Python in algorithmic trading - page 23
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Balancing RNN sequence data - Deep Learning w/ Python, TensorFlow and Keras p.10
Balancing RNN sequence data - Deep Learning w/ Python, TensorFlow and Keras p.10
Hello everybody, and welcome to another deep learning with Python, TensorFlow, and Keras tutorial video. In this video, we're going to continue building on our future cryptocurrency price movement predictor using a recurrent neural network (RNN)."
The tutorial video is focused on building a predictor for cryptocurrency price movement using deep learning techniques.
The presenter mentions that they have already performed pre-processing steps, including building sequential data and separating out the validation data. They have also normalized the data.
The next step in the process is to balance the data. It is important to have an equal number of buy and sell instances in the dataset. If there is an imbalance, it can affect the model's performance. The presenter suggests that even if there is a slight imbalance, it is better to balance the data to avoid the model favoring one class over the other.
To balance the data, the presenter creates two lists: buys and sells. They iterate over the sequential data and check if the target is a 0 (sell) or 1 (buy). If it is a sell, they append the sequence to the sells list. If it is a buy, they append it to the buys list. Afterward, they shuffle both lists.
Next, they find the minimum length between the two lists (buys and sells). They update the buys and sells lists to contain only the elements up to the length of the shorter list. This ensures that both lists have an equal number of instances.
Then, the presenter combines the buys and sells lists into the sequential_data list. They shuffle the sequential_data list again to further randomize the order of the data.
The next step is to split the sequential_data into features (X) and labels (Y). They create empty lists x and y to store the features and labels, respectively. They iterate over the sequential_data and append the sequence to the x list and the target to the y list.
Finally, they return the arrays of x and y as the pre-processed data.
The presenter then proceeds to print out some statistics about the data, such as the sizes of the training and validation datasets, and the balance between the buy and sell instances.
In the next video, they plan to build and train the model using the pre-processed data.Cryptocurrency-predicting RNN Model - Deep Learning w/ Python, TensorFlow and Keras p.11
Cryptocurrency-predicting RNN Model - Deep Learning w/ Python, TensorFlow and Keras p.11
Hello everyone and welcome to yet another deep learning tutorial with Python, TensorFlow, and Keras. In this tutorial, we will continue from where we left off in the previous tutorial. Our goal is to predict the future price movements of a specific cryptocurrency based on its historical prices, volume, and other major cryptocurrencies. We will achieve this using a recurrent neural network (RNN).
To begin, we need to import the necessary libraries. We will import the time library for later use. Next, we define a few constants. The first constant is the number of epochs we want to train the model for. We set the batch size to 64 initially, but we can adjust it later if needed. Finally, we define a name for the model using an F-string. It is important to have a unique name for the model and the TensorBoard logs for easy comparison and identification.
Now, we import the required TensorFlow modules. We import TensorFlow as tf and the required submodules: tf.keras.models, tf.keras.layers, tf.keras.optimizers, tf.keras.callbacks, and tf.keras.backend. We also import numpy and matplotlib for data processing and visualization.
Next, we start building our model. We create a sequential model using model = tf.keras.models.Sequential(). Inside the model, we add layers using the model.add() function. Our first layer is an LSTM layer with 128 nodes. We set return_sequences=True since we want to pass the output to the next layer. We specify the input shape as train_X.shape[1:], where train_X is the input data. We also add a dropout layer with a rate of 0.2 and a batch normalization layer.
We repeat this process two more times, adding two more LSTM layers with 128 nodes each. We remove return_sequences=True for the last LSTM layer since it will be followed by a dense layer. We also add dropout and batch normalization layers to each LSTM layer.
After the LSTM layers, we add a dense layer with 32 nodes and a rectified linear activation function. We add a dropout layer with a rate of 0.2 and finally, the output layer with two nodes and a softmax activation function.
Now, we specify the optimizer for our model. We use the Adam optimizer with a learning rate of 0.001 and a decay rate of 1e-6. We compile the model using model.compile() and specify the loss function as sparse categorical cross-entropy and the metrics as accuracy.
Next, we define the callbacks for our model. We create a TensorBoard callback with the log directory set to 'logs'. We also create a ModelCheckpoint callback to save the best model during training. We specify the file path for saving the checkpoints using string formatting.
Finally, we train the model using model.fit(). We pass the training data (train_X and train_Y), the batch size, the number of epochs, and the validation data (validation_X and validation_Y). We also pass the callbacks we defined earlier.
After training, we can save the model using model.save() for future use.
That's it! We have successfully built and trained a recurrent neural network model for predicting the future price movements of a cryptocurrency. We have also visualized the training progress using TensorBoard.
Algorithmic Trading Strategy in Python
Algorithmic Trading Strategy in Python
In today's tutorial, we will be implementing an algorithmic stock trading strategy using Python. Let's dive right into it.
