Other Examples
About 4 min
Other example
Additional examples of the Python SDK are constantly being updated and will be synced to the Github code repository as they become available.
Get historical K-lines in bulk
import datetime
import time
import unittest
import pandas as pd
import pytz
from tigeropen.common.consts import BarPeriod
from tigeropen.quote.quote_client import QuoteClient
from tigeropen.tiger_open_config import get_client_config
pd.set_option('display.max_columns', 500)
pd.set_option('display.max_rows', 100)
pd.set_option('display.width', 1000)
client_config = get_client_config(private_key_path='your private key path',
tiger_id='your tiger id',
account='your account'
)
quote_client = QuoteClient(client_config)
SYMBOL = "symbol"
TIME = "time"
CLOSE = "close"
DATE = "date"
# total number of requested bars.
BARS_TOTAL_NUMBER = 2000
# number of bars per request, the system limits each symbol to a maximum of 1200 bars per request.
BARS_BATCH_SIZE = 300
# number of symbols per request, the system limits each request to a maximum of 50 symbols.
SYMBOLS_BATCH_SIZE = 50
# The interval between each request, to prevent requests too fast to trigger the system rate limit. Time unit: second
REQUEST_INTERVAL = 0.5
class QuoteExamples(unittest.TestCase):
def setUp(self) -> None:
print(f'quote permissions:{quote_client.grab_quote_permission()}')
def test_get_history(self):
"""
Example of a batch request for historical data
The processed historical data prints out like this:
all history:
time open high low close volume
date symbol
2022-02-11 15:41:00+08:00 00700 1644565260000 476.60 477.20 476.60 477.20 49200
01810 1644565260000 16.56 16.60 16.56 16.56 408000
2022-02-11 15:42:00+08:00 00700 1644565320000 477.00 477.20 476.20 476.20 95500
01810 1644565320000 16.56 16.58 16.54 16.56 199200
2022-02-11 15:43:00+08:00 00700 1644565380000 476.20 476.40 475.40 475.40 73110
... ... ... ... ... ... ...
2022-02-22 11:08:00+08:00 01810 1645499280000 15.62 15.62 15.60 15.62 128400
2022-02-22 11:09:00+08:00 00700 1645499340000 434.80 435.00 434.20 434.60 83988
01810 1645499340000 15.60 15.64 15.60 15.62 243400
2022-02-22 11:10:00+08:00 00700 1645499400000 434.60 434.80 434.20 434.60 29399
01810 1645499400000 15.60 15.62 15.58 15.62 234200
close data:
date symbol
2022-02-14 09:40:00+08:00 00700 469.00
01810 16.16
2022-02-14 09:41:00+08:00 00700 468.60
01810 16.06
2022-02-14 09:42:00+08:00 00700 468.00
...
2022-02-22 11:37:00+08:00 01810 15.64
2022-02-22 11:38:00+08:00 00700 436.00
01810 15.62
2022-02-22 11:39:00+08:00 00700 436.00
01810 15.64
Name: close, Length: 4200, dtype: float64
history of 00700:
date time open high low close volume
symbol
00700 2022-02-14 09:40:00+08:00 1644802800000 470.0 470.2 468.6 469.0 61900
00700 2022-02-14 09:41:00+08:00 1644802860000 469.0 469.4 468.4 468.6 87800
00700 2022-02-14 09:42:00+08:00 1644802920000 468.6 468.6 467.6 468.0 131770
00700 2022-02-14 09:43:00+08:00 1644802980000 468.0 468.8 467.8 468.4 115694
00700 2022-02-14 09:44:00+08:00 1644803040000 468.8 468.8 468.4 468.6 29607
... ... ... ... ... ... ... ...
00700 2022-02-22 11:35:00+08:00 1645500900000 436.4 436.4 436.0 436.0 39400
00700 2022-02-22 11:36:00+08:00 1645500960000 436.2 436.2 435.8 436.0 47000
00700 2022-02-22 11:37:00+08:00 1645501020000 436.0 436.4 435.8 436.2 40802
00700 2022-02-22 11:38:00+08:00 1645501080000 436.2 436.4 436.0 436.0 43102
00700 2022-02-22 11:39:00+08:00 1645501140000 436.0 436.2 436.0 436.0 1700
[2100 rows x 7 columns]
close of 00700:
symbol
00700 469.0
00700 468.6
00700 468.0
00700 468.4
00700 468.6
...
