intellectia-stock-screenerGet stock screener list data from Intellectia API (no auth) and summarize results.
Install via ClawdBot CLI:
clawdbot install intellectiaAI/intellectia-stock-screenerBase URL: https://api.intellectia.ai
GET /gateway/v1/stock/screener-listFull URL:
https://api.intellectia.ai/gateway/v1/stock/screener-listsymbol_type (int): Asset type 0=stock 1=etf 2=cryptoperiod_type (int): Period 0=day 1=week 2=monthtrend_type (int): Trend 0=bullish 1=bearishprofit_asc (bool): Sort by profit ascending (true = small → large)market_cap (int): Market cap filter0=any1=micro <300M2=small 300M-2B3=mid 2B-10B4=large 10B-200B5=mega >200Bprice (int): Price filter0=any1=<52=<503=>54=>505=5-50page (int): Page number (example: 1)size (int): Page size (example: 20)Example response (shape):
{
"ret": 0,
"msg": "",
"data": {
"list": [
{
"code": "BKD.N",
"symbol": "BKD",
"symbol_type": 0,
"name": "Brookdale Senior Living Inc",
"logo": "https://intellectia-public-documents.s3.amazonaws.com/image/logo/BKD_logo.png",
"pre_close": 14.5,
"price": 15,
"change_ratio": 3.45,
"timestamp": "1769749200",
"simiar_num": 10,
"probability": 80,
"profit": 5.27,
"klines": [{ "close": 15, "timestamp": "1769749200" }],
"trend_list": [
{
"symbol": "BKD",
"symbol_type": 0,
"is_main": true,
"list": [{ "change_ratio": 5.27, "timestamp": "1730260800", "close": 16 }]
}
],
"update_time": "1769806800"
}
],
"total": 3,
"detail": {
"cover_url": "https://d159e3ysga2l0q.cloudfront.net/image/cover_image/stock-1.png",
"name": "Stocks Bullish Tomorrow",
"screener_type": 1011,
"params": "{}",
"desc": "..."
}
}
}
Top-level:
ret (int): Status code (typically 0 means success)msg (string): Message (empty string when OK)data (object): Payloaddata:
data.list (array): Result rowsdata.total (int): Total number of rowsdata.detail (object): Screener metadataEach item in data.list:
code (string): Full instrument code (may include exchange suffix, e.g. BKD.N)symbol (string): Ticker symbol (e.g. BKD)symbol_type (int): Asset type (0=stock 1=etf 2=crypto)name (string): Display namelogo (string): Logo URLpre_close (number): Previous close priceprice (number): Current pricechange_ratio (number): Percent change vs previous closetimestamp (string): Quote timestamp (Unix seconds)simiar_num (int): Similarity count (as returned by API; spelling kept as-is)probability (int): Model confidence (0-100)profit (number): Predicted/expected return (as returned by API)klines (array): Price seriesklines[].close (number): Close priceklines[].timestamp (string): Unix secondstrend_list (array): Trend comparison seriestrend_list[].symbol (string): Symbol for the series (may be empty for non-main series)trend_list[].symbol_type (int): Asset typetrend_list[].is_main (bool): Whether this is the main seriestrend_list[].list (array): Time pointstrend_list[].list[].change_ratio (number): Percent change at that pointtrend_list[].list[].timestamp (string): Unix secondstrend_list[].list[].close (number): Close price at that pointupdate_time (string): Last update time (Unix seconds)data.detail:
cover_url (string): Cover image URLname (string): Screener titlescreener_type (int): Screener type IDparams (string): Serialized params (often JSON string)desc (string): Screener descriptionnum (int, optional): As returned by API (may be absent)curl -sS "https://api.intellectia.ai/gateway/v1/stock/screener-list?symbol_type=0&period_type=0&trend_type=0&profit_asc=false&market_cap=0&price=0&page=1&size=20"
python3 - <<'PY'
import requests
base_url = "https://api.intellectia.ai"
params = {
"symbol_type": 0,
"period_type": 0,
"trend_type": 0,
"profit_asc": False,
"market_cap": 0,
"price": 0,
"page": 1,
"size": 20,
}
r = requests.get(f"{base_url}/gateway/v1/stock/screener-list", params=params, timeout=30)
r.raise_for_status()
payload = r.json()
print("ret:", payload.get("ret"))
print("msg:", payload.get("msg"))
data = payload.get("data") or {}
rows = data.get("list") or []
print("total:", data.get("total"))
for row in rows[:10]:
print(row.get("symbol"), row.get("price"), row.get("change_ratio"), row.get("probability"), row.get("profit"))
PY
size and add backoff/retry in client code.Generated Mar 1, 2026
Individual investors can use this skill to screen for stocks, ETFs, or cryptocurrencies based on bullish or bearish trends, market cap, and price filters. It helps identify potential investment opportunities by providing predicted returns and model confidence scores, enabling users to make informed decisions without needing financial expertise.
Financial advisors can leverage this skill to generate stock lists for client portfolios, filtering by asset type, market cap, and trend direction. It assists in quickly identifying securities that match client risk profiles and investment goals, streamlining research and enhancing portfolio diversification strategies.
Quantitative analysts and developers can use the screener data to backtest trading strategies by analyzing historical price trends and predicted profits. The API provides detailed klines and trend lists, allowing for simulation of trades based on specific criteria like period type and profit sorting.
This skill serves as a practical tool for students learning about stock markets, enabling them to explore real-time data on different asset classes. They can experiment with filters to understand how market cap, price ranges, and trends impact stock selection, reinforcing classroom concepts with hands-on experience.
Financial bloggers, journalists, and media outlets can use the screener to generate data-driven content, such as lists of top-performing stocks or crypto assets. It provides up-to-date information on changes and probabilities, helping create engaging articles or reports on market trends without extensive manual research.
Offer basic screener access for free to attract users, then charge for premium features like higher rate limits, advanced filters, or historical data. This model can generate revenue through subscription tiers, targeting both individual investors and small businesses that need reliable stock data without upfront costs.
License the screener data to other financial platforms, such as trading apps, robo-advisors, or investment websites. This provides a steady revenue stream by integrating the API into third-party services, which can enhance their offerings with real-time stock insights and trend analysis.
Monetize by partnering with brokerage firms or investment services, referring users who use the screener to open accounts or make trades. This model leverages the skill's ability to drive targeted traffic, generating commissions based on user actions without charging end-users directly.
💬 Integration Tip
Ensure client code includes error handling for rate limits by implementing backoff strategies, and validate query parameters to avoid API errors.
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