Merge branch 'main' into hotfix/proxy
ValueRaider
GitHub
commit
056b84d8fe
|
|
@ -9,7 +9,7 @@ assignees: ''
|
|||
|
||||
# IMPORTANT
|
||||
|
||||
If you want help, you got to read this first, follow the instructions.
|
||||
# Read and follow these instructions carefully. Help us help you.
|
||||
|
||||
### Are you up-to-date?
|
||||
|
||||
|
|
@ -25,19 +25,20 @@ and comparing against [PIP](https://pypi.org/project/yfinance/#history).
|
|||
|
||||
### Does Yahoo actually have the data?
|
||||
|
||||
Are you spelling ticker *exactly* same as Yahoo?
|
||||
Are you spelling symbol *exactly* same as Yahoo?
|
||||
|
||||
Then visit `finance.yahoo.com` and confirm they have the data you want. Maybe your ticker was delisted, or your expectations of `yfinance` are wrong.
|
||||
Then visit `finance.yahoo.com` and confirm they have the data you want. Maybe your symbol was delisted, or your expectations of `yfinance` are wrong.
|
||||
|
||||
### Are you spamming Yahoo?
|
||||
|
||||
Yahoo Finance free service has rate-limiting depending on request type - roughly 60/minute for prices, 10/minute for info. Once limit hit, Yahoo can delay, block, or return bad data. Not a `yfinance` bug.
|
||||
Yahoo Finance free service has rate-limiting depending on request type - roughly 60/minute for prices, 10/minute for info. Once limit hit, Yahoo can delay, block, or return bad data -> not a `yfinance` bug.
|
||||
|
||||
### Still think it's a bug?
|
||||
|
||||
Delete this default message (all of it) and submit your bug report here, providing the following as best you can:
|
||||
**Delete these instructions** and replace with your bug report, providing the following as best you can:
|
||||
|
||||
- Simple code that reproduces your problem, that we can copy-paste-run
|
||||
- Exception message with full traceback, or proof `yfinance` returning bad data
|
||||
- `yfinance` version and Python version
|
||||
- Operating system type
|
||||
- Simple code that reproduces your problem, that we can copy-paste-run.
|
||||
- Run code with [debug logging enabled](https://github.com/ranaroussi/yfinance#logging) and post the full output.
|
||||
- If you think `yfinance` returning bad data, give us proof.
|
||||
- `yfinance` version and Python version.
|
||||
- Operating system type.
|
||||
|
|
|
|||
|
|
@ -1,14 +0,0 @@
|
|||
---
|
||||
name: Feature request
|
||||
about: Request a new feature
|
||||
title: ''
|
||||
labels: ''
|
||||
assignees: ''
|
||||
|
||||
---
|
||||
|
||||
**Describe the problem**
|
||||
|
||||
**Describe the solution**
|
||||
|
||||
**Additional context**
|
||||
|
|
@ -13,9 +13,9 @@ jobs:
|
|||
runs-on: ubuntu-latest
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||||
|
||||
steps:
|
||||
- uses: actions/checkout@v2
|
||||
- uses: actions/checkout@v3
|
||||
- name: Set up Python
|
||||
uses: actions/setup-python@v2
|
||||
uses: actions/setup-python@v4
|
||||
with:
|
||||
python-version: '3.x'
|
||||
- name: Install dependencies
|
||||
|
|
|
|||
|
|
@ -1,6 +1,68 @@
|
|||
Change Log
|
||||
===========
|
||||
|
||||
0.2.24
|
||||
------
|
||||
Fix info[] missing values #1603
|
||||
|
||||
0.2.23
|
||||
------
|
||||
Fix 'Unauthorized' error #1595
|
||||
|
||||
0.2.22
|
||||
------
|
||||
Fix unhandled 'sqlite3.DatabaseError' #1574
|
||||
|
||||
0.2.21
|
||||
------
|
||||
Fix financials tables #1568
|
||||
Price repair update: fix Yahoo messing up dividend and split adjustments #1543
|
||||
Fix logging behaviour #1562
|
||||
Fix merge future div/split into prices #1567
|
||||
|
||||
0.2.20
|
||||
------
|
||||
Switch to `logging` module #1493 #1522 #1541
|
||||
Price history:
|
||||
- optimise #1514
|
||||
- fixes #1523
|
||||
- fix TZ-cache corruption #1528
|
||||
|
||||
0.2.18
|
||||
------
|
||||
Fix 'fast_info' error '_np not found' #1496
|
||||
Fix bug in timezone cache #1498
|
||||
|
||||
0.2.17
|
||||
------
|
||||
Fix prices error with Pandas 2.0 #1488
|
||||
|
||||
0.2.16
|
||||
------
|
||||
Fix 'fast_info deprecated' msg appearing at Ticker() init
|
||||
|
||||
0.2.15
|
||||
------
|
||||
Restore missing Ticker.info keys #1480
|
||||
|
||||
0.2.14
|
||||
------
|
||||
Fix Ticker.info dict by fetching from API #1461
|
||||
|
||||
0.2.13
|
||||
------
|
||||
Price bug fixes:
|
||||
- fetch big-interval with Capital Gains #1455
|
||||
- merging dividends & splits with prices #1452
|
||||
|
||||
0.2.12
|
||||
------
|
||||
Disable annoying 'backup decrypt' msg
|
||||
|
||||
0.2.11
|
||||
------
|
||||
Fix history_metadata accesses for unusual symbols #1411
|
||||
|
||||
0.2.10
|
||||
------
|
||||
General
|
||||
|
|
|
|||
93
README.md
93
README.md
|
|
@ -42,11 +42,6 @@ Yahoo! finance API is intended for personal use only.**
|
|||
|
||||
---
|
||||
|
||||
## News [2023-01-27]
|
||||
Since December 2022 Yahoo has been encrypting the web data that `yfinance` scrapes for non-market data. Fortunately the decryption keys are available, although Yahoo moved/changed them several times hence `yfinance` breaking several times. `yfinance` is now better prepared for any future changes by Yahoo.
