 import yfinance as yf
import talib
import time

# Define the ticker symbol
tickerSymbol = '^NSEI'

# Get the historical prices for this ticker
tickerDf = yf.download(tickerSymbol, period='1d')

while True:
    # Calculate the Bollinger Bands
    upper, middle, lower = talib.BBANDS(tickerDf['Close'])

    # Calculate the MACD indicator
    macd, macdsignal, macdhist = talib.MACD(tickerDf['Close'], fastperiod=12, slowperiod=26, signalperiod=9)

    # Calculate the RSI indicator
    rsi = talib.RSI(tickerDf['Close'], timeperiod=14)

    # Get the last closing price
    last_price = tickerDf['Close'].iloc[-1]

    # Calculate the upper and lower bands of the Bollinger Bands
    last_upper_band = upper[-1]
    last_lower_band = lower[-1]

    # Calculate the MACD signal
    last_macd_signal = macdsignal[-1]

    # Calculate the last MACD histogram value
    last_macd_histogram = macdhist[-1]

    # Calculate the last RSI value
    last_rsi = rsi[-1]

    # Determine if the last closing price is above the upper Bollinger Band
    if last_price > last_upper_band:
        print("Sell signal generated. Consider selling Nifty50.")

    # Determine if the last closing price is below the lower Bollinger Band
    elif last_price < last_lower_band:
        print("Buy signal generated. Consider purchasing Nifty50.")

    # Determine if the MACD signal crossed above the MACD histogram
    elif last_macd_signal > last_macd_histogram:
        print("Buy signal generated. Consider purchasing Nifty50.")

    # Determine if the MACD signal crossed below the MACD histogram
    elif last_macd_signal < last_macd_histogram:
        print("Sell signal generated. Consider selling Nifty50.")

    # Determine if the RSI is oversold
    elif last_rsi < 30:
        print("Buy signal generated. Consider purchasing Nifty50.")

    # Determine if the RSI is overbought
    elif last_rsi > 70:
        print("Sell signal generated. Consider selling Nifty50.")

    # If none of the above conditions are met, do not take any action
    else:
        print("No clear signal generated. Hold Nifty50.")

    # Wait for 30 seconds before fetching the latest data
    time.sleep(30)

created 2 years ago

Python Online Compiler

Write, Run & Share Python code online using OneCompiler's Python online compiler for free. It's one of the robust, feature-rich online compilers for python language, supporting both the versions which are Python 3 and Python 2.7. Getting started with the OneCompiler's Python editor is easy and fast. The editor shows sample boilerplate code when you choose language as Python or Python2 and start coding.

Taking inputs (stdin)

OneCompiler's python online editor supports stdin and users can give inputs to programs using the STDIN textbox under the I/O tab. Following is a sample python program which takes name as input and print your name with hello.

import sys
name = sys.stdin.readline()
print("Hello "+ name)

About Python

Python is a very popular general-purpose programming language which was created by Guido van Rossum, and released in 1991. It is very popular for web development and you can build almost anything like mobile apps, web apps, tools, data analytics, machine learning etc. It is designed to be simple and easy like english language. It's is highly productive and efficient making it a very popular language.

Tutorial & Syntax help

Loops

1. If-Else:

When ever you want to perform a set of operations based on a condition IF-ELSE is used.

if conditional-expression
    #code
elif conditional-expression
    #code
else:
    #code

Note:

Indentation is very important in Python, make sure the indentation is followed correctly

2. For:

For loop is used to iterate over arrays(list, tuple, set, dictionary) or strings.

Example:

mylist=("Iphone","Pixel","Samsung")
for i in mylist:
    print(i)

3. While:

While is also used to iterate a set of statements based on a condition. Usually while is preferred when number of iterations are not known in advance.

while condition  
    #code

Collections

There are four types of collections in Python.

1. List:

List is a collection which is ordered and can be changed. Lists are specified in square brackets.

Example:

mylist=["iPhone","Pixel","Samsung"]
print(mylist)

2. Tuple:

Tuple is a collection which is ordered and can not be changed. Tuples are specified in round brackets.

Example:

myTuple=("iPhone","Pixel","Samsung")
print(myTuple)

Below throws an error if you assign another value to tuple again.

myTuple=("iPhone","Pixel","Samsung")
print(myTuple)
myTuple[1]="onePlus"
print(myTuple)

3. Set:

Set is a collection which is unordered and unindexed. Sets are specified in curly brackets.

Example:

myset = {"iPhone","Pixel","Samsung"}
print(myset)

4. Dictionary:

Dictionary is a collection of key value pairs which is unordered, can be changed, and indexed. They are written in curly brackets with key - value pairs.

Example:

mydict = {
    "brand" :"iPhone",
    "model": "iPhone 11"
}
print(mydict)

Supported Libraries

Following are the libraries supported by OneCompiler's Python compiler

Name	Description
NumPy	NumPy python library helps users to work on arrays with ease
SciPy	SciPy is a scientific computation library which depends on NumPy for convenient and fast N-dimensional array manipulation
SKLearn/Scikit-learn	Scikit-learn or Scikit-learn is the most useful library for machine learning in Python
Pandas	Pandas is the most efficient Python library for data manipulation and analysis
DOcplex	DOcplex is IBM Decision Optimization CPLEX Modeling for Python, is a library composed of Mathematical Programming Modeling and Constraint Programming Modeling