Zipline is a powerful algorithmic trading library that allows traders and analysts to implement financial algorithms, including handling complex order execution and commission strategies. Understanding and customizing these order execution dynamics and commission models can significantly impact your backtesting results, providing more realistic simulations that closely match live trading environments.
Introduction to Order Execution Models
In Zipline, the order execution model is responsible for deciding how market orders are matched with available transactions. By default, Zipline assumes instant execution with no delay or market impact. However, this isn't always the case in live markets. Customizing the order execution model can help simulate slippage and delay occurring in real-world executions.
Implementing a Custom Slippage Model
A slippage model introduces a more realistic outcome on trade execution prices based on order size relative to the market’s volume. Here’s how you can define and apply a custom slippage model in Zipline:
from zipline.finance.slippage import VolumeShareSlippage
class CustomSlippageModel(VolumeShareSlippage):
def __init__(self, volume_limit=0.25, price_impact=0.1):
super(CustomSlippageModel, self).__init__(volume_limit, price_impact)
def __call__(self, order, transaction):
# Add custom logic here
# Example: Adjust price impact based on order size
return super(CustomSlippageModel, self).__call__(order, transaction)
# Apply this to your algorithm
algo = TradingAlgorithm()
algo.set_slippage(slippage=CustomSlippageModel())This example modifies the default VolumeShareSlippage model to account for custom order size impacts.
Commission Models
Commissions are an unavoidable aspect of trading and critical in algorithm evaluation. Zipline allows traders to define what costs look like, whether it is a fixed cost, a per-share cost, or a more dynamic structure.
Creating a Custom Commission Model
A common commission strategy involves fixed commission rates per trade. Let's implement it through a custom model:
from zipline.finance.commission import PerShare, PerTrade
class FixedCommissionModel(PerTrade):
def __init__(self, cost=1.0):
super(FixedCommissionModel, self).__init__(cost)
# Usage example in the algorithm
algo.set_commission(commission=FixedCommissionModel(1.00))This instruction sets up a fixed $1 commission per trade. Similarly, per-share costs or more intricate fee schedules can be implemented based on trading needs.
Advanced Commission Techniques
For an even more nuanced approach, consider creating commission models that reflect a mixture of both per-share and fixed costs:
from zipline.finance.commission import PerShare, PerTrade
class CustomCommissionModel(PerShare):
def __init__(self, cost=1.00, per_share=0.05):
self.per_share = per_share
self.fixed_cost = cost
def calculate(self, order, transaction):
# Calculate based on per-share and fixed strategies
return self.fixed_cost + self.per_share * transaction.amount
# Apply this to your algorithm
algo.set_commission(commission=CustomCommissionModel(cost=1.00, per_share=0.01))This example hybrid model implements a strategy with a fixed cost and additional per-share cost, better simulating real broker charges.
Applying to Strategy
After customizing slippage and commission models, incorporate them into your backtesting setup and analyze how these adjustments affect trading outcomes. Custom models can be tailored to reflect your insights or hedge against biases in algorithm performance.
Conclusion
By thoughtfully implementing custom order execution and commission models in Zipline, users can adapt the simulation environment closer to real-world scenarios. This ultimately enhances the reliability of the backtesting results, providing better insights into how algorithmic strategies would perform when executed live. Experiment with different models and ensure they reflect realistic assumptions about market behavior and transaction costs.
