Vnstock (vnstock-hq/vnstock-agent-guide)

Vnstock

https://github.com/vnstock-hq/vnstock-agent-guide
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Vnstock is a comprehensive Python library for accessing Vietnamese stock market data and financial...

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# VnStock Agent Guide

VnStock Agent Guide is a comprehensive documentation repository designed specifically for AI Agents (Claude, Gemini, GitHub Copilot, Cursor, Windsurf) to provide accurate, up-to-date guidance on using the VnStock Python library ecosystem for Vietnamese stock market data. The ecosystem consists of five core libraries: `vnstock` (free tier for basic market data), `vnstock_data` (sponsor tier with advanced features and Unified UI), `vnstock_ta` (technical analysis indicators), `vnstock_news` (news crawling and sentiment analysis), and `vnstock_pipeline` (data pipelines and real-time streaming).

The library supports multiple data sources including KBS (recommended for stability), VCI (comprehensive data), and others, providing access to stock quotes, company information, financial statements, macroeconomic data, market insights, and more. The Unified UI introduced in `vnstock_data` v3.0.0 offers a chained API design with seven data layers: Reference, Market, Fundamental, Analytics, Macro, Insights, and News, making it easier to access the full breadth of Vietnamese market data programmatically.

---

## Installation

Install the vnstock library from PyPI for the free tier functionality.

```bash
# Install from PyPI (stable)
pip install vnstock

# Install from GitHub (latest development)
pip install git+https://github.com/vnstock-lab/vnstock.git

# Dependencies are automatically installed:
# pandas>=1.3.0, requests>=2.25.0, beautifulsoup4>=4.9.0, lxml>=4.6.0
# pydantic>=1.8.0, tenacity>=8.0.0, aiohttp>=3.7.0, tqdm>=4.60.0
```

---

## API Key Registration and Authentication

VnStock supports different usage tiers with corresponding rate limits. Register for a free API key to increase your request quota from 20 to 60 requests per minute.

```python
from vnstock.core.utils.auth import register_user, change_api_key, check_status

# Interactive registration - will guide you to https://vnstocks.com/login
register_user()

# Change API key if needed
change_api_key("your_api_key_here")

# Check current status
status = check_status()
# Output:
# API key: ab12***ef34
# Tier: Community
# Limit: 60 requests/minute

# After registration, API key is automatically used for all requests
from vnstock import Quote
quote = Quote(source="KBS", symbol="VCI")
df = quote.history(start="2024-01-01", end="2024-12-31")
```

---

## Listing API - Stock Discovery and Filtering

The Listing API provides methods to search, filter, and retrieve information about securities available on the market including stocks, indices, bonds, warrants, and ETFs.

```python
from vnstock import Listing

# Initialize with KBS source (recommended for stability)
listing = Listing(source="KBS")

# Get all stock symbols
all_symbols = listing.all_symbols(to_df=True)
print(f"Total symbols: {len(all_symbols)}")  # ~1565 symbols
# Columns: ['symbol', 'organ_name']

# Get as list instead of DataFrame
symbols_list = listing.all_symbols(to_df=False)
print(symbols_list[:5])  # ['DPP', 'SDA', 'SDC', 'SDH', 'SDS']

# Filter by exchange (HOSE, HNX, UPCOM)
hose_stocks = listing.symbols_by_exchange(exchange="HOSE", to_df=True)
print(f"HOSE stocks: {len(hose_stocks)}")
# Columns: ['symbol', 'organ_name', 'en_organ_name', 'exchange', 'type', 'id']

# Filter by industry classification (ICB)
by_industry = listing.symbols_by_industries()

# Get VN30 index components
vn30 = listing.symbols_by_group(group="VN30")
print(vn30)  # Returns Series with 30 symbols

# Get all indices
indices = listing.all_indices()

# Get ETFs (KBS exclusive)
etfs = listing.all_etf()

# Get bonds and warrants
bonds = listing.all_bonds()
warrants = listing.all_covered_warrant()
```

