Python in algorithmic trading - page 15

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Download, Transform and Write Data to Excel with Python || Stock Analysis with Python Part 6

Download, Transform and Write Data to Excel with Python || Stock Analysis with Python Part 6

Welcome to part six of my series on stock analysis with Python. In the video description, you'll find links to the previous five videos, as well as a link to the GitHub repository containing the code.

In the previous part, we explored different ways of plotting closing prices for selected stocks. Now, in part six, we'll take a different approach to obtain data and work with Excel files. We'll create a function called "get_return_data" that takes at least one ticker as input (multiple tickers can be separated by commas or stored in a Python collection object). Additionally, the function allows users to specify a date and choose between the close or adjusted close prices. The data will be saved to an Excel file and can also be stored in a variable.

To begin, we need to set up the API client. As before, we'll use the "end of day historical data" API. You'll need to replace the "key" variable with your own API key.

Next, we create a temporary DataFrame to store the downloaded data. We loop over the tickers passed to the function and use a try-except block to handle any potential errors. Depending on whether the user wants adjusted close or close prices, we append the corresponding column from the API call result to the ticker column in the DataFrame.

Once we've downloaded and stored the data, we can perform any desired transformations. In this case, we calculate the instantaneous rate of return using NumPy and drop the first row. We also calculate the regular percent change using a built-in method.

Finally, we write the data to an Excel file using the pandas Excel writing object within a context manager. This step requires providing the file name and optional date format. Each variable is inserted into the "returns" file, and a message is printed to indicate the completion of the task. The function returns the closing prices and can be expanded to return other data as well.

Testing the function with four tickers, we can see the generated file containing the downloaded data. The file can be opened in Excel to view the data.

That concludes part six of the series. In part seven, we'll explore how to plot the performances of multiple stocks in a single figure.

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Python for Stock Analysis: Plotting Performance Grid of Multiple Securities w\matplotlib || Part 7

Python for Stock Analysis: Plotting Performance Grid of Multiple Securities w\matplotlib || Part 7

Welcome to part seven of my series on using Python for stock analysis. In the video description, you'll find links to the previous six videos as well as the GitHub repository containing the code.

In part six, we developed a function to download data, perform transformations, and save it to an Excel file. Now, in part seven, we'll focus on plotting the relative performances of multiple stocks onto a single graph.

To begin, we'll read all the files in a specific folder. In this case, we'll be looking in the "energy" folder. We'll skip any files that start with zero.

Using the matplotlib library, we'll create a subplot object. The number of rows will be determined by the ceiling of the length of the files divided by four, as we want to display four graphs per row. We'll make the figure large to accommodate potentially many graphs.

Next, we'll keep track of the number of graphs added and use a nested for loop to iterate through the rows and columns of the figure. If we reach the last row and it's not full, we'll handle the exception by incrementing the count.

Within the loop, we'll read the closing prices from each file and transform the data into relative performance. We'll plot the relative performance in the corresponding axes and add a horizontal line at 0. To format the data as a percentage, we'll import the ticker module from matplotlib.

In the exception block, we won't take any action, as we know we've run out of data to plot. We'll simply pass and increment the count to move on to the next file.

Once all the data has been plotted, we'll show the graph.

Testing the code with the files in the "energy" folder, we can see the resulting graph displaying the relative performances of 21 securities over approximately one year.

That concludes part seven of the series. In the next video, we'll explore additional data cuts from the end-of-day historical data API.

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Download List of Stocks About to Announce Earnings or Dividends || Stock Analysis with Python Part 8

Download List of Stocks About to Announce Earnings or Dividends || Stock Analysis with Python Part 8

Welcome to part eight of my series on using Python for stock analysis. In this video, we'll delve deeper into the end-of-day historical data API and explore additional functionalities beyond retrieving prices. The API is comprehensive, and while we won't cover everything, I'll show you a couple of examples that you might find useful. The documentation provided by the API is extensive and can guide you in exploring different data sets.

