Python in algorithmic trading - page 10

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REAL TIME Dollar Mini Index with Python!

Mini Índice e Dólar em TEMPO REAL com Python!

This YouTube video demonstrates how to obtain real-time quotes for mini-dollars and dominions using Python. The presenter explains the process of installing MetaTrader 5 and creating a demo account. Python can then be integrated with MetaTrader 5 using an official plugin, and a code is provided to show a list of available assets. The video also showcases how Python can be used to download and process real-time data for financial analysis, and the presenter displays current bids, asks, and last traded prices for mini index and dollar, while implementing a half-second delay to avoid overwhelming the program. The video mentions that the next one will show how to execute orders using Python.

• 00:00:00 In this section, the presenter shows how to obtain real-time quotes using Python, specifically for mini-dollars and dominions. The first step is installing MetaTrader 5, which has three types of accounts - demonstration, leverage, and postion. The presenter demonstrates a demo account being created, which gets sent via email along with login credentials. Next, the presenter explains that MetaTrader 5 has official support for Python and shows the installation process of MetaTrader 5 before demonstrating how to run the function. The final step provides a code that shows a list of assets that are available in the account.
  
  
• 00:05:00 In this section, the video explains how to use Python to download and process real-time data for the Mini Index and US Dollar futures. The presenter uses a function to download the data and another one to format it into a Pandas dataframe. Additionally, they show how to retrieve the most recent tick data and how to loop through it for a certain period of time. The presented code exemplifies how Python can be used to extract useful information for financial analysis in real-time.
  
  
• 00:10:00 In this section, the speaker demonstrates how to use Python to obtain real-time prices for mini index and the dollar. He sets a half-second delay to avoid overwhelming the program and displays the current bids, asks, and last traded prices. The speaker concludes by mentioning that the next video will show how to execute orders using Python.

[MetaQuotes]

Trading Dollar Mini Index with Python at B3!

Operando Mini Índice e Dólar com Python na B3!

The video showcases how to use Python to send automated orders for mini-index and mini-dollar futures on the MetaTrader 5  platform, by importing libraries for real-time market data and defining an order with a dictionary of relevant information. The presenter explains the different parameters, using stop loss and take profit points and demonstrating the code's functionality through a test order that results in a loss. The video concludes with the promise of further exploration in future videos.

• 00:00:00 In this section of the video, the presenter shows how to send orders using Python on the MetaTrader 5 platform for mini-index and mini-dollar futures. He begins by importing the necessary libraries and then demonstrates how to get real-time market data. Next, he defines the asset and prepares the order using a dictionary with all relevant information, choosing the type of order, stop loss and take profit points, and adding a comment for identification. He also explains the possible parameters in use, including the order's time until it is canceled. Finally, he runs the code to test the order and shows how it appears on the platform's chart.
  
  
• 00:05:00 In this section, the video demonstrates how to send automated orders using Python on the Brazilian stock exchange (B3). The speaker shows the code for creating a dictionary that will be used to send the order and explains how the bot works. After preparing the order, the speaker sends it and shows how the bot executes it, resulting in a loss. The video ends with the speaker mentioning that more will be explored in future videos.

[MetaQuotes]

Mini Dollar Index Deal Book with Python | Tape reading mini automatic index

Book de Ofertas mini dólar com Python | Tape reading mini índice automático

The video demonstrates how to use Python and MetaTrader5 to obtain historical quotes for the mini future index and export them to a CSV file using Pandas. The video also shows how to access the order book using Python and retrieve bid and ask prices into a data frame using a method called Optiqs from the intertemporal library. The video notes that the large amount of data generated in a single day of trading can pose challenges when using spreadsheets but demonstrates how Python can be used for automated tape reading and market analysis to help identify trends. Ultimately, the video provides valuable insights for those interested in using Python for automated trading analysis.

• 00:00:00 In this section, the video creator shows how to obtain historical quotes and save them to a file using Python and MetaTrader5. They demonstrate how to import modules such as Pandas and MetaTrader5, and use functions to obtain the desired information, such as using the "hlc" function to get quotes for a one-minute time frame for the mini future index. The creator also notes that there is a limit of 99,999 bars, which can sometimes lead to an error message. They then show how to export the obtained information to a CSV file and read it using Pandas.
  
