TQQQ–BIL Strategy Tracker

Background — how this page came to be

I came across this strategy while researching ways to trade TQQQ. I usually swing trade TQQQ, but this rules-based rotation caught my eye. It looked like a more systematic way to hold a leveraged ETF without staring at charts all day.

The original concept comes from Jason Kelly. Up front, I don't subscribe to his services and I haven't read his book. I pieced these rules together from public discussions about how the strategy works, so my version may not perfectly match his.

After running some early backtests in a spreadsheet, I asked an AI coding agent to do a more rigorous backtest. I went the AI route because I couldn't find a free backtesting tool that did what I wanted. Most of what you see on this page is the AI's output, shaped by my prompts and reviewed along the way.

One last thing. This is a personal learning project, not financial advice. Your results will vary based on the inputs you set, and what looked great in a backtest has a way of looking different when real money is on the line. If you're putting actual capital at risk, talk to a registered advisor first.

What is this strategy?

The TQQQ–BIL strategy is a rules-based, systematic approach that pairs a 3× leveraged Nasdaq ETF (TQQQ) with a short-term treasury ETF (BIL) as a defensive cash reserve. Instead of buy-and-hold, the portfolio is rebalanced on a fixed schedule using a growth target — capturing gains when markets run and buying more when they fall.

Set a growth target Choose how much you expect TQQQ to grow each period (e.g. 9% per quarter). This becomes your rebalance anchor — not a prediction, but a disciplined trigger.

Rebalance on schedule At each rebalance date (monthly, quarterly, or annually), compare TQQQ's current value to the target. Only act if the deviation exceeds the tolerance threshold.

Sell high into BIL If TQQQ is above target — it outran expectations. Sell the surplus TQQQ and park proceeds in BIL. Lock in gains, rebuild your reserve.

Buy low from BIL If TQQQ is below target — it lagged. Deploy BIL reserves to buy discounted TQQQ. Systematically average down without emotion.

Reset the target After each rebalance, the new target is set to the post-trade TQQQ value multiplied by the growth rate. The process repeats next period.

Risk notice: TQQQ is a 3× leveraged ETF that resets daily. It can lose 90%+ of its value in a severe or prolonged downturn. This strategy does not guarantee profits. Past backtest results do not predict future performance. Only invest capital you can afford to lose entirely.

Strategy configuration

Saved presets

Target growth / period (%)

Rebalance frequency

Starting capital ($)

Initial TQQQ allocation (%)

Start date

Commission + slippage (%)

Rebalance tolerance (%)

Risk-free rate (%, annual)

Cash flow — contributions & withdrawals

Amount per period ($)

Direction

Frequency

Cash goes into

TQQQ3× leveraged Nasdaq-100 ETF · BIL1–3 month US T-bills · QQQNasdaq-100 ETF (1×) · SPYS&P 500 ETF Data through: —

Return

Risk

Quick reality check before you get excited. This is a learning tool, not financial advice. Every number on this page came straight from the inputs you set above — change the start date or growth target and the whole picture shifts. Play with the dials, see what moves, but don't mistake a backtest for a guarantee.

Heads up for Canadian readers. All returns shown are in USD. Holding US-listed ETFs from Canada adds currency risk (CAD/USD can swing 10–15% in a year), foreign withholding tax on distributions (0% in RRSP, 15% in TFSA on US dividends, treaty in non-registered), and every rebalance is a taxable event in a non-registered account. The numbers above ignore all of that. Real after-tax, after-FX returns in CAD will be lower.

Next rebalance signal

Portfolio vs. signal target vs. benchmarks (USD)

Start-date sensitivity — rolling CAGR by start date

Drawdown from peak

Annual returns

Allocation over time

TQQQ % BIL %

Cumulative contributions vs. portfolio value

What follows are simulations, not predictions. The market doesn't know we ran them — and neither do taxes, fees, or how you'll actually feel when your account is down 50%. Use these to get a sense of the risk, not to plan around a specific outcome.

Monte Carlo — 10-year forward projection (2,000 paths)

What is this telling me?

We ran 2,000 imaginary versions of the next 10 years by replaying real historical daily return patterns in random order. Each "path" is one possible future. Together they show the full range — from lucky outcomes to unlucky ones — so you can see what this strategy might realistically do going forward.

Green line (median path)

Half of all 2,000 futures ended above this line, half below. Think of it as the single most representative outcome — not a guarantee, just the middle of the range.

Orange bands

The darker band shows where the middle 50% of futures landed. The lighter outer band covers 90% of all simulated futures. A wider band means more uncertainty.

Median CAGR

The typical yearly growth rate across all simulations — like a compound interest rate. A 30% CAGR means your money roughly triples every ~4 years in the median scenario.

Risk of Ruin

The % of futures where your portfolio fell below 50% of what you started with. This is the worst-case catastrophic loss risk — you should be comfortable with this number before investing.

Probability of Loss

The % of futures where you ended 10 years with less money than you started. Even over a long horizon, some paths go negative — this tells you how often that happened.

Mean Max Drawdown

On average across all futures, how far did the portfolio fall from its peak before recovering? For example, -60% means at some point you'd be watching your account drop by more than half. This is the emotional pain number.

⚠️ Important: This is based on historical patterns and does not predict the future. TQQQ is a 3× leveraged ETF — it can lose 90%+ of its value in a severe market crash. Past performance does not guarantee future results.

