Synthetic TQQQ
- hypothetical · Annual Return (Compounded)
- 12.7%
- Max Drawdown
- 69.4%
- Trades
- 37
- Win Trades
- 75.7%
- Profit Factor
- 2.40
- Win Months
- 58.1%
About this strategy
This is a long-only system that only trades one ETF, TQQQ. This system is designed for non-marginable accounts such as IRAs, 401ks and Cash accounts, and the risk can be controlled by yourself. On average you only expose 40%-60% of your account capital to the market.
This system is comprised of five individual sub-strategies. Each sub-strategy trades TQQQ independently. Each sub-strategy is fundamentally different, i.e. they have no correlation to each other. All of the five sub-strategies use the end of day close data to generate the signals. The five sub-strategies are a mix of trend following and mean-reverting strategies. The benefit of these mixed sub-strategies is that the result adds up each individual strategy's profit while reducing the total drawdown. In modern portfolio theory, you can reduce the overall risk in an investment portfolio and even boost your overall returns by investing in asset combinations that are not correlated.
In a bear market, this system still trades; however, it will adjust the parameters automatically. The trend-following sub-strategies will not generate positions, i.e. filtered by moving average indicators, while the mean-reverting sub-strategies will generate positions trying to buy at low and sell at high with different parameters for a bear market.
Each sub-strategy has a different holding length ranging from 3 to 10 trading days. In C2, it counts one trade after all positions are sold. Therefore you may find one trade lasts for more than two weeks instead of 3 to 10 days, which is an additive result of all five sub-strategies. However, when you click on the 'Show AutoTrade data' button in the trading record, you will find the details of each sub-strategy's transactions. Most of the time, only two or three sub-strategies will have positions. That means on average you only expose 40%-60% of your account capital to the market. Rarely will all five sub-strategies have positions at the same time.
For instance, If you have an IRA account of $25,000, you can trade $5,000 ($25,000/5=$5,000) for each sub-strategy. If TQQQ's price is $70/per share at that time, you can trade 70 shares ($5,000/70=71.4, rounded to 70) per sub-strategy. In C2, I trade 100 shares per sub-strategy. If you want to auto-trade this system, you can set AutoTrade Scaling to 70%.
This system is designed for non-marginable accounts, but it can also be traded with a marginal account. For instance, if you have a marginal account, the maximum amount you can trade is $35,000. You can trade 100 ($35,000/5/70=100) shares per sub-strategy, assuming that TQQQ's price is $70/per share at that time.
The best part of this system is that you can control the risk by yourself. For instance, as with the above example, you have an account that can trade up to $35,000. Based on the standard calculation, you will trade 100 shares for each sub-strategy. If at any moment, you do not feel it is advantageous to trade in such a volume, but would still like to participate in the market, you can reduce the shares of each sub-strategy from 100 to 50. This way, you are still in the market but will reduce the risk by 50%.
On average only two or three sub-strategies have positions at any given time, which means you only use 40% to 60% of your account capital. If you are comfortable with the current market and would like to take more risk for higher returns at that time, you can increase the shares of each sub-strategy.
This system is comprised of five individual sub-strategies. Each sub-strategy trades TQQQ independently. Each sub-strategy is fundamentally different, i.e. they have no correlation to each other. All of the five sub-strategies use the end of day close data to generate the signals. The five sub-strategies are a mix of trend following and mean-reverting strategies. The benefit of these mixed sub-strategies is that the result adds up each individual strategy's profit while reducing the total drawdown. In modern portfolio theory, you can reduce the overall risk in an investment portfolio and even boost your overall returns by investing in asset combinations that are not correlated.
In a bear market, this system still trades; however, it will adjust the parameters automatically. The trend-following sub-strategies will not generate positions, i.e. filtered by moving average indicators, while the mean-reverting sub-strategies will generate positions trying to buy at low and sell at high with different parameters for a bear market.
Each sub-strategy has a different holding length ranging from 3 to 10 trading days. In C2, it counts one trade after all positions are sold. Therefore you may find one trade lasts for more than two weeks instead of 3 to 10 days, which is an additive result of all five sub-strategies. However, when you click on the 'Show AutoTrade data' button in the trading record, you will find the details of each sub-strategy's transactions. Most of the time, only two or three sub-strategies will have positions. That means on average you only expose 40%-60% of your account capital to the market. Rarely will all five sub-strategies have positions at the same time.
