Summary

This article introduces Technical Analysis (TA) with Python, using Bitcoin data and libraries such as yfinance, talib, and matplotlib to perform analysis and visualization.

Abstract

Technical Analysis (TA) is a method of analyzing stocks based on historical time series data, such as prices and volume. This article demonstrates how Python can be used to perform TA, specifically on Bitcoin data. The author uses the yfinance library to download Bitcoin data and the talib library to calculate the Relative Strength Index (RSI), an indicator used to determine if an asset is overbought or oversold. The matplotlib library is used to visualize the RSI and closing price. The article also covers other indicators, such as Moving Average and Bollinger Bands, and concludes by mentioning the potential use of TA in manual trading or building a trading bot.

Opinions

The author believes that TA can be useful for predicting future behaviors of assets.
The author suggests that Python can be a powerful tool for performing TA.
The author implies that TA can be used for manual trading or building a trading bot.
The author encourages readers to follow and clap for the article if they find value in it.
The author promotes a cost-effective AI service, ZAI.chat, as an alternative to ChatGPT Plus(GPT-4).

Technical Analysis with Python

Technical Analysis (TA) is the analysis of stocks based on previous time series features as prices and volume. The series can be analyzed statistically, or plotted, to spot patterns that helps to predict future behaviors.

We can use Python to fuel this analysis. This article is an introduction to TA with Python. We will be using Bitcoin data that is available at https://finance.yahoo.com/quote/BTC-USD/history?p=BTC-USD to download the data we are going to use the library yfinance:

To do the Technical Analysis we can use the library talib:

import talib

Now let’s get the Relative Strength Index (RSI), this indicator can help to determine if an asset is overbought or oversold. The range of RSI values is between 0 and 100.

rsi = talib.RSI(data["Close"])

Note that we are using the Close price column to calculate the rsi.

rsi.tail()

Date
2022-07-29    60.589750
2022-07-30    59.438286
2022-07-31    56.931639
2022-08-01    56.748439
2022-08-02    56.878482
dtype: float64

Plotting the rsi using the matplotlib library:

Plotting also the closing price:

Moving Average

The Moving Average is one of the most used indicators to see trends, reducing the noise in the price signal. Here we are calculating a 200 days Moving Average:

Bollinger Bands

The Bolinger Bands are basically ranges of deviations. If some prices lies outside can be considered as kind of abnormal behavior. It can be use to see if the asset price is overextended. They could signal times for buy or sell.

Conclusion

We show you 3 Technical Indicators, there are several more indicators that can be calculated using Python. How this can be helpful? Well you can trade manually using the Technical Analysis derived from the use of Python or you can build a bot to trade when you think the indicator meet certain criteria.

If you find value in this article, please consider clapping and following. On the next article we will be making a simple bot to trade, stay tune, see you!