Stocker can be run from an interative Python 3. I recommend installing the Anaconda Python 3. Stocker is a work in progress. Let me know of any issues and feel free to make your own contributions! These can be installed with pip from the command line some of these might require running the command prompt as administrator. If you run into any other errors installing packages, check out Stack Overflow.
Once the required packages have been installed, get started exploring a stock by running an interactive python session or Jupyter Notebook in the same folder as stocker. Instantiate a stocker object by calling Stocker with a valid stock ticker. If using one of the tickers in the list, only the ticker symbol needs to be passed.
If using a stock not on the list, try using a different exchange:. The Stocker object includes 8 main methods for analyzing and predicting stock prices. Call any of the following on your stocker object, replacing Stocker with your object for example microsoft :. The default stat is Adjusted Closing price and default start and end dates are the beginning and ending dates of the data.
The number of training years for any Prophet model can be set with the Stocker. The default number of training years is 3.Batocera nvidia enable
Make a Prophet Additive Model using the specified number of training years and make predictions number of days into the future. Also plots the historical data with the predictions and uncertainty overlaid. Returns the prophet model, and the future dataframe which can be used for plotting components of the time series. Finds the most significant changepoints in the dataset from a prophet model trained using the assigned years of training data.
The changepoints represent where the change in the rate of change of the data is the greatest in either the negative or positive direction. The changepoints occur where the change in the rate of the time series is greatest. This method prints the 5 most significant changepoints ranked by the change in the rate and plots the 10 most significant overlaid on top of the stock price data.
A special bonus feature of this method is a Google Search Trends analysis. If a search term is passed to the method, the method retrieves the Google Search Frequency for the specified term and plots on the same graph as the changepoints and the stock price data. It also displays related search queries and related rising search queires. If no term is specified then this capability is not used. You can use this to determine if the stock price is correlated to certain search terms or if the changepoints coincide with an increase in particular searches.
Evaluates a buy and hold strategy from the start date to the end date with the specified number of shares. If no start date and end date are specified, these default to the start and end date of the data. The buy and hold strategy means buying the stock on the start date and holding to the end date when we sell the stock. Prints the expected profit and plots the profit over time. Recommended for those planning a trip back in time to maximize profits. Makes a prophet model with each of the specified changepoint prior scales cps.
The cps controls the amount of overfitting in the model: a higher cps means a more flexible model which can lead to overfitting the training data more varianceand a lower cps leads to less flexibility and the possiblity of underfitting high bias.
Each model is fit with the assigned number of years of data and makes predictions for 6 months.Make and lose fake fortunes while learning real Python. Trying to predict the stock market is an enticing prospect to data scientists motivated not so much as a desire for material gain, but for the challenge. We see the daily up and downs of the market and imagine there must be patterns we, or our models, can learn in order to beat all those day traders with business degrees. Naturally, when I started using additive models for time series prediction, I had to test the method in the proving ground of the stock market with simulated funds.
Inevitably, I joined the many others who have tried to beat the market on a day-to-day basis and failed. However, in the process, I learned a ton of Python including object-oriented programming, data manipulation, modeling, and visualization. I also found out why we should avoid playing the daily stock market without losing a single dollar all I can say is play the long game! While option three is the best choice on an individual and community level, it takes the most courage to implement.
I can selectively choose ranges when my model delivers a handsome profit, or I can throw it away and pretend I never spent hours working on it.
That seems pretty naive! We advance by repeatedly failing and learning rather than by only promoting our success. Moreover, Python code written for a difficult task is not Python code written in vain! In a previous articleI showed how to use Stocker for analysis, and the complete code is available on GitHub for anyone wanting to use it themselves or contribute to the project.
Stocker is a Python tool for stock exploration. Once we have the required libraries installed check out the documentation we can start a Jupyter Notebook in the same folder as the script and import the Stocker class:. The class is now accessible in our session.
We construct an object of the Stocker class by passing it any valid stock ticker bold is output :. Just like that we have 20 years of daily Amazon stock data to explore!Ue4 rotate character with mouse
Stocker is built on the Quandl financial library and with over stocks to use. The analysis capabilities of Stocker can be used to find the overall trends and patterns within the data, but we will focus on predicting the future price.
