Python in algorithmic trading - page 13

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EMA IN PYTHON - TECHNICAL ANALISYS LIBRARY

EMA EN PYTHON - TECHNICAL ANALISYS LIBRARY

The instructor demonstrates how to load the exponential moving average indicator in Python using pre-loaded data. The first step is to obtain the data from MetaTrader 5, which can be done by selecting the desired market and dates and then exporting the data. Once the data is obtained, it needs to be converted into candles using a function. The instructor then installs the necessary library and imports the EMA indicator class from the library. To compute the EMA, a data frame column, such as the close data, is passed to the constructor. The window parameter specifies the number of periods to use for the EMA. Finally, the EMA values are obtained using the ema_indicator method, which creates a data frame with the values.

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SMA IN PYTHON - TECHNICAL ANALYSIS LIBRARY

SMA EN PYTHON!! - TECHNICAL ANALYSIS LIBRARY

The video talks about the Simple Moving Average (SMA) and how it can be used in Python. The presenter explains that SMA is easy to use with past data, which can be obtained through MetaTrader5. The video goes on to demonstrate how to create candles from tick data and compute the SMA value using the AlgoTraderTrends library. The presenter provides a step-by-step guide on how to import and use the library to compute the SMA value from a specific column of the data frame. The video concludes with a call to action for viewers to like, subscribe, and share the video if they found it helpful.

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How to import stock price data from MetaTrader 5 into Python?

How to import stock price data from MetaTrader 5 into Python?

In this YouTube video, different methods to import stock price data from MetaTrader 5 into Python are explained. The methods include importing necessary libraries, setting the desired time frame and time zone, defining a function called "get data," manipulating the resulting data frame, using the tqtndm package, creating a rates frame, and utilizing two data frames to retrieve prices and date/time information. The speaker suggests putting the loops into a function to make the code cleaner, and using these methods, users can easily import data for numerous symbols without much difficulty.

• 00:00:00 In this section, the speaker explains how to import stock price data from MetaTrader5 into Python. The first step is to import all the necessary libraries, including pandas, pytz, datetime, tqdm, and MetaTrader5. Then, the speaker initializes MetaTrader5 and sets the desired time zone and time frame. The speaker defines a function called "get data" that requires the symbol, the number of candles needed, and the time frame. The function returns the desired data, and the speaker explains what each input and output does in the function.
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• 00:05:00 In this section, the speaker explains a function used to import stock price data from MetaTrader5 into Python. The function takes in a symbol, a time frame, and a date, and returns a data frame containing the requested data. The speaker goes through steps to manipulate the resulting data frame, including converting the time column to daytime and dropping unnecessary columns. Additionally, the use of a for loop is suggested to make it easier to call data for multiple assets.
  
  
• 00:10:00 In this section, the speaker explains how to import stock price data from MetaTrader5 into Python using the tqtndm package. They use the try function and accept function to call a rates function previously defined that takes in the symbol and the number of days set to 400. The returned data is appended to a dictionary, and any not available data is dropped. The speaker suggests putting the loop into a function to make the code cleaner. Overall, the process involves creating a rates frame, appending the data to a dictionary, and then running the script.
  
  
• 00:15:00 In this section, the speaker explains that with the use of two data frames, users can easily import stock price data from metatrader5 to Python by retrieving the prices and date/time information. This method can be used for numerous symbols without much difficulty.

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RSI TRADING BOT IN PYTHON!!- GETTING DATA FROM METATRADER 5

RSI BOT DE TRADING EN PYTHON!! - COGIENDO DATOS DE MT5

The video provides an in-depth tutorial on creating a Python bot using the RS index on MetaTrader 5 (MT5) for algorithmic trading. The process involves configuring MT5 for algorithmic trading and web requests, creating a bot file using the Mt5 library, and importing the RS trading class, with a constructor that receives parameters such as the time period, lot size, and market string. To activate the bot, the presenter uses the "set" function to initiate an event, and the "join" function to end the process correctly. The video also covers the creation of a server module to receive data from MT5 and the definition of a function to open and close bot trades. The presenter tests the bot by loading it onto a chart and analyzing its behavior. Overall, the video provides an extensive guide on how to set up and test the RSI bot for automated trading.

