|
via Udemy |
Go to Course: https://www.udemy.com/course/design-backtest-and-run-your-binance-trading-bot-on-gcp/
Certainly! Here’s a comprehensive review and recommendation of the Coursera course titled "Design, Backtest and Run your Binance Trading Bot on GCP." --- **Course Review: "Design, Backtest and Run your Binance Trading Bot on GCP"** This course offers an in-depth, hands-on approach to developing a fully operational cryptocurrency trading bot using Python, Docker, and Google Cloud Platform (GCP). It is ideal for traders, developers, and enthusiasts who want to leverage automation and cloud computing for cryptocurrency trading. **Course Highlights:** - **Practical Learning:** The course is project-based, guiding you step-by-step through building and backtesting a trading strategy for Bitcoin (BTCUSD). You will learn how to integrate key technical indicators such as RSI, Bollinger Bands, and Engulfing Pattern, which are fundamental in technical analysis. - **Comprehensive Technical Skills:** - Setting up a local environment with Docker and Jupyter Notebooks. - Interfacing with Binance API to fetch data, execute trades, and manage orders. - Programming with Python using Ta-Lib for technical analysis. - Visualizing data with plots. - Backtesting strategies on Google Cloud's Compute Engine, with logs stored in BigQuery for analysis. - Containerizing applications with Docker and deploying on GCP for continuous trading. - **Advanced Cloud Integration:** The course teaches container management, deploying, and running your trading bot 24/7 on Google Cloud. This is particularly valuable for aspiring quant traders and developers interested in scalable, reliable automation. - **Hands-On Approach:** The instructions for building Docker images, pushing to Artifact Registry, and the step-by-step deployment to Google Cloud make this course very practical and ready-to-implement. - **Additional Support:** The course includes a detailed appendix on installing necessary tools and setting permissions, which simplifies the initial setup process. **What You Will Gain:** - Skills to develop, backtest, and deploy a cryptocurrency trading bot. - Practical experience in integrating multiple APIs and tools. - Understanding of cloud deployment for real-time trading automation. - Knowledge of containerization, Docker, and GCP essential for scalable trading systems. --- **Course Review Summary:** This course is comprehensive, well-structured, and ideal for those eager to combine financial strategies with cloud and programming skills. It offers a perfect blend of theory and practice, making complex concepts accessible and actionable. --- **Recommendation:** If you're interested in algorithmic trading, especially in the cryptocurrency space, this course is highly recommended. It empowers you to build your own trading bot from scratch, backtest its performance accurately, and deploy it on a robust cloud platform to operate continuously. The technical depth, combined with real-world application, makes it suitable for intermediate learners with some programming background. --- **Final thoughts:** Whether you're a trader looking to automate your strategy or a developer keen on cloud deployment, this course provides all the tools and knowledge needed to take your trading to the next level. Given its practical focus and detailed guidance, I strongly recommend enrolling if you aim to develop scalable, professional-grade trading bots. --- Let me know if you'd like a more concise summary or any additional insights!
DescriptionIn this course you will learn an example of how to combine three technical indicators (RSI, Bollinger Bands and Engulfing Pattern) to define a trading strategy for Bitcoin (BTCUSD) using Python, Ta-Lib and Binance API. You will perform a backtesting of this strategy to see if it is a successful strategy or not. The execution of the backtesting will take several hours, so you will learn how to containerize your Python script using Docker and how to push it to Google Cloud Platform, specifically how to push the container to Artifact Registry and then run the container on Google Compute Engine. Finally, you will see how to run your trading bot 24/7 on Google Cloud again by using Docker, Artifact Registry and Google Compute Engine.Overview of the contentsSection 1: BasicsCreate local environment for our experiments using Docker (a Jupyter notebook with specific libraries).Usage of Binance API: getting credentials, extraction of historical prices, checking filters for BTCUSDT, executing market and OCO orders, getting the id and status of an order. Also canceling an order.Reviewing RSI, Bollinger Bands and Engulfing Pattern.Programming them using Ta-Lib and building some basic plots.Section 2: Defining and Visualizing the Trading StrategyDescribe the trading strategy.Programming the buy signal.Plotting simultaneously the Bollinger Bands, RSI and Engulfing Pattern.Based on previous plot, define the stoploss and takeprofit.Check a couple examples (a winning trade and a losing trade).Section 3: Backtesting the Trading Strategy on Google Compute EngineCreation of Dockerfile, requirements.txt and the main Python script (bot_backtesting).The bot_backtesting script Includes a connection to BigQuery to save logs during the execution and to save the final result at the end of the execution. Also it will handle Binance fees and any existing open trades.In bot_backtesting script, construct the main function (compute_sl_tp) steps: 1 Browsing for buy signals. 2 Setting entry, takeprofit and stoploss prices. 3 Determine when we reach each one of them. 4 Which one happened first. 5 Retrieve additional information.Building container with Docker, push it to Artifact Registry and run it on Google Compute Engine.Analyze backtesting results.Section 4: Building and Running the Trading Bot on Google Compute Engine.Creation of Dockerfile, requirements.txt and the main script (main py) which includes a class called TradingBot.The class will handle buy signals. Also it will define the entry, takeprofit and stoploss prices, estimated fees and returns, and whether we have an existing open trade or not.The class will define the required conditions to enter a trade.It will create market and OCO orders. Also it will check and update the status of both market and OCO orders.Running the class TradingBot every minute.Building container with Docker, push it to Artifact Registry and run it on Google Compute Engine. Also see how to run it on local machine.Results after running trading bot.Section 5: AppendixInstalling basic tools: Notepad++, Google Cloud SDK, Docker.Enable Google Cloud components: Compute Engine, Artifact Registry and BigQueryCreate Json Service Account for connection from Python to BigQuery.Provide additional permissions to SDK: Gmail, Docker, Artifact Registry and repositories in Artifact Registry.