1. System Objectives and Architecture DesignIn quantitative investment research, building a backtesting framework is just the beginning. Faced with thousands of listed companies, the real challenge is how to efficiently identify high-potential stocks, monitor their key metrics daily, and receive alerts when risks arise. This article introduces a lightweight and automated stock screening and risk management system that supports a daily data-driven investment workflow.The system is composed of three core modules:Screener: Filters stocks based on customizable financial indicators;Tracker: Fetches and records daily stock statistics for historical tracking and visualization;Alert Engine: Detects abnormal changes in metrics and pushes real-time alerts.All data is sourced from Luckdata’s structured wrapper of Yahoo Financials API, which offers a wide variety of financial, valuation, institutional, and insight data through the following endpoints:/screeners/get-filters: Lists available screening fields;/screeners/get-symbols-by-predefined: Returns symbols from popular stock universes;/stock/v4/get-statistics: Fetches financial ratios like ROE, gross margin, and debt ratio;/stock/get-earnings: Provides earnings calendar and EPS performance;/stock/v3/get-holders: Tracks institutional holding changes;/stock/v2/get-insights: Delivers AI-driven insights and alpha signals.2. Screener: Constructing Factor-Based Stock Selection LogicThe heart of the screener lies in your financial factor logic. Here’s a sample condition set for selecting fundamentally strong growth stocks:ROE > 15%: Indicates strong capital efficiency;Revenue 3-year CAGR > 10%: Demonstrates sustained top-line growth;Net Profit Margin > 10%: Reflects solid profitability;Market Cap > $5 Billion: Filters out micro-caps and improves liquidity/stability.Start by retrieving symbols from a popular stock universe, then filter using your selected factors:import requests def fetch_screener_symbols(): url = "https://luckdata.io/yahu-financials/ke6gzl1mrinc" params = { "scrIds": "most_actives", "count": 100, "start": 0 } headers = {"x-api-key": "YOUR_API_KEY"} res = requests.get(url, headers=headers, params=params) symbols = [item['symbol'] for item in res.json()['finance']['result'][0]['quotes']] return symbols After fetching the initial list, apply your filters using statistics:def filter_stocks_by_factors(symbols): selected = [] for symbol in symbols: stats = get_statistics(symbol) if stats["roe"] > 15 and stats["revenueGrowth3Y"] > 0.1 and stats["netMargins"] > 0.1 and stats["marketCap"] > 5e9: selected.append(symbol) return selected 3. Tracker: Daily Monitoring of Core Stock MetricsOnce you’ve selected your stocks, you need to fetch their metrics daily and store them for trend analysis and alert detection. Use the following function to get statistics:def get_statistics(symbol): url = "https://luckdata.io/yahu-financials/nu0a98y0vcj3" params = { "region": "US", "lang": "en-US", "symbol": symbol } headers = {"x-api-key": "YOUR_API_KEY"} res = requests.get(url, params=params, headers=headers) stats = res.json() return stats Data can be stored in CSV files or databases (e.g., SQLite or PostgreSQL) to build a historical database. You can also use /stock/get-earnings to retrieve upcoming earnings dates and monitor stocks ahead of announcements.4. Alert Engine: Abnormal Condition Detection and NotificationDefining meaningful alert conditions is crucial for managing risk. Here are a few key scenarios:Financial Deterioration: ROE or gross margins declining across consecutive quarters;Earnings Misses: EPS significantly below consensus estimates;Valuation Surge: PE or PS ratios spiking to unsustainable levels;Institutional Sell-off: Major institutional holdings drop by more than 5%.Here’s an example of simple alert logic:def check_risk_flags(stats): alerts = [] if stats.get("roe", 100) < 10: alerts.append("ROE dropped below 10%") if stats.get("grossMargins", 1) < 0.3: alerts.append("Gross margin is low") if stats.get("forwardPE", 0) > 80: alerts.append("Potential valuation bubble") return alerts Alerts can be pushed via Email, Slack, Feishu (Lark), or Line bot for real-time notifications.5. Visualization and Daily Report GenerationFor better transparency and analysis, consider generating a daily report with visual charts and summaries, including:Newly selected stocks;List of triggered alerts;Historical trends of key metrics (charts);Earnings date reminders.Here’s a simple Matplotlib function for plotting metric changes:import matplotlib.pyplot as plt def plot_metric_change(symbol, dates, values, metric_name="ROE"): plt.figure(figsize=(10, 4)) plt.plot(dates, values, marker="o", color="blue") plt.title(f"{symbol} - {metric_name} Trend") plt.xlabel("Date") plt.ylabel(metric_name) plt.grid(True) plt.tight_layout() plt.show() For an interactive dashboard experience, consider tools like Streamlit, Dash, or Tableau to build dynamic interfaces and extend to backtesting or screening control panels.6. Advantages of Luckdata API for Investment ResearchLuckdata transforms raw Yahoo Financials data into structured, clean, and easily accessible API endpoints that offer several advantages:Consistent and clean API format reduces parsing complexity;Comprehensive data coverage, including fundamentals, valuation, estimates, dividends, sentiment, and more;Plug-and-play compatibility with Python, JavaScript, Rust, and other research environments;Scalable for use in screening, backtesting, alerting, and report generation within a unified framework.Whether you're a beginner or an advanced quant, Luckdata’s API allows you to build robust investment tools with minimal setup.7. Conclusion and Future ExtensionsThis article demonstrated how to use Luckdata’s Yahu Financials API to build a fully functional investment research system—from screening and monitoring to risk alerting and report generation. It’s a modular, lightweight, and scalable architecture suitable for both personal and professional use.Future extensions may include:NLP-powered news sentiment analysis;Weighted multi-factor ranking and scoring system;Integration with backtesting modules to evaluate factor performance;Real-time price data and technical indicators for hybrid strategies.With this framework, automated investment research is no longer the privilege of large institutions—it becomes an accessible and powerful tool for every serious investor.Articles related to APIs :Quantitative Trading Strategy Development and Backtesting Based on Yahu APIBuild Your Financial Data System from Scratch: Bring the Financial World into Your Local Project via APIDecoding Deep Stock Insights: Build Your Stock Analysis Radar with the Yahu APIFinancial Forums Aren’t Just Noise: Using the Yahu API to Decode Market SentimentThe Next Evolution in Portfolio Management: Building Your "Asset Pool + NAV Dashboard" with the Watchlists ModuleReal-Time Market Monitoring & Risk Alert: Build Your Automated System with Luckdata Yahu Financials API

In today’s investment research landscape, quantitative strategies have become increasingly mainstream, rooted in the principle of "letting the data speak." With the continuous evolution of data platforms and tools, building a fully functional quantitative trading system is no longer exclusive to financial institutions. All you need is a simple Python environment and a powerful data provider like Luckdata’s Yahu Financials API to efficiently retrieve data, construct trading strategies, perform backtesting, and visualize results — completing a full cycle from concept to verification.This article provides a complete hands-on example that walks you through building an API-based quantitative strategy backtesting system, covering the full loop of "Data Acquisition → Strategy Implementation → Backtest Analysis → Visual Evaluation," helping you master the full data-to-insight pipeline.1. Why Use API + Python for Quantitative Backtesting?Real-time data access via API for automated connection from market data to fundamentals: APIs enable on-demand access to the latest market and financial data without tedious manual updates.Python is the most popular language for quant analysis: With robust data analysis libraries like pandas, numpy, matplotlib, and scikit-learn, and a vast open-source quant community, you can quickly prototype and test ideas.Independence from broker platforms for greater flexibility and portability: