yfinance-data

Name: yfinance-data
Author: himself65

1.9kinstalls351forks

How to install yfinance-data?

himself65/finance-skills/yfinance-data

$npx -y skills add himself65/finance-skills --skill yfinance-data

Installs into the current project.

Summary

Fetch financial and market data using the yfinance Python library. Use this skill whenever the user asks for stock prices, historical data, financial statements, options chains, dividends, earnings, analyst recommendations, or any market data. Triggers include: any mention of sto

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SKILL.md

1	# yfinance Data Skill
2
3	Fetches financial and market data from Yahoo Finance using the [yfinance](https://github.com/ranaroussi/yfinance) Python library.
4
5	Important: yfinance is not affiliated with Yahoo, Inc. Data is for research and educational purposes.
6
7	---
8
9	## Step 1: Ensure yfinance Is Available
10
11	Current environment status:
12
13	```
14	!`python3 -c "import yfinance; print('yfinance ' + yfinance.__version__ + ' installed')" 2>/dev/null \|\| echo "YFINANCE_NOT_INSTALLED"`
15	```
16
17	If `YFINANCE_NOT_INSTALLED`, install it before running any code:
18
19	```python
20	import subprocess, sys
21	subprocess.check_call([sys.executable, "-m", "pip", "install", "-q", "yfinance"])
22	```
23
24	If yfinance is already installed, skip the install step and proceed directly.
25
26	---
27
28	## Step 2: Identify What the User Needs
29
30	Match the user's request to one or more data categories below, then use the corresponding code from `references/api_reference.md`.
31
32	\| User Request \| Data Category \| Primary Method \|
33	\|---\|---\|---\|
34	\| Stock price, quote \| Current price \| `ticker.info` or `ticker.fast_info` \|
35	\| Price history, chart data \| Historical OHLCV \| `ticker.history()` or `yf.download()` \|
36	\| Balance sheet \| Financial statements \| `ticker.balance_sheet` \|
37	\| Income statement, revenue \| Financial statements \| `ticker.income_stmt` \|
38	\| Cash flow \| Financial statements \| `ticker.cashflow` \|
39	\| Dividends \| Corporate actions \| `ticker.dividends` \|
40	\| Stock splits \| Corporate actions \| `ticker.splits` \|
41	\| Options chain, calls, puts \| Options data \| `ticker.option_chain()` \|
42	\| Earnings, EPS \| Analysis \| `ticker.earnings_history` \|
43	\| Analyst price targets \| Analysis \| `ticker.analyst_price_targets` \|
44	\| Recommendations, ratings \| Analysis \| `ticker.recommendations` \|
45	\| Upgrades/downgrades \| Analysis \| `ticker.upgrades_downgrades` \|
46	\| Institutional holders \| Ownership \| `ticker.institutional_holders` \|
47	\| Insider transactions \| Ownership \| `ticker.insider_transactions` \|
48	\| Company overview, sector \| General info \| `ticker.info` \|
49	\| Compare multiple stocks \| Bulk download \| `yf.download()` \|
50	\| Screen/filter stocks \| Screener \| `yf.Screener` + `yf.EquityQuery` \|
51	\| Sector/industry data \| Market data \| `yf.Sector` / `yf.Industry` \|
52	\| News \| News \| `ticker.news` \|
53
54	---
55
56	## Step 3: Write and Execute the Code
57
58	### General pattern
59
60	```python
61	import subprocess, sys
62	subprocess.check_call([sys.executable, "-m", "pip", "install", "-q", "yfinance"])
63
64	import yfinance as yf
65
66	ticker = yf.Ticker("AAPL")
67	# ... use the appropriate method from the reference
68	```
69
70	### Key rules
71
72	1. Always wrap in try/except — Yahoo Finance may rate-limit or return empty data
73	2. Use `yf.download()` for multi-ticker comparisons — it's faster with multi-threading
74	3. For options, list expiration dates first with `ticker.options` before calling `ticker.option_chain(date)`
75	4. For quarterly data, use `quarterly_` prefix: `ticker.quarterly_income_stmt`, `ticker.quarterly_balance_sheet`, `ticker.quarterly_cashflow`
76	5. For large date ranges, be mindful of intraday limits — 1m data only goes back ~7 days, 1h data ~730 days
77	6. Print DataFrames clearly — use `.to_string()` or `.to_markdown()` for readability, or select key columns
78	7. Timezone handling — yfinance returns tz-aware datetime indices (e.g., `America/New_York`). When comparing dates, always use `pd.Timestamp(..., tz=...)` or strip timezones with `.tz_localize(None)`. See the reference file for details.
79
80	### Valid periods and intervals
81
82	\| Periods \| `1d`, `5d`, `1mo`, `3mo`, `6mo`, `1y`, `2y`, `5y`, `10y`, `ytd`, `max` \|
83	\|---\|---\|
84	\| Intervals \| `1m`, `2m`, `5m`, `15m`, `30m`, `60m`, `90m`, `1h`, `1d`, `5d`, `1wk`, `1mo`, `3mo` \|
85
86	---
87
88	## Step 4: Present the Data
89
90	After fetching data, present it clearly:
91
92	1. Summarize key numbers in a brief text response (current price, market cap, P/E, etc.)
93	2. Show tabular data formatted for readability — use markdown tables or formatted DataFrames
94	3. Highlight notable items — earnings beats/misses, unusual volume, dividend changes
95	4. Provide context — compare to sector averages, historical ranges, or analyst consensus when relevant
96
97	If the user seems to want a chart or visualization, combine with an appropriate v

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yfinance-data

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View on GitHub

How to install yfinance-data?

himself65/finance-skills/yfinance-data

$npx -y skills add himself65/finance-skills --skill yfinance-data

Installs into the current project.