Predictions in Stocker are made using an additive model which considers a time series as a combination of an overall trend along with seasonalities on different time scales such as daily, weekly, and monthly. Stocker uses the prophet package developed by Facebook for additive modeling. Creating a model and making a prediction can be done with Stocker in a single line:.
Notice that the prediction, the green line, contains a confidence interval. The confidence interval grows wide further out in time because the estimate has more uncertainty as it gets further away from the data.
Any time we make a prediction we must include a confidence interval. Although most people tend to want a simple answer about the future, our forecast must reflect that we live in an uncertain world! For us to trust our model we need to evaluate it for accuracy. There are a number of methods in Stocker for assessing model accuracy.
To calculate accuracy, we need a test set and a training set. We need to know the answers — the actual stock price — for the test set, so we will use the past one year of historical data in our case.
When training, we do not let our model see the answers to the test set, so we use three years of data previous to the testing time frame —Add an item to the end of the list.
Extend the list by appending all the items from the iterable. Insert an item at a given position. The first argument is the index of the element before which to insert, so a. Remove the first item from the list whose value is equal to x. It raises a ValueError if there is no such item.
Remove the item at the given position in the list, and return it. If no index is specified, a. The square brackets around the i in the method signature denote that the parameter is optional, not that you should type square brackets at that position.
You will see this notation frequently in the Python Library Reference. Remove all items from the list. Equivalent to del a[:]. Return zero-based index in the list of the first item whose value is equal to x. Raises a ValueError if there is no such item. The optional arguments start and end are interpreted as in the slice notation and are used to limit the search to a particular subsequence of the list.
The returned index is computed relative to the beginning of the full sequence rather than the start argument. Sort the items of the list in place the arguments can be used for sort customization, see sorted for their explanation.
Return a shallow copy of the list. Equivalent to a[:]. You might have noticed that methods like insertremove or sort that only modify the list have no return value printed — they return the default None.
Another thing you might notice is that not all data can be sorted or compared. To add an item to the top of the stack, use append. To retrieve an item from the top of the stack, use pop without an explicit index. For example:. While appends and pops from the end of list are fast, doing inserts or pops from the beginning of a list is slow because all of the other elements have to be shifted by one.
To implement a queue, use collections. List comprehensions provide a concise way to create lists.In software, it's said that all abstractions are leakyand this is true for the Jupyter notebook as it is for any other software. I most often see this manifest itself with the following issue:. This issue is a perrennial source of StackOverflow questions e. Fundamentally the problem is usually rooted in the fact that the Jupyter kernels are disconnected from Jupyter's shell ; in other words, the installer points to a different Python version than is being used in the notebook.
In the simplest contexts this issue does not arise, but when it does, debugging the problem requires knowledge of the intricacies of the operating system, the intricacies of Python package installation, and the intricacies of Jupyter itself. In other words, the Jupyter notebook, like all abstractions, is leaky. In the wake of several discussions on this topic with colleagues, some online exhibit Aexhibit B and some off, I decided to treat this issue in depth here.
This post will address a couple things:. SecondI'll dive into some of the background of exactly what the Jupyter notebook abstraction is doing, how it interacts with the complexities of the operating system, and how you can think about where the "leaks" are, and thus better understand what's happening when things stop working. ThirdI'll talk about some ideas the community might consider to help smooth-over these issues, including some changes that the Jupyter, Pip, and Conda developers might consider to ease the cognitive load on users.
This post will focus on two approaches to installing Python packages: pip and conda. Other package managers exist including platform-specific tools like yumapthomebrewetc. If you're just looking for a quick answer to the question, how do I install packages so they work with the notebookthen look no further. First, a few words on pip vs. For many users, the choice between pip and conda can be a confusing one. I wrote way more than you ever want to know about these in a post last year, but the essential difference between the two is this:.
If you already have a Python installation that you're using, then the choice of which to use is easy:. If you installed Python using Anaconda or Miniconda, then use conda to install Python packages. If conda tells you the package you want doesn't exist, then use pip or try conda-forgewhich has more packages available than the default conda channel.
If you installed Python any other way from source, using pyenv, virtualenv, etc. Finally, because it often comes up, I should mention that you should never use sudo pip install. It will always lead to problems in the long term, even if it seems to solve them in the short-term. Doing this can have bad consequences, as often the operating system itself depends on particular versions of packages within that Python installation. If you're in the jupyter notebook and you want to install a package with conda, you might be tempted to use the!
