Data API¶

Retrieve historical quotes, real-time data, financial information, A-share specific data, etc.

get_price¶

Retrieve historical price data.

get_price(security, start_date=None, end_date=None, frequency='daily', fields=None, count=None)

Parameter	Type	Required	Description
`security`	`str` or `list`	Yes	Stock code
`start_date`	`str`/`date`	No	Start date
`end_date`	`str`/`date`	No	End date
`frequency`	`str`	No	`'daily'` / `'1d'`; use `get_price_minute` for minutes
`fields`	`list`	No	Specify returned fields
`count`	`int`	No	Return the most recent N bars

Returns a DataFrame (single stock) or dict[str, DataFrame] (multiple stocks).

history¶

Retrieve count bars of data looking back from the current backtest time. Only available inside strategy callbacks.

history(count, unit='1d', field='close', security=None, df=False)

attribute_history¶

Retrieve historical attribute data for a single stock.

attribute_history(security, count, unit='1d', fields=('close',), df=True, skip_paused=True, fq='pre')

Returns a DataFrame with columns: open, high, low, close, volume, money, pct_change, turnover.

get_current_data¶

Retrieve real-time snapshots for all A-shares. Returns dict[str, dict] containing fields such as code, name, price, pct_change, volume, pe, pb, total_value, etc.

get_security_info¶

Retrieve basic information for a single stock. Returns a SecurityInfo object (code, name, industry, total_shares, float_shares, total_value, list_date).

get_valuation¶

Retrieve valuation data. Returns dict or None.

scan_market¶

Scan A-shares with filters.

scan_market(min_price=10, min_pct_change=3, max_pct_change=5, max_pe=50)

get_financial_screen¶

Screen by financial metrics.

get_financial_screen(min_pe=None, max_pe=None, min_pb=None, max_pb=None, min_roe=None)

get_all_securities¶

Retrieve a list of all A-shares. Returns a DataFrame (code, name).

get_trade_days¶

Retrieve the trading calendar. Returns list[date].

Indices & Industry¶

API	Description	Returns
`get_index_stocks(index_code)`	Index constituent stocks	`DataFrame`
`get_industry_list()`	All industry sectors	`list[str]`
`get_industry_stocks(industry_name)`	Stocks in a given industry	`DataFrame`
`get_industry(code)`	Industry classification for a single stock	`dict` or `None`
`get_index_weights(index_code, date=None)`	Index constituent weights	`DataFrame`

Concept Sectors¶

API	Description	Returns
`get_concept_list()`	All concept sectors	`list[str]`
`get_concept_stocks(concept_name)`	Constituent stocks of a concept	`DataFrame`

Minute-Level Data¶

fetch_minute_data(code, period='5m', start_date=None, end_date=None, adjust='qfq')
get_price_minute(security, count=None, period='5m', fields=None, adjust='qfq')

Supported periods: 1m, 5m, 15m, 30m, 60m.

Tick Data¶

get_tick_data(code, trade_date=None)

Money Flow & Billboard¶

get_money_flow(code, start_date=None, end_date=None, count=None)
get_billboard_list(stock_list=None, date=None, start_date=None, end_date=None)

get_north_money_flow¶

Northbound money flow (Shanghai-Hong Kong Stock Connect + Shenzhen-Hong Kong Stock Connect aggregate).

get_north_money_flow(start_date=None, end_date=None)

Parameter	Type	Description
`start_date`	`str`/`date`	Start date; defaults to 30 days ago
`end_date`	`str`/`date`	End date; defaults to today (China timezone)

Returns a DataFrame with the following columns:

Column	Description
`date`	Trading date
`net_buy`	Net buy amount (100 million yuan)
`total_buy`	Total buy amount (100 million yuan)
`total_sell`	Total sell amount (100 million yuan)

from eqlib import get_north_money_flow

# Get northbound flow for the last 3 months
north = get_north_money_flow(start_date="2024-01-01", end_date="2024-03-31")

# Calculate net buy over the last 5 days
recent_5d = north["net_buy"].tail(5).sum()
if recent_5d > 50:
    print("Strong northbound capital inflow")

Note: Uses China timezone (UTC+8) to determine "today"; cached for 1 hour.

get_margin_data¶

Margin trading data (market-wide aggregate).

get_margin_data(start_date=None, end_date=None)

Returns a DataFrame with the following columns:

Column	Description
`date`	Trading date
`margin_balance`	Margin balance (100 million yuan)
`margin_buy`	Margin buy amount (100 million yuan)
`margin_repay`	Margin repayment amount (100 million yuan) — NaN for the first row
`short_balance`	Short selling balance (100 million yuan)

from eqlib import get_margin_data

margin = get_margin_data(start_date="2024-01-01", end_date="2024-03-31")

