finmetry 0.1.4

Stock market data analysis

Finmetry

This is the project on stock market data analysis using modern data anlysis tools, AI and ML.

In depth tests on various stock market tools are conducted.

The current version is

0.1.3

Importing and initiating

%load_ext autoreload

%autoreload 2
import finmetry as fm

import pandas as pd
pd.set_option('display.max_rows', 5)
pd.set_option('display.max_columns', 20)
import numpy as np
import datetime as dtm

The API has Stock class which helps in managing the data related to any security. Clients are classes of various data providing external libraries such as py5paisa, yfinance etc. finmetry API has the class based of the python API of such external libraries for ease of use with Stock class. This version of finmetry is mainly developed around client py5paisa, as it provieds enough data for in-depth market analysis.

Initializing Stock

Stock class API provides handy tools for managing various data provided by clients. The Stock instance is provided to various clients which will then download the data for those stocks. There are multiple clients which provide variety of data on security. Further information on these can be found on specific module or through help of specific methods. The information about intializing Client5paisa can be found here.

The Stock class consists of various methods to get historical data and managing the local storage of data. If the store_local is False then no local storage is done. It is advisable to keep a folder for market data which can be provided to Stock class for proper handling. Also, if the historical data is already downloaded then this helps in directly accesing offline data instead of downloading it again.

from finmetry import Stock

market_foldpath = r"D:\Finmetry\local_data"

s1 = Stock(
    symbol="RELIANCE",
    exchange="N",
    exchange_type="C",
    store_local=True,
    local_data_foldpath=market_foldpath,
)

As store_local = True, Folder with name RELIANCE is created. Now all the historical and other types of data will be stored in this folder. If there is already some historical data downloaded then API would directly access that data.

Intitalizing Client5paisa

Client5paisa is the class derived from py5paisa.FivePaisaClient. This is done to ease the data handling process.

First, we will initialize Client5paisa which will be used for data downloading for various stocks. For this the user needs 5paisa account and the API keys. User can refer to py5paisa for further details on getting API credentials. User need, API credentials (cred), email-id (email), password (password) of 5paisa account and date of birth (dob). Here, the credentials are already loaded on variables and not shown for security reasons.

c_5p = fm.client_5paisa.Client5paisa(email=email, password=password, dob=dob, cred=cred)

 19:32:23 | Logged in!!

ScripMaster

py5paisa provides the .csv file containing symbols and other information of majority of the Indian securities. The file is around 40Mb (while writing this and may vary in future) which may take about few seconds to download depending upon the internet speed. It is advisable to save it offline and use that .csv again for faster intialization. The local file should be updated often for any changes in .csv file, if any.

Downloading the file

scrip_master = fm.client_5paisa.ScripMaster()

Save the file locally

scrip_master_filepath = r"D:\Finmetry\local_data\scrip_master.csv"
scrip_master.save(scrip_master_filepath)

Loading from local file

scrip_master_filepath = r"D:\Finmetry\local_data\scrip_master.csv"
scrip_master = fm.client_5paisa.ScripMaster(filepath=scrip_master_filepath)
scrip_master.data

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	Exch	ExchType	Scripcode	Name	Series	Expiry	...	Multiplier	Underlyer	Root	TickSize	CO BO Allowed	Symbol
0	B	C	199117	SETFN50INAV	EQ	1980-01-01 00:00:00	...	1	NaN	SETFN50INAV	0.01	N	SETFN50INAV
1	B	C	199118	MOHEALTINAV	EQ	1980-01-01 00:00:00	...	1	NaN	MOHEALTINAV	0.01	N	MOHEALTINAV
...	...	...	...	...	...	...	...	...	...	...	...	...	...
202732	M	D	256029	NATURALGAS 23 JUN 2023 CE 130.00 ...		2023-06-23 23:59:59	...	1	23 Jun 2023	NATURALGAS	0.05	Y	NATURALGAS 23 JUN 2023 CE 130.00 ...
202733	M	D	256030	NATURALGAS 23 JUN 2023 PE 130.00 ...		2023-06-23 23:59:59	...	1	23 Jun 2023	NATURALGAS	0.05	Y	NATURALGAS 23 JUN 2023 PE 130.00 ...

202734 rows × 20 columns

User can repeat the process of downloading and saving to update the local file. This scrip_master will be used for generating various inputs for data fatching methods. The Exch, ExchType, Scripcode are some of the parameters required to get the market data of various securities. Hence this scrip_master should be provided to client_5paisa instance (i.e, c_5p).

Provide scrip_master to Client5paisa

c_5p.scrip_master = scrip_master

User can also provide the scrip_master instance during initialization of Client5paisa class.

c_5p = fm.client_5paisa.Client5paisa(
    email=email, password=password, dob=dob, cred=cred, scrip_master=scrip_master
)

Historical data

Historical data for list of stocks can be downloaded using c_5p.download_historical_data. If Stock.store_local is True then the data will be stored locally with separate file for each months data.

syms = ["RELIANCE", "ITC", "TCS"]
sl1 = []
for sym in syms:
    s1 = Stock(
        symbol=sym.upper(),
        exchange="N",
        exchange_type="C",
        store_local=True,
        local_data_foldpath=market_foldpath,
    )
    sl1.append(s1)

c_5p.download_historical_data(
    stocklist=sl1, interval="1m", start="2019-04-1", end="2019-04-30"
)

Accessing local data

Stock class has the method to access the data stored locally.

s1 = sl1[0]
data = s1.load_historical_data(interval="1m", start="2019-04-1", end="2019-04-30")
data

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	Open	High	Low	Close	Volume
Datetime
2019-04-01 09:15:00	1371.00	1373.15	1369.50	1370.10	73889
2019-04-01 09:16:00	1370.30	1370.75	1368.90	1369.00	45357
...	...	...	...	...	...
2019-04-30 15:29:00	1392.15	1393.00	1391.00	1393.00	33141
2019-04-30 15:30:00	1392.45	1392.45	1392.45	1392.45	25

7137 rows × 5 columns

Live Market Data

Various live market data is provided by py5paisa, like Market Depth and Market Feed.

