Python in algorithmic trading - page 14

 

Make Candlestick Chart with Python (Pandas Library Notes)



Hacer Gráfico de Velas con Python (Apuntes de librería Pandas)

The video provides a step-by-step guide on how to create candlestick charts using Python's Pandas library with CSV data. It starts with importing the CSV data and creating a data frame to use for chart creation. The tutorial also covers the installation of the mpl finance library used for financial plots. The video highlights the importance of understanding Pandas to effectively use it in Python and how to alter the chart type and date range for accurate representations of the data. This is useful even for tasks such as modeling for automated trading.

  • 00:00:00 In this section, the YouTuber explains how to create candlestick charts using Python's Pandas library with CSV data. The first step is to obtain historical data in CSV format, which can be downloaded from various sources online. Once the CSV data is imported into the script, a data frame is created, which is a collection of data that can be used to create the candlestick chart. The YouTuber then goes on to show how to select specific columns from the data frame to create the chart. These charts can be used in conjunction with machine learning algorithms for automated trading.

  • 00:05:00 In this section, the video discusses how to create a candlestick chart with Python using the Pandas library. The tutorial begins by selecting columns from a dataset and reversing the order of a DataFrame, which is an important step when working with stock market data. The video also covers the installation of mpl finance, a library used for creating financial plots. The tutorial then proceeds to import Pandas and mpl finance before selecting the CSV file to be used.

  • 00:10:00 In this section of the tutorial, the speaker explains how to create candlestick charts using Python's Pandas library. By using Data Frames, the closing values of stocks can be shown on a graph. The speaker explains how to select different types of charts by using the mpf library, and how to alter the date range to zoom in on specific periods. The tutorial provides a step-by-step guide and highlights the importance of understanding Pandas to effectively use this library in Python.

  • 00:15:00 In this section, the video shows how to create a candlestick chart using Python's Pandas library. The first step is to create a DataFrame called "Chart" by reading data from a CSV file. The next step is to use the DataFrame to create a candlestick graph using the "mpl_finance" library. The video shows how to customize the graph and change it to line charts or other types of charts. The code used to create the graph is also provided in the video description. The video also emphasizes the importance of selecting the appropriate date range when selecting data from the DataFrame, as this can be useful for other tasks such as modeling.
Hacer Gráfico de Velas con Python (Apuntes de librería Pandas)
Hacer Gráfico de Velas con Python (Apuntes de librería Pandas)
  • 2022.12.28
  • www.youtube.com
Formación: https://inteligencia-artificial.devCódigo: https://inteligencia-artificial.dev/apuntes-pandas-python/Clase para repasar un poco la librería de pan...
 

Algorithmic Trading with Python (Decision Tree)



Trading Algorítmico con Python (Árbol de decisiones)

In this video about algorithmic trading with Python, the speaker uses a decision tree approach to predict the price movement of Bitcoin based on historical correlations between SP500, gold, and ethereum. The model is trained using current day data, and then used to predict the next day’s results. While decision trees can be useful for predictions, the speaker notes that it may not be perfect with only a small data sample. Viewers can access more algorithmic trading strategies, courses and articles related to artificial intelligence and Python on the website provided in the video.

  • 00:00:00 In this section of the transcript, the speaker introduces the decision-making process using an algorithmic approach, with a focus on decision trees. The decision tree methodology is applied to data from historical price movement correlations between p500, gold, and ethereum to predict Bitcoin price movement. The training model is based on X data for the current day, which is used to predict the data for the following day. The speaker states that they have developed their approach, applying intuition and logic to the process.

  • 00:05:00 In this section, the video demonstrates how to use an algorithmic trading strategy to predict whether the price of Bitcoin will go up or down the following day. The algorithm uses a decision tree model that is trained using existing data, and then predicts future outcomes based on the input features. The example shown in the video has a limited data set, but the same approach could be used with larger data sets to improve accuracy. Additionally, the video provides a website where viewers can access more algorithmic trading strategies, as well as courses and articles related to artificial intelligence and Python.

  • 00:10:00 In this section, the speaker explains how to use decision trees to make algorithmic trading predictions with Python. They demonstrate the use of decision trees by assigning numerical values to the terms "up" and "down" to analyze correlations in data. The speaker suggests that while decision trees can be useful to make predictions, it may not be perfect with only a small data sample. They also suggest that decision trees can be applied to predict other types of charts, and that for more learning, people can enter artificial intelligence.
Trading Algorítmico con Python (Árbol de decisiones)
Trading Algorítmico con Python (Árbol de decisiones)
  • 2022.11.26
  • www.youtube.com
Código: https://inteligencia-artificial.dev/arbol-decisiones-trading-algoritmico/En este vídeo os enseñaré a aplicar el algoritmo de Árbol de Decisiones, par...
 

Python for investments: How to get Dollar Index? DXY



Python para inversiones: ¿Cómo sacar Dollar Index? DXY

The video covers the extraction of the DXY Index data, which measures the strength of the US dollar against other currencies, using Python's Beautiful Soup and Pandas libraries. The presenter provides a code that extracts variation percentage information from the DXY data and saves it in a CSV file for further data analysis and machine learning purposes. Additionally, they share their website with free courses on Python, finance, and algorithmic trading. Thus, the video is a useful guide for extracting financial data using Python.

  • 00:00:00 In this section, the speaker introduces the concept of DXY Index, which measures the strength of the US dollar against other currencies such as the euro, yen, pound, Canadian dollar, Swiss franc, and Swedish krona. The speaker explains that this data can be useful for automated investments, data analysis, and machine learning. He then provides a code to extract DXY data from a web page using Python's Beautiful Soup library, with a focus on obtaining the variation of the index. The speaker also shares his Instagram handle and the web page from which he scrapes the data.

  • 00:05:00 In this section of the video, the presenter demonstrates how to extract the Dollar Index (DXY) using Python for investment purposes. The presenter uses a piece of code that saves DXY information in a CSV file for later analysis. The code first extracts the percentage information from the DXY data, and then separates it from the date information using the "split" and "replace" functions. Finally, the code saves the extracted information into a CSV file that includes two columns – one with the date and the other with the variation percentage. The presenter suggests that the extracted data can be used for machine learning and data analysis purposes.

