Python in algorithmic trading - page 5

[MetaQuotes]

Build Your Own MetaTrader 5 Python Trading Bot: Advanced Order Management

Get the code at GitHub: https://github.com/jimtin/algorithmic_trading_bot

Build Your Own MetaTrader 5 Python Trading Bot: Advanced Order Management

This section of the video covers advanced order management in a MetaTrader 5 Python trading bot using filters on symbols and comments. By adding a function to the MT5 lib file, the bot can retrieve a filtered list of orders based on comment and symbol for each strategy, allowing for effective management of drawdown and identification of trades. The video also discusses managing order cancellations by checking if orders exist before canceling them using a Boolean value and emphasizing the importance of managing order cancellations as part of risk management. Finally, the video concludes by demonstrating the functioning of the trading bot and offering viewers updates on its progress via Discord.

• 00:00:00 In this section of the video, the speaker talks about managing multiple strategies on the same MetaTrader 5 account. To do this, the speaker chooses to distinguish between strategies using a filter on symbol and comment, which allows for managing total drawdown and identifying which trades belong to which strategy. To create this filtering capability, the viewer is instructed to add a function to the mt5 lib file that retrieves a filtered list of orders based on symbol and comment. The speaker emphasizes the importance of commenting code and provides a link to their GitHub repository where viewers can access the code.
  
  
• 00:05:00 In this section, the video discusses how to turn a symbol into a data frame for easier filtering of active orders. Once the orders are in a data frame, they can be filtered by comment, and the open order numbers are added to a variable called "open orders." The video also shows how to create a function that combines the order filtering function with the previous "cancel order" function, resulting in a Boolean value indicating whether or not the order can be canceled.
  
  
• 00:10:00 In this section of the video, the presenter discusses how to manage order cancellations in a trading bot. They receive a filtered list of orders and check if any orders exist before cancelling them using the `cancel order` function. If there are no orders to cancel, they simply return `false`. They use a `Boolean` for the cancel outcome to ensure that the execution results in a definite outcome. They also demonstrate getting the comment string from the strategy and making sure that it is named correctly in the function. The presenter emphasizes the importance of managing order cancellations as a critical part of a risk management strategy.
  
  
• 00:15:00 In this section, the author concludes the video series and demonstrates the functioning of the trading bot by showing that there are no trades for USDJPY and no sleeping candle. The author also offers to send updates on the bot's progress to viewers via Discord upon request.

[MetaQuotes]

Build Your Own MACD Zero Cross Strategy: Python Trading Bot

Get the code at GitHub: https://github.com/jimtin/algorithmic_trading_bot

Build Your Own MACD Zero Cross Strategy: Python Trading Bot

In this video tutorial, the presenter demonstrates how to create a MACD zero-cross strategy signal using Python trading bot. The presenter explains how the MACD line is generated and how it oscillates around a zero value to form the basis of the zero-cross strategy. They guide the viewer through the steps of creating a file for the MACD zero-cross strategy, importing necessary libraries, structuring code, and passing parameters such as symbol and time frame. The video emphasizes the importance of testing the strategy before implementing any buy and sell trades and provides code for setting up stop-loss and take-profit orders based on the MACD zero cross.

• 00:00:00 In this section, the video tutorial discusses how to generate a MACD zero-cross strategy signal using Python trading bot. In order to do so, the user must have access to Candlestick data (open-high-low-close data) from an exchange of their choice and generate their own MACD indicator consisting of the MACD line, MACD signal line, and histogram. The video provides an overview of how the MACD line is generated and how it oscillates around a zero value, which is the basis of the zero-cross strategy. The tutorial then walks through the steps of creating a file for the MACD zero-cross strategy, importing necessary libraries, structuring code, and passing parameters such as symbol and time frame. The video emphasizes the importance of testing the strategy before implementing any buy and sell trades.
  
  
• 00:05:00 In this section, the speaker discusses the MACD zero cross strategy and provides an overview of the code structure. The MACD indicator requires several pieces of information, and the default values are provided, but it is recommended to backtest different values specific to individual symbols. The speaker emphasizes the importance of commenting on the code for future reference and includes a link to their GitHub. The pseudo code for the strategy includes getting the necessary data, calculating the indicators, and generating the signals from zero line crosses. Placeholders for each function are included, and the code can be expanded to include other parameters such as take profit and stop loss ratios for backtesting.
  