Market Depth

d1 = c_5p.get_market_depth(sl1)
d1

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	AverageTradePrice	BuyQuantity	Close	Exchange	ExchangeType	High	LastQuantity	LastTradeTime	LastTradedPrice	Low	...	Open	OpenInterest	ScripCode	SellQuantity	TotalBuyQuantity	TotalSellQuantity	UpperCircuitLimit	Volume	Datetime	Symbol
0	2342.62	0	2352	N	C	2359	1	/Date(1681986517000)/	2346.05	2332.1	...	2354.1	0	2885	0	0	783	2587.20	3233882	2023-04-20 19:37:00.123152	RELIANCE
1	400.07	0	398.75	N	C	402.65	5	/Date(1681986598000)/	400.30	397.7	...	400	0	1660	0	279	0	438.60	6667781	2023-04-20 19:37:00.123152	ITC
2	3094.25	0	3089.6	N	C	3113	11	/Date(1681986560000)/	3104.80	3078	...	3090	0	11536	0	0	14	3398.55	2419999	2023-04-20 19:37:00.123152	TCS

3 rows × 23 columns

Market Feed

c_5p.get_market_feed(sl1)

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	Chg	ChgPcnt	Exch	ExchType	High	LastRate	Low	PClose	Symbol	TickDt	Time	Token	TotalQty	Datetime
0	-5.95	-0.252976	N	C	2359.00	2346.05	2332.1	2352.00	RELIANCE	/Date(1681986517000+0530)/	35917	2885	3233882	2023-04-20 19:37:03.343834
1	1.55	0.388715	N	C	402.65	400.30	397.7	398.75	ITC	/Date(1681986598000+0530)/	35998	1660	6667781	2023-04-20 19:37:03.343834
2	15.20	0.491973	N	C	3113.00	3104.80	3078.0	3089.60	TCS	/Date(1681986560000+0530)/	35960	11536	2419999	2023-04-20 19:37:03.343834

Market Data

For feed_type='md', user gets the data for Buyers and seller for 5 depth. Here, Last traded price is not there.

d1 = c_5p.get_market_data(sl1,feed_type='md')
d1

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	Exch	ExchType	Token	TBidQ	TOffQ	Details	TimeStamp	Time
0	N	C	2885	0	783	[{'Quantity': 0, 'Price': 0, 'NumberOfOrders':...	0	/Date(1681986590946)/
1	N	C	1660	279	0	[{'Quantity': 279, 'Price': 400.3, 'NumberOfOr...	0	/Date(1681986598666)/
2	N	C	11536	0	14	[{'Quantity': 0, 'Price': 0, 'NumberOfOrders':...	0	/Date(1681986591595)/

for feed_type='mf', user gets the data for nearest bid and offer and the last traded price.

d1 = c_5p.get_market_data(sl1,feed_type='mf')
d1

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	Exch	ExchType	Token	LastRate	LastQty	TotalQty	High	Low	OpenRate	PClose	AvgRate	Time	BidQty	BidRate	OffQty	OffRate	TBidQ	TOffQ	TickDt	ChgPcnt
0	N	C	2885	2346.05	1	3233882	2359.00	2332.1	2354.1	2352.00	2342.62	35918	0	0.0	783	2346.05	0	783	/Date(1681986518000)/	0
1	N	C	1660	400.30	5	6667781	402.65	397.7	400.0	398.75	400.07	35999	279	400.3	0	0.00	279	0	/Date(1681986599000)/	0
2	N	C	11536	3104.80	11	2419999	3113.00	3078.0	3090.0	3089.60	3094.25	35961	0	0.0	14	3104.80	0	14	/Date(1681986561000)/	0

Getting Option Chain data

For getting option chain data, user needs the expiry date for the option chain. Available expiry dates can be weekly or monthly depending upon the type of security. For indices, the options can have weekly expiry and for all of the stocks the expiry is monthly.

dates1 = c_5p.get_monthly_option_expiry()
dates1 = c_5p.get_weekly_option_expiry()
dates1

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	Exch	ExchType	ExpiryDate	Timestamp	Date
0	N	D	/Date(1681981200000+0530)/	1681981200000	2023-04-20
1	N	D	/Date(1682586000000+0530)/	1682586000000	2023-04-27
...	...	...	...	...	...
5	N	D	/Date(1685005200000+0530)/	1685005200000	2023-05-25
6	N	D	/Date(1688029200000+0530)/	1688029200000	2023-06-29

7 rows × 5 columns

c_5p.get_option_chain(sl1[0],'2023-04-27')

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	CPType	ChangeInOI	EXCH	ExchType	LastRate	Name	OpenInterest	Prev_OI	PreviousClose	ScripCode	StrikeRate	Volume
0	CE	0	N	D	0.0	RELIANCE 27 APR 2023 CE 1160.00	0	0	0.0	55506	1160	0
1	PE	0	N	D	0.0	RELIANCE 27 APR 2023 PE 1160.00	0	0	0.0	55507	1160	0
...	...	...	...	...	...	...	...	...	...	...	...	...
232	CE	0	N	D	0.0	RELIANCE 27 APR 2023 CE 3480.00	0	0	0.0	41363	3480	0
233	PE	0	N	D	0.0	RELIANCE 27 APR 2023 PE 3480.00	0	0	0.0	41366	3480	0

234 rows × 12 columns

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