  • 00:10:00 In this section, the speaker demonstrates how to extract Dollar Index using Python. They show excerpts from a Python script and explain how it works. The code uses the pandas library to extract data from an HTML table and store it in a CSV file for further analysis. The speaker also provides a link to their website with free courses on Python, algorithmic trading, and finance. Overall, the video provides a helpful guide for anyone looking to extract financial data using Python.
Python para inversiones: ¿Cómo sacar Dollar Index? DXY
Python para inversiones: ¿Cómo sacar Dollar Index? DXY
  • 2023.04.10
  • www.youtube.com
Formación: https://inteligencia-artificial.dev/formacion/Código: https://inteligencia-artificial.dev/python-dollar-index-dxy/En este vídeo de Python para Bol...
 

Algorithmic Trading Using Python - Full Course



Algorithmic Trading Using Python - Full Course

00:00:00 - 01:00:00 This video explains how to use Python to trade stocks algorithmically. It starts by introducing the basics of the pandas library, and then demonstrates how to use it to create a data frame. The course then shows how to execute an HTTP request for a stock and how to use the results of the API call to populate the data frame. Finally, the course discusses how to improve the performance of the code by batching API requests.

01:00:00 - 02:00:00 This video covers how to use Python to trade stocks using an automated algorithm. It starts by creating a list of stocks and symbols, and then transforming those stocks into strings. Next, the video creates a URL to launch an HTTP request using that string. Finally, the video demonstrates how to run that request and get the data back.

02:00:00 - 03:00:00 This video teaches how to use Python to create an algorithmic trading strategy that uses the price-earnings ratio as a value metric. The strategy screens for stocks based on this metric and makes buy and sell recommendations based on the current stock price.

03:00:00 - 04:00:00 This video tutorial explains how to use Python to trade stocks algorithmically. It covers how to calculate various indicators of success, how to deal with missing data, and how to use Exception Handling to replace missing values.

04:00:00 - 04:30:00 This video explains how to calculate percentile scores for different stock metrics using Python. It also covers how to use Python to create an Excel file containing stock prices and position sizes for calls and puts, and how to format the file for printing.

Part 1:

  • 00:00:00 This course is on algorithmic trading and Python. The first project is building an equal weight version of the popular S&P 500 index fund. The second project is a quantitative momentum strategy that selects the best stocks based on a variety of momentum, investing metrics. The third project is a quantitative value screener that selects stocks that are attractive based on a number of value metrics.

  • 00:05:00 This course is designed to teach Python for algorithmic trading. Python is a popular language for this type of trading, but it is a slow language. Many practitioners have found that Python is often used as a glue language to trigger code that actually runs in other languages. In this course, we will be using Python to develop three algorithmic trading strategies.

  • 00:10:00 This video introduces the Python programming language and shows how to use it to access data from online APIs. The course will focus on using the IRS code API to gather stock market data.

  • 00:15:00 In this video, the instructor introduces the concepts of algorithmic trading and how to create various investing strategies in Python. The first project is to create an equal weight version of the S&P 500 index fund.

  • 00:20:00 This Python video tutorial explains how to use the libraries NumPy, pandas, requests, and XLS to create an algorithmic trading strategy.

  • 00:25:00 This video is a tutorial on how to use Python to trade stocks. The first step is to save a list of the 500 stocks in the S&P 500 into a CSV file. Then, we import into our Jupyter Notebook the IE x cloud API key and use it to acquire an API token. We start by importing into our script the secrets.py file, which stores sensitive information such as the API key. We then use the API key to acquire financial data from the IE x cloud API. The data is stored in a panda's data frame, and we can print the data to verify that it works.

  • 00:30:00 This part of the Python tutorial covers how to use the cloud API to query for market capitalization and stock prices for individual stocks.

  • 00:35:00 The video explains how to use the requests library to execute an HTTP request and store the results in a variable. It shows how to create an API endpoint using the curl command line tool and how to execute the request. The data variable contains the response object from the request, which has the status code and other information.

  • 00:40:00 This video explains how to use the Python programming language to trade stocks using algorithms. The video shows how to set up a Python environment and how to use the dot JSON method to convert data from an HTTP request into a JSON object. The data variable in the Python environment behaves the same way as the extended change variable in the iX Cloud docs. The video explains that the price data in the iX Cloud may not be accurate, and shows how to test for accuracy using a Google search.

  • 00:45:00 In this video, the author explains how to parse an API call, price stocks, and calculate their market capitalization. They then explain how to append data points to a panda's data frame, and show how to do so by printing the data frame as a list.

  • 00:50:00 This video describes how to use Python to trade stocks algorithmically. The course starts by introducing the basics of pandas, a data analysis library, and then moves on to creating a data frame and a pandas series. The course then demonstrates how to execute an HTTP request for a stock and how to use the results of the API call to populate a panda's data frame. Finally, the course shows how to improve the performance of the code by batching API requests.

  • 00:55:00 This video provides an overview of the topic of algorithmic trading using Python, with a focus on the use of the pandas data frame library and the use of batch API calls to speed up the code. The video discusses how to split a list of stocks into chunks of 100, and how to use the chunks function to generate a list of lists of pandas series, each of which is limited to 100 items. Finally, a for loop is used to execute batch API calls for each stock in the list of stocks, and the information from each stock is appended to the final data frame.
Part 2:
  • 01:00:00 This Python video tutorial demonstrates how to use the Python programming language to trade stocks using an automated algorithm. The video starts by creating a list of stocks and symbols, and then transforming those stocks into strings. Next, the video creates a URL to launch an HTTP request using that string. Finally, the video demonstrates how to run that request and get the data back.

  • 01:05:00 This video teaches how to use Python to perform algorithmic trading. It covers how to create an API call URL, parse data from the API call, and use the requests library to get the data. The video also shows how to add an ignore index to the pandas series class, and how to run the code.

  • 01:10:00 This Python script quickly calculates the number of shares to buy for a portfolio size entered by the user.

  • 01:15:00 This Python script calculates the position size and buys shares of a stock in order to reach that position size. It uses the round down function of the math module to round down the number of shares to be bought.

