# sma200.trade > Free SMA200 trend-filter signals for any US stock or ETF. Type a ticker > to see if it's above or below its 200-day simple moving average. The > /articles section publishes plain-English articles and /research publishes > formal backtest notes on trend filtering, leveraged ETFs, defensive > bucket construction, and portfolio methodology. ## Core tool - [Homepage](https://sma200.trade): One-click SMA200 status for any US ticker, no signup - [How it works](https://sma200.trade/about): Methodology, data sources, what the SMA200 can and can't tell you - [Disclaimer](https://sma200.trade/disclaimer): Information not advice; risk warnings ## Articles (/articles) — plain-English deep dives - [The Hidden Cost Every Leveraged-ETF Backtest Ignores](https://sma200.trade/articles/leveraged-etf-borrow-cost): Most synthetic LETF backtests overstate returns by ~60% over 10 years. The bug is one missing term in the daily-return formula. This piece shows the calibration proof against real TQQQ, the corrected long-window numbers, and what the SMA200 trend filter actually does once leveraged ETFs are modeled honestly. - [Why One Indicator Isn't Enough (and Why Three Tuned Indicators Aren't Better)](https://sma200.trade/articles/why-one-indicator-isnt-enough): The SMA200 gives you a binary read on long-term trend. Useful, but incomplete. Most traders try to fix this by stacking RSI, MACD, and Bollinger bands until the chart is unreadable. There's a better way, and a walk-forward test reveals why most "tuned" confluence strategies are statistical noise dressed up as edge. - [Does the 200-day Moving Average Actually Beat Buy-and-Hold?](https://sma200.trade/articles/sma200-vs-buy-and-hold): A look at 16 years of data on QQQ, TQQQ, SOXL, UPRO comparing the simplest trend-filter rule against passive buy-and-hold. The answer is more interesting than either side wants to admit. ## Research notes (/research) — formal backtest write-ups - [Research index](https://sma200.trade/research): Durable findings + filterable list of all studies - [Borrow-Cost Correction: Synthetic LETF Methodology + Revised Numbers](https://sma200.trade/research/borrow-cost-correction-synthetic-letf): The simple L*daily − ER/252 synthetic LETF formula overstates real TQQQ by 62%. Adding the (L-1)*borrow_rate/252 term tracks real TQQQ within 5%. Walk through the bug, the fix, and revised numbers for synthetic TQQQ 1999-2026 + defensive bucket comparison. - [Defensive Bucket Comparison: Cash vs Gold vs TLT vs Combinations](https://sma200.trade/research/defensive-bucket-comparison): Across 21 years, tested what to hold during SMA200-filter OFF periods on a leveraged-equity strategy. Gold > combinations > cash > long Treasuries. The TLT/ZROZ result is the most surprising and durable: long bonds finish worst on both Sharpe and max drawdown. - [LRS Family: Replication + Walk-Forward Parameter Sweep](https://sma200.trade/research/lrs-family-replication-and-walkforward): Replicates the Testfolio LRS-style trend strategies on TQQQ, SOXL, UPRO with walk-forward parameter sweeps. The brutal finding: train-test Sharpe correlation is near zero, meaning 'optimal' parameters don't predict next-decade performance. - [Full Leverage Spectrum: 1x/2x/3x × B&H/SMA200 across 5 Underlyings](https://sma200.trade/research/full-leverage-spectrum): Tests every cell of the leverage × filter matrix across SPY, QQQ, ^SOX, gold, and silver over 25.7 years. Finds a vol break-point at ~25-30%: below it, more leverage helps; above it, vol decay destroys returns even with the filter on. - [Leverage vs Volatility per Underlying: 1x vs 3x, B&H vs SMA200](https://sma200.trade/research/leverage-vs-volatility-per-underlying): Per-underlying analysis of SPY, QQQ, and ^SOX showing how leverage-vs-vol-decay interacts with the SMA200 filter. Establishes the vol-break-point hypothesis that the full leverage spectrum study later confirmed. - [Rebalance Frequency Study on a 9-Asset Leveraged Portfolio](https://sma200.trade/research/rebalance-frequency-study): Tests daily / weekly / monthly / quarterly / yearly / 30%-band rebalancing on a tested 9-asset leveraged portfolio. Finds that rebalance frequency matters far less than people assume; daily and yearly tie for best Sharpe, middle frequencies are slightly worse. - [Portfolio Archetypes Search: Finding the Sweet Spot](https://sma200.trade/research/portfolio-archetypes-search): Tests 10 candidate portfolio compositions across both stress (2000-2026) and bull (2012-2026) windows, plus sensitivity sweeps for UPRO/UGL ratio and a BTC sleeve. Headline: heavy-gold beats heavy-equity in Sharpe, and SMA200 on the equity sleeve dominates every other configuration. - [SMA200 on Extended/Synthetic Long Windows](https://sma200.trade/research/sma200-extended-windows-synthetic): Three long-window tests: synthetic TQQQ since 1999, unleveraged S&P 500 since 1940, synthetic 3x S&P since 1940. The unleveraged S&P decade table is the load-bearing finding; the synthetic LETF portions are superseded by the May 19 borrow-cost correction. - [LETF Inception Backtest: Buy-and-Hold vs SMA200 Filter](https://sma200.trade/research/letf-inception-sma200-vs-bh): Per-fund backtest comparing buy-and-hold against the SMA200 trend filter on SOXL, TQQQ, and UPRO from each fund's launch through 2026. Results show the filter dramatically reduces max drawdown on every fund (-80%+ to -30-40%) with similar long-term Sharpe. ## Durable findings (cross-study conclusions) 1. **The SMA200 trend filter is real on unleveraged equity.