You’re free from platform constraints, making your strategies easier to deploy across environments or in the cloud.Luckdata’s Yahu API offers structured endpoints for multi-dimensional data: These include market data, fundamentals, news sentiment, and valuation metrics — supporting comprehensive inputs for strategy design.2. Data Preparation: Fetching Historical Market Data from the APILet’s use Apple Inc. (AAPL) as an example to retrieve daily historical price data from 2020 to 2024 using the Yahu API:import requests import pandas as pd import datetime def fetch_price_history(symbol="AAPL", start="2020-01-01", end="2024-12-31"): endpoint = "https://luckdata.io/yahu-financials/azr83087xcq0" params = { "symbol": symbol, "region": "US", "type": "history", "period1": start, "period2": end } headers = {"x-api-key": "YOUR_API_KEY"} res = requests.get(endpoint, params=params, headers=headers) data = res.json() prices = pd.DataFrame(data["prices"]) prices["date"] = pd.to_datetime(prices["date"], unit="s") return prices[["date", "open", "high", "low", "close", "volume"]] The output will be a structured dataset of historical daily prices:dateopenhighlowclosevolume2023-01-03130.28130.90124.17125.07112117500..................This dataset serves as the foundation for building and testing the strategy.3. Strategy Implementation: Moving Average Crossover StrategyWe’ll implement a classic technical trading model: Buy when the 5-day moving average crosses above the 20-day moving average, and sell when it crosses below.def compute_ma_cross(prices): prices["ma_short"] = prices["close"].rolling(5).mean() prices["ma_long"] = prices["close"].rolling(20).mean() prices["signal"] = 0 prices.loc[prices["ma_short"] > prices["ma_long"], "signal"] = 1 prices.loc[prices["ma_short"] < prices["ma_long"], "signal"] = -1 return prices This strategy captures trend shifts by comparing short-term and long-term price momentum — a widely used method in swing and trend-following strategies.4. Strategy Backtesting: Simulating Capital GrowthNext, we simulate the performance of the strategy starting with an initial capital of $100,000 and evaluate its growth based on the generated signals.def backtest_strategy(prices, initial_capital=100000): prices = prices.copy() prices["position"] = prices["signal"].shift(1) prices["daily_return"] = prices["close"].pct_change() prices["strategy_return"] = prices["daily_return"] * prices["position"] prices["capital"] = (1 + prices["strategy_return"]).cumprod() * initial_capital return prices This function calculates daily returns and applies the position signal to model capital growth over time, producing data ready for visualization.5. Visualizing Backtest ResultsUsing matplotlib, we can compare the performance of our strategy to a basic buy-and-hold benchmark:import matplotlib.pyplot as plt def plot_performance(prices): plt.figure(figsize=(12,6)) plt.plot(prices["date"], prices["capital"], label="Strategy Capital") plt.plot(prices["date"], (1 + prices["daily_return"]).cumprod() * 100000, label="Buy & Hold") plt.legend() plt.title("Strategy vs. Buy & Hold Capital Curve") plt.xlabel("Date") plt.ylabel("Capital ($)") plt.grid(True) plt.show() The resulting chart will show two lines — one for your strategy’s performance, and one for holding AAPL throughout the period. It provides clear visual feedback on whether the strategy added value.6. Optional Enhancements and AutomationTo expand this system into a robust research or trading tool, consider the following upgrades:Multi-asset backtesting with performance comparison: Run the strategy across multiple tickers and rank based on risk-adjusted returns.Use professional backtesting frameworks: Leverage tools like Backtrader or vectorbt for advanced features like transaction costs, slippage modeling, and capital allocation.Automated scheduling with APScheduler or cron: Set up a job to fetch data and rerun the backtest daily or weekly, enabling automated strategy reports.Add fundamental screening using Yahu API’s screeners: Use APIs for financial data to filter high-quality stocks as inputs.Integrate sentiment/news data: Use Yahu’s news and sentiment modules to filter signals based on market sentiment, potentially improving strategy robustness.7. ConclusionIn this article, we walked through the full cycle of building a quantitative strategy using Luckdata’s Yahu Financials API — from fetching structured market data to constructing a trading model, running a backtest, and visualizing results. The API’s rich and structured data endpoints eliminate the burden of manual data handling and empower flexible, scalable strategy development in Python. Whether you're a beginner or an experienced quant developer, this framework provides a powerful launchpad for exploring and refining your trading ideas.? Interested in trying it yourself? Visit the Luckdata website to register and get a free API key for Yahu Financials — and begin your own strategy research journey.[Luckdata Official Site: https://luckdata.io]Articles related to APIs :Comprehensive Overview: Practical Guide to Yahu Financials API ModulesBuild Your Financial Data System from Scratch: Bring the Financial World into Your Local Project via APIDecoding Deep Stock Insights: Build Your Stock Analysis Radar with the Yahu APIFinancial Forums Aren’t Just Noise: Using the Yahu API to Decode Market SentimentThe Next Evolution in Portfolio Management: Building Your "Asset Pool + NAV Dashboard" with the Watchlists ModuleReal-Time Market Monitoring & Risk Alert: Build Your Automated System with Luckdata Yahu Financials API

In financial investment research, understanding a company goes far beyond looking at its stock price or P/E ratio. True value discovery requires a multi-dimensional approach—fundamentals, valuation, profitability, financial health, analyst ratings, key events calendar, shareholder structure, and more. These components collectively form a "panoramic analysis view" that gives investors a comprehensive understanding of a security.This article dives into the Stock Module in the Yahu Financials API and demonstrates how to use it in a structured way to pull and integrate core data, enabling a "research engine" tailored for investment research, quant teams, or content creators.I. Stock Module Overview: A Panoramic Data HubThe Stock Module can be thought of as a multi-dimensional data hub revolving around a single stock. It provides various types of data capabilities across the following categories:CategoryCore CapabilitiesKey EndpointsBasic Info / ProfileCompany background, sector classification, fund dataget-profile, get-fundamentalsFinancials & ValuationFinancial summaries, valuation multiples, profitability metricsget-statistics, get-timeseriesAnalyst ViewsRating trends, target prices, upgrades/downgradesget-recommendations, get-recommendation-trendInstitutional HoldingsShareholder structure, top institutional holdingsget-holders, get-top-holdingsNews & InsightsMarket views, trend analysis, ESG insightsget-insights, get-esg-scoresRisk & FeesRisk factors, fee structuresget-fees-and-expenses, get-sec-filingsEvent CalendarEarnings dates, IPOs, SEC filingsget-events-calendarTogether, these modules create a flexible, modular architecture that can be tailored to various research goals.II. Practical Guide: Building a Panoramic Stock Analysis InterfaceSuppose we want to conduct a comprehensive analysis of the stock AAPL (Apple Inc.). Below is a Python-based approach for aggregating data across multiple modules.1. Fetch Company Profile and Overviewimport requests BASE = "https://luckdata.io/api/yahu-financials" HEADERS = {"X-Luckdata-Api-Key": "your-api-key"} # Fetch company overview and fund information resp = requests.get( f"{BASE}/g6qnrm4550pq?region=US&symbol=AAPL&modules=assetProfile,summaryProfile,fundProfile", headers=HEADERS ) profile_data = resp.json() This returns industry, company background, management details, and fund characteristics—ideal for understanding the company’s foundation and market positioning.2. Retrieve Financial Metrics and Earnings Trends# Pull valuation, financials, and profitability metrics resp = requests.get( f"{BASE}/nu0a98y0vcj3?region=US&symbol=AAPL", headers=HEADERS ) stats_data = resp.json() This includes metrics like market cap, P/E ratio, ROE, and profit margins—essential for assessing financial health and investment appeal.3. Analyst Rating Trendsresp = requests.get( f"{BASE}/uy59c4zdch38?region=US&symbol=AAPL", headers=HEADERS ) rating_trend = resp.json() Analyst ratings and price targets offer insight into market sentiment and projected performance. These metrics are helpful in validating investment hypotheses or identifying sentiment shifts.4. Institutional Holdings and Trendsresp = requests.get( f"{BASE}/4xxnpjqfjprp?region=US&symbol=AAPL&straddle=true", headers=HEADERS ) holders_info = resp.json() Tracking institutional and insider holdings helps identify “smart money” activity and shifts in institutional confidence.5. ESG Scores and Investment Insightsresp = requests.get( f"{BASE}/b4n6g03ozlsm?region=US&symbol=AAPL", headers=HEADERS ) insights_data = resp.json() Environmental, social, and governance (ESG) scores are increasingly important for long-term investors. Investment insights also include news sentiment, analyst commentary, and event interpretations.III. Structured Output: Analysis Report TemplateCombining the data from above modules, we can assemble the following structured JSON report for downstream use—e.g., rendering on a dashboard, feeding a BI system, or generating automated reports.