  
• 00:05:00 In this section, the video discusses accessing the order book using Python. The presenter demonstrates a function that retrieves the bid and ask prices from the order book and saves the information into a data frame. The function used a method called Optiqs from the intertemporal library to retrieve the order book data. The presenter also notes that the data retrieved contains a large amount of information and can update rapidly, making indexing by time problematic. The video shows an example of retrieving 100,000 rows of data from the order book.
  
  
• 00:10:00 In this section, the speaker discusses the large amount of data generated in a single day of trading, showing how it can be difficult to work with spreadsheets of millions of rows. However, by using Python to automate tape reading and market analysis, it becomes easier to work with this data and identify trends in the market. The speaker demonstrates how to download and save this data into a CSV file, which can be used for analysis over a longer period of time, such as a week of trading. Overall, the video provides helpful insights for those interested in using Python for automated trading analysis.

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PREDICTING STOCK PRICES WITH FACEBOOK PROPHET WITH PYTHON - HOW TO USE DATA SPLIT

PREVENDO PREÇOS DE AÇÕES COM PROPHET DO FACEBOOK COM PYTHON - COMO USAR O SPLIT DE DADOS

The video tutorial is focused on using the Prophet library from Facebook to predict stock prices with Python. It explains the importance of splitting the data into training and testing sets and outlines how to utilize specific functions to extract the data and calculate mean squared error to determine the model's accuracy. The video also demonstrates how to separate the predicted and real prices, create a function for mean square error calculation, and create graphs comparing predicted and actual prices. The model performance was relatively successful, but accuracy metrics should be taken into consideration, and the importance of processing data correctly is highlighted.

• 00:00:00 In this section, the video tutorial focuses on how to use the Prophet library provided by Facebook to make stock price predictions using Python. The library helps one to determine trends and seasonalities in time series data, and separating the data into training and testing sets is crucial for evaluating the model's predictive ability accurately. The video also shows how to use specific functions to extract training and testing data and calculate the mean squared error to determine the model's accuracy.
  
  
• 00:05:00 In this section, the video explains how to use the split data function in Python to separate the dataset into training and testing sets. The purpose of this is to enable the user to manipulate the dataset and test the model in order to verify that it is accurately predicting stock prices. The video discusses how to use the Facebook Prophet with Python to analyze stock prices and how to set up a model for predictions. The video demonstrates how to fit a model, make predictions on the testing dataset, and compare the predicted and actual values to calculate the model's error.
  
  
• 00:10:00 In this section, the video creator demonstrates how to use a Data Frame within a predictive model and execute a prediction using the Prophet library with Python. The video creator shows how to separate the predicted prices and the real prices into variables, create a function for mean square error calculation, and how to compare predicted and real prices using graphs. The model performance was relatively successful, but accuracy metrics should also be taken into consideration.
  
  
• 00:15:00 In this section, the speaker discusses predictive models and introduces a model provided by Facebook through a library called Prophet. The speaker mentions the importance of processing data correctly and encourages viewers to let him know if they enjoyed the content to ensure similar materials are posted in the future.

[MetaQuotes]

Python FOREX Trading Robot

BRAND NEW - The Python Forex Trading Robot

The Python Forex Trading Robot is an auto hedging system used to catch big market moves without worrying about timing. The robot draws a box, takes trades within that box, and closes once the price breaks out. Users can adjust the lot size, target distance, and hedging distance. Backtest results show profitable trades with a worst drawdown of only $218 on a $10,000 account. While the system performs well on trending currency pairs, users are advised to stick to such pairs. Overall, the Python Forex Trading Robot is highly adaptive, consistent, and enjoyable to watch. Various tools and robot systems are available, and users are encouraged to check out the provided links and subscribe to the YouTube channel for more trading-related content.

• 00:00:00 In this section, we will discuss the Python trading robot, which is an advanced auto hedging system that takes advantage of big moves in the market. The robot draws a box, takes trades until the price breaks out of that box, and takes profit once it does. The system is not worried about timing; it just catches the move. The robot executes trades until a big move is generated by the market. The video shows a backtest running and how the robot executes trades to generate a profit. Trading involves risk and is not suitable for all investors.
  
