DEV Community: abhiphull

Revolutionizing Your Portfolio: Top 10 AI-Powered Stock Trading Apps of the Year 2024

abhiphull — Sat, 23 Mar 2024 00:14:14 +0000

In the rapidly evolving domain of stock trading and investing, the integration of Generative AI technologies has ushered in a new era of applications designed to optimize investment strategies, provide in-depth market analysis, and automate trading processes. These platforms leverage advanced AI to cater to a wide array of investor needs, from seasoned traders to those just beginning their investment journey. Below is a curated list of ten notable AI stock trading apps that have made a significant impact over the last year:

Pionex - Specializes in AI-powered bot strategies for cryptocurrency trading, supporting a vast range of cryptocurrencies. Visit Pionex
Coinrule - Tailored for beginners, allowing users to create custom trading rules using the IFTTT model for automated trading across numerous exchanges. Visit Coinrule
Wealthfront - Manages diversified portfolios with AI algorithms, featuring tax-loss harvesting and automatic rebalancing. Visit Wealthfront
Trade Ideas - Features Holly AI for after-hours market analysis and trade suggestions, ideal for information-driven traders. Visit Trade Ideas
TrendSpider - Offers extensive technical analysis and AI alerts, catering to advanced traders seeking a wide range of options. Visit TrendSpider
eToro’s CopyTrader - Enables users to follow the trades of top investors, providing a dynamic way to learn and trade. Visit eToro
Signal Stack - Converts trading platform alerts into executed orders in brokerage accounts, automating the trading process. Visit signal stack

Each of these apps represents the forefront of AI-driven trading, providing tools and features that cater to various aspects of the trading and investment process. From automated cryptocurrency trading bots to comprehensive platforms for managing diversified portfolios, these applications are reshaping how individuals and institutions engage with financial markets.

For those interested in exploring even more AI trading platforms, this comprehensive list offers a wider selection of sites dedicated to leveraging AI for trading and investment purposes. It's an excellent resource for anyone looking to dive deeper into the world of AI-assisted trading. Visit the comprehensive list.

How To Set Up Django Social User Management Using Allauth

abhiphull — Fri, 15 Jan 2021 02:25:08 +0000

In this post, I will go in brief about setting up Django Social login system. There are lot of articles on this topic online already. I will not repeat the stuff which is already available online. I want to go through couple of pain points which I faced while setting allauth user management.

First follow the following tutorial to setup the Django Allauth
https://django-allauth.readthedocs.io/en/latest/installation.html

Important Points:

For Google login. make sure your Redirect URI in the google console follows following pattern... http://domain_or_localhost/accounts/google/login/callback/
- For localhost do... http://127.0.0.1/accounts/google/login/callback/
- For your IP http://IP/accounts/google/login/callback/
- For your domain http://domain/accounts/google/login/callback/
  - If you are using actual domain, make sure you add following in your settings.py file...
```
 USE_X_FORWARDED_HOST = True
```
If you want to restrict the social login to pre-approved users only look at following snippet of code... https://www.code-fetcher.com/code-examples/django-allauth-social-login-for-pre-approved-users-only/
SITE_ID is another important parameter in the settings.py file. For me SITE_ID = 2 worked for Google login but it could be SITE_ID = 1 depending upon which version of the allauth you are using. So make sure you check that If you run in to any issues.

I hope these suggestions would help.

Best Python Resources And Sites

abhiphull — Wed, 21 Oct 2020 01:39:49 +0000

In this post, I will go over the list of useful Python sites and blogs which every Python programmer should have on his/her list.

Planetpython.org - Great resource to know the latest in the field of Python. You would find everything here about Python from latest courses, libraries to coding snippets.

Plant Scipy - This one is from the Scipy guys. Great resource to remain up to speed in the field of Machine and Deep learning.

Real Python - Anything you want to learn about Python, This is the place to go. This site has great Python tutorials.

Nbshare - nbshare.io is about Python and R notebooks. You can upload your notebooks on this site and share it easily with any one. Also site has great content around Python, R and Data science.

Python.org - Well this is the official Python site. Also check out their blog https://blog.python.org/ for latest information about Python.

Let me know if any useful Python site you know of. I will add to the list here.

