安达机器人

oanda-bot 是一个用于自动交易机器人的 python 库，在 Python 3.6 及更高版本上带有 oanda rest api。

安装

$ pip install oanda-bot

用法

基本运行

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
        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()
        )

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

基本回测

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
        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()
        )

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

基本报告

from oanda_bot import Bot

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

高级运行

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 take profit none)
        self.stop_loss = 20 # stop loss pips (default=0 stop loss none)

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()

高级回测

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 take profit none)
        self.stop_loss = 20 # stop loss pips (default=0 stop loss none)

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

高级报告

from oanda_bot import Bot

Bot(
    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

现场运行

from oanda_bot import Bot

class MyBot(Bot):
    def atr(self, *, period: int = 14, price: str = "C"):
        a = (self.df.H - self.df.L).abs()
        b = (self.df.H - self.df[price].shift()).abs()
        c = (self.df.L - self.df[price].shift()).abs()

        df = pd.concat([a, b, c], axis=1).max(axis=1)
        return df.ewm(span=period).mean()

    def strategy(self):
        rsi = self.rsi(period=10)
        ema = self.ema(period=20)
        atr = self.atr(period=20)
        lower = ema - atr
        upper = ema + atr
        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()

支持的指标

• 简单移动平均线'sma'
• 指数移动平均线'ema'
• 移动平均收敛散度'macd'
• 相对强度指数'rsi'
• 布林带'bbands'
• 市场动量“妈妈”
• 随机振荡器'stoch'
• 真棒振荡器'ao'

入门

如需开始使用 OANDA REST API 的帮助，请查看我们的在线文档。

贡献

1. 叉它
2. 创建您的功能分支 ( git checkout -b my-new-feature)
3. 提交您的更改 ( git commit -am 'Add some feature')
4. 推送到分支 ( git push origin my-new-feature)
5. 创建新的拉取请求