|
||||
|
||||
Why is Yahoo doing this? We don't know. Is it to stop scrapers? Maybe, so we've implemented changes to reduce load on Yahoo. In December we rolled out version 0.2 with optimised scraping. Then in 0.2.6 introduced `Ticker.fast_info`, providing much faster access to some `info` elements wherever possible e.g. price stats and forcing users to switch (sorry but we think necessary). `info` will continue to exist for as long as there are elements without a fast alternative.
|
||||
|
||||
## Quick Start
|
||||
|
||||
### The Ticker module
|
||||
|
|
@ -58,10 +53,8 @@ import yfinance as yf
|
|||
|
||||
msft = yf.Ticker("MSFT")
|
||||
|
||||
# get all stock info (slow)
|
||||
# get all stock info
|
||||
msft.info
|
||||
# fast access to subset of stock info (opportunistic)
|
||||
msft.fast_info
|
||||
|
||||
# get historical market data
|
||||
hist = msft.history(period="1mo")
|
||||
|
|
@ -76,9 +69,6 @@ msft.splits
|
|||
msft.capital_gains # only for mutual funds & etfs
|
||||
|
||||
# show share count
|
||||
# - yearly summary:
|
||||
msft.shares
|
||||
# - accurate time-series count:
|
||||
msft.get_shares_full(start="2022-01-01", end=None)
|
||||
|
||||
# show financials:
|
||||
|
|
@ -98,25 +88,6 @@ msft.major_holders
|
|||
msft.institutional_holders
|
||||
msft.mutualfund_holders
|
||||
|
||||
# show earnings
|
||||
msft.earnings
|
||||
msft.quarterly_earnings
|
||||
|
||||
# show sustainability
|
||||
msft.sustainability
|
||||
|
||||
# show analysts recommendations
|
||||
msft.recommendations
|
||||
msft.recommendations_summary
|
||||
# show analysts other work
|
||||
msft.analyst_price_target
|
||||
msft.revenue_forecasts
|
||||
msft.earnings_forecasts
|
||||
msft.earnings_trend
|
||||
|
||||
# show next event (earnings, etc)
|
||||
msft.calendar
|
||||
|
||||
# Show future and historic earnings dates, returns at most next 4 quarters and last 8 quarters by default.
|
||||
# Note: If more are needed use msft.get_earnings_dates(limit=XX) with increased limit argument.
|
||||
msft.earnings_dates
|
||||
|
|
@ -154,6 +125,8 @@ msft.option_chain(..., proxy="PROXY_SERVER")
|
|||
...
|
||||
```
|
||||
|
||||
### Multiple tickers
|
||||
|
||||
To initialize multiple `Ticker` objects, use
|
||||
|
||||
```python
|
||||
|
|
@ -167,24 +140,18 @@ tickers.tickers['AAPL'].history(period="1mo")
|
|||
tickers.tickers['GOOG'].actions
|
||||
```
|
||||
|
||||
### Fetching data for multiple tickers
|
||||
To download price history into one table:
|
||||
|
||||
```python
|
||||
import yfinance as yf
|
||||
data = yf.download("SPY AAPL", start="2017-01-01", end="2017-04-30")
|
||||
data = yf.download("SPY AAPL", period="1mo")
|
||||
```
|
||||
|
||||
`yf.download()` and `Ticker.history()` have many options for configuring fetching and processing, e.g.:
|
||||
#### `yf.download()` and `Ticker.history()` have many options for configuring fetching and processing. [Review the Wiki](https://github.com/ranaroussi/yfinance/wiki) for more options and detail.
|
||||
|
||||
```python
|
||||
yf.download(tickers = "SPY AAPL", # list of tickers
|
||||
period = "1y", # time period
|
||||
interval = "1d", # trading interval
|
||||
ignore_tz = True, # ignore timezone when aligning data from different exchanges?
|
||||
prepost = False) # download pre/post market hours data?
|
||||
```
|
||||
### Logging
|
||||
|
||||
Review the [Wiki](https://github.com/ranaroussi/yfinance/wiki) for more options and detail.
|
||||
`yfinance` now uses the `logging` module to handle messages, default behaviour is only print errors. If debugging, use `yf.enable_debug_mode()` to switch logging to debug with custom formatting.
