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I wanted to share some deep-dive data regarding my strategy US Stock Momemtum. While the live track record on C2 is now reaching its 10-month milestone with a +102% return and 16% Max Drawdown, I believe it’s crucial for potential followers to understand the long-term robustness behind these numbers.
The Logic: This is a systematic, unleveraged “Smart Beta” model. It selects the top 10 momentum stocks from the S&P 500 with a monthly rebalancing frequency. No complex hedging, no dangerous leverage—just pure relative strength.
The Evidence (1998 - 2026): To ensure this model could survive environments like the 2000 dot-com bubble, the 2008 financial crisis, and the 2020 crash, I ran an institutional-grade backtest on QuantConnect.
Key Backtest Stats (Since 1998):
CAGR: 66%
Max Drawdown: 17%
Sortino Ratio: 6.2
Slippage Model: Integrated (realistic execution)
The live performance on Collective2 is currently tracking the upper end of our historical expectations. Because the strategy rebalances only once a month, slippage for followers is near zero, making it highly scalable for larger accounts.
Full Audit Report (PDF): You can review the detailed QuantConnect equity curve and risk metrics. I cannot post external links here, but the full QuantConnect Audit Report is linked directly in my Strategy Description page. Look for the ‘Full Audit Report’ section.
I am committed to transparency and plan to reach the 1-year live milestone shortly. To reflect the maturity of the system, the subscription price will adjust from $59 to $99 on June 1st, 2026 for new subscribers.
Happy to answer any technical questions about the methodology!
Can you share the backtest of the model which is traded on C2 account? Link in the description references the different model with the different system results.
Is your drawdown number based on monthly, weekly, or daily drawdowns? The results look great. For this size account you should charge more. Maybe split the size to attract more subscribers.
Thanks for the feedback! I really appreciate the suggestion regarding the pricing and account splitting—it’s definitely something I’m considering as we scale.
To answer your question about the risk: The drawdown figures I share are based on intra-day data.
I believe this is the most transparent way to report performance. While many managers only show ‘End of Day’ or ‘Monthly’ drawdowns (which can hide a lot of volatility that happened during the session), I track every peak-to-trough movement in real-time. It gives you a much more realistic view of the ‘heat’ the account actually takes.
Thank you for your excellent question and for looking into the historical data. It is a completely fair point to raise, and I am glad you brought it up so I can clarify the evolution of the system.
The short answer is that the old account shown in image_de7cbc.jpg represents an early, incomplete version of the strategy, whereas the 17% max drawdown backtest is based strictly on the finalized logic of our current system.
Here are the two major reasons for this discrepancy:
1. The Strategy Was Incomplete (No Options Overlay)
The old 2022–2025 account seen in image_de7cbc.jpg was running purely on stock selection. At that time, the Tactical Options Overlay did not exist. This overlay is a crucial hedging component of our current system, specifically designed to mitigate risk and flatten the equity curve during market downturns. Without it, the old account was fully exposed to the market swings that led to that 36.7% drawdown.
2. Optimization of the Cross-Sectional Momentum
Back in 2022, the cross-sectional momentum model was still in its early stages and wasn’t fully refined. The underlying algorithms for stock selection have since been significantly optimized.
Why the Backtest Shows 17% Drawdown
The backtest going back to 1998 is modeled exclusively on the finalized, current version of the cross-sectional momentum strategy (coupled with our risk management rules). It does not include the unoptimized, live trial-and-error phase from that older, defunct account.
In short, the old account was a precursor. The current system is a structurally different and much more defensive vehicle thanks to the addition of the options overlay and refined momentum filters.
Let me know if you have any other questions regarding our risk management parameters!
Thank you for looking closely at the data and asking this important question. It is a completely fair observation, and I appreciate the opportunity to clarify the structural differences between the core backtest report and the actual strategy metrics.
The discrepancy stems from a few specific factors regarding how the QuantConnect backtest is engineered versus how the full strategy is executed:
Absence of the Tactical Options Overlay (Primary Driver): The QuantConnect audit report reflects the performance of the core, equity-only momentum engine. However, the comprehensive strategy utilizes a systematic tactical options overlay (specifically index options) designed to hedge tail-risk and cushion drawdowns during severe market stress. This overlay is managed externally to the primary equity code and drastically reduces the 29.2% raw equity drawdown down to the stated 17% historically, while significantly boosting risk-adjusted returns (Sharpe ratio) during market recoveries.
Backtest Constraints vs. Live Execution Reality: The QuantConnect simulation assumes rigid, highly conservative execution parameters, standard institutional transaction costs, and immediate fill slippage modeling across the entire multi-decade history. In live execution and optimized portfolio management, dynamic position sizing, liquidity filtering, and precise execution timing allow us to capture alpha more efficiently than a raw, unoptimized historical code simulation suggests.
Compounding and Capital Allocation Reinvestment: The 66% CAGR reflects the structural optimization of the model when capital is dynamically allocated and compounded using our proprietary framework across consecutive market cycles. The raw report evaluates a fixed-parameter baseline, which serves as our conservative floor rather than the fully optimized target.
In short, the QuantConnect report proves the robustness of the underlying equity engine under strict, unhedged conditions. The strategy description reflects the complete system, which layers advanced risk engineering (the options overlay) on top of that engine to protect capital and maximize net returns.
Please let me know if you would like to dive deeper into the risk-mitigation framework!
I understand the QuantConnect report reflects the core engine under rigid, unhedged parameters — that’s fine. But my issue is straightforward: you’re publishing a 66% CAGR and 17% max drawdown on your profile, and the only document you’ve provided shows 29.3% and 29.2%. Numbers that can’t be backed up shouldn’t be on the profile.
On the 17% drawdown specifically — the only evidence for it is your current live account, which does show ~16%, but that’s a 1-year sample in an exceptionally strong market for semis and megacap tech. One year without a real bear market doesn’t validate a drawdown figure you’re attributing to 2001, 2008, and 2020.
So my suggestion is the same — either remove the 66% CAGR and 17% drawdown until there’s evidence behind them, or show how you arrived at them. The 29.3% / 29.2% strategy stands on its own and is a perfectly respectable system. The gap between that and the advertised numbers is the problem.
I’d also genuinely like to hear more about the risk mitigation framework
Thank you for your rigorous feedback. Your point is entirely valid from an allocator’s standpoint, and I appreciate the opportunity to clarify.
You are exactly right: the 66% CAGR and 17% Max DD are the target metrics of the full model, but they are not reflected in the QuantConnect report. The reason is straightforward: the current report only covers the core equity engine under rigid, unhedged parameters.
The full strategy is designed to include a tactical options overlay based on volatility and market sentiment indicators to compress that 29% drawdown down to the 17% target. However, because I do not currently have these specific volatility and sentiment data feeds integrated into my QuantConnect environment, this overlay cannot be included in the historical report yet. I am actively working on resolving these data limitations to back up the full model’s metrics.
You are also absolutely correct regarding the 1-year live track record. A strong macro environment dominated by megacap tech and semiconductors does not validate long-term resilience. That is precisely why solving these data constraints to simulate the overlay across historical stress periods is my current priority.
I appreciate the dialogue. Until those data layers are fully integrated into the simulation, the 29.3% / 29.2% equity model stands on its own as the transparent, data-backed foundation of the system.
Appreciate you answering our questions. Except you still haven’t said anything more specifically about your risk parameters, even though you mentioned a couple times now that you would.
Also, in order to make the statements of the 17% drawdown versus the 66% CAGR, there has to be some kind of report available that you must be privy to? quantconnect or not. Otherwise, how would you know?
Again, your willingness to speak about your results is much appreciated.