Before we begin, I want to emphasize that this video is not intended as investment or financial advice. I am not a stock professional, investing expert, or financial professional. The purpose of this video is to demonstrate how to implement an algorithmic trading strategy using Python. Whether you choose to use it for your investments is entirely your decision and responsibility. I will only be focusing on the programming aspect.
To start, we need to install two libraries: "pandas-datareader" to fetch stock data and "matplotlib" for data visualization. Open the command prompt (CMD) and install these libraries. Once installed, we can proceed with the coding.
First, we import the necessary modules: "datetime" for handling date and time, "matplotlib.pyplot" for plotting graphs, and "pandas_datareader" as "web" to retrieve stock data.
Next, we define the moving averages (MA) we will be using. Moving averages represent the average stock price over a specified time period, such as 30 days or 100 days. We create two variables, "ma1" and "ma2," which will be set to 30 and 100, respectively. These values determine the length of the moving averages we will analyze.
Now, let's set the time frame for our analysis. We define the start and end dates using the current date and a time delta of 3 years. This time frame will be used to fetch stock data.
Using the "datareader" library, we retrieve the stock data for a specific company (in this case, Facebook) from the Yahoo Finance API. We pass the start and end dates to fetch the relevant data.
To calculate the moving averages, we add two new columns to the data DataFrame. We use the "rolling" function to calculate the moving average for both "ma1" and "ma2" periods. The "adjusted close" column represents the closing price adjusted for stock splits. We store the moving averages in the respective columns.
Before proceeding further, let's visualize the data and moving averages. We plot the adjusted close values as the stock price, and the moving averages are plotted on the same graph. We set the graph style to use a dark background and give appropriate labels and colors to differentiate the lines. Finally, we add a legend to identify the plotted lines and display the graph.
Now, let's move on to implementing the algorithmic trading strategy. We create two empty lists, "buy_signals" and "sell_signals," which will store the buy and sell signals, respectively. Additionally, we introduce a "trigger" variable that will help us track changes in the strategy.
Using a for loop, we iterate over the data DataFrame. Inside the loop, we check two conditions: if the first moving average ("ma1") is larger than the second moving average ("ma2"), and if the trigger is not equal to 1. If both conditions are met, we add a buy signal to the "buy_signals" list and append "NaN" (not a number) to the "sell_signals" list. We also update the trigger to 1.
In the opposite case, where "ma1" is less than "ma2" and the trigger is not equal to -1, we add a sell signal to the "sell_signals" list and append the actual stock price to the "buy_signals" list. We update the trigger to -1.
If none of the conditions are met, we append "NaN" values to both lists to maintain consistent lengths.
Finally, we add two more columns to the data DataFrame to store the buy and sell signals.
We're going to scatter the sell signals as well, so we'll add another scatter plot using plt.scatter. This time, we'll scatter the sell signals on data.index as the x-values, and the corresponding sell signal prices as the y-values. We'll label this scatter plot as "sell signal" to differentiate it from the buy signals.
Finally, we'll add a legend to the plot using plt.legend to display the labels for the different elements. Then, we'll call plt.show() to display the plot.
The code:
Remember, this is just a simple algorithmic trading strategy implemented in Python, and it's not meant to be taken as financial or investment advice. It's important to do thorough research and consult with professionals before making any investment decisions.
Introduction to Algorithmic Trading Using Python - How to Create & Test Trading Algorithm
Introduction to Algorithmic Trading Using Python - How to Create & Test Trading Algorithm
In this video, we are going to delve into the development of an algorithmic trading strategy. It is crucial to note that this video is intended for educational purposes only and should not be considered as investment advice. The strategy we will be exploring is commonly known as a momentum strategy, although the term itself can be ambiguous and open to interpretation.
Essentially, a momentum strategy involves identifying securities that are exhibiting a clear directional movement. In our case, we will begin by conducting a screening process to identify securities that are trading above their 50-day moving average or any other predetermined metric that we have researched.
This strategy is often referred to as a trend-following strategy, as it aims to capitalize on securities that are moving in a specific direction, whether upward or downward. It is important to note that this approach requires thorough research to identify potential profitable trading signals that can be applied across various securities.
For the purposes of this video, we will focus on analyzing a single security to determine its potential. To begin, we will import the necessary libraries and set up our environment. The notebook containing the code will be made available on a GitHub link, which can be found in the video description.
Next, we will download the required data for analysis. In this case, we will be using the Yahoo Finance API to retrieve data for the Gold ETF (Exchange Traded Fund) with the symbol "GLD." We will utilize the Pandas data reader to fetch the data, specifying the symbol and leaving the start and end dates as default, which should provide us with approximately five years of data. Once downloaded, we will examine the first few rows of the data to ensure its accuracy.