For instance, If you have an IRA account of $25,000, you can trade $5,000 ($25,000/5=$5,000) for each sub-strategy. If TQQQ's price is $70/per share at that time, you can trade 70 shares ($5,000/70=71.4, rounded to 70) per sub-strategy. In C2, I trade 100 shares per sub-strategy. If you want to auto-trade this system, you can set AutoTrade Scaling to 70%.
This system is designed for non-marginable accounts, but it can also be traded with a marginal account. For instance, if you have a marginal account, the maximum amount you can trade is $35,000. You can trade 100 ($35,000/5/70=100) shares per sub-strategy, assuming that TQQQ's price is $70/per share at that time.
The best part of this system is that you can control the risk by yourself. For instance, as with the above example, you have an account that can trade up to $35,000. Based on the standard calculation, you will trade 100 shares for each sub-strategy. If at any moment, you do not feel it is advantageous to trade in such a volume, but would still like to participate in the market, you can reduce the shares of each sub-strategy from 100 to 50. This way, you are still in the market but will reduce the risk by 50%.
On average only two or three sub-strategies have positions at any given time, which means you only use 40% to 60% of your account capital. If you are comfortable with the current market and would like to take more risk for higher returns at that time, you can increase the shares of each sub-strategy.
Hypothetical Monthly Returns (includes fees/commissions)
| Year | Jan | Feb | Mar | Apr | May | Jun | Jul | Aug | Sep | Oct | Nov | Dec | YTD |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 2019 | 2.4 | 4.4 | 8.0 | 6.8 | 23.3 | ||||||||
| 2020 | 4.9 | -24.2 | 7.1 | 20.6 | 12.8 | 7.8 | 8.5 | 8.1 | -1.9 | 0.1 | 4.1 | 5.4 | 57.9 |
| 2021 | -0.4 | -4.1 | 0.2 | 5.0 | -4.4 | 6.8 | 2.2 | 7.0 | -10.0 | 5.9 | 0.2 | 0.5 | 7.7 |
| 2022 | -11.6 | 0.0 | 0.3 | -5.0 | -1.2 | -4.8 | 0.3 | -7.4 | -7.1 | -3.3 | 1.7 | -6.3 | -37.0 |
| 2023 | 1.4 | -1.1 | 6.3 | -0.4 | 5.3 | 6.5 | 4.8 | -10.9 | -3.7 | -4.6 | 18.3 | 15.8 | 39.8 |
| 2024 | 4.2 | 16.8 | 4.4 | -21.1 | 12.9 | 26.5 | -6.2 | -31.4 | -8.2 | -4.6 | 16.3 | -0.3 | -6.4 |
| 2025 | 6.0 | -8.5 | -21.1 | -2.2 | 20.8 | 7.6 | 5.7 | 0.4 | 5.7 | 3.0 | -0.6 | -0.0 | 11.7 |
| 2026 | -0.2 | -3.8 | -5.2 | 13.3 | 21.3 | -2.2 | -11.5 | 5.4 | 4.7 | 0.9 | 20.5 |
Statistics
Overview
| Strategy began | 9/24/2019 |
|---|---|
| Suggested Minimum Capital | $25,000 |
| Age | 86 months |
| What it trades | Stocks |
| # Trades | 37 |
| # Profitable | 28 |
| % Profitable | 75.7% |
| Avg trade duration | 64.3 days |
| Max peak-to-valley drawdown | 69.4% |
| drawdown period | July 10, 2024 - April 08, 2025 |
| Annual Return (Compounded) | 12.7% |
| Avg win | $2,549 |
| Avg loss | $3,343 |
Ratios
| W:L ratio | 2.43 |
|---|---|
| Sharpe Ratio | 0.40 |
| Sortino Ratio | 0.57 |
| Calmar Ratio | 0.32 |
CORRELATION STATISTICS
| Correlation to SP500 | 0.51 |
|---|---|
| Return Percent SP500 (cumu) during strategy life | 160.0% |
| Return of Strat Pcnt - Return of SP500 Pcnt (cumu) | -30.1% |
Return Statistics
| Ann Return (w trading costs) | 12.7% |
|---|---|
| Return Pcnt (Compound or Annual, age-based, NFA compliant) | 0.1% |
| Return Pcnt Since TOS Status | 0.0% |
| Ann Return (Compnd, No Fees) | 15.2% |