---

## Quote API - Historical and Real-time Price Data

The Quote API provides historical OHLCV data, intraday tick data, and order book depth information for stocks, indices, and other securities.

```python
from vnstock import Quote

# Initialize Quote adapter
quote = Quote(symbol="VCI", source="VCI")

# Get historical OHLCV data with date range
df = quote.history(start="2024-01-01", end="2024-12-31", interval="1D")
print(df[['time', 'open', 'high', 'low', 'close', 'volume']].head())
# Output:
#         time   open   high    low  close   volume
# 0 2024-01-02  32.10  32.50  31.80  32.30  1234567

# Use length parameter for lookback periods
df_1m = quote.history(length="1M", interval="1D")    # Last 1 month
df_3m = quote.history(length="3M", interval="1D")    # Last 3 months (90 days)
df_1y = quote.history(length="1Y", interval="1D")    # Last 1 year
df_100 = quote.history(length=100, interval="1D")    # Last 100 days
df_50b = quote.history(length="50b", interval="1D")  # Last 50 bars/candles

# Supported intervals: 1m, 5m, 15m, 30m, 1H, 1D, 1W, 1M
# Aliases: m (minute), h (hour), d (day), w (week), M (month)

# Get intraday tick data
intraday = quote.intraday(page_size=100)
print(intraday[['time', 'price', 'volume', 'match_type']].head())
# Output:
#              time   price  volume match_type
# 0  09:00:05.123    32.50    1000         BU

# Get order book / price depth (VCI only)
depth = quote.price_depth()
print(depth)
# Shows bid/ask levels with prices and volumes
```

---

## Trading API - Real-time Price Board

The Trading API provides real-time market price board data including current prices, bid/ask levels, and trading statistics for multiple symbols at once.

```python
from vnstock import Trading

# Initialize with KBS (29 columns, standardized)
trading = Trading(source="KBS", symbol="VCI")

# Get price board for multiple symbols
board = trading.price_board(symbols_list=['VCI', 'VCB', 'ACB', 'HPG', 'FPT'])
print(f"Shape: {board.shape}")  # (5, 29)

# Key columns available
print(board[['symbol', 'exchange', 'reference_price', 'price_change', 'percent_change']])
# Output:
#   symbol exchange  reference_price  price_change  percent_change
# 0    VCI     HOSE            34850           -50         -0.1435
# 1    VCB     HOSE            84500           100          0.1183

# Access bid/ask data
print(board[['symbol', 'bid_price_1', 'bid_vol_1', 'ask_price_1', 'ask_vol_1']])

# Filter stocks by conditions
risers = board[board['price_change'] > 0]
high_volume = board[board['volume_accumulated'] > 1000000]

# VCI source provides more detailed data (77 columns)
trading_vci = Trading(source="VCI", symbol="VCI")
board_vci = trading_vci.price_board(symbols_list=['VCI', 'VCB'])
print(board_vci[['symbol', 'ref_price', 'match_price', 'total_volume']])
```

---

## Company API - Corporate Information

The Company API provides comprehensive company information including profiles, shareholders, management, subsidiaries, news, and events.

```python
from vnstock import Company

# Initialize with KBS (recommended)
company = Company(source="KBS", symbol="VCI")

# Get company overview (30 columns with KBS)
overview = company.overview()
print(overview[['symbol', 'charter_capital', 'exchange', 'number_of_employees']])
# Output:
#   symbol  charter_capital exchange  number_of_employees
# 0    VCI   8501000000000     HOSE                  850

# Get major shareholders
shareholders = company.shareholders()
print(shareholders[['name', 'shares_owned', 'ownership_percentage']])
# Output:
#       name  shares_owned  ownership_percentage
# 0   To Hai     128889403                 17.95

# Get company officers/management
officers = company.officers()
print(officers[['name', 'position', 'from_date']])
# Output:
#              name                        position  from_date
# 0         To Hai      Chairman of the Board            2007

# Get subsidiaries
subsidiaries = company.subsidiaries()

# Get company news with pagination (KBS feature)
news = company.news(page=1, page_size=10)

# Get corporate events
events = company.events()

# KBS-exclusive methods
ownership = company.ownership()           # Ownership structure
capital_history = company.capital_history()  # Capital changes
insider = company.insider_trading()       # Insider transactions
```