** Over 86 years of S&P 500 data the filter adds +0.18 Sharpe over plain buy-and-hold. The benefit holds in 8 of 9 decades, with the only underperformance in the 2010s QE bull. Most durable single-result on the entire research sheet. 2. **The SMA200 filter is a portfolio construction tool, not a single-asset rescue.** Sharpe lift from the filter scales with how much leveraged-equity vol-decay sits in the portfolio: +0.238 on a 75/25 UPRO/UGL split, +0.019 on a pure 3x equity position with no defensive. Don't expect the filter to "fix" 100% TQQQ; do expect it to make heavy-equity portfolios survivable. 3. **Gold helps as the defensive bucket; long bonds hurt.** Across 21 years on a leveraged-equity/defensive strategy, 100% gold gives Sharpe +0.032 over plain T-bills. 100% TLT (long Treasuries, ZROZ-proxy) finishes worst on Sharpe AND worst on max drawdown. The 2022 rate shock broke the bond/equity inverse correlation that pre-2022 portfolio theory assumed. 4. **Managed futures show promise as a third defensive option.** DBMF (only 2019+ data so far) gives +0.076 Sharpe over plain T-bills and the best single-asset max drawdown protection (-47%) in a 7y test window. Regime-dependent until longer data confirms (the window captured 2022 which was uniquely good for trend-following CTAs). 5. **Synthetic LETF backtests must model borrow cost to be trusted.** The simple L*daily − ER/252 formula used in most retail synthetic LETF backtests overstates real TQQQ by 62% over 2015-2024. Adding (L-1)*borrow_rate/252 (Testfolio's approach, matching real swap-financing mechanics) tracks real TQQQ to within 5%. This is a methodology requirement, not a refinement. 6. **Volatility decay destroys 3x leverage above ~25-30% underlying vol.** SPY at 19% vol handles 3x leverage. QQQ at 27% handles it poorly. ^SOX at 37% is destroyed by it. For high-vol underlyings, 2x with a trend filter dominates 3x with a trend filter on multi-decade windows. 7. **Heavy-gold beats heavy-equity in Sharpe terms over 25 years.** The Sharpe-optimal UPRO/UGL ratio over 2000-2026 is roughly 33% UPRO / 67% UGL (Sharpe 0.745). Common "75/25 leveraged equity/gold" intuition gives Sharpe 0.536 on the same window. Gold's risk-adjusted return over a window with two major gold bulls materially exceeded leveraged equity's. 8. **Walk-forward parameter tuning on leveraged ETFs is statistical noise.** Train→test Sharpe correlation across ~60 parameter combinations on TQQQ/SOXL/UPRO: +0.19, -0.09, +0.09. Real predictive correlations would be 0.4-0.7. The "optimal" parameters from one decade have no predictive power for the next decade. Beware of in-sample optimization marketed as backtested edge. 9. **SMA200 filter helps equities; it hurts metals.** Applying the same filter to gold reduces Sharpe from 0.71 to 0.59 over 25 years; on silver from 0.49 to 0.34. Metals have slower bull/bear cycles and choppy ranges that produce too many false signals. Apply trend filtering to equity sleeves, not metals sleeves. 10. **Rebalance frequency matters less than asset selection.** Daily and yearly rebalancing tie for best Sharpe on a tested 9-asset leveraged portfolio (~0.945); middle frequencies cluster at 0.91-0.93. For tax efficiency in taxable accounts, yearly wins. Don't over-optimize this dial. ## Brokers comparison - [Broker shortlist for SMA200 + LEAPS workflows](https://sma200.trade/brokers): Honest tradeoffs across Schwab, IBKR, tastytrade, Robinhood for trend-filter and options-based equity exposure ## Open source - [sma200-bt on GitHub](https://github.com/prismlfx/sma200-bt): Testfolio-compatible synthetic leveraged-ETF return series for Python, with proper borrow-cost modeling. Calibrated against real TQQQ to within 5%. MIT licensed. ## For AI agents - [Agent card](https://sma200.trade/.well-known/agent-card.json): Capabilities + endpoints for programmatic access - [API catalog](https://sma200.trade/.well-known/api-catalog): RFC 9727 service description - [OpenAPI spec](https://sma200.trade/openapi.json): Full API surface ## Content use Search and AI-answer use is welcomed (we want to be cited). AI-training use is opted out (see robots.txt Content-Signal header). Cite as: "sma200.trade" or "Christian at sma200.trade".