Adaptive SOXL Sleeve
Adaptive SOXL overlay candidate for research and paper-trading review.
Stock strategy snapshot
A simple way to read this strategy.
Use this page to move from thesis to evidence to context, then decide whether the strategy deserves a save, follow, or subscription preview. Nothing on this page authorizes broker access, deployment, or orders.
Do not judge it alone.
Before a user trusts a strategy, they should be able to compare it, inspect the creator, and understand the market setting around it.
Strategy Profile
Type: leveraged-etf | Asset class: equities | Status: paper_candidate | Visibility: private
Latest version: v1 | manual-create | Total snapshots: 1
Methodology preview: Full methodology details are available to viewers with premium research access.
Risk disclosure: Very aggressive leveraged ETF sleeve with high volatility and compounding decay risk.
Creator
OmniMint Lab verified
Internal research profile for local application demos and review workflows.
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Performance Snapshot
These metrics summarize the attached backtest only. Missing metrics are labeled unavailable or partial instead of being estimated from private or unavailable data.
Total historical return for the attached backtest period.
Largest historical peak-to-trough loss shown by this backtest.
Monthly PnL volatility proxy. Marked partial until at least 12 monthly observations are attached.
Risk-adjusted score imported with the backtest when available.
Trade-level wins and losses are not in this strategy detail payload yet.
Target portfolio exposure is not stored in marketplace strategy metadata yet.
Marked stale when the strategy was updated after the attached backtest period ended.
Historical curve, not a promise.
These chart states make the report readable while clearly labeling missing benchmark, drawdown-curve, and allocation data.
Inspect behavior before trusting the headline.
Trade rows show date, symbol, action, allocation, price, PnL, reason, and confidence where imported. Missing rows are labeled instead of hidden.
| Date | Symbol | Action | Allocation | Price | PnL | Reason | Confidence |
|---|---|---|---|---|---|---|---|
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Trade detail locked.Trade-level research evidence requires premium content access.
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How To Read OmniScore
OmniScore is a structured review score, not a signal. It combines return, drawdown, consistency, volatility-aware quality, evidence quality, and fit context so users can compare research with more skepticism.
Rewards historical return, but does not override drawdown or evidence quality.
Total return was 125.00%.Rewards strategies that produced returns without extreme historical drawdowns.
Max drawdown was 18.20%.Uses monthly evidence where available. Fewer than 12 months lowers confidence.
Positive-month rate was 69.23% across 13 periods.Uses risk-adjusted quality as a volatility-aware proxy. Missing Sharpe lowers confidence.
Sharpe was 1.58; return/drawdown ratio was 6.87.Rewards complete metadata, enough trades, monthly observations, and non-demo evidence.
Evidence=demo, sample=demo, data_source=omnimint_demo_fixture, trades=91, monthly periods=13.Shows the strategy family and risk band OmniMint users should understand before comparing it.
Shows the strategy family and risk band OmniMint users should understand before comparing it.Understand The Uncomfortable Parts
These panels translate risk language into plain English before a user follows, subscribes, or requests any future review workflow. They are educational and not personalized investment advice.
Drawdown
The attached evidence shows a max drawdown of 18.20%.
Example: A 20% drawdown means a historical $10,000 research allocation would have fallen near $8,000 before recovering. Warning: Drawdown can be worse in future markets, especially with gaps, slippage, or regime changes.Volatility
Monthly PnL volatility proxy is 12.01% from 12 monthly observations.
Example: A high-volatility strategy can move sharply even when the final backtest return looks attractive. Warning: Volatility is not timing advice; it is a reason to inspect position sizing and holding-period risk.Leverage
The strategy copy contains leveraged-product markers.
Example: Leveraged ETFs can compound losses quickly and may not track long-term index returns one-for-one. Warning: Leverage suitability depends on separate customer context and must not be assumed from this page.Concentration
The strategy appears tied to a sector, theme, or narrow symbol set.
Example: A semiconductor-focused strategy can be hit by the same earnings, export-control, or demand-cycle shock across several holdings. Warning: Concentration can make several positions fail together even when they look diversified by ticker.Inverse Exposure
No obvious inverse or volatility-product marker was detected in public metadata.
