Auto Quant
Project description
AutoQuant
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.
Quick Start
Installation
pip install --upgrade autoquant
Data Preparation
from 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)
)
Backtest
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()
Advanced Topics
Market
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.US
Index
AutoQuant support the indexes in multiple markets now.
Use StocksIndex Enum in codes:
from autoquant import StocksIndex
StocksIndex.ZZ500
StocksIndex.HS300
StocksIndex.SZ50
Use FundsIndex Enum in codes:
from autoquant import FundsIndex
FundsIndex.CN_ALL
FundsIndex.CN_ETF
Metrics
Exclusive Metrics
- Gross Rate Of Return
- CAGR(Compound Annual Growth Rate)
TA-Lib Metrics
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)
Providers
Price Provider
Provides List
- BaostockProvider
- TushareProvider
- EastmoneyProvider
API
def daily_prices(self, market: Market, code: str, start: date, end: date, **kwargs)
Financial Statement Provider
Provides List
- SnowballProvider
API
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)
Index Provider
Provides List
- BaostockProvider
- EastmoneyProvider
API
def stocks_of_index(self, index: StocksIndex, **kwargs)
def funds_of_index(self, index: FundsIndex, **kwargs)
Contribution Guide
Test
Test all
PYTHONPATH=./ pytest
Test specified test
PYTHONPATH=./ pytest tests/<YOUR_DISIRE_FILE>.py -k "<YOUR_DISIRE_TEST_CASE>" -s
Development
Generate Requirements
pipreqs ./ --encoding=utf8 --force
Package Update
python3 -m build
python3 -m twine upload dist/*
Project details
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