• 00:00:00 In this section, the video creator explains how to create a trading bot using the RS index in Python with MetaTrader 5 (MT5). The first step is to configure MT5 to enable algorithmic trading and web requests. Then, the creator shows how to create the bot by creating a bot file using the Mt5 library, importing the RS trading class, and creating a constructor that receives parameters such as time period, lot size, and market string. Finally, the creator demonstrates how to stop the bot correctly using an event and a dictionary to store trading data. Users can access the complete code on the creator's GitHub profile.
  
  
• 00:05:00 In this section of the video, the presenter discusses the creation of functions to open and close the three bots, as well as how to activate an event and stop the bots once they have been initiated. The presenter uses the "set" function to activate the event and the "join" function to end the process correctly. Additionally, they create a separate function to wait for the user to press "enter" before stopping the program and stopping the bots using the "join" function. They go on to define a function for each of the three bots, with the only difference between them being the text printed to the console.
  
  
• 00:10:00 In this section, the video discusses creating a module called "python rs6" which will act as a server and receive data from MT5, acting as the client. The socket library is used to create the server, with a port number of 889 and the address set to localhost. The video then goes on to explain how to create a function to initiate the socket and listen for connections, using the accept function to accept incoming connections. The main loop of the function is set to run indefinitely until the stop event is set, with the function receiving messages and decoding them while the stop event is not set. Overall, the video covers the basics of creating a Python bot for trading with RSI data.
  
  
• 00:15:00 In this section of the video, the presenter creates an RSI trading bot in Python that retrieves data from MT5. The bot, named "rs player," is created in the Expert Advisor section of MT5 and loaded with the R&S and socket connections to connect to a Python server at a specified address. With an executed tick, the bot sends information to the server, which is a string that contains the RSI value from the previous tick and the current RSI value. In Python, the data received is split by a comma and converted to a float, allowing for further manipulation of the data. The bot is closed by closing the connection and server socket with an added function to handle any errors encountered. The presenter tests the bot by creating a main file that imports MT5 and the bot class, initializes an instance, and starts the live trading session.
  
  
• 00:20:00 In this section, the video demonstrates how to load the bot onto a chart and test if it is receiving data correctly. The bot is set to receive 1 trade of 0.01 in 60 seconds, with variables to establish orders and the amount of leverage used. To execute the bot, the video shows users how to save the code and compile it in MQL5, and subsequently close the bot by clicking 'enter'. Then the video shows the creation of an order file with macros, including 'candles' and 'between operations', to ensure that only one trade is executed per set of candles. Overall, the video offers a detailed guide on how to set up and test the RSI bot for algorithmic trading.
  
  
• 00:25:00 In this section of the transcript excerpt, the speaker explains how to create a function called "borders" that receives the stop event, shared data dictionary, and bot information dictionary as inputs. The function includes variables to save the time of the last operation and the current time. The section also covers the logic behind opening a buy or sell position based on certain conditions and the importance of taking into account the time between operations. The speaker mentions that a function called "open position" needs to be created to open a position.
  
  
• 00:30:00 In this section, the YouTuber explains how to define a function for trading with the MetaTrader 5 platform using Python. The function is called "open position" and it receives the market, the lot, and the type of operation. The YouTuber explains that the code for this function can be found on a Google Chrome page for MT5 Python order send, and it can be directly copied and pasted. However, there are several things that need to be customized, such as the order type, stop loss, and take profit, which depend on the user's broker. The YouTuber goes on to define the stop loss and take profit for the bot, and then shows how to import the function and create a bot to execute trades.
  
  
• 00:35:00 In this section, the YouTuber demonstrates how to access the last element of a list in the Ceret Orders code by typing in "[-1]" into the code, which would return the last element of the list. The next step is to set the buy trigger at a certain price point by changing the code to buy if the price goes above 50. The bot can be executed and if the criteria are met, such as the price going above the threshold, it will execute a buy order. The user can then check to see that the take profit and stop loss levels are set correctly by checking the code and observing the bot's behavior. The YouTuber encourages users to check out the bot on GitHub and to like, subscribe, and share the video.