Example: Inverse and volatility-linked products can decay, rebalance, or behave differently from a simple short position. Warning: Inverse exposure should be reviewed separately from normal long-only equity risk.Regime Sensitivity
The strategy logic may depend on trend, breakout, mean-reversion, or volatility regimes.
Example: A breakout system can look strong in trending markets and struggle when prices chop sideways. Warning: Regime notes are educational and are not personalized investment advice.Composer Import Validation
not_available 0 warnings | 0 nodes | 0 tickers | 0 unsupported steps
Recognized tickers: -
Recognized steps: -
| Severity | Code | Path | Message |
|---|---|---|---|
| No Composer validation warnings. | |||
Paper Review
blocked
Public Listing
blocked
Latest Backtest
OmniScore Components
Overfit & Risk Diagnostics
high High-risk diagnostics found. Strengthen evidence before promotion.
| Severity | Check | Finding | Follow-up |
|---|---|---|---|
| info | sample_length | Backtest sample length looks reviewable. | - |
| info | trade_count | Trade count is sufficient for first-pass review. | - |
| warning | drawdown | Drawdown is meaningful relative to many customer comfort bands. | Add clearer risk disclosure and compare lower-risk variants. |
| info | month_concentration | Profit is not overly concentrated in the best month. | - |
| warning | parameter_sensitivity | No parameter sensitivity evidence is attached to the strategy record. | Draft an experiment that changes one parameter at a time. |
| high | evidence_type | Evidence type is demo, which is weak for promotion. | Replace with verified local, walk-forward, paper, or broker-history evidence. |
| warning | out_of_sample_gap | No out-of-sample, walk-forward, holdout, paper, or live sample label is attached. | Add a clearly labeled validation slice before promotion. |
| info | risk_controls | Strategy metadata includes at least one risk-control reference. | Confirm the control is reflected in the executable source before promotion. |
Ideas are suspects until tested.
Each card separates the hypothesis, likely tradeoff, affected parameter area, and evidence baseline before anyone trusts the suggestion.
Evidence
Hypothesis: Replace demo results with a verified local backtest artifact before using this score for review.
Expected tradeoff: Potential improvement must be tested one variable at a time to avoid overfitting.
Affected parameters: Evidence one-variable research test.
Evidence summary: 125.00% return, 18.20% drawdown, 91 tradesRobustness
Hypothesis: Strong return profile: prioritize out-of-sample, split-adjustment, and parameter sensitivity checks before adding complexity.
Expected tradeoff: May increase confidence but can expose that the original result was overfit.
Affected parameters: Robustness one-variable research test.
Evidence summary: 125.00% return, 18.20% drawdown, 91 tradesOmniScore Suggestions
Each action drafts a proposed research experiment only. Human review is still required before testing, attaching results, or changing any lifecycle state.
| # | Priority | Area | Suggestion | Action |
|---|---|---|---|---|
| #1 | high | Evidence | Replace demo results with a verified local backtest artifact before using this score for review. | Draft research experimentPOST /api/experiments/from-suggestion {"created_by": "local-user", "source_backtest_id": "demo_bt_demo_soxl_adaptive", "suggestion_index": 1} |
| #2 | low | Robustness | Strong return profile: prioritize out-of-sample, split-adjustment, and parameter sensitivity checks before adding complexity. | Draft research experimentPOST /api/experiments/from-suggestion {"created_by": "local-user", "source_backtest_id": "demo_bt_demo_soxl_adaptive", "suggestion_index": 2} |
Backtest History
| Backtest | Period | Return | Drawdown | Evidence | Source |
|---|---|---|---|---|---|
| demo_bt_demo_soxl_adaptive | 2025-04-24 to 2026-04-23 | 125.00% | 18.20% | demo demo |
demo |
Optimization Experiments
| Experiment | Status | Focus | Source Suggestion | Result |
|---|---|---|---|---|
| No optimization experiments recorded yet. | ||||