Backtrader is a powerful open-source Python framework for trading strategy backtesting and trading system development. One crucial aspect of simulating realistic trading behavior within Backtrader is handling commissions and slippage. These factors can significantly impact the performance of a trading strategy when applied in a live market. In this article, we'll explore how to incorporate commission and slippage into your Backtrader strategies, complete with practical examples.
Understanding Commission in Trading
Commission is the fee a trader pays for executing trades through a broker. In the real world, commissions vary with different brokers and trading platforms, and they can be flat fees or based on a percentage of the trade's value. Modeling these costs accurately is essential for realistic backtesting.
Setting Up Basic Commission Schemes in Backtrader
Backtrader provides several tools to account for commission in your trading strategies. The most straightforward way is using the Broker member function setCommission().
Let's start with a simple example of setting a fixed commission:
cerebro = bt.Cerebro()
cerebro.broker.setcommission(commission=0.01)In this case, 1% of the transaction's value is accounted for as a commission fee.
Advanced Commission Structures
Besides a simple percentage, Backtrader supports more complex commission models, including fractional shares, forex trading, or even specific broker models.
Example for setting up Forex style commissions:
cerebro.broker.setcommission(commission=0.0005, stocklike=False)Here, each trade costs 0.0005 of the transaction size, and stocklike=False indicates that the commission is percentage-based like in Forex trading, where size-calculated commissions are common.
Understanding Slippage in Trading
Slippage is the difference between the expected price of a trade and the price at which the trade is actually executed. Slippage can adversely affect trading performance, especially in a volatile market.
Implementing Slippage in Backtrader
Backtrader provides various slippage models that you can incorporate into your simulations. A common approach is the FixedSlippage model:
from backtrader import sizers
from backtrader.commissions import slippage
cerebro.broker.set_slippage_perc(slippage.FixedSlippageAdv(sizefrac=0.1))In this snippet, we set a fixed slippage impact of 10% on trade sizes.
Custom Slippage Models
For more advanced strategies or specific conditions, you can define a custom slippage model by subclassing SlippageBase. Here's a quick template:
from backtrader.commissions.slippage import SlippageBase
class MySlippage(SlippageBase):
def __call__(self, broker, order, price,
exectype, simulated, **kwargs):
my_slippage = 0.05 # Define slippage computation logic
return price * (1 + my_slippage)This custom slippage impacts the execution price by a fixed 5% of the trade's size.
Then integrate the custom slippage model as follows:
cerebro.broker = bt.brokers.BackBroker(slippage=MySlippage())Combining Commissions and Slippage
Most realistic strategies will need both commissions and slippage factored in simultaneously. In Backtrader, you can do this by specifying these models directly on your broker setup. By adjusting both the commission and slippage settings as illustrated above, you gain a realistic representation of trading impacting your backtest results.
Testing and Validation
Once your trading strategy includes realistic commission and slippage models, it's crucial to test it comprehensively. Use historical data with known outcomes to gauge accuracy, and examine multiple timeframes or trading conditions to ensure robustness.
By correctly integrating and adjusting for commission and slippage, backtests in Backtrader can better mimic actual market conditions, providing safer insights into the performance and viability of trading strategies before venturing into live markets.