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How to download data from the Stock Market with MetaTrader 5 and Python

Como baixar dados da Bolsa de Valores com MetaTrader 5 e Python

The video tutorial "Como baixar dados da Bolsa de Valores com MetaTrader5 e Python" explains how to download stock market data using MetaTrader5 and Python. The tutorial demonstrates how to create a Python script to access desired assets and export the data to a CSV file. The video covers topics such as securely storing login credentials, manipulating data with Pandas, and extracting data from candles using cop rates. The high-quality and free data available is a valuable resource for developing tools to attract more people to the stock market. The video concludes with a call to visit the Develop Academy website for further learning and connecting via Instagram.

• 00:00:00 In this section, the speaker discusses the importance of having quality and abundant data for quantitative traders and highlights the difficulty of finding good public data sources. He introduces a solution that he recently discovered for using the MetaTrader 5 system to download data, categorize it in an internal database, and develop strategies locally on one's computer without paying anything. He walks through the steps of installation and exporting data, demonstrating the unique feature of MetaTrader 5 in allowing users to export data into a CSV file that can be easily imported into other data analysis software.
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• 00:05:00 In this section, the speaker explains how to download stock market data using MetaTrader 5 and Python. By creating a script in Python, users can programatically request data from MetaTrader 5 and export the information for any desired asset. The first step is to install the MetaTrader5 module in the Python environment, and then create a Python script to replicate the process of logging into MetaTrader 5 and accessing the desired assets. The speaker also shares a tip on creating an external file to securely store login credentials.
  
  
• 00:10:00 In this section, the video explains how to download data from the stock exchange using MetaTrader 5 and Python. The process involves connecting to a broker's specific server, and then using MetaTrader 5 methods to extract and organize data. The video demonstrates how to pull data on Bradesco ticker, including setting up dates and flagging types of ticks to pull. After the request is completed, the video shows how to use Pandas to manipulate the data and create a dataframe. In this case, the data returned covers all the operations that occurred in Bradesco between the beginning of the month of March to March 10th.
  
  
• 00:15:00 In this section, the speaker talks about how to download stock market data using MetaTrader 5 and Python. They explain how to retrieve data on the volume traded, the aggressor, and location of the trades, which can be used to develop high-frequency trading strategies. The data is of high-quality and is available for free, which is a great resource for developers to create tools that can be used to attract more people to the stock market. The speaker also demonstrates how to handle timestamps and convert them to real data, as well as how to extract data from candles using the cop rates Range function. Overall, the tutorial provides valuable information on how to efficiently gather stock market data for trading analysis.
  
  
• 00:20:00 In this final section, the speaker gives a brief closing statement, thanking the viewers for watching and providing the link to their website, Develop Academy, for those interested in learning more about quantitative trading strategies. The speaker encourages viewers with questions to leave comments on the video or reach out to them on Instagram.

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Online Trading by Python in MetaTrader 5 + Get Data from MQL5

Get the code at GitHub: https://github.com/Hesamtps/online-trading-by-python-in-MetaTrader5-get-data-from-mql5

Online Trading by Python in MetaTrader 5 + Get Data from MQL5

The tutorial demonstrates how to download a dataset from MetaTrader and conduct online trading deals using Python. The instructor imports the MetaTrader5, pandas, and datetime libraries, specifies the asset and timeframe for the dataset, and downloads the last hundred data points. They explain how to manage a position in MetaTrader5 by setting stop loss, take profit, and using the GTC command for a specified duration. While the section provides a basic understanding of the different commands required to manage a position, it's unclear what the overall trading strategy being employed is.

• 00:00:00 In this section of the tutorial, the instructor demonstrates downloading a dataset from MetaTrader and conducting simple online trading deals using Python. The MetaTrader5 library is imported, and the software shortcut path is addressed to Python. The pandas and datetime libraries are also imported, and the current time is used to specify the time of the last data in the dataset. The symbol key of the desired asset is written, and the timeframe (daily timeframe in this case) is selected for the dataset. The last one hundred data points are downloaded, and a format command is used to store the data in the user's personal system. Online trading is carried out by determining the assets and volume of the transaction, defining the unit of price as a pip, and using either ask or bid price depending on the position entered.
  
