Nse500 Exploratory Data Analysis
Nifty 500 Technical Exploratory Data Analysis and Stock Selection for Investment:
Professional trading and investment in stock market is one of the most elite and respected profession in modern world. This profession is considered both a science and an art. As it require a great deal of analysis to find a good stock for investment, and an art as it is a skill which anyone can learn with some honest hardwork and practice.
People who are successfully sustaining in the markets and making a fortune have few things in common. May it be a person who follows technical analysis or fundamental analysis, some thing that are common for both are:
- deciding a stoploss before making a position in any stock
- selecting a stock on a technical or fundamental basis rahter than making a speculation
- deciding a target (it's not mandator, as some people exit seeing the price action)
- keeping the approach mechanical and monotonous.
This exploratory data analysis on NIFTY 500 stocks will assist you in coming over some of those problems. Some of the things which will be covered extensively are:
- analysing the stocks on basis of relative strength
- assessing some KPIs for the stocks
- screening out stocks on basis of a technical screener
- setting a stop-loss as per the stock price volatility
- understanding the breadth of the stock
- understanding the underlying momentum for the trend of the stock
- confirming the selection with help of a widely used indicator for medium and long term investments.
#installation !pip install yfinance mplfinance ta --upgrade --quiet #importation import pandas as pd import ta import yfinance as yf import warnings import mplfinance as mpf import plotly.express as px import plotly.graph_objects as go from datetime import date, timedelta from sklearn.preprocessing import StandardScaler warnings.filterwarnings(action='ignore',category=FutureWarning)
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 63.2/63.2 kB 3.5 MB/s eta 0:00:00 ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 73.1/73.1 kB 4.5 MB/s eta 0:00:00 Preparing metadata (setup.py) ... done Building wheel for ta (setup.py) ... done