Slump
| Current Slump as Pcnt Equity | 36.9% |
|---|---|
| Current Slump, time of slump as pcnt of strategy life | 0.3% |
Instruments
| Percent Trades Forex | 0.0% |
|---|---|
| Percent Trades Futures | 0.0% |
| Percent Trades Options | 0.0% |
| Short Options - Percent Covered | 100.0% |
| Percent Trades Stocks | 1.0% |
Risk of Ruin (Monte-Carlo)
| Chance of 10% account loss | 73.0% |
|---|---|
| Chance of 20% account loss | 45.0% |
| Chance of 30% account loss | 26.0% |
| Chance of 40% account loss | 16.5% |
| Chance of 50% account loss | 5.5% |
| Chance of 60% account loss (Monte Carlo) | 0.5% |
| Chance of 70% account loss (Monte Carlo) | 0.0% |
| Chance of 80% account loss (Monte Carlo) | 0.0% |
| Chance of 90% account loss (Monte Carlo) | 0.0% |
Automation
| Percentage Signals Automated | 2.2% |
|---|
Popularity
| Popularity (Today) | 0 |
|---|---|
| Popularity (Last 6 weeks) | 0 |
| Popularity (7 days, Percentile 1000 scale) | 0 |
Trading Style
| Any stock shorts? 0/1 | 0 |
|---|
Trades-Own-System Certification
| Trades Own System? | 0 |
|---|---|
| TOS percent | 0.0% |
Win / Loss
| Avg Loss | $3,343 |
|---|---|
| Avg Win | $2,549 |
| # Winners | 28 |
| Sum Trade PL (losers) | $30,089 |
| Sum Trade PL (winners) | $71,360 |
| Num Months Winners | 51 |
| # Losers | 9 |
| % Winners | 75.7% |
Dividends
| Dividends Received in Model Acct | 1834 |
|---|
Age
| Num Months filled monthly returns table | 86 |
|---|
Frequency
| Avg Position Time (mins) | 92576.18 |
|---|---|
| Avg Position Time (hrs) | 1542.94 |
| Avg Trade Length | 64.30 |
| Last Trade Ago | 564 |
Leverage
| Daily leverage (average) | 1.59 |
|---|---|
| Daily leverage (max) | 5 |
Regression
| Alpha | 0.01 |
|---|---|
| Beta | 0.80 |
| Treynor Index | 0.05 |
Maximum Adverse Excursion (MAE)
| MAE:Equity, average, all trades | 0.11 |
|---|---|
| MAE:Equity, 95th Percentile Value for this strat | 0.26 |
| MAE:Equity, average, losing trades | 0.23 |
| MAE:Equity, losing trades only, 95th Percentile Value for this strat | — |
| MAE:Equity, average, winning trades | 0.07 |
| MAE:Equity, win trades only, 95th Percentile Value for this strat | — |
| Avg(MAE) / Avg(PL) - All trades | 3.95 |
| MAE:PL (avg, all trades) | 0.70 |
| MAE:PL (avg, losing trades) | — |
| MAE:PL (avg, winning trades) | — |
| MAE:PL - worst single value for strategy | — |
| Avg(MAE) / Avg(PL) - Winning trades | 0.91 |
| Avg(MAE) / Avg(PL) - Losing trades | -2.58 |
| Hold-and-Hope Ratio | 0.30 |
RATIO STATISTICS
| Mean | 0.22 |
|---|---|
| SD | 0.36 |
| Sharpe ratio (Glass type estimate) | 0.62 |
| Sharpe ratio (Hedges UMVUE) | 0.61 |
| df | 66 |
| t | 1.46 |
| p | 0.07 |
| Lowerbound of 95% confidence interval for Sharpe Ratio | -0.22 |
| Upperbound of 95% confidence interval for Sharpe Ratio | 1.45 |
| Lowerbound of 95% CI (Gibbons, Hedeker & Davis approximation | -0.23 |
| Upperbound of 95% CI (Gibbons, Hedeker & Davis approximation | 1.45 |
| Sortino ratio | 0.97 |
| Upside Potential Ratio | 2.58 |
| Upside part of mean | 0.59 |
| Downside part of mean | -0.37 |
| Upside SD | 0.28 |
| Downside SD | 0.23 |
| N nonnegative terms | 41 |
| N negative terms | 26 |
| N of observations | 67 |
| Mean of predictor | 0.16 |
| Mean of criterion | 0.22 |
| SD of predictor | 0.18 |
| SD of criterion | 0.36 |
| Covariance | 0.04 |
| r | 0.67 |
| b (slope, estimate of beta) | 1.34 |