  
• 00:05:00 In this section, the video explains how to set stop loss and take profit for a position using Python commands in MetaTrader5. It also shows how to close the position by specifying the position ticket number. The GTC command is explained for keeping a transaction active for a specified duration. The video also shows an example of a USDJPY transaction with an active stop loss and take profit. Overall, this section provides a basic understanding of the different commands required to manage a position in MetaTrader5 through Python.
  
  
• 00:10:00 In this section, we learn that the long position was successfully closed. Unfortunately, without further context, it is unclear what the long position refers to or what the overall trading strategy being employed is.

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Trading with Python - How to execute orders on the stock market?

Trading con Python - ¿Cómo ejecutar órdenes en bolsa?

In this video I show you how to execute stock orders with Python. For those who want to apply their knowledge in data analysis and economics, in the stock market.

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Part 2: TRADING with Python - How to do automated INVESTMENTS?

Parte2: TRADING con Python - ¿Cómo hacer INVERSIONES automatizadas?

This is part two, on how to trade with Python. In the first part I explained how to launch orders. In this part I teach to launch orders based on the price of stocks and their relationships between them, automatically, with data from web scraping and apply algorithmic Trading.

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Algorithmic Trading with Python (MACD Indicator)

Trading Algorítmico con Python (Indicador MACD)

In this video on "Trading Algorítmico con Python (Indicador MACD)," the instructor provides a detailed explanation of how the MACD indicator can be used to create trading algorithms in Python. The video covers the three parameters used by the MACD indicator, and how they dictate buying and selling decisions. Libraries like Pandas, NumPy, and Yahoo Finance are used to obtain and analyze stock data, while data cleaning techniques and dictionaries are used to retrieve key information. Overall, the video provides a practical overview of building trading algorithms with Python and the MACD indicator.

• 00:00:00 In this section, the instructor discusses trading algorithms using the MACD indicator with Python. They explain that the MACD indicator uses three parameters to determine whether the market is in an uptrend or downtrend, and based on this, orders are placed for buying or selling. The instructor demonstrates how the code for this indicator works, and how it can be customized depending on different trading strategies. He also explain how to use libraries such as Yahoo Finance, Data Time, Pandas, and NumPy to obtain and analyze data on stock prices. Finally, they show how to use data cleaning techniques and dictionaries to retrieve the last date for a stock purchase, based on the MACD indicator.
  
  
• 00:05:00 In this section, the speaker explains how to create a graph to illustrate the algorithmic trading strategy using the MACD indicator in Python. He suggests downloading the code from the website, "inteligencia-artificial," and accessing the training section. The video also encourages viewers to leave comments or raise any questions they may have.

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Algorithmic Trading with Python (Bollinger Bands Indicator)

Trading Algorítmico con Python (Indicador Bandas de Bollinger)

In this video, the speaker discusses Bollinger Bands, how they measure market volatility, and how to create an automated order system based on them using Python. The speaker explains the main libraries used, such as Yahoo Finance and Pandas, and emphasizes the importance of specifying parameters to customize the system for each stock analyzed. They also demonstrate adding data to the buy and sell columns and how to compare the last sale date with the current date and initiate a sale if they match. Finally, the speaker reminds viewers that technical analysis is not always accurate and suggests combining various indicators and using artificial intelligence to make informed trading decisions.

• 00:00:00 In this section, the speaker introduces the concept of Bollinger Bands, which measure market volatility and include a central moving average line and upper and lower deviation lines. The speaker then discusses how to create an automated order system based on these Bands, which can execute a buy or sell order when certain criteria are met. The speaker provides an overview of the code used to create this system, including the main libraries used, such as Yahoo Finance for data retrieval and Pandas for data analysis. Additionally, the speaker explains the importance of specifying parameters, such as n20 and n2, to customize the system for each stock analyzed.
  
  
• 00:05:00 In this section, the speaker explains how to add data to the buy and sell columns that were created earlier in the code. They demonstrate that data can be added to the "happen" part of the column or left empty using "np.nal". The speaker also explains that they have specialized in this algorithm to only work with sales data, eliminating empty columns and saving the data in a CSV file. They then show how to compare the last sale date with the current date and initiate a sale if they match. Finally, they remind viewers that technical analysis is not always 100% accurate and suggests combining various indicators and creating algorithms with artificial intelligence to make informed trading decisions.