To facilitate our analysis, we will add additional columns to the data frame. First, we will include a day counter column to keep track of the day in the time series. This will be accomplished using the NumPy library to create a range array matching the number of observations in the gold data frame. The day counter column will then be added to the data frame, and we will adjust the column order to ensure that the day follows the date column.
Additionally, we will drop unnecessary columns such as the adjusted close and volume for our specific analysis. It is worth mentioning that for certain securities, the adjusted close may be useful, especially in cases where there have been stock splits. However, for our purposes, we will exclude these columns.
Having completed the preliminary steps, we can examine the structure of the data by running the info command on the data frame. This will provide information about the number of observations and data types present in the data frame, confirming that all columns are numerical.
Next, we will introduce the momentum strategy by adding moving average columns to the data frame. We will utilize two moving averages, a fast one (9-day) and a slow one (21-day), to trigger trades. Specifically, we will enter a trade when the fast moving average crosses above the slow moving average, and we will exit or go short when the fast moving average crosses below the slow moving average. It is essential to note that this strategy assumes that we are always in a trade, either long or short.
To calculate the moving averages based on the closing price, we will use the rolling method provided by Pandas. This method allows us to specify the number of days for the rolling average. Additionally, we can apply different aggregators if desired, such as standard deviation or median. In this case, we will focus solely on the rolling average. We will duplicate this process for both the fast and slow moving averages, resulting in two additional columns in the data frame.
Since the moving averages are lagged by 9 and 21 days, respectively, we need to adjust them by shifting the data forward by one day using the shift method in Pandas.
After calculating the moving averages, we will adjust them by shifting the data forward by one day using the shift method in Pandas. This ensures that the current day's moving averages are aligned with the next day's price action.
Now that we have the moving average columns adjusted, we can generate trading signals based on the crossover of the fast and slow moving averages. To do this, we will create a new column called "Signal" and assign it a value of 1 when the fast moving average is above the slow moving average, indicating a bullish signal, and -1 when the fast moving average is below the slow moving average, indicating a bearish signal.
To determine the entry and exit points for our trades, we will add another column called "Position." Initially, we will assign it a value of 0, indicating that we are not in a trade. When a bullish signal occurs (fast moving average crosses above slow moving average) and we are not currently in a trade (Position = 0), we will assign a value of 1 to the Position column, indicating a long position. Conversely, when a bearish signal occurs (fast moving average crosses below slow moving average) and we are not currently in a trade, we will assign a value of -1 to the Position column, indicating a short position. This ensures that we enter a trade only when there is a new signal and we are not already in a trade.
To track the daily returns of our trading strategy, we will create another column called "Strategy Returns." We will calculate the daily returns by multiplying the Position column with the daily percentage change in the closing price. This will give us the return we would have achieved if we had followed the trading signals.
Finally, we will calculate the cumulative returns of our strategy by applying the cumprod method to the Strategy Returns column. This will provide us with the overall performance of our strategy over the specified time period.
At this point, we can visualize the performance of our strategy by plotting the cumulative returns. We will use the matplotlib library to create a line plot that shows the growth of our strategy's returns over time.
In the plot, we can observe the cumulative returns curve, which should give us an idea of the effectiveness of our momentum strategy. Positive cumulative returns indicate profitability, while negative cumulative returns indicate losses.
Keep in mind that this is a simplified example of a momentum trading strategy, and there are many factors to consider when developing a real-world trading strategy, such as transaction costs, slippage, and risk management. It's important to thoroughly backtest and validate any trading strategy before applying it to real-world trading.
<How to Use Alpha Vantage Free Real Time Stock API & Python to Extract Time of Daily Highs and Lows
How to Use Alpha Vantage Free Real Time Stock API & Python to Extract Time of Daily Highs and Lows
In this video, we are going to use a Jupyter Notebook to explore the Alpha Vantage API and extract the high and low trading prices for a stock using one-minute trading data. Alpha Vantage is one of several APIs available for obtaining real-time trading data and operates on a freemium model. To get started, we will need to sign up for a free account and obtain an API key from the Alpha Vantage website.
We will be using the Alpha Vantage helper library called "alpha_vantage," which simplifies the process of making API calls. If you don't have the library installed, you can do so by running the command "pip install alpha_vantage" in your command line.
To begin, we set up our environment by importing necessary third-party libraries. Once that is done, we store our API key in a variable. If you prefer to keep your API key private, you can store it in a separate text file and read it into your notebook. Next, we create a time series object by specifying the API key and the desired output format. In this case, we choose to use the pandas library as it provides an easier way to work with the output data, which is in JSON format by default.