| a (intercept, estimate of alpha) | 0.01 |
| Mean Square Error | 0.07 |
| DF error | 65 |
| t(b) | 7.30 |
| p(b) | 0 |
| t(a) | 0.09 |
| p(a) | 0.47 |
| Lowerbound of 95% confidence interval for beta | 0.97 |
| Upperbound of 95% confidence interval for beta | 1.71 |
| Lowerbound of 95% confidence interval for alpha | -0.22 |
| Upperbound of 95% confidence interval for alpha | 0.24 |
| Treynor index (mean / b) | 0.17 |
| Jensen alpha (a) | 0.01 |
| Mean | 0.16 |
| SD | 0.36 |
| Sharpe ratio (Glass type estimate) | 0.43 |
| Sharpe ratio (Hedges UMVUE) | 0.43 |
| df | 66 |
| t | 1.03 |
| p | 0.15 |
| Lowerbound of 95% confidence interval for Sharpe Ratio | -0.40 |
| Upperbound of 95% confidence interval for Sharpe Ratio | 1.27 |
| Lowerbound of 95% CI (Gibbons, Hedeker & Davis approximation | -0.40 |
| Upperbound of 95% CI (Gibbons, Hedeker & Davis approximation | 1.26 |
| Sortino ratio | 0.62 |
| Upside Potential Ratio | 2.19 |
| Upside part of mean | 0.55 |
| Downside part of mean | -0.39 |
| Upside SD | 0.26 |
| Downside SD | 0.25 |
| N nonnegative terms | 41 |
| N negative terms | 26 |
| N of observations | 67 |
| Mean of predictor | 0.14 |
| Mean of criterion | 0.16 |
| SD of predictor | 0.18 |
| SD of criterion | 0.36 |
| Covariance | 0.04 |
| r | 0.65 |
| b (slope, estimate of beta) | 1.30 |
| a (intercept, estimate of alpha) | -0.03 |
| Mean Square Error | 0.08 |
| DF error | 65 |
| t(b) | 6.95 |
| p(b) | 0 |
| t(a) | -0.22 |
| p(a) | 0.59 |
| Lowerbound of 95% confidence interval for beta | 0.93 |
| Upperbound of 95% confidence interval for beta | 1.67 |
| Lowerbound of 95% confidence interval for alpha | -0.26 |
| Upperbound of 95% confidence interval for alpha | 0.21 |
| Treynor index (mean / b) | 0.12 |
| Jensen alpha (a) | -0.03 |
| VaR(95%) | 0.15 |
| Expected Shortfall on VaR | 0.18 |
| VaR(95%) | 0.06 |
| Expected Shortfall on VaR | 0.12 |
| Mean | 0.20 |
| SD | 0.31 |
| Sharpe ratio (Glass type estimate) | 0.64 |
| Sharpe ratio (Hedges UMVUE) | 0.64 |
| df | 1472 |
| t | 1.51 |
| p | 0.48 |
| Lowerbound of 95% confidence interval for Sharpe Ratio | -0.19 |
| Upperbound of 95% confidence interval for Sharpe Ratio | 1.46 |
| Lowerbound of 95% CI (Gibbons, Hedeker & Davis approximation | -0.19 |
| Upperbound of 95% CI (Gibbons, Hedeker & Davis approximation | 1.46 |
| Sortino ratio | 0.91 |
| Upside Potential Ratio | 7.23 |
| Upside part of mean | 1.57 |
| Downside part of mean | -1.37 |
| Upside SD | 0.22 |
| Downside SD | 0.22 |
| N nonnegative terms | 795 |
| N negative terms | 678 |
| N of observations | 1473 |
| Mean of predictor | 0.17 |
| Mean of criterion | 0.20 |
| SD of predictor | 0.23 |
| SD of criterion | 0.31 |
| Covariance | 0.04 |
| r | 0.52 |
| b (slope, estimate of beta) | 0.72 |
| a (intercept, estimate of alpha) | 0.07 |
| Mean Square Error | 0.07 |
| DF error | 1471 |
| t(b) | 23.63 |
| p(b) | 0.18 |
| t(a) | 0.67 |
| p(a) | 0.49 |
| Lowerbound of 95% confidence interval for beta | 0.66 |
| Upperbound of 95% confidence interval for beta | 0.78 |
| Lowerbound of 95% confidence interval for alpha | -0.14 |
| Upperbound of 95% confidence interval for alpha | 0.29 |
| Treynor index (mean / b) | 0.27 |
| Jensen alpha (a) | 0.07 |
| Mean | 0.15 |
| SD | 0.31 |
| Sharpe ratio (Glass type estimate) | 0.48 |
| Sharpe ratio (Hedges UMVUE) | 0.48 |
