Python in algorithmic trading - page 12

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PYTHON TRADING BOT #3 - MetaTrader 5, data for backtesting trading

BOT DE TRADING PYTHON #3 - Metatrader 5, datos para backtesting de trading

In this video section, the presenter highlights the usefulness of MetaTrader 5's feature that allows users to save tick data from a specified date range for backtesting purposes. He emphasizes the significance of having ample historical data to develop precise trading algorithms and recommends exporting the data as a CSV file to load into Python for creating trading models. He also predicts that this feature may no longer be free due to the increasing use of bots and AI in trading. The presenter encourages viewers to utilize this feature and direct further interest in his website if they have any interest in trading and cryptocurrencies.

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PYTHON TRADING BOT #4 - MetaTrader 5 and Python

BOT DE TRADING PYTHON #4 - MetaTrader 5 y Python

This video explains how to use MetaTrader 5 with Python and create a login. The audience needs to install the MetaTrader 5 package for Python first, and the YouTuber demonstrates how to import libraries by providing an example code. He also mentions that there are many resources available on how to create MetaTrader 5 bots using Python. The video concludes with a recommendation to visit the YouTuber's website for inexpensive and useful trading materials.

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PYTHON TRADING BOT #5 - Getting tick prices

BOT DE TRADING PYTHON #5 - Obteniendo ticks (precios)

The video tutorial demonstrates two ways to obtain ticks (prices) from the MetaTrader 5 trading application using Python. The first method involves using the function symbolinfo.tick, which returns the current price of a market with the option to choose the bid or ask tick. The second method is slightly more complex, involving the function copyticksrange and datetime library to retrieve tick data from a specified date until the current time. The tutorial includes a graph of tick data collected every 15 minutes and demonstrates how to compare it to a real graph to ensure accuracy. Finally, the video promotes a website that offers affordable and useful trading documents for those interested in cryptocurrency trading.

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PYTHON TRADING BOT #6 - Open and modify trades

BOT DE TRADING PYTHON #6 - Abrir y modificar operaciones

This section of the video tutorial on trading bots with Python covers the process of opening and modifying operations using Python. The presenter gives guidance on defining a purchase for opening an operation through a step-by-step guide using a dictionary and order send function. Other factors the dictionary should cover includes the trade's type, stop-loss, and take-profit values. A suggestion is given to check the cost-effectiveness of the trade using a low volume. Additionally, the presenter explains the utilization of the symbol info function to access the market price and point. Similarly, the video demonstrates modifying open trades through the order send function, beginning with ticket number acquisition through a built-in function named "positions get." A dictionary format can be used to modify the stop loss and take profit values with the "position" key containing the value of the ticket number. The presenter finishes by recommending an affordable and useful trading document webpage.

• 00:00:00 In this section of the video, the presenter explains how to open and modify operations using Python. The presenter provides a step-by-step guide to open an operation by defining a purchase using a dictionary and then using the order send function with that dictionary as an argument. The dictionary should include information such as the market symbol, the volume (representing the amount of money used for the operation), the order type, and the stop loss and take profit values. The presenter advises using a low volume and provides a brief explanation of how the stop loss and take profit work. The presenter also explains how to obtain the market price and point using the symbol info function. Lastly, the presenter recommends visiting a webpage to access all the error codes related to the feeling type in the order dictionary.
  
  
• 00:05:00 In this section, the video explains how to modify an open trade using a function called "order send." The first step is to obtain the ticket number of the open trade using a built-in function called "positions get." Once the ticket number is obtained, the user can modify the stop loss or take profit values using a dictionary format. It is important to have the "position" key with the value of the ticket number in the dictionary. This method can be applied to both buying and selling trades. The video concludes with a website recommendation for affordable and helpful trading materials.

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PYTHON TRADING BOT #7 - Trailing stop

BOT DE TRADING PYTHON #7 - Trailing stop

In this video, the presenter explains how to program a trading stop in Python, as it cannot be done automatically in MetaTrader 5. He demonstrates how to set the stop loss manually to minimize the loss if the price drops and ensure a profit if the price goes up, using a simple example on a price chart. The presenter gives a step-by-step guide and code on the screen, and suggests visiting his website for helpful trading documents.

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PYTHON TRADING BOT #8 - Generating data for the AI

BOT DE TRADING PYTHON #8 - Generando datos para la IA

In this video, the presenter demonstrates how to generate backtesting data to create a successful artificial intelligence algorithm for trading scripts and cryptocurrencies. They suggest using a small file for generating data and patching the file's format before generating various attributes such as the trade type, profit, success rate, slope, and indicators. They also explain how to overcome the challenge of opening and searching the data file repeatedly by opening the mining file in binary mode. Additionally, they emphasize the importance of saving values of indicators when training the AI for effective trading.