00700 436.0
00700 436.0
00700 436.2
00700 436.0
00700 436.0
Name: close, Length: 2100, dtype: float64
"""
# HK market
symbol1 = '00700'
symbols = [symbol1, '01810']
timezone = 'Asia/Shanghai'
# US market
# symbol1 = 'AAPL'
# symbols = [symbol1, 'TSLA']
# timezone = 'US/Eastern'
end = int(datetime.datetime.today().timestamp() * 1000)
history = pd.DataFrame()
for i in range(0, BARS_TOTAL_NUMBER, BARS_BATCH_SIZE):
if i + BARS_BATCH_SIZE <= BARS_TOTAL_NUMBER:
limit = BARS_BATCH_SIZE
else:
limit = i + BARS_BATCH_SIZE - BARS_TOTAL_NUMBER
end_time = datetime.datetime.fromtimestamp(end/1000, pytz.timezone(timezone))
print(f'query {len(symbols)} symobls history, end_time:{end} -- {end_time}, limit:{limit}')
# This request is for the minute k line, for other periods, can change 'period' parameter
part = self._request_bars(symbols=symbols, period=BarPeriod.ONE_MINUTE, end_time=end, bars_batch_size=BARS_BATCH_SIZE)
part[DATE] = pd.to_datetime(part[TIME], unit='ms').dt.tz_localize('UTC').dt.tz_convert(timezone)
end = min(part[TIME])
history = history.append(part)
history.set_index([DATE, SYMBOL], inplace=True)
history.sort_index(inplace=True)
print(f'all history:\n{history}')
close_data = history[CLOSE]
print(f'close data:\n{close_data}')
hist_of_symbol1 = history.reset_index().set_index(SYMBOL).loc[symbol1]
close_of_symbol1 = hist_of_symbol1[CLOSE]
print(f'history of {symbol1}:\n {hist_of_symbol1}')
print(f'close of {symbol1}:\n{close_of_symbol1}')
return history
@staticmethod
def _request_bars(symbols, period, end_time, bars_batch_size):
"""
Request history bars.
:param symbols: like ['AAPL', 'TSLA']
:param period: tigeropen.common.consts.BarPeriod. like BarPeriod.DAY
:param end_time: end time in timestamp format. like 1645499400000
:param bars_batch_size: bars limit size of each symbol
:return:
"""
symbols = list(symbols)
result = pd.DataFrame()
for i in range(0, len(symbols), SYMBOLS_BATCH_SIZE):
part = symbols[i:i + SYMBOLS_BATCH_SIZE]
quote = quote_client.get_bars(part, period=period, end_time=end_time, limit=bars_batch_size)
result = result.append(quote)
# to avoid rate limit
time.sleep(REQUEST_INTERVAL)
return result
Option Indicator Caculation Tool
SDK code path: tigeropen/examples/option_helpers/helpers.py ， calculate Greek values and implied Volatility,also support option price predictions. this tool will depend on this library: quantlib,Need to install before use: pip install quantlib
Usage 1
FDAmericanDividendOptionHelper is American option calculation tool FDEuropeanDividendOptionHelper is European option calculation class
import quantlib as ql
from tigeropen.examples.option_helpers.helpers import FDAmericanDividendOptionHelper
# Calculate option prices based on implied volatility:
ql.Settings.instance().evaluationDate = ql.Date(19, 4, 2022)
helper = FDAmericanDividendOptionHelper(option_type=ql.Option.Call, # PUT/CALL
underlying=985, # Settlement date stock price
strike=990, # strike price
risk_free_rate=0.017, # risk free rate
dividend_rate=0, # dividend rate
volatility=0.6153, # implied volatility
settlement_date=ql.Date(14, 4, 2022), # settlement date
expiration_date=ql.Date(22, 4, 2022)) # option expiration date
print(f'value:{helper.NPV()}')
print(f'delta:{helper.delta()}')
print(f'gamma:{helper.gamma()}')
print(f'theta:{helper.theta()}')
print(f'vega:{helper.vega()}')
print(f'rho:{helper.rho()}')
# Calculation of implied volatility based on option prices:
ql.Settings.instance().evaluationDate = ql.Date(19, 4, 2022)
helper = FDAmericanDividendOptionHelper(option_type=ql.Option.Call,
underlying=985,
strike=990,
risk_free_rate=0.017,
dividend_rate=0,
volatility=0, # implied volatility
settlement_date=ql.Date(14, 4, 2022),
expiration_date=ql.Date(22, 4, 2022))
# calculation implied volatility
volatility = helper.implied_volatility(33.6148)
helper.update_implied_volatility(volatility)
print(f'implied volatility:{volatility}')
print(f'value:{helper.NPV()}')
print(f'delta:{helper.delta()}')
print(f'gamma:{helper.gamma()}')
print(f'theta:{helper.theta()}')
print(f'vega:{helper.vega()}')
print(f'rho:{helper.rho()}')
Usage 2
# calculation option price
python helpers.py -t PUT -e '2022-05-20' -s 2022-04-24 -p 215 -u 215.52 -r 0.0078 -v 0.5919
# Calculate implied volatility based on option prices. -n option price
python helpers.py -t CALL -e '2022-04-22' -s 2022-04-14 -p 990 -u 985 -r 0.017 -n 33.6148
# Calculate implied volatility from options intraday data.(use ask bid average price)
python helpers.py -t CALL -e '2022-04-22' -s 2022-04-14 -p 990 -u 985 -r 0.017 -a 35 -b 36
# help command
python helpers.py -h