A lightweight algorithmic trading and backtesting library. Currently in development.
Running a backtest can be done in a few lines of code. Let's say we want to try out a crossover strategy on General Motors' stock using 5-day and 20-day exponential moving averages.
from algofin.data import fetch_data
from algofin.backtest import Backtest
from algofin.strategies import EMACrossover
df = fetch_data('GM', '2015-01-01', '2021-01-01')
ema_crossover = EMACrossover(
t1=5, t2=20, short=True,
take_profit=1.15, stop_loss=0.95, size=0.25, cost=0.03
)
backtest = Backtest(ema_crossover, df, capital=100000)
backtest.run()
backtest.print_report()----------BACKTEST REPORT----------
Start Date 2015-01-29 00:00:00
End Date 2020-12-31 00:00:00
Realized P&L 52821.2
Total P&L 52779.9
Annual Return 0.074192
Sharpe Ratio 2.31421
No. of Trades 88
Win Rate 0.571429
Max Drawdown -0.147667
We can also easily visualise our strategy's P&L and capital management throughout the backtest.
backtest.plot_pl()
backtest.plot_capital()
We can also run a backtest on a portfolio of instruments using the PortfolioBacktest class.
from algofin.backtest import PortfolioBacktest
labels = ['FB', 'AMZN', 'AAPL', 'NFLX', 'GOOGL']
portfolio = [fetch_data(i, '2015-01-01', '2021-01-01') for i in labels]
capital_allocation = [0.25, 0.25, 0.2, 0.15, 0.15]
backtest_2 = PortfolioBacktest(
ema_crossover, portfolio, capital=1000000,
capital_allocation=capital_allocation, labels=labels
)
backtest_2.run()
backtest_2.plot_pl_breakdown()
Algofin comes with popular indicators for technical analysis. For example, we can quickly take a look at the relative strength index of Bitcoin for the last 5 months.
from algofin.data import fetch_data
from algofin.indicators import RSI, plot_RSI
df = fetch_data('BTC-USD', '2020-11-20', '2021-04-20')
df['RSI_14'] = RSI(df['Close'])
plot_RSI(df['RSI_14'])