  • 01:20:00 The video shows how to use Python to trade stocks. The first section of the video explains how to access and use data in a dataframe. The second section explains how to save the dataframe to an Excel file.

  • 01:25:00 This video demonstrates how to use Python to create algorithms for trading. The first step is to create an Excel file and pass in the dataframe from a pandas module. Next, formatting is introduced, with different formats for string, dollar, and integer cells. Finally, the format is applied to the cells in the Excel file.

  • 01:30:00 This video teaches how to use Python to trade stocks using an automated algorithm. The first part of the video explains how to create a column format in a dictionary, which is used to format the columns in an Excel spreadsheet. The second part of the video shows how to create two loops that automatically format the columns in the spreadsheet.

  • 01:35:00 This video explains how to create an algorithmic trading strategy in Python using a virtual environment. First, the instructor opens a Jupyter Notebook to start the virtual environment. Next, they activate the virtual environment and create a new project. The project includes a stock price prediction model, a market capitalization prediction model, and a number of shares to buy prediction model. The instructor then shows how to loop through the models and output the predictions to a file. Finally, they show how to save the file and end the lesson.

  • 01:40:00 In this video, notebookist explains how to use Python to trade stocks using an algorithmic trading strategy. The course covers how to import libraries, how to make API calls, and how to build a momentum-based strategy.

  • 01:45:00 This video teaches how to use Python for algorithmic trading. It covers the basics of how to use the language for performance analysis and trading. The video shows how to use the Requests library to make an easy API call to get stock data.

  • 01:50:00 In this video, the author demonstrates how to use Python to create batch API calls for algorithmic trading. The author first divides a list of stocks into groups of 100, then creates a blank Panda's data frame and instantiates it. For each stock in the symbol strings list, the author creates a batch API call URL, and calls the JSON method on it to transform it from a request subject to a JSON object.

  • 01:55:00 The video explains how to use Python to trade stocks using algorithms. The instructor demonstrates how to loop over stocks, parse the relevant metrics, and append them to a dataframe.

Part 3:

  • 02:00:00 This video tutorial explains how to use Python to trade stocks using algorithms. It covers how to create a data frame, loop over symbols, make API calls, and sort data. The final data frame is used to determine which stocks to buy.

  • 02:05:00 This Python video tutorial shows how to use the pandas library to calculate the number of shares to buy for a momentum strategy. The tutorial first sorts the rows of a data frame based on one year price returns and then uses the inplace equals true parameter to modify the original data frame, instead of returning a temporary copy. Next, the data frame is modified to only contain the 50 stocks with the highest price momentum. Finally, the function to calculate the number of shares to buy for the momentum strategy is created and specialized to accept only value errors. The strategy is then run and successful.

  • 02:10:00 This course covers how to write code to create a simple algorithmic trading strategy, how to test the strategy, and how to use the strategy to buy and sell stocks. The strategy is based on the assumption that a stock is either a high quality momentum stock or a low quality momentum stock. The strategy works by buying stocks when the price is low and selling stocks when the price is high.

  • 02:15:00 This Python video teaches how to create a quantitative momentum strategy using a data frame of stock prices and return values over different time periods. The strategy uses a variety of momentum metrics to identify high quality stocks.

  • 02:20:00 The author discusses how to create an algorithm for trading stocks using Python. They first create a list of metrics to track, and then use the Sai pi library to calculate percentile scores for each metric. Next, they create a loop to calculate the percentile scores for each column in the data frame. Finally, they use the loop to create a strategy for trading stocks.

  • 02:25:00 This video teaches how to use the Python programming language to trade stocks algorithmically. The instructor uses the LFC (linear least squares) method to calculate the percentile score for each column in a dataframe.

  • 02:30:00 This course explains how to use Python to perform algorithmic trading. The course starts with an introduction to Python programming and explains how to use the stats module to calculate percentile scores for different time periods and asset classes. The Mean function is then used to calculate the average of these scores. Next, a loop is used to calculate the HTM score for each row in the HTM dataframe. Finally, the HM dataframe is printed to verify that the calculations were successful.

  • 02:35:00 In this video, the author teaches how to use Python to calculate an "HTM score" for each stock in a dataframe, and to select the 50 best momentum stocks using the HTM score as a filter.

  • 02:40:00 This video demonstrates how to use Python to trade stocks using algorithms. The author initializes a dataframe, and then formats the data into an Excel document for non-technical users.

  • 02:45:00 The author of the video teaches how to use Python to create an algorithmic trading system. The first step is to create a dictionary of column letters to column values, and then loop through the dictionary to apply the appropriate format to each column. The second step is to use the writer object to write the data to an Excel file. The third step is to use the set column method to dynamically apply the formats to each column in the data frame.

  • 02:50:00 In this video, the instructor shows how to format Excel files for use in algorithmic trading, and how to create a value investing strategy based on one metric.

  • 02:55:00 This video demonstrates how to use Python to build an algorithmic trading strategy that uses the price-earnings ratio as a value metric. The strategy screens for stocks based on this metric and makes buy and sell recommendations based on the current stock price.

Part 4:

  • 03:00:00 The video discusses how to use the Python programming language to create algorithmic trading models. It demonstrates how to use the requests library to make HTTP requests, and how to transform data values into JSON objects. The video then shows how to calculate price and earnings ratios using the stock prices from two different exchanges.

  • 03:05:00 This video explains how to execute a batch API call using Python.

  • 03:10:00 The video demonstrates how to use the Python requests library to execute a get request to get data from the stock market. This data is then parsed and used to generate a panda's series.

  • 03:15:00 This course explains how to use Python to perform algorithmic trading. First, the instructor explains how to create a dataframe in Python. Next, the instructor explains how to remove glamour stocks from a dataframe. Finally, the instructor explains how to return the top 50 stocks from a dataframe according to an order schema.

  • 03:20:00 In this video, the instructor explains how to use Python to perform algorithmic trading. First, they sorted the data frame to ensure that the stocks with the lowest price-to-earnings ratios were at the top. Next, they used the drop method to delete new index column. They then sorted the data frame again using the in place equals true parameter to keep the original data frame modified. They then calculated the position size and calculated the price per share for each stock. Finally, they used the portfolio input function to calculate the position size for each stock in the data frame.