  
• 00:10:00 In this section, the focus is on the getData function, which retrieves data from an exchange to be used in the strategy. This function is similar to the one used in the previous episode on the MACD crossover function, and it is argued that it may be strategy-dependent, so it is left in the same file. However, it is crucial to retrieve enough candlesticks, as exponential moving averages are used, and a rule of thumb is to have at least five times as many candlesticks as the EMA size being calculated. This is important for exchanges like Binance, which may limit the number of candlesticks retrieved at any given time. Once the data is retrieved, step two involves calculating the two indicators required for the strategy, namely the MACD and the MACD zero cross, which is generated after defining the function.
  
  
• 00:15:00 In this section, the speaker discusses how to calculate the MACD (Moving Average Convergence Divergence) indicator and the zero cross using Python. To calculate the MACD, the speaker refers to the indicator library and uses the "calc_macd" function. To calculate the zero cross, one needs to create two extra columns on the dataframe, position, and pre-position, and compare them against zero using a Boolean true/false statement. A lambda function is used to remove any N/A values, and the position and pre-position values are dropped to leave the zero cross column.
  
  
• 00:20:00 In this section of the video, the presenter adds a MACD zero cross indicator to the code using a reusable library to save time in the future. They explain that the values they use for buy and sell stops and take profit are arbitrary and not tested, so users should generate their own. The presenter then adds blank columns for order types, price, stop loss, and take profit to the dataframe, and iterates through to generate signals based on a zero cross. The code checks if a zero cross is true and continues only if it is, allowing for the direction of the MACD to be tested.
  
  
• 00:25:00 strategy to include stop loss and take profit orders based on the MACD zero cross. In this section, the speaker explains the process for setting up sell stop and buy stop orders, including determining the stop price and take profit distance. For sell stop orders, the stop price is set to the previous candle's low, while the stop price for buy stop orders is set to the previous candle's high. The take profit distance is calculated based on the difference between the stop price and stop loss and added back to the relevant price point. The speaker also includes comments to guide users in updating their own information to calculate their stop price and stop loss values.
  
  
• 00:30:00 In this section, the video focuses on updating the main function to make it more stable. The data frame with calculated values is now passed back to the function, but an error can occur if the function tries to call the first row because the take profit and stop loss values are based on the previous row's value. To fix this issue, a few lines of code are added to the for loop, telling it to work only after passing the zeroth row, which makes the code more stable. The video then updates the main function to extract the data frame rows only where the zero cross is equal to true to make the results more useful.
  

[MetaQuotes]

Build Your Own MACD Crossover Strategy with MetaTrader 5 and Python

Get the code at GitHub: https://github.com/jimtin/algorithmic_trading_bot

Build Your Own MACD Crossover Strategy with MetaTrader 5 and Python

This video provides a comprehensive guide on how to create a MACD crossover strategy using Python and MetaTrader 5. The process involves obtaining OHLC data, calculating indicators, generating signals, and developing a function for crossovers that can be used with different technical indicators. The presenter emphasizes the importance of backtesting before using the strategy in live trading and provides code and resources for quick implementation. The video also covers calculating values for sell signals, updating data frame values, and filtering results for easier backtesting. Overall, the video is a great resource for traders interested in developing and implementing a MACD crossover strategy.

• 00:00:00 In this section of the video, the presenter outlines the necessary tools needed to create a MACD crossover strategy using Python and MetaTrader 5. The MACD indicator is made up of three separate pieces of information: the MACD line, the MACD signal line, and the MACD histogram. For this strategy, the focus is on the MACD line and the MACD signal line, crossing each other to generate buy and sell signals. The presenter provides an outline of how to create a strategy, defining a function that brings all the necessary pieces together and specifying inputs such as the symbol, time frame, and MACD inputs. The presenter also emphasizes the importance of commenting as a way to make an investment that will save unnecessary time in the future.
  
  
• 00:05:00 In this section, the instructor discusses the steps involved in developing a MACD crossover strategy on MetaTrader 5 using Python. The first step is to obtain the OHLC (open, high, low, close) data for the chosen symbol, time frame, and exchange. Then, the MACD indicator and another indicator (not specified) are calculated based on input values for the MACD fast, slow, and signal. Next, the signals are generated using the indicators and arbitrary values for rules. The instructor advises against using these values for live trading without backtesting. Finally, if desired, an auto trading bot could be created to execute trades based on the generated signals.
  
  
• 00:10:00 In this section, the speaker explains the importance of retrieving an adequate number of candlesticks when using EMAs in a trading strategy. A rule of thumb is to retrieve at least five times the number of the EMA being calculated. If the exchange being used has a limit on the number of candlesticks that can be retrieved, this may impact the use of certain EMA values in backtesting. Additionally, the speaker walks through the process of calculating the MACD indicator using ta-lib and explains that a simple crossover indicator needs to be built into the strategy to identify when the MACD line and signal line cross each other.
  