| df | 1472 |
| t | 1.14 |
| p | 0.49 |
| Lowerbound of 95% confidence interval for Sharpe Ratio | -0.35 |
| Upperbound of 95% confidence interval for Sharpe Ratio | 1.31 |
| Lowerbound of 95% CI (Gibbons, Hedeker & Davis approximation | -0.35 |
| Upperbound of 95% CI (Gibbons, Hedeker & Davis approximation | 1.31 |
| Sortino ratio | 0.67 |
| Upside Potential Ratio | 6.93 |
| Upside part of mean | 1.54 |
| Downside part of mean | -1.39 |
| Upside SD | 0.22 |
| Downside SD | 0.22 |
| N nonnegative terms | 795 |
| N negative terms | 678 |
| N of observations | 1473 |
| Mean of predictor | 0.14 |
| Mean of criterion | 0.15 |
| SD of predictor | 0.23 |
| SD of criterion | 0.31 |
| Covariance | 0.04 |
| r | 0.52 |
| b (slope, estimate of beta) | 0.72 |
| a (intercept, estimate of alpha) | 0.05 |
| Mean Square Error | 0.07 |
| DF error | 1471 |
| t(b) | 23.52 |
| p(b) | 0.18 |
| t(a) | 0.41 |
| p(a) | 0.49 |
| Lowerbound of 95% confidence interval for beta | 0.66 |
| Upperbound of 95% confidence interval for beta | 0.78 |
| Lowerbound of 95% confidence interval for alpha | -0.17 |
| Upperbound of 95% confidence interval for alpha | 0.26 |
| Treynor index (mean / b) | 0.21 |
| Jensen alpha (a) | 0.05 |
| VaR(95%) | 0.03 |
| Expected Shortfall on VaR | 0.04 |
| VaR(95%) | 0.01 |
| Expected Shortfall on VaR | 0.02 |
| Mean | 0.86 |
| SD | 0.50 |
| Sharpe ratio (Glass type estimate) | 1.73 |
| Sharpe ratio (Hedges UMVUE) | 1.72 |
| df | 130 |
| t | 1.23 |
| p | 0.45 |
| Lowerbound of 95% confidence interval for Sharpe Ratio | -1.05 |
| Upperbound of 95% confidence interval for Sharpe Ratio | 4.51 |
| Lowerbound of 95% CI (Gibbons, Hedeker & Davis approximation | -1.06 |
| Upperbound of 95% CI (Gibbons, Hedeker & Davis approximation | 4.50 |
| Sortino ratio | 3.00 |
| Upside Potential Ratio | 9.49 |
| Upside part of mean | 2.73 |
| Downside part of mean | -1.87 |
| Upside SD | 0.41 |
| Downside SD | 0.29 |
| N nonnegative terms | 77 |
| N negative terms | 54 |
| N of observations | 131 |
| Mean of predictor | 0.65 |
| Mean of criterion | 0.86 |
| SD of predictor | 0.30 |
| SD of criterion | 0.50 |
| Covariance | 0.09 |
| r | 0.62 |
| b (slope, estimate of beta) | 1.03 |
| a (intercept, estimate of alpha) | 0.19 |
| Mean Square Error | 0.15 |
| DF error | 129 |
| t(b) | 9.05 |
| p(b) | 0.13 |
| t(a) | 0.35 |
| p(a) | 0.48 |
| Lowerbound of 95% confidence interval for beta | 0.81 |
| Upperbound of 95% confidence interval for beta | 1.26 |
| Lowerbound of 95% confidence interval for alpha | -0.91 |
| Upperbound of 95% confidence interval for alpha | 1.30 |
| Treynor index (mean / b) | 0.84 |
| Jensen alpha (a) | 0.19 |
| Mean | 0.74 |
| SD | 0.49 |
| Sharpe ratio (Glass type estimate) | 1.52 |
| Sharpe ratio (Hedges UMVUE) | 1.51 |
| df | 130 |
| t | 1.08 |
| p | 0.45 |
| Lowerbound of 95% confidence interval for Sharpe Ratio | -1.26 |
| Upperbound of 95% confidence interval for Sharpe Ratio | 4.30 |
| Lowerbound of 95% CI (Gibbons, Hedeker & Davis approximation | -1.27 |
| Upperbound of 95% CI (Gibbons, Hedeker & Davis approximation | 4.29 |
| Sortino ratio | 2.50 |
| Upside Potential Ratio | 8.93 |
| Upside part of mean | 2.65 |
| Downside part of mean | -1.91 |
| Upside SD | 0.39 |
| Downside SD | 0.30 |
| N nonnegative terms | 77 |
| N negative terms | 54 |
| N of observations | 131 |