  • 03:25:00 This video introduces the concept of algorithmic trading, and how Python can be used to create a value strategy. The video then demonstrates how to use Python to access the i x Cloud API to pull in data for multiple valuation metrics. If all goes well, the data will be returned as a panda dataframe, and the video will indicate that the data is working correctly.

  • 03:30:00 In this video, the author demonstrates how to use Python to perform algorithmic trading. The author first shows how to get the price earnings ratio and then parses this value from the IRS cloud API. Next, the author demonstrates how to assign a value to each metric and how to parse this data from the IRS cloud API. Finally, the author shows how to use the price to sales ratio to find the price to book ratio.

  • 03:35:00 This video tutorial explains how to use Python to perform algorithmic trading. The course begins by teaching how to calculate the price-to-sales ratio of a company, using a different metric (price to book). Next, the course teaches how to calculate enterprise value, earnings before interest, taxes, depreciation, and amortization (EBITDA), and enterprise value to gross profit (EV/EBIT). Finally, the course shows how to calculate the end-of-the-period value-to-earnings (EV/EBIT) ratio for a company.

  • 03:40:00 In this video, the author shows how to use Python for algorithmic trading. They start by showing how to calculate valuation metrics, and then go on to create a loop to populate a data frame with the calculated information. They conclude the video by printing the status codes for the API calls.

  • 03:45:00 In this video, the author demonstrates how to use Python to trade stocks algorithmically. They first create a dataframe of stock symbols and data values, and then use the append method to add data to the dataframe for each ticker. Next, they use the head method to parse out data points for each ticker, and then use the append method to add those data points to the pandas dataframe. Finally, they use the percentile method to calculate stock percentile scores.

  • 03:50:00 This 1-hour video tutorial covers how to use Python to trade stocks, using various calculations to generate indicators of success. When one of the calculations fails, the instructor uses Exception Handling to replace the value with a dummy value.

  • 03:55:00 In this video, the instructor explains how to deal with missing data in a data frame using pandas. First, they explain how to identify which columns in a data frame contain missing data. Then, they show how to use the fill in a method to replace the missing data with an average value from a different column.

Part 5:

  • 04:00:00 This video explains how to calculate percentile scores for different stock metrics using Python. First, the transcript excerpt shows how to create a dictionary of stock metrics and then how to use the pandas library's "LLC" method to access the percentile scores for each metric.

  • 04:05:00 The video teaches how to use Python to perform algorithmic trading. The first part of the tutorial shows how to use the percentile of score function from the Sai pi dot stats module to access percentile scores for a given data set. The second part of the tutorial shows how to calculate the RV score for a given row in a data set using the LSC method.

  • 04:10:00 The video explains how to use Python to calculate various valuation metrics, including P/E ratios, PB ratios, PS ratios, Evie/EBIT ratios, and Eb/gross profit. Once the metrics are calculated, the video demonstrates how to print the data and compare it to expected values.

  • 04:15:00 In this Python video, the author demonstrates how to calculate the position size for a portfolio using the Python library pandas. First, they create a dataframe with the index of 50 stocks that are cheapest in their universe. Next, they filter the dataframe to only include the 50 stocks and reset the index to underscore. They then pass in the drop parameter to avoid duplicating the existing index and create a for loop to calculate the position size for each stock. Finally, they print the position size for each stock and compare it to the desired position size of $50,000.

  • 04:20:00 This video teaches how to use Python to trade stocks algorithmically. The first part of the video covers the basics of how to create a dataframe and print it out. The second part of the video covers how to use Python to create an Excel file containing the stock prices and position sizes for calls and puts. Finally, the video shows how to format the Excel file and send it to a printer.

  • 04:25:00 This video demonstrates how to use Python to perform algorithmic trading. The instructor demonstrates how to format a spreadsheet to hold data for trading purposes and how to use various Python functions to perform various tasks in the spreadsheet.

  • 04:30:00 This tutorial introduces the use of Python for quantitative value investing, using a method to identify the 50 cheapest stocks in the S&P 500. The tutorial provides step-by-step instructions for formatting and running the code, and concludes with a completed spreadsheet.
Algorithmic Trading Using Python - Full Course
Algorithmic Trading Using Python - Full Course
  • 2020.12.04
  • www.youtube.com
Learn how to perform algorithmic trading using Python in this complete course. Algorithmic trading means using computers to make investment decisions. Comput...
 

Algorithmic Trading Python 2023 - FULL TUTORIAL Beginner


Algorithmic Trading Python 2023 - FULL TUTORIAL Beginner

In this video tutorial, the author delves into the process of installing and utilizing a Python program for algorithmic trading. They provide step-by-step instructions on creating a basic Python 3 file specifically designed for housing code related to algorithmic trading strategies. Moreover, they demonstrate how to execute the code and print the resulting outputs for analysis. The tutorial primarily focuses on harnessing the power of the Python programming language for algorithmic trading purposes. It covers a range of essential functions and libraries applicable to algorithmic trading, including the yfinance library. The tutorial highlights the significance of using these functions and libraries while also exploring data download and processing techniques using spreadsheets.

Additionally, the video tutorial showcases the process of writing and reading CSV files using Python. It explains the necessary steps for creating a CSV file and demonstrates how to read and manipulate the file within a Python environment. Continuing with the theme of Python-based stock trading, the tutorial elucidates the creation of a stock index and demonstrates how the Python function "convert" can be used to modify the index format. Furthermore, it explains how the Python function "start.columns" facilitates changes to the column list specifically for stocks.

The next video tutorial also revolves around using Python for stock trading. It commences by illustrating the download and parsing of stock data, followed by employing the "describe" function to analyze the acquired data effectively. Lastly, it demonstrates the utilization of the "dot lock" function to monitor and track stock prices. Moving on, the subsequent video tutorial provides a comprehensive explanation of using Python to create algorithms for stock trading. It begins by visualizing different starting points for three distinct stocks, subsequently illustrating the normalization of values to represent them within a uniform 100-point range. The tutorial then guides viewers on plotting the normalized closing prices of a stock and utilizing the "dot" (mole) function to multiply the values by 100, enhancing readability.