---

## Finance API - Financial Statements

The Finance API provides access to financial reports including income statements, balance sheets, cash flow statements, and financial ratios.

```python
from vnstock import Finance

# Initialize Finance adapter
finance = Finance(source="KBS", symbol="VCI")

# Get income statement (quarterly data)
income = finance.income_statement(period="quarter")
print(f"Shape: {income.shape}")  # (90, 10) - 90 line items
print(income.columns)
# ['item', 'item_id', 'unit', 'levels', 'row_number', '2025-Q3', '2025-Q2', ...]

# Get yearly income statement
income_year = finance.income_statement(period="year")

# Display mode options for field names
from vnstock.explorer.kbs.financial import FieldDisplayMode

# Standardized (Vietnamese only)
df_std = finance.income_statement(period="quarter", display_mode=FieldDisplayMode.STD)

# All fields (Vietnamese + English + item_id)
df_all = finance.income_statement(period="quarter", display_mode=FieldDisplayMode.ALL)

# English only
df_en = finance.income_statement(period="quarter", display_mode='en')

# Get balance sheet (162 line items)
balance = finance.balance_sheet(period="quarter")

# Get cash flow statement (159 line items)
cashflow = finance.cash_flow(period="quarter")

# Get financial ratios (27 ratios with KBS)
ratios = finance.ratio(period="quarter")
print(ratios[ratios['item_id'].isin(['roe', 'roa', 'eps', 'pe'])])
```

---

## Unified UI - Reference Layer (vnstock_data)

The Reference Layer in the Unified UI provides static reference data about companies, equities, indices, industries, funds, bonds, and market events.

```python
from vnstock_data import Reference

ref = Reference()

# === Company Information ===
# Get company profile
profile = ref.company("TCB").info()
print(profile)

# Get major shareholders
shareholders = ref.company("VIC").shareholders()
print(shareholders)

# Get company officers
officers = ref.company("HPG").officers()

# Get subsidiaries
subsidiaries = ref.company("VNM").subsidiaries()

# Get company news and events
news = ref.company("TCB").news()
events = ref.company("TCB").events()

# === Equity Listings ===
# All equities (1700+ symbols)
all_equities = ref.equity.list()
print(f"Total: {len(all_equities)}")

# Filter by index group
vn30 = ref.equity.list_by_group("VN30")

# Filter by exchange
by_exchange = ref.equity.list_by_exchange()

# Filter by industry
by_industry = ref.equity.list_by_industry()

# === Index Information ===
indices = ref.index.list()
index_groups = ref.index.groups()
vn30_members = ref.index.members("VN30")
vn30_info = ref.index("VN30").info()

# === Other Reference Data ===
industries = ref.industry.list()
funds = ref.fund.list()
etfs = ref.etf.list()
bonds = ref.bond.list(bond_type='all')  # 'corporate' or 'government'
events_calendar = ref.events.calendar(start='2026-03-01', end='2026-03-31')
futures = ref.futures().list()
warrants = ref.warrant().list()
```

---

## Unified UI - Market Layer (vnstock_data)

The Market Layer provides real-time and historical trading data including OHLCV, order books, trades, foreign flow, and market statistics.

```python
from vnstock_data import Market

mkt = Market()

# === Equity Market Data ===
# Historical OHLCV data
ohlcv = mkt.equity("VIC").ohlcv(start="2026-02-01", end="2026-03-01")
print(ohlcv)
# Columns: ['time', 'open', 'high', 'low', 'close', 'volume']

# Trading history statistics
history = mkt.equity("VIC").trade_history(start="2026-02-01", end="2026-03-01")

# Intraday trades (Time & Sales)
trades = mkt.equity("TCB").trades()

# Order book depth
orderbook = mkt.equity("VNM").order_book()

# Current quote / price board
quote = mkt.equity("HPG").quote()

# Session statistics
session = mkt.equity("VIC").session_stats()

# Foreign investor flow
foreign = mkt.equity("VIC").foreign_flow()

# Proprietary trading flow
proprietary = mkt.equity("VIC").proprietary_flow()

# Block trades (negotiated deals)
blocks = mkt.equity("VIC").block_trades()

# Volume profile
vol_profile = mkt.equity("VJC").volume_profile()

# Summary information
summary = mkt.equity("VIC").summary()

# === Index Market Data ===
vnindex = mkt.index("VNINDEX").ohlcv(start="2026-01-01", end="2026-03-01")
index_quote = mkt.index("VNINDEX").quote()

# === Multi-symbol Quote ===
quotes = mkt.quote(['VCI', 'VCB', 'ACB', 'HPG'])

# === Other Markets ===
# Futures, warrants, ETFs, funds
futures_data = mkt.futures("VN30F2503").ohlcv(start="2026-01-01", end="2026-03-01")
etf_data = mkt.etf("E1VFVN30").ohlcv(start="2026-01-01", end="2026-03-01")
fund_nav = mkt.fund("DCDS").nav_history()

# International markets
crypto = mkt.crypto("BTCUSDT").ohlcv(length="1M")
forex = mkt.forex("EURUSD").ohlcv(length="1M")
commodity = mkt.commodity("XAUUSD").ohlcv(length="1M")
```