{ "symbol": "AAPL", "companyProfile": { "sector": "Technology", "industry": "Consumer Electronics", "summary": "...", "CEO": "Tim Cook" }, "financialSnapshot": { "marketCap": "2.5T", "peRatio": 27.4, "roe": 146.7, "profitMargin": "25%" }, "analystRatings": { "recommendationTrend": [ {"period": "3mo ago", "buy": 20, "hold": 5, "sell": 1}, {"period": "now", "buy": 22, "hold": 4, "sell": 0} ], "targetPriceRange": {"low": 140, "high": 210, "mean": 185} }, "ownership": { "topInstitutions": [...], "insiderHolding": "0.07%" }, "esgScores": { "environment": 54, "social": 67, "governance": 58, "total": 60 } } This format supports:Frontend visualization and dashboardsAutomated PDF or Markdown report generationIntegration with robo-advisory tools or research systemsIV. Best Practices & Implementation SuggestionsEncapsulate as a Service LayerCreate a backend service like get_full_stock_snapshot(symbol) to abstract and centralize multi-module data fetches, improving maintainability and development efficiency.Field StandardizationNormalize field names across modules (e.g., peRatio, marketCap, roe) to ensure consistency for front-end developers and data analysts.Integrate with Market, Screener, and Sentiment ModulesStock research should not exist in a silo—combine with real-time market data (Market), screening filters (Screener), and sentiment analysis (Conversations) to form a closed-loop research workflow.V. Conclusion: From "API Integration" to "Research Architecture"The Stock Module offers nearly complete coverage of a stock’s critical aspects in structured data form. It is one of the most in-depth and versatile modules within financial data APIs and serves as the foundation for building scalable and intelligent research engines.Next steps for applying this framework include:Pulling panoramic data on the Top 10 stocks in a specific sector (e.g., semiconductors) for benchmarkingBuilding a personalized “core holdings” database with search, filter, and sort capabilitiesIntegrating analysis results with BI tools or generating automated research reportsThe future of investment research lies not just in acquiring data, but in elevating the research framework. The Stock Module is your launchpad into that next era.Articles related to APIs :Comprehensive Overview: Practical Guide to Yahu Financials API ModulesBuild Your Financial Data System from Scratch: Bring the Financial World into Your Local Project via APIReal-Time Market Monitoring & Risk Alert: Build Your Automated System with Luckdata Yahu Financials APIA Powerful Tool for Finding Potential Stocks: Building a Multi-Dimensional Financial Screener with Luckdata Yahu Financials APISeizing the Edge in Sentiment: Building a Financial News Aggregator and Sentiment Analysis Engine with Yahu Financials API

When handling massive product data from Taobao, manual analysis is not only inefficient but also prone to subjective errors and scalability issues. With machine learning techniques, we can automate product classification, price prediction, anomaly detection, and more, greatly enhancing data utility and decision-making efficiency.This article presents a practical guide from data preparation and feature engineering to model training and deployment, aiming to build an intelligent prediction system tailored for Taobao product data—ideal for e-commerce platforms or data analysts.1. Application ScenariosMachine learning can be applied to various real-world tasks in Taobao product data analysis, such as:Product Category Prediction: Automatically classify products into categories (e.g., phones, clothing, home goods) based on their titles and descriptions.Price Prediction Modeling: Predict reasonable price ranges using both textual and structured features, useful for spotting anomalies or identifying high-value items.Sales Forecasting (Extended): Estimate future sales based on historical data and product attributes to guide inventory and marketing decisions.2. Data Preparation: Feature and Label DesignAssume the raw data collected from an API or web crawler is structured as follows:{ "title": "Xiaomi Bluetooth Headset Pro Noise Cancellation Edition", "category": "Headphones & Audio", "price": 199.00, "shop_name": "Xiaomi Official Store", "sales": 3200, "description": "Bluetooth 5.3 | Active Noise Cancellation | Long Battery Life | Lightweight Fit", "timestamp": "2024-04-22" } We need to engineer features and define labels to provide structured input and learning targets for machine learning models.Feature EngineeringFeatureTypeProcessing MethodtitleTextVectorization via TF-IDF, Word2Vec, or BERTshop_nameCategoricalLabel EncodingsalesNumericalStandardization (e.g., StandardScaler)timestampTemporalExtract month, day, weekday, etc.The description field can also be leveraged as an additional semantic feature to enhance model performance.3. Classification Task: Product Category Prediction (Multiclass)Product classification is a multiclass task aimed at automatically assigning a product to its most likely category, aiding search optimization, recommendation systems, and data management.Model ChoicesTraditional Models: RandomForest, XGBoost—fast and interpretable.Deep Learning Models: TextCNN, LSTM—effective in capturing sequential patterns in text.Semantic Models: BERT + classifier layers—high precision, strong contextual understanding, but resource-intensive.Example: TF-IDF + XGBoost for Classificationfrom sklearn.feature_extraction.text import TfidfVectorizer from xgboost import XGBClassifier from sklearn.model_selection import train_test_split from sklearn.metrics import classification_report X = df['title'] y = df['category'] vectorizer = TfidfVectorizer(max_features=3000) X_vec = vectorizer.fit_transform(X) X_train, X_test, y_train, y_test = train_test_split(X_vec, y, test_size=0.2, random_state=42) model = XGBClassifier() model.fit(X_train, y_train) y_pred = model.predict(X_test) print(classification_report(y_test, y_pred)) This example demonstrates how to build a classification model using simple text vectorization and a powerful gradient boosting classifier.4. Price Prediction: Multi-Feature Regression ModelingPrice prediction is a regression task aimed at estimating a product's reasonable price based on its description and structured data—helpful in anomaly detection and pricing strategy.Modeling StrategyThe input features should combine various data types:Title (vectorized)Sales (numerical)Category and Shop (categorical, label encoded)Description (optional semantic feature)Recommended models include:Traditional: Linear Regression, RandomForestRegressor, XGBoostRegressorDeep Learning: MLPs or hybrid architectures with semantic embeddingsExample: RandomForest for Regressionfrom sklearn.ensemble import RandomForestRegressor from sklearn.preprocessing import LabelEncoder from sklearn.metrics import mean_squared_error df['shop_encoded'] = LabelEncoder().fit_transform(df['shop_name']) df['category_encoded'] = LabelEncoder().fit_transform(df['category']) X_features = df[['sales', 'shop_encoded', 'category_encoded']] y_price = df['price'] X_train, X_test, y_train, y_test = train_test_split(X_features, y_price, test_size=0.2, random_state=42) model = RandomForestRegressor() model.fit(X_train, y_train) preds = model.predict(X_test) print("MSE:", mean_squared_error(y_test, preds)) This