Similarly, another video tutorial focuses on utilizing Python to create stock trading algorithms. The tutorial outlines the process of creating a new column within a dataset to store information regarding closed stocks. It further explains the utilization of the "shift" function to relocate data to the bottom of the column. Additionally, it showcases the calculation of percentage changes in stock prices from the previous day. Shifting gears, another tutorial introduces learners to utilizing Python for statistical calculations related to algorithmic trading. It provides guidance on employing functions such as "shift," "subtract," and "divide" to compute lag and diff-related data.

Next, the video delves into calculating percentage changes for financial assets using Python. It demonstrates modifying the "change" function to improve readability by renaming it as "pst." Furthermore, it sets the "periods" variable to one and multiplies the percentage change by 100 to represent it in point value format. The video also covers calculating the standard change for an asset, subtracting it from the percentage change to eliminate the impact of the first day. The dataframe for a specific asset is renamed as "change," and the "change" column is created. The tutorial concludes with running a check on the "change" column using "aafl" and saving the dataframe.

Moreover, the tutorial author explains how to calculate mean, standard deviation, percentage change, and returns for a given dataset. They also demonstrate plotting a histogram and creating a hit system graph.

Continuing with statistical calculations, another video tutorial explains calculating the mean, variance, and standard deviation of a stock's returns. Additionally, it provides guidance on determining the annual mean return and the annual variance return.

Expanding further, the tutorial showcases calculating the annual standard deviation of a stock's returns using the "std" function in Python. This approach efficiently analyzes large datasets by taking data from a ticker symbol instead of individual data points. The tutorial also demonstrates creating columns to track the mean and standard deviation of a stock's return, as well as the mean and standard deviation of a stock's percentage change. It further explains calculating the mean and standard deviation of a stock's return using the "summary" function.

The author also covers the creation of scatter plots and annotating them to illustrate the return and risk associated with different stocks. This visualization helps in understanding the relationship between returns and risks in the context of stock trading. Moving on, the video tutorial delves into using Python to create algorithms for trading stocks. It explores the usage of for loops and functions such as covariance and correlation. Additionally, it showcases the graphical representation of the algorithm's results, enabling traders to visualize and analyze the performance of their trading strategies effectively.

Furthermore, the tutorial explains how to leverage the seaborn library to create a heatmap depicting stock correlations. It provides a step-by-step guide along with a code download for the entire project, facilitating the implementation of stock correlation analysis using Python. Shifting focus, the presenter in a video tutorial educates viewers on calculating the risk and reward potential of a portfolio of stocks using Python. They discuss the limitations of simple returns and introduce the concept of log returns, demonstrating their practical application in assessing risk and reward. This analysis helps traders make informed decisions regarding their portfolio composition and risk management.

Another tutorial elucidates the process of calculating a simple moving average using the "rolling" function in Python. By applying this technique, traders can smoothen the fluctuations in stock prices and identify trends more effectively. In addition, a tutorial demonstrates the calculation of the mean, median, and moving average of a dataset, emphasizing their significance in analyzing and understanding data patterns.

Moreover, a video tutorial showcases the calculation of various moving averages, including the 50-day moving average, 200-day moving average, and EMA (earnings-to-price) of a stock. These moving averages are then plotted on a graph, aiding traders in identifying key trends and potential trading signals. Continuing with data manipulation techniques, a video tutorial explains the utilization of the re-index function in pandas to replace missing values within a dataframe. It also covers the application of forward and backward fill functions to manage data when encountering holidays and weekends.

The video tutorial further demonstrates the calculation of returns for a stock over time, encompassing buy and hold returns, cumulative returns, and maximum returns. Additionally, it explores the computation of cumulative maximum returns and visualizes the data through graph plotting. Furthermore, the tutorial explains how to calculate drawdowns for a stock, as well as the maximum cumulative return and maximum cumulative drawdown. Understanding drawdowns helps traders assess the risk associated with investments and identify potential loss scenarios. In a similar vein, another video tutorial discusses calculating drawdown and maximum drawdown for a stock. Additionally, it provides an overview of calculating percent drawdown, a crucial metric in risk management.

A Python 2023 tutorial on YouTube introduces viewers to creating a moving average crossover strategy for trading. This strategy involves utilizing two moving averages, a 50-day moving average, and a 100-day moving average, to determine the stock's trend and generate trading signals accordingly. Moreover, a video tutorial explains how to write Python code for trading stocks. It demonstrates the process of determining whether to buy or sell a stock based on its current price and past price data. It also covers using a library to track a stock's position over time, allowing traders to monitor and manage their portfolio effectively.

The tutorial video enlightens viewers on backtesting an algorithmic trading strategy using returns and standard deviation. It showcases a strategy that outperforms a 50-day moving average in terms of returns but comes with higher standard deviation, highlighting the trade-off between risk and reward. Additionally, the video tutorial guides users through creating an investment strategy and comparing it to other strategies. It emphasizes that the strategy with the best returns is the one with a long bias, indicating a preference for bullish positions.

Furthermore, the author introduces a function to create a test strategy for algorithmic trading. This function takes parameters such as stock name, start and end dates, and returns key performance metrics such as daily return, cumulative return, and SMA (Simple Moving Average). By utilizing this function, traders can assess the effectiveness of their trading strategies and make data-driven decisions. The tutorial then proceeds to demonstrate how to build an algorithmic trading Python script. The script incorporates a simple stop-loss and take-profit strategy, aiming to achieve better overall performance compared to a traditional buy-and-hold investment approach. This script serves as a foundation for developing more sophisticated trading algorithms.

The presenter also showcases the process of backtesting a trading strategy written in Python. The strategy, created by the presenter, is tested on historical stock market data from 2017, enabling traders to evaluate its performance and viability. Moreover, the tutorial explains how to code a Python2023 algorithm for trading stocks and cryptocurrencies. It covers the utilization of APIs to access data from various stock and cryptocurrency exchanges, enabling traders to analyze real-time market data and implement trading strategies accordingly. The video tutorial further explores using Python to trade stocks and cryptocurrencies. It encompasses data entry, analysis, storage, manipulation, and the execution of trade strategies using API services. By leveraging these techniques, traders can automate their trading processes and efficiently manage their portfolios.