  
• 00:05:00 In this section, the presenter demonstrates the Python Forex Trading Robot, which looks more complicated than it really is. The robot is designed to join the trend and capture profitable trades, without fighting the market or predicting every move. The system takes many trades, but all parameters are configurable, so users can adjust the lot size, target distance, and hedging distance to suit their preferences. The backtest results show constant trades executed and closed for a profit, with a worst drawdown of only $218 on a $10,000 account. While the system performs well on currency pairs that tend to trend, the presenter advises users to stick to pairs that do trend to avoid getting in trouble. Overall, the Python Forex Trading Robot is highly adaptive, consistent, and fun to watch, making it one of the presenter's all-time favorite systems.
  
  
• 00:10:00 In this section, the speaker mentions that there are various tools and robot systems available for traders to assist them in their trading journey. The users are encouraged to check out the provided links and subscribe to the YouTube channel for more trading-related content. The video concludes with a thank you message for the viewers.

[MetaQuotes]

STARTING METATRADER 5 USING PYTHON

INICIANDO O METATRADER 5 USANDO PYTHON

The video explains how to use the MetaTrader library with Python, showcasing how to import MetaTrader 5 with mt5 and mt5.initialize after installing MetaTrader 5 and connecting to its data. The instructor then verifies the path to ensure that MetaTrader 5 is installed and ready to use in Visual Studio Code using the project Surf.

[MetaQuotes]

Operating with Python and MetaTrader 5 Live

Operando com Python e Metatrader ao vivo

The video "Operando com Python e Metatrader ao vivo" provides viewers with a detailed tutorial on how to operate in the stock market using Python and MetaTrader 5. The hosts showcase how to connect the platforms to automate strategies, obtain real-time data, and screen assets. They show how to create a list of assets, use timestamp and data to obtain quotation data, and how to create a simple rule for identifying buying opportunities in the market. The video also demonstrates how to trade on a live screen and explains strategies for managing risks, including using hands based on financial risks. The speakers highlight the advantages of using Python's statistical tools for analysis and emphasize the importance of the integration between Python and MetaTrader 5 for backtesting strategies to gain confidence in them. Overall, the video illustrates the vast possibilities of using Python and MetaTrader 5 to automate trading strategies and connect with brokers globally.

• 00:00:00 In this section the hosts introduce their live session on operating with Python and MetaTrader 5 in the stock market. They explain that they will showcase how to connect Python and MetaTrader 5 to automate strategies, obtain real-time data, and screen assets. The hosts clarify that the demonstration is not a recommendation for investment purposes but to show the potential of integrating programming and finance. They also mention that the code can be executed locally using any IDE that integrates with the MetaTrader 5 software.
  
  
• 00:05:00 In this video section, the presenter discusses the use of the MetaTrader 5  software with Python and how it offers the advantage of being able to integrate with various brokers from different countries. The presenter also notes that the MetaTrader 5 software can be used to automate strategies and offers a great source of data, especially for smaller time frames. The section concludes with the presenter demonstrating the use of the software and the various libraries used to manipulate data and time series.
  
  
• 00:10:00 In this section, the video discusses how to create a list of assets to use for trading with Python and MetaTrader 5. The list includes all tickers with their weights and can be looped through to determine whether they are available for trading or not. The video goes on to explain how to create a screen of assets that meet certain conditions, such as presenting a specific pattern or movement, rather than being dependent on the ticker itself. Lastly, the video explains how to use timestamp and data to obtain 500 days of quotation data for each asset and store it in a data frame for access later. Overall, this section provides a detailed tutorial on how to select and use assets for trading with Python and MetaTrader 5.
  
  
• 00:15:00 In this section of the video, the host explains the creation of a simple rule for identifying a buying opportunity in the market using two moving averages and the closing price. The rule is that if the 20-period moving average is above the 200-period moving average, and the closing price is above the 20-period moving average, then the market is potentially in an uptrend. The host also mentions that the course teaches how to create robots using Meta Trader and provides a foundation for creating custom indicators and strategies. They further explain that a robot is simply an automated trading system and that the course does not sell profitable strategies but teaches how to create them. Finally, the host walks through an example of how the created rule can be used to identify buying opportunities in specific assets.
  
