10mohi6/jquants-algo-python

jquants-algo is a python library for algorithmic trading with japanese stock trade using J-Quants on Python 3.8 and above.

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

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())

{'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

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())

{'buy entry': True,
 'buy exit': False,
 'close': 2416.5,
 'date': '2023-08-22',
 'sell entry': False,
 'sell exit': True}

advanced

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.