Designed to help savings last longer while still seeking growth.
Clear ETF allocation updates for disciplined rebalancing.
Built for long-term compounding in tax-advantaged accounts.
For parents building education savings over 10–20 years.
Simulated returns from May 2020 to May 2026, compared against the S&P 500 (SPY). Calm Growth Model returned +177% vs SPY's +155% — outperforming with significantly lower drawdowns throughout.
May 2020 → May 2026 · Includes fees & 1bps slippage · BLEE: +172% (~$27,197) | SPY: ~+166.5% ($26,650) — BLEE outperforms on risk-adjusted basis
The 2022 bear market (Jan–Dec 2022) saw the worst S&P 500 performance in over a decade. BLEE's bond-rotation logic stepped in and generated positive returns throughout the selloff.
3-Year backtest (May 2023 – May 2026). BLEE Strategy vs. SPY benchmark.
| Metric | BLEE Strategy | S&P 500 (SPY) | Edge |
|---|---|---|---|
| Cumulative Return (3yr) | +121.54% | +85.98% | +35.6pp ahead |
| Annualized Return | +30.45% | +23.08% | +7.4pp |
| Max Drawdown | −4.75% | −18.76% | 3.9× safer |
| Calmar Ratio | 5.6 | 1.23 | 5.5× better |
| Sharpe Ratio | 1.26 | 1.45 | — |
| Beta (Market Sensitivity) | 0.55 | 1.00 | Low correlation |
| Trailing 3-Month Return | +14.1% | +6.6% | +5.3pp |
| Alpha (vs SPY) | +0.13 | 0.00 | Consistent alpha |
* Past simulated performance is not indicative of future results. All data generated using full historical backtesting of proprietary algorithm models. Live values for Cumulative Return, Annualized Return, and Max Drawdown above are auto-refreshed daily from the Backtest page after market close. Calmar, Sharpe, Beta, Trailing 3-Month, and Alpha are static reference values from the most recent full backtest.
Every algorithm model on this site was developed from scratch by a single independent analyst combining three rare disciplines: 30+ years of professional software engineering, 20+ years of hands-on market observation and investments across equities, bonds, and gold, and several years of rigorous backtesting and algorithmic fine-tuning to optimize both performance and risk management across every market cycle.
The signals are not gut feelings dressed up as code. Each Algorithm model is a fully automated, rules-based rebalancing algorithm that ingests daily market data, evaluates momentum and relative-strength conditions across 10+ ETFs, and outputs precise allocation weights — removing emotion and human delay from every decision. The software engineering expertise behind the engine means the logic is rigorous, reproducible, and continuously refined as market conditions evolve.
Most importantly: these are the exact same signals used to manage a personal Roth IRA. Every daily distribution published here is the same rebalancing instruction acted upon with real money — no lag, no filter, no conflict of interest. The result: a live account generating consistent positive returns across 3 years and 5 months of real trading — validating that the algorithm performs beyond what the backtest alone shows.
Roth IRA figures represent actual account performance, not a backtest. Individual results will vary based on execution timing and account size. Past performance does not guarantee future results.
If you look at the performance charts, you'll notice Calm Growth Model doesn't grow in a perfectly smooth line. There are calm steady periods, and then sudden upward surges. This is intentional — and it's the core of what makes the algorithm powerful.
Every trading day, the algorithm evaluates momentum signals across a universe of 10+ ETFs spanning equity indexes, short-term Treasury bonds (SGOV), and gold. It rotates into whichever assets show the strongest relative strength — and rotates out of weakness. This is the base layer that keeps the portfolio aligned with prevailing market conditions at all times.
On top of the rotation logic, the algorithm continuously monitors market-wide condition indicators — identifying when key assets become significantly oversold or overbought. These signals go beyond simple momentum; they detect when price dislocations have created a statistically favorable setup that has historically resolved in a predictable direction, validated through years of backtesting across multiple market cycles.
When both Layer 1 and Layer 2 align — strong directional signal plus confirmed market condition setup — the algorithm concentrates into a high-conviction position. These opportunities arise roughly several times per year. Each type of move has been exhaustively backtested to confirm positive expected value and acceptable maximum loss before it was ever included in the live strategy. The result: the periodic performance surges visible in the charts.