|
||||
|
||||
### Smarter scraping
|
||||
|
||||
|
|
@ -206,11 +173,12 @@ Combine a `requests_cache` with rate-limiting to avoid triggering Yahoo's rate-l
|
|||
from requests import Session
|
||||
from requests_cache import CacheMixin, SQLiteCache
|
||||
from requests_ratelimiter import LimiterMixin, MemoryQueueBucket
|
||||
from pyrate_limiter import Duration, RequestRate, Limiter
|
||||
class CachedLimiterSession(CacheMixin, LimiterMixin, Session):
|
||||
""" """
|
||||
pass
|
||||
|
||||
session = CachedLimiterSession(
|
||||
per_second=0.9,
|
||||
limiter=Limiter(RequestRate(2, Duration.SECOND*5)), # max 2 requests per 5 seconds
|
||||
bucket_class=MemoryQueueBucket,
|
||||
backend=SQLiteCache("yfinance.cache"),
|
||||
)
|
||||
|
|
@ -231,21 +199,7 @@ yfinance?](https://stackoverflow.com/questions/63107801)
|
|||
- How to download single or multiple tickers into a single
|
||||
dataframe with single level column names and a ticker column
|
||||
|
||||
### Timezone cache store
|
||||
|
||||
When fetching price data, all dates are localized to stock exchange timezone.
|
||||
But timezone retrieval is relatively slow, so yfinance attemps to cache them
|
||||
in your users cache folder.
|
||||
You can direct cache to use a different location with `set_tz_cache_location()`:
|
||||
```python
|
||||
import yfinance as yf
|
||||
yf.set_tz_cache_location("custom/cache/location")
|
||||
...
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## `pandas_datareader` override
|
||||
### `pandas_datareader` override
|
||||
|
||||
If your code uses `pandas_datareader` and you want to download data
|
||||
faster, you can "hijack" `pandas_datareader.data.get_data_yahoo()`
|
||||
|
|
@ -262,6 +216,18 @@ yf.pdr_override() # <== that's all it takes :-)
|
|||
data = pdr.get_data_yahoo("SPY", start="2017-01-01", end="2017-04-30")
|
||||
```
|
||||
|
||||
### Timezone cache store
|
||||
|
||||
When fetching price data, all dates are localized to stock exchange timezone.
|
||||
But timezone retrieval is relatively slow, so yfinance attemps to cache them
|
||||
in your users cache folder.
|
||||
You can direct cache to use a different location with `set_tz_cache_location()`:
|
||||
```python
|
||||
import yfinance as yf
|
||||
yf.set_tz_cache_location("custom/cache/location")
|
||||
...
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## Installation
|
||||
|
|
@ -272,6 +238,11 @@ Install `yfinance` using `pip`:
|
|||
$ pip install yfinance --upgrade --no-cache-dir
|
||||
```
|
||||
|
||||
Test new features by installing betas, provide feedback in [corresponding Discussion](https://github.com/ranaroussi/yfinance/discussions):
|
||||
``` {.sourceCode .bash}
|
||||
$ pip install yfinance --upgrade --no-cache-dir --pre
|
||||
```
|
||||
|
||||
To install `yfinance` using `conda`, see
|
||||
[this](https://anaconda.org/ranaroussi/yfinance).
|
||||
|
||||
|
|
@ -289,11 +260,15 @@ To install `yfinance` using `conda`, see
|
|||
- [html5lib](https://pypi.org/project/html5lib) \>= 1.1
|
||||
- [cryptography](https://pypi.org/project/cryptography) \>= 3.3.2
|
||||
|
||||
### Optional (if you want to use `pandas_datareader`)
|
||||
#### Optional (if you want to use `pandas_datareader`)
|
||||
|
||||
- [pandas\_datareader](https://github.com/pydata/pandas-datareader)
