tseopt is a Python library for fetching and processing option data from the Tehran Stock Exchange using various public APIs.
Check if Python is installed and available from the command line by running:
python3 --version # Unix/macOSor
py --version # WindowsIf you do not have Python, please install the latest 3.x version from python.org
python3 -m pip --version # Unix/macOSor
py -m pip --version # WindowsIf pip isn’t already installed, then first try to bootstrap it from the standard library:
python3 -m ensurepip --default-pip # Unix/macOSor
py -m ensurepip --default-pip # WindowsNow that you have Python and pip set up, you can create a virtual environment. Navigate to your project directory and run the following command:
python3 -m venv venv # Unix/macOSor
py -m venv venv # WindowsNext, you need to activate the virtual environment:
source venv/bin/activate # Unix/macOSor
venv\Scripts\activate # WindowsAfter activation, your command prompt should change to indicate that you are now working within the virtual environment.
python3 -m pip install --upgrade pip setuptools wheel # Unix/macOSor
py -m pip install --upgrade pip setuptools wheel # WindowsUse the package manager pip to install tseopt.
pip install --upgrade tseoptFetches all Bourse and FaraBours data (suitable for screening the total market).
from tseopt import get_all_options_data
entire_option_market_data = get_all_options_data()
print(entire_option_market_data.head(5))
print(entire_option_market_data.iloc[0])import pandas as pd
from tseopt.use_case.screen_market import OptionMarket, convert_to_billion_toman
option_market = OptionMarket(entire_option_market_data=entire_option_market_data)
print(f"total_trade_value: {option_market.total_trade_value / 1e10:.0f} B Toman", end="\n\n")
most_trade_value_calls = pd.DataFrame(option_market.most_trade_value.get("call"))
most_trade_value_calls['ticker'] = most_trade_value_calls['ticker'].astype(str)
most_trade_value_calls["trades_value"] = convert_to_billion_toman(most_trade_value_calls["trades_value"])
most_trade_value_puts = pd.DataFrame(option_market.most_trade_value.get("put"))
most_trade_value_puts['ticker'] = most_trade_value_puts['ticker'].astype(str)
most_trade_value_puts["trades_value"] = convert_to_billion_toman(most_trade_value_puts["trades_value"])
most_trade_value_by_underlying_asset = pd.DataFrame(option_market.most_trade_value_by_underlying_asset)
most_trade_value_by_underlying_asset[["call", "put", "total"]] =convert_to_billion_toman(most_trade_value_by_underlying_asset[["call", "put", "total"]])
print(most_trade_value_calls)
print(most_trade_value_puts)
print(most_trade_value_by_underlying_asset)from tseopt.use_case.options_chains import Chains
chains = Chains(entire_option_market_data)
# Display underlying asset information to help select ua_tse_codes
print("Underlying Asset Information:")
print(chains.underlying_asset_info.head(5))
ua_tse_code = "17914401175772326" # اهرم
# Option types can be "call", "put", or "both"
options = chains.options(ua_tse_code=ua_tse_code, option_type="both")
date_chain = chains.make_date_chains(ua_tse_code=ua_tse_code, option_type="both")
strike_price_chain = chains.make_strike_price_chains(ua_tse_code=ua_tse_code, option_type="call")
display(options)
# strike_price_chain and date_chain are generators.
# If you're not familiar with generators (and if you're wondering what the heck they are!),
# uncomment the lines below to convert them to lists
# strike_price_chain = list(strike_price_chain)
# date_chain = list(date_chain)
for chain in date_chain:
name = chain.loc[0, "name"]
jalali_date = name.split("-")[2]
print("Date: ", jalali_date)
display(chain)
print("\n\n")
for chain in strike_price_chain:
print("Strike Price: ", chain.loc[0, "strike_price"])
display(chain)
print("\n\n")
from tseopt import fetch_historical_lob, take_lob_screenshot
jalali_date = "1403-10-24"
tse_code = "17091434834979599" # ضهرم1110
all_lob = fetch_historical_lob(tse_code=tse_code, jalali_date=jalali_date)
display(all_lob)
specific_time = "10:50"
lob = take_lob_screenshot(entire_data=all_lob, specific_time=specific_time)
display(lob)
Provides low latency and more detailed data (such as initial margin and order book). This may be suitable for obtaining data for actual trading.
from tseopt import tadbir_api
isin_list = ["IRO9AHRM2501", "IROATVAF0621", "IRO9BMLT2771", "IRO9TAMN8991", "IRO9IKCO81M1"]
bulk_data = tadbir_api.get_last_bulk_data(isin_list=isin_list)
detail_data = tadbir_api.get_detail_data(isin_list[0])
symbol_info = detail_data.get("symbol_info")
order_book = pd.DataFrame(detail_data.get("order_book"))
print(bulk_data)
print(symbol_info)
print(order_book)Fetches all data which mercantile exchange website provides.
from tseopt import make_a_mercantile_data_object
md = make_a_mercantile_data_object()
md.update_data(timeout=20)
print(md.gavahi[0])
print(md.sandoq[0])
print(md.salaf[0])
print(md.future[0])
print(md.markets_info[0])
print(md.cdc[0])
print(md.all_market)
print(md.future_date_time)| English Word | Farsi Translation |
|---|---|
| ua_tse_code | کد نماد دارایی پایه |
| ua_ticker | نماد معاملاتی دارایی پایه |
| days_to_maturity | روزهای باقیمانده تا سررسید |
| strike_price | قیمت اعمال |
| contract_size | اندازه قرارداد |
| ua_close_price | قیمت پایانی دارایی پایه |
| ua_yesterday_price | قیمت روز گذشته دارایی پایه |
| begin_date | تاریخ شروع قرارداد |
| end_date | تاریخ سررسید قرارداد |
| tse_code | کد نماد آپشن |
| ticker | نماد معاملاتی آپشن |
| trades_num | تعداد معاملات آپشن |
| trades_volume | حجم معاملات آپشن |
| trades_value | ارزش معاملات آپشن |
| last_price | آخرین قیمت آپشن |
| close_price | قیمت پایانی آپشن |
| yesterday_price | قیمت روز گذشته آپشن |
| open_positions | موقعیتهای باز |
| yesterday_open_positions | موقعیتهای باز روز گذشته |
| notional_value | ارزش اسمی |
| bid_price | قیمت پیشنهادی خرید |
| bid_volume | حجم پیشنهادی خرید |
| ask_price | قیمت پیشنهادی فروش |
| ask_volume | حجم پیشنهادی فروش |
Pull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.