dt <- clean_logs(import_logs())
metrics <- compute_metrics(dt)
datatable(metrics)
plot_monthly_returns_heatmap(calculate_calendar_returns(dt)$calendar_returns,calculate_calendar_returns(dt)$yearly_returns )
plot_returns_distribution(dt, "profit_loss_percent")
create_frequency_table(dt, "profit_loss_percent")
## bin_start bin_end count proportion
## <num> <num> <int> <char>
## 1: -0.5056698 -0.48300 8 0.74%
## 2: -0.4830000 -0.46100 0 0.00%
## 3: -0.4610000 -0.43800 1 0.09%
## 4: -0.4380000 -0.41500 1 0.09%
## 5: -0.4150000 -0.39300 0 0.00%
## 6: -0.3930000 -0.37000 0 0.00%
## 7: -0.3700000 -0.34700 0 0.00%
## 8: -0.3470000 -0.32500 0 0.00%
## 9: -0.3250000 -0.30200 1 0.09%
## 10: -0.3020000 -0.27900 2 0.18%
## 11: -0.2790000 -0.25700 1 0.09%
## 12: -0.2570000 -0.23400 2 0.18%
## 13: -0.2340000 -0.21100 4 0.37%
## 14: -0.2110000 -0.18900 5 0.46%
## 15: -0.1890000 -0.16600 11 1.01%
## 16: -0.1660000 -0.14300 10 0.92%
## 17: -0.1430000 -0.12100 25 2.31%
## 18: -0.1210000 -0.09780 20 1.85%
## 19: -0.0978000 -0.07520 25 2.31%
## 20: -0.0752000 -0.05250 46 4.24%
## 21: -0.0525000 -0.02980 71 6.55%
## 22: -0.0298000 -0.00716 76 7.01%
## 23: -0.0071600 0.01550 147 13.56%
## 24: 0.0155000 0.03820 172 15.87%
## 25: 0.0382000 0.06080 110 10.15%
## 26: 0.0608000 0.08350 44 4.06%
## 27: 0.0835000 0.10600 273 25.18%
## 28: 0.1060000 0.12900 11 1.01%
## 29: 0.1290000 0.15200 7 0.65%
## 30: 0.1520000 0.17400 2 0.18%
## 31: 0.1740000 0.19700 3 0.28%
## 32: 0.1970000 0.22000 3 0.28%
## 33: 0.2200000 0.24200 0 0.00%
## 34: 0.2420000 0.26500 1 0.09%
## 35: 0.2650000 0.28800 0 0.00%
## 36: 0.2880000 0.33100 2 0.18%
## bin_start bin_end count proportion
plot_correlation_heatmap(dt)
stat_metrics <- compute_statistical_metrics(dt)
datatable(stat_metrics)
plot_trades_scatter(dt)