# Margin balance rate of change
margin["change_pct"] = margin["margin_balance"].diff(5) / margin["margin_balance"].shift(5) * 100

Note: margin_repay is NaN for the first row (no prior balance to compute from). Users can handle this with dropna() or fillna().

get_limit_up_down_stats¶

Limit up/down statistics (daily count of stocks hitting price limit up / limit down).

get_limit_up_down_stats(start_date=None, end_date=None)

Returns a DataFrame with the following columns:

Column	Description
`date`	Trading date
`limit_up_count`	Number of stocks hitting the upper price limit
`limit_down_count`	Number of stocks hitting the lower price limit
`api_error_count`	Number of failed API calls (for data quality monitoring)

from eqlib import get_limit_up_down_stats

stats = get_limit_up_down_stats()

# Systemic risk alert
latest_down = stats["limit_down_count"].iloc[-1]
if latest_down > 100:
    print("⚠️ Systemic risk alert")

Note: The API only supports the most recent 30 trading days; a warning is issued if the requested range exceeds this limit.

get_restriction_release¶

Restricted share unlock schedule (list of upcoming unlocks within the next N days).

get_restriction_release(days=30)

Parameter	Type	Description
`days`	`int`	Number of days to look ahead; defaults to 30

Returns a DataFrame with the following columns:

Column	Description
`code`	Stock code
`name`	Stock name
`release_date`	Unlock date
`release_amount`	Unlock quantity (10,000 shares)
`release_value`	Unlock market value (100 million yuan)
`release_pct`	Percentage of pre-unlock float market cap

from eqlib import get_restriction_release

# Get unlocks for the next 30 days
releases = get_restriction_release(days=30)

# Alert on large unlocks
large = releases[releases["release_value"] > 50]
print(f"Large unlocks: {len(large)} stocks")

Financial Data¶

get_financial_abstract(code)

Chained Stock Selection API¶

query(*fields)
get_fundamentals(query_or_code, date=None)

Available fields are accessed via the valuation namespace: code, market_cap, total_value, float_value, pe, pb, turnover, price, pct_change.

Chained methods: .filter(), .order_by(), .limit().

q = query(valuation.code, valuation.market_cap, valuation.pe) \
    .filter(valuation.market_cap.between(20, 30), valuation.pe > 0) \
    .order_by(valuation.market_cap.asc()) \
    .limit(5)
df = get_fundamentals(q)

get_current_data_object¶

Retrieve real-time market snapshots with attribute access. Returns dict[str, _StockDataObj].

get_extras¶

Retrieve additional data fields ('is_st' or 'net_value').

Universe Management¶

set_universe(security_list)   # Set the strategy universe
get_universe()                 # Get the current universe

Local Files¶

API	Description
`download_stock_data(code, start_date, end_date, adjust='qfq', output_dir=None)`	Download daily data as CSV
`load_csv(path, index_col='date', parse_dates=True)`	Load data from a local CSV
`clear_cache()`	Clear in-memory cache
`save_stock_local(security, start_date, end_date)`	Download and save locally
`load_stock_local(security, start_date, end_date)`	Load from local storage
`has_local_data(security)`	Check if local data exists
`list_local_stocks()`	List all local files
`remove_local_data(security)`	Delete a single local file
`clear_all_local_data()`	Delete all local files

Historical price and adjustment contracts¶

count must be a positive integer (not a boolean). Results contain the last N rows in the window, or fewer when data is unavailable. String, date and datetime inputs are supported. With an active backtest context, get_price follows attribute_history: only daily bars before the current date are visible, and explicit future end dates are clamped. Research queries without a backtest context use the requested range. frequency="1m" raises NotImplementedError rather than silently returning daily bars.

Preloaded prices must match fq: default qfq preload supports fq="pre"; requesting fq=None (raw prices) raises ValueError unless the preload explicitly contains unadjusted data. Use genuine raw prices rather than treating adjusted prices as raw. Mutating returned data does not mutate the shared cache.

get_price_minute also enforces simulation visibility: only bar timestamps strictly before current_dt are returned, conservatively excluding the current bar. Supported periods are 1m, 5m, 15m, 30m and 60m; rows are sorted before taking the last count bars.

Data API¶

get_price¶

history¶

attribute_history¶

get_current_data¶

get_security_info¶

get_valuation¶

scan_market¶

get_financial_screen¶

get_all_securities¶

get_trade_days¶

Indices & Industry¶

Concept Sectors¶

Minute-Level Data¶

Tick Data¶

Money Flow & Billboard¶

A-Share Specific Data¶

get_north_money_flow¶

get_margin_data¶

get_limit_up_down_stats¶

get_restriction_release¶

Financial Data¶

Chained Stock Selection API¶

get_current_data_object¶

get_extras¶

Universe Management¶

Local Files¶

Historical price and adjustment contracts¶