There are several ways to compute support and resistance prices in Python, depending on the data you have and the method you want to use. Here are a few examples:
- Using the
ta-liblibrary: This library provides a wide range of technical indicators, including support and resistance levels. You can install it using
pip install TA-Lib. Once installed, you can use the
TA_Libclass to compute support and resistance levels for a given stock or other financial asset.
- Using the
numpylibraries: You can use these libraries to perform data manipulation and calculations on your stock data, respectively, and then use those calculations to identify support and resistance levels.
- Using Machine Learning: You could use a machine learning algorithm like a Random Forest or Decision Tree to predict the support and resistance levels.
Here is an example with pandas
import pandas as pd # Compute the high, low, and closing prices for the stock high = prices["High"] low = prices["Low"] close = prices["Close"] # Find the rolling maximum and minimum for the high and low prices rolling_max = high.rolling(20).max().mean() rolling_min = low.rolling(20).min().mean() # Identify the support levels as the rolling minimum support = rolling_min # Identify the resistance levels as the rolling maximum resistance = rolling_max # Print the support and resistance levels print("Support levels:", support) print("Resistance levels:", resistance)
It's important to note that the accuracy of the support and resistance level predictions can vary greatly depending on the method used and the quality of data.
It's also important to note that support and resistance level predictions are not always accurate, so it's important to use them as part of a broader trading strategy, not as standalone predictions.