First, we'll focus on obtaining earnings data for companies reporting this week. To begin, we'll initialize our API client by passing in our API key (replace it with your own). We'll then download the data and store it in a DataFrame.

The function we'll use for this task doesn't require any parameters. Once we have the DataFrame, we'll extract the symbols of the companies reporting earnings this week and store them in a list.

To filter the data for a specific exchange, such as the U.S., we'll loop through each row in the DataFrame and check if the symbol ends with "us." Stock symbols are referred to as codes in the end-of-day historical data, and their endings correspond to the exchange they belong to. We'll append the relevant symbols to our list, excluding the exchange information.

After looping through all the rows, we'll print the number of companies reporting earnings this week and return the list of symbols for further analysis.

Moving on, let's explore how to retrieve dividends for a specific ex-date. We'll start with today's date. The function setup will be similar to the previous one, where we create an API client and download the data into a DataFrame.

The function we'll use this time is called get_bulk_market. From this function, you can obtain various data points, including closing prices for an entire market. For dividends, we'll specify the data we're interested in as "dividends."

After making the call, we'll return the resulting DataFrame.

Testing this function, we'll retrieve dividends with today's ex-date. The code will print the dividends, assuming the default values for the U.S. market and today's date.

The resulting DataFrame will display the dividends with their respective rates. Since we're looking at the U.S. market, the dividends will be in U.S. dollars. Additionally, the DataFrame provides information about the frequency of dividend payments.

That wraps up part eight. In part nine, we'll conclude section one by building a simple screener.

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How to Create a Stock Screener Using an API || Stock Analysis with Python Part 9

How to Create a Stock Screener Using an API || Stock Analysis with Python Part 9

This is part 9 of our series on stock analysis with Python. You can find links to the previous videos in the description, as well as the code on GitHub.

In this final segment of section 1, we will explore an example of a stock screener. The goal is to create a simple screener that analyzes the 52-week high, current price, and price-to-earnings ratio of multiple securities. This will help us identify securities for further analysis.

To accomplish this, we will use the end-of-day historical data fundamental feed. Let's begin by examining the data we get from a call to this feed. We will create a client object and retrieve fundamental data for a specific security, such as Apple. This will give us a large data frame with various information, including financials, balance sheets, and more. We can explore specific sections by using index locations.

Next, we will focus on the technicals of the index and use both the end-of-day historical data helper library and an endpoint to bulk download closing prices for the most recent day. We'll store the data in a data frame and reset the index. This call fetches closing prices for all securities on the US stock exchange. We can then filter the data to include only the securities we're interested in.

To build our stock screener, we'll create a client and loop through the symbols we want to analyze. We'll populate a dictionary with the 52-week high for each security. If a security doesn't have this information available, we'll skip it and continue the loop.

After obtaining the necessary data, we'll merge the closing prices, 52-week highs, and calculate the price-to-earnings ratio. We'll return the resulting data frame containing the securities, their closing prices, highs, and ratios.

To test our screener, we'll retrieve symbols from the S&P 500 using the get_sp function and print the result. This will show the closing prices, 52-week highs, and ratios for the first 10 securities in the S&P 500.

In Part 10, we will delve into analyzing individual securities as we start building a class for that purpose. Join us for the next part to learn more about analyzing securities on an individual level.

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Stock Analysis Python: How to Automatically Analyze Stocks with Python || Part 10

Stock Analysis Python: How to Automatically Analyze Stocks with Python || Part 10

This is going to be Part 10 of my series on Python for stock analysis. You can find the links to the previous videos in the description below, as well as a link to the GitHub repository where all the code is available. In this part, we'll start focusing on individual securities instead of entire stock exchanges or large lists of stock symbols.

To begin, I have already included the necessary imports for this part, such as datetime, matplotlib, numpy, pandas, and seaborn. I also defined a symbolic constant that represents a date about a year ago, which can be changed by the user using an ISO-formatted date.