Additionally, the tutorial provides comprehensive guidance on using Python to trade stocks and other financial assets. It covers fundamental concepts such as price analysis and trading, as well as advanced topics like backtesting and utilizing APIs for data integration. This tutorial equips traders with the necessary knowledge and tools to engage in algorithmic trading effectively.

In conclusion, these tutorials and videos offer a wealth of information on using Python for algorithmic trading. They cover a wide range of topics, including data processing, statistical analysis, visualization, strategy development, backtesting, and real-time trading. By following these tutorials, traders can enhance their understanding of algorithmic trading principles and leverage Python's capabilities to make informed trading decisions.

  • 00:00:00 In this video, the author discusses how to install and use an algorithmic trading Python program. Next, they explain how to create a basic Python 3 file to hold code for an algorithmic trading strategy. Finally, they show how to run the code by printing the results.

  • 00:05:00 This tutorial explains how to use the Python programming language to perform algorithmic trading. The tutorial covers various functions and libraries that can be used in algorithmic trading, such as the y finance library. The tutorial also shows how to download and process data in a spreadsheet.

  • 00:10:00 This YouTube video demonstrates how to write a CSV file and how to read it in Python.

  • 00:15:00 This tutorial explains how to use Python to trade stocks. The video first explains how to create a stock index, and then shows how to use the Python function convert to change the index format. Finally, it explains how to use the Python function start.columns to change the column list for stocks.

  • 00:20:00 This video tutorial discusses how to use Python to trade stocks. The first part of the tutorial covers how to download and parse stock data. Next, the tutorial covers how to use the describe function to analyze the data. Finally, the tutorial covers how to use the dot lock function to keep track of stock prices.

  • 00:25:00 This video tutorial explains how to use the Python programming language to create an algorithm to trade stocks. The tutorial starts by displaying the different starting points for three different stocks, and then demonstrates how to normalize the values so that they are all represented in 100-point ranges. Next, the tutorial shows how to plot the norm of a stock's closing price, and how to use the
    dot (mole) function to multiply the values by 100 to make them easier to read.

  • 00:30:00 This video tutorial demonstrates how to use the Python programming language to create algorithms to trade stocks. The first step is to create a new column of data to store the information about the stocks that have been closed. Next, the video explains how to use the shift function to move the data to the bottom of the column. Finally, the tutorial shows how to calculate the percentage change in stock prices from the previous day.

  • 00:35:00 In this tutorial, you will learn how to use the Python programming language to calculate various statistical data related to algorithmic trading. You will learn how to use the shift, subtracted, and divided by functions to calculate data related to lag and diff.

  • 00:40:00 The video covers how to calculate the percentage change for a financial asset using Python. The change function is changed to pst to make it easier to read, and then the periods variable is set to equal one. The percentage change is then multiplied by hundred to convert to a point value. The standard change for the asset is then calculated and subtracted from the percentage change to remove the first day's effect. The dataframe apple is renamed to change and the column change is created. Aafl is run to check the column changes and the dataframe is saved.

  • 00:45:00 In this tutorial, the author demonstrates how to calculate the mean and standard deviation of a particular data set, as well as the percentage change and returns on the monthly change. He also demonstrates how to plot a histogram and hit system graph.

  • 00:50:00 This video explains how to calculate the mean, variance, and standard deviation of a stock's returns. The video also explains how to calculate the annual mean return and how to calculate the annual var return.

  • 00:55:00 This video tutorial explains how to calculate the annual standard deviation of a given stock's return using the std function. The std function takes in data from a ticker symbol, rather than individual data points, which makes it more efficient for analyzing large data sets. The tutorial also shows how to create a column to track the mean and standard deviation of a stock's return, as well as a column to track the mean and standard deviation of a stock's percentage change. Finally, it explains how to calculate the mean and standard deviation of a stock's return using the summary function.

  • 01:00:00 The author explains how to create a scatter plot and annotate it to show the return and risk associated with various stocks.

  • 01:05:00 This video tutorial explains how to use the Python programming language to create algorithms for trading stocks. The tutorial covers the use of for loops and the covariance and correlation functions, as well as a graphical representation of the results.

  • 01:10:00 This tutorial explains how to use the seaborn library to create a heat map of stock correlations. The tutorial also includes a code download for the entire project.

  • 01:15:00 In this video, the presenter teaches how to calculate the risk and reward potential of a portfolio of stocks using Python. He discusses the limitations of simple returns and log returns and demonstrates how they work in practice.

  • 01:20:00 This tutorial explains how to calculate a simple moving average using the rolling function in Python.

  • 01:25:00 This tutorial demonstrates how to calculate the mean and median of a set of values, as well as the moving average.

  • 01:30:00 This video demonstrates how to calculate the 50-day moving average, 200-day moving average, and ema (or "earnings-to-price") of a stock. The video also demonstrates how to plot these averages on a graph.

  • 01:35:00 In this video, dot day explains how to use the re-index function in pandas to replace missing values in a dataframe. The video also covers how to use the forward and backward fill functions to manage data when there are holidays and Saturdays and Sundays included.

  • 01:40:00 This video explains how to calculate the returns for a stock over time, including buy and hold returns, cumulative returns, and maximum returns. It also discusses how to calculate cumulative maximum returns and how to plot a graph of the data.

  • 01:45:00 This video explains how to calculate the drawdowns for a stock, and how to calculate the maximum cumulative return and maximum cumulative maximum for a stock.

  • 01:50:00 The video discusses how to calculate the drawdown and maximum drawdown for a stock, and also provides an overview of how to calculate the percent drawdown.

  • 01:55:00 In this YouTube video, a Python 2023 tutorial explains how to create a moving average crossover strategy. The strategy involves using two moving averages, a 50 day and a 100 day, to determine the stock's trend.

  • 02:00:00 This video tutorial explains how to use Python to write code to trade stocks. The video demonstrates how to write code to determine whether a stock should be bought or sold, based on its current price and past price. The video also explains how to use a library to track a stock's position over time.

