You start developing a trading strategy with an idea that you test on historical data by applying quantitative analysis. You need a tool like Matlab to do the job but will spend up to 80% of your time developing the historical data interface to give your analytics access to market data. You will test new strategies on end-of-day data first but will still require real-time data for testing afterwards. You will make the decision of whether to rebalance your portfolio at the end of the day based on closing prices but you will still need to track stop loss and position closing orders throughout the day to minimize risk. You will also need to collect and store historical tick data and process it.

If testing of your system is profitable on historical data you will need to paper-trade it. This requires integration of a real-time feed and usually a new datafeed interface. Backtesting is done by processing ticks from disk while the real-time datafeed requires an event-driven approach. You will probably need to change your analysis and decision-making code to support the event-driven nature of real-time data. You will also need to ask your traders to simulate buy/sell orders in the current market using their trading expertise. If paper trading works then you will need to integrate trade execution into the system or develop a production version in C++ or other programming language. You will also need to link your newly minted system to your back office, risk management, reporting and other processes.

OpenQuant frees you from all of the unneeded hassle. We include in our framework all of the blocks required for building a trading system. Our macro and script editor is designed for fast prototyping. It's not just a text editor with a .NET compiler attached. No, we include a whole user front-end! When you run the macro editor you are instantly plugged into our market data warehouse, data feeds, execution, etc. You can jump up in the middle of the night and test your strategy without spending any time on unnecessary groundwork. This is what we call fast prototyping.

You can work with a simulated data feed and virtual trade execution. You can play back historical tick data to simulate a real-time market data feed. You can quickly prove that your strategy works on true real-time market data. Just plug in a ESignal or IB datafeed adapter instead of the included simulated data provider into your code. Simulate stop, limit, stop limit and other types of orders with our simulated execution interface and quickly switch over to Interactive Brokers. You can collect executed orders into a portfolio and compile performance statistics of your trading strategy.

The ability to switch from simulated to real-time market trading by replacing two lines in your code gives you a few key advantages such as
 
1. Time savings. All standard blocks are included and you don't need to reinvent the wheel. Quickly put just the required blocks of your trading system together.
 
2. Fast prototyping. Everything needed for analysis is included in OpenQuant. No need to be concerned with anything but designing and testing of new trading strategies. You expect from your quants new ideas that will bring in more money. You do not expect from them new tools to process market data.
 
3. OpenQuant smashes the barrier between analysis and trading. Execution is seamlessly integrated into the system. No need to integrate disparate systems such as Matlab, Excel, Flex Trade or broker API.
 
4. Finally, we will always implement your trading ideas for you and let you concentrate on trading. We are the implementation wizards! You can focus on making money while we concentrate on coding.

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