# Drawdown

Drawdown refers to the maximum difference between the portfolio's maximum value and any point thereafter. We will be observing the relationship between the net profit and the drawdown of the algorithm, and how these two can be used to determine performance against the underlying. Drawdown can be calculated as follows:

$DrawdownPercentage=\frac{PortfolioMaximumValue}{LowestSubsequentPortfolioValue}$

In this section, we will be discussing the drawdown of the 6-year, 2-year, and 1-year backtests. Based on our backtests, we get the following drawdowns for the tested securities:

Security | 2016-2022 | 2020-2022 | 2021-2022 |
---|---|---|---|

AAPL | 24.73% | 21.83% | 13.19% |

AMZN | 27.76% | 27.53% | 27.52% |

NVDA | 42.8% | 33.95% | 33.95% |

TGT | 73.57% | 24.97% | 24.97% |

From these recorded values, we can calculate the average drawdown per backtest to be as follows:

Timeframe | Drawdown |
---|---|

2016-2020 | 42.22% |

2020-2022 | 27.07% |

2021-2022 | 24.91% |

Overall the drawdown remained relatively consistent throughout the timeframes tested, excluding the 2016-2020 period where TGT skewed the results to be higher than average. Excluding this, our results help to support our initial claim of the algorithm performing stronger in more liquid market conditions. Overall, these numbers can be scaled down with relation to the net profit, as these tests were performed using 100% of the account balance for clearer results. With future iterations of the algorithm, as with all categories, we hope to improve the drawdown especially during less liquid market cycles.

Last modified 7mo ago