stock-liquidity

仓库: himself65/finance-skills
安装量: 518
排名: #6773

安装

npx skills add https://github.com/himself65/finance-skills --skill stock-liquidity
Contains Shell Commands
This skill contains shell command directives (
!command
) that may execute system commands. Review carefully before installing.
Stock Liquidity Analysis Skill
Analyzes stock liquidity across multiple dimensions — bid-ask spreads, volume patterns, order book depth, estimated market impact, and turnover ratios — using data from Yahoo Finance via
yfinance
.
Liquidity matters because it determines the real cost of trading. The quoted price is not what you actually pay — spreads, slippage, and market impact all eat into returns, especially for larger positions or less liquid names.
Important
This is for research and educational purposes only. Not financial advice. yfinance is not affiliated with Yahoo, Inc.
Step 1: Ensure Dependencies Are Available
Current environment status:
!python3 -c "import yfinance, pandas, numpy; print(f'yfinance={yfinance.__version__} pandas={pandas.__version__} numpy={numpy.__version__}')" 2>/dev/null || echo "DEPS_MISSING"
If
DEPS_MISSING
, install required packages:
import
subprocess
,
sys
subprocess
.
check_call
(
[
sys
.
executable
,
"-m"
,
"pip"
,
"install"
,
"-q"
,
"yfinance"
,
"pandas"
,
"numpy"
]
)
If already installed, skip and proceed.
Step 2: Route to the Correct Sub-Skill
Classify the user's request and jump to the matching section. If the user asks for a general liquidity assessment without specifying a particular metric, run
Sub-Skill A
(Liquidity Dashboard) which computes all key metrics together.
User Request
Route To
Examples
General liquidity check, "how liquid is X"
Sub-Skill A: Liquidity Dashboard
"how liquid is AAPL", "liquidity analysis for TSLA", "is this stock liquid enough"
Bid-ask spread, trading costs, effective spread
Sub-Skill B: Spread Analysis
"bid-ask spread for AMD", "what's the spread on NVDA options", "trading cost estimate"
Volume, ADTV, dollar volume, volume profile
Sub-Skill C: Volume Analysis
"volume analysis MSFT", "average daily volume", "volume profile for SPY"
Order book depth, market depth, level 2
Sub-Skill D: Order Book Depth
"order book depth for AAPL", "market depth", "show me the book"
Market impact, slippage, execution cost for large orders
Sub-Skill E: Market Impact
"how much would 50k shares move the price", "slippage estimate", "market impact of $1M order"
Turnover ratio, trading activity relative to float
Sub-Skill F: Turnover Ratio
"turnover ratio for GME", "float turnover", "how actively traded is this"
Compare liquidity across multiple stocks
Sub-Skill A
(multi-ticker mode)
"compare liquidity AAPL vs TSLA", "which is more liquid AMD or INTC"
Defaults
Parameter
Default
Lookback period
3mo
(3 months)
Data interval
1d
(daily)
Market impact model
Square-root model
Intraday interval (when needed)
5m
Sub-Skill A: Liquidity Dashboard
Goal
Produce a comprehensive liquidity snapshot combining all key metrics for one or more tickers. A1: Fetch data and compute all metrics import yfinance as yf import pandas as pd import numpy as np def liquidity_dashboard ( ticker_symbol , period = "3mo" ) : ticker = yf . Ticker ( ticker_symbol ) info = ticker . info hist = ticker . history ( period = period ) if hist . empty : return None

--- Spread metrics (from current quote) ---

bid

info . get ( "bid" , None ) ask = info . get ( "ask" , None ) current_price = info . get ( "currentPrice" ) or info . get ( "regularMarketPrice" ) or hist [ "Close" ] . iloc [ - 1 ] spread = None spread_pct = None if bid and ask and bid

0 and ask

0 : spread = round ( ask - bid , 4 ) midpoint = ( ask + bid ) / 2 spread_pct = round ( ( spread / midpoint ) * 100 , 4 )

