ThetaData Backtesting#

Important

You can get an username and password at thetadata.net. Please use the full link to give us credit for the sale (https://www.thetadata.net), it helps support this project. You can use the coupon code ‘LUMI’ for 10% off.

ThetaData backtester allows for flexible and robust backtesting. It uses the thetadata API to fetch pricing data for stocks, options, forex, and cryptocurrencies. This backtester simplifies the process of getting pricing data; simply use the thetadata DataSource and it will automatically fetch pricing data when you call get_last_price() or get_historical_prices().

As of this writing, ThetaData provides historical data for free. If you pay for an API you can get many years of data and the backtesting will download data much faster because it won’t be rate limited.

This backtesting method caches the data on your computer making it faster for subsequent backtests. So even if it takes a bit of time the first time, the following backtests will be much faster.

To use this feature, you need to obtain an API key from thetadata, which is free and you can get in the Dashboard after you have created an account. You must then replace username and password with your own.

Start by importing the ThetaDataBacktesting, BacktestingBroker and other necessary classes:

from datetime import datetime

from lumibot.backtesting import BacktestingBroker, ThetaDataBacktesting
from lumibot.strategies import Strategy
from lumibot.traders import Trader

Next, create a strategy class that inherits from the Strategy class. This class will be used to define the strategy that will be backtested. In this example, we will create a simple strategy that buys a stock on the first iteration and holds it until the end of the backtest. The strategy will be initialized with a symbol parameter that will be used to determine which stock to buy. The initialize method will be used to set the sleeptime to 1 day. The on_trading_iteration method will be used to buy the stock on the first iteration. The strategy will be run from 2023-01-01 to 2023-05-01.

class MyStrategy(Strategy):
    parameters = {
        "symbol": "AAPL",
    }

    def initialize(self):
        self.sleeptime = "1D"

    def on_trading_iteration(self):
        if self.first_iteration:
            symbol = self.parameters["symbol"]
            price = self.get_last_price(symbol)
            qty = self.portfolio_value / price
            order = self.create_order(symbol, quantity=qty, side="buy")
            self.submit_order(order)

Set the start and end dates for the backtest:

backtesting_start = datetime.datetime(2023, 1, 1)
backtesting_end = datetime.datetime(2023, 5, 1)

Finally, run the backtest:

result = MyStrategy.run_backtest(
    ThetaDataBacktesting,
    backtesting_start,
    backtesting_end,
    benchmark_asset="SPY")

Alternatively, if you want to use polygon for stock data, and thetadata for option data, you can pass a dictionary with both data sources as the first argument to run_backtest. In this example, we are using thetadata for both stock and option data.

result = MyStrategy.run_backtest(
    {"STOCK":PolygonDataBacktesting,"OPTION":ThetaDataBacktesting},
    backtesting_start,
    backtesting_end,
    benchmark_asset="SPY")

Here’s the full code:

Make sure to replace the `THETADATA_USERNAME` and `THETADATA_PASSWORD` (it’s free)

from datetime import datetime

from lumibot.backtesting import BacktestingBroker, ThetaDataBacktesting
from lumibot.strategies import Strategy
from lumibot.traders import Trader


class MyStrategy(Strategy):
    parameters = {
        "symbol": "AAPL",
    }

    def initialize(self):
        self.sleeptime = "1D"

    def on_trading_iteration(self):
        if self.first_iteration:
            symbol = self.parameters["symbol"]
            price = self.get_last_price(symbol)
            qty = self.portfolio_value / price
            order = self.create_order(symbol, quantity=qty, side="buy")
            self.submit_order(order)


if __name__ == "__main__":
    backtesting_start = datetime(2023, 1, 1)
    backtesting_end = datetime(2023, 5, 1)
    result = MyStrategy.run_backtest(
        ThetaDataBacktesting,
        backtesting_start,
        backtesting_end,
        benchmark_asset="SPY")

Important

In summary, the ThetaData backtester is a powerful tool for fetching pricing data for backtesting various strategies. With its capability to cache data for faster subsequent backtesting and its easy integration with thetadata API, it is a versatile choice for any backtesting needs.