To retrieve the trading data, we make a call to the Alpha Vantage API using the "get_intraday" function. This function allows us to specify the symbol of the stock and the desired interval, such as one minute, five minutes, or one hour. We can also set the output size, which determines the amount of historical data we want to retrieve. For this video, we set it to "full," which gives us approximately ten days of data.
Once we have fetched the data, we can examine the metadata associated with it by accessing the "meta" attribute. The meta object provides information about the downloaded data, such as the interval, the date of the data, and the columns it contains. We can then inspect the data itself by calling the "info" method, which displays the column names and the date-time index.
To get a visual representation of the data, we can plot one of the columns, such as the closing prices. However, the column names returned by Alpha Vantage may not be convenient to work with, so we can rename them to more meaningful names.
Next, we extract the data corresponding to regular trading hours, excluding after-hours trading, which can introduce distortions. We create a new variable called "market" by applying a time-based filter to the data. Pandas provides a convenient function, "between_time," which allows us to specify the start and end times for the market.
At this point, we are ready to extract the dates and times of the highs and lows. We do this in two ways. First, we group the data by trade date and use the "ag" method to calculate the minimum and maximum values for the low and high columns, respectively. This approach gives us the actual low and high values for each trading day.
Second, we take a different approach and focus on the minute when the low and high occurred. We use the "loc" function to locate the specific rows where the lows and highs occur within each trading day. Then, we extract the index (date and time) for the minimum and maximum values, respectively. This allows us to identify the exact minute when the low and high prices were reached.
By examining the results, we can observe interesting patterns, such as the timing of lows and highs throughout the trading days.
This video provides a basic overview of using the Alpha Vantage API to retrieve minute-by-minute trading data and extract the highs and lows for analysis. It serves as a starting point for exploring and utilizing the Alpha Vantage API in your own projects.
In conclusion, this video tutorial demonstrates how to use the Alpha Vantage API and the Alpha Vantage helper library in a Jupyter Notebook to extract high and low trading prices for a stock using one-minute trading data. By following the steps outlined in the video, you can retrieve real-time trading data, analyze it using pandas, and gain insights into the timing of highs and lows within a given trading day.
It's important to note that the Alpha Vantage API offers various functionalities and data options beyond what was covered in this video. You can explore different intervals, such as five-minute or one-hour data, as well as different types of data, including daily or historical data. The API also provides additional features, such as technical indicators and fundamental data.
To further enhance your analysis, you can incorporate additional data manipulation and visualization techniques. For example, you can calculate additional metrics based on the extracted high and low prices, perform statistical analyses, or create interactive visualizations to present the data in a more intuitive manner.
Remember to refer to the Alpha Vantage documentation for detailed information on available API calls, parameters, and options. Additionally, make sure to adhere to the terms and conditions of the Alpha Vantage API, including any limitations or usage restrictions associated with your free account.
By leveraging the Alpha Vantage API and combining it with the capabilities of Jupyter Notebook and the pandas library, you can unlock a wealth of trading data and explore various strategies and insights to support your investment decisions and quantitative analyses.
Introduction to Scatter Plots with matplotlib Python for Data Science
Introduction to Scatter Plots with matplotlib Python for Data Science
This is the second video in my introductory series to Matplotlib. In this video, we will be focusing on scatter plots. Scatter plots are a visual aid that helps us determine the strength and nature of a relationship between two variables. We will cover the basics of creating scatter plots, including setting themes, adding a color map, creating a bubble chart, and adding dimensionality.
To start, let's set up our environment by importing the necessary libraries. We will import NumPy, Matplotlib, and the Pandas data reader. The Pandas data reader will allow us to download real data to work with. In this case, we will download three to four months worth of data for Google, Amazon, and the gold ETF.
Once we have the data, we can take a look at the first few rows to familiarize ourselves with the dataset. We can see that the data starts on August 1st and includes the closing prices.
Now, let's create a basic scatter plot using Matplotlib's scatter method. We can choose two columns from the dataset and plot them. However, this basic scatter plot doesn't provide much information about the relationship between the variables.
To investigate further, we can calculate the instantaneous rate of return for each security. This will give us a better understanding of how the changes in price relate to each other. We remove the absolute price and reduce it to a percentage change. Looking at the first few observations, we can see that all the securities went down, with Amazon and Google experiencing a decrease of over 1%, while gold remained relatively unchanged.
Next, we remove the first observation, which is not a number, and plot a scatter plot to see if the change in Google is relevant to the change in Amazon. This scatter plot tells a different story than the previous one. We can observe a general tendency that as Google goes up, so does Amazon, indicating a strong positive relationship between the two variables.
Now that we have the baseline scatter plot, we can add some features to enhance it. First, let's change the size of the plot to make it more visible. We can do this by importing the parameters module from Matplotlib.
We can also add visual appeal to the scatter plot by adding guide lines to show the movement of points in different directions. By plotting lines through the zero on the X and Y axes, we can quickly identify when the points are moving together, moving apart, or in opposite directions.