--- Volume metrics ---

avg_volume

hist [ "Volume" ] . mean ( ) median_volume = hist [ "Volume" ] . median ( ) avg_dollar_volume = ( hist [ "Close" ] * hist [ "Volume" ] ) . mean ( ) volume_std = hist [ "Volume" ] . std ( ) volume_cv = volume_std / avg_volume if avg_volume

0 else None

coefficient of variation

--- Turnover ratio ---

shares_outstanding

info . get ( "sharesOutstanding" , None ) float_shares = info . get ( "floatShares" , None ) base_shares = float_shares or shares_outstanding turnover_ratio = round ( avg_volume / base_shares , 6 ) if base_shares else None

--- Amihud illiquidity ratio ---

Average of |daily return| / daily dollar volume

returns

hist [ "Close" ] . pct_change ( ) . dropna ( ) dollar_volume = ( hist [ "Close" ] * hist [ "Volume" ] ) . iloc [ 1 : ]

align with returns

amihud_values

returns . abs ( ) / dollar_volume amihud = amihud_values [ amihud_values . replace ( [ np . inf , - np . inf ] , np . nan ) . notna ( ) ] . mean ( )

--- Market impact estimate (square-root model) ---

For a hypothetical order of 1% of ADV

adv

avg_volume order_size = adv * 0.01 daily_volatility = returns . std ( ) sigma = daily_volatility participation_rate = order_size / adv if adv

0 else 0 impact_bps = sigma * np . sqrt ( participation_rate ) * 10000

in basis points

return
{
"ticker"
:
ticker_symbol
,
"current_price"
:
round
(
current_price
,
2
)
,
"bid"
:
bid
,
"ask"
:
ask
,
"spread"
:
spread
,
"spread_pct"
:
spread_pct
,
"avg_daily_volume"
:
int
(
avg_volume
)
,
"median_daily_volume"
:
int
(
median_volume
)
,
"avg_dollar_volume"
:
round
(
avg_dollar_volume
,
0
)
,
"volume_cv"
:
round
(
volume_cv
,
3
)
if
volume_cv
else
None
,
"shares_outstanding"
:
shares_outstanding
,
"float_shares"
:
float_shares
,
"turnover_ratio"
:
turnover_ratio
,
"amihud_illiquidity"
:
round
(
amihud
*
1e9
,
4
)
if
not
np
.
isnan
(
amihud
)
else
None
,
"daily_volatility"
:
round
(
daily_volatility
*
100
,
2
)
,
"impact_1pct_adv_bps"
:
round
(
impact_bps
,
2
)
,
"observations"
:
len
(
hist
)
,
}
A2: Interpret and present
Present as a summary card. For the Amihud illiquidity ratio, multiply by 1e9 for readability (standard convention).
Liquidity grade
(use these rough thresholds for US equities):
Grade
Avg Dollar Volume
Spread (%)
Amihud (×10⁹)
Very High
> $500M/day
< 0.03%
< 0.01
High
$50M–$500M/day
0.03–0.10%
0.01–0.1
Moderate
$5M–$50M/day
0.10–0.50%
0.1–1.0
Low
$500K–$5M/day
0.50–2.00%
1.0–10
Very Low
< $500K/day
> 2.00%
> 10
When comparing multiple tickers, show a side-by-side table and highlight which is more liquid and why.
Sub-Skill B: Spread Analysis
Goal
Detailed bid-ask spread analysis including current spread, historical context from options data, and effective spread estimates.
B1: Current spread from quote
import
yfinance
as
yf
def
spread_analysis
(
ticker_symbol
)
:
ticker
=
yf
.
Ticker
(
ticker_symbol
)
info
=
ticker
.
info
bid
=
info
.
get
(
"bid"
,
0
)
ask
=
info
.
get
(
"ask"
,
0
)
bid_size
=
info
.
get
(
"bidSize"
,
None
)
ask_size
=
info
.
get
(
"askSize"
,
None
)
current_price
=
info
.
get
(
"currentPrice"
)
or
info
.
get
(
"regularMarketPrice"
,
0
)
result
=
{
"bid"
:
bid
,
"ask"
:
ask
,
"bid_size"
:
bid_size
,
"ask_size"
:
ask_size
}
if
bid
>
0
and
ask
>
0
:
midpoint
=
(
bid
+
ask
)
/
2
result
[
"absolute_spread"
]
=
round
(
ask
-
bid
,
4
)
result
[
"relative_spread_pct"
]
=
round
(
(
ask
-
bid
)
/
midpoint
*
100
,
4
)
result
[
"relative_spread_bps"
]
=
round
(
(
ask
-
bid
)
/
midpoint
*
10000
,
2
)
return
result
B2: Options spread context
Options data from yfinance includes bid/ask for each strike, which gives a sense of derivatives liquidity. Use the nearest expiration, extract near-the-money calls and puts, and compute spread and spread percentage for each.
See
references/liquidity_reference.md
§ "Options Spread Analysis" for the full code template.
B3: Present results
Show:
Current quoted spread (absolute, relative %, basis points)
Bid/ask sizes if available
Near-the-money options spreads for context
How the spread compares to typical ranges for this market cap tier
Sub-Skill C: Volume Analysis
Goal
Analyze trading volume patterns — averages, trends, relative volume, and dollar volume. C1: Compute volume metrics import yfinance as yf import pandas as pd import numpy as np def volume_analysis ( ticker_symbol , period = "3mo" ) : ticker = yf . Ticker ( ticker_symbol ) hist = ticker . history ( period = period ) if hist . empty : return None vol = hist [ "Volume" ] close = hist [ "Close" ] dollar_vol = vol * close