Next, I'll write a class called "Stock" that will handle individual securities. The class will be initialized with parameters for the stock symbol, API key, and date (with a default value). Additionally, it will allow the user to specify a folder where data can be saved, with a default value of None.

Inside the class, I'll define a method called "get_data" that will fetch the stock data. It will first check if the data is already available in the specified folder by comparing the symbol to the available files. If the data is found, it will be read into a DataFrame and standardized. Otherwise, it will fetch the data using the API and return it as a DataFrame.

By initializing a Stock instance, the symbol, API key, date, and data will be stored as instance variables. To test the functionality, I'll create a Stock object and print out the data.

That concludes Part 10 of the series. In Part 11, we'll add more data transformations to the Stock class.

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Python for Stock Analysis: Automatically Calculate & Graph Stock Returns & Volatility || Part 11

Python for Stock Analysis: Automatically Calculate & Graph Stock Returns & Volatility || Part 11

Welcome to Part 11 of my series on Python for stock analysis. You can find the links to the previous videos and the GitHub repository in the description below.

In Part 10, we created a simple class to initialize a stock symbol and fetch its data either from a local folder or through an API. In this part, we will delve further into data transformations and begin plotting the data.

First, I'll add a new method called "calculate_volatility" that takes a DataFrame as input. To integrate this method with the "get_data" method, I will modify it accordingly. Inside the "calculate_volatility" method, I'll add several columns to the DataFrame. The first column will be the returns, calculated as the logarithmic difference of the close prices with four decimal precision.

Next, I'll calculate the rolling volatility, which represents the standard deviation of returns over a 21-day period. I'll round the result to four decimals. Additionally, I'll include the absolute change, high-low spread, and expected change columns, with appropriate rounding.

To analyze the magnitude of the stock's movement, I'll calculate a column called "magnitude" representing the actual change divided by the expected change, rounded to two decimals. Lastly, I'll include an absolute value column for potential graphing purposes.

I'll drop the initial rows that contain NaN values resulting from the calculations, and then I'll call the "calculate_volatility" method within the "get_data" method.

Let's test the code by initializing a Stock instance and printing the DataFrame.

Now, let's plot the return distribution by creating a histogram. I'll determine the start and end dates, and then plot the histogram with 20 bins and an edge color. To enhance the title, I'll use a supertitle with two lines, specifying the date range. Finally, I'll display the plot.

Let's run the code and examine the histogram, which provides an overview of the stock's return distribution over the past year.

That concludes Part 11. In Part 12, we will continue working on additional plots before moving on to other data transformations.

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How to Calculate & Normalize Expected Stock Returns || Python Stock Analysis Part 12

How to Calculate & Normalize Expected Stock Returns || Python Stock Analysis Part 12

Welcome to Part 12 of my series on Python for stock analysis. You can find the code and links to other videos in the description below.

In Part 11, we performed basic data transformations in our Stock class by adding columns to the DataFrame and plotting the distribution of returns. In this video, we'll continue with more plotting options that may be useful for stock analysis.

To begin, let's plot the volatility of the stock. The setup will be similar to what we've done before, including the super title and the start and end dates. We'll create a scatter plot, where the x-axis represents the returns and the y-axis represents the absolute magnitude of the change in standard deviations. We'll add horizontal and vertical lines for reference.

Let's run the code and examine the scatter plot of volatility. We can observe the range of percent changes and the number of standard deviations. For example, over the past year, there haven't been any changes beyond three and a half standard deviations.

Next, let's plot the performance of the stock. Again, most of the code remains the same, so we can copy it and make a few adjustments. Instead of a scatter plot, we'll create a line plot to track the relative performance over time. We'll divide the closing prices by the closing price on the first day, subtract 1 to obtain the percentage change, multiply by 100, and format it as a percentage on the y-axis. We'll keep the horizontal line for reference.

Running the code will display the line plot of the stock's performance. We can see how the stock has fared over the given time period, comparing it to the starting price. In this case, we can observe a positive trend followed by a decline in recent months.

Feel free to customize the figure size and explore other plot options according to your needs.