Percentile fan chart — portfolio value (block-bootstrap, not assuming normality)

50th pct (median) 25th–75th pct band 5th–95th pct band

Stress tests — strategy performance during market crises

What is this telling me?

Unlike the Monte Carlo which simulates imaginary futures, stress tests look at what actually happened during real historical market crises. This strategy mechanically buys more TQQQ when prices drop, so crashes affect it differently than a buy-and-hold approach.

Return %

How much the total portfolio gained or lost from the start to the end of that crisis window. A -39% return means if you had $100,000 at the start, you'd have about $61,000 at the end of that period.

Max Drawdown

The worst single drop from a peak during that period — the lowest your account would have looked. This is deeper than the overall return because it captures the scariest moment before any recovery.

Days

How many trading days the period covered. Shorter crashes (like COVID in 2020) recovered faster. Longer downturns (like 2022) dragged on and gave the strategy less opportunity to average down.

Why some periods are missing

TQQQ only began trading in February 2010, so crises before that date (like the 2008 Financial Crisis) cannot be shown — there is no real TQQQ price data from that time.

💡 Tip: Large negative returns during crashes are expected with this strategy. The key is whether the strategy recovers — look at the Post-COVID Recovery row to see how dramatically the strategy can rebound after a crash.

Return % per crisis period

Stress period detail

Period	Return	Max DD	Days

Date	Action	TQQQ px	BIL px	Pre TQQQ	Target	Deviation	Trade $	Post total	New target	TQQQ %	BIL %

Date	Type	Amount	Into	Before	After	TQQQ %	BIL %

Metric	TQQQ-BIL	TQQQ B&H	QQQ	SPY

Year	Inflation	TQQQ-BIL Strategy		QQQ		SPY
Year	Inflation	Return	Balance	Return	Balance	Return	Balance

How these numbers are calculated

Data source

Prices come from Yahoo Finance via yfinance with auto_adjust=True, so closes are dividend-adjusted (BIL, QQQ, SPY distributions are included). All series are USD daily closes.

CAGR

(end_value / start_value)^(1/years) − 1, where years = trading_days / 252.

Annualized volatility

std(daily_returns) × √252. Population std (divisor = n). Assumes daily returns are independent — leveraged ETFs have mild negative serial autocorrelation, so the true annualized vol is slightly lower than this calculation.

Sharpe ratio

(mean_daily − rfr_daily) / std_daily × √252, where rfr_daily = (1 + rfr_annual)^(1/252) − 1. The risk-free rate is whatever you set in the config above. Default 2% is a rough time-weighted average for 2015–2022 (T-bill yield was near 0% in 2015–2021, ~5% in 2023+). For period-accurate ratios, override to match your chosen backtest window. Also reported with a standard error band: SE(Sharpe) = √((1 + 0.5·Sharpe²) / T_years) (Lo 2002, IID approximation).

Sortino ratio

(mean_daily − rfr_daily) × 252 / downside_dev_annualized, where downside deviation only counts days with returns below rfr_daily, summed-of-squares divided by total n (Sortino & Price 1994 convention).

Maximum drawdown

Largest peak-to-trough percentage loss using daily close prices only. Intraday lows are not captured, so actual lived drawdowns may be a few percent worse.

Monte Carlo

Block-bootstrap resampling of historical daily portfolio returns. Default: 2,000 paths, 10-year horizon, 5-day blocks, RNG seed = 42 on first render (click "Re-run with new seed" to see how stable the bands are). Block-bootstrapping preserves short-term autocorrelation and volatility clustering — it does not assume normal/Gaussian returns. Risk of Ruin = % of paths ending below 50% of the anchor value; Probability of Loss = % ending below the anchor value.

The MC projection anchor is configurable via the toggle above the section. Default: initial capital (matches a fresh investor's mental model: "if I start today with $X, what range of outcomes might I see in 10 years?"). The alternative is anchoring on the backtest's ending value (useful when asking "if I'm already at $X, what's next?").

Limitation: 5-day blocks preserve weekly autocorrelation but not regime/crisis dependencies. Heavier-tailed events than what's in the historical sample won't appear.

Stress tests

Hardcoded historical windows (2018 vol, COVID 2020, post-COVID recovery, 2022 rate hikes, 2022 tech crash, plus GFC 2008 if data exists). Only windows with ≥5 trading days of overlapping data are shown. TQQQ began trading 2010-02-11, so anything before that returns no data.

Transaction frictions

Commission + slippage is applied symmetrically — the sell side receives price × (1 − f), the buy side pays price × (1 + f). Default 0.15% per side ≈ 0.30% round-trip, realistic for Canadian discount brokers on leveraged ETFs.

What's NOT modeled

Taxes — every rebalance is a taxable event in a non-registered account
Currency / FX — all returns are USD, ignoring CAD/USD volatility
Foreign withholding tax on US ETF distributions (treatment depends on account type)
Inflation — returns are nominal, not real (subtract ~2–3% per year for real CAGR)
Settlement delays — assumes T+0 cash availability
Bid-ask spread variability — uses closing price, not actual fill price

Source code

All calculations are open and inspectable in the public repository — Python tools (Monte Carlo, stress tests) and JS in this page itself.

The bottom line: this is a learning tool, full stop. Every number depends on the inputs you chose, so different choices give you different results. TQQQ is a 3× leveraged ETF — in a bad market it can drop 90% or more, and no backtest changes that. Before you put real money on the line, sit down with a registered Canadian advisor. That's what they're there for.