  
• 00:15:00 In this section of the video, the presenter explains how to create a generic crossover function that takes any two columns of a data frame and tells when they cross each other. By using this function, traders can easily use different technical indicators with crossing mechanisms. The function creates two additional columns on the data frame that compares them together. Once a cross has been identified, a third column is added, leaving only the new cross column. The presenter emphasizes the importance of dropping NA values and using in-place equals true to ensure the function is fast when doing algorithmic trading. Traders can access the code on the Algorithmic Trading Bot Github page https://github.com/jimtin/algorithmic_trading_bot.
  
  
• 00:20:00 In this section, the video covers the calculation of signals for the MACD crossover strategy. The function takes in a data frame and allows users to customize the take profit, stop loss, and buy/sell prices. The function starts by adding columns for order type, stop price, stop loss, and take profit, which serve as placeholders for trades that haven't been made. The function then iterates through each line of the data frame and checks if there is a crossover, indicating that a trade needs to occur. If there is a crossover, the function checks if the MACD line is above or below the signal line to determine the direction of the trade, which is indicated by a signal value of either 1 or -1. The function then returns a data frame with the buy and sell signals for easy backtesting and review.
  
  
• 00:25:00 In this section, the speaker explains how to calculate values for a sell signal in the MACD crossover strategy using Python and MetaTrader 5. After the cross happens and MACD goes below the signal, it is an indication to sell, which means the price is about to or is already going down. Hence, the order type is set to buy stop, stop price to the high of the previous candle, and stop loss to the low of the previous candle. The take profit is set to a one-to-one ratio. For a sell signal, the opposite parameter values are used, i.e., stop loss is set to the high of the previous candle, sell stop to the low of the previous candle, and take profit at a one-to-one ratio.
  
  
• 00:30:00 In this section of the video, the speaker discusses updating the values in the data frame of the MACD crossover strategy. They note that while the previous code took care of when the signal was equal to 1 or negative one, the vast majority of the time, it's going to be equal to zero, leading to potential errors. To avoid any errors, they add default values back in for when there is no order type, stop price, stop loss, or take profit value. Additionally, the speaker notes that if the very first row of the data frame is the one that generates a true value, then there is no minus one to do to get there, which could lead to further errors. To address this issue, a simple function is written to skip the very first row of the data frame.
  
  
• 00:35:00 In this section, the presenter explains that the existing code in the main function of the Python script calls a few start-up functions and imports the MACD crossover strategy. He proceeds to update the main function to use the ETH/USD currency pair with a one-hour timeframe and prints it to the screen. However, since there are a lot of instances where nothing happens, he suggests filtering the results to only print the values that are true to make it easier to backtest the strategy. The video then shows how the code can be modified to achieve this.

[MetaQuotes]

How to Install TA Lib on Windows

Get the code at GitHub: https://github.com/jimtin/algorithmic_trading_bot

How to Install TA Lib on Windows

This YouTube tutorial provides detailed instructions on how to install TA Lib, a technical analysis library, on Windows 10. The steps include downloading and installing Microsoft Visual Studio build tools and TA Lib, using Microsoft Powershell to unpack the TA Lib file, compiling the program using the developer command prompt and running "pip install ta lib" to install the program. The tutorial warns that installing TA Lib on Windows can be challenging but provides a clear guide for users with administrator access.

• 00:00:00 In this section, the YouTuber provides instructions on how to install TA Lib on a Windows 10 machine with administrator access. They explain that TA Lib is a technical analysis library with over 150 indicators used by trading companies and traders. They warn that installing it on Windows can be challenging and explain the three downloads needed, including Microsoft Visual Studio build tools and TA Lib itself. They then detail the steps for downloading and installing these tools and using Microsoft Powershell to unpack the TA Lib file.
  
  
• 00:05:00 In this section, the transcript explains how to compile TA Lib on Windows after having installed Visual Studio tools. The video highlights a change that now requires using the developer command prompt and entering the location of the file before running the end make command to compile the program. Finally, users are advised to run "pip install ta lib" to install the program.

[MetaQuotes]

How to Trade with MetaTrader 5 using Python?

✅¿Cómo Operar con Metatrader5 usando Python?✅

The video goes over how to use Python to connect with MetaTrader 5 in order to program investment robots and automate forex market strategies. Various libraries are introduced, and the MetaTrader 5 library's functions for initialization and retrieving data are explained. The presenter also demonstrates how to extract financial data from MetaTrader 5 using Python, specifying date ranges and symbols and plotting the data using a plot function to show the ask and bid, last, and volume. The video provides examples of code and offers the potential for creating a course on creating an investment robot with the help of the order send function.