model effectively captures non-linear interactions among features, suitable for complex pricing patterns in e-commerce.5. Deployment and Service IntegrationAfter training, the model can be deployed as a web API to provide real-time prediction services.Building a Simple Prediction API with Flaskfrom flask import Flask, request, jsonify import joblib app = Flask(__name__) model = joblib.load('price_predictor.pkl') @app.route('/predict', methods=['POST']) def predict(): data = request.json input_vec = [data['sales'], data['shop_encoded'], data['category_encoded']] prediction = model.predict([input_vec]) return jsonify({'predicted_price': round(prediction[0], 2)}) if __name__ == '__main__': app.run(port=5000) This API can be consumed by frontend systems or other modules, and further extended to production-level deployment using Docker, Gunicorn, or cloud services.6. Extended Applications and Optimization DirectionsTo further improve model robustness and performance, consider the following:Semantic Embedding Models: Replace TF-IDF with BERT or similar embeddings for better context understanding.Automated Labeling Pipeline: Use high-confidence predictions to create new training data in semi-supervised learning settings.Model Versioning and A/B Testing: Use tools like MLFlow or DVC to track experiments and compare model versions in production.Multi-Task Learning: Train classification and price prediction jointly in one deep learning architecture for enhanced generalization.ConclusionBy leveraging machine learning, we can efficiently classify and predict prices for massive product datasets, laying a solid foundation for search optimization, anomaly detection, and recommendation systems. Combined with modern data infrastructure (e.g., Kafka, ELK stack), a comprehensive intelligent product analysis platform can be built to support business growth and user experience optimization.Articles related to APIs :From Data to Product: Building Search, Visualization, and Real-Time Data ApplicationsBuilding a Real-Time Monitoring Pipeline: Tracking Taobao Price and Promotion Changes Using Kafka + Spark StreamingEnhanced Data Insights: Analyzing Taobao Product Trends and Anomalies with the ELK StackIntroduction to Taobao API: Basic Concepts and Application ScenariosTaobao API: Authentication & Request Flow Explained with Code ExamplesUsing the Taobao API to Retrieve Product Information and Implement Keyword SearchIf you need the Taobao API, feel free to contact us : support@luckdata.com

To quickly launch data-driven research and investment operations in a highly competitive financial market, a comprehensive and easy-to-integrate API service is essential. Luckdata's Yahu Financials API is built exactly for that purpose. Covering market data, fundamentals, news, screeners, sentiment, and portfolio tracking, this API supports a wide range of use cases — from quantitative backtesting and risk monitoring to building full-featured investment research platforms.I. Module Overview and Core CapabilitiesModuleCore UsageExample Endpoints (GET / POST)MarketReal-time/historical quotes, top movers, mini charts• quotes: /api/yahu-financials/0h6ilfhtvlta?region=US&symbols=AAPL,TSLA• movers: /api/yahu-financials/b2tkd65h755m?lang=en-US&count=6&region=US• spark: /api/yahu-financials/gw5ycny2aaae?range=1d&symbols=AMZN&interval=1mStockFundamental data, analyst ratings, institutional holdings, event calendar• get-fundamentals: /api/yahu-financials/g6qnrm4550pq?symbol=AMRN&modules=assetProfile,summaryProfile• get-recommendations: /api/yahu-financials/6nwd8jn6id18?symbol=INTC• get-statistics: /api/yahu-financials/nu0a98y0vcj3?symbol=AMRNNewsAggregated financial news, article details by UUID• news/v2/get-details: /api/yahu-financials/4t4jbotgu79n?uuid=<UUID>&region=USScreenerFilter stocks based on financial metrics or presets• /screener/overview (field list)• /screener: POST, with multi-dimensional filters, sorts, limitConversationsScrape investment community discussions (sentiment)• list: /api/yahu-financials/wjbchky2ls76?messageBoardId=finmb_24937&count=16&sort_by=newest• count: /api/yahu-financials/l2mlcgr2myz0?messageBoardId=finmb_24937WatchlistsCreate/query/manage custom watchlists, view performance• watchlists: /api/yahu-financials/olza8ffcp4mf• watchlist-performance: /api/yahu-financials/j93gsfo4ho89?pfId=...&symbols=^GSPCPortfolioSync broker holdings, backtest portfolios• watchlist-detail: /api/yahu-financials/1w4fvjj0dvnr?pfId=...Tip: Only core endpoints are listed above. Full parameter details are available in the Luckdata documentation center.II. Sample Use Case: Concurrent Data Requests in PythonIn real-world projects, it’s common to pull data from multiple modules in parallel. Below is a simple Python example using requests with concurrent.futures to concurrently fetch market, screener, and news data, and write them into local JSON files.import requests import json from concurrent.futures import ThreadPoolExecutor API_KEY = "your-luckdata-key" BASE_URL = "https://luckdata.io/api/yahu-financials" HEADERS = { "X-Luckdata-Api-Key": API_KEY, "Content-Type": "application/json" } def fetch_market_quotes(): url = f"{BASE_URL}/0h6ilfhtvlta?region=US&symbols=AAPL,TSLA,GOOG" res = requests.get(url, headers=HEADERS) with open("market_quotes.json", "w") as f: json.dump(res.json(), f) def fetch_screener(): url = f"{BASE_URL}/0h6ilfhtvlta/screener" payload = { "region": "US", "filters": [{"field": "trailingPE", "operator": "<", "value": 25}], "limit": 10 } res = requests.post(url, headers=HEADERS, json=payload) with open("screener_results.json", "w") as f: json.dump(res.json(), f) def fetch_news_detail(): uuid = "9803606d-a324-3864-83a8-2bd621e6ccbd" url = f"{BASE_URL}/4t4jbotgu79n?uuid={uuid}&region=US" res = requests.get(url, headers=HEADERS) with open("news_detail.json", "w") as f: json.dump(res.json(), f) with ThreadPoolExecutor() as executor: executor.submit(fetch_market_quotes) executor.submit(fetch_screener) executor.submit(fetch_news_detail) print("Data successfully fetched:") print(" - Market: market_quotes.json") print(" - Screener: screener_results.json") print(" - News: news_detail.json") With this script, you can fetch three key types of data in parallel with one click. You can later enhance it with JSON parsing, database insertion, alerting, and more.III. Choose Modules Based on Your NeedsMarket monitoring only → Focus on the Market module: quotes, movers, sparkIn-depth stock research → Dive into the Stock module: combine fundamentals, statistics, holders, recommendationsAutomated screening → Use the Screener module to filter by dividends, valuations, etc., using presetsSentiment and news analysis → Combine News + Conversations modules for media and community insightsPortfolio management → Use Watchlists + Watchlist-performance for real-time NAV tracking and benchmarkingIn upcoming in-depth articles, we will explore:Practical stock research (Stock module)Building a portfolio dashboard (Watchlists module)Advanced screening techniques (Screener module)Sentiment and conversational research (News + Conversations)IV. Summary & Action PlanLuckdata’s Yahu Financials API offers developers a one-stop shop for financial data services. With this panoramic overview, you now understand the core capabilities and how to call key endpoints across the major modules.Next steps you can take:Register and obtain your API KeyTry the Python script to fetch data in parallelChoose a module to explore in depth and integrate into your projectV. Bonus Section: Development and Deployment TipsFor production deployments, we recommend the following:Wrap API requests in reusable modules for better code organizationAdd logging and error handling to increase system stabilityUse schedulers like cron or Airflow for periodic data collectionImplement concurrency limits and retry mechanisms to avoid quota issues or blocksBy applying these practices, you can robustly integrate Yahu Financials API into enterprise-grade data pipelines to support real-time, informed decision-making.Articles related to APIs :Build Your Financial Data System from Scratch: Bring the Financial World into Your Local Project via APIReal-Time Market Monitoring & Risk Alert: Build Your Automated System with Luckdata Yahu Financials APIA Powerful Tool for Finding Potential Stocks: Building a Multi-Dimensional Financial Screener with Luckdata Yahu Financials APISeizing the Edge in Sentiment: Building a Financial News Aggregator and Sentiment Analysis Engine with Yahu Financials APIUnderstanding the Other Side of Market Sentiment: Practical Analysis of Investor Comments via the Yahu API