|
||||
\>= 0.4.0
|
||||
|
||||
## Developers: want to contribute?
|
||||
|
||||
`yfinance` relies on community to investigate bugs and contribute code. Developer guide: https://github.com/ranaroussi/yfinance/discussions/1084
|
||||
|
||||
---
|
||||
|
||||
### Legal Stuff
|
||||
|
|
|
|||
|
|
@ -1,5 +1,5 @@
|
|||
{% set name = "yfinance" %}
|
||||
{% set version = "0.2.10" %}
|
||||
{% set version = "0.2.24" %}
|
||||
|
||||
package:
|
||||
name: "{{ name|lower }}"
|
||||
|
|
|
|||
3
setup.py
3
setup.py
|
|
@ -63,9 +63,8 @@ setup(
|
|||
'requests>=2.26', 'multitasking>=0.0.7',
|
||||
'lxml>=4.9.1', 'appdirs>=1.4.4', 'pytz>=2022.5',
|
||||
'frozendict>=2.3.4',
|
||||
# 'pycryptodome>=3.6.6',
|
||||
'cryptography>=3.3.2',
|
||||
'beautifulsoup4>=4.11.1', 'html5lib>=1.1'],
|
||||
# Note: Pandas.read_html() needs html5lib & beautifulsoup4
|
||||
entry_points={
|
||||
'console_scripts': [
|
||||
'sample=sample:main',
|
||||
|
|
|
|||
|
|
@ -15,6 +15,9 @@ Sanity check for most common library uses all working
|
|||
|
||||
import yfinance as yf
|
||||
import unittest
|
||||
import logging
|
||||
|
||||
logging.basicConfig(level=logging.DEBUG)
|
||||
|
||||
symbols = ['MSFT', 'IWO', 'VFINX', '^GSPC', 'BTC-USD']
|
||||
tickers = [yf.Ticker(symbol) for symbol in symbols]
|
||||
|
|
|
|||
|
|
@ -7,3 +7,37 @@ _src_dp = _parent_dp
|
|||
sys.path.insert(0, _src_dp)
|
||||
|
||||
import yfinance
|
||||
|
||||
|
||||
# Optional: see the exact requests that are made during tests:
|
||||
# import logging
|
||||
# logging.basicConfig(level=logging.DEBUG)
|
||||
|
||||
|
||||
# Setup a session to rate-limit and cache persistently:
|
||||
import datetime as _dt
|
||||
import os
|
||||
import appdirs as _ad
|
||||
from requests import Session
|
||||
from requests_cache import CacheMixin, SQLiteCache
|
||||
from requests_ratelimiter import LimiterMixin, MemoryQueueBucket
|
||||
class CachedLimiterSession(CacheMixin, LimiterMixin, Session):
|
||||
pass
|
||||
from pyrate_limiter import Duration, RequestRate, Limiter
|
||||
history_rate = RequestRate(1, Duration.SECOND*2)
|
||||
limiter = Limiter(history_rate)
|
||||
cache_fp = os.path.join(_ad.user_cache_dir(), "py-yfinance", "unittests-cache")
|
||||
if os.path.isfile(cache_fp + '.sqlite'):
|
||||
# Delete local cache if older than 1 day:
|
||||
mod_dt = _dt.datetime.fromtimestamp(os.path.getmtime(cache_fp + '.sqlite'))
|
||||
if mod_dt.date() < _dt.date.today():
|
||||
os.remove(cache_fp + '.sqlite')
|
||||
session_gbl = CachedLimiterSession(
|
||||
limiter=limiter,
|
||||
bucket_class=MemoryQueueBucket,
|
||||
backend=SQLiteCache(cache_fp, expire_after=_dt.timedelta(hours=1)),
|
||||
)
|
||||
# Use this instead if only want rate-limiting:
|
||||
# from requests_ratelimiter import LimiterSession
|
||||
# session_gbl = LimiterSession(limiter=limiter)
|
||||
|
||||
|
|
|
|||
|
|
@ -0,0 +1,23 @@
|
|||
Date,Open,High,Low,Close,Adj Close,Volume,Dividends,Stock Splits
|
||||
2023-04-14 00:00:00+09:00,4126,4130,4055,4129,4129,7459400,0,0
|
||||
2023-04-13 00:00:00+09:00,4064,4099,4026,4081,4081,5160200,0,0
|
||||
2023-04-12 00:00:00+09:00,3968,4084,3966,4064,4064,6372000,0,0
|
||||
2023-04-11 00:00:00+09:00,3990,4019,3954,3960,3960,6476500,0,0
|
||||
2023-04-10 00:00:00+09:00,3996,4009,3949,3964,3964,3485200,0,0
|
||||
2023-04-07 00:00:00+09:00,3897,3975,3892,3953,3953,4554700,0,0
|
||||
2023-04-06 00:00:00+09:00,4002,4004,3920,3942,3942,8615200,0,0
|
||||
2023-04-05 00:00:00+09:00,4150,4150,4080,4088,4088,6063700,0,0
|
||||
2023-04-04 00:00:00+09:00,4245,4245,4144,4155,4155,6780600,0,0
|
||||
2023-04-03 00:00:00+09:00,4250,4259,4162,4182,4182,7076800,0,0
|
||||
2023-03-31 00:00:00+09:00,4229,4299,4209,4275,4275,9608400,0,0
|
||||
2023-03-30 00:00:00+09:00,4257,4268,4119,4161,4161,5535200,55,5
|
||||