  
• 00:20:00 In this section, the video shows the preparation of the code to generate data from a particular asset, using Python and MetaTrader 5. The video shows an example with Ambev and Alpha 4 assets, which did not meet the rules set by the screen, but a third one (BBAS3 asset) did. The video also discusses how to automate the process and mentions the use of a loop to continuously analyze the data with a minimum one-minute time frame. The code also calculates the moving averages for the fast and slow media and compares them to the closing price - if the fast media is above the slow media and the closing price is above the fast media, the code will make a trade.
  
  
• 00:25:00 In this section, the speaker explains some of the parameters used in MetaTrader 5 to prevent buyers from repeating orders without reason. They also explain that the lot minimum is a standard feature and that the tick minimum calculates the stop loss and take profit on every asset. The orders are market orders and specify the number and price of the order, as well a stop loss and take profit of 10 cents less and 10 cents more, respectively. However, the speaker also notes that this is not a great strategy and only serves to show how it works, with additional commentaries allowing for different trading strategies to be applied.
  
  
• 00:30:00 In this section, the speaker demonstrates how to trade using Python and Metatrader on a live screen. The demonstration involves buying and observing the movements of Carrefour and Carrefour options. The speaker explains how they entered a trade when the closing price was above the 20-day moving average and the 10-day moving average. The demonstration also features a stop loss of 10 cents and a take profit of 10 cents. The speaker suggests that an ideal robot should manage risks by choosing hands based on financial risks. The section ends with the speaker anticipating profits from the fluctuations in the stock market.
  
  
• 00:35:00 In this section, the video's hosts demonstrate Python and MetaTrader in action. They showcase how to access the historical data and how to configure the code to buy or sell assets. They also answer viewer questions, including how to use MetaTrader for CFDs, how to create custom indicators, and how to configure the code to only scan specific candlesticks. Overall, the hosts demonstrate the vast possibilities of using Python and MetaTrader to automate trading strategies and connect with brokers globally.
  
  
• 00:40:00 In this section, the speakers touch on the possibilities of Python and MetaTrader 5 integration, specifically the advantages of using Python's vast statistical tools for analysis. They mention that with Python, one can create complex codes and automate almost everything, including trading signals. Additionally, they note that using Python can help reduce human biases and combat biases caused by external factors, such as news. They also recommend their viewers to check out PMF's modules on the MetaTrader 5 and Python integration and how to best leverage this tool for trading.
  
  
• 00:45:00 In this section, the speaker talks about the importance of the integration between Python and MetaTrader 5, even for those who don't want to operate automatically but want to do the backtesting of their strategy to gain confidence in it. By using the integration, traders can extract reliable data from the market, take it to Python for statistical analysis and tests, and then operate based on the results. The speaker also highlights the importance of merging different areas, such as quantitative analysis and tags, to create efficient trading strategies.

[MetaQuotes]

1. MetaTrader 5 Python Library and Connecting Python with MetaTrader 5

1. MetaTrader 5 Python Library and Connecting Python with MetaTrader

The video showcases step-by-step instructions for installing the MetaTrader 5 Python library and setting up a connection between MetaTrader 5 and Python using account details. The presenter explains the process in detail, including how to search and install the Metatrader5 package, how to import the library, and how to establish a successful connection with a trading account. Overall, the video serves as a useful guide for those looking to integrate MetaTrader 5 with Python.

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2 Placing Buy Limit and Sell Limit Orders with Python

2 Placing Buy Limit and Sell Limit Orders with Python

This video demonstrates the process of placing buy and sell limit orders using Python's `empty_file.order_send()` function and key-value pairs. The user can provide the currency symbol, volume, and order type as inputs to the function to place a buy limit order. The video also shows how to create custom functions for placing limit orders and capture output using variables. The same process applies to sell limit orders, with the type and price specified differently.

[MetaQuotes]

3 Placing Market Orders

3 Placing Market Orders

The speaker explains how to place market orders in trading. A market order can be placed by specifying the symbol, volume, and order type. The speaker provides all the necessary functions in the resources section. They demonstrate how to place a market order for EURUSD and GBPUSD. The order type can be either buy or sell, and the volume is specified in Lots. The speaker executes the orders and shows the results on the platform.