• 00:00:00 In this section, the video discusses how to connect Python with MetaTrader 5 to program investment robots and automate forex market strategies. The host introduces libraries including DateTime, a date library, pandas, a data visualization library, and the star library, MetaTrader 5. The MetaTrader 5 library has functions for initialization and retrieving data, which are explained. The video includes examples of code and shows how to make requests using the library functions to program investment robots.
  
  
• 00:05:00 In this section, the presenter demonstrates how to extract financial data from MetaTrader 5 using Python. The demo includes specifying the range of dates and symbols to be extracted and converting the data to a data frame. The data is then plotted using a plot function to show the ask and bid, last, and volume. The presenter suggests that one could use these data frames to create strategies or robots, with the help of the order send function. The presenter also offers the possibility of creating a course on how to create an investment robot.

[MetaQuotes]

RSI Trading Bot with Python and MetaTrader 5

Get the code at GitHub: https://github.com/kecoma1/Trading_BOT/tree/main/ta/RSI/BOT

RSI TRADING BOT WITH PYTHON AND METATRADER 5!!!

The video explains how to create a trading bot using Python, MetaTrader 5 and RSI indicator. It covers different trading threads and functions, such as the candle thread and the function to load previous candles, and highlights the importance of handling exceptions while dealing with the tick values. The video also covers setting a proper stop loss and take profit and the process of sending and checking the orders. The presenter demonstrates the workings of the bot by opening a cell position in the EVP and concludes by encouraging viewers to like, share, and subscribe to their channel.

• 00:00:00 In this section of the video, the presenter explains how to create a Python bot using Metatrader 5 and the indicator RSI. The video goes through the main function of the bot, the threads used, and the bot class constructor where the lotage, time period, and market are set. Additionally, the presenter provides a breakdown of the different trading threads and functions, including the candle thread and the function to load previous candles. The code for this bot is also available on the presenter's GitHub.
  
  
• 00:05:00 In this section, the video explains how the candle file and RSI functions work together in the trading bot. The candle file creates candles based on the tick data received, and checks for every period whether to create a new candle or update the current one. The RSI file waits until the candle thread signals that it's ready to compute the RSI indicator, creates a data frame, and uses the TA library to compute the RSI value. The video emphasizes using pandas data frame for storing and processing the values, and highlights the importance of handling exceptions while dealing with the tick values.
  
  
• 00:10:00 In this section, the video goes over how to load the RSI indicator using the RSI object and creating a data frame with the close column from the candle file. The RSI then computes the value based on the close column with a set parameter of 14. The last two non-moving values of the RSI are stored in the data dictionary shared by all the threads. The thread orders are then called to open a buy or sell position, depending on the argument given for the type. The open position function takes the market, lot size, and type arguments, and defines the stop loss and take profit points. The main thread then waits for the RSI to load before checking if the conditions with the indicator are met and if so, will open a position, checking that enough time has passed between the last operation.
  
  
• 00:15:00 In this section of the video, the speaker discusses setting a proper stop loss and take profit, calculating the number of points above or below the desired price, and creating a dictionary that includes the necessary information such as action, market, lot size, type, current price, stop loss, and take profit. The speaker also explains the process of sending the order and checking for any errors that may occur. Additionally, the video demonstrates killing the threads and shutting down the Metatrader 5 software, and includes a test of opening a position in the EUR/USD and AUD/USD pairs.
  
  
• 00:20:00 In this section, the presenter demonstrates how the RSI trading bot with Python and MetaTrader 5 works by opening a cell position in the EVP and checking if the stop loss and take profits are okay. The take profit is set at 500 points and the stop loss is set at 100 points. The presenter concludes the video by reiterating that creating a bot with the TA library and RSI in MetaTrader5 is easy and encourages viewers to like, share, subscribe, and watch the next video.

[MetaQuotes]

How to connect Python to MetaTrader 5 - MQL5

COMO CONECTAR PYTHON AO METATRADER 5 - MQL5

This video introduces a series on how to connect Python to MQL5, which allows for the development of robots that can perform quantitative and multifaceted analyses using Python libraries for MetaTrader 5 trading platforms. The speaker explains the process of downloading Anaconda and MetaTrader 5 and emphasizes the importance of using the Windows operating system for the best results. The video demonstrates downloading the MetaTrader 5 library into Anaconda and connecting Python to MetaTrader 5 through MQL5. Additionally, the presenter demonstrates how to check if a connection works and how to use the autocomplete feature to aid in programming.

