Strategies: HiddenQuant TrendFollow CTA Strategy

Tearsheet (generated by QuantStats)

In our quantitative investment philosophy, diversification is paramount—it’s the only "free lunch" available. With this principle at our core, we are dedicated to developing a portfolio of strategies that manage both our own and a select amount of custodial funds. Our diversified strategy suite includes, but is not limited to, time series trend-following, cross-sectional multi-factor, momentum, mean reversion, and statistical arbitrage strategies. Starting today, we will begin publishing a series of historical performance data for these strategies.

Let’s start with our CTA trend-following strategy, which features:

  • Dynamic coin pool: Defines the tradable cryptocurrencies over a certain period based on market volatility levels and specific screening strategies.
  • Multi-strategy signals: Incorporates hundreds of trend-following signals.
  • Comprehensive parameter coverage: Aims to avoid overfitting by covering all retrospective periods.
  • Multiple time periods: Includes 15-minute, 1-hour, 4-hour, daily, and weekly periods.

Below is the detailed performance report:

HiddenQuant TF CTA Strategy
Backtest Range: 31 Dec, 2020 - 30 Sep, 2024

2024-10-11T10:08:45.043632 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/
2024-10-11T10:08:45.350487 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/
2024-10-11T10:08:45.583811 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/
2024-10-11T10:08:45.740810 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/
2024-10-11T10:08:45.934695 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/
2024-10-11T10:08:46.195744 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/
2024-10-11T10:08:46.445793 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/
2024-10-11T10:08:48.694090 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/
2024-10-11T10:08:49.287832 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/
2024-10-11T10:08:49.583784 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/
2024-10-11T10:08:50.141045 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/
2024-10-11T10:08:50.856844 image/svg+xml Matplotlib v3.7.1, https://matplotlib.org/

Key Performance Metrics

Metric Strategy
Risk-Free Rate 0.0%
Time in Market 100.0%
Cumulative Return 3,621.54%
CAGR﹪ 94.69%
Sharpe 0.38
Prob. Sharpe Ratio 100.0%
Smart Sharpe 0.38
Sortino 0.56
Smart Sortino 0.55
Sortino/√2 0.39
Smart Sortino/√2 0.39
Omega 1.09
Max Drawdown -32.32%
Longest DD Days 270
Volatility (ann.) 8.24%
Calmar 2.93
Skew 0.39
Kurtosis 36.51
Expected Daily 0.01%
Expected Monthly 8.18%
Expected Yearly 106.13%
Kelly Criterion 4.03%
Risk of Ruin 0.0%
Daily Value-at-Risk -0.84%
Expected Shortfall (cVaR) -0.84%
Max Consecutive Wins 12
Max Consecutive Losses 11
Gain/Pain Ratio 0.47
Gain/Pain (1M) 6.04
Payoff Ratio 1.07
Profit Factor 1.09
Common Sense Ratio 1.19
CPC Index 0.59
Tail Ratio 1.1
Outlier Win Ratio 5.15
Outlier Loss Ratio 5.16
MTD 2.14%
3M 24.32%
6M 58.35%
YTD 128.69%
1Y 170.1%
3Y (ann.) 51.62%
5Y (ann.) 94.69%
10Y (ann.) 94.69%
All-time (ann.) 94.69%
Best Day 11.07%
Worst Day -11.63%
Best Month 82.87%
Worst Month -15.83%
Best Year 562.48%
Worst Year 0.0%
Avg. Drawdown -3.16%
Avg. Drawdown Days 6
Recovery Factor 12.56
Ulcer Index 0.11
Serenity Index 0.98
Avg. Up Month 13.52%
Avg. Down Month -7.5%
Win Days 50.3%
Win Month 80.0%
Win Quarter 86.67%
Win Year 100.0%

EOY Returns

Year Return Cumulative
2020 0.0% 0.0%
2021 211.65% 562.48%
2022 45.96% 47.03%
2023 57.59% 67.07%
2024 90.68% 128.69%

Worst 10 Drawdowns

Started Recovered Drawdown Days
2022-06-13 2023-03-10 -32.32 270
2024-03-05 2024-06-18 -25.66 105
2021-01-10 2021-01-15 -25.31 6
2021-01-29 2021-02-04 -19.14 7
2021-06-22 2021-07-20 -18.77 28
2021-05-19 2021-06-21 -18.26 33
2021-02-20 2021-04-14 -16.02 53
2022-02-24 2022-05-09 -15.37 75
2023-06-13 2023-08-17 -13.99 66
2021-01-07 2021-01-08 -13.96 1

Disclaimer: All information provided by HiddenQuant.com is intended solely for the purpose of studying topics related to crypto trading and is in no way intended as a specific investment or trading recommendation. We are not a registered broker or investment advisor. I mention specific financial products, especially cryptocurrencies, always and only for the purpose of studying crypto trading. We are not responsible for the specific decisions of individual readers of this presentation. Trading and investing in financial instruments (and cryptocurrencies in particular) is high risk. The decision to trade cryptocurrencies is the responsibility of each individual and only they are fully responsible for their decisions. Never engage in trades that you do not fully understand the merits of. Remember, the crypto exchange has rules that you need to understand before risking your own money!