oanda-bot 0.1.0

oanda-bot is a python library for automated trading bot with oanda rest api on Python 3.6 and above.

oanda-bot

oanda-bot is a python library for automated trading bot with oanda rest api on Python 3.6 and above.

Installation

$ pip install oanda-bot

Usage

basic run

from oanda_bot import Bot

class MyBot(Bot):
    def strategy(self):
        fast_ma = self.sma(period=5)
        slow_ma = self.sma(period=25)
        # golden cross
        bt.sell_exit = bt.buy_entry = (fast_ma > slow_ma) & (
            fast_ma.shift() <= slow_ma.shift()
        )
        # dead cross
        bt.buy_exit = bt.sell_entry = (fast_ma < slow_ma) & (
            fast_ma.shift() >= slow_ma.shift()
        )

MyBot(
    account_id='<your practice account id>',
    access_token='<your practice access token>',
).run()

basic backtest

from oanda_bot import Bot

class MyBot(Bot):
    def strategy(self):
        fast_ma = self.sma(period=5)
        slow_ma = self.sma(period=25)
        # golden cross
        bt.sell_exit = bt.buy_entry = (fast_ma > slow_ma) & (
            fast_ma.shift() <= slow_ma.shift()
        )
        # dead cross
        bt.buy_exit = bt.sell_entry = (fast_ma < slow_ma) & (
            fast_ma.shift() >= slow_ma.shift()
        )

MyBot(
    account_id='<your practice account id>',
    access_token='<your practice access token>',
).backtest()

basic report

from oanda_bot import Bot

class MyBot(Bot):
    def strategy(self):
        fast_ma = self.sma(period=5)
        slow_ma = self.sma(period=25)
        # golden cross
        bt.sell_exit = bt.buy_entry = (fast_ma > slow_ma) & (
            fast_ma.shift() <= slow_ma.shift()
        )
        # dead cross
        bt.buy_exit = bt.sell_entry = (fast_ma < slow_ma) & (
            fast_ma.shift() >= slow_ma.shift()
        )

MyBot(
    account_id='<your practice account id>',
    access_token='<your practice access token>',
).report()

advanced run

from oanda_bot import Bot

class MyBot(Bot):
    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.C < lower)
        self.sell_entry = (rsi > 70) & (self.df.C > upper)
        self.sell_exit = ema > self.df.C
        self.buy_exit = ema < self.df.C
        self.units = 1000 # currency unit (default=10000)
        self.take_profit = 50 # take profit pips (default=0)
        self.stop_loss = 20 # stop loss pips (default=0)

MyBot(
    account_id='<your practice account id>',
    access_token='<your practice access token>',
    # trading environment (default=practice)
    environment='practice',
    # trading currency (default=EUR_USD)
    instrument='USD_JPY',
    # 1 minute candlesticks (default=D)
    granularity='M1',
    # trading time (default=Bot.SUMMER_TIME)
    trading_time=Bot.WINTER_TIME,
    # Slack notification when an error occurs
    slack_webhook_url='<your slack webhook url>',
    # Line notification when an error occurs
    line_notify_token='<your line notify token>',
    # Discord notification when an error occurs
    discord_webhook_url='<your discord webhook url>',
).run()

advanced backtest

from oanda_bot import Bot

class MyBot(Bot):
    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.C < lower)
        self.sell_entry = (rsi > 70) & (self.df.C > upper)
        self.sell_exit = ema > self.df.C
        self.buy_exit = ema < self.df.C

MyBot(
    account_id='<your practice account id>',
    access_token='<your practice access token>',
    instrument='USD_JPY',
    granularity='S15', # 15 second candlestick
).backtest(from_date="2020-7-7", to_date="2020-7-13", filename="backtest.png")

total profit        3910.000
total trades         374.000
win rate              59.091
profit factor          1.115
maximum drawdown    4220.000
recovery factor        0.927
riskreward ratio       0.717
sharpe ratio           0.039
average return         9.787
stop loss              0.000
take profit            0.000

advanced report

from oanda_bot import Bot

class MyBot(Bot):
    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.C < lower)
        self.sell_entry = (rsi > 70) & (self.df.C > upper)
        self.sell_exit = ema > self.df.C
        self.buy_exit = ema < self.df.C

MyBot(
    account_id='<your practice account id>',
    access_token='<your practice access token>',
    instrument='USD_JPY',
    granularity='S15', # 15 second candlestick
).report(filename="report.png", days=-7) # from 7 days ago to now

total profit        -4960.000
total trades          447.000
win rate               59.284
profit factor          -0.887
maximum drawdown    10541.637
recovery factor        -0.471
riskreward ratio       -0.609
sharpe ratio           -0.043
average return        -10.319

live run

from oanda_bot import Bot

class MyBot(Bot):
    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.C < lower)
        self.sell_entry = (rsi > 70) & (self.df.C > upper)
        self.sell_exit = ema > self.df.C
        self.buy_exit = ema < self.df.C
        self.units = 1000

MyBot(
    account_id='<your live account id>',
    access_token='<your live access token>',
    environment='live',
    instrument='EUR_GBP',
    granularity='H12', # 12 hour candlesticks
    trading_time=Bot.WINTER_TIME,
    slack_webhook_url='<your slack webhook url>',
).run()

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'
• Awesome Oscillator 'ao'

Getting started

For help getting started with OANDA REST API, view our online documentation.

Contributing

1. Fork it
2. Create your feature branch (git checkout -b my-new-feature)
3. Commit your changes (git commit -am 'Add some feature')
4. Push to the branch (git push origin my-new-feature)
5. Create new Pull Request