| Mean of predictor | 0.60 |
| Mean of criterion | 0.74 |
| SD of predictor | 0.30 |
| SD of criterion | 0.49 |
| Covariance | 0.09 |
| r | 0.63 |
| b (slope, estimate of beta) | 1.04 |
| a (intercept, estimate of alpha) | 0.12 |
| Mean Square Error | 0.14 |
| DF error | 129 |
| t(b) | 9.23 |
| p(b) | 0.13 |
| t(a) | 0.22 |
| p(a) | 0.49 |
| Lowerbound of 95% confidence interval for beta | 0.81 |
| VAR (95 Confidence Intrvl) | 0.03 |
| Upperbound of 95% confidence interval for beta | 1.26 |
| Lowerbound of 95% confidence interval for alpha | -0.96 |
| Upperbound of 95% confidence interval for alpha | 1.19 |
| Treynor index (mean / b) | 0.72 |
| Jensen alpha (a) | 0.12 |
| VaR(95%) | 0.05 |
| Expected Shortfall on VaR | 0.06 |
| VaR(95%) | 0.01 |
| Expected Shortfall on VaR | 0.03 |
ORDER STATISTICS
| Number of observations | 67 |
|---|---|
| Minimum | 0.76 |
| Quartile 1 | 0.96 |
| Median | 1.03 |
| Quartile 3 | 1.07 |
| Maximum | 1.31 |
| Mean of quarter 1 | 0.89 |
| Mean of quarter 2 | 1.00 |
| Mean of quarter 3 | 1.05 |
| Mean of quarter 4 | 1.14 |
| Inter Quartile Range | 0.10 |
| Number outliers low | 2 |
| Percentage of outliers low | 0.03 |
| Mean of outliers low | 0.76 |
| Number of outliers high | 1 |
| Percentage of outliers high | 0.01 |
| Mean of outliers high | 1.31 |
| Extreme Value Index (moments method) | 0.21 |
| VaR(95%) (moments method) | 0.10 |
| Expected Shortfall (moments method) | 0.17 |
| Extreme Value Index (regression method) | -0.32 |
| VaR(95%) (regression method) | 0.11 |
| Expected Shortfall (regression method) | 0.14 |
| Number of observations | 1473 |
| Minimum | 0.89 |
| Quartile 1 | 1.00 |
| Median | 1.00 |
| Quartile 3 | 1.01 |
| Maximum | 1.18 |
| Mean of quarter 1 | 0.98 |
| Mean of quarter 2 | 1.00 |
| Mean of quarter 3 | 1.00 |
| Mean of quarter 4 | 1.02 |
| Inter Quartile Range | 0.01 |
| Number outliers low | 121 |
| Percentage of outliers low | 0.08 |
| Mean of outliers low | 0.96 |
| Number of outliers high | 90 |
| Percentage of outliers high | 0.06 |
| Mean of outliers high | 1.04 |
| Extreme Value Index (moments method) | 0.57 |
| VaR(95%) (moments method) | 0.02 |
| Expected Shortfall (moments method) | 0.05 |
| Extreme Value Index (regression method) | 0.19 |
| VaR(95%) (regression method) | 0.02 |
| Expected Shortfall (regression method) | 0.03 |
| Number of observations | 131 |
| Minimum | 0.89 |
| Quartile 1 | 1.00 |
| Median | 1.00 |
| Quartile 3 | 1.01 |
| Maximum | 1.18 |
| Mean of quarter 1 | 0.97 |
| Mean of quarter 2 | 1.00 |
| Mean of quarter 3 | 1.00 |
| Mean of quarter 4 | 1.04 |
| Inter Quartile Range | 0.01 |
| Number outliers low | 15 |
| Percentage of outliers low | 0.11 |
| Mean of outliers low | 0.95 |
| Number of outliers high | 16 |
| Percentage of outliers high | 0.12 |
| Mean of outliers high | 1.06 |
| Extreme Value Index (moments method) | 0.38 |
| VaR(95%) (moments method) | 0.02 |
| Expected Shortfall (moments method) | 0.04 |
| Extreme Value Index (regression method) | -0.02 |
| VaR(95%) (regression method) | 0.03 |
| Expected Shortfall (regression method) | 0.04 |
DRAW DOWN STATISTICS
| Number of observations | 6 |
|---|---|
| Minimum | 0.01 |
| Quartile 1 | 0.12 |
| Median | 0.20 |
| Quartile 3 | 0.30 |
| Maximum | 0.49 |
| Mean of quarter 1 | 0.05 |
| Mean of quarter 2 | 0.19 |
| Mean of quarter 3 | 0.22 |
