Algorithmic trading has seen great traction in recent years and the numbers of students, engineering graduates, and finance professionals looking to explore this lucrative domain has been growing exponentially with each passing year.
Are you among the ones looking to learn quant skills and also make money with your trading ideas? Let us explore the Quantiacs platform which allows one to create, run and implement your https://www.quantinsti.com/blog/python-trading-library/Python trading strategy. Quantiacs offers great earning opportunities for successful quants.
The Quantiacs toolbox is free and open-source. Quantiacs provides up to 25 years of free data for 49 futures and S&P 500 stocks. The toolkit allows the user to create a trading strategy and backtest it with data all the way back to 1990. In addition to futures data, Quantiacs has recently added macroeconomic data which can be used in conjunction with the price time series data to improve the trading algorithms. Quantiacs supports both Python and Matlab. In this post, we will explore the Python toolbox and illustrate a toy strategy using it.
Quantiacs Python Toolbox
Quantiacs has created a simple yet powerful Python framework which can be used to create different types of algorithmic strategies. It provides for defining trading system settings like loading market data, trading costs, custom fields, capital etc. Others features of the Python toolbox include evaluating the trading system, optimization, visualization of results etc. Let us explore some features of the Python framework here.
Loading the market data:
Quantiacs trades in both stock and futures markets. Here is what the data fields look like for a stock:
We can load the stock data in Python using the quantiacsToolbox.loadData function.
As can be seen, the data is in the form of a Python dictionary. Let us check the data type of the key-value pairs.
To create a Python trading strategy we will have to manipulate the numpy array and it is required that you have a good understanding of Python numpy arrays and the myriad functions that it supports. Here’s a list of some useful functions.
Candle High-Low Python Strategy
Now let us take a very simple candle high-low strategy and try to code it using the Quantiacs toolbox. The step-by-step process has been illustrated below.
Step 1: Define the Settings
We test our sample strategy on Apple Inc. (AAPL) and Amazon Inc. (AMZN) stocks. The backtest period is defined in settings[‘beginInSample’] and settings[‘endInSample’] variables. We also define the lookback days, capital and the slippage.
Step 2: Python Trading Strategy
We have kept our strategy simple. In the first step, we define the number of candles which represent the number of the previous prices that will be considered for generating a buy/sell signal. We then compute the price difference of the last ‘n’ candles. If all the price differences are positive we go short expecting a mean reversion behaviour. If all the price differences are negative we go long.
The long position is indicated by the value 1, while the short position takes the value of -1.
Step 3: Run the Strategy
To execute our strategy, we use the quantiacsToolbox.runts command and specify the respective Python file.
Step 4: Visualize the results
Upon execution, the Python framework displays a very informative chart which includes the markets, an option to select the exposure type, various performance metrics etc.
As can be seen, the Quantiacs Python framework is easy to use and can be used to develop varied trading strategies.
Python algorithmic trading has gained traction in the quant finance community as it makes it easy to build intricate statistical models with ease due to the availability of sufficient scientific libraries like Pandas, NumPy, PyAlgoTrade, Pybacktest and more.
In case you are looking to master the art of using Python to generate trading strategies, backtest, deal with time series, generate trading signals, predictive analysis and much more, you can enroll for our course on Python for Trading!
Disclaimer: All investments and trading in the stock market involve risk. Any decisions to place trades in the financial markets, including trading in stock or options or other financial instruments is a personal decision that should only be made after thorough research, including a personal risk and financial assessment and the engagement of professional assistance to the extent you believe necessary. The trading strategies or related information mentioned in this article is for informational purposes only.
Download Python Code
- Candle High Low Strategy – Python Code