In this chapter, we were introduced to the evolution of trading from the pits to the electronic trading platform, and learned how algorithmic trading came about. We looked at some brokers offering API access to their trading service offering. To help us get started on our journey in developing an algorithmic trading system, we used the TWS of IB and the IbPy Python module.

In our first trading program, we successfully sent an order to our broker through the TWS API using a demonstration account. Next, we developed a simple algorithmic trading system. We started by requesting the market data and account updates from the server. With the captured real-time information, we implemented a mean-reverting algorithm to trade the markets. Since this trading system uses only one indicator, more work would be required to build a robust, reliable, and profitable trading system.

We also discussed currency trading with the OANDA REST API with the help of the oandapy Python module. After setting up our account for API access, our first step to explore the OANDA API is to fetch rates for a single currency pair and send a limit order to the server. Using the fxTrade Practice platform, we can track our current trades, orders, and positions. Next, we developed a trend-following algorithm to trade the EUR/USD currency pair with the use of streaming rates and market orders.

One critical aspect of trading is to manage risk effectively. In the financial industry, the VaR is the most common technique used to measure risk. Using Python, we took a practical approach to calculate the daily VaR of a set of stock prices from Yahoo! Finance.

Once we have built a working algorithmic trading system, we can explore the other ways to measure the performance of our trading strategy. One of these areas is backtesting. We will discuss this topic in the next chapter.