use this project to learn smart pointers
install required package:
pip install pybind11clone this project & build:
git clone https://github.com/Phimos/fast-limit-order-book
cd fast-limit-order-book
python setup.py install
you can use this project as a python package, to display the details of the order book:
import flob
from flob import LimitOrderBook, Quote
lob = LimitOrderBook(decimal_places=2)
lob.write(Quote(12, 1186, 300, 1664529300000000000, flob.Bid, flob.LimitOrder))
lob.write(Quote(13, 1187, 100, 1664529500000000000, flob.Bid, flob.LimitOrder))
lob.write(Quote(14, 1188, 200, 1664530200000000000, flob.Bid, flob.LimitOrder))
lob.write(Quote(15, 1187, 400, 1664531500000000000, flob.Ask, flob.LimitOrder))
lob.show(n=1)expected output:
┌────────────────────┬────────────────────┐
│ Price │ Quantity │
╞════════════════════╪════════════════════╡
│ 11.87│ 100│
├────────────────────┼────────────────────┤
├────────────────────┼────────────────────┤
│ 11.86│ 300│
└────────────────────┴────────────────────┘you can also see the list of transactions:
lob.show_transactions()expected output:
┌────────────────────┬────────────────────┬────────────────────┐
│ Timestamp │ Price │ Quantity │
╞════════════════════╪════════════════════╪════════════════════╡
│ 09:51:40.000│ 11.88│ 200│
│ 09:51:40.000│ 11.87│ 100│
└────────────────────┴────────────────────┴────────────────────┘you can load quotes from a csv file and batch process them:
the columns of the csv file must be the same as the sample file
import pandas as pd
from flob import LimitOrderBook
lob = LimitOrderBook(decimal_places=2)
lob.load("data/sample.csv")
lob.set_status("CallAuction")
lob.until(pd.Timedelta("09:25:00").value)
lob.match_call_auction()
lob.set_status("ContinuousTrading")
lob.until(pd.Timedelta("11:30:00").value)
lob.show()expected output:
┌────────────────────┬────────────────────┐
│ Price │ Quantity │
╞════════════════════╪════════════════════╡
│ 12.00│ 760600│
│ 11.99│ 307500│
│ 11.98│ 422100│
│ 11.97│ 315800│
│ 11.96│ 323000│
│ 11.95│ 316600│
│ 11.94│ 191200│
│ 11.93│ 158600│
│ 11.92│ 93400│
│ 11.91│ 11000│
├────────────────────┼────────────────────┤
├────────────────────┼────────────────────┤
│ 11.90│ 95334│
│ 11.89│ 316400│
│ 11.88│ 289300│
│ 11.87│ 281535│
│ 11.86│ 171600│
│ 11.85│ 207800│
│ 11.84│ 131200│
│ 11.83│ 264500│
│ 11.82│ 507200│
│ 11.81│ 333800│
└────────────────────┴────────────────────┘you can also see the list of transactions:
lob.show_transactions()expected output:
┌────────────────────┬────────────────────┬────────────────────┐
│ Timestamp │ Price │ Quantity │
╞════════════════════╪════════════════════╪════════════════════╡
│ 11:29:47.110│ 11.91│ 200│
│ 11:29:47.110│ 11.91│ 1100│
│ 11:29:50.480│ 11.90│ 300│
│ 11:29:53.350│ 11.91│ 1000│
│ 11:29:53.350│ 11.91│ 1000│
│ 11:29:53.350│ 11.91│ 2600│
│ 11:29:53.350│ 11.91│ 200│
│ 11:29:53.350│ 11.91│ 1000│
│ 11:29:53.350│ 11.91│ 1000│
│ 11:29:53.350│ 11.91│ 200│
└────────────────────┴────────────────────┴────────────────────┘there are example scripts in the example folder. it can be used to generate transactions and tick data in any frequency.
python example/transaction.py \
--data data/sample.csv \
--schedule AShare \
--output data/transactions.csvpython example/tick.py \
--data data/sample.csv \
--schedule AShare \
--snapshot_gap 3s \
--topk 5 \
--output data/ticks.csvNote:
- this project is still under development, so the API is not stable.
- I will very grateful if you can provide the data of Hong Kong/US stocks