Relative volume (today vs average)

rvol

vol . iloc [ - 1 ] / vol . mean ( ) if vol . mean ( )

0 else None

Volume trend (linear regression slope over the period)

x

np . arange ( len ( vol ) ) slope , _ = np . polyfit ( x , vol . values , 1 ) if len ( vol )

1 else ( 0 , 0 ) trend_pct = ( slope * len ( vol ) ) / vol . mean ( ) * 100

% change over period

Volume profile by day of week

hist_copy

hist . copy ( ) hist_copy [ "DayOfWeek" ] = hist_copy . index . dayofweek day_names = { 0 : "Mon" , 1 : "Tue" , 2 : "Wed" , 3 : "Thu" , 4 : "Fri" } vol_by_day = hist_copy . groupby ( "DayOfWeek" ) [ "Volume" ] . mean ( ) vol_by_day . index = vol_by_day . index . map ( day_names )

High/low volume days

high_vol_days

hist
.
nlargest
(
5
,
"Volume"
)
[
[
"Close"
,
"Volume"
]
]
low_vol_days
=
hist
.
nsmallest
(
5
,
"Volume"
)
[
[
"Close"
,
"Volume"
]
]
return
{
"avg_volume"
:
int
(
vol
.
mean
(
)
)
,
"median_volume"
:
int
(
vol
.
median
(
)
)
,
"avg_dollar_volume"
:
round
(
dollar_vol
.
mean
(
)
,
0
)
,
"current_volume"
:
int
(
vol
.
iloc
[
-
1
]
)
,
"relative_volume"
:
round
(
rvol
,
2
)
if
rvol
else
None
,
"volume_trend_pct"
:
round
(
trend_pct
,
1
)
,
"volume_by_day"
:
vol_by_day
.
to_dict
(
)
,
"high_vol_days"
:
high_vol_days
,
"low_vol_days"
:
low_vol_days
,
"max_volume"
:
int
(
vol
.
max
(
)
)
,
"min_volume"
:
int
(
vol
.
min
(
)
)
,
}
C2: Present results
Show:
Average daily volume (shares and dollar) with median for comparison
Relative volume (RVOL) — today's volume vs. the average. RVOL > 1.5 is elevated; RVOL < 0.5 is unusually quiet
Volume trend — is trading activity increasing or declining?
Day-of-week pattern (if meaningful variation exists)
Top 5 highest-volume days with context (earnings? news?)
Sub-Skill D: Order Book Depth
Goal
Estimate order book depth using available bid/ask data from the equity quote and options chain.
Yahoo Finance does not provide full Level 2 / order book data. Be upfront about this limitation. What we can do:
Equity quote
bid, ask, bid size, ask size (top of book only)
Options chain
bid/ask and open interest across strikes give a proxy for derivatives depth
Intraday volume distribution
how volume is distributed within the day suggests how deep the continuous market is
D1: Gather available depth data
Collect three data points:
Top of book
— bid, ask, bidSize, askSize from
ticker.info
Intraday volume distribution
— 5-min bars over the last 5 days, grouped by time-of-day and normalized to percentage of daily volume
Options open interest
— total call/put OI and volume from the nearest expiration as a derivatives depth proxy
See
references/liquidity_reference.md
§ "Order Book Depth Proxy" for the full code template.
D2: Present results
Show:
Top of book
current bid/ask with sizes
Intraday volume shape
where volume concentrates (open/close vs. midday)
Options depth
total open interest and volume as a proxy for derivatives liquidity
Honest limitation
"Yahoo Finance provides top-of-book only. For full Level 2 depth, a direct market data feed (e.g., NYSE OpenBook, NASDAQ TotalView) is needed."
Sub-Skill E: Market Impact
Goal