That concludes Part 12. In Part 13, we will dive into additional data transformations.

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Python for Stock Analysis: Filter Data by Option Expiration and Volatility || Part 13

Python for Stock Analysis: Filter Data by Option Expiration and Volatility || Part 13

Welcome to Part 13 of my series on Python for stock analysis. You can find the code and links to other videos on GitHub in the description below.

In Part 12, we explored various plots that could be useful for stock analysis. In this video, we'll extend our Stock class by adding a couple of methods to examine different slices of the data.

First, let's write a method to identify days where options expire. These are the third Fridays of each month. We'll call this method "option_expiration." To accomplish this, we'll create a mask using NumPy's np.where function. We'll specify three conditions: the index of the data should be greater than the 14th (earliest possible third Friday), less than the 21st (latest possible third Friday), and correspond to a Friday (day of the week 4). We'll return the data with this mask applied, resulting in a DataFrame that only includes the expiration Fridays.

Next, we'll write our final method, which determines how long low volatility has lasted since the last two standard deviation move. We'll call this method "low_vol_duration." To implement this, we'll add a column to the data called "days less than 2 standard deviation" and initialize it with zeros. Then, we'll iterate over the data and check the magnitude of each row. If the magnitude is less than two, we'll increment the count and update the corresponding row in the "days less than 2 standard deviation" column. If the magnitude is two or greater, we'll reset the count to zero. Finally, we'll return the DataFrame containing only the rows with two standard deviation moves, showing the number of days between each move.

Let's run the code and examine the results. We can see the days of option expiration, which fall between the 15th and the 21st of each month. Additionally, we have the low volatility duration, indicating the number of days since the previous two standard deviation move. For example, about a year ago, we had gone two days since the previous two standard deviation move, followed by periods of 32, 41, and so on.

That wraps up Part 13 and concludes Section 2. I encourage you to continue developing and expanding the Stock class according to your needs. In Part 14, we'll explore how to package our functions into a Python package that can be installed using pip.

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Python Stock Analysis: Create & Publish Your Own Custom Python Packages with Pip in VS Code

Python Stock Analysis: Create & Publish Your Own Custom Python Packages with Pip in VS Code

In this video, I will guide you through the process of packaging your Python code into a library that can be installed using pip. This is the final video in my series on Python for stock analysis, where we covered topics like data retrieval, data transformation, visualization, and data screening. You can find links to the previous videos in the description below, as well as a link to the GitHub repository.

The goal of packaging our code is to make it more convenient to use and reuse in specific Python projects. Although I will demonstrate the process as if we were publishing the code to PyPI (Python Package Index), it is important to note that the code might not be ready for PyPI right away. We will first set it up and install it locally to understand the process. However, in the future, you might modify the code and create something that you would like to share with a wider audience.

The packaging process can vary depending on the complexity of the project. In our case, since our code consists of two modules, the process is relatively simple. Here's an overview of the steps:

1. Create a parent folder to store the source code and other files.
2. Within the parent folder, create a "source" folder and a "tests" folder. The source folder will contain the actual package when installed.
3. Copy the two code files from the previous segments into the source folder.
4. Create a double underscore "init.py" file in the source folder to allow importing the modules.
5. Create a license file to protect your code and minimize legal risks.
6. Add a README file to provide documentation and serve as the homepage on GitHub or PyPI.
7. Create configuration files that define how Python interacts with your code.
   • Create a "pyproject.toml" file for the build system and specify dependencies.
   • Create a "setup.cfg" file to provide metadata about your project (name, version, description, license, etc.) and specify package dependencies.

Once you have set up the folders and configuration files, you can install the library locally using pip. Open the command line, navigate to the parent folder, and run the command "pip install .". This will install the library, making it accessible from any Python environment.

After the installation, you can test the library by launching Python and importing the "analyze" module. You can also use the "help" function to view metadata about the package.

Remember that the packaging process may evolve over time, and there are multiple ways to package code for installation. The steps I've outlined here are a starting point, and you can explore additional options as your projects become more complex.