| Mean of quarter 4 | 0.41 |
| Inter Quartile Range | 0.17 |
| Number outliers low | 0 |
| Percentage of outliers low | 0 |
| Mean of outliers low | 0 |
| Number of outliers high | 0 |
| Percentage of outliers high | 0 |
| Mean of outliers high | 0 |
| Extreme Value Index (moments method) | 0 |
| VaR(95%) (moments method) | 0 |
| Expected Shortfall (moments method) | 0 |
| Extreme Value Index (regression method) | 0 |
| VaR(95%) (regression method) | 0 |
| Expected Shortfall (regression method) | 0 |
| Number of observations | 48 |
| Minimum | 0.00 |
| Quartile 1 | 0.00 |
| Median | 0.02 |
| Quartile 3 | 0.05 |
| Maximum | 0.61 |
| Mean of quarter 1 | 0.00 |
| Mean of quarter 2 | 0.01 |
| Mean of quarter 3 | 0.03 |
| Mean of quarter 4 | 0.18 |
| Inter Quartile Range | 0.05 |
| Number outliers low | 0 |
| Percentage of outliers low | 0 |
| Mean of outliers low | 0 |
| Number of outliers high | 4 |
| Percentage of outliers high | 0.08 |
| Mean of outliers high | 0.37 |
| Extreme Value Index (moments method) | 0.67 |
| VaR(95%) (moments method) | 0.19 |
| Expected Shortfall (moments method) | 0.63 |
| Extreme Value Index (regression method) | 0.76 |
| VaR(95%) (regression method) | 0.19 |
| Expected Shortfall (regression method) | 0.78 |
| Number of observations | 13 |
| Minimum | 0.00 |
| Quartile 1 | 0.00 |
| Median | 0.01 |
| Quartile 3 | 0.04 |
| Maximum | 0.25 |
| Mean of quarter 1 | 0.00 |
| Mean of quarter 2 | 0.01 |
| Mean of quarter 3 | 0.03 |
| Mean of quarter 4 | 0.15 |
| Inter Quartile Range | 0.03 |
| Number outliers low | 0 |
| Percentage of outliers low | 0 |
| Mean of outliers low | 0 |
| Number of outliers high | 2 |
| Percentage of outliers high | 0.15 |
| Mean of outliers high | 0.18 |
| Extreme Value Index (moments method) | -2.26 |
| VaR(95%) (moments method) | 0.10 |
| Expected Shortfall (moments method) | 0.11 |
| Extreme Value Index (regression method) | 0.07 |
| VaR(95%) (regression method) | 0.22 |
| Expected Shortfall (regression method) | 0.35 |
| Strat Max DD how much worse than SP500 max DD during strat life? | -642282048 |
| Max Equity Drawdown (num days) | 272 |
| Last 4 Months - Pcnt Negative | 0.2% |
COMBINED STATISTICS
| Annualized return (arithmetic extrapolation) | 0.32 |
|---|---|
| Compounded annual return (geometric extrapolation) | 0.20 |
| Calmar ratio (compounded annual return / max draw down) | 0.41 |
| Compounded annual return / average of 25% largest draw downs | 0.50 |
| Compounded annual return / Expected Shortfall lognormal | 1.11 |
| j156mfCOMBRisPar | 0 |
| j157mfCOMBRisPar | 0 |
| Annualized return (arithmetic extrapolation) | 0.30 |
| Compounded annual return (geometric extrapolation) | 0.19 |
| Calmar ratio (compounded annual return / max draw down) | 0.32 |
| Compounded annual return / average of 25% largest draw downs | 1.10 |
| Compounded annual return / Expected Shortfall lognormal | 5.08 |
| j313dfCOMBRisPar | 0 |
| j314dfCOMBRisPar | 0 |
| Annualized return (arithmetic extrapolation) | 0.94 |
| Compounded annual return (geometric extrapolation) | 1.16 |
| Calmar ratio (compounded annual return / max draw down) | 4.61 |
| Compounded annual return / average of 25% largest draw downs | 7.69 |
| Compounded annual return / Expected Shortfall lognormal | 20.16 |
Trading record
Placed 705 trades in real-life brokerage accounts.