To improve the visibility of the guide lines, we can set their color to a gray shade using the RGB notation. Additionally, we can set the line style to dashed for a different visual effect.
To further enhance the scatter plot, we can add a color scheme. Although we don't have a third variable to represent, we can still add a color map to the scatter plot. We modify the scatter plot code to include the color of returns for Amazon, and we choose the spectral color map. This color map assigns different colors to the points based on the values of the returns, with red representing the most negative values and violet representing the most positive values.
However, some points in the middle range may be difficult to see due to the color gradient. To address this, we can change the edge color of the points to black, making them more distinct.
To provide additional information about the color gradations, we can add a color bar. The color bar plots a legend that indicates the color mapping based on the returns.
Furthermore, we can improve the overall appearance of the plot by applying a theme. We can use Seaborn as a theme, which is a wrapper around Matplotlib that provides a visually appealing style. This theme changes the background and adds gridlines without detracting from the plotted data.
Lastly, we can adjust the limits of the plot to center the guide lines.
Lastly, we can adjust the limits of the plot to center the guide lines and make the scatter plot more visually balanced. We can set the x-axis and y-axis limits to the minimum and maximum values of the returns to ensure that the guide lines intersect at the center of the plot. This adjustment helps us visualize the movement of points in relation to the guide lines more effectively.
Now that we have made these enhancements, our scatter plot is more informative and visually appealing. We can clearly see the relationship between the returns of Google and Amazon, as well as the distribution of returns based on the color map. The guide lines provide a visual reference for interpreting the movement of points in different directions.
In addition to basic scatter plots, we can also create a bubble chart using Matplotlib. A bubble chart adds a third dimension to the plot by varying the size of the markers based on a third variable. In our case, we can use the volume of each security as the third variable.
To create a bubble chart, we modify our scatter plot code by specifying the size parameter and passing the volume of each security as the marker size. This creates circles with sizes proportional to the volume of each security, allowing us to visualize the relationship between returns, volume, and the movement of points.
By adding this third dimension to the scatter plot, we gain a deeper understanding of the relationship between the variables. We can see that larger circles represent higher trading volumes, and the movement of points can be correlated to both returns and volume.
In conclusion, scatter plots and bubble charts are powerful visualization tools that help us understand the relationship between variables. We can use them to analyze and interpret data, identify patterns and trends, and make informed decisions. With Matplotlib, we can create customized and visually appealing scatter plots and enhance them with various features, such as color maps, guide lines, and themes.
Introduction to Algorithmic Trading with Python: Create a Mean Reverting Trading Algorithm
Introduction to Algorithmic Trading with Python: Create a Mean Reverting Trading Algorithm
In this video, we will explore a mean-reverting trading algorithm for educational purposes only. It is important to note that this video does not provide investment advice. The algorithm will be implemented using a Jupyter Notebook, and a link to download the notebook will be provided in the video description. This video serves as a companion to the momentum trading strategy discussed previously, and a link to that video will also be provided.
The mean-reverting trading strategy assumes that a security will move back towards an average value whenever it deviates too far from it. There are multiple ways to approach this strategy, such as using linear regression or a moving average. The determination of "too far" and the measurement used can vary. Some people use an absolute dollar value, while in this video, we will use percentiles. Additionally, a moving average will be used to determine the mean value.
To begin, we import the necessary libraries, including Pandas for data manipulation, the Pandas DataReader to download live data (other services can be used as well), NumPy for numerical operations, Matplotlib for graphing, and Seaborn for styling the plots. The required libraries are imported by running the respective code cell.
Next, we obtain the data for analysis. While a good trading algorithm should be generalizable to multiple securities, this video focuses on a single security: the gold ETF. The Pandas DataReader is used to download approximately five years' worth of data for the gold ETF. Since only the closing price is of interest, we limit the download to that column. Once the data is obtained, we examine the first few rows to ensure its proper retrieval.
After obtaining the data, we add some columns to the data frame. The first column added is for the moving average. We set a variable to define the moving average period, which can be easily manipulated. The instantaneous rate of return from the previous day's close is calculated and stored in a new column. Another column, named "moving average," is created to track the mean value based on the closing price using a 21-day (or one trading month) average. Additionally, a "ratio" column is added, representing the division of the closing price by the moving average. This column helps determine when the price is too far from the mean.
Descriptive statistics are computed for the "ratio" column to gain insights into the data distribution. As expected, the prices generally remain close to the mean value. The 25th and 75th percentiles define the lower and upper boundaries of the data, while the minimum and maximum values indicate extreme deviations from the mean. Additional price points are selected for analysis, such as the 5th, 10th, 90th, and 95th percentiles, to determine significant deviations from the mean. The numpy percentile function is used to calculate the respective values based on the "gold ratio" column. Before performing the calculation, missing values are dropped.
