A detailed documentation is available here, where you can find many useful information about the toolbox.
 

You need to be logged-in to download the toolbox and submit a system.

1.    Install Python 3.7

Download and install Python 3.7. There are several distributions of Python 3.7 that can be used. For an easy installation process, we recommend Anaconda since it will reliably install all the necessary dependencies.

Installing Python with Anaconda

To download Anaconda, please follow the instructions on the installation page. You might have another Python version pre-installed on your computer. When installing the Anaconda distribution, it is important that you know how to access it from the command line. This is different for each operating system:

• Windows: When installing Anaconda be sure to leave the ‘Add Anaconda to my PATH environment variable’ box checked. This makes the Anaconda distribution available from the command line. Additionally, after installation Anaconda supplies a command prompt that is pointed to the Anaconda distribution.
• Linux/OSX: Following the instruction on the Anaconda installation page, be sure to select yes when prompted to add the Anaconda install location to your PATH. This will make the Anaconda distribution available from your command line.

Installing the necessary packages manually

If you are experienced with Python, you can install the necessary dependencies manually. We recommend however to set up a virtual environment using Anaconda.

2.    Install the Quantiacs Toolbox

The easiest way to install the toolbox is to use Anaconda and create a virtual environment for Quantiacs as follows:

- First, create the environment:

        conda create --name quantiacsbox

- Then, activate it using:

        conda activate quantiacsbox

- Finally install the Quantiacs toolbox:

        conda install -c quantiacspkg quantiacstoolbox

Anaconda will take care of installing the required dependencies.

You can also use the pip installer in Python. Having the latest version of pip is recommended. To upgrade pip, type the following in the command line:

	pip install -U pip

Now you are ready to install the Quantiacs Toolbox via pip. It is highly recommended that you use the latest version of the toolbox. You can install or upgrade to this version by running the following at the command line:

	pip install -U quantiacsToolbox

If you don’t want to install the toolbox through pip, download the toolbox from my area on the Quantiacs site. Make sure that you have the necessary dependencies installed (numpy, pandas, and matplotlib) and that they are up to date. Extract the contents of the toolbox download, open a command prompt within the folder and type:

	python setup.py install

If you have any issues write us to info@quantiacs.com and we’ll respond ASAP.

3.    Run a sample trading algorithm

With the toolbox installed, you can now backtest your trading algorithms. We put a simple sample algorithm example on github as a starting point.  To evaluate this sample algorithm type in the command line:

	python path/to/trading_system.py

This runs the evaluation of the sample system on the defined data set. Once the system is evaluated it opens a GUI with important performance indicators like the equity curve, the long vs short exposure, marketwise performance, and the Sharpe Ratio.

The Sharpe Ratio is one of several important performance numbers. It compares the realized performance against the risk (or volatility) taken to achieve that performance. Investors usually prefer systems with high performance and low risk. Here you can find more about important aspects of a trading algorithm and its performance.

4.    Make your first changes

Finally, we change the sample system, rerun it and submit a test to the Quantiacs marketplace. Open the downloaded sample strategy with your preferred editor (IDLE, atom, sublime, etc.). The sample trading system uses a Simple Moving Average (SMA) as an indicator to predict trends. The basic idea of an SMA is to smooth out fluctuating data points to lower the noise and detect signals. Here is an illustration of what these SMAs might look like.


Within, 'myTradingSystem', the algorithm evaluates two SMAs over two different time periods: longerPeriod = 200 days, shorterPeriod = 40 days. You can define how much the data is smoothened by setting the number of days over which we average the data. The higher the number, the more days you average, the smoother the SMA.

	longerPeriod=200

	shorterPeriod=40

Change the numbers of longerPeriod and/or shorterPeriod, save the changes and rerun the strategy. You will see how this changes the equity curve and the Sharpe Ratio.

To submit your tweaked strategy to the Quantiacs marketplace simply click the button ‘Submit Trading System’ at the bottom of the GUI. This redirects you to the website where you can set the name for your system and hit the submit button.
 

	python

	import quantiacsToolbox

	quantiacsToolbox.submit(‘/Path/to/your/TradingSystem.py’,’mySystemName’)

A detailed documentation is available here, where you can find many useful information about the toolbox.
 

You need to be logged-in to download the toolbox and submit a system.

1.    Install the Quantiacs Toolbox

To get the Matlab Toolbox, download it from my area. Add the opened zip-file to your Matlab search path (Environment -> Set Path -> Add with Subfolders…). This will make the toolbox functions available from the command window.

2.    Run a sample trading algorithm

With the toolbox in your matlab search path, you can now backtest your trading algorithms. We put a simple sample algorithm example on github as a starting point. To evaluate this sample algorithm place the file in your Matlab search path (the current folder is recommended). Then type in the command line:

	runts(‘trading_system_name’)

This runs the evaluation of the sample system on the defined market list. Once the system is evaluated it opens a GUI with important performance indicators like the equity curve, the long vs short exposure, marketwise performance, and Sharpe Ratio.

The Sharpe Ratio is one of several important performance numbers. It compares the realized performance against the risk (or volatility) taken to achieve that performance. Investors usually prefer systems with high performance and low risk. Here you can find more about important aspects of a trading algorithm and its performance.

3.    Make your first changes

Finally, we change the sample system, rerun it and submit it to Quantiacs. The sample trading system uses a Simple Moving Average (SMA) as an indicator to predict trends. The basic idea of an SMA is to smooth out fluctuating data points to lower the noise and detect signals. Here is an illustration of what these SMAs might look like.


Within, 'myTradingSystem', the algorithm evaluates two SMAs over two different time periods: longerPeriod = 200 days, shorterPeriod = 40 days. You can define how much the data is smoothened by setting the number of days over which we average the data. The higher the number, the more days you average, the smoother the SMA.

	longerPeriod=200

	shorterPeriod=40

Change the numbers of longerPeriod and/or shorterPeriod, save the changes and rerun the strategy. You will see how this changes the Sharpe Ratio.

To submit your tweaked strategy to the Quantiacs marketplace simply click the button ‘Submit Trading System’ in the upper right of the GUI. This redirects you to the website where you can set the name for your system and hit the submit button. 

You can also submit your system via the command window. Simply type:
 

	submit(‘Trading_System_file’, ‘system_name’)