2023-03-29 00:00:00+09:00,4146,4211,4146,4206,4151,6514500,0,0
|
||||
2023-03-28 00:00:00+09:00,4200,4207,4124,4142,4087.837109375,4505500,0,0
|
||||
2023-03-27 00:00:00+09:00,4196,4204,4151,4192,4137.183203125,5959500,0,0
|
||||
2023-03-24 00:00:00+09:00,4130,4187,4123,4177,4122.379296875,8961500,0,0
|
||||
2023-03-23 00:00:00+09:00,4056,4106,4039,4086,4032.569140625,5480000,0,0
|
||||
2023-03-22 00:00:00+09:00,4066,4128,4057,4122,4068.0984375,8741500,0,0
|
||||
2023-03-20 00:00:00+09:00,4000,4027,3980,3980,3927.95546875,7006500,0,0
|
||||
2023-03-17 00:00:00+09:00,4018,4055,4016,4031,3978.28828125,6961500,0,0
|
||||
2023-03-16 00:00:00+09:00,3976,4045,3972,4035,3982.236328125,5019000,0,0
|
||||
2023-03-15 00:00:00+09:00,4034,4050,4003,4041,3988.1578125,6122000,0,0
|
||||
|
|
|
@ -0,0 +1,23 @@
|
|||
Date,Open,High,Low,Close,Adj Close,Volume,Dividends,Stock Splits
|
||||
2023-04-14 00:00:00+09:00,4126,4130,4055,4129,4129,7459400,0,0
|
||||
2023-04-13 00:00:00+09:00,4064,4099,4026,4081,4081,5160200,0,0
|
||||
2023-04-12 00:00:00+09:00,3968,4084,3966,4064,4064,6372000,0,0
|
||||
2023-04-11 00:00:00+09:00,3990,4019,3954,3960,3960,6476500,0,0
|
||||
2023-04-10 00:00:00+09:00,3996,4009,3949,3964,3964,3485200,0,0
|
||||
2023-04-07 00:00:00+09:00,3897,3975,3892,3953,3953,4554700,0,0
|
||||
2023-04-06 00:00:00+09:00,4002,4004,3920,3942,3942,8615200,0,0
|
||||
2023-04-05 00:00:00+09:00,4150,4150,4080,4088,4088,6063700,0,0
|
||||
2023-04-04 00:00:00+09:00,4245,4245,4144,4155,4155,6780600,0,0
|
||||
2023-04-03 00:00:00+09:00,4250,4259,4162,4182,4182,7076800,0,0
|
||||
2023-03-31 00:00:00+09:00,4229,4299,4209,4275,4275,9608400,0,0
|
||||
2023-03-30 00:00:00+09:00,4257,4268,4119,4161,4161,5535200,55,5
|
||||
2023-03-29 00:00:00+09:00,4146,4211,4146,4206,4151,6514500,0,0
|
||||
2023-03-28 00:00:00+09:00,21000,21035,20620,20710,20439.185546875,901100,0,0
|
||||
2023-03-27 00:00:00+09:00,20980,21020,20755,20960,20685.916015625,1191900,0,0
|
||||
2023-03-24 00:00:00+09:00,20650,20935,20615,20885,20611.896484375,1792300,0,0
|
||||
2023-03-23 00:00:00+09:00,20280,20530,20195,20430,20162.845703125,1096000,0,0
|
||||
2023-03-22 00:00:00+09:00,20330,20640,20285,20610,20340.4921875,1748300,0,0
|
||||
2023-03-20 00:00:00+09:00,20000,20135,19900,19900,19639.77734375,1401300,0,0
|
||||
2023-03-17 00:00:00+09:00,20090,20275,20080,20155,19891.44140625,1392300,0,0
|
||||
2023-03-16 00:00:00+09:00,19880,20225,19860,20175,19911.181640625,1003800,0,0
|
||||
2023-03-15 00:00:00+09:00,20170,20250,20015,20205,19940.7890625,1224400,0,0
|
||||
|
|
|
@ -0,0 +1,30 @@
|
|||
Date,Open,High,Low,Close,Adj Close,Volume,Dividends,Stock Splits
|
||||
2023-04-20 00:00:00+02:00,3,3,2,3,3,2076,0,0
|
||||
2023-04-21 00:00:00+02:00,3,3,2,3,3,2136,0,0
|
||||
2023-04-24 00:00:00+02:00,3,3,1,1,1,77147,0,0
|
||||
2023-04-25 00:00:00+02:00,1,2,1,2,2,9625,0,0
|
||||
2023-04-26 00:00:00+02:00,2,2,1,2,2,5028,0,0
|
||||
2023-04-27 00:00:00+02:00,2,2,1,1,1,3235,0,0
|
||||
2023-04-28 00:00:00+02:00,2,2,1,2,2,10944,0,0
|
||||
2023-05-02 00:00:00+02:00,2,2,2,2,2,12220,0,0
|
||||
2023-05-03 00:00:00+02:00,2,2,2,2,2,4683,0,0
|
||||
2023-05-04 00:00:00+02:00,2,2,1,2,2,3368,0,0
|
||||
2023-05-05 00:00:00+02:00,2,2,1,2,2,26069,0,0
|
||||
2023-05-08 00:00:00+02:00,1,2,1,1,1,70540,0,0
|
||||
2023-05-09 00:00:00+02:00,1,2,1,1,1,14228,0,0
|
||||
2023-05-10 00:00:00+02:00,1.08000004291534,1.39999997615814,0.879999995231628,1,1,81012,0,0.0001
|
||||
2023-05-11 00:00:00+02:00,1.03999996185303,1.03999996185303,0.850000023841858,1,1,40254,0,0
|
||||
2023-05-12 00:00:00+02:00,0.949999988079071,1.10000002384186,0.949999988079071,1.01999998092651,1.01999998092651,35026,0,0
|
||||
2023-05-15 00:00:00+02:00,0.949999988079071,1.01999998092651,0.860000014305115,0.939999997615814,0.939999997615814,41486,0,0
|
||||
