How AI Can Help Your Investing

  •  AI allows investors to process real-time datasets from various sources such as stock prices, financial reports, social media sentiment, portfolio management resources, etc.
  • Further, AI can make predictions on market movements, optimize investment portfolios, manage trading strategy in terms of ROI-risk balance, and automatically build a risk-aware customized portfolio.



  • AI plays a key role in financial risk analysis - a process of assessing and evaluating potential risks and their impact on an organization, project, or decision-making.


  • AI-Powered Stock Technical Indicators:
  • Thanks to high-level automation and integration of multiple tasks, AI can simultaneously analyze hundreds of technical indicators, run simulations and forecasts, perform real-time technical analysis, and generate trading signals and alerts at a speed and frequency that is impossible for a human trader.

  • AI Breakout Trading Strategies implemented as a stock scanner in Python. AI can download historical data from Yahoo finance. We look at the “filtered” price where we observe consolidation that meets the growth criteria at a given date/time.  This scanner is very simple and efficient in that it can be applied to a 1000's of stocks to identify potential consolidation/breakout patterns of interest to swing traders.


  • Other useful applications of AI in fintech include Fibonacci Retracement Trading, (Back)Test Portfolio Asset Allocation and Diversification, Market State Analysis, Stock Value at Risk (VaR), Stock Correlations, Bear/Bull Market Strategies, Portfolio Risk/Return Optimization, Cryptocurrency Trading, Predicting Stock Prices, Market NLP Sentiment Analysis, Asset technical/Fundamental Analysis, Stock Screener App & Dashboard, An Automated Query Interface, DCA Investment Strategies, and Fraud Detection.

 

Case Examples:

Read more:

https://wp.me/PdMwZd-3nr

Location: Rotterdam, Netherlands

Comments

Post a Comment

Popular posts from this blog

Short-term Stock Market Price Prediction using Deep Learning Models

Image
  This blog is about short-term stock market price trend prediction using a comprehensive deep learning LSTM model. Results show that t he model achieves overall high accuracy for stock market trend prediction .  The following end-to-end sequence  provides the detailed Python/Jupyter workflow from data processing to prediction, including the data exploration: 1. Data Preparation Phase #import libraries import pandas as pd import numpy as np # To remove the scientific notation from numpy arrays np.set_printoptions(suppress=True)   # install the nsepy library to get stock prices !pip install nsepy   ############################################ # Getting Stock data using nsepy library from nsepy import get_history from datetime import datetime   startDate=datetime(2021, 1,1) endDate=datetime(2022, 10, 3)   # Fetching the data StockData=get_history(symbol='INFY', start=startDate, end=endDate) print(StockData.shape) StockData.head() # Creating a column as date StockData['TradeDa
Read more

Webscraping in R - IMDb ETL Showcase

Image
  Web scraping in R is an ETL pipeline that perform web data mining by reading HTML tags and converting them  to the structured format which can easily be visualized using tidyverse . Let's  scrape movies from IMDb into a data frame in R by invoking the rvest library and then visualize the data frame using ggplot2 and qplot functions: Importing the key R libraries library(rvest) #scraping library(dplyr) #piping library('ggplot2') #plotting Specifying the URL for desired website to be scraped url <- 'http://www.imdb.com/search/title?count=100&release_date=2016,2016&title_type=feature' Reading the HTML code from the website webpage <- read_html(url) Using CSS selectors to scrape the rankings section rank_data_html <- html_nodes(webpage,'.text-primary') Converting the ranking data to text rank_data <- html_text(rank_data_html) Let's have a look at the rankings head(rank_data) [1] "1." "2." "3." "4.&q
Read more

Supervised ML/AI Stock Prediction using Keras LSTM Models

Image
  (the image was created using Visme [1]). Introduction Stock markets are analyzed either technically or fundamentally [2]. Fundamental analysis studies supply and demand relationships that define the stock price at any given time.  Technical analysis uses specialized methods of predicting prices by analyzing past price patterns and levels.  T here are many techniques used to examine stock price lines and patterns [2]: bar or high/low/close charts moving averages trend lines channels cycles resistance and support planes corrections double tops and bottoms head and shoulders formation trading volume open interest.  Theere are numerous limitations of these techniques: moving averages responds to general trends only is not highly precise short-term moving averages can give false indications, especially in times of volatile prices Trend lines work best with sustained trends positioning of trend lines is subjective and takes practice trends must be established before they become recognizabl
Read more