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https://github.com/10mohi6/oanda-bot-python
oanda-bot is a python library for automated trading bot with oanda rest api on Python 3.6 and above.
https://github.com/10mohi6/oanda-bot-python
api automated backtest bot fx oanda python trading
Last synced: 3 days ago
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oanda-bot is a python library for automated trading bot with oanda rest api on Python 3.6 and above.
- Host: GitHub
- URL: https://github.com/10mohi6/oanda-bot-python
- Owner: 10mohi6
- License: mit
- Created: 2020-06-22T13:59:27.000Z (over 4 years ago)
- Default Branch: master
- Last Pushed: 2022-12-08T07:45:17.000Z (about 2 years ago)
- Last Synced: 2025-01-06T04:09:51.191Z (19 days ago)
- Topics: api, automated, backtest, bot, fx, oanda, python, trading
- Language: Python
- Homepage:
- Size: 85 KB
- Stars: 34
- Watchers: 9
- Forks: 19
- Open Issues: 9
-
Metadata Files:
- Readme: README.md
- License: LICENSE.txt
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README
# oanda-bot
[](https://pypi.org/project/oanda-bot/)
[](https://opensource.org/licenses/MIT)
[](https://codecov.io/gh/10mohi6/oanda-bot-python)
[](https://travis-ci.com/10mohi6/oanda-bot-python)
[](https://pypi.org/project/oanda-bot/)
[](https://pepy.tech/project/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
```python
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='',
access_token='',
).run()
```
### basic backtest
```python
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='',
access_token='',
).backtest()
```
### basic report
```python
from oanda_bot import Bot
Bot(
account_id='',
access_token='',
).report()
```
### advanced run
```python
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='',
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='',
# Line notification when an error occurs
line_notify_token='',
# Discord notification when an error occurs
discord_webhook_url='',
).run()
```
### advanced backtest
```python
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='',
access_token='',
instrument='USD_JPY',
granularity='S15', # 15 second candlestick
).backtest(from_date="2020-7-7", to_date="2020-7-13", filename="backtest.png")
```
```python
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
```python
from oanda_bot import Bot
Bot(
account_id='',
access_token='',
instrument='USD_JPY',
granularity='S15', # 15 second candlestick
).report(filename="report.png", days=-7) # from 7 days ago to now
```
```python
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
```python
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='',
access_token='',
environment='live',
instrument='EUR_GBP',
granularity='H12', # 12 hour candlesticks
trading_time=Bot.WINTER_TIME,
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](https://developer.oanda.com/rest-live-v20/introduction/).
## 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