In the world of quantitative research and investment analysis, individual stock research is only the starting point—true execution begins with portfolio management. The real challenge lies in systematically tracking the performance of an investment portfolio, dynamically monitoring valuation, trends, and risk changes, and integrating both your "watchlist" and "portfolio NAV" into a single, visual dashboard.In this article, we’ll take a deep dive into the Watchlists module of Yahu Financials API, demonstrating how to use it to build a professional-grade Asset Pool + NAV Dashboard. This helps investors close the loop between research and trading with a real-world, executable system.1. Module Definition: Watchlists ≠ Simple FavoritesFor many, a Watchlist is simply a "favorites folder" for stocks—an easy way to bookmark interesting tickers. But in Yahu Financials API, a Watchlist is a structured data container with far more powerful capabilities:Function TypeDescriptionCreate/Read PortfoliosManage multiple portfolios with structured and scalable data architectureTrack Portfolio PerformanceRetrieve historical returns, compare performance vs. benchmarksGet Portfolio DetailsAccess full holdings, sector breakdown, weight allocationReal-Time Market MonitoringLive updates on quotes, micro charts (spark), price movers, and rankingThis means a Watchlist is not just a collection of stocks—it’s the core infrastructure for data-driven strategy execution.2. API Landscape OverviewYahu Financials offers several key API endpoints for building powerful portfolio tools:CapabilityEndpointDescriptionGet Watchlist List/watchlistsReturns all watchlists for a user, including IDs, names, timestampsGet Watchlist Details/watchlist-detailRetrieves full component data: symbols, weights, sector classificationGet Watchlist Performance/watchlist-performanceProvides daily/accumulated returns for a watchlist, by user and benchmarkThese can be combined with market endpoints (quotes, spark, summary) to create functionalities similar to Snowball Portfolios or mutual fund NAV pages.3. In Practice: Building a “Berkshire Portfolio” Visual DashboardLet’s walk through building a dashboard for the sample portfolio the_berkshire_hathaway_portfolio:Step 1. Get Watchlist Listresp = requests.get( f"{BASE}/olza8ffcp4mf", headers=HEADERS ) watchlists = resp.json() This fetches all watchlists for a user, including IDs and metadata. It’s the foundation for accessing specific portfolios.Step 2. Get Portfolio Componentsresp = requests.get( f"{BASE}/1w4fvjj0dvnr?pfId=the_berkshire_hathaway_portfolio&userId=X3NJ2A7VDSABUI4URBWME2PZNM", headers=HEADERS ) components = resp.json() This provides full holding details, including each symbol's weight and sector. Ideal for building front-end radar charts, pie charts, and detailed tables.Step 3. Get Daily Portfolio Performance (NAV Chart)resp = requests.get( f"{BASE}/j93gsfo4ho89?pfId=the_berkshire_hathaway_portfolio&region=US&userId=X3NJ2A7VDSABUI4URBWME2PZNM&symbols=^GSPC", headers=HEADERS ) performance_data = resp.json() Returns the portfolio’s daily performance vs. a benchmark (e.g., S&P 500), perfect for generating a NAV trend chart:{ "date": ["2024-01-01", "2024-01-02", ...], "portfolioNAV": [100, 100.8, 101.2, ...], "benchmark": [100, 100.5, 101.0, ...] } 4. Expandable Features: Building a Visual Asset DashboardFor those building a research or investment decision platform, Watchlists can be combined with other modules to deliver comprehensive visual tools:1. Asset Allocation Radar ChartExtract sector data from watchlist-detailVisualize balance and diversification using a polar chart2. Performance Comparison TableDisplay weekly/monthly/quarterly returns for each assetCompare with benchmarks like S&P500, QQQ, etc.3. Portfolio HeatmapClassify assets using daily change, PE, PEG, etc.Switch between data dimensions for deeper insights4. Risk Monitoring ModuleCombine spark (minute-level K lines), quote (real-time price), and statistics (volatility)Dynamically score and monitor portfolio risk levels5. Best Practices & Implementation Advice1. Unified ID Management SystemUse userId + pfId to uniquely identify each portfolio—essential for team collaboration, access control, and multi-user systems.2. Support Multi-Dimensional AnalyticsWatchlists shouldn’t be limited to return tracking. Incorporate ESG scores, analyst ratings, earnings performance, and more to create a multi-factor scoring model.3. Integrate Chat/News Modules for Event MonitoringUse the conversations and news APIs to track market sentiment, trending discussions, and breaking news related to portfolio assets—enabling responsive decision-making.6. ConclusionA portfolio is not just a list of favorite stocks—it’s a strategic container that reflects research, strategy, and execution. With the Watchlists module from Yahu Financials, you can build a flexible, dynamic, and structured portfolio system that integrates seamlessly with live market data, fundamental research, and sentiment analysis.This marks not just an evolution in tools—but a transformation in how portfolios are built, monitored, and optimized in the modern investment workflow.Articles related to APIs :Comprehensive Overview: Practical Guide to Yahu Financials API ModulesBuild Your Financial Data System from Scratch: Bring the Financial World into Your Local Project via APIReal-Time Market Monitoring & Risk Alert: Build Your Automated System with Luckdata Yahu Financials APIAdvanced Equity Research: Building a "360-Degree Panoramic Analysis View" with the Stock ModuleA Powerful Tool for Finding Potential Stocks: Building a Multi-Dimensional Financial Screener with Luckdata Yahu Financials APISeizing the Edge in Sentiment: Building a Financial News Aggregator and Sentiment Analysis Engine with Yahu Financials API

Introduction: The "Off-Chart" Signals of the Investment WorldBeyond cold financial data and technical charts, another force is quietly and powerfully influencing capital markets—the voice of online communities. This "off-chart" noise, amplified across platforms like Reddit, StockTwits, and Yahoo Finance, has become an increasingly vital element in modern investment decisions.These forums are not just casual spaces for opinions—they are hotbeds of information diffusion and emotional contagion. In today’s hyper-connected world, community discussions often reflect potential market dynamics before traditional news sources do. The Conversations module of the Yahu Financials API offers a structured way to capture these fragmented signals of market sentiment.In this article, we’ll explore why investor discussion boards matter, how to use the API to extract useful data, and how to translate these unstructured texts into actionable investment insights.1. Why Should You Pay Attention to Investor Discussions?1.1 From GameStop to Mass Action: A Case in PointThe 2021 GameStop saga marked a turning point in financial history. A fundamentally mediocre retail stock exploded in value, not because of earnings surprises or analyst upgrades, but because of a grassroots movement in Reddit’s WallStreetBets forum. The result? A historic short squeeze that rattled hedge funds worldwide.This episode proved that social media buzz isn’t just noise—it’s a form of action that can rewrite market trajectories.1.2 Forums Offer Predictive Market ValueReal-Time Response: Forum posts offer immediate investor sentiment—often ahead of formal news releases.Crowd Consensus: When many users echo similar opinions or emotions, collective behavior is more likely to be triggered.Dense, Low-Barrier Info Flow: Insights and rumors spread and get validated fast—without waiting for analyst reports.1.3 Sentiment as a Strategic InputWhile institutional investors still rely on earnings and valuation models, short-term traders and event-driven strategists are increasingly factoring in sentiment as a core signal. Whether for trend following or risk avoidance, tracking the "buzz" on forums can deliver early warnings or opportunity alerts.2. How to Use the Conversations API to Extract Forum DataThe Yahu Financials API Conversations module offers complete access to stock discussion threads and activity metrics. It requires an