Lawyers for FTX’s disgraced former boss, Sam Bankman-Fried (SBF), have reached an agreement with prosecutors allowing him to contact certain FTX employees. Besides certain restrictions, the 30-year-old may contact such parties through a host of new mediums. The New Rules Updated terms surrounding Bankman-Fried’s contact restrictions were sent to Lewis A. Kaplan – a judge for the Southern District of New York – in a letter on Monday. Bankman-Fried’s lawyers said the updated conditions were a response to the government…
What is Crypto Backtesting?
Crypto backtesting is evaluating a trading strategy by testing it on historical data. This allows traders to see how the strategy would have performed in the past and can help identify potential issues or areas for improvement.
One example of crypto backtesting would be testing a simple moving average (SMA) crossover strategy on historical price data for a specific cryptocurrency. The trader would set up the strategy to buy the cryptocurrency when the short-term SMA crosses above the long-term SMA and sell when the short-term SMA crosses below the long-term SMA. They would then run the strategy on historical price data and see how it would have performed.
For example, using a 30-day SMA and a 90-day SMA, the backtesting shows that the strategy would have resulted in buying the cryptocurrency on January 15, 2019, and selling on April 15, 2019, with a profit of 10%.
How to Use Crypto Backtesting
Using backtesting in crypto involves a few key steps:
- First, define the trading strategy: This includes identifying the entry and exit rules and any additional indicators or conditions used.
- Gather historical data: This can include price, trading volumes, and other relevant market data. Then, again, if it’s a large enough dataset, make the backtesting results statistically significant.
- Set up the backtesting environment: This includes creating a program or using backtesting software to run the trading strategy on historical data and generate results.
- Run the backtesting: The trading strategy is then applied to the historical data, and the results are generated, including profit and loss, number of trades, win-loss ratio, and other relevant metrics.
- Analyze the results: The results of the backtesting can be analyzed to identify any issues or areas for improvement in the trading strategy.
- Optimize the strategy: Based on the backtesting results, traders can adjust the strategy and run the backtesting again to see if the changes result in improved performance.
Backtesting does not guarantee future performance, and that real-world trading conditions can differ. Backtesting should be used as a tool to test and refine strategies, but ultimately traders should not rely solely on backtesting results when making trading decisions.