This library will have many tools for algorithmic trading. So far it only supports comprehensive backtests. To install:
pip3 install git+https://github.com/numberjuani/strategy.git
Create a pandas dataframe with your trading signals in each row. These must be 1 for long, -1 for short, 0 for hold. Once you have that, do this:
from strategy.performance_report import StrategyPerformanceReport
backtest = StrategyPerformanceReport('SPY','demo_strategy',df,'Signal')
performance_report,trades = backtest.get_backtest()
| All Trades | Long Trades | Short Trades | |
|---|---|---|---|
| start_date | 2017-06-06 | 2017-07-20 | 2017-06-06 |
| end_date | 2022-02-08 | 2022-01-11 | 2022-02-08 |
| trading_period_days | 1708 | 1636 | 1708 |
| trading_period_years | 4.67945205479452 | 4.482191780821918 | 4.67945205479452 |
| total_net_profit | 6719.000000000008 | 6233.0000000000055 | 486.000000000004 |
| gross_profit | 22738.000000000004 | 14271.000000000002 | 8467.000000000004 |
| gross_loss | -16018.999999999995 | -8037.999999999995 | -7980.999999999998 |
| profit_factor | 1.4194394156938643 | 1.775441652152278 | 1.0608946247337434 |
| total_trades | 91 | 45 | 46 |
| percent_profitable | 29.67032967032967 | 28.88888888888889 | 30.434782608695652 |
| winning_trades | 27 | 13 | 14 |
| losing_trades | 64 | 32 | 32 |
| avg_trade_net_profit | 73.83516483516493 | 138.51111111111123 | 10.565217391304435 |
| avg_win_trade_pnl | 842.1481481481483 | 1097.769230769231 | 604.7857142857146 |
| avg_lose_trade_pnl | -250.29687499999991 | -251.18749999999986 | -249.40624999999994 |
| ratio_avg_win_loss | 3.364597133496567 | 4.370317912990223 | 2.4249019993914134 |
| max_win_trade_pnl | 3809.0000000000005 | 3809.0000000000005 | 1797.0 |
| max_lose_trade_pnl | -953.0000000000001 | -953.0000000000001 | -712.9999999999995 |
| average_trade_duration | 18 days 18:28:00 | 21 days 02:08:00 | 16 days 12:00:00 |
| avg mfe/mae | -1.4760575792507435 | 5.805316889950003 | -8.599141299121039 |
| average_favorable_excursion | 499.2637362637362 | 520.1333333333333 | 478.84782608695645 |
| average_adverse_excursion | -339.53846153846155 | -407.3777777777777 | -273.17391304347836 |
| perfect_profit_correlation | 47.113805244082855 | 39.19153595367113 | 45.92621312184276 |
| max_drawdown | 4652.0 | 2620.0 | 2832.0 |
| annualized_sharpe_ratio | 0.3369222136547682 | 0.4099999066550984 | -0.029656852937467643 |
| strategy_return | 6.719000000000008 | 6.233000000000004 | 0.4860000000000042 |
| annualized_return | 1.4358518735363015 | 1.390614303178485 | 0.10385831381733111 |
| annualized_volatility | 1.6 | 1.23 | 0.9 |
| total_commission_paid | 0 | 0 | 0 |
| total_slippage_paid | 0.0 | 0.0 | 0.0 |
| total_costs | 0.0 | 0.0 | 0.0 |
| Trade Number | position | entry_date | entry_price | exit_date | exit_price | trade_duration | max_favorable_excursion | max_adverse_excursion | mfemae_ratio | commissions | slippage | pnl | strategy_equity | strategy_returns | high_watermark | drawdown | perfect_profit_line |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | short | 2017-06-06 14:00:00 | 78.61 | 2017-07-20 14:00:00 | 70.6 | 44 days | 496.9999999999999 | -801.0000000000005 | 0.6204744069912604 | 0 | 0.0 | 801.0000000000005 | 100801.0 | 0.8010000000000019 | 100801.0 | -0.0 | 100000.0 |
| 1 | long | 2017-07-20 14:00:00 | 70.6 | 2017-07-25 14:00:00 | 69.38 | 5 days | 198.0000000000004 | -71.99999999999989 | 2.7500000000000098 | 0 | 0.0 | -121.99999999999989 | 100679.0 | 0.679000000000002 | 100801.0 | -122.0 | 100073.83516483517 |
| 2 | short | 2017-07-25 14:00:00 | 69.38 | 2017-07-26 14:00:00 | 70.3 | 1 days | 112.99999999999955 | -71.99999999999989 | 1.5694444444444406 | 0 | 0.0 | -92.00000000000017 | 100587.0 | 0.5870000000000033 | 100801.0 | -214.0 | 100147.67032967033 |
| 3 | long | 2017-07-26 14:00:00 | 70.3 | 2018-02-05 14:00:00 | 108.39 | 194 days | 826.0000000000005 | -3884.9999999999995 | 0.21261261261261274 | 0 | 0.0 | 3809.0000000000005 | 104396.0 | 4.396000000000001 | 104396.0 | -0.0 | 100221.5054945055 |
| 4 | short | 2018-02-05 14:00:00 | 108.39 | 2018-04-05 14:00:00 | 97.94 | 59 days | 1109.0000000000005 | -1250.0 | 0.8872000000000003 | 0 | 0.0 | 1045.0000000000002 | 105441.0 | 5.4410000000000025 | 105441.0 | -0.0 | 100295.34065934065 |