API key (provided by Luckdata) and supports two key endpoints:conversations/v2/list: Fetches the latest forum messages for a given stock.conversations/count: Returns total message volume for a stock, useful for trend tracking.Extended Example: Sentiment Tagging with Basic NLPHere’s an upgraded Python example that not only fetches messages but also applies simple sentiment scoring using TextBlob:import requests import datetime from textblob import TextBlob headers = {'X-Luckdata-Api-Key': 'your-api-key'} message_board_id = 'finmb_24937' # AAPL's message board ID # Fetch the latest 30 messages url = f'https://luckdata.io/api/yahu-financials/wjbchky2ls76?count=30&offset=0&sort_by=newest&messageBoardId={message_board_id}' response = requests.get(url, headers=headers) data = response.json() print("Recent Sentiment Analysis for AAPL Discussion Board:\n") positive, negative, neutral = 0, 0, 0 for msg in data.get("messages", []): content = msg["content"] created_at = datetime.datetime.fromtimestamp(msg["createdAt"] / 1000) blob = TextBlob(content) sentiment = blob.sentiment.polarity if sentiment > 0.1: positive += 1 tag = "Positive" elif sentiment < -0.1: negative += 1 tag = "Negative" else: neutral += 1 tag = "Neutral" print(f"User: {msg['userName']} | Time: {created_at.strftime('%Y-%m-%d %H:%M:%S')}") print(f"Message: {content}") print(f"Sentiment: {tag}") print("------") print(f"\nSummary: Positive {positive} | Negative {negative} | Neutral {neutral}") This code gives a quick breakdown of the emotional tone of posts, providing quantitative insight for strategies or dashboards.3. Use Cases: Applying Sentiment Data in Practice3.1 Building Sentiment + Price ModelsPull hourly data from the API and cross-reference message volume and tone with price and volume action. When forum activity surges before price reacts, you may have found a valuable leading indicator.3.2 Cross-Stock Sentiment ComparisonsAnalyze multiple stocks by comparing their post volumes and sentiment swings. Construct a “relative sentiment heatmap” to uncover under-the-radar stocks or highlight overheated ones.3.3 AI-Powered ForecastingLong-term collection of sentiment data enables training deep learning models (RNNs, LSTMs) to forecast price swings or news impact within a 1-3 day horizon. This is already in use by quant hedge funds.3.4 News Precursor & Rumor ScannerIf a piece of unconfirmed info ("company X may be acquired") starts spreading rapidly on forums, real-time scanning via the API can alert you long before the media or analysts react.4. From Data Stream to Trading SystemPractical Integration IdeasFeed API data into Power BI or Tableau to build a "Sentiment + Technical Indicators" dashboard.Integrate with stock signal generators—only trigger alerts when both technical and sentiment thresholds are met.Use in trading bots to automate entries/exits based on emotion metrics and buzz spikes.Strategy ExamplesVolume Spike Filter: If post volume exceeds 3x the 7-day average, flag the stock for pre-market watchlist review.Sentiment Reversal Play: If negative posts drop for 3 straight days but the stock holds support, it could signal short-term rebound potential.Event-Driven Scan: Watch for spikes in specific keywords (e.g., “layoff,” “merger”) coupled with high engagement to detect emerging catalysts.5. Conclusion: Finding Structure in “Irrational” MarketsThe financial markets are not driven solely by math—they're driven by emotion, anticipation, and collective behavior. When you shift your lens from charts to people, you uncover a different kind of edge.The Yahu Financials API and its Conversations module allow you to turn social noise into structured, analyzable data. What once seemed like chaotic chatter can now become an organized signal—a view into early momentum shifts, brewing risks, or overlooked opportunities.In tomorrow’s markets, sentiment data won’t be optional—it will be essential. Now is the perfect time to start building your own emotion radar system, using the crowd’s voice to predict the market’s next move.Articles related to APIs :Comprehensive Overview: Practical Guide to Yahu Financials API ModulesBuild Your Financial Data System from Scratch: Bring the Financial World into Your Local Project via APIDecoding Deep Stock Insights: Build Your Stock Analysis Radar with the Yahu APIThe Next Evolution in Portfolio Management: Building Your "Asset Pool + NAV Dashboard" with the Watchlists ModuleReal-Time Market Monitoring & Risk Alert: Build Your Automated System with Luckdata Yahu Financials APIAdvanced Equity Research: Building a "360-Degree Panoramic Analysis View" with the Stock Module

Introduction: Where Does Stock Research Begin?In the world of investing, choosing what to buy is often not the hardest part—why you buy is the real key to long-term success. As data science and financial technology increasingly converge, the dimensions of stock analysis have grown more complex. From fundamentals, valuation models, and institutional holdings to market sentiment and analyst ratings—everything requires integration and real-time insight.Traditionally, stock research involved manually collecting data, poring over financial reports, and analyzing news—a time-consuming and error-prone process. Today, APIs (Application Programming Interfaces) have emerged as essential tools for modern investors. The Yahu Financials API is a powerful, flexible platform that enables users to programmatically access comprehensive company data, supporting deep and systematic equity research.This article will guide you through using the Yahu Financials API to collect and synthesize multidimensional data on individual stocks, helping you build a customized “Stock Analysis Radar” that empowers smarter investment decisions.I. What Are the Core Dimensions of Stock Information?Before constructing a robust equity analysis system, it’s essential to understand which types of information are investment-worthy. The Yahu API provides a wide range of data modules and fields that help you build a full picture of any company:Company Profile: Name, ticker, business overview, sector classification, company history, executive team, and more—providing a foundation for industry and competitive analysis.Financial Indicators: Revenue, gross and net profit, EPS, ROE, debt ratio, etc.—used to assess company performance and financial health.Valuation & Analyst Expectations: Current and historical P/E and P/B ratios, target prices, analyst recommendation breakdowns—essential for judging over- or under-valuation.Ownership Structure: Track institutional and fund ownership levels and changes—often considered “smart money” signals.Market Sentiment & News: Natural language processing (NLP)-powered analysis of media coverage and news sentiment—helps anticipate potential market reactions.ESG Ratings & Governance: Scores for environment, social responsibility, and governance practices—important for long-term, values-based investors.Technical Data: Moving averages, RSI, MACD, Bollinger Bands, and other technical indicators—useful for short-term trades and chart analysis.By integrating these diverse data modules, you can construct a 360-degree profile of any stock.II. Case Study: Building a Stock Dashboard Using AMRNLet’s walk through a hands-on example using Amarin Corporation (AMRN) to build a multi-dimensional stock analysis dashboard using the Yahu API.1. Retrieve Basic Company Informationimport requests headers = {'X-Luckdata-Api-Key': 'your-api-key'} url = 'https://luckdata.io/api/yahu-financials/zwbwjphohej0?lang=en-US®ion=US&symbol=AMRN' response = requests.get(url, headers=headers) print(response.json()) This call returns company name, industry sector, website, executive leadership, and more—crucial for categorization and competitive analysis.2. Get Key Financial & Fund Dataurl = 'https://luckdata.io/api/yahu-financials/g6qnrm4550pq?lang=en-US®ion=US&symbol=AMRN&modules=assetProfile,summaryProfile,fundProfile' response = requests.get(url, headers=headers) print(response.json()) Here you can get an overview of assets, fund-related info, and a financial summary—ideal for understanding capital structure and fund interest.3. Analyze Analyst Recommendation Trendsurl = 'https://luckdata.io/api/yahu-financials/uy59c4zdch38?lang=en-US®ion=US&symbol=AMRN' response = requests.get(url, headers=headers) print(response.json()) This call returns the trend of analyst ratings over time (Buy, Hold, Sell), providing insight into institutional sentiment shifts.4. Explore Institutional Holdingsurl = 'https://luckdata.io/api/yahu-financials/q120tpjkfvcy?lang=en-US®ion=US&symbol=AMRN&straddle=true' response = requests.get(url, headers=headers) print(response.json()) See which funds and institutions hold positions in the stock—critical for following the money trail.5. Add Technical Indicators & Historical Pricesurl = 'https://luckdata.io/api/yahu-financials/x9s1kqjfsh82?lang=en-US®ion=US&symbol=AMRN&modules=priceHistory,technicalIndicators' response = requests.get(url, headers=headers) print(response.json()) These data can power charts such as candlestick plots, moving averages, RSI levels, and MACD—perfect for blending technical and fundamental analysis.III. Combining Data into a Visual Stock Intelligence DashboardData alone isn’t enough. How it’s visualized and interpreted makes the real difference. Here's how to turn the data into actionable intelligence:Use tools like Tableau, Power BI, or Python libraries (Plotly, Dash) to create interactive dashboards.Set up automated API calls to refresh data daily or weekly.Use a modular layout: profile & summary on the left, live news & technicals in the center, institutional ownership and analyst trends on the right.Incorporate visual scoring systems (traffic lights, radar charts) to summarize attractiveness across multiple dimensions.Such a system helps investors make confident decisions and can also serve as the foundation for quantitative models or alerts.IV. Who Needs This Capability?This type of automated, data-driven insight system is useful for a wide range of users:Individual Investors: Move from intuition to data-backed decisions.Asset Managers & Analysts: Automate parts of the research process and generate deeper reports.FinTech Entrepreneurs: Build smart investment products and data platforms.Financial Media Creators: Power content with authoritative, real-time analytics.Educators & Trainers: Use as real-world teaching examples in finance and data science.V. Conclusion: From “Watching Stocks” to “Understanding Stocks”The true power of data lies in connection and insight. The Yahu Financials API is more than just a data provider—it’s a bridge between you and the market. It enables you to move beyond static, time-consuming analysis and into a new realm of automated, multidimensional, and real-time understanding.If you aspire to evolve from a retail investor to a professional-grade decision maker, the journey starts with the right tools and methodology. Start with the stocks you care about most, plug the Yahu API into your workflow, and watch your investment perspective transform.The future of investing belongs to those who combine insight with infrastructure—and you’re now one step closer.Articles related to APIs :Comprehensive Overview: Practical Guide to Yahu Financials API ModulesBuild Your Financial Data System from Scratch: Bring the Financial World into Your Local Project via APIThe Next Evolution in Portfolio Management: Building Your "Asset Pool + NAV Dashboard" with the Watchlists ModuleReal-Time Market Monitoring & Risk Alert: Build Your Automated System with Luckdata Yahu Financials APIAdvanced Equity Research: Building a "360-Degree Panoramic Analysis View" with the Stock ModuleA Powerful Tool for Finding Potential Stocks: Building a Multi-Dimensional Financial Screener with Luckdata Yahu Financials API

In the vast ocean of the stock market, identifying high-potential stocks among thousands of tickers is a challenging task. A financial screener is an essential tool that helps investors quickly filter stocks based on multiple financial indicators like PE ratio, PB ratio, revenue growth, and ROE. It turns stock picking from "needle in a haystack" into "targeted selection."This article will walk you through building a flexible, multi-dimensional financial screener using the Luckdata Yahu Financials API, particularly focusing on the screeners module. We will also show how to present the filtered results through a simple interactive web interface built with Flask and Bootstrap.Why Use an API for Financial ScreeningStructured and Real-Time DataUnlike many broker websites that offer limited filtering options and outdated data, using an API provides real-time, structured JSON data directly from the source.Highly Customizable FilteringYou can define flexible screening criteria like "PE < 20", "ROE > 15%", and "revenue growth > 10%" to target very specific stock characteristics.Automation-ReadyScreening logic can be embedded into automated trading systems or scheduled jobs, enabling a complete end-to-end investment pipeline.Luckdata’s screeners module is designed for this exact use case, offering several key endpoints:/screeners/get-filters: Retrieve all available screening fields and categories;/screeners/get-symbols-by-predefined: Get a list of stocks based on predefined screening IDs;/screeners/list-by-ticker: Query detailed screening data for an individual stock.Overview of Screener Filters and FieldsTo build a customizable screener, we first need to know what fields we can screen on. The following API call returns all available filters by region and category:GET https://luckdata.io/api/yahu-financials/vclchw21z2no?region=US&category=keystats,financials,valuation,profitability_ratios_and_dividends&eType=equity Sample response (partial):{ "filters": [ { "id": "trailingPE", "name": "PE Ratio (TTM)", "type": "number" }, { "id": "priceToBook", "name": "Price to Book", "type": "number" }, { "id": "revenueQuarterlyGrowth", "name": "Quarterly Revenue Growth (%)", "type": "number" }, { "id": "returnOnEquity", "name": "Return on Equity (ROE, %)", "type": "number" } // … More fields … ] } These fields can be used to dynamically build user-selectable filters in your frontend.Practical Example: Filter Stocks by PE, ROE, and Revenue GrowthCurrently, Luckdata does not support direct POST requests with multiple custom filters. However, we can combine the /screeners/get-symbols-by-predefined endpoint to fetch a base list, and then apply our logic by calling /screeners/list-by-ticker for each stock.Step 1: Fetch Most Active Tickersimport requests API_KEY = 'your-luckdata-key' url = 'https://luckdata.io/api/yahu-financials/ke6gzl1mrinc' params = { 'count': 100, 'start': 0, 'scrIds': 'MOST_ACTIVES' } headers = {'X-Luckdata-Api-Key': API_KEY} response = requests.get(url, headers=headers, params=params) tickers = [item['symbol'] for item in response.json().get('finance', {}).get('result', [])[0].get('quotes', [])] Step 2: Check Each Ticker's Financial Dataqualified = [] for ticker in tickers: detail_url = 'https://luckdata.io/api/yahu-financials/5xbac871zs11' params = {'ticker': ticker} resp = requests.get(detail_url, headers=headers, params=params) data = resp.json() try: pe = data['trailingPE'] roe = data['returnOnEquity'] rev_growth = data['revenueQuarterlyGrowth'] if pe < 20 and roe > 15 and rev_growth > 10: qualified.append({ 'symbol': ticker, 'PE': pe, 'ROE': roe, 'RevenueGrowth': rev_growth }) except KeyError: continue for stock in qualified: print(f"{stock['symbol']}: PE={stock['PE']}, ROE={stock['ROE']}%, Revenue Growth={stock['RevenueGrowth']}%") Build a Simple Frontend with Flask + BootstrapTo make it interactive, we’ll use Flask to create a form where users can input custom filters.Flask Backend Code:from flask import Flask, render_template, request import requests app = Flask(__name__) API_KEY = 'your-luckdata-key' @app.route('/', methods=['GET', 'POST']) def index(): results = [] if request.method == 'POST': pe = float(request.form.get('pe')) roe = float(request.form.get('roe')) rev = float(request.form.get('rev')) ticker_resp = requests.get( 'https://luckdata.io/api/yahu-financials/ke6gzl1mrinc', headers={'X-Luckdata-Api-Key': API_KEY}, params={'scrIds': 'MOST_ACTIVES', 'count': 100, 'start': 0} ) tickers = [i['symbol'] for i in ticker_resp.json()['finance']['result'][0]['quotes']] for ticker in tickers: detail = requests.get( 'https://luckdata.io/api/yahu-financials/5xbac871zs11', headers={'X-Luckdata-Api-Key': API_KEY}, params={'ticker': ticker} ).json() try: if detail['trailingPE'] < pe and detail['returnOnEquity'] > roe and detail['revenueQuarterlyGrowth'] > rev: results.append({ 'symbol': ticker, 'PE': detail['trailingPE'], 'ROE': detail['returnOnEquity'], 'RevenueGrowth': detail['revenueQuarterlyGrowth'] }) except: continue return