2023-05-16 00:00:00+02:00,0.899999976158142,0.944000005722046,0.800000011920929,0.800000011920929,0.800000011920929,43583,0,0
|
||||
2023-05-17 00:00:00+02:00,0.850000023841858,0.850000023841858,0.779999971389771,0.810000002384186,0.810000002384186,29984,0,0
|
||||
2023-05-18 00:00:00+02:00,0.779999971389771,0.78600001335144,0.740000009536743,0.740000009536743,0.740000009536743,24679,0,0
|
||||
2023-05-19 00:00:00+02:00,0.78600001335144,0.78600001335144,0.649999976158142,0.65200001001358,0.65200001001358,26732,0,0
|
||||
2023-05-22 00:00:00+02:00,0.8299999833107,1.05999994277954,0.709999978542328,0.709999978542328,0.709999978542328,169538,0,0
|
||||
2023-05-23 00:00:00+02:00,0.899999976158142,1.60800004005432,0.860000014305115,1.22000002861023,1.22000002861023,858471,0,0
|
||||
2023-05-24 00:00:00+02:00,1.19400000572205,1.25999999046326,0.779999971389771,0.779999971389771,0.779999971389771,627823,0,0
|
||||
2023-05-25 00:00:00+02:00,0.980000019073486,1.22000002861023,0.702000021934509,0.732999980449677,0.732999980449677,1068939,0,0
|
||||
2023-05-26 00:00:00+02:00,0.660000026226044,0.72000002861023,0.602999985218048,0.611999988555908,0.611999988555908,631580,0,0
|
||||
2023-05-29 00:00:00+02:00,0.620000004768372,0.75,0.578999996185303,0.600000023841858,0.600000023841858,586150,0,0
|
||||
2023-05-30 00:00:00+02:00,0.610000014305115,0.634999990463257,0.497000008821487,0.497000008821487,0.497000008821487,552308,0,0
|
||||
2023-05-31 00:00:00+02:00,0.458999991416931,0.469999998807907,0.374000012874603,0.379999995231628,0.379999995231628,899067,0,0
|
||||
|
|
|
@ -0,0 +1,30 @@
|
|||
Date,Open,High,Low,Close,Adj Close,Volume,Dividends,Stock Splits
|
||||
2023-04-20 00:00:00+02:00,3.0,3.0,2.0,3.0,3.0,2076,0.0,0.0
|
||||
2023-04-21 00:00:00+02:00,3.0,3.0,2.0,3.0,3.0,2136,0.0,0.0
|
||||
2023-04-24 00:00:00+02:00,3.0,3.0,1.0,1.0,1.0,77147,0.0,0.0
|
||||
2023-04-25 00:00:00+02:00,1.0,2.0,1.0,2.0,2.0,9625,0.0,0.0
|
||||
2023-04-26 00:00:00+02:00,2.0,2.0,1.0,2.0,2.0,5028,0.0,0.0
|
||||
2023-04-27 00:00:00+02:00,2.0,2.0,1.0,1.0,1.0,3235,0.0,0.0
|
||||
2023-04-28 00:00:00+02:00,2.0,2.0,1.0,2.0,2.0,10944,0.0,0.0
|
||||
2023-05-02 00:00:00+02:00,2.0,2.0,2.0,2.0,2.0,12220,0.0,0.0
|
||||
2023-05-03 00:00:00+02:00,2.0,2.0,2.0,2.0,2.0,4683,0.0,0.0
|
||||
2023-05-04 00:00:00+02:00,2.0,2.0,1.0,2.0,2.0,3368,0.0,0.0
|
||||
2023-05-05 00:00:00+02:00,2.0,2.0,1.0,2.0,2.0,26069,0.0,0.0
|
||||
2023-05-08 00:00:00+02:00,9.999999747378752e-05,0.00019999999494757503,9.999999747378752e-05,9.999999747378752e-05,9.999999747378752e-05,705399568,0.0,0.0
|
||||
2023-05-09 00:00:00+02:00,1.0,2.0,1.0,1.0,1.0,14228,0.0,0.0
|
||||
2023-05-10 00:00:00+02:00,1.0800000429153442,1.399999976158142,0.8799999952316284,1.0,1.0,81012,0.0,0.0001
|
||||
2023-05-11 00:00:00+02:00,1.0399999618530273,1.0399999618530273,0.8500000238418579,1.0,1.0,40254,0.0,0.0
|
||||
2023-05-12 00:00:00+02:00,0.949999988079071,1.100000023841858,0.949999988079071,1.0199999809265137,1.0199999809265137,35026,0.0,0.0
|
||||
2023-05-15 00:00:00+02:00,0.949999988079071,1.0199999809265137,0.8600000143051147,0.9399999976158142,0.9399999976158142,41486,0.0,0.0
|
||||
2023-05-16 00:00:00+02:00,0.8999999761581421,0.9440000057220459,0.800000011920929,0.800000011920929,0.800000011920929,43583,0.0,0.0
|
||||
2023-05-17 00:00:00+02:00,0.8500000238418579,0.8500000238418579,0.7799999713897705,0.8100000023841858,0.8100000023841858,29984,0.0,0.0
|
||||
2023-05-18 00:00:00+02:00,0.7799999713897705,0.7860000133514404,0.7400000095367432,0.7400000095367432,0.7400000095367432,24679,0.0,0.0
|
||||
2023-05-19 00:00:00+02:00,0.7860000133514404,0.7860000133514404,0.6499999761581421,0.6520000100135803,0.6520000100135803,26732,0.0,0.0
|
||||
2023-05-22 00:00:00+02:00,0.8299999833106995,1.059999942779541,0.7099999785423279,0.7099999785423279,0.7099999785423279,169538,0.0,0.0
|
||||