| Symbol | Side | Qty | Opened | Closed | P/L |
|---|---|---|---|---|---|
| TQQQ | long | 900 | Aug 21, 2024 | Sep 6, 2024 | ($9,825) |
| TQQQ | long | 3200 | May 3, 2024 | Aug 5, 2024 | ($2,422) |
| TQQQ | long | 2200 | Feb 20, 2024 | Apr 18, 2024 | ($2,386) |
| TQQQ | short | 400 | Feb 16, 2024 | Feb 16, 2024 | ($454) |
| TQQQ | long | 11600 | Aug 17, 2022 | Feb 13, 2024 | $9,478 |
| TQQQ | long | 2450 | Mar 7, 2022 | Jul 18, 2022 | ($2,897) |
| TQQQ | long | 10100 | Apr 14, 2021 | Mar 4, 2022 | ($3,027) |
| TQQQ | long | 400 | Apr 12, 2021 | Apr 13, 2021 | $1,637 |
| TQQQ | long | 2680 | Feb 16, 2021 | Apr 9, 2021 | ($2,830) |
| TQQQ | long | 1160 | Jan 27, 2021 | Feb 12, 2021 | $810 |
| TQQQ | short | 80 | Jan 21, 2021 | Jan 25, 2021 | $84 |
| TQQQ | long | 660 | Jan 4, 2021 | Jan 21, 2021 | $1,701 |
| TQQQ | long | 140 | Dec 18, 2020 | Dec 29, 2020 | $945 |
| TQQQ | long | 150 | Dec 9, 2020 | Dec 16, 2020 | $772 |
| TQQQ | long | 910 | Oct 13, 2020 | Dec 4, 2020 | $657 |
| TQQQ | long | 120 | Oct 8, 2020 | Oct 13, 2020 | $1,375 |
| TQQQ | long | 480 | Sep 4, 2020 | Oct 8, 2020 | $301 |
| TQQQ | long | 540 | Jul 22, 2020 | Aug 24, 2020 | $3,899 |
| TQQQ | long | 320 | Jul 14, 2020 | Jul 20, 2020 | $1,107 |
| TQQQ | long | 1100 | Jun 8, 2020 | Jul 7, 2020 | $3,510 |
| TQQQ | long | 200 | Jun 3, 2020 | Jun 5, 2020 | $671 |
| TQQQ | long | 300 | Jun 1, 2020 | Jun 3, 2020 | $668 |
| TQQQ | long | 300 | May 21, 2020 | May 29, 2020 | $759 |
| TQQQ | long | 180 | May 19, 2020 | May 20, 2020 | $747 |
| TQQQ | long | 1890 | Apr 16, 2020 | May 18, 2020 | $4,456 |
| TQQQ | long | 300 | Apr 7, 2020 | Apr 14, 2020 | $2,635 |
| TQQQ | long | 400 | Mar 27, 2020 | Apr 6, 2020 | $1,311 |
| TQQQ | long | 1250 | Feb 24, 2020 | Mar 26, 2020 | ($1,970) |
| TQQQ | long | 380 | Feb 19, 2020 | Feb 24, 2020 | ($4,716) |
| TQQQ | long | 205 | Feb 4, 2020 | Feb 7, 2020 | $663 |
Past results are not necessarily indicative of future results.
These results are based on simulated or hypothetical performance results that have certain inherent limitations. Unlike the results shown in an actual performance record, these results do not represent actual trading. Also, because these trades have not actually been executed, these results may have under-or over-compensated for the impact, if any, of certain market factors, such as lack of liquidity. Simulated or hypothetical trading programs in general are also subject to the fact that they are designed with the benefit of hindsight. No representation is being made that any account will or is likely to achieve profits or losses similar to these being shown.