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Description:

jquantsml 0.1.4

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
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="<your J-Quants mail address>",
password="<your J-Quants password>",
ticker="7203", # TOYOTA
size=100, # 100 shares
)
pprint.pprint(ml.backtest())

{'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
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="<your J-Quants mail address>",
password="<your J-Quants password>",
ticker="7203", # TOYOTA
size=100, # 100 shares
)
pprint.pprint(ml.predict())

{'Date': '2023-08-01', 'Price': 2445.5, 'Sign': 'short'}

advanced
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="<your J-Quants mail address>",
password="<your J-Quants 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.