To visualize the movement of the ratio column around the mean, a plot is generated. The irrelevant values are dropped, and the ratio column is plotted with a legend. Horizontal lines are added to represent the price breaks at the selected percentiles (5th, 50th, and 95th). This visual representation helps observe the cyclic movement of the ratio column around the mean, indicating a tendency to correct deviations.
Next, specific thresholds are defined to determine when to go short or long. The short position is defined as the 95th percentile, while the long position is defined as the 5th percentile. A new column is added to the data frame, indicating whether the position is long or short. Numpy's "where" function is used to assign values based on the gold ratio column. A value of -1 is assigned when the ratio is less than the short threshold, indicating a short position, and a value of 1 is assigned when the ratio is greater than the long threshold, indicating a long position. Finally, a plot is generated to visualize the positions. The plot shows the ratio column and highlights the long and short positions with different colors.After identifying the positions, the next step is to calculate the daily returns. This is done by multiplying the position column with the daily rate of return column, which gives the return for each day based on the position held. A new column named "strategy" is added to the data frame to store the daily returns.
To evaluate the performance of the strategy, cumulative returns are calculated. The cumulative returns are obtained by taking the cumulative product of the strategy returns, adding 1 to the result, and multiplying by 100 for percentage representation. A plot is generated to visualize the cumulative returns over time.
Next, additional performance metrics are calculated to assess the strategy's performance. The total return, average daily return, standard deviation of daily returns, Sharpe ratio, and maximum drawdown are computed. These metrics provide insights into the strategy's profitability, risk, and risk-adjusted return. The values are printed for easy reference.
Finally, a plot is generated to compare the cumulative returns of the mean-reverting strategy with the buy-and-hold strategy. The buy-and-hold strategy assumes holding the asset for the entire period without any trading decisions. This plot allows for a visual comparison of the two strategies.
In summary, this video demonstrates the implementation of a mean-reverting trading strategy using Python and Jupyter Notebook. It covers data retrieval, calculation of moving averages, determination of thresholds, visualization of positions, calculation of daily returns, evaluation of performance metrics, and comparison with a buy-and-hold strategy. The accompanying Jupyter Notebook provides a step-by-step guide to recreate the strategy and further explore its implementation. Remember that this video is for educational purposes only and does not provide investment advice.
Python Pandas || Moving Averages and Rolling Window Statistics for Stock Prices
Python Pandas || Moving Averages and Rolling Window Statistics for Stock Prices
In this video tutorial, I will demonstrate how to use the pandas rolling method, which automates calculations for moving averages and rolling standard deviations. The rolling method is a powerful tool for performing rolling window aggregations, and it can be easily implemented using pandas version 0.21. I will provide a link to the Jupyter Notebook used in this tutorial for reference.
To begin, we need to set up the environment. We import the numpy library since pandas relies on it. Although we won't directly use numpy extensively, it is necessary for pandas to function correctly. Additionally, we import the pandas data reader for fetching data and matplotlib for plotting purposes. By using the magic function, we ensure that the plots are displayed within the Jupyter Notebook.
Next, we acquire the data for analysis. I will download the data for the gold ETF (Exchange-Traded Fund) from Yahoo Finance. To specify the desired timeframe, I set the start date to the day of the presidential election, approximately a year ago. To confirm the data has been correctly fetched, I display the first few lines of the dataset. Since we are primarily interested in the closing prices for this demonstration, I remove the other columns. Furthermore, as the data frame that is downloaded is a series of series and lacks certain properties I require, I cast it as a data frame.
Now we are ready to demonstrate the rolling method. I will add a new column to the data frame called "MA9" to represent the nine-day moving average. Using the rolling method, I calculate the average for the specified number of periods or rows. I repeat this process for a longer window of 21 days. These two new columns represent the moving averages we computed. To visualize the data, I plot the closing prices, the nine-day moving average, and the 21-day moving average.
Sometimes it is useful to lag the moving averages. By adding the parameter "center=True" when using the rolling method, we can shift the moving average line back by ten days for the 21-day window. This creates a lagged line that aligns with the corresponding data. We can observe this shift in the plotted graph.
I should note that when calculating moving averages, the current observation is included in the average. If you want to use it as a traditional forecasting tool, you may want to shift the moving average forward. By using the shift method and specifying a positive integer, we can shift the moving average forward by the desired number of periods. This ensures that the current observation is not included in the average.
Additionally, the rolling method offers other parameters, such as the ability to set any lag value or even shift the moving average to the first day using a negative integer.
Furthermore, I demonstrate how to calculate historical volatility, often used in options pricing. To do this, we need to add another column to the data frame. Using numpy, I calculate the logarithmic returns by dividing the closing prices by the previous day's close. Plotting these returns displays a noisy graph centered around zero.
