AutoQuant is an out-of-the-box quantitative investment platform.
It contains the full ML pipeline of data processing, strategy building(includes AI & traditionals), back-testing, and covers the entire chain of quantitative investment: alpha seeking, risk modeling, portfolio optimization, and order execution.
With AutoQuant, users can easily try ideas to create better Quant investment strategies.
pip install --upgrade autoquantfrom autoquant.collector import Collector
from autoquant import Market
from datetime import date
collector = Collector.default()
data = collector.daily_prices(
market=Market.SZ,
code='002594',
start=date(2021, 11, 1),
end=date(2021, 11, 5)
)
data = collector.quarter_statement(
market=Market.SH,
code='601318',
quarter=date(2021, 9, 30)
)
from autoquant.collector import Collector
from autoquant.workflow import Workflow
from autoquant.broker import Broker
from autoquant import Market
from datetime import date
from autoquant.workflow import Workflow
from autoquant.strategy import MA_CrossOver
class SmaCross(MA_CrossOver):
params = dict(fast=5, slow=20)
collector = Collector.default()
broker = Broker.default(kick_start=100000, commission=0.01)
data = collector.daily_prices(market=Market.SZ, code='002594', start=date(2020, 1, 1), end=date(2021, 11, 1))
w = Workflow().with_broker(broker).with_strategy(SmaCross).backtest(data)
w.visualize()AutoQuant support Shanghai, Shenzhen, HongKong and US markets now. Use Market Enum in codes:
from autoquant import Market
Market.SZ
Market.SH
Market.HK
Market.CN
Market.USAutoQuant support the indexes in multiple markets now.
Use StocksIndex Enum in codes:
from autoquant import StocksIndex
StocksIndex.ZZ500
StocksIndex.HS300
StocksIndex.SZ50Use FundsIndex Enum in codes:
from autoquant import FundsIndex
FundsIndex.CN_ALL
FundsIndex.CN_ETF
FundsIndex.CN_QDII
FundsIndex.HUAXIA_SECTOR_ETF- ParityIndex
- AdjustedMomentum
All the indicators in Backtrader are available in AutoQuant.
For Example, if you were using the indicators of Backtrader like this:
from backtrader.indicators import MomentumYou can simply change the import sentence to use the indicators in AutoQuant. The codes would be:
from autoquant.indicators import Momentum- Gross Rate Of Return
- CAGR(Compound Annual Growth Rate)
All the metrics in TA-Lib are available in AutoQuant.
For Example, if you were using the metrics of TA-Lib like this:
from talib import SMA
close = numpy.random.random(100)
output = MOM(close, timeperiod=5)You can simply change the import sentence to use the metrics in AutoQuant. The codes would be:
from AutoQuant import SMA
close = numpy.random.random(100)
output = MOM(close, timeperiod=5)- BaostockProvider
- TushareProvider
- EastmoneyProvider
def daily_prices(self, market: Market, code: str, start: date, end: date, **kwargs)- SnowballProvider
def quarter_statement(self, market: Market, code: str, quarter: date, **kwargs)
def yearly_balance_sheet(self, market: Market, code: str, years: list, **kwargs)
def yearly_income_sheets(self, market: Market, code: str, years: list, **kwargs)
def yearly_flow_sheets(self, market: Market, code: str, years: list, **kwargs)- BaostockProvider
- EastmoneyProvider
def stocks_of_index(self, index: StocksIndex, **kwargs)
def funds_of_index(self, index: FundsIndex, **kwargs)PYTHONPATH=./ pytestPYTHONPATH=./ pytest tests/<YOUR_DISIRE_FILE>.py -k "<YOUR_DISIRE_TEST_CASE>" -spipreqs ./ --encoding=utf8 --forcepython3 -m build
python3 -m twine upload dist/*