Intraday Scanners: Build a Free Screener That Surfaces Real Setups (Most Picks Are Noise)

Most Scanner Picks Are Statistical Noise

Before you build your first screener, understand the base rate. Across 660,005 backtests covering 903 assets and 382 indicators, only 26% of indicator-asset combinations beat a simple buy-and-hold strategy. That means the other 74% of signals a scanner might fire on are, at best, random noise and, at worst, a reliable way to underperform doing nothing.

A scanner is just a filter. If the indicators powering it have no edge on the assets you're trading, you're not surfacing opportunities — you're surfacing noise faster. The first step in building a useful screener is not picking a platform or writing alert code. It's understanding which signals have demonstrated edge and which ones produce high win rates that still lose money over time.

The Win-Rate Trap That Burns Most Screener Users

Many popular scanner filters look great on paper. Murrey Math Lines signals land on the right side of price 74.3% of the time. Holy Grail Confluence trades come in at 73.3%. RSI Mean-Reversion clocks 71.7%. CCI wins 71% of the time. Those numbers sound like an edge worth building a screener around.

But when you ask whether those strategies actually beat buy-and-hold — not just win trades, but generate risk-adjusted returns worth the effort — the picture inverts. Murrey Math Lines beats buy-and-hold on only 11% of assets tested. Holy Grail Confluence: 8%. RSI Mean-Reversion: 10%. CCI: 9%. SMC Liquidity Sweep, one of the most-discussed filters in retail trading communities, wins 71.2% of trades and beats buy-and-hold on just 8% of assets tested. Across our entire dataset, no Smart Money Concepts indicator beat buy-and-hold on any tested asset class.

A screener built around these indicators will hand you a steady stream of green trade confirmations while quietly underperforming holding the asset. Win rate is not edge. It is not even correlated with edge.

Which Indicators Actually Show Edge for Stocks

Among the 903 assets and 382 indicators tested, stocks have a cleaner picture than most asset classes. Fibonacci Pivots came out as the top-performing indicator on 22 stock assets — the widest margin of any single indicator in the stock category. Projection Bands, Intraday Momentum Index, and Camarilla Pivots each led on 16 assets. Markov Regime topped 14.

What these have in common is structure. Fibonacci Pivots and Camarilla Pivots establish discrete price levels derived from prior-session data — levels the market has tended to respect rather than levels an oscillator computed from an arbitrary lookback period. Intraday Momentum Index weighs open-to-close movement against the day's range, information that is directly relevant to intraday price behaviour. Markov Regime identifies whether the current environment favours trending or mean-reverting strategies, which changes which indicator you should even be scanning for.

None of these are the indicators that dominate scanner tutorials or get the most attention on trading forums. That gap between attention and evidence is, for the most part, the story of retail scanner use.

Building a Screener Around the Evidence

Most free scanner tools — the built-in screener on your broker platform, TradingView's screener, Finviz for US equities — let you filter on the same underlying indicator outputs. The platform matters less than what you put into it.

Start with a universe filter: sector, average daily volume, price range. Thin, illiquid stocks distort indicator readings and make execution worse; filter them out before adding any signal logic. Then add an indicator-based filter using signals with demonstrated edge in your target asset class. For stocks, that means pivot-based levels (Fibonacci Pivots, Camarilla Pivots) and regime inputs (Markov Regime) rather than oscillators with a good story but no evidence.

Our backtests cover the 1-Hour, 4-Hour, Daily, and Weekly timeframes. The 1-Hour resolution is the shortest we have tested. Results at sub-hourly timeframes — 5-minute, 15-minute — are not in this dataset; do not assume they generalize down. Treat the screener output as a candidate list, not a trade list. A setup that passes your scan still requires your own read of the chart, context, and risk management before you act.

What These Results Are and Are Not

Every figure in this article comes from out-of-sample backtests run with realistic transaction cost assumptions across 903 assets. The results are hypothetical — they show what would have happened if these rules had been followed mechanically over the test period, not what any real account earned. Past backtest performance does not guarantee future results, and nothing here is financial advice.

The median best Sharpe ratio across all tested strategies was 0.62 — real but modest, not the spectacular numbers scanner marketing typically leads with. Short-side signals showed edge on only 17.4% of tested combinations, which is worth knowing if you are building a screener around short setups. Use this data to inform your own research process, not to replace it. See the full methodology here.