To obtain the historical volatility, we employ a rolling standard deviation with a window of 21 days, as there are typically 21 trading days in a month. This calculation includes the twenty-first observation, so to accurately reflect the volatility, we shift the result forward by one day. This avoids implying that we have future knowledge. Plotting the volatility provides a clearer representation of the data and reveals periods of high and low volatility in gold prices.
In a future video, I will cover additional price analysis techniques using pandas. I hope this tutorial has provided a helpful introduction to using the rolling method in pandas for moving averages and rolling standard deviations.
Quantitative Stock Price Analysis with Python, pandas, NumPy matplotlib & SciPy
Quantitative Stock Price Analysis with Python, pandas, NumPy matplotlib & SciPy
In this video, the speaker introduces quantitative analytical methods for analyzing stock price changes. The main objective is to determine whether the stock price change follows a normal distribution, identify any directional bias in the daily change, and assess if the price movement can be described as a random walk. The speaker mentions using a Jupyter notebook and provides a link for downloading the notebook.
The speaker starts by setting up the environment and importing data analysis libraries such as NumPy, Pandas, and Matplotlib. They also mention using the Pandas Data Reader library to download live data from Yahoo API. The speaker then retrieves the stock data for Amazon, specifying the start and end dates, which defaults to the last five years of price data.
After obtaining the data, the speaker examines the first few rows to verify the available information. They point out the columns representing high, low, open, close, volume, and adjusted close prices. Since they are primarily interested in the closing price, they discuss the option of using either the "close" or "adjusted close" column, with the latter being useful for stocks that have undergone splits. In this case, since Amazon's last split was in 1999, the choice between the two columns does not matter significantly.
Next, the speaker extracts the closing price column into a separate variable and calculates the instantaneous rate of return using the logarithm of the difference between consecutive closing prices. They display the resulting values, noting that the first row contains a NaN (not a number) value because the return cannot be calculated for the first day.
The speaker then visualizes the daily price change by plotting the data as a line graph using Matplotlib. They observe that the price change fluctuates considerably and clusters around zero, with occasional large events occurring unpredictably throughout the five-year period. To analyze a specific time frame, they plot the last year's worth of data, which shows less density but retains the same overall pattern.
Descriptive statistics for the price movement are obtained using the Pandas "describe" function. The speaker mentions the possibility of obtaining the statistics individually or using other tools but finds the Pandas method sufficient for their purposes. They also introduce the SciPy library and demonstrate another way to calculate descriptive statistics using the "describe" function from SciPy stats. They mention that some values appear as "NaN" due to handling missing values in NumPy and SciPy.
To make the numbers more interpretable, the speaker multiplies the values by 100 to convert them into percentages. This change improves the readability of the output without altering the data.
Moving on, the speaker compares the distribution of the daily price change to a sample drawn from a normal distribution. They plot a histogram of the Amazon return data and observe that it shows significant activity around the center, with returns spreading out to the left and right, indicating fatter tails compared to a normal distribution. They then generate a sample of the same size from a normal distribution using the SciPy stats module and plot it as a histogram alongside the Amazon return data. The normal distribution sample appears squatter and more uniformly spread than the Amazon data.
Next, the speaker performs a statistical test, specifically a kurtosis test, on both the normal variable and the Amazon returns. The kurtosis test examines whether the distribution can be considered normal, with the null hypothesis assuming a normal distribution. The test statistic and p-value are obtained, with the speaker explaining the interpretation of the results. For the normal variable, the test statistic is slightly negative, indicating no strong evidence against the null hypothesis. In contrast, for the Amazon returns, the test statistic is much larger, suggesting a rejection of the null hypothesis and concluding that the price change of Amazon cannot be described as normally distributed.
To further visualize the difference, the speaker modifies the histogram by displaying proportions instead of frequencies and how well it fits the distribution. To do this, I'll make a couple of changes to the initial histogram. Instead of displaying frequencies, I'll display proportions. This way, I can overlay a theoretical normal curve over the histogram and see how well it fits.
Let's go ahead and plot the histogram with the normal curve. I'll use the norm module from scipy.stats to generate the normal curve, and then I'll plot it on the same graph as the histogram.
Upon observing the plot, we can see that the histogram of Amazon returns deviates significantly from the shape of a normal distribution. The distribution has fatter tails, indicating a higher occurrence of extreme price movements compared to what we would expect from a normal distribution. This aligns with our previous analysis of kurtosis, which indicated excess kurtosis in the Amazon returns data.
