In this post, I will dive into my Moving Average Extrapolator, a tool that I created to help traders predict future price movements based on past data. I will discuss the underlying logic, its limitations, and the importance of accounting for delays in the moving average. The following code, my Moving Average Extrapolator, will serve as the basis for our discussion.
The Moving Average Extrapolator uses a simple moving average (SMA) to analyze past price movements and make predictions about future price movements. It uses a Monte Carlo simulation to generate possible future price movements based on historical probabilities.
Let's start by understanding the different components of the code:
The movement_probability function calculates the probability of green and red price movements, where green movements indicate an increase in price, and red movements indicate a decrease in price.
The monte function generates an array of potential price movements using a Monte Carlo simulation.
The sim function uses the generated Monte Carlo array to simulate potential future price movements based on the probabilities calculated earlier.
The draw_lines function draws lines connecting the current price to the extrapolated future price movements.
The extrapolate function calculates the extrapolated future price movements based on the provided source, length, and accuracy.
Limitations of My Moving Average Extrapolator:
Reliance on historical data: My Moving Average Extrapolator relies heavily on historical data to make future price predictions. This can be a limitation, as past performance does not guarantee future results. Market conditions can change, making the extrapolator less reliable in predicting future price movements.
Inherent randomness: The Monte Carlo simulation introduces an element of randomness in the extrapolator's predictions. While this can help in exploring various scenarios, it may not always accurately predict future price movements.
Delay in the moving average: Moving averages inherently have a delay, as they are based on past data. This delay can cause my Moving Average Extrapolator to be less accurate in predicting immediate price movements.
Accounting for Delays in the Moving Average:
It is essential to account for the delay in the moving average to improve the accuracy of my Moving Average Extrapolator. I have taken this into account by introducing a delay variable (delay) in the draw_lines function. The delay variable calculates the delay as half the moving average's length and adjusts the time axis accordingly.
This adjustment helps in reducing the lag in the extrapolator's predictions, making it more accurate and useful for traders. However, it is important to note that even with this adjustment, my Moving Average Extrapolator is still subject to the limitations discussed earlier.
Adding Custom Lookback Period to My Moving Average Extrapolator:
To enhance the functionality and adaptability of my Moving Average Extrapolator, I have implemented an option to set a custom lookback period. The lookback period determines how far back in the historical data the Moving Average Extrapolator should start its analysis.
To achieve this, I have included a method to obtain the current bar index and then calculate the starting bar index by subtracting the desired lookback period.
Here's how to implement the custom lookback period in the Moving Average Extrapolator:
Get the current bar index: I use the bar_index built-in variable to get the current bar index, which represents the current position in the historical data.
Set the start index: To set the start index, you can subtract the desired lookback period from the current bar index. In the code, I have defined a user-input number variable, which can be set to the desired lookback period. By default, it is set to 20800. The starting index for the Moving Average Extrapolator's analysis is calculated as bar_index - number.
Here's the relevant code snippet:
number = input.int(20800, "Bar Start")
And to conditionally run the calculations:
if bar_index > number
draw_lines(avg, extrapolate(close, length, 10), length, extrapolate)
By implementing this custom lookback period, users can easily adjust the starting point of the Moving Average Extrapolator based on their preferences and trading strategies. This allows for more flexibility and adaptability to different market scenarios and ensures that the Moving Average Extrapolator remains a valuable tool for traders.
Conclusion:
My Moving Average Extrapolator can be a valuable tool for traders looking to predict future price movements based on historical data. However, it is essential to understand its limitations and the need to account for the delay in the moving average. By considering these factors, traders can make better-informed decisions and use my Moving Average Extrapolator to complement their trading strategies effectively.