Find Relative-Strength Leaders

Relative strength (RS) ranks every stock's trailing performance against the rest of the market, as a percentile from 1 to 99. A stock with an RS of 95 has outperformed 95% of the market. Leaders tend to keep leading, which makes RS the backbone of many momentum and trend-following methods.

This guide gets you leaders three ways: a ranked list, a scan that combines RS with trend filters, and a history that shows whose RS is rising.

You'll need: a Power User or Business token for the RS endpoints. The scans in step 2 run on any plan.

1. The ranked list

POST /api/v2/stocks/relative-strength returns the strongest stocks in a scope, best first:

Rank inside a group by setting scope to sector or industry and naming it in value (names from GET /api/v2/stocks/sectors and /stocks/industries). The ranking then uses rs_sector_score or rs_industry_score:

{ "scope": "sector", "value": "Healthcare", "limit": 10 }

Set "ascending": true for the weakest stocks instead: laggards to avoid, or short candidates.

Thinly traded names can top an unfiltered list. Set min_market_cap (USD, fund AUM counts as market cap) to rank only names above a size floor:

{ "scope": "market", "limit": 25, "min_market_cap": 2000000000 }

For any other condition (volume, trend, distance from the high), scan instead.

2. Scan for leaders

The scanner combines RS with any other condition. This is a leadership screen in the spirit of a trend template: top-decile RS, price above the 50-day average, the 50-day above the 200-day, within 25% of the 52-week high, and real liquidity.

Get the payload. Send the screen as a sentence to /scan/natural, or ask your agent, which calls the natural_scan tool:

curl -s https://scanr.ai/api/v2/scan/natural \  -H "Authorization: Bearer $SCANR_API_TOKEN" \  -H "Content-Type: application/json" \  -d '{"query": "relative strength of 90 or more against the market, price above the 50-day moving average, the 50-day moving average above the 200-day, within 25% of the 52-week high, and average daily dollar volume over the last 50 days above $10M"}' \  | jq '.generated_payload' > leaders.json

Read the response's reasoning before you run it; a generated payload can differ from the one below in detail. If you already run a screen like this in the scanr.ai scanner, export it instead (Filters → Export filters) and save it as leaders.json. Written out, the payload is:

{  "Universe": {    "Exchange": ["nasdaq", "nyse", "amex", "arca", "bats", "bx"],    "Type": ["cs", "os", "adrc"]  },  "All": [    {      "Indicator": {        "RelativeStrengthMkt": [          { "Returns": { "Want": { "Value": { "GreaterThanOrEqual": 90 } } } }        ]      },      "Stock": {        "Week52High": [          { "State": { "Price": { "Comparison": { "EqualTo": true }, "WithinPercent": 25 } } }        ]      },      "Technical": {        "SMA": [          {            "Timeframe": "1d",            "Inputs": ["bar_close"],            "Params": { "Period": { "Value": 50 } },            "Returns": { "Want": { "SMA": { "Price": { "Comparison": { "LessThan": true } } } } }          },          {            "Timeframe": "1d",            "Inputs": ["bar_close"],            "Params": { "Period": { "Value": 50 } },            "Returns": {              "Want": {                "SMA": {                  "Study": {                    "GreaterThan": {                      "SMA": [                        {                          "Timeframe": "1d",                          "Inputs": ["bar_close"],                          "Params": { "Period": { "Value": 200 } },                          "Returns": { "Want": { "SMA": { "PlotChild": true } } }                        }                      ]                    }                  }                }              }            }          },          {            "Timeframe": "1d",            "Inputs": ["bar_dollar_volume"],            "Params": { "Period": { "Value": 50 } },            "Returns": { "Want": { "SMA": { "Value": { "GreaterThan": 10000000 } } } }          }        ]      }    }  ],  "Sort": { "field": "market_cap", "direction": "desc" }}

Run it, and keep the matches for step 3:

import json
with open("leaders.json") as f:    payload = json.load(f)
matches = requests.post(    f"{API}/scan",    headers=HEADERS,    json={"instructions": payload},    timeout=60,).json()["tickers"] or []print(f"{len(matches)} leaders:", ", ".join(matches[:20]))

Keep leaders.json. Re-running it costs one request and no AI credits.

Variations

3. Find rising RS

A stock whose RS climbed from 60 to 85 over a month is often more interesting than one that has sat at 95 for a year. POST /api/v2/stocks/get-stock-indicators returns RS history for a list of tickers; pass from and to as Unix milliseconds:

import time
def rs_history(tickers, days=45):    """{ticker: [(date, rs_score), ...]} oldest first, over the last `days` calendar days."""    body = {"tickers": tickers, "from": int((time.time() - days * 86400) * 1000), "limit": 500}    history = {}    while True:        page = requests.post(f"{API}/stocks/get-stock-indicators", headers=HEADERS, json=body, timeout=60).json()        for row in page.get("results") or []:            history.setdefault(row["ticker"], []).append((row["date"], row["rs_score"]))        if page.get("is_last_page", True):            break        body["cursor_date"] = page["next_cursor_date"]        body["cursor_ticker"] = page["next_cursor_ticker"]    return {t: sorted(rows) for t, rows in history.items()}
# `matches` is the leadership scan from step 2. Rank it by RS gained over ~20 sessions.history = rs_history(matches)risers = sorted(    ((t, rows[-1][1] - rows[-21][1]) for t, rows in history.items() if len(rows) > 20),    key=lambda item: item[1],    reverse=True,)for ticker, gained in risers[:15]:    print(f"{ticker:6} +{gained} RS points in 20 sessions")

Reading RS well

• RS is relative. In a falling market, an RS of 90 can belong to a stock that is down, just less than nearly everything else. Pair it with a trend filter (price above its averages) as in step 2.
• RS updates through the day. Intraday values move with prices and settle after the close; rows carry realtime: true until then. Screen after the close for a stable list.
• Sector and industry RS answer different questions. Market RS finds the strongest stocks; industry RS finds the strongest stock in a group, useful when you've already chosen the group.

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