I hope this tutorial helps you get started with creating your own Python packages.

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Backtesting and live trading with Interactive Brokers using Python.

Backtesting and live trading with Interactive Brokers using Python.

Dr. Julio begins the session by providing an introduction to algorithmic trading and its benefits. He explains that algorithmic trading allows traders to reduce the pressure of constantly monitoring the market, minimize human errors, and create more free time for other activities. He highlights the four major components of algorithmic trading, which are brokers, the internet, programs, and computers.

The focus then shifts to Interactive Brokers (IB), which is introduced as the largest electronic trading platform in the United States. Dr. Julio explains that IB offers an advanced API technology that enables traders to use programs to trade, providing competitive pricing and access to global markets. He emphasizes that Python programs can be used to algorithmically trade with IB.

Next, Dr. Julio introduces a Python software called Hybrid Fat Pack, which allows traders to set up an algorithmic trading platform on their local or cloud computers. He highlights the main advantage of this platform, which is the ability to protect intellectual property by not needing to disclose or upload any information on the internet. Traders can also perform backtesting and live trading in one place, manage multiple accounts, and trade with different brokers using various Python packages such as TensorFlow and Scikit-learn. He provides instructions on how to download and set up the Hybrid Fat Pack platform from the AI Bridge PI's website.

Moving on, Dr. Julio explains the steps required to set up the tools for backtesting and live trading with Interactive Brokers using Python. He advises users to download and save the necessary tools, including Interactive Brokers, IP official terminals, and Python. He also provides links to tutorials, documentation, and a community forum for additional support. Dr. Julio demonstrates how to configure WS (Workstation) and IB Gateway and shows how to open a Python environment using Anaconda. He runs a Python code to showcase the setup process and provides tips on organizing windows for better productivity.

The speaker proceeds to explain the initial steps of using Interactive Brokers with Python. He instructs users to open a file named 'Romina Eva' and locate their account code, which needs to be updated in the file accordingly. The speaker demonstrates how to choose and run a Python code, initializing a trader and displaying account balance, cash value, portfolio value, and pending orders. Users can trade various securities such as stocks, options, futures, and Forex using Hybrid Fat Pack. The speaker mentions that orders placed cannot be manipulated and are listed with a permit ID.

The process of retrieving real-time and historical price data from Interactive Brokers is demonstrated. By commenting/uncommenting specific code lines, the speaker shows how to retrieve either real-time or historical data and print it in a user-friendly format. The code structure and three essential functions in Python code are explained, including the "initialize" function that runs at the beginning, and the "handle data" function where trading decisions are made and executed every 2 seconds by default.

Dr. Julio explains how to make trading decisions using Python and Interactive Brokers. Users can configure the code to make decisions at fixed schedules, whether every second, hour, day, or at specific times. He introduces three crucial functions for creating trading decisions: initialization, handling data, and placing an order. To demonstrate the process, sample codes are provided to fetch historical data and print the ask price of the I Spears ETF. The speaker utilizes Panda's data frame to retrieve and print historical data, showcasing how Python can be utilized for trading decisions.

The speaker discusses placing orders using global variables and real-time prices, providing examples of buying shares. Interactive Brokers is used to search securities, apply filters such as the US major market and a price over $100, and use the cat scan result function to obtain relevant information. The steps to build an algo strategy are explained, including contract identification, frequency of trading decisions using handle data or scheduled functions, requesting historical data, and choosing the order type. An example of a daily close reversion strategy is presented, where trading decisions are made based on the previous day's closing price. A schedule function is defined to run daily at a specific time to trigger the decision-making process and subsequent actions.

The video covers two examples of trading strategies in Python. The first example demonstrates a basic strategy based on the closing prices of two consecutive days. If today's close is higher than yesterday's, all positions are sold off; otherwise, SPY is bought. The code is explained and is relatively simple. The video then introduces the concept of backtesting and presents a more advanced example of a moving average crossover strategy. The process of applying a strategy to historical data and evaluating its performance is discussed. The code for this strategy is explained and remains straightforward. The video also covers retrieving and processing data, as well as analyzing testing results and viewing portfolio values.