Estimate how much a given order size would move the price, using the square-root market impact model. The standard model in practice is: Impact (%) = σ × √(Q / V) where σ is daily volatility, Q is order size in shares, and V is average daily volume. This is a simplified version of the Almgren-Chriss framework used by institutional traders. E1: Compute market impact estimate import yfinance as yf import numpy as np def market_impact ( ticker_symbol , order_shares = None , order_dollars = None , period = "3mo" ) : ticker = yf . Ticker ( ticker_symbol ) hist = ticker . history ( period = period ) info = ticker . info if hist . empty : return None current_price = info . get ( "currentPrice" ) or hist [ "Close" ] . iloc [ - 1 ] avg_volume = hist [ "Volume" ] . mean ( ) daily_volatility = hist [ "Close" ] . pct_change ( ) . dropna ( ) . std ( )

Determine order size in shares

if order_dollars and not order_shares : order_shares = order_dollars / current_price elif not order_shares :

Default: estimate for various sizes

order_shares

avg_volume * 0.01

1% of ADV

participation_rate

order_shares / avg_volume if avg_volume

0 else 0 pct_adv = ( order_shares / avg_volume * 100 ) if avg_volume

0 else 0

Square-root impact model

impact_pct

daily_volatility * np . sqrt ( participation_rate ) * 100 impact_bps = impact_pct * 100 impact_dollars = impact_pct / 100 * current_price * order_shares

Generate impact curve for multiple order sizes

sizes

[ 0.001 , 0.005 , 0.01 , 0.02 , 0.05 , 0.10 , 0.20 , 0.50 ]

as fraction of ADV

curve

[
]
for
s
in
sizes
:
q
=
avg_volume
*
s
imp
=
daily_volatility
*
np
.
sqrt
(
s
)
*
100
curve
.
append
(
{
"pct_adv"
:
round
(
s
*
100
,
1
)
,
"shares"
:
int
(
q
)
,
"dollars"
:
round
(
q
*
current_price
,
0
)
,
"impact_bps"
:
round
(
imp
*
100
,
1
)
,
"impact_dollars_per_share"
:
round
(
imp
/
100
*
current_price
,
4
)
,
}
)
return
{
"ticker"
:
ticker_symbol
,
"current_price"
:
round
(
current_price
,
2
)
,
"avg_daily_volume"
:
int
(
avg_volume
)
,
"daily_volatility_pct"
:
round
(
daily_volatility
*
100
,
2
)
,
"order_shares"
:
int
(
order_shares
)
,
"order_dollars"
:
round
(
order_shares
*
current_price
,
0
)
,
"pct_of_adv"
:
round
(
pct_adv
,
2
)
,
"estimated_impact_bps"
:
round
(
impact_bps
,
1
)
,
"estimated_impact_pct"
:
round
(
impact_pct
,
4
)
,
"estimated_impact_total_dollars"
:
round
(
impact_dollars
,
2
)
,
"impact_curve"
:
curve
,
}
E2: Present results
Show:
The estimated impact for the user's specific order size
An impact curve table showing how cost scales with order size
Context: "This uses the square-root market impact model, a standard institutional estimate. Actual impact depends on execution strategy (VWAP, TWAP, etc.), time of day, and current market conditions."
If impact > 50 bps, flag that the order is large relative to liquidity and suggest the user consider algorithmic execution or splitting the order across days
Sub-Skill F: Turnover Ratio
Goal
Measure how actively a stock trades relative to its shares outstanding and free float. F1: Compute turnover metrics import yfinance as yf import pandas as pd import numpy as np def turnover_analysis ( ticker_symbol , period = "3mo" ) : ticker = yf . Ticker ( ticker_symbol ) hist = ticker . history ( period = period ) info = ticker . info if hist . empty : return None avg_volume = hist [ "Volume" ] . mean ( ) shares_outstanding = info . get ( "sharesOutstanding" ) float_shares = info . get ( "floatShares" ) result = { "avg_daily_volume" : int ( avg_volume ) , "shares_outstanding" : shares_outstanding , "float_shares" : float_shares , } if shares_outstanding : daily_turnover = avg_volume / shares_outstanding result [ "daily_turnover_ratio" ] = round ( daily_turnover , 6 ) result [ "annualized_turnover" ] = round ( daily_turnover * 252 , 2 ) result [ "days_to_trade_float" ] = round ( ( float_shares or shares_outstanding ) / avg_volume , 1 ) if avg_volume

