Backtrader is a popular backtesting library for Python that allows you to simulate the performance of trading strategies. One of its powerful features is the ability to create custom indicators and scripts, which can help in optimizing strategies and understanding market trends better. In this article, we'll explore the process of creating advanced indicators and custom scripts using Backtrader.
Getting Started with Custom Indicators
Backtrader offers several built-in indicators, but sometimes, you might need an indicator that isn't provided. This is where custom indicators come in. A custom indicator in Backtrader is essentially a Python class that inherits from the Indicator base class.
Here is a basic outline of creating a custom indicator:
from backtrader.indicators import Indicator
class MyCustomIndicator(Indicator):
lines = ("line_name",)
def __init__(self):
self.addminperiod(1)
def next(self):
self.lines.line_name[0] = self.datas[0].close[0] * 2 # Simple logic example
In this example, MyCustomIndicator simply doubles the closing price of the data feed. The lines attribute allows you to define the outputs of your indicator.
Implementing a Moving Average Convergence Divergence (MACD) Indicator
Let's create a more practical custom indicator: the MACD, which is often used to spot changes in momentum.
class MACD(Indicator):
lines = ('macd', 'signal', 'histogram')
params = (
('fast', 12),
('slow', 26),
('signal', 9),
)
def __init__(self):
exp1 = bt.indicators.EMA(self.data, period=self.params.fast)
exp2 = bt.indicators.EMA(self.data, period=self.params.slow)
self.lines.macd = exp1 - exp2
self.lines.signal = bt.indicators.EMA(self.lines.macd, period=self.params.signal)
self.lines.histogram = self.lines.macd - self.lines.signal
In this MACD example, we compute two Exponential Moving Averages (EMAs): a fast and a slow. The MACD line is the difference between these two EMAs, and the signal line is a smoothing of the MACD line. The histogram is the difference between the MACD and the signal line, providing insight into the trend direction.
Creating Custom Scripts for Backtesting Strategies
Apart from indicators, writing custom scripts to implement and test trading strategies is equally crucial. This involves defining a class that inherits from the bt.Strategy class. The key methods you'll need to define or customize are:
__init__(): Initialize your indicators and variables.next(): Define the logic for evaluating and executing trades.notify_trade(): Monitor opened or closed trades.
Here is a simple example of a momentum trading strategy script:
class MomentumStrategy(bt.Strategy):
params = (('period', 14),)
def __init__(self):
self.momentum = bt.indicators.Momentum(self.data.close, period=self.params.period)
self.order = None
def next(self):
if self.order:
return # waiting for pending orders
if self.momentum[0] > 100:
if not self.position:
self.order = self.buy()
elif self.momentum[0] < 100:
if self.position:
self.order = self.sell()
def notify_trade(self, trade):
if not trade.isclosed:
return
print(f'Trade PnL: Gross {trade.pnl}, Net {trade.pnlcomm}')
The MomentumStrategy initiates trades based on a simple momentum calculation. When the momentum is greater than 100, it buys, and it sells when it drops below 100. The notify_trade method prints the trade's profit or loss once it's closed.
Conclusion
Custom indicators and scripts in Backtrader provide the flexibility needed to model complex strategies and explore unique trading insights tailored to your individual needs. By creating custom indicators, you can visualize more information about the trend and momentum. Writing custom scripts helps in executing these strategies and measuring their effectiveness. While this guide presents basic examples, the possibilities for expansion and customization are vast and allow traders significant opportunities to refine and improve their systems.