Summary

Files · 1

View on GitHub

SKILL.md

1	# yfinance Data Skill
2
3	Fetches financial and market data from Yahoo Finance using the [yfinance](https://github.com/ranaroussi/yfinance) Python library.
4
5	Important: yfinance is not affiliated with Yahoo, Inc. Data is for research and educational purposes.
6
7	---
8
9	## Step 1: Ensure yfinance Is Available
10
11	Current environment status:
12
13	```
14	!`python3 -c "import yfinance; print('yfinance ' + yfinance.__version__ + ' installed')" 2>/dev/null \|\| echo "YFINANCE_NOT_INSTALLED"`
15	```
16
17	If `YFINANCE_NOT_INSTALLED`, install it before running any code:
18
19	```python
20	import subprocess, sys
21	subprocess.check_call([sys.executable, "-m", "pip", "install", "-q", "yfinance"])
22	```
23
24	If yfinance is already installed, skip the install step and proceed directly.
25
26	---
27
28	## Step 2: Identify What the User Needs
29
30	Match the user's request to one or more data categories below, then use the corresponding code from `references/api_reference.md`.
31
32	\| User Request \| Data Category \| Primary Method \|
33	\|---\|---\|---\|
34	\| Stock price, quote \| Current price \| `ticker.info` or `ticker.fast_info` \|
35	\| Price history, chart data \| Historical OHLCV \| `ticker.history()` or `yf.download()` \|
36	\| Balance sheet \| Financial statements \| `ticker.balance_sheet` \|
37	\| Income statement, revenue \| Financial statements \| `ticker.income_stmt` \|
38	\| Cash flow \| Financial statements \| `ticker.cashflow` \|
39	\| Dividends \| Corporate actions \| `ticker.dividends` \|
40	\| Stock splits \| Corporate actions \| `ticker.splits` \|
41	\| Options chain, calls, puts \| Options data \| `ticker.option_chain()` \|
42	\| Earnings, EPS \| Analysis \| `ticker.earnings_history` \|
43	\| Analyst price targets \| Analysis \| `ticker.analyst_price_targets` \|
44	\| Recommendations, ratings \| Analysis \| `ticker.recommendations` \|
45	\| Upgrades/downgrades \| Analysis \| `ticker.upgrades_downgrades` \|
46	\| Institutional holders \| Ownership \| `ticker.institutional_holders` \|
47	\| Insider transactions \| Ownership \| `ticker.insider_transactions` \|
48	\| Company overview, sector \| General info \| `ticker.info` \|
49	\| Compare multiple stocks \| Bulk download \| `yf.download()` \|
50	\| Screen/filter stocks \| Screener \| `yf.Screener` + `yf.EquityQuery` \|
51	\| Sector/industry data \| Market data \| `yf.Sector` / `yf.Industry` \|
52	\| News \| News \| `ticker.news` \|
53
54	---
55
56	## Step 3: Write and Execute the Code
57
58	### General pattern
59
60	```python
61	import subprocess, sys
62	subprocess.check_call([sys.executable, "-m", "pip", "install", "-q", "yfinance"])
63
64	import yfinance as yf
65
66	ticker = yf.Ticker("AAPL")
67	# ... use the appropriate method from the reference
68	```
69
70	### Key rules
71
72	1. Always wrap in try/except — Yahoo Finance may rate-limit or return empty data
73	2. Use `yf.download()` for multi-ticker comparisons — it's faster with multi-threading
74	3. For options, list expiration dates first with `ticker.options` before calling `ticker.option_chain(date)`
75	4. For quarterly data, use `quarterly_` prefix: `ticker.quarterly_income_stmt`, `ticker.quarterly_balance_sheet`, `ticker.quarterly_cashflow`
76	5. For large date ranges, be mindful of intraday limits — 1m data only goes back ~7 days, 1h data ~730 days
77	6. Print DataFrames clearly — use `.to_string()` or `.to_markdown()` for readability, or select key columns
78	7. Timezone handling — yfinance returns tz-aware datetime indices (e.g., `America/New_York`). When comparing dates, always use `pd.Timestamp(..., tz=...)` or strip timezones with `.tz_localize(None)`. See the reference file for details.
79
80	### Valid periods and intervals
81
82	\| Periods \| `1d`, `5d`, `1mo`, `3mo`, `6mo`, `1y`, `2y`, `5y`, `10y`, `ytd`, `max` \|
83	\|---\|---\|
84	\| Intervals \| `1m`, `2m`, `5m`, `15m`, `30m`, `60m`, `90m`, `1h`, `1d`, `5d`, `1wk`, `1mo`, `3mo` \|
85
86	---
87
88	## Step 4: Present the Data
89
90	After fetching data, present it clearly:
91
92	1. Summarize key numbers in a brief text response (current price, market cap, P/E, etc.)
93	2. Show tabular data formatted for readability — use markdown tables or formatted DataFrames
94	3. Highlight notable items — earnings beats/misses, unusual volume, dividend changes
95	4. Provide context — compare to sector averages, historical ranges, or analyst consensus when relevant
96
97	If the user seems to want a chart or visualization, combine with an appropriate v

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