0 else None if float_shares : float_turnover = avg_volume / float_shares result [ "float_turnover_daily" ] = round ( float_turnover , 6 ) result [ "float_turnover_annualized" ] = round ( float_turnover * 252 , 2 )

Turnover trend

vol

hist
[
"Volume"
]
base
=
float_shares
or
shares_outstanding
if
base
:
hist_copy
=
hist
.
copy
(
)
hist_copy
[
"turnover"
]
=
hist_copy
[
"Volume"
]
/
base
recent_turnover
=
hist_copy
[
"turnover"
]
.
tail
(
20
)
.
mean
(
)
older_turnover
=
hist_copy
[
"turnover"
]
.
head
(
20
)
.
mean
(
)
if
older_turnover
>
0
:
result
[
"turnover_trend_pct"
]
=
round
(
(
recent_turnover
-
older_turnover
)
/
older_turnover
*
100
,
1
)
return
result
F2: Present results
Show:
Daily and annualized turnover ratios (vs. outstanding and float)
"Days to trade the float" — how many days at average volume to turn over the entire free float
Turnover trend — is the stock becoming more or less actively traded?
Context:
Turnover (Annualized)
Interpretation
> 500%
Extremely active — likely speculative or momentum-driven
100–500%
Actively traded
30–100%
Moderate activity
< 30%
Thinly traded — likely institutional buy-and-hold or neglected
Step 3: Respond to the User
After running the appropriate sub-skill:
Always include
The
lookback period
used for historical metrics
The
data timestamp
— spreads and quotes are snapshots, not real-time
Any tickers that returned
empty data
(invalid symbol, delisted, etc.)
Always caveat
Yahoo Finance quote data has a
15-minute delay
for most exchanges — spreads shown may not reflect the current live market
Full order book (Level 2) data is
not available
through Yahoo Finance
Market impact estimates are
models, not guarantees
— actual execution costs depend on strategy, timing, and market conditions
Liquidity can
change rapidly
— a stock that's liquid today may not be tomorrow (especially around events, halts, or during extended hours)
Practical guidance (mention when relevant)
Position sizing
If estimated impact exceeds 25 bps, the position may be too large for the stock's liquidity
Small/micro-cap warning
Stocks with < $1M daily dollar volume require careful execution
Spread costs compound
A 0.10% spread on a round-trip (buy + sell) costs 0.20% — this adds up for active strategies
Illiquidity premium
Less liquid stocks historically earn higher returns as compensation — but the transaction costs can eat this premium
Important
Never recommend specific trades. Present liquidity data and let the user make their own decisions. Reference Files references/liquidity_reference.md — Detailed formulas, extended code templates, metric interpretation guides, and academic references for all liquidity measures Read the reference file when you need exact formulas, edge case handling, or deeper background on liquidity metrics.
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