• 00:00:00 In this section, the speaker introduces a set of videos that will connect the powerful Python programming language to the MQL5 language, which develops robots for the MetaTrader 5 trading platform. Through this connection, the computational power of Python and the implementation of ideas in the MQL5 language can be combined to create robots for quantitative and multi-faceted analyses using Python libraries, ultimately leading to production of robots for buying and selling based on these analyses. The speaker guides the viewer through the process of downloading two important tools, Anaconda and MetaTrader 5, and explains the importance of utilizing the Windows operating system for the most effective use of these tools.
  
  
• 00:05:00 In this section, the video shows the download and installation of Anaconda, a platform where one can develop applications, algorithms, and scripts for data mining, artificial intelligence, and more. Within Anaconda, there are various tools such as Spider, Jupyter Notebooks, and Navigator, but the focus is on Jupyter Notebook. The video walks through the installation of a library through Anaconda's prompt that connects Python to the MQL5 language, which allows for the integration of trading functionality with MetaTrader 5. The importation of the successfully installed MetaTrader 5 library and its Python syntax are also demonstrated.
  
  
• 00:10:00 In this section of the video, the presenter demonstrates how to connect Python with MQL5. The first step is to initialize the platform by entering a command with two quick clicks. They saved the file in the MQL5 folder and checked if the connection worked by executing a command that displayed the values of the terminal info. The presenter also notes the importance of using the autocomplete function to help with programming and that they will be posting several videos on how to use the functionality of the meta trader with the MQL5 language.

[MetaQuotes]

How to collect MetaTrader 5 tick prices for Python

COMO COLETAR PREÇOS DE TICKS DO METATRADER 5 PARA PYTHON

In the video "COMO COLETAR PREÇOS DE TICKS DO METATRADER 5 PARA PYTHON," the presenter explains how to connect MetaTrader 5 with Python and collect price data for a specific symbol. They use a dictionary object to manipulate and parse data, as well as import necessary libraries to transform timestamp data and display it using the pandas library. The collected data includes information such as time in milliseconds, volume, and real volume, which can be useful for analyzing price trends. Finally, they transform the data into a Data Frame structure for more efficient analysis, visualize the data, and perform a quick descriptive analysis before closing the connection with the MetaTrader 5 terminal.

• 00:00:00 In this section of the video, the presenter shows how to connect MetaTrader 5 with Python and how to acquire prices within the MetaTrader platform using Python. First, the presenter imports the necessary libraries and initializes the terminal. Then, the object of the terminal is changed into a dictionary object to make it easier to manipulate. Next, the presenter demonstrates how to use a dictionary to parse through data and return all associated keys and values. Finally, the presenter shows how to collect and save data from MetaTrader into a variable named "dados" in Python.
  
  
• 00:05:00 In this section, the speaker explains how to collect price information for a specific symbol using Python programming language. The user specifies the symbol, the starting date, and the number of data points to collect. The speaker then imports the necessary libraries to transform the timestamp data and displays the collected data using the pandas library. The collected data includes information such as time in milliseconds, volume, and real volume, which can be useful for analyzing price trends.
  
  
• 00:10:00 In this section, the speaker demonstrates how to transform data into a Data Frame structure, which is similar to a spreadsheet, to allow for quicker and more efficient data analysis. They import several Python libraries, including Pandas, and execute the code to create a new Data Frame variable. They then show how to make the format more readable and visualize the data for easier analysis. The speaker also demonstrates how to close the connection with the MetaTrader terminal and performs a quick descriptive analysis of the data collected. The video is part of a series on connecting a MetaTrader platform with Python for data analysis and AI.

[MetaQuotes]

How to work with Python and MetaTrader 5 in MetaEditor

Como trabalhar com Python e MT5 no MetaEditor

The video tutorial "Como trabalhar com Python e MT5 no MetaEditor" explains how to use Python and MT5 in the MetaEditor program. It covers accessing the terminal, creating a new Python file, and popular Python libraries. It is possible to install a Python compiler in the MetaEditor platform, but the speaker advises using alternative environments such as Spider or Jupyter Notebook. The video suggests other options for Python development, including cloud-based platforms like Google Colab.

[MetaQuotes]

How to trade demo,real,B3 MARKET and FOREX accounts in MetaTrader 5 and Python

COMO TROCAR CONTAS DEMO, REAL, MERCADO B3 E FOREX NO MT5 E PYTHON

In the video, the presenter provides a tutorial on how to connect MetaTrader 5 with Python in order to change account types, such as demo to real, as well as accessing various market accounts for different brokers. The tutorial covers initializing and accessing accounts by inputting login details, server details, and passwords. The instructions are straightforward and easy to follow.