Rosetta Analytics : RL One (S&P 500 long/short)

Year-to-Date
3.90%
Sep Performance
-1.57%
Min Investment
$ 1,000k
Mgmt. Fee
2.00%
Perf. Fee
25.00%
Annualized Vol
10.53%
Sharpe (RFR=1%)
0.82
CAROR
-
Assets
$ 5.0M
Worst DD
-2.42
S&P Correlation
0.30

Growth of 1,000 - VAMI

Monthly Performance

Export Data
Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec YTD DD

Past performance is not necessarily indicative of future results. The risk of loss in trading commodity futures, options, and foreign exchange ("forex") is substantial.

Period Returns

Program / Index Sep Qtr YTD 1yr 3yr 5yr 10yr Since
5/2020
RL One (S&P 500 long/short) -1.57 1.90 3.90 - - - - 3.90
S&P 500 -3.92 8.47 4.09 - - - - 10.47
+/- S&P 500 2.35 -6.58 -0.20 - - - - -6.57

Strategy Description

Summary

Deep reinforcement learning solves multi-asset optimization problems by using state-of-the-art neural networks to map market data to investible actions in a non-linear way. In our case, the problem is what is the optimal exposure long or short exposure to the S&P 500 Index on a t+1... Read More

Account & Fees

Type Managed Account
Minimum Investment $ 1,000k
Trading Level Incremental Increase $ 500k
CTA Max Funding Factor 2.00
Management Fee 2.00%
Performance Fee 25.00%
Average Commission $5.00
Available to US Investors Yes

Subscriptions

High Water Mark Yes
Subscription Frequency 7-14 Days
Redemption Frequency 7-14 Days
Investor Requirements QEP
Lock-up Period 0

Trading

Trading Frequency 500 RT/YR/$M
Avg. Margin-to-Equity 10%
Targeted Worst DD -10.00%
Worst Peak-to-Trough 3.00%
Sector Focus Stock Index Traders

Holding Periods

Over 12 Months 0%
4-12 Months 0%
1-3 Months 0%
1-30 Days 100.00%
Intraday 0%

Decision-Making

Discretionary 0%
Systematic 100.00%

Strategy

Other
100.00%
Strategy Pie Chart

Composition

Stock Indices
100.00%
Composition Pie Chart

Summary

Deep reinforcement learning solves multi-asset optimization problems by using state-of-the-art neural networks to map market data to investible actions in a non-linear way. In our case, the problem is what is the optimal exposure long or short exposure to the S&P 500 Index on a t+1 basis? It solves this problem by maximizing a specific, long-term reward (in our case, P&L), taking into account delayed rewards, confidence, and transaction costs. Through periodically retraining, the model adapts dynamically to different market environments. The output of the model is a holistic investment action to buy [sell] a percentage X% of S&P 500 E-mini futures and hold Y% in cash equivalent. The action must always sum to an absolute value of 1.00.

Investment Strategy

RL One (S&P 500 Long/Short) uses Rosetta’s proprietary reinforcement learning model to predict the optimal long or short exposure to the S&P 500 Index™ on a close-to-close basis. It implements the predictions by taking long or short positions in S&P 500 E-Mini futures contracts. The Strategy currently does not employ leverage.

Risk Management

Risk management is embedded in the model: the deep reinforcement learning model considers risk and transaction costs when determining the optimal long or short allocation to the S&P 500 Index.

   

Past performance is not necessarily indicative of future results. The risk of loss in trading commodity futures, options, and foreign exchange ("forex") is substantial.

Reward
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Past performance is not necessarily indicative of future results. The risk of loss in trading commodity futures, options, and foreign exchange ("forex") is substantial.

Note: Figures shown in the Monthly column are the greatest figures (or worst for losses/drawdowns) for any particular month. The Annual figures are the greatest for any calendar year.

Drawdown Report

Depth Length (Mos.) Recovery (Mos.) Peak Valley
-2.42 1 1 1/1/0001 5/1/2020
-1.57 1 - 8/1/2020 9/1/2020
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Consecutive Gains

Run-up Length (Mos.) Start End
8.17 3 6/1/2020 8/1/2020
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Consecutive Losses

Run-up Length (Mos.) Start End
-2.42 1 5/1/2020 5/1/2020
-1.57 1 9/1/2020 9/1/2020
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Time Windows Analysis

 1 Month
Number of Periods5.00
Percent Profitable60.00
Average Period Return0.80
Average Gain2.67
Average Loss-2.00
Best Period4.49
Worst Period-2.42
Standard Deviation3.04
Gain Standard Deviation2.29
Loss Standard Deviation0.60
Sharpe Ratio (1%)0.24
Average Gain / Average Loss1.34
Profit / Loss Ratio2.01
Downside Deviation (10%)1.55
Downside Deviation (5%)1.34
Downside Deviation (0%)1.29
Sortino Ratio (10%)0.26
Sortino Ratio (5%)0.54
Sortino Ratio (0%)0.62

Top Performer Badges

Index Award Type Rank Performance Period

Past performance is not necessarily indicative of future results. The risk of loss in trading commodity futures, options, and foreign exchange ("forex") is substantial.