2023-05-23 00:00:00+02:00,0.8999999761581421,1.6080000400543213,0.8600000143051147,1.2200000286102295,1.2200000286102295,858471,0.0,0.0
|
||||
2023-05-24 00:00:00+02:00,1.194000005722046,1.2599999904632568,0.7799999713897705,0.7799999713897705,0.7799999713897705,627823,0.0,0.0
|
||||
2023-05-25 00:00:00+02:00,0.9800000190734863,1.2200000286102295,0.7020000219345093,0.7329999804496765,0.7329999804496765,1068939,0.0,0.0
|
||||
2023-05-26 00:00:00+02:00,0.6600000262260437,0.7200000286102295,0.6029999852180481,0.6119999885559082,0.6119999885559082,631580,0.0,0.0
|
||||
2023-05-29 00:00:00+02:00,0.6200000047683716,0.75,0.5789999961853027,0.6000000238418579,0.6000000238418579,586150,0.0,0.0
|
||||
2023-05-30 00:00:00+02:00,0.6100000143051147,0.6349999904632568,0.4970000088214874,0.4970000088214874,0.4970000088214874,552308,0.0,0.0
|
||||
2023-05-31 00:00:00+02:00,0.45899999141693115,0.4699999988079071,0.37400001287460327,0.3799999952316284,0.3799999952316284,899067,0.0,0.0
|
||||
|
|
|
@ -0,0 +1,11 @@
|
|||
Date,Open,High,Low,Close,Adj Close,Volume,Dividends,Stock Splits
|
||||
2023-05-18 00:00:00+01:00,193.220001220703,200.839996337891,193.220001220703,196.839996337891,196.839996337891,653125,0,0
|
||||
2023-05-17 00:00:00+01:00,199.740005493164,207.738006591797,190.121994018555,197.860000610352,197.860000610352,822268,0,0
|
||||
2023-05-16 00:00:00+01:00,215.600006103516,215.600006103516,201.149993896484,205.100006103516,205.100006103516,451009,243.93939,0.471428571428571
|
||||
2023-05-15 00:00:00+01:00,215.399955531529,219.19995640346,210.599967302595,217.399987792969,102.39998147147,1761679.3939394,0,0
|
||||
2023-05-12 00:00:00+01:00,214.599988664899,216.199965558733,209.599965558733,211.399977329799,99.573855808803,1522298.48484849,0,0
|
||||
2023-05-11 00:00:00+01:00,219.999966430664,219.999966430664,212.199987357003,215.000000871931,101.269541277204,3568042.12121213,0,0
|
||||
2023-05-10 00:00:00+01:00,218.199954659598,223.000000435965,212.59995640346,215.399955531529,101.457929992676,5599908.78787879,0,0
|
||||
2023-05-09 00:00:00+01:00,224,227.688003540039,218.199996948242,218.399993896484,102.87100982666,1906090,0,0
|
||||
2023-05-05 00:00:00+01:00,220.999968174526,225.19996686663,220.799976457868,224.4,105.697140066964,964523.636363637,0,0
|
||||
2023-05-04 00:00:00+01:00,216.999989972796,222.799965558733,216.881988961356,221.399965994698,104.284055655343,880983.93939394,0,0
|
||||
|
|
|
@ -0,0 +1,11 @@
|
|||
Date,Open,High,Low,Close,Adj Close,Volume,Dividends,Stock Splits
|
||||
2023-05-18 00:00:00+01:00,193.220001220703,200.839996337891,193.220001220703,196.839996337891,196.839996337891,653125,0,0
|
||||
2023-05-17 00:00:00+01:00,199.740005493164,207.738006591797,190.121994018555,197.860000610352,197.860000610352,822268,0,0
|
||||
2023-05-16 00:00:00+01:00,215.600006103516,215.600006103516,201.149993896484,205.100006103516,205.100006103516,451009,243.93939,0.471428571428571
|
||||
2023-05-15 00:00:00+01:00,456.908996582031,464.969604492188,446.727203369141,461.151489257813,217.21208190918,830506,0,0
|
||||
2023-05-12 00:00:00+01:00,455.212097167969,458.605987548828,444.605987548828,448.424194335938,211.217269897461,717655,0,0
|
||||
2023-05-11 00:00:00+01:00,466.666595458984,466.666595458984,450.121185302734,456.060607910156,214.814178466797,1682077,0,0
|
||||
2023-05-10 00:00:00+01:00,462.848388671875,473.030303955078,450.969604492188,456.908996582031,215.213790893555,2639957,0,0
|
||||
2023-05-09 00:00:00+01:00,224,227.688003540039,218.199996948242,218.399993896484,102.87100982666,1906090,0,0
|
||||
2023-05-05 00:00:00+01:00,468.787811279297,477.696899414063,468.363586425781,476,224.2060546875,454704,0,0
|
||||
2023-05-04 00:00:00+01:00,460.303009033203,472.605987548828,460.052703857422,469.636291503906,221.208602905273,415321,0,0
|
||||
|
|
|