Dr. Julio discusses the process of backtesting and live trading with Interactive Brokers using Python. He explains the default mode of running the handle data function every minute for testing purposes and the importance of having a historical data injection plan. He details how to supply minute-to-minute and daily data to the code and how to specify the time frame and frequency for backtesting with Interactive Brokers. A code example is demonstrated, showcasing how to run a backtester, retrieve and manage account information, and check the output folder for account balance and cash value.

The speaker emphasizes the benefits of supplying historical data provided by the user to improve code performance. While exact simulation data from Interactive Brokers is necessary for debugging purposes, requesting unnecessary information can be time-consuming. Dr. Julio suggests supplying only a portion of historical data or using random data, which can significantly improve code performance. He demonstrates how to supply historical data from a local CSV file, specify the desired time range and data type, and run tests faster and more efficiently using a custom time list and the 'render' data provider.

Dr. Julio discusses performance analysis and its importance in evaluating the performance of an algorithmic trading strategy. He explains the need for historical data to test and refine code, and how a performance analysis chart can calculate variables such as the Sharpe ratio to assist in this process. Once comfortable with the backtesting results, the speaker advises switching to a paper account to run the strategy against real market conditions. He also demonstrates how to use iBridgePy to handle multiple accounts, which is crucial for fund managers.

Dr. Julio concludes by highlighting the flexibility and user-friendly nature of the Hybrid Fat Pack software, making it a powerful tool for algorithmic trading.

• 00:00:00 Dr. Julio gives an introduction on algorithmic trading and Interactive Brokers, followed by explaining a simple trading strategy called "Daily Close Reverse". He explains the implementation of this strategy using iBridgePy and backtesting using historical data provided by Interactive Brokers or other data providers. Once the backtesting is completed, it is analyzed for potential results, and if the strategy performs well, the live trading strategy is implemented using iBridgePy, and orders are placed to martillo accounts. Dr. Julio emphasizes that this is an essential feature for hedge fund managers.
  
  
• 00:05:00 The speaker talks about the benefits of algorithmic trading, which include less pressure from constantly watching the market, fewer human errors, and more free time. The four major components of algorithmic trading are brokers, the internet, programs, and computers. The speaker then goes on to discuss Interactive Brokers (IB), which is an LLC and the largest electronic trading platform in the United States. IB offers an advanced API technology that enables traders to use programs to trade, providing competitive pricing and global market access. To algorithmically trade with IB, traders can use Python programs and connect them to IB to begin trading.
  
  
• 00:10:00 The speaker introduces a Python software called Hybrid Fat Pack, which allows traders to set up an algo trading platform on their local or cloud computers. The main advantage of using this platform is that traders can protect their intellectual properties, as they do not need to disclose or upload any information on the internet like other competitors do. Moreover, traders can backtest and live trade together in one place, manage multiple accounts, and trade with different brokers using any Python packages, including TensorFlow and Scikit-learn. The speaker also provides instructions on how to set up the platform, which can be downloaded from the AI Bridge PI's website.
  
  
• 00:15:00 The speaker explains the steps needed to set up the tools required for backtesting and live trading with Interactive Brokers using Python. The first step is to download and save the necessary tools, including Interactive Brokers, IP official terminals, and Python. The speaker also provides links to tutorials, documentation, and a community forum that provide helpful information to users. The speaker then gives a demo on how to configure WS and IB gateway before demonstrating how to open a Python environment using Anaconda and run a Python code. The section concludes with tips on organizing windows for better productivity.
  
  
• 00:20:00 The speaker explains the initial steps required to use Interactive Brokers with Python by opening a file called 'Romina Eva.' The user needs to find their account code, and then change the account code in the file to reflect their own account. The speaker demonstrates how to choose a Python code and run it, and then shows how to initialize a trader and display the account balance, cash value, portfolio value, and pending orders. The user can trade stocks, options, futures, Forex, and other contracts using hybrid path. If the user places orders, which cannot be manipulated, then it will be listed with a permit ID.
  
