Use fastquant to easily optimize your trading strategy’s parameters automatically! However, this can easily start getting tedious as you would have to run hundreds.
If you want to consistently earn money with your investments, backtesting is one of the best ways to assess the effectiveness of your trading strategies — of course, assuming that you implement it properly. The idea is that you can experiment with the different parameters for your chosen strategies, and see which combinations give you the best returns for your investment.
However, this can easily start getting tedious as you would have to run hundreds or even thousands of parameter combinations manually.
To solve this problem, we can use fastquant to implement a technique called “grid search”, which basically allows you to run a backtest across each of the parameter combinations that you want to run for your strategy.
For the rest of the article, I’ll be demonstrating how to apply automated grid search when backtesting your trading strategies.
Note: If you’re not yet familiar with how to do basic backtesting with fastquant, you may want to check out my previous [article_](https://towardsdatascience.com/backtest-your-trading-strategy-with-only-3-lines-of-python-3859b4a4ab44?source=friends_link&sk=ec647b6bb43fe322013248fd1d473015) on how to do this in 3 lines of code._
Data science is being used to provide a unique understanding of the stock market and financial data. Securities, commodities, and stocks follow some basic principles for stock trading. We can either sell, buy, or hold. Data Analytics and Stock Trading: How to Use Data Science in Stock Market Analysis
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