Exploratory Data Analysis With 2 Lines Of Python Pandas

abhiphull — Sat, 04 Jul 2020 03:12:32 +0000

In this post, I will talk about how to do data analysis using Python Pandas. The purpose of this post is not to introduce Pandas. There is already ton of tutorials and wikis on Pandas. I would suggest following links If you want to learn about the basics of Python Pandas...

https://pandas.pydata.org/
https://www.nbshare.io/

This post is about introducing a Pandas package which would make the data analysis much faster.

Requirements

Python 3.5+
Jupyter Notebook
Pandas-Profiling

For the example, I have downloaded the covid-19 US data from following github link...

https://raw.githubusercontent.com/nytimes/covid-19-data/master/us-states.csv

Let us first read the above CSV and read in to our Pandas dataframe.

import numpy as np
import pandas as pd
df = pd.read_csv('us-states.csv')

Now our data frame is ready. Let us now do some initial data analysis. Pandas has describe() function which gives us nice summary. But in this post, I will introduce Pandas library Pandas-Profiling which is kind of extension to describe() utility but with lot more data analysis and information with just one line of code.
Note: Pandas-Profiling will work in the Jupyter-notebook, it will embed a interactive widget.
Let us import the Pandas-Profiling and run the exploratory analysis.

from pandas_profiling import ProfileReport
profile = ProfileReport(df, title='Pandas Analysis', explorative=True)
profile.to_notebook_iframe()

Once you run the above code, you would see lot of information. Below is few of the snapshots that I have put together to give you a glimpse of output which you would see after running the above code.

Remember all the above snapshots are widgets and in your notebook, you would be able to interact and choose different options too. Before i wrap up this post, I want you to look at few more pandas packages which are excellent addons to have.
https://nbshare-io.github.io/top-pandas-packages/

Must Have Shell Utilities

abhiphull — Thu, 25 Jun 2020 06:40:10 +0000

Zsh If you have bash as your default shell. I bet you would like working in zsh too. Zsh has lot of plugins which makes working in zsh fun. For installing these plugins, install ohmyzsh. Once you install and enable ohmyzsh, your command prompt should look like this.

Fzf command line fuzzy finder This is great utility to have. fzf is especially useful for scanning through the command line history. When you press Ctl+R, it prompts you through the list of previously used commands from History and then you can search through the commands based on regular expressions and select it. Therefore it is a kind of autocomplete utility for history command. see more here

Csvkit If you work with CSVs, you would love this utility. This is a command line utility to parse and do some data analysis for csv files which are small to medium size. Once you install, you would get following utilities csvclean, csvcut, csvformat, csvgrep, csvjoin, csvjson, csvlook, csvpy, csvsort, csvsql, csvstack,csvstat available in your shell environment.

Z Last but not least to jump to most frequent directories in Unix, install z. Z keeps track of the directories that you visit. Z also ranks the directories. Z ranks most frequent directories in Linux higher. From any directory one can jump to previously visited directory which z does based on fuzzy directory name match if partial name of directory is given.

Must Have Vim Plugins

abhiphull — Sun, 14 Jun 2020 18:28:52 +0000

In this post, I will talk about top vim plugins which everyone should have.

Nerdtree Nerdtree is a file explorer for the vim editor. Once you have nerdtree installed, it is easy to navigate through sub directories and files.

Ctrlp Ctrlp is a fuzzy file finder. Ctrlp is a great utility to find files in a current directory and sub directories. You can use find files based on fuzzy logic or vim's regular expressions. With Ctrlp, you don't have to get out of vim. Ctrlp also keeps track of previously opened files.

Vim-surround Surround.vim is all about "surroundings": parentheses, brackets, quotes, XML tags, and more. The plugin provides mappings to easily delete, change and add such surroundings in pairs.

Vim-foldsearch This plugin is must have plugin if you deal with large files. It makes searching through files very effective. When you search a word or pattern using vim-foldsearch, it folds the lines which don't have pattern matched and only show the lines with patterns.

Vim-visual-star-search This is small plugin but very useful. This allows you to select some text using Vim's visual mode and then hit *
and # to search for it elsewhere in the file. For example, hit V, select a strange sequence of characters like "$! $!", and hit star. You'll find all other runs of "$! $!" in the file.

Vim-colors-solarized Great plugin to select from pre-configured color schemes.

Vim rename multiple Files There are multiple plugins to do this. Please checkout the above post to know more about these plugins.