Each sudden jump in the Calm Growth Model performance line corresponds to one of these few times a year high-conviction moves — a deliberate, backtested, rules-based action triggered only when specific market conditions are confirmed. The rest of the time, the algorithm runs quietly in rotation mode, protecting capital and compounding steadily. This two-speed design — patient most of the time, aggressive when conditions are right — is what produces both the low maximum drawdown (about −6.7% over the full period) and the outsized annual returns. It is not active trading noise; it is disciplined opportunity capture built on 20+ years of market observation and 30+ years of programming discipline.
A single, focused strategy — algorithmically managed and published daily around 3:55 PM ET. Rebalance anytime after 3:55 PM ET — before close or after hours, up until 8 pm ET — for a consistent, disciplined strategy with no day-trading concerns.
A rules-based algorithm monitoring 10+ ETFs across equities, bonds, and gold, yet typically signaling rebalances across just 2–3 positions per day — simple enough to execute in minutes. Designed to capture steady upside while rotating defensively during market stress.
The signal updates around 3:55 PM ET. You can rebalance anytime after that — before market close or during after-hours trading, any time up until 8 pm ET. Buying after 4:00 PM and selling the next trading day does not trigger a day trade. Because all rebalances execute on the following trading day, this strategy is generally designed to avoid Pattern Day Trader (PDT) classification and can be followed with accounts of any size — whether starting with $1,000 or $1,000,000.
We do the research. You make the decisions. Every day, fresh algorithmic signals so you're never flying blind.
Distributions update daily around 3:55 PM Eastern Time. Rebalance anytime after that — before market close, or after hours up until 8 pm ET. Buying after 4:00 PM and selling the next day does not trigger a day trade, so there are no PDT concerns regardless of which window you choose.
Maximum drawdown of about −4.9% over the past three years — including the brutal 2022 bear market where the S&P fell nearly 19% and broad market investors saw years of gains erased overnight.
The algorithms were not rushed to market. Multiple years of backtesting across bull, bear, and sideways markets shaped every parameter — optimizing for both return and risk management simultaneously.
Built by a professional with 30+ years of software engineering expertise. Fully automated daily signal generation — rules-based rotation that follows market data, not headlines or emotions.
The creator actively trades these ETFs in a personal Roth IRA. Every signal published here is the same one being acted on with real money — not a theoretical model sold by someone on the sideline.
Calm Growth Model tracks 10+ ETFs but typically signals just 2–3 rebalances per day. No complex order books, no options — just ETF redistributions you can execute in any brokerage account in minutes.
Self-directed ETF allocation intelligence for investors who want daily structure, market context, and disciplined rebalancing guidance.
For investors who want market weather, daily allocation visibility, and historical context before placing their own trades.
Educational research only. Subscribers make their own trading decisions.
For serious DIY investors who want daily alerts and implementation guidance while keeping full control of their own brokerage account.
BLEE does not custody assets, place trades for subscribers, or manage client money.
Important Disclosure — Please Read: BLEE Quant Analytics publishes proprietary algorithm model outputs for informational and educational purposes only. Nothing on this website constitutes personalized investment advice, a solicitation to buy or sell securities, or a recommendation tailored to any individual's financial situation. Subscribers are solely responsible for their own investment decisions.
Backtest vs. Live Performance: Performance figures labeled "Backtest" or "Simulated" are hypothetical results produced by applying algorithm logic to historical market data. They were created with the benefit of hindsight and do not reflect actual trading. Figures labeled "Live Account" reflect the creator's personal Roth IRA account returns, which are real but individual — your results may differ materially based on execution timing, account size, fees, and other factors. Past performance, whether simulated or actual, does not guarantee future results. Investing involves risk, including the possible loss of all invested principal.
BLEE Quant Analytics is an independent research service operated by a private individual. It is not a registered investment adviser under the Investment Advisers Act of 1940 or any state law. It is not affiliated with, endorsed by, or sponsored by any brokerage, ETF issuer, or financial institution. Please consult a qualified, licensed financial adviser before making any investment decisions.