https://github.com/agrawal-rohit/futon
Create automated crypto bots that trade for you while you sleep
https://github.com/agrawal-rohit/futon
backtesting-engine cryptocurrency stock-analysis trading-bot trading-strategy
Last synced: 8 months ago
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Create automated crypto bots that trade for you while you sleep
- Host: GitHub
- URL: https://github.com/agrawal-rohit/futon
- Owner: agrawal-rohit
- License: gpl-3.0
- Created: 2021-05-28T21:13:22.000Z (about 5 years ago)
- Default Branch: main
- Last Pushed: 2024-01-28T21:44:21.000Z (over 2 years ago)
- Last Synced: 2025-09-15T19:27:40.782Z (9 months ago)
- Topics: backtesting-engine, cryptocurrency, stock-analysis, trading-bot, trading-strategy
- Language: Python
- Homepage:
- Size: 42.3 MB
- Stars: 8
- Watchers: 2
- Forks: 6
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- Changelog: CHANGELOG.md
- Funding: FUNDING.yml
- License: LICENSE
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README
# Installation
```shell
$ pip install futon
```
# Usage
## Step 1: Initialize a data provider
A data provider refers to a source from where an instruments historical data can be fetched. Currently, **Binance** is the only supported provider (more are being added actively)
```python
from futon.data.providers import Binance
# Add your developer API keys here
api_key = ''
secret_key = ''
binance = Binance(api_key, secret_key)
```
## Step 2: Choose an instrument
```python
coin = futon.instruments.Crypto(base_asset = 'DOGE',
quote_asset = 'USDT',
provider = binance,
interval = '30-min',
start_date = '2021-05-01 00:00:00')
```
When you initialize an instrument, historical data for the instrument is downloaded by _default_
If you're a chart guy, then you can create an interactive OHLCV chart right in your jupyter notebook:
```python
from bokeh.io import output_notebook, show, push_notebook
output_notebook()
coin.plot_candles()
```
## Step 3: Create a trading strategy
```python
from futon.strategy import TradingStrategy
class MACDCrossover(TradingStrategy):
# Runs only once before trading has started
def setup(self):
# Initialize the indicators you want to use here
self.macd = futon.indicators.MACD(fastperiod = 6,
slowperiod = 18,
signalperiod = 5,
plot_separately = True)
#
self.indicators = [self.macd]
# Live trading logic
def logic(self, account, lookback):
today = lookback.iloc[-1]
macd_today, signal_today, _ = self.macd.lookback[-1]
macd_yest, signal_yest, _ = self.macd.lookback[-2]
# Buying
buy_signal = (macd_today > signal_today) and (macd_yest < signal_yest)
if buy_signal:
entry_price = today.close
entry_capital = account.buying_power
account.buy(entry_capital=entry_capital, entry_price=entry_price)
# Selling
sell_signal = (macd_today < signal_today) and (macd_yest > signal_yest)
if sell_signal:
percent = 1
exit_price = today.close
account.sell(percent=percent, current_price=exit_price)
strat = MACDCrossover(coin)
```
## Step 4: Run a backtest on historical data
```python
strat.backtest(start_date = '2021-06-1 00:00:00', commision = 0.001, show_trades = True)
```
**Output**
Performing backtest from: 01 June, 2021 (00:00:00) to 21 June, 2021 (16:00:00)
-------------- Results ----------------
Relative Returns: -2.55%
Relative Profit: -25.49
Strategy : -36.4%
Net Profit : -363.96
Buy and Hold : -33.85%
Net Profit : -338.47
Buys : 75
Sells : 75
--------------------
Total Trades : 150
---------------------------------------