In conclusion, based on the quantitative analytical methods we have employed, we can determine that the stock price change of Amazon cannot be described as a normally distributed phenomenon. The data exhibits characteristics such as skewness and excess kurtosis, indicating deviations from a normal distribution. The daily price changes are more clustered around zero, with frequent occurrence of larger, unpredictable movements. This information is valuable for understanding the nature of Amazon's stock price behavior and can be useful in developing investment strategies or risk management approaches.
Linear Regression Model Techniques with Python, NumPy, pandas and Seaborn
Linear Regression Model Techniques with Python, NumPy, pandas and Seaborn
In this video, we will explore some simple regression techniques in Python. There are several tools available for implementing regression in Python, but we will focus on a couple of them, specifically using NumPy. Please note that this tutorial is not meant to be exhaustive and we won't be running any statistical tests. We will simply fit a line and visualize the output.
You can download the notebook from the link provided in the video description on GitHub to follow along. Let's start by setting up our environment and importing the necessary libraries. We will be using NumPy, pandas, Yahoo Finance API to fetch live data, matplotlib for visualization, and seaborn to apply a theme to our plots.
Next, we need to retrieve the data. We will get data for Google and the S&P 500 ETF, going back about a year. We will use the pandas data reader and the Yahoo Finance API for this purpose. Once we have the data, we can take a quick look at it. Since we are only interested in the "close" prices for our analysis, we will adjust the data accordingly.
To perform regression, we will calculate the instantaneous rate of returns for both Google and the S&P 500 ETF. After dropping any invalid values, we are ready to calculate the correlation between the two variables. We find that they are strongly correlated, but we won't try to determine causality in this analysis. Instead, we will consider the S&P 500 as the independent variable.
To make visualization easier, we will sample a smaller subset of data points. In this case, we randomly sample 60 returns and observe the correlation, which remains similar to the overall dataset. We then proceed to visualize the data by plotting a scatter plot with the S&P 500 on the x-axis and Google on the y-axis.
Moving on to regression, we will fit a linear model using NumPy's polyfit function. We pass the sample data, with the S&P 500 as the independent variable and Google as the dependent variable, along with the degree of our polynomial (1 for simple linear regression). This gives us the slope and y-intercept of the best-fit line, which can be interpreted as the beta value.
To plot the trend line, we use NumPy's polyval function, passing in the regression and the independent variable. We can overlay this trend line on the scatter plot. Additionally, regression can be used as a technical indicator to predict future prices. In this example, we regress the S&P 500 closing prices against time.
After obtaining the regression coefficients, we can calculate predicted values for future time points. We plot the actual data against time, add the trend line, and create a channel by adding and subtracting one standard deviation from the linear model. This channel provides a visual representation of the confidence interval of the prediction.
Finally, we demonstrate how to make predictions for specific time points using the regression model. By creating a poly1d object with the regression coefficients, we can plug in a value (representing a future time point) and obtain the predicted value. We also briefly mention Seaborn's regplot, which provides an alternative way to visualize the scatter plot with a trend line and confidence interval.
By following this tutorial, you can get started with linear regression in Python and explore various techniques for analysis and prediction.
Now that we have covered the basics of linear regression in Python, let's explore some additional techniques and concepts.
One important aspect of regression analysis is evaluating the goodness of fit of the model. In other words, how well does the linear regression line represent the relationship between the variables? There are various statistical measures that can be used to assess the fit, such as the R-squared value, which indicates the proportion of the variance in the dependent variable that can be explained by the independent variable(s).
To calculate the R-squared value, we can use the statsmodels library in Python. We will import the necessary module and fit the linear regression model to our data. Then, we can extract the R-squared value using the rsquared attribute of the model.
Let's demonstrate this with an example. Suppose we have a dataset with two variables, X and Y, and we want to fit a linear regression model to predict Y based on X. We will use the sm.OLS (Ordinary Least Squares) function from the statsmodels library to perform the regression analysis.
First, we import the required modules:
print("R-squared:", r_squared)The R-squared value ranges from 0 to 1, with 1 indicating a perfect fit. Generally, a higher R-squared value suggests a better fit of the model to the data.In addition to the R-squared value, it is also important to examine the residual plots to check for any patterns or trends that might indicate violations of the assumptions of linear regression. Residuals are the differences between the observed and predicted values of the dependent variable. A good linear regression model should have random and evenly distributed residuals around zero.
To visualize the residuals, we can plot a scatter plot of the predicted values against the residuals. If the plot shows a pattern or any systematic deviation from randomness, it suggests that the linear regression model may not be appropriate for the data.
To create the residual plot, we can use the matplotlib library in Python:
In conclusion, linear regression is a powerful and widely used technique for modeling the relationship between variables. By fitting a regression line to the data, we can make predictions and gain insights into the dependent variable based on the independent variables. It is important to evaluate the goodness of fit using measures like the R-squared value and to check the residual plots to assess the validity of the regression model.