Practical ML/AI Guide for Businesses

 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

  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

  What is ML? Machine Learning (ML) is defined as follows: A code learns from experience E with respect to a task T and a performance measure P, if its performance on T, as measured by P, improves with E. [1].   Example 1: your code monitors spam and classifies emails as spam or not spam (SNS). In this case, T = classifying emails as SNS; E = watching you label emails as SNS; P = the fraction of emails correctly classified as SNS. Example 2: playing checkers. E = the experience of playing many games of checkers; T = the task of playing checkers; P = the probability that the program will win the next game. ML is a part of Artificial Intelligence (AI). ML algorithms build a model based on sample data, known as training data, in order to make predictions or decisions without being explicitly programmed to do so [2]. ML is an important subset of data science. Through the use of statistical methods, data science algorithms are trained to make classifications or predictions, uncove