Most back testing frameworks model slippage. It is worth knowing what it is.
Slippage is an important component of market simulation
In order for strategy back tests to be realistic we need to accurately simulate transaction costs. Transaction costs include many factors, such as: Commissions, broker inefficiencies, volatility and market impact and others. At Lucena, our product QuantDesk enables clients to directly input a broker’s commission structure for their back tests. And for simplicity we group the remaining factors together as “slippage.” Here’s a definition of slippage from wikipedia:
Slippage is the difference between estimated transaction costs and the amount actually paid. Brokers may not always be effective enough at executing orders. Market-impacted, liquidity, and frictional costs may also contribute.
As an example from live trading, assume you issue an order to buy 100 shares of stock XYZ at 10:00AM and the most recent transaction for XYZ was at $100.00. If your order is filled at $100.05, slippage was 0.05% or 5bps. Now, most savvy traders do not trade using market orders, and they might argue that because they use limit orders they do not experience any slippage. Unfortunately, that’s not really true. The key here is market impact, and any “buy” order is visible in the marketplace, thus in an efficient market buy orders influence the price to go up. In a similar manner, “sell” orders cause the price to go down.
In QuantDesk, we model slippage as a single fixed factor that counts against the strategy each time it trades. For “buy” orders we assume the executed price is slightly higher than the historically recorded price. For “sell” orders we assume the price is slightly lower.
How should you estimate slippage for your own back tests? The key factor is volume: Assuming your trading is less than 0.1% of the market, we suggest the following slippage values:
- For large cap stocks: 2.5bps
- For mid cap stocks: 5.0bps
- for micro cap stocks: 10.0bps
If your trading accounts for a more significant volume slippage is likely higher than these numbers. In general, slippage increases significantly as strategy trading volume becomes a higher percentage of market volume. You will need a more sophisticated approach.
mathtrading
March 28, 2014
Hi,
Nice blog and good point regarding your % volume.
Personally I like to look at the bid/ask spread at the time the model trades as my base slippage estimation for each market.
One could then modify this base unit according to his % volume, maybe even in a simple multiplicative fashion.
A simple model, but which could allow the modelling of some execution algos (and should make obvious the rational behind slicing high volume orders)
Next step could probably be to compare your volume against the bid/ask sizes, but then I guess we start moving into the HFT space.
Andrea
Abi
August 18, 2014
Didn’t know what slippage was, so this was really useful!