  • 02:05:00 The video explains how to backtest an algorithm trading strategy using returns and standard deviation. The strategy achieves a higher return than a 50-day moving average, but has a high standard deviation.

  • 02:10:00 This video explains how to create a strategy for a given investment, and how to compare it to other strategies. The strategy with the best returns is the strategy with the long bias.

  • 02:15:00 The author introduces a function to create a test strategy for algorithmic trading. The function takes in a stock name, start and end date, and returns the daily return, the cumulative return, and the sma.

  • 02:20:00 This tutorial shows how to create a Python algorithm to trade stocks, and how to use it to make predictions about future stock prices. The tutorial includes a demonstration of how to calculate the return on an investment in a stock, as well as the standard deviation of that return.

  • 02:25:00 The sma backtester class is used to create a strategy that calculates returns and standard deviation. The class also includes a function to get data.

  • 02:30:00 The video demonstrates how to use the getdata function to download stock data, how to create a test result function, and how to calculate the performance and outperformance of a buy and hold strategy using the data.

  • 02:35:00 The author demonstrates how to calculate the performance and out performance of an algorithmic trading strategy. The author also demonstrates how to create a function to plot the results.

  • 02:40:00 In this tutorial, the author teaches how to build an algorithmic trading Python script. The script uses a simple stop-loss and take-profit strategy to achieve an overall performance advantage over a buy-and-hold investment.

  • 02:45:00 This video shows how to back test a trading strategy written in Python. The strategy was written by the presenter and was tested on the stock market in 2017.

  • 02:50:00 This tutorial explains how to code a Python2023 algorithm for trading stocks and cryptocurrencies. The tutorial also covers how to use an API to access data from various stock and cryptocurrency exchanges.

  • 02:55:00 This video tutorial explains how to use Python to trade stocks and cryptocurrencies. The video covers how to enter and analyze data, how to store and manipulate data, and how to send a trade strategy using API services.

  • 03:00:00 This tutorial explains how to use Python to trade stocks and other financial assets. The course covers basic concepts such as price analysis and trading, as well as more advanced topics such as backtesting and using APIs.
Algorithmic Trading Python 2023 - FULL TUTORIAL Beginner
Algorithmic Trading Python 2023 - FULL TUTORIAL Beginner
  • 2022.01.14
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We have a created an Algorithmic Trading Course in python for pure beginners wherein we discuss multiple concepts from a basic zero to hero framework. The vi...
 

How to Get a List of Stocks on an Exchange || Stock Analysis with Python Part 1



How to Get a List of Stocks on an Exchange || Stock Analysis with Python Part 1

This is the first part of my series on using Python for stock analysis. The series will be divided into three sections. In the first section, we will focus on obtaining and organizing data for analysis. Part two will cover individual securities, and finally, I will demonstrate how to package our code for easy installation using pip. The code will be available on GitHub (link in the video description). You can use any text editor of your choice.

The most important aspect of stock analysis is the data source. I will be using comprehensive end-of-day historical data, which includes global data. You can start with a free plan, but it has limitations on the number of API calls per day. If you require more, you can subscribe to a data bundle with special pricing (link in the video description).

Let's begin by importing the necessary modules. We will start by downloading metadata about securities from a specific exchange. We need to provide an API key and specify the exchange (defaulting to the New York Stock Exchange). I will provide some examples for the US market, but you can explore international markets using the documentation on the end-of-day historical data website.

Once we have the documentation set up, we will make a call to the API endpoint, passing the exchange and API key. We will use the requests module for this. The response will be in JSON format, which we will transform into a pandas DataFrame.

Finally, I will add some print statements to show the progress, and we can test the code by running an entry point function. Make sure to replace the API key with your own. The result will be a DataFrame containing the retrieved data. We can further filter this data based on the types of securities we are interested in, which will be covered in the next video.

I hope you find this information helpful, and I look forward to seeing you in the next part of the series.

How to Get a List of Stocks on an Exchange || Stock Analysis with Python Part 1
How to Get a List of Stocks on an Exchange || Stock Analysis with Python Part 1
  • 2022.06.06
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@MattMacarty #python #stockmarket #dataanalytics ✅ Please SUBSCRIBE:https://www.youtube.com/subscription_center?add_user=mjmacartyVideos:[Part 1](https://y...
 

How to Download S&P 500 Stock Symbols, Filter by Sector || Stock Analysis with Python Part 2



How to Download S&P 500 Stock Symbols, Filter by Sector || Stock Analysis with Python Part 2

This is part two of my series on stock analysis with Python. If you haven't seen part one, I recommend checking it out. In the previous video, we downloaded data from a specific stock exchange and obtained a DataFrame with metadata. Now, we want to filter out symbols based on their security type, such as common stock, ETF, or fund. This will allow us to focus on specific securities before downloading data.

I'll add a new function called "get_security_type" that takes the exchange data DataFrame as input. By default, we'll filter for common stock, but you can specify a different type if needed. The function will return a list of symbols that match the specified security type.

Additionally, I noticed that the end-of-day historical data doesn't include the S&P 500 symbols. So, I'll write another function called "get_sp500_symbols" to fetch the symbols from a CSV file. This function accepts an optional "sector" parameter to filter the symbols based on sectors. By default, it returns all symbols. The function will return a DataFrame with symbol, name, and sector columns.

To implement this, we'll use the pandas library to read the CSV file and apply the desired filters based on the parameters provided.

Once implemented, we can test the functions. By default, "get_security_type" will return all symbols, and "get_sp500_symbols" will also return all symbols. We can specify a sector to filter the S&P 500 symbols.

With these functions in place, we can now filter symbols based on security types and explore stocks from the S&P 500. In the next part of the series, we will focus on downloading and curating the data.

I hope you find this information useful, and I look forward to continuing the series with you.

How to Download S&P 500 Stock Symbols, Filter by Sector || Stock Analysis with Python Part 2
How to Download S&P 500 Stock Symbols, Filter by Sector || Stock Analysis with Python Part 2
  • 2022.06.09
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@MattMacarty #dataanalytics #pythonprogramming #stockmarket ✅ Please SUBSCRIBE:https://www.youtube.com/subscription_center?add_user=mjmacartyVideos:...
 