render_template('index.html', results=results) HTML Template templates/index.html<!doctype html> <html> <head> <meta charset="utf-8"> <title>Financial Screener Example</title> <link href="https://cdn.jsdelivr.net/npm/bootstrap@5.0.0/dist/css/bootstrap.min.css" rel="stylesheet"> </head> <body class="p-4"> <h1 class="mb-4">Multi-Dimensional Financial Screener</h1> <form method="post" class="row g-3 mb-4"> <div class="col-auto"> <label class="form-label">PE <=</label> <input name="pe" type="number" step="0.1" value="20" class="form-control"> </div> <div class="col-auto"> <label class="form-label">ROE >=</label> <input name="roe" type="number" step="0.1" value="15" class="form-control"> </div> <div class="col-auto"> <label class="form-label">Revenue Growth >=</label> <input name="rev" type="number" step="0.1" value="10" class="form-control"> </div> <div class="col-auto align-self-end"> <button type="submit" class="btn btn-primary">Screen</button> </div> </form> {% if results %} <table class="table table-striped"> <thead> <tr> <th>Symbol</th><th>PE</th><th>ROE (%)</th><th>Revenue Growth (%)</th> </tr> </thead> <tbody> {% for s in results %} <tr> <td>{{ s.symbol }}</td> <td>{{ s.PE }}</td> <td>{{ s.ROE }}</td> <td>{{ s.RevenueGrowth }}</td> </tr> {% endfor %} </tbody> </table> {% endif %} </body> </html> Advanced Tips and ExtensionsDynamic Filter OptionsAutomatically load available filters from /screeners/get-filters and create a flexible UI for users to define their own conditions.Chart VisualizationCombine historical data with visualization tools like ECharts or Recharts for trend analysis.Automation and AlertsUse scheduled jobs to run daily screenings and push alerts via email, Slack, or LINE.Integrate with Backtesting PlatformsExport results to backtesting tools for validation and strategy refinement.ConclusionFrom understanding available filters, fetching a base list, to applying custom screening logic per ticker, this guide shows how to build a flexible multi-dimensional financial screener using Luckdata Yahu Financials API. With high customizability and integration capabilities, this system empowers investors to find high-potential stocks and streamline their decision-making process.Articles related to APIs :How to Get Real-Time Financial Data Through the Yahu Financials API: A Complete GuideBuild Your Financial Data System from Scratch: Bring the Financial World into Your Local Project via APIReal-Time Market Monitoring & Risk Alert: Build Your Automated System with Luckdata Yahu Financials API

In the fast-paced world of investing, markets can shift in the blink of an eye—a single headline, economic data release, or rumor can send prices soaring or plummeting. For individual investors and small teams, being able to detect these "abnormal moves" in real time means staying ahead of the curve, seizing opportunities, and minimizing risk. In this article, we'll walk you through how to use the Luckdata Yahu Financials API to build a real-time market monitoring and risk alert system from scratch in Python. Your program will automatically notify you when a stock’s price movement exceeds a defined threshold.1. Why Build Real-Time Monitoring and AlertingCatch Opportunities EarlyIdentify "black horse" gainers or "crashing" losers on the day’s top/bottom movers list, and react promptly with buy/sell decisions.Risk ManagementReceive automated alerts during sharp market movements and reduce reliance on manual monitoring.Automation EfficiencyDelegate tedious data querying and filtering to scripts, freeing you up to focus on strategic decision-making.✅ Ideal for: day traders, quantitative strategists, market sentiment followers, and risk-conscious investors.2. Technology Choices and API Rate LimitsPolling vs WebSocketMethodPollingWebSocketMechanismClient sends periodic requestsServer pushes data updatesComplexitySimpleHigher; requires long-lived connectionsReal-time CapabilityLow to mediumHighSuitable forLow/medium frequency (1–5 min intervals)High-frequency or millisecond-level updatesSince Yahu Financials API uses HTTP-based endpoints, we’ll use polling, which is straightforward and sufficient for most intra-day use cases.Movers EndpointGET https://luckdata.io/api/yahu-financials/b2tkd65h755m ?lang=en-US &count=6 &start=0 &region=US symbol: Stock tickershortName: Company namepercentChange: % price change for the dayregularMarketPrice: Current pricemarketCap: Market capitalization (optional)You can adjust the count to fetch more movers and switch region for different markets (e.g., US, HK, CN).API Plans & Rate LimitsPlanMonthly CallsRate LimitUse CaseFree500 calls/month5 calls/secLearning, testingBasic10,000 calls/month5 calls/secRegular monitoringPro50,000 calls/month5 calls/secMulti-market or higher frequencyUltra2,500,000 calls/month10 calls/secHigh-frequency, enterprise scaleExample: For polling once per minute during trading hours (12 hrs/day), you’ll need ~21,600 calls/month → consider Basic or Pro.3. Hands-On: Python Script for Monitoring & Email AlertsHere’s a complete working example of polling and alerting logic using Python:import time import requests import smtplib from email.mime.text import MIMEText # —— 1. Config Section —— # API_URL = ( 'https://luckdata.io/api/yahu-financials/' 'b2tkd65h755m?lang=en-US&count=50&start=0&region=US' ) API_KEY = 'your-luckdata-key' # Replace with your actual key THRESHOLD = 5.0 # Price change threshold (%) POLL_INTERVAL = 60 # Polling interval in seconds SMTP_SERVER = 'smtp.example.com' SMTP_PORT = 587 SMTP_USER = 'alert@example.com' SMTP_PASS = 'your-smtp-password' ALERT_TO = ['youremail@example.com'] # —— 2. Fetch Movers —— # def fetch_movers(): headers = {'X-Luckdata-Api-Key': API_KEY} resp = requests.get(API_URL, headers=headers) resp.raise_for_status() return resp.json().get('movers', []) # —— 3. Send Email Alert —— # def send_email(subject: str, body: str): msg = MIMEText(body) msg['Subject'] = subject msg['From'] = SMTP_USER msg['To'] = ', '.join(ALERT_TO) with smtplib.SMTP(SMTP_SERVER, SMTP_PORT) as server: server.starttls() server.login(SMTP_USER, SMTP_PASS) server.send_message(msg) # —— 4. Main Loop —— # if __name__ == '__main__': alerted = set() # Prevent duplicate alerts while True: try: movers = fetch_movers() for m in movers: symbol = m['symbol'] change = m['percentChange'] if abs(change) >= THRESHOLD and symbol not in alerted: subject = f'[Risk Alert] {symbol} moved {change:.2f}%' body = ( f'Symbol: {symbol}\n' f'Price Change: {change:.2f}%\n' f'Time: {time.strftime("%Y-%m-%d %H:%M:%S")}\n' f'Source: Yahu Financials API (movers endpoint)' ) send_email(subject, body) alerted.add(symbol) except Exception as e: print(f'Monitoring error: {e}') time.sleep(POLL_INTERVAL) ✅ Suggestions:Replace email with Slack, Discord, LINE Bot, or enterprise chat toolsAdd multiple threshold levels (e.g. >5% alert, <-7% urgent alert)Store alerts in file/Redis to persist state across restarts4. Performance Optimization & Advanced IdeasParallel Monitoring for Multiple MarketsUse aiohttp or httpx with asyncio to poll multiple markets (e.g., US, HK, CN) in parallel, increasing coverage.Visualization & Alert IntegrationsConnect with Slack, Teams, LINE, Feishu botsIntegrate with Grafana + Prometheus for visual alert dashboardsUse Streamlit or Flask to build simple dashboardsIntelligent ThresholdsDynamically adjust thresholds based on volatility indicators like historical standard deviation or Bollinger Bands.Multi-Endpoint FusionEnrich alerts by combining data from:/market/quotes: real-time prices, volume, and market data/news: recent headlines to explain price action/spark: intraday trend lines for pattern analysis5. ConclusionWith this lightweight, automated setup powered by the Luckdata Yahu Financials API, you now have a practical and extendable risk alert engine. Key advantages include:Low entry barrier: clean API design and simple HTTP integrationFlexible configuration: customizable frequency, thresholds, and alerting channelsStrong scalability: integrates with dashboards, messaging apps, and trading logicWhether you're building a personal trading assistant or enhancing a broader risk management system, this type of tool can be a crucial part of your trading workflow.Articles related to APIs :How to Get Real-Time Financial Data Through the Yahu Financials API: A Complete GuideBuild Your Financial Data System from Scratch: Bring the Financial World into Your Local Project via API