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How To Automate Multi-Asset Trading Strategies In Python
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In this video, we explore multi-asset trading automation in Python, demonstrating how to test and optimize a trading strategy across various assets. Using Python, we backtest strategies on major Forex pairs and Gold to enhance win rates and total returns. We cover setting up a trailing stop on the daily timeframe to maximize profits, using moving averages to detect trends, and utilizing Bollinger bands for entry signals.
Follow along as we code the strategy in Python, automate the backtesting process, and optimize the parameters efficiently. This video provides a comprehensive guide to multi-asset trading automation, making it easier for you to implement and enhance your own trading strategies.
This approach on multi-asset trading automation in Python will provide valuable insights and practical steps to improve trading strategies and backtest on different markets.
°°° Discount Vouchers for my Algorithmic Trading and Python courses:
Live Trading Bot Example:
To rerun the tests on your own data you can download the Python code from the link:
And the data files I used:
Follow along as we code the strategy in Python, automate the backtesting process, and optimize the parameters efficiently. This video provides a comprehensive guide to multi-asset trading automation, making it easier for you to implement and enhance your own trading strategies.
This approach on multi-asset trading automation in Python will provide valuable insights and practical steps to improve trading strategies and backtest on different markets.
°°° Discount Vouchers for my Algorithmic Trading and Python courses:
Live Trading Bot Example:
To rerun the tests on your own data you can download the Python code from the link:
And the data files I used:
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