# jquants-algo
[](https://pypi.org/project/jquants-algo/)
[](https://opensource.org/licenses/MIT)
[](https://codecov.io/gh/10mohi6/jquants-algo-python)
[](https://github.com/10mohi6/jquants-algo-python/actions/workflows/python-package.yml)
[](https://pypi.org/project/jquants-algo/)
[](https://pepy.tech/project/jquants-algo)
jquants-algo is a python library for algorithmic trading with japanese stock trade using J-Quants on Python 3.8 and above.
## Installation
$ pip install jquants-algo
## Usage
### backtest
```python
from jquants_algo import Algo
import pprint
class MyAlgo(Algo):
def strategy(self):
fast_ma = self.sma(period=3)
slow_ma = self.sma(period=5)
# golden cross
self.sell_exit = self.buy_entry = (fast_ma > slow_ma) & (
fast_ma.shift() <= slow_ma.shift()
)
# dead cross
self.buy_exit = self.sell_entry = (fast_ma < slow_ma) & (
fast_ma.shift() >= slow_ma.shift()
)
algo = MyAlgo(
mail_address="<your J-Quants mail address>",
password="<your J-Quants password>",
ticker="7203", # TOYOTA
size=100, # 100 shares
)
pprint.pprint(algo.backtest())
```
```python
{'long': {'average return': '0.156',
'maximum drawdown': '49350.000',
'profit': '11450.000',
'profit factor': '1.080',
'riskreward ratio': '1.455',
'sharpe ratio': '0.038',
'trades': '54.000',
'win rate': '0.426'},
'short': {'average return': '-0.238',
'maximum drawdown': '42050.000',
'profit': '-31020.000',
'profit factor': '0.754',
'riskreward ratio': '1.319',
'sharpe ratio': '-0.091',
'trades': '55.000',
'win rate': '0.364'},
'total': {'average return': '-0.043',
'maximum drawdown': '79950.000',
'profit': '-19570.000',
'profit factor': '0.927',
'riskreward ratio': '1.423',
'sharpe ratio': '-0.013',
'trades': '109.000',
'win rate': '0.394'}}
```
### predict
```python
from jquants_algo import Algo
import pprint
class MyAlgo(Algo):
def strategy(self):
fast_ma = self.sma(period=3)
slow_ma = self.sma(period=5)
# golden cross
self.sell_exit = self.buy_entry = (fast_ma > slow_ma) & (
fast_ma.shift() <= slow_ma.shift()
)
# dead cross
self.buy_exit = self.sell_entry = (fast_ma < slow_ma) & (
fast_ma.shift() >= slow_ma.shift()
)
algo = MyAlgo(
mail_address="<your J-Quants mail address>",
password="<your J-Quants password>",
ticker="7203", # TOYOTA
size=100, # 100 shares
)
pprint.pprint(algo.predict())
```
```python
{'buy entry': True,
'buy exit': False,
'close': 2416.5,
'date': '2023-08-22',
'sell entry': False,
'sell exit': True}
```
### advanced
```python
from jquants_algo import Algo
import pprint
class MyAlgo(Algo):
def strategy(self):
rsi = self.rsi(period=10)
ema = self.ema(period=20)
lower = ema - (ema * 0.001)
upper = ema + (ema * 0.001)
self.buy_entry = (rsi < 30) & (self.df.Close < lower)
self.sell_entry = (rsi > 70) & (self.df.Close > upper)
self.sell_exit = ema > self.df.Close
self.buy_exit = ema < self.df.Close
algo = MyAlgo(
mail_address="<your J-Quants mail address>",
password="<your J-Quants password>",
ticker="7203", # TOYOTA
size=100, # 100 shares
outputs_dir_path="outputs",
data_dir_path="data",
)
pprint.pprint(algo.backtest())
pprint.pprint(algo.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/).
