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200-Day SMA Regime Filter on Webull

Q: How do I automate 200-day regime filter on Webull?

Connect Webull to DeployQuant (trade & read permissions only), pick the 200-Day SMA Regime Filter template or describe it in English, backtest it on your chosen ETF, allocate cash, and confirm live trading. The strategy then runs every session in its own sleeve inside your Webull account.

Q: Why the 200-day average specifically?

Convention and evidence: it approximates a year of trading days and has been studied across decades of data. It isn't optimal everywhere — the per-ETF backtests here show where it helped and where it didn't.

Own the asset when price closes above its 200-day average; hold cash when it closes below. Deployed to your own Webull account — no code, your assets never leave the broker.

The short version: connect Webull once, deploy the 200-day regime filter template with its own cash allocation, and DeployQuant runs the rules every session — entries, resting exits, and the journal that explains each decision. Median backtested CAGR across 59 ETFs: 1.6% (2021-01-04 → 2026-07-17).

Setup on Webull

Connect your Webull account from the Brokerage page — the connection carries trade and read permissions only, never withdrawals.
Build a strategy in the Lab (blocks or AI), or fork one from the community, and backtest it on years of minute data.
Deploy it to Webull with a cash allocation — the strategy runs in its own isolated sleeve, so other holdings and other strategies are untouched.
Type the live-trading confirmation. Until you do, nothing real is sent.
Profit targets rest at Webull as real GTC limit orders; pause the strategy or the whole connection anytime.

The strategy

WHEN the market opens · IF not invested AND yesterday's close > SMA(200) · THEN buy with 98% of the sleeve
WHEN the market opens · IF invested AND yesterday's close < SMA(200) · THEN sell the whole position

Good for: a first systematic strategy — it's simple enough to fully understand and audit every trade.
Watch out: price whips around the 200-day line during volatile bottoms, generating clusters of buy-sell pairs; some traders add a small buffer band to reduce churn.

Strongest backtests for this strategy

ETF	CAGR	max DD	trades
SOXL	31.6%	−63.6%	19
TQQQ	23.6%	−36.6%	13
QLD	19.6%	−28.8%	12
SOXX	17.4%	−39.2%	19
XLK	14.8%	−19.6%	11
ROM	13.8%	−42.2%	18
IAU	13.6%	−19.9%	21
SSO	12.1%	−31.2%	17

Top-8 by CAGR shown of 59 tested — hindsight selection; see the full table including the losers.

Run 200-day regime filter on Webull yourself — free →

Build it from blocks (or type it in English), backtest it on 5.5 years of minute data in seconds, tweak any parameter, then paper trade it on live data. No card, no broker needed to start.

Frequently asked questions

How do I automate 200-day regime filter on Webull?

Connect Webull to DeployQuant (trade & read permissions only), pick the 200-Day SMA Regime Filter template or describe it in English, backtest it on your chosen ETF, allocate cash, and confirm live trading. The strategy then runs every session in its own sleeve inside your Webull account.

Why the 200-day average specifically?

Convention and evidence: it approximates a year of trading days and has been studied across decades of data. It isn't optimal everywhere — the per-ETF backtests here show where it helped and where it didn't.

Backtests are hypothetical, computed by DeployQuant's engine on minute-resolution consolidated US market data (2021-01-04 to 2026-07-17, $10,000 starting capital, no margin, fees and slippage not modeled) and do not guarantee future results. Nothing on this page is investment advice. Live trading involves risk of loss.