---

## Unified UI - Fundamental Layer (vnstock_data)

The Fundamental Layer provides financial statement data and ratios for fundamental analysis, with support for auto-scoring based on industry classification.

```python
from vnstock_data import Fundamental

fun = Fundamental()

# === Financial Statements ===
# Income statement (4 recent periods)
income = fun.equity("TCB").income_statement(limit=4)
print(income)

# Balance sheet - using utility function syntax
balance = fun.equity.balance_sheet("VIC", limit=4)

# Cash flow statement
cashflow = fun.equity("VNM").cash_flow(limit=4)

# Financial ratios (PE, PB, ROE, Debt/Equity, etc.)
ratios = fun.equity("HPG").ratio(limit=4)

# BCTC Notes (detailed footnotes)
notes = fun.equity("FPT").note()

# === Financial Health Scorecard ===
# Auto-detect industry and apply appropriate scorecard
health_auto = fun.equity("SSI").financial_health(
    scorecard="auto",  # auto, banking, securities, insurance, generic
    lang="vi",
    limit=4
)

# Force banking scorecard for bank analysis
health_bank = fun.equity("TCB").financial_health(
    scorecard="banking",
    lang="en",
    limit=4
)

# Parameters:
# - limit: Number of periods (quarters/years)
# - period_type: 1=Year, 2=Quarter
# - lang: "vi" (Vietnamese) or "en" (English)
# - scorecard: "auto", "banking", "securities", "insurance", "generic"
```

---

## Unified UI - Macro Layer (vnstock_data)

The Macro Layer provides macroeconomic data including GDP, CPI, FDI, exchange rates, interest rates, and commodity prices.

```python
from vnstock_data import Macro

mac = Macro()

# === Economy Data (Vietnam) ===
# GDP growth
gdp_quarterly = mac.economy().gdp(start="2020-01", end="2026-03", period="quarter")
gdp_yearly = mac.economy().gdp(period="year")

# Consumer Price Index
cpi = mac.economy().cpi(period="month", length=24)  # Last 24 months

# Industrial production
industry = mac.economy().industry_prod(period="month", length=12)

# Import/Export trade balance
trade = mac.economy().import_export(period="month")

# Retail sales
retail = mac.economy().retail(period="month")

# Foreign Direct Investment
fdi = mac.economy().fdi(period="month")

# Money supply
money = mac.economy().money_supply(period="month")

# Population and labor statistics
labor = mac.economy().population_labor(period="year")

# === Currency & Interest Rates ===
# Daily exchange rates
exchange = mac.currency().exchange_rate(
    start="2026-02-06",
    end="2026-03-06",
    period="day"
)
print(exchange[['report_time', 'USD', 'EUR', 'JPY']])

# Monthly average exchange rates
exchange_monthly = mac.currency().exchange_rate(period="month", length=12)

# Interest rates
interest = mac.currency().interest_rate(period="day", length=365)

# === Commodity Prices ===
# Gold prices
gold = mac.commodity().gold(length="1M")

# Oil prices
oil = mac.commodity().oil(length="3M")

# Steel, agriculture, etc.
steel = mac.commodity().steel(length="6M")
```

---

## Unified UI - Insights Layer (vnstock_data)

The Insights Layer provides stock rankings and screening tools to identify trading opportunities and market trends.

```python
from vnstock_data import Insights

ins = Insights()

# === Stock Rankings ===
# Top gainers (price increase)
gainers = ins.ranking().gainer(limit=10)
gainers_vn = ins.ranking().gainer(index='VNINDEX', limit=10)

# Top losers (price decrease)
losers = ins.ranking().loser(limit=10)

# Top by trading volume
volume = ins.ranking().volume(limit=10)

# Top by trading value
value = ins.ranking().value(limit=10)

# Top foreign buying
foreign_buy = ins.ranking().foreign_buy(limit=10)

# Top foreign selling
foreign_sell = ins.ranking().foreign_sell(limit=10)

# Top block trades (negotiated deals)
deals = ins.ranking().deal(limit=10)

# === Stock Screener ===
# Get available screening criteria
criteria = ins.screener().criteria(lang='vi')
print(criteria)

# Screen stocks with custom filters
params = {
    "filter": [
        {"name": "exchange", "conditionOptions": [{"type": "value", "value": "hsx"}]},
        {"name": "marketCap", "conditionOptions": [{"from": 1e12, "to": 1e14}]},
        {"name": "ttmPe", "conditionOptions": [{"from": 5, "to": 15}]},
        {"name": "ttmRoe", "conditionOptions": [{"from": 15, "to": 50}]},
        {"name": "rsi", "conditionOptions": [{"from": 30, "to": 70}]}
    ]
}
screened = ins.screener().filter(params=params, limit=100)
print(screened)

# Filter criteria include:
# - exchange, sectorLv1, marketCap, marketPrice
# - dailyPriceChangePercent, adtv, avgVolume, priceReturn
# - stockStrength, rs, rsi, priceEma, macd, adx
# - ttmPe, ttmPb, ttmRoe, debtToEquity, epsGrowth
```

