a very profitable strategy and one that performs poorly. Slippage is a function of the underlying asset volatility, the latency between the trading system and the exchange and the type of strategy being carried out.

An instrument with higher volatility is more likely to be moving and so prices between signal and execution can differ substantially. Latency is defined as the time difference between signal generation and point of execution. Higher frequency strategies are more sensitive to latency issues and improvements of milliseconds on this latency can make all the difference towards profitability. The type of strategy is also important. Momentum systems suffer more from slippage on average because they are trying to purchase instruments that are already moving in the forecast direction. The opposite is true for mean-reverting strategies as these strategies are moving in a direction opposing the trade.

3.) Market Impact:

Market impact is the cost incurred to traders due to the supply/demand dynamics of the exchange (and asset) through which they are trying to trade. A large order on a relatively illiquid asset is likely to move the market substantially as the trade will need to access a large component of the current supply. To counter this, large block trades are broken down into smaller “chunks” which are transacted periodically, as and when new liquidity arrives at the exchange. On the opposite end, for highly liquid instruments such as the S&P500 E-Mini index futures contract, low volume trades are unlikely to adjust the “current price” in any great amount.

More illiquid assets are characterised by a larger spread, which is the difference between the current bid and ask prices on the limit order book. This spread is an additional transaction cost associated with any trade. Spread is a very important component of the total transaction cost – as evidenced by the myriad of UK spread-betting firms whose advertising campaigns express the “tightness” of their spreads for heavily traded instruments.

Major Transaction Costs In Algo Trading;

Transaction costs are a major factor in determining overall profitability in algorithmic trading. These expenses can be broadly divided into two categories: explicit and implicit.

A.] Explicit Costs (Direct Costs)

1. Brokerage Fees
2. Exchange Fees
3. Slippage
4. Spread Cost
5. Latency Costs

B.] Implicit Costs (Hidden Costs)

1. Market Impact
2. Opportunity Cost
3. Taxes and Regulatory Costs

C.] Strategies to Reduce Transaction Costs

1. Smart Order Routing (SOR)
2. Order Execution Algorithms
3. Dark Pool Trading
4. Co-Location and Low-Latency Infrastructure

In algorithmic trading, transaction costs which fall into two categories: explicit and implicit have a big influence on profitability. Slippage, spreads, brokerage fees, and exchange fees are examples of explicit charges that have a direct impact on transaction execution. Inefficiencies in order execution lead to implicit expenses such as opportunity cost and market impact. Because even slight slippage or spread fluctuations can reduce profits, high-frequency traders are especially vulnerable to these variables. The total cost of trading also includes regulatory fees and delay.

Traders employ dark pools, execution algorithms (VWAP, TWAP), and smart order routing (SOR) to decrease expenses and lessen market impact. Infrastructure that is low-latency and co-located speeds up execution. For long term profitability, a well optimized plan must strike a balance between transaction costs and order execution effectiveness.

Just as crucial to algo trading as having a successful strategy is reducing transaction costs. To maximize performance, traders need to strike a balance between market liquidity, order size, and execution speed.

Read Also; Difference Between Algo Trading And Quant Trading