• 00:00:00 In this section, the YouTuber demonstrates how to generate backtesting data that is essential in creating an effective artificial intelligence algorithm. He recommends using a small file for generating data as larger ones can be tedious to use. To generate backtesting data, he patches the format of the files and then generates various attributes to train the AI, such as the trade type, profit, success rate, slope, and indicators. The YouTuber's algorithm simulates trading while loading the variables and indicators for each line of the file, and then tests whether a trade can be made or not, and checks for the successful or failed outcome of the trade. One of the YouTuber's errors is not storing the values of the indicators, which he mentions is an important attribute for training the AI.
  
  
• 00:05:00 In this section of the video, the presenter talks about a problem they faced when checking if an operation was successful in their data mining file. They found that they had to keep opening and searching the file repeatedly, which was inefficient. To overcome this challenge, they suggest opening the mining file in binary mode, which allows them to move directly to the line they need to check. They explain that while having a powerful computer with a lot of RAM could allow for a quick and easy fix, it's not always feasible. The presenter shares that training an effective AI requires saving a lot of attributes and that while there can be errors, it's possible to find simple solutions.

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PYTHON TRADING BOT #9 - Creating an AI

BOT DE TRADING PYTHON #9 - Creando una IA

In this video, the host demonstrates how to train an AI using data from the previous video by using a Jupyter notebook to visualize output and Skyler library to create a decision tree and neural network. Graphs and histograms are created to classify successful and failed operations visually. Different AI models are used to classify the data, and the host recommends creating a small file with loops to find the best configuration for the model using the highest score. He recommends using an odd number of decision trees when creating a model to achieve better results and shares a 64% accuracy rate of his model. Viewers are encouraged to visit Sky Learn website to learn more about decision trees, forests, and neural networks, like, subscribe, and share the video, and visit the host's website to purchase affordable trading and cryptocurrency-related documents.

• 00:00:00 In this section of the video, the host explains how to train an artificial intelligence using the data generated in the previous video. The host uses a Jupyter notebook to visualize the output of different test cases and Skyler library, which is associated with artificial intelligence, to create a decision tree and a neural network. The data is passed to Pandas through a function, and the data frame is used to define the load types of each attribute column. The host then shares how to create graphs and histograms to classify the successful and failed operations visually. Finally, different AI models are used to classify the data, and the host recommends creating a small file with loops to find the best configuration for the model using the highest score.
  
  
• 00:05:00 In this section, the speaker recommends using an odd number of decision trees when creating a model to achieve better results. He also reveals that the model he created has a 64% accuracy rate, which is only 1% lower than another similar model. To help those who are unfamiliar with decision trees, forests, and neural networks, he recommends visiting the Sky Learn website, which is a great resource for learning about these topics.
  

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TRADING CANDLES IN PYTHON

VELAS DE TRADING EN PYTHON

The author explains how to obtain trading data from MetaTrader 5 and convert it to candles using Python. He begins by showcasing how one can obtain tick data from a specific market on MT5 and export it to a CSV file. He then demonstrates how he uses a simple algorithm to transform the tick data into candles of different time periods―15 minutes, 1 minute, 30 seconds, etc.―which are saved in a Pandas DataFrame. The YouTuber emphasizes the importance of having a DataFrame with a "close" column, as this enables the user to load technical indicators into Python with the help of the TA-Lib library. The video is part of a series where he teaches how to use Pandas and other libraries for trading analytics.

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MACD IN PYTHON - TECHNICAL ANALYSIS LIBRARY

MACD EN PYTHON - TECHNICAL ANALYSIS LIBRARY

The video showcases a tutorial on loading MACD values in Python using the technical analysis library, with a focus on generating those values from an existing data file with a close column. The video also demonstrates the installation of the technical analysis library and the computation of the MACD and Signal line values using an object constructor from the library. Finally, the speaker showcases the plotting of the resulting values using the Matplotlib library. In conclusion, the video provides a comprehensive guide on the process of loading MACD values in Python using the technical analysis library.

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RSI IN PYTHON - TECHNICAL ANALISYS LIBRARY

RSI EN PYTHON - TECHNICAL ANALISYS LIBRARY

In this video, the speaker explains the process of loading the RS indicator in Python using the TIA library. The steps involved include defining a data frame containing the column on which to load the indicator, importing the TIA library, creating an object with the RS indicator constructor function, and calling the RS indicator method to create a data frame with the RS indicator information. The video shows how to compare the RS indicator data with real market data to ensure the accuracy of the implementation. In summary, the speaker provides a simple approach that can be followed to load the RS indicator in Python.