---

## Technical Analysis (vnstock_ta)

The vnstock_ta library provides 25+ technical indicators for trend analysis, momentum, volatility, and volume analysis.

```python
from vnstock_data import Market
from vnstock_ta import Indicator

# Get OHLCV data
mkt = Market()
df = mkt.equity("VCB").ohlcv(start="2024-09-01", end="2024-12-02")
df = df.set_index('time')

# Initialize indicator calculator
indicator = Indicator(data=df)

# === Trend Indicators ===
# Simple Moving Average
sma_20 = indicator.sma(length=20)
sma_50 = indicator.sma(length=50)

# Exponential Moving Average
ema_12 = indicator.ema(length=12)
ema_26 = indicator.ema(length=26)

# Volume Weighted Average Price
vwap = indicator.vwap(anchor='D')  # Daily VWAP

# Volume Weighted Moving Average
vwma = indicator.vwma(length=20)

# === Momentum Indicators ===
# Relative Strength Index
rsi = indicator.rsi(length=14)

# MACD
macd = indicator.macd(fast=12, slow=26, signal=9)
# Returns DataFrame with columns: MACD, MACD_signal, MACD_hist

# Stochastic
stoch = indicator.stochastic(k=14, d=3, smooth_k=3)
# Returns DataFrame with columns: %K, %D

# === Volatility Indicators ===
# Bollinger Bands
bb = indicator.bollinger_bands(length=20, std=2)
# Returns DataFrame with columns: BBL, BBM, BBU, BBB, BBP

# Average True Range
atr = indicator.atr(length=14)

# === Volume Indicators ===
# On-Balance Volume
obv = indicator.obv()

# Money Flow Index
mfi = indicator.mfi(length=14)

# Add indicators to DataFrame
df['SMA_20'] = sma_20
df['RSI'] = rsi
df['MACD'] = macd['MACD']
print(df[['close', 'SMA_20', 'RSI', 'MACD']].tail())
```

---

## News Crawling (vnstock_news)

The vnstock_news library provides tools to crawl financial news from Vietnamese news sources via RSS feeds and sitemaps.

```python
from vnstock_news import Crawler, BatchCrawler, AsyncBatchCrawler
import pandas as pd

# === Basic Crawler (RSS + Sitemap) ===
crawler = Crawler(site_name="vnexpress")

# Get articles from RSS feed
articles = crawler.get_articles_from_feed(limit_per_feed=10)
df = pd.DataFrame(articles)
print(df[['url', 'title', 'publish_time', 'source']].head())

# Get articles (RSS first, fallback to sitemap)
articles = crawler.get_articles(limit=100)

# Custom config for new sites
custom_config = {
    "name": "My Custom News",
    "domain": "mynews.com",
    "rss_urls": ["https://mynews.com/feed.xml"],
    "sitemap_url": "https://mynews.com/sitemap.xml",
    "config": {"title_selector": {"class": "title"}}
}
custom_crawler = Crawler(custom_config=custom_config)

# === Batch Crawler (with resume support) ===
batch = BatchCrawler(
    site_name="cafef",
    request_delay=1.5,      # Delay between requests
    output_path="./data",   # Output directory
    debug=False
)

# Fetch article details in batch
articles = batch.fetch_articles(limit=100)

# Fetch from specific sitemap
articles = batch.fetch_articles(
    sitemap_url="https://cafef.vn/latest-news-sitemap.xml",
    limit=500
)

# === Async Batch Crawler (high performance) ===
async_crawler = AsyncBatchCrawler(
    site_name="cafef",
    max_concurrent=5,       # Concurrent requests
    request_delay=0.5
)

# Async fetch
import asyncio
articles = asyncio.run(async_crawler.fetch_articles(limit=1000))
```

