Beginner’s Introduction to Backtrader

Backtrader is a popular open-source Python library for backtesting and trading strategies. In backtrader, a platform is a way to connect to a brokerage or exchange in order to trade financial instruments. In this article, we will explore the different platform concepts in backtrader and how they can be used to connect to different brokerages and exchanges.

Data Feeds

A data feed is a source of financial market data that can be used to feed data into backtrader. Backtrader has a number of built-in data feeds for different exchanges and asset classes, including:

• BacktraderCSVData: A data feed that reads data from a CSV file.
• BacktraderPandasData: A data feed that reads data from a Pandas DataFrame.
• BacktraderYahooFinanceData: A data feed that fetches data from Yahoo Finance.

In addition to the built-in data feeds, backtrader also has a number of third-party data feed libraries that can be used to connect to other exchanges and data sources. Some popular examples include:

• ccxt: A library for connecting to a wide variety of cryptocurrency exchanges.
• pandas_datareader: A library for fetching financial data from a number of sources, including Yahoo Finance, Google Finance, and FRED.
• To use a data feed in backtrader, it must be passed to the feed parameter of the Cerebro class when creating a new instance of the Cerebro class. For example:

from backtrader import Cerebro, BacktraderCSVData

# Load data from a CSV file
data = BacktraderCSVData(dataname='data.csv')

# Create a new instance of the Cerebro class
cerebro = Cerebro()

# Add the data feed to the Cerebro instance
cerebro.adddata(data)

Brokers

A broker is a platform that allows traders to place orders to buy or sell financial instruments. In backtrader, a broker is represented by the Broker class. The Broker class has several methods that can be used to place orders and track the status of orders, including:

• buy: Place a buy order.
• sell: Place a sell order.
• getposition: Get the current position (i.e. the number of contracts or shares held) for a given instrument.

Backtrader has a number of built-in brokers that can be used to connect to different exchanges and brokerages, including:

• BacktraderBroker: A broker that can be used to simulate trades in a backtesting environment.
• IbBroker: A broker that can be used to connect to the Interactive Brokers API.

Strategies

A strategy is a set of rules that define how to trade a financial instrument. In backtrader, a strategy is represented by a subclass of the Strategy class. The Strategy class has several methods that can be overridden to implement a custom trading strategy, including:

• start: Called when the strategy is started.
• next: Called on every new tick of data.
• notify_order: Called when an order is placed or filled.
• notify_trade: Called when a trade is executed.

To use a custom strategy in backtrader, it must be passed to the strategy parameter of the Cerebro class when creating a new instance of the Cerebro class. For example:

from backtrader import Cerebro, Strategy

class MyStrategy(Strategy):
    def start(self):
        print('Starting strategy')
    
    def next(self):
        print('Next tick')
    
    def notify_order(self, order):
        print('Order notification:', order)
    
    def notify_trade(self, trade):
        print('Trade notification:', trade)

# Create a new instance of the Cerebro class
cerebro = Cerebro()

# Add the strategy to the Cerebro instance
cerebro.addstrategy(MyStrategy)

Indicators

An indicator is a mathematical calculation that is used to analyze financial data and make trading decisions. In backtrader, indicators are represented by subclasses of the Indicator class. The Indicator class has several methods that can be overridden to implement a custom indicator, including:

• next: Called on every new tick of data.

To use a custom indicator in backtrader, it must be passed to the indicators parameter of the Cerebro class when creating a new instance of the Cerebro class. For example:

from backtrader import Cerebro, Indicator

class MyIndicator(Indicator):
    def next(self):
        print('Next tick')

# Create a new instance of the Cerebro class
cerebro = Cerebro()

# Add the indicator to the Cerebro instance
cerebro.addindicator(MyIndicator)

Observers

An observer is a component that listens for events in backtrader and takes some action based on those events. In backtrader, observers are represented by subclasses of the Observer class. The Observer class has several methods that can be overridden to implement a custom observer, including:

• start: Called when the observer is started.
• stop: Called when the observer is stopped.
• next: Called on every new tick of data.

To use a custom observer in backtrader, it must be passed to the observers parameter of the Cerebro class when creating a new instance of the Cerebro class. For example:

from backtrader import Cerebro, Observer

class MyObserver(Observer):
    def start(self):
        print('Starting observer')
    
    def stop(self):
        print('Stopping observer')
    
    def next(self):
        print('Next tick')

# Create a new instance of the Cerebro class
cerebro = Cerebro()

# Add the observer to the Cerebro instance
cerebro.addobserver(MyObserver)

Conclusion

In this article, we have explored the different platform concepts in backtrader and how they can be used to build custom trading systems. By combining data feeds, brokers, strategies, indicators, and observers, it is possible to create powerful and flexible trading systems with backtrader.