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"description": "# jquants-algo\n\n[](https://pypi.org/project/jquants-algo/)\n[](https://opensource.org/licenses/MIT)\n[](https://codecov.io/gh/10mohi6/jquants-algo-python)\n[](https://github.com/10mohi6/jquants-algo-python/actions/workflows/python-package.yml)\n[](https://pypi.org/project/jquants-algo/)\n[](https://pepy.tech/project/jquants-algo)\n\njquants-algo is a python library for algorithmic trading with japanese stock trade using J-Quants on Python 3.8 and above.\n\n## Installation\n\n $ pip install jquants-algo\n\n## Usage\n\n### backtest\n\n```python\nfrom jquants_algo import Algo\nimport pprint\n\nclass MyAlgo(Algo):\n def strategy(self):\n fast_ma = self.sma(period=3)\n slow_ma = self.sma(period=5)\n # golden cross\n self.sell_exit = self.buy_entry = (fast_ma > slow_ma) & (\n fast_ma.shift() <= slow_ma.shift()\n )\n # dead cross\n self.buy_exit = self.sell_entry = (fast_ma < slow_ma) & (\n fast_ma.shift() >= slow_ma.shift()\n )\n\nalgo = MyAlgo(\n mail_address=\"<your J-Quants mail address>\",\n password=\"<your J-Quants password>\",\n ticker=\"7203\", # TOYOTA\n size=100, # 100 shares\n)\npprint.pprint(algo.backtest())\n```\n\n\n\n```python\n{'long': {'average return': '0.156',\n 'maximum drawdown': '49350.000',\n 'profit': '11450.000',\n 'profit factor': '1.080',\n 'riskreward ratio': '1.455',\n 'sharpe ratio': '0.038',\n 'trades': '54.000',\n 'win rate': '0.426'},\n 'short': {'average return': '-0.238',\n 'maximum drawdown': '42050.000',\n 'profit': '-31020.000',\n 'profit factor': '0.754',\n 'riskreward ratio': '1.319',\n 'sharpe ratio': '-0.091',\n 'trades': '55.000',\n 'win rate': '0.364'},\n 'total': {'average return': '-0.043',\n 'maximum drawdown': '79950.000',\n 'profit': '-19570.000',\n 'profit factor': '0.927',\n 'riskreward ratio': '1.423',\n 'sharpe ratio': '-0.013',\n 'trades': '109.000',\n 'win rate': '0.394'}}\n```\n\n### predict\n\n```python\nfrom jquants_algo import Algo\nimport pprint\n\nclass MyAlgo(Algo):\n def strategy(self):\n fast_ma = self.sma(period=3)\n slow_ma = self.sma(period=5)\n # golden cross\n self.sell_exit = self.buy_entry = (fast_ma > slow_ma) & (\n fast_ma.shift() <= slow_ma.shift()\n )\n # dead cross\n self.buy_exit = self.sell_entry = (fast_ma < slow_ma) & (\n fast_ma.shift() >= slow_ma.shift()\n )\n\nalgo = MyAlgo(\n mail_address=\"<your J-Quants mail address>\",\n password=\"<your J-Quants password>\",\n ticker=\"7203\", # TOYOTA\n size=100, # 100 shares\n)\npprint.pprint(algo.predict())\n```\n\n```python\n{'buy entry': True,\n 'buy exit': False,\n 'close': 2416.5,\n 'date': '2023-08-22',\n 'sell entry': False,\n 'sell exit': True}\n```\n\n### advanced\n\n```python\nfrom jquants_algo import Algo\nimport pprint\n\nclass MyAlgo(Algo):\n def strategy(self):\n rsi = self.rsi(period=10)\n ema = self.ema(period=20)\n lower = ema - (ema * 0.001)\n upper = ema + (ema * 0.001)\n self.buy_entry = (rsi < 30) & (self.df.Close < lower)\n self.sell_entry = (rsi > 70) & (self.df.Close > upper)\n self.sell_exit = ema > self.df.Close\n self.buy_exit = ema < self.df.Close\n\nalgo = MyAlgo(\n mail_address=\"<your J-Quants mail address>\",\n password=\"<your J-Quants password>\",\n ticker=\"7203\", # TOYOTA\n size=100, # 100 shares\n outputs_dir_path=\"outputs\",\n data_dir_path=\"data\",\n)\npprint.pprint(algo.backtest())\npprint.pprint(algo.predict())\n```\n\n## Supported indicators\n\n- Simple Moving Average 'sma'\n- Exponential Moving Average 'ema'\n- Moving Average Convergence Divergence 'macd'\n- Relative Strenght Index 'rsi'\n- Bollinger Bands 'bbands'\n- Market Momentum 'mom'\n- Stochastic Oscillator 'stoch'\n- Average True Range 'atr'\n\n## Getting started\n\nFor help getting started with J-Quants, view our online [documentation](https://jpx-jquants.com/).\n",
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