---

## Data Pipeline (vnstock_pipeline)

The vnstock_pipeline library provides a framework for building data pipelines with fetching, validation, transformation, and export stages.

```python
from vnstock_pipeline.tasks.ohlcv import run_task
from vnstock_pipeline.tasks.financial import run_financial_task
from vnstock_pipeline.tasks.intraday import run_intraday_task
from vnstock_pipeline.tasks.price_board import run_price_board_task

# === OHLCV Pipeline ===
# Fetch historical price data for multiple symbols
tickers = ['VCB', 'ACB', 'HPG', 'FPT', 'VNM']
run_task(
    tickers,
    start="2024-01-01",
    end="2024-12-31",
    interval="1D"
)
# Output: ./data/ohlcv/VCB.csv, ./data/ohlcv/ACB.csv, ...

# === Financial Pipeline ===
# Fetch financial reports for multiple companies
run_financial_task(
    tickers=['VCB', 'ACB'],
    balance_sheet_period="year",
    income_statement_year_period="year",
    lang="vi",
    output_dir="./data/financial"
)
# Output: ./data/financial/VCB_balance_sheet.csv, etc.

# === Intraday Pipeline ===
run_intraday_task(
    tickers=['VCB', 'ACB'],
    output_dir="./data/intraday"
)

# === Price Board Pipeline ===
run_price_board_task(
    tickers=['VCB', 'ACB', 'HPG'],
    output_dir="./data/price_board"
)

# === Custom Pipeline with Scheduler ===
from vnstock_pipeline.core.scheduler import Scheduler
from vnstock_pipeline.template.vnstock import VNFetcher, VNValidator, VNTransformer
from vnstock_pipeline.core.exporter import CSVExporter

scheduler = Scheduler(
    fetcher=VNFetcher(),
    validator=VNValidator(),
    transformer=VNTransformer(),
    exporter=CSVExporter(output_dir="./data")
)

# Run for list of symbols with parallel processing
results = scheduler.run(
    symbols=['VCB', 'ACB', 'HPG', 'FPT'],
    params={'start': '2024-01-01', 'end': '2024-12-31'}
)
```

---

## Real-time Streaming (vnstock_pipeline)

The streaming module provides WebSocket-based real-time data streaming with low latency for the entire market.

```python
from vnstock_pipeline.stream.client import WSSClient
from vnstock_pipeline.stream.processors import QuoteProcessor, TradeProcessor

# Initialize WebSocket client
client = WSSClient()

# Define callback for quote updates
def on_quote(data):
    print(f"Quote: {data['symbol']} - Price: {data['price']}")

# Define callback for trade updates
def on_trade(data):
    print(f"Trade: {data['symbol']} - {data['volume']} @ {data['price']}")

# Subscribe to symbols
client.subscribe(
    symbols=['VCB', 'ACB', 'HPG'],
    on_quote=on_quote,
    on_trade=on_trade
)

# Start streaming (blocking)
client.start()

# Or run in background
import threading
thread = threading.Thread(target=client.start)
thread.daemon = True
thread.start()

# Unsubscribe when done
client.unsubscribe(['VCB'])
client.stop()
```

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## Summary

The VnStock ecosystem provides a comprehensive toolkit for Vietnamese stock market data analysis, supporting use cases ranging from individual research to production trading systems. For basic data access, the free `vnstock` library offers Quote, Listing, Company, Finance, and Trading APIs with KBS and VCI data sources. For advanced features, sponsor-tier libraries unlock the Unified UI with seven data layers (Reference, Market, Fundamental, Analytics, Macro, Insights), technical indicators via vnstock_ta, news crawling via vnstock_news, and production-grade pipelines with real-time streaming via vnstock_pipeline.

Integration patterns typically follow a layered approach: use Reference layer for static lookups (symbols, companies, indices), Market layer for price data and trading activity, Fundamental layer for financial analysis, Insights layer for screening and rankings, and Macro layer for economic context. For production deployments, vnstock_pipeline's scheduler handles parallel processing of 1600+ symbols with automatic retry logic, while the streaming module enables real-time monitoring. The libraries support multiple output formats (DataFrame, CSV, Parquet, DuckDB) and integrate seamlessly with pandas, making it easy to build custom analysis workflows, backtesting systems, and trading dashboards for the Vietnamese market.