  
• 00:25:00 The presenter demonstrates how to run a bridge between Python and Interactive Brokers to obtain real-time and historical price data. By commenting in/out specific code lines, the presenter shows how to retrieve real-time or historical data and print it out in a straightforward manner. The presenter then explains the code structure and the three basic functions used in the Python code, including the "initialize" function, which runs at the beginning of code execution, and the "handle data" function, where trading decisions are made and runs every 2 seconds by default.
  
  
• 00:30:00 The speaker explains how to use Python to make trading decisions with Interactive Brokers. By configuring the code, users can set a fixed schedule to make decisions either every second, hour, day, or at specific times. In addition, the speaker introduces three basic functions that are essential in creating trading decisions, including initializing a function, handling data, and placing an order. To demonstrate the process, the speaker provides sample codes that fetch historical data and print the ask price of the I Spears ETF. Using Panda's data frame, the speaker is able to retrieve historical data and print it, further demonstrating how Python can be used to make trading decisions.
  
  
• 00:35:00 The presenter discusses how to place orders using global variables, real-time prices, and examples to buy shares. The presenter also introduces the use of Interactive Brokers to search securities, add filters such as the US major market and a price over $100, and use the function cat scan result to obtain relevant information. He then explains the steps to building an algo strategy, which includes identifying the contract, frequency of trading decisions using handle data or a scheduled function, requesting historical data, and choosing the type of order. The presenter provides an example of a daily close reversion strategy, which involves making trading decisions based on the previous day's closing price. He also defines a schedule function to run every day at a specific time to trigger the daily function where the decisions and actions are made.
  
  
• 00:40:00 The video goes through two examples of trading strategies in Python. The first example uses a basic strategy based on the closing prices of two days, where if today's close is greater than yesterday, sell off all positions, otherwise, buy SPY. The code is then explained and is quite straightforward. The video then moves onto the concept of backtesting, and a more advanced example of a moving average crossover strategy is detailed. The process of applying a strategy to historical data to see how well it estimates trade results is discussed. The code for this strategy is explained and is still quite simple. The video also explains how to retrieve and process this data, as well as how to analyze testing results and view them in portfolio values.
  
  
• 00:45:00 The speaker discusses the process of backtesting and live trading with Interactive Brokers using Python. They begin by explaining the default mode of running handle data every minute for testing and the need for a historical data injection plan. The speaker then explains how to supply minute-to-minute and daily data to your code. They also cover how to tell Interactive Brokers the time frame and frequency for backtesting. The speaker demonstrates a code example of running a backtester and retrieving and managing account information and how to check the output folder for account balance and cash value.
  
  
• 00:50:00 The speaker discusses the benefits of supplying historical data by the user to improve code performance. The speaker explains that for debugging purposes, exact simulation data from Interactive Brokers (IB) for every test is needed, but requesting a lot of information is unnecessary and wastes a lot of time. Sometimes, only a portion of historical data is required, and supplying random data can suffice. This can vastly improve the performance of one's code. The speaker demonstrates how to supply historical data from a local CSV file and how to specify the desired time range and type of data needed. Additionally, the speaker shows how to run tests much faster and more efficiently by specifying a custom time list and using the 'render' data provider.
  
  
• 00:55:00 The speaker discusses performance analysis and how to use it to analyze the performance of your algorithmic trading strategy. To begin, historical data is needed to test and refine your code, and the speaker explains how to use a performance analysis chart to calculate variables such as Sharpe ratio to aid in this process. Once you're comfortable with the results of your backtesting, you can switch to a paper account to run your strategy against real market conditions. Finally, the speaker demonstrates how to use I Bridge PY to handle multiple accounts, an important feature for fund managers. Overall, the averages and software are flexible and easy to use, making them a powerful tool for algorithmic trading.