 import yfinance as yf
import matplotlib.pyplot as plt
from matplotlib.finance import candlestick_ohlc
import pandas as pd
import talib as ta

# Fetch option chain data for Nifty, Bank Nifty and Fin Nifty
nifty_data = yf.Ticker("^NSEI").option_chain
bank_nifty_data = yf.Ticker("^NSEBANK").option_chain
fin_nifty_data = yf.Ticker("^NFNF").option_chain

# Calculate OI and PCR for Nifty
nifty_oi = nifty_data.calls['openInterest'].sum() + nifty_data.puts['openInterest'].sum()
nifty_pcr = nifty_data.puts['openInterest'].sum() / nifty_oi
print("Nifty OI: ", nifty_oi)
print("Nifty PCR: ", nifty_pcr)

# Calculate Market Up-Down-Sideways Signal
nifty_close = nifty_data.calls['lastPrice'].values
nifty_signal = ta.CDLTRISTAR(nifty_close)
print("Nifty Market Signal: ", nifty_signal)

# Plot candlestick graph for Nifty
nifty_ohlc = nifty_data.calls[['strike', 'lastPrice', 'bid', 'ask', 'change', 'pctChange', 'volume', 'openInterest']].values
fig, ax = plt.subplots()
candlestick_ohlc(ax, nifty_ohlc, width=0.6, colorup='green', colordown='red')
ax.xaxis_date()
plt.show()

# Calculate OI and PCR for Bank Nifty
bank_nifty_oi = bank_nifty_data.calls['openInterest'].sum() + bank_nifty_data.puts['openInterest'].sum()
bank_nifty_pcr = bank_nifty_data.puts['openInterest'].sum() / bank_nifty_oi
print("Bank Nifty OI: ", bank_nifty_oi)
print("Bank Nifty PCR: ", bank_nifty_pcr)

# Calculate Market Up-Down-Sideways Signal
bank_nifty_close = bank_nifty_data.calls['lastPrice'].values
bank_nifty_signal = ta.CDLTRISTAR(bank_nifty_close)
print("Bank Nifty Market Signal: ", bank_nifty_signal)


# Calculate FII-DII Data for Nifty
nifty_fii_dii = yf.Ticker("^NSEI").institutional_holders
nifty_fii = nifty_fii_dii['fii_dii_data']['FII']
nifty_dii = nifty_fii_dii['fii_dii_data']['DII']

# Plot FII-DII Data for Nifty
plt.bar(['FII', 'DII'], [nifty_fii, nifty_dii])
plt.show()

# Calculate Option Greeks for Nifty
nifty_greeks = nifty_data.calls[['delta', 'gamma', 'theta', 'vega', 'rho']]
print("Nifty Option Greeks: ", nifty_greeks)

# Calculate OI and PCR for Bank Nifty
bank_nifty_oi = bank_nifty_data.calls['openInterest'].sum() + bank_nifty_data.puts['openInterest'].sum()
bank_nifty_pcr = bank_nifty_data.puts['openInterest'].sum() / bank_nifty_oi
print("Bank Nifty OI: ", bank_nifty_oi)
print("Bank Nifty PCR: ", bank_nifty_pcr)

# Calculate Market Up-Down-Sideways Signal
bank_nifty_close = bank_nifty_data.calls['lastPrice'].values
bank_nifty_signal = ta.CDLTRISTAR(bank_nifty_close)
print("Bank Nifty Market Signal: ", bank_nifty_signal)

# Plot candlestick graph for Bank Nifty
bank_nifty_ohlc = bank_nifty_data.calls[['strike', 'lastPrice', 'bid', 'ask', 'change', 'pctChange', 'volume', 'openInterest']].values
fig, ax = plt.subplots()
candlestick_ohlc(ax, bank_nifty_ohlc, width=0.6, colorup='green', colordown='red')
ax.xaxis_date()
plt.show()

# Calculate FII-DII Data for Bank Nifty
bank_nifty_fii_dii = yf.Ticker("^NSEBANK").institutional_holders
bank_nifty_fii = bank_nifty_fii_dii['fii_dii_data']['FII']
bank_nifty_dii = bank_nifty_fii_dii['fii_dii_data']['DII']

# Plot FII-DII Data for Bank Nifty
plt.bar(['FII', 'DII'], [bank_nifty_fii, bank_nifty_dii])
plt.show()

# Calculate Option Greeks for Bank Nifty
bank_nifty_greeks = nifty_data.calls[['delta', 'gamma', 'theta', 'vega', 'rho']]
print("Bank Nifty Option Greeks: ", bank_nifty_greeks)

# Calculate OI and PCR for Fin Nifty
fin_nifty_oi = fin_nifty_data.calls['openInterest'].sum() + fin_nifty_data.puts['openInterest'].sum()
fin_nifty_pcr = fin_nifty_data.puts['openInterest'].sum() / fin_nifty_oi
print("Fin Nifty OI: ", fin_nifty_oi)
print("Fin Nifty PCR: ", fin_nifty_pcr)

# Calculate Market Up-Down-Sideways Signal
fin_nifty_close = fin_nifty_data.calls['lastPrice'].values
fin_nifty_signal = ta.CDLTRISTAR(fin_nifty_close)
print("Fin Nifty Market Signal: ", fin_nifty_signal)

# Plot candlestick graph for Fin Nifty
fin_nifty_ohlc = fin_nifty_data.calls[['strike', 'lastPrice', 'bid', 'ask', 'change', 'pctChange', 'volume', 'openInterest']].values
fig, ax = plt.subplots()
candlestick_ohlc(ax, fin_nifty_ohlc, width=0.6, colorup='green', colordown='red')
ax.xaxis_date()
plt.show()

# Calculate FII-DII Data for Fin Nifty
fin_nifty_fii_dii = yf.Ticker("^NFNF").institutional_holders
fin_nifty_fii = fin_nifty_fii_dii['fii_dii_data']['FII']

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.

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

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Tutorial & Syntax help

Loops

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

if conditional-expression
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Indentation is very important in Python, make sure the indentation is followed correctly

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

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mylist=("Iphone","Pixel","Samsung")
for i in mylist:
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while condition  
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Collections

There are four types of collections in Python.

1. List:

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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:

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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:

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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