Trading system with python
Data analysis is a crucial part of finance. Besides learning to handle dataframes using Pandas, there are a few specific topics that you should pay attention to while dealing with trading data. One of the most important packages in the Python data science stack is undoubtedly Pandas. You can accomplish almost all major tasks using the functions defined in the package.
Trading data is all about time-series analysis. You should learn to resample or reindex the data to change the frequency of the data, from minutes to hours or from the end of day OHLC data to end of week data. For example, you can convert 1-minute time series into 3-minute time series data using the resample function:. A career in quantitative finance requires a solid understanding of statistical hypothesis testing and mathematics.
A good grip over concepts like multivariate calculus, linear algebra, probability theory will help you lay a good foundation for designing and writing algorithms. You can start by calculating moving averages on stock pricing data, writing simple algorithmic strategies like moving average crossover or mean reversion strategy and learning about relative strength trading. After taking this small yet significant leap of practicing and understanding how basic statistical algorithms work, you can look into the more sophisticated areas of machine learning techniques.
These require a deeper understanding of statistics and mathematics. The next step is to expose this strategy to a stream of historical trading data, which would generate trading signals. This is called backtesting. Backtesting requires you to be well-versed in many areas, like mathematics, statistics, software engineering, and market microstructure.
Here are some concepts you should learn to get a decent understanding of backtesting:. Once you understand the strategy confidently, the following performance metrics can help you learn how good or bad the strategy actually is:. This article served as a suggested curriculum to help you get started with algorithmic trading. It is a good list of concepts to master.
Here are a few classic books and useful courses with assignments and exercises that I found helpful:.
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Forum Donate. Before we deep dive into the details and dynamics of stock pricing data, we must first understand the basics of finance. If you are someone who is familiar with finance and how trading works, you can skip this section and click here to go to the next one. A stock is a representation of a share in the ownership of a corporation, which is issued at a certain amount. These stocks are then publicly available and are sold and bought.
The process of buying and selling existing and previously issued stocks is called stock trading. There is a price at which a stock can be bought and sold, and this keeps on fluctuating depending upon the demand and the supply in the share market.
Traders pay money in return for ownership within a company, hoping to make some profitable trades and sell the stocks at a higher price. Another important technique that traders follow is short selling. Quantitative traders at hedge funds and investment banks design and develop these trading strategies and frameworks to test them. It requires profound programming expertise and an understanding of the languages needed to build your own strategy.
It is being adopted widely across all domains, especially in data science, because of its easy syntax, huge community, and third-party support. Make sure to brush up on your Python and check out the fundamentals of statistics. You can create your first notebook by clicking on the New dropdown on the right.
Make sure you have created an account on Quandl.
Follow the steps mentioned here to create your API key. After the packages are imported, we will make requests to the Quandl API by using the Quandl package:.
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All you had to do was call the get method from the Quandl package and supply the stock symbol, MSFT, and the timeframe for the data you need. With the data in our hands, the first thing we should do is understand what it represents and what kind of information it encapsulates. An index can be thought of as a data structure that helps us modify or reference the data. Time-series data is a sequence of snapshots of prices taken at consecutive, equally spaced intervals of time. In trading, EOD stock pricing data captures the movement of certain parameters about a stock, such as the stock price, over a specified period of time with data points recorded at regular intervals.
We can learn about the summary statistics of the data, which shows us the number of rows, mean, max, standard deviations, and so on. Try running the following line of code in the Ipython cell:. We can specify the time intervals to resample the data to monthly, quarterly, or yearly, and perform the required operation over it. A financial return is simply the money made or lost on an investment. A return can be expressed nominally as the change in the amount of an investment over time.
It can be calculated as the percentage derived from the ratio of profit to investment. Here is how you can calculate returns:. This will print the returns that the stock has been generating on a daily basis.
Extracting data from the Quandl API
Multiplying the number by will give you the percentage change. After resampling the data to months for business days , we can get the last day of trading in the month using the apply function. The lambda function is an anonymous function in Python which can be defined without a name, and only takes expressions in the following format:. The concept of moving averages is going to build the base for our momentum-based trading strategy.
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In finance, analysts often have to evaluate statistical metrics continually over a sliding window of time, which is called moving window calculations. Moving averages help smooth out any fluctuations or spikes in the data, and give you a smoother curve for the performance of the company. And you can see the difference for yourself, how the spikes in the data are consumed to give a general sentiment around the performance of the stock.
Here comes the final and most interesting part: designing and making the trading strategy. Momentum-based strategies are based on a technical indicator that capitalizes on the continuance of the market trend. We purchase securities that show an upwards trend and short-sell securities which show a downward trend. The SMAC strategy is a well-known schematic momentum strategy. It is a long-only strategy.
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Momentum, here, is the total return of stock including the dividends over the last n months. This period of n months is called the lookback period. There are 3 main types of lookback periods: short term, intermediate-term, and long term.
We need to define 2 different lookback periods of a particular time series. A buy signal is generated when the shorter lookback rolling mean or moving average overshoots the longer lookback moving average. A sell signal occurs when the shorter lookback moving average dips below the longer moving average. We have created 2 lookback periods. We have created a new DataFrame which is designed to capture the signals. These signals are being generated whenever the short moving average crosses the long moving average using the np. It assigns 1. The positions columns in the DataFrame tells us if there is a buy signal or a sell signal, or to stay put.
We're basically calculating the difference in the signals column from the previous row using diff. Now, you can clearly see that whenever the blue line short moving average goes up and beyond the orange line long moving average , there is a pink upward marker indicating a buy signal. Quantopian is a Zipline-powered platform that has manifold use cases.