Design, Backtest and Run Your Binance Trading Bot on GCP

Design, Backtest and Run your Binance Trading Bot on GCP
.MP4, AVC, 1280x720, 30 fps | English, AAC, 2 Ch | 5h 21m | 2.62 GB
Instructor: Antonio de Jesus Campos Rodriguez

• How to execute Market Orders and OCO Orders using Binance API
  
• Combine technical indicators to build a trading strategy
  
• Backtest your trading strategy to check if it really works
  
• Build a Docker container with your Python script and push it to Google Cloud
  
• Create an instance in Google Compute Engine to run your backtest
  
• Running your trading bot 24/7 on Google Cloud Compute Engine
  

• Python is strongly recommended.
  
• Some basic knowledge of Docker and Google Cloud.
  
• Some familiarity with technical indicators would be useful.
  
• Some basic knowledge of crypto and Binance.
  

• Create 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.
  

• Describe 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).
  

• Creation 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.
  

• 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.
  

• Installing basic tools: Notepad++, Google Cloud SDK, Docker.
  
• Enable Google Cloud components: Compute Engine, Artifact Registry and BigQuery
  
• Create Json Service Account for connection from Python to BigQuery.
  
• Provide additional permissions to SDK: Gmail, Docker, Artifact Registry and repositories in Artifact Registry.
  

• Python developers interested in trading bots and cryptocurrencies.
  
• Traders with programming skills.