
Double Tops and Back Testing in Python
A double top is a bearish reversal pattern that is formed after an uptrend. It is characterized by two consecutive peaks at approximately the same price level, with a moderate trough in between them. The double-top pattern is completed when the price falls below the low point of the trough.
It’s important to keep in mind that backtesting is not a perfect representation of how a strategy will perform in the future. Market conditions and investor sentiment can change over time, so it’s always a good idea to test your strategy in a demo account before risking real money.
How do I backtest a double-top strategy in python on the ES futures?
To backtest a double-top trading strategy onOvernight Session More the ES futures using Python, you will need to follow these steps:
- Gather historical data for the ES futures. You can use a financial data provider or web scraping tool to obtain this data. Make sure to get data for a sufficiently long period of time, as backtesting over a shorter period may not provide accurate results.
- Clean and prepare the data. This may include formatting the data, handling missing values, and removing outliers.
- Identify double-top patterns in the data. You can do this by writing a script that searches for two consecutive peaks at approximately the same price level, with a moderate trough in between them.
- Determine your entry and exit points. As mentioned earlier, you may choose to enter a short position as soon as the price breaks below the low point of the trough and exit when the price reaches a certain level of support or when a trailing stop loss is triggered.
- Simulate trades onOvernight Session More the data. Use your entry and exit points to simulate trades onOvernight Session More the historical data and track the performance of your strategy.
- Evaluate the results. Calculate key performance metrics, such as net profit, return onOvernight Session More investment, and risk-to-reward ratio, to assess the viability of your strategy.
It’s important to keep in mind that backtesting is not a perfect representation of how a strategy will perform in the future. Market conditions and investor sentiment can change over time, so it’s always a good idea to test your strategy in a demo account before risking real money.
Here is some example code. Notebook File Included

This code defines a function backtest_double_bottom
that takes in three arguments: the data to be used for backtesting, an entry threshold, and an exit threshold. The entry threshold is the maximum allowed distance between the entry point and the high point of the trough, expressed as a percentage. The exit threshold is the minimum allowed distance between the exit point and the close of the trough, also expressed as a percentage.
The function then cleans and prepares the data, identifies double bottom patterns, determines the entry and exit points, and simulates a trade.
You can then use this function for backtesting the strategy using different variables and timeframes by calling the function with different arguments and data subsets. In the example above, the strategy is backtested onOvernight Session More the entire data set, onOvernight Session More the data for the first half of 2022, and onOvernight Session More the data for the second half of 2022, with different entry and exit thresholds for each time frame.
It’s worth noting that this is just one example of how you might backtest a double bottom reversal strategy onOvernight Session More the ES futures using Python in a Jupyter notebook, with the ability to change the variables and timeframe.