Note that we use --yes to automatically answer y if and when conda asks for user confirmation. For various reasons that I'll outline more fully below, this will not generally work if you want to use these installed packages from the current notebook, though it may work in the simplest cases.
That bit of extra boiler-plate makes certain that conda installs the package in the currently-running Jupyter kernel thanks to Min Ragan-Kelley for suggesting this approach. I'll discuss why this is needed momentarily.
Python will make you rich in the stock market!
If you're using the Jupyter notebook and want to install a package with pipyou similarly might be inclined to run pip directly in the shell:. That bit of extra boiler-plate makes certain that you are running the pip version associated with the current Python kernel, so that the installed packages can be used in the current notebook. This is related to the fact that, even setting Jupyter notebooks aside, it's better to install packages using.Exploring financial data with object-oriented programming and additive models.
After spending a little bit of time with the quandl financial library and the prophet modeling libraryI decided to try some simple stock data exploration.
Several days and lines of Python later, I ended up with a complete stock analysis and prediction tool. Although I am not confident or foolish enough to use it to invest in individual stocks, I learned a ton of Python in the process and in the spirit of open-source, want to share my results and code so others can benefit. I had tried several times to conquer classes, the foundation of object-oriented programming in Python, but as with most programming topics, they never quite made sense to me when I read the books.
It was only when I was deep in a project faced with a problem I had not solved before that the concept finally clicked, showing once again that experience beats theoretical explanations! In addition to an exploration of Stocker, we will touch on some important topics including the basics of a Python class and additive models. For anyone wanting to use Stocker, the complete code can be found on GitHub along with documentation for usage. Stocker was designed to be easy to use even for those new to Pythonand I encourage anyone reading to try it out.
After installing the required libraries, the first thing we do is import the Stocker class into our Python session. We can do this from an interactive Python session or a Jupyter Notebook started in the directory with the script.
We now have the Stocker class in our Python session, and we can use it to create an instance of the class. In Python, an instance of a class is called an object, and the act of creating an object is sometimes called instantiation or construction. In order to make a Stocker object we need to pass in the name of a valid stock ticker bold indicates output. Now, we have a microsoft object with all the properties of the Stocker class. Stocker is built on the quandl WIKI database which gives us access to over US stocks with years of daily price data full list.
For this example, we will stick to Microsoft data. Although Microsoft might be seen as the opposite of open-source, they have recently made some changes that make me optimist they are embracing the open-source community including Python. A class in Python is comprised of two main parts: attributes and methods. Without going into too much detail, attributes are values or data associated either with the class as a whole or with specific instances objects of the class.
Methods are functions contained in the class which can act on that data. One attribute of a Stocker object is stock data for a specific company that is attribute is associated with the object when we construct it.
We can access the attribute and assign it to another variable for inspection:. The benefit of a Python class is that the methods functions and the data they act on are associated with the same object. We can use a method of the Stocker object to plot the entire history of the stock. This is a pretty basic plot that we could have found from a Google Search, but there is something satisfying about doing it ourselves in a few lines of Python!
By default, this method plots the Adjusted Closing price for the entire date range, but we can choose the range, the stats to plot, and the type of plot. For example, if we want to compare the Daily Change in price with the Adjusted Volume number of shares traded, we can specify those in the function call. Notice the y-axis is in percentage change relative to the average value for the statistic.
This scale is necessary because the daily volume is originally in shares, with a range in the hundreds of millions, while daily price change typically is a few dollars!
By converting to percentage change we can look at both datasets on a similar scale. The plot shows there is no correlation between the number of shares traded and the daily change in price.
Stock Prediction in Python
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Asked 4 years, 3 months ago. Active 4 years, 3 months ago. Viewed 1k times. I have just installed python 3. I am on windows and writing it in the windows cmd. Anyone have any solutions? Marc Erhardtsen Marc Erhardtsen 19 7 7 bronze badges. Active Oldest Votes. Try the command python -m pip install numpy without the version number. Frank Frank 33 5 5 bronze badges. I still get the same error. Sign up or log in Sign up using Google. Sign up using Facebook. Sign up using Email and Password.
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