https://github.com/marcnuth/autoquant
AutoQuant is an out-of-the-box quantitative investment platform.
https://github.com/marcnuth/autoquant
ai-trade backtrader python qlib quant quantitative-finance trading
Last synced: 8 months ago
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AutoQuant is an out-of-the-box quantitative investment platform.
- Host: GitHub
- URL: https://github.com/marcnuth/autoquant
- Owner: Marcnuth
- License: mit
- Created: 2021-09-23T10:27:25.000Z (over 4 years ago)
- Default Branch: main
- Last Pushed: 2023-08-01T13:29:12.000Z (almost 3 years ago)
- Last Synced: 2024-04-26T20:22:43.490Z (about 2 years ago)
- Topics: ai-trade, backtrader, python, qlib, quant, quantitative-finance, trading
- Language: JavaScript
- Homepage: https://autoquant.qogir.fund
- Size: 66.5 MB
- Stars: 12
- Watchers: 3
- Forks: 3
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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# 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.
- [AutoQuant](#autoquant)
- [Quick Start](#quick-start)
- [Installation](#installation)
- [Data Preparation](#data-preparation)
- [Backtest](#backtest)
- [Advanced Topics](#advanced-topics)
- [Market](#market)
- [Index](#index)
- [Indicators](#indicators)
- [Specific Indicators](#specific-indicators)
- [Backtrader Indicators](#backtrader-indicators)
- [Metrics](#metrics)
- [Specific Metrics](#specific-metrics)
- [TA-Lib Metrics](#ta-lib-metrics)
- [Providers](#providers)
- [Price Provider](#price-provider)
- [Provides List](#provides-list)
- [API](#api)
- [Financial Statement Provider](#financial-statement-provider)
- [Provides List](#provides-list-1)
- [API](#api-1)
- [Index Provider](#index-provider)
- [Provides List](#provides-list-2)
- [API](#api-2)
- [Contribution Guide](#contribution-guide)
- [Test](#test)
- [Test all](#test-all)
- [Test specified test](#test-specified-test)
- [Development](#development)
- [Generate Requirements](#generate-requirements)
- [Package Update](#package-update)
# Quick Start
## Installation
```shell
pip install --upgrade autoquant
```
## Data Preparation
```python
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
```python
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:
```python
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:
```python
from autoquant import StocksIndex
StocksIndex.ZZ500
StocksIndex.HS300
StocksIndex.SZ50
```
Use FundsIndex Enum in codes:
```python
from autoquant import FundsIndex
FundsIndex.CN_ALL
FundsIndex.CN_ETF
FundsIndex.CN_QDII
FundsIndex.HUAXIA_SECTOR_ETF
```
## Indicators
### Specific Indicators
- ParityIndex
- AdjustedMomentum
### Backtrader Indicators
All the indicators in Backtrader are available in AutoQuant.
For Example, if you were using the indicators of Backtrader like this:
```python
from backtrader.indicators import Momentum
```
You can simply change the import sentence to use the indicators in AutoQuant. The codes would be:
```python
from autoquant.indicators import Momentum
```
## Metrics
### Specific 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:
```python
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:
```python
from AutoQuant import SMA
close = numpy.random.random(100)
output = MOM(close, timeperiod=5)
```
## Providers
### Price Provider
#### Provides List
- BaostockProvider
- TushareProvider
- EastmoneyProvider
#### API
```python
def daily_prices(self, market: Market, code: str, start: date, end: date, **kwargs)
```
### Financial Statement Provider
#### Provides List
- SnowballProvider
#### API
```python
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)
```
### Index Provider
#### Provides List
- BaostockProvider
- EastmoneyProvider
#### API
```python
def stocks_of_index(self, index: StocksIndex, **kwargs)
def funds_of_index(self, index: FundsIndex, **kwargs)
```
# Contribution Guide
## Test
### Test all
```shell
PYTHONPATH=./ pytest
```
### Test specified test
```shell
PYTHONPATH=./ pytest tests/.py -k "" -s
```
## Development
### Generate Requirements
```shell
pipreqs ./ --encoding=utf8 --force
```
### Package Update
```shell
python3 -m build
python3 -m twine upload dist/*
```