How to Download and Save Stock Price Data || Stock Analysis with Python Part 3



How to Download and Save Stock Price Data || Stock Analysis with Python Part 3

This is part three of my series on using Python for stock analysis. You can find the first two videos in the link provided in the video description. All the code used in the series is available on a GitHub repository, which is also linked in the description.

In the previous videos, we learned how to obtain a list of securities on a specific exchange and how to filter them based on specific criteria. We also wrote a function to filter out the S&P 500 stocks. In this video, we will focus on downloading and organizing the data we are interested in.

To get started, we need to install a helper library called "eod" that simplifies the interaction with end-of-day data. We can use this library instead of making traditional API calls. After installing it using pip, we import the "EodHistoricalData" class from the library, which allows us to make API calls easily. Additionally, we import the "datetime" module for setting time limits and the "os" module for working with the file system.

Next, we set some default dates for the time period we want to retrieve data for. In this case, we set it to be approximately a year. We also set the current date as a reference if we need both the start and end dates.

Now, we can proceed to write the main function called "get_data." This function accepts a variety of inputs, including a single symbol, a comma-separated list of symbols, or a list of symbols. It also requires an API key and a path where the data will be stored. The function retrieves data for the specified tickers using the EodHistoricalData class and saves it as a CSV file in the specified folder. It keeps track of the number of downloaded and skipped securities and provides information about the download process.

After writing the function, we can test it by passing in some sample arguments and checking the output. We can see the downloaded securities and any skipped ones. The function successfully retrieves and saves the data in the specified folder.

In the upcoming videos, we will work with the downloaded data, such as extracting closing prices and returns, and explore visualization techniques.

How to Download and Save Stock Price Data || Stock Analysis with Python Part 3
How to Download and Save Stock Price Data || Stock Analysis with Python Part 3
  • 2022.06.13
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​ @Matt Macarty #dataanalytics #pythonprogramming #stockmarket ✅ Please SUBSCRIBE:https://www.youtube.com/subscription_center?add_user=mjmacartyVid...
 

Stock Analysis with Python: How to Extract Price Data from Files || Part 4



Stock Analysis with Python: How to Extract Price Data from Files || Part 4

Welcome to part four of my Python series for stock analysis. You can find links to the first three parts in the video description and also access the code on the GitHub repository.

In the previous videos, we covered obtaining a list of securities, filtering the list, and downloading data into CSV files. Now, in this video, we will focus on extracting the closing price column from the downloaded data.

To do this, we'll create a function called "get_closing_prices." The function takes a folder as input, with a default value set for the data files. By default, it extracts the close column, but you can choose the adjusted close column if desired.

First, we read all the valid files in the specified folder, excluding the file starting with "zero" to avoid duplication. Then, we set up an empty DataFrame.

Next, we loop through the files and check if the adjusted close is set to true. If it is, we create a temporary DataFrame using pandas' read_csv function, specifying the folder and file. We set the index column to be the date and select the adjusted close column. Finally, we rename the column to the ticker symbol.

If the adjusted close is not true, we follow a similar process for the close column. We replace the existing DataFrame with the temporary DataFrame for the first file and concatenate the new DataFrame onto the existing one for subsequent files.

Finally, we return the DataFrame containing the closing prices. Additionally, we write the DataFrame to a CSV file named "closes.csv" if desired.

You can test the function by calling it with the desired folder name. The function will return the DataFrame with the closing prices. In the example shown, it successfully extracted the closing price column for the specified securities.

In part five, we will calculate return data based on these closing prices.

Stock Analysis with Python: How to Extract Price Data from Files || Part 4
Stock Analysis with Python: How to Extract Price Data from Files || Part 4
  • 2022.06.16
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​ @Matt Macarty #dataanalytics #pythonprogramming #stockmarket ✅ Please SUBSCRIBE:https://www.youtube.com/subscription_center?add_user=mjmacartyVid...
 

Calculate Returns, Create Correlation Matrix, Plot Performance || Stock Analysis with Python Part 5



Calculate Returns, Create Correlation Matrix, Plot Performance || Stock Analysis with Python Part 5

Welcome to part five of my series on using Python for stock analysis. In the video description, you'll find links to the previous four parts as well as a link to the GitHub repository where you can access the code.

In part four, we obtained the closing prices for selected securities and saved them into a file. Now, in part five, we'll focus on calculating returns based on these closing prices. Before we proceed, we need to import the NumPy library.

We'll create a function called "calculate_returns" that takes a folder and a file name as input. To handle potential errors, we'll use a try-except block. Inside the function, we'll use pandas to read the data from a CSV file. We'll set the index column to the date and return the DataFrame of returns.

We can test the function by printing the result, passing the folder name and the file name. In the example shown, it successfully calculates the returns for the selected securities.

From here, there are several possible next steps. One common task is to calculate the correlations between the securities. While I won't dive into the specific implementation, you can use the result of the previous function to build a function for calculating correlations. You can explore different options, such as reading the data from a file (e.g., Excel or CSV) to enhance flexibility.

Another useful function we can write is one to plot the closing prices. For this, we need to import the matplotlib library. The function "plot_closes" takes the closes as input, which can be a CSV or Excel file. Additionally, we can choose to plot the prices relative to the starting price.

Within the function, we read the data using pandas, and based on the relative parameter, we plot the prices as they are or plot the performance relative to the starting price. We can customize the plot with options like grid lines and a horizontal line at zero (or one, depending on the desired representation).

Testing the function, we can see the resulting plot for the selected securities. By setting the relative parameter to true, we can observe the performance relative to the starting price.

In part six, we will continue working with closing prices and changes, focusing on saving that data to a separate file.
Calculate Returns, Create Correlation Matrix, Plot Performance || Stock Analysis with Python Part 5
Calculate Returns, Create Correlation Matrix, Plot Performance || Stock Analysis with Python Part 5
  • 2022.06.20
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​ @Matt Macarty #dataanalytics #pythonprogramming #stockmarket ✅ Please SUBSCRIBE:https://www.youtube.com/subscription_center?add_user=mjmacartyVid...