This post is the part of trading series. In the past, I gave you a brief intro to Ta-Lib and how it can be used in technical analysis, in this post, I am going to discuss how you can RSI indicator to generate buy or sell signals in Python by using the TA-Lib library. Before I write code about the implementation, let’s discuss a bit about signal generation and RSI. What Are Signals In the world of trading, signals are cues or indicators that are derived from various kinds of analyses and guide investors on when to buy, sell, or hold shares. They help traders to make informed decisions based…
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Automate Your Recipe Posts on Facebook Page with Python
A step by step guide how you can automate and schedule your Facebook Page posts using Facebook Graph API in Python
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Getting started with On-chain Data Analysis in Python using getblock.io
On-chain data analysis involves studying the information recorded on a blockchain to gain insights into transaction patterns, market trends, and network behavior. By examining the data stored on the blockchain, analysts can uncover valuable information about user behavior, market sentiment, and the overall health of a blockchain network. In this post, I am going to discuss some basics about on-chain data analysis for blockchain and then will be discussing how we can leverage the getblock.io platform to perform on-chain analysis of transactions using Python language. What is On-chain Data Analysis? On-chain data analysis refers to the process of studying the information recorded on a blockchain. It involves analyzing transaction details,…
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Create Stock Sentiment Analysis in Python using chatGPT
ChatGPT is a large language model developed by OpenAI that has gained immense popularity for its ability to generate human-like text responses to prompts. With its advanced natural language processing capabilities, ChatGPT has become a powerful tool for a variety of applications, including sentiment analysis. By analyzing text data and identifying the underlying sentiment, ChatGPT can provide valuable insights into customer feedback, social media sentiment, and other aspects of public opinion. In this blog post, I’ll explore how ChatGPT can be used for sentiment analysis without using any library or code to write the main logic. Let’s proceed! If you are in a hurry or not interested in technical details…
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Creating an e-commerce bot to buy online items with ScrapingBee and Python
I wrote about ScrapingBee a couple of years ago where I gave a brief intro about the service. ScrapingBee is a cloud-based scraping service that provides both headless and lightweight typical HTTP request-based scraping services. Recently I discovered that they are providing some cool features which other online services are not providing as such. What are those features? I thought to explore and explain them with a real use case. I used Python language to automate the Daraz group’s shopping website, a famous e-commerce website service in Asian countries like Pakistan, Nepal, Bangladesh, and Sri Lanka. I am automating DarazPK since I am in Pakistan. You can view the demo…
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Getting started with Rocksdb and Python
In this post, I am going to discuss RocksDB. RocksDB is an embeddable persistent key-value store system developed by Facebook. It was originally forked from LevelDB which was created by Google. According to Wikipedia: RocksDB is a high performance embedded database for key-value data. It is a fork of Google’s LevelDB optimized to exploit many CPU cores, and make efficient use of fast storage, such as solid-state drives (SSD), for input/output (I/O) bound workloads. It is based on a log-structured merge-tree (LSM tree) data structure. It is written in C++ and provides official language bindings for C++, C, and Java; alongside many third-party language bindings. RocksDB has particularly been optimized…
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Introduction to technical Analysis in Python using TA-Lib
This post is the part of trading series. In this tutorial, I am going to discuss TA-Lib, a technical analysis library for Python apps. Before I move on and discuss how you can do technical analysis in Python, allow me to discuss what technical analysis is and how it helps to make a decision about whether you buy an asset, sell, or hold it. What is Technical Analysis From Investopedia: Technical analysis is a trading discipline employed to evaluate investments and identify trading opportunities by analyzing statistical trends gathered from trading activity, such as price movement and volume. In short, it is the study of past and current data and…
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Getting started with Celery and Python
In this post, I am going to talk about Celery, what it is, and how it is used. What is Celery From the official website: Celery is a simple, flexible, and reliable distributed system to process vast amounts of messages, while providing operations with the tools required to maintain such a system. Wikipedia says: Celery is an open source asynchronous task queue or job queue which is based on distributed message passing. While it supports scheduling, its focus is on operations in real time. In short, Celery is good to take care of asynchronous or long-running tasks that could be delayed and do not require real-time interaction. It can also…
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Develop Ali Express Scraper in Python with Scraper API
This is another post in ScrapeTheFamous, in which I will be parsing some famous websites and will discuss my development process. The posts will be using Scraper API for parsing purposes which makes me free from all worries about blocking and rendering dynamic sites since Scraper API takes care of everything. In this post, we are going to scrape AliExpress. AliExpress is a Chinese B2C portal to buy stuff. The script I am going to make consists of two parts, or I say, two functions: fetch and parse. The fetch will accept a category and return all links of individual items and parse will parse an individual entry and returns a few data points in…
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Develop Google scraper in Python with Scraper API
This is another post in ScrapeTheFamous, in which I will be parsing some famous websites and will discuss my development process. The posts will be using Scraper API for parsing purposes which makes me free from all worries about blocking and rendering dynamic sites since Scraper API takes care of everything. So this post is about scraping Google search results, the script will accept a keyword and would return results across multiple pages. The data will be stored in a text file in JSON format. The code that is parsing the result is pretty straightforward and given below: def google_scraper(query, start=0): records = [] try: URL_TO_SCRAPE = "http://www.google.com/search?q=" + query.replace(' ', '+') +…
