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https://github.com/10mohi6/jquants-ml-python
jquants-ml is a python library for machine learning with japanese stock trade using J-Quants on Python 3.8 and above.
https://github.com/10mohi6/jquants-ml-python
j-quants japanese-stock jquants machine-learning python
Last synced: 5 days ago
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jquants-ml is a python library for machine learning with japanese stock trade using J-Quants on Python 3.8 and above.
- Host: GitHub
- URL: https://github.com/10mohi6/jquants-ml-python
- Owner: 10mohi6
- License: mit
- Created: 2023-09-25T04:30:45.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2023-10-24T13:16:13.000Z (over 1 year ago)
- Last Synced: 2024-04-24T18:55:30.368Z (9 months ago)
- Topics: j-quants, japanese-stock, jquants, machine-learning, python
- Language: Python
- Homepage:
- Size: 992 KB
- Stars: 2
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# jquants-ml
[](https://pypi.org/project/jquants-ml/)
[](https://opensource.org/licenses/MIT)
[](https://codecov.io/gh/10mohi6/jquants-ml-python)
[](https://github.com/10mohi6/jquants-ml-python/actions/workflows/python-package.yml)
[](https://pypi.org/project/jquants-ml/)
[](https://pepy.tech/project/jquants-ml)
jquants-ml is a python library for machine learning with japanese stock trade using J-Quants on Python 3.8 and above.
## Installation
$ pip install jquants-ml
## Usage
### backtest
```python
from jquants_ml import Ml
import pprint
class MyMl(Ml):
def features(self):
self.X["close"] = self.df.Close
self.X["ror"] = self.df.Close.pct_change(1)
self.X["sma"] = self.sma(period=5)
ml = MyMl(
mail_address="",
password="",
ticker="7203", # TOYOTA
size=100, # 100 shares
)
pprint.pprint(ml.backtest())
```
```python
{'long': {'maximum drawdown': '15250.000',
'profit': '12700.000',
'profit factor': '1.183',
'riskreward ratio': '1.213',
'sharpe ratio': '0.063',
'trades': '81.000',
'win rate': '0.494'},
'short': {'maximum drawdown': '50100.000',
'profit': '-43800.000',
'profit factor': '0.413',
'riskreward ratio': '0.478',
'sharpe ratio': '0.298',
'trades': '41.000',
'win rate': '0.463'},
'total': {'maximum drawdown': '47200.000',
'profit': '-31100.000',
'profit factor': '0.784',
'riskreward ratio': '0.837',
'sharpe ratio': '0.149',
'trades': '122.000',
'win rate': '0.484'}}
```
### predict
```python
from jquants_ml import Ml
import pprint
class MyMl(Ml):
def features(self):
self.X["close"] = self.df.Close
self.X["ror"] = self.df.Close.pct_change(1)
self.X["sma"] = self.sma(period=5)
ml = MyMl(
mail_address="",
password="",
ticker="7203", # TOYOTA
size=100, # 100 shares
)
pprint.pprint(ml.predict())
```
```python
{'Date': '2023-08-01', 'Price': 2445.5, 'Sign': 'short'}
```
### advanced
```python
from jquants_ml import Ml
class MyMl(Ml):
# Awesome Oscillator
def ao(self, *, fast_period: int = 5, slow_period: int = 34):
return ((self.df.H + self.df.L) / 2).rolling(fast_period).mean() - (
(self.df.H + self.df.L) / 2
).rolling(slow_period).mean()
def features(self):
self.X["ao"] = self.ao(fast_period=5, slow_period=34)
self.X["close"] = self.df.Close
self.X["ror"] = self.df.Close.pct_change(1)
self.X["sma"] = self.sma(period=5)
self.X["ema"] = self.ema(period=5)
self.X["upper"], self.X["mid"], self.X["lower"] = self.bbands(
period=20, band=2
)
self.X["macd"], self.X["signal"] = self.macd(
fast_period=12, slow_period=26, signal_period=9
)
self.X["k"], self.X["d"] = self.stoch(k_period=5, d_period=3)
self.X["rsi"] = self.rsi(period=14)
self.X["atr"] = self.atr(period=14)
self.X["mom"] = self.mom(period=10)
ml = MyMl(
mail_address="",
password="",
ticker="7203", # TOYOTA
size=100, # 100 shares
outputs_dir_path="outputs",
model_dir_path="model",
data_dir_path="data",
)
pprint.pprint(ml.backtest())
pprint.pprint(ml.predict())
```
## Supported indicators
- Simple Moving Average 'sma'
- Exponential Moving Average 'ema'
- Moving Average Convergence Divergence 'macd'
- Relative Strenght Index 'rsi'
- Bollinger Bands 'bbands'
- Market Momentum 'mom'
- Stochastic Oscillator 'stoch'
- Average True Range 'atr'
## Getting started
For help getting started with J-Quants, view our online [documentation](https://jpx-jquants.com/).