@ -0,0 +1,24 @@
|
|||
Date,Open,High,Low,Close,Adj Close,Volume,Dividends,Stock Splits
|
||||
2023-05-31 00:00:00+10:00,0.120290003716946,0.120290003716946,0.120290003716946,0.120290003716946,0.120290003716946,0,0,0
|
||||
2023-05-30 00:00:00+10:00,0.120290003716946,0.120290003716946,0.120290003716946,0.120290003716946,0.120290003716946,0,0,0.4406
|
||||
2023-05-29 00:00:00+10:00,0.120290003716946,0.120290003716946,0.120290003716946,0.120290003716946,0.120290003716946,0,0,0
|
||||
2023-05-26 00:00:00+10:00,0.120290003716946,0.120290003716946,0.120290003716946,0.120290003716946,0.120290003716946,0,0,0
|
||||
2023-05-25 00:00:00+10:00,0.120290003716946,0.120290003716946,0.120290003716946,0.120290003716946,0.120290003716946,0,0,0
|
||||
2023-05-24 00:00:00+10:00,0.120290003716946,0.120290003716946,0.120290003716946,0.120290003716946,0.120290003716946,0,0,0
|
||||
2023-05-23 00:00:00+10:00,0.120290003716946,0.120290003716946,0.120290003716946,0.120290003716946,0.120290003716946,0,0,0
|
||||
2023-05-22 00:00:00+10:00,0.120290003716946,0.120290003716946,0.120290003716946,0.120290003716946,0.120290003716946,0,0,0
|
||||
2023-05-19 00:00:00+10:00,0.120290003716946,0.120290003716946,0.120290003716946,0.120290003716946,0.120290003716946,0,0,0
|
||||
2023-05-18 00:00:00+10:00,0.120290003716946,0.120290003716946,0.120290003716946,0.120290003716946,0.120290003716946,0,0,0
|
||||
2023-05-17 00:00:00+10:00,0.120290003716946,0.120290003716946,0.120290003716946,0.120290003716946,0.120290003716946,0,0,0
|
||||
2023-05-16 00:00:00+10:00,0.120290003716946,0.120290003716946,0.120290003716946,0.120290003716946,0.120290003716946,0,0,0
|
||||
2023-05-15 00:00:00+10:00,0.120290003716946,0.120290003716946,0.120290003716946,0.120290003716946,0.120290003716946,0,0,0
|
||||
2023-05-12 00:00:00+10:00,0.120290003716946,0.120290003716946,0.120290003716946,0.120290003716946,0.120290003716946,0,0,0
|
||||
2023-05-11 00:00:00+10:00,0.120290003716946,0.120290003716946,0.120290003716946,0.120290003716946,0.120290003716946,0,0,0
|
||||
2023-05-10 00:00:00+10:00,0.120290003716946,0.120290003716946,0.120290003716946,0.120290003716946,0.120290003716946,0,0,0
|
||||
2023-05-09 00:00:00+10:00,0.120290003716946,0.120290003716946,0.120290003716946,0.120290003716946,0.120290003716946,0,0,0
|
||||
2023-05-08 00:00:00+10:00,0.120290003716946,0.120290003716946,0.120290003716946,0.120290003716946,0.120290003716946,0,0,0
|
||||
2023-05-05 00:00:00+10:00,0.120290003716946,0.120290003716946,0.120290003716946,0.120290003716946,0.120290003716946,0,0,0
|
||||
2023-05-04 00:00:00+10:00,0.120290003716946,0.120290003716946,0.120290003716946,0.120290003716946,0.120290003716946,0,0,0
|
||||
2023-05-03 00:00:00+10:00,0.120290003716946,0.120290003716946,0.120290003716946,0.120290003716946,0.120290003716946,0,0,0
|
||||
2023-05-02 00:00:00+10:00,0.120290003716946,0.120290003716946,0.120290003716946,0.120290003716946,0.120290003716946,0,0,0
|
||||
2023-05-01 00:00:00+10:00,0.120290003716946,0.120290003716946,0.120290003716946,0.120290003716946,0.120290003716946,0,0,0
|
||||
|
|
|
@ -0,0 +1,24 @@
|
|||
Date,Open,High,Low,Close,Adj Close,Volume,Dividends,Stock Splits
|
||||
2023-05-31 00:00:00+10:00,0.120290003716946,0.120290003716946,0.120290003716946,0.120290003716946,0.120290003716946,0,0,0
|
||||
2023-05-30 00:00:00+10:00,0.120290003716946,0.120290003716946,0.120290003716946,0.120290003716946,0.120290003716946,0,0,0.4406
|
||||
2023-05-29 00:00:00+10:00,0.120290003716946,0.120290003716946,0.120290003716946,0.120290003716946,0.120290003716946,0,0,0
|
||||
2023-05-26 00:00:00+10:00,0.120290003716946,0.120290003716946,0.120290003716946,0.120290003716946,0.120290003716946,0,0,0
|
||||
2023-05-25 00:00:00+10:00,0.120290003716946,0.120290003716946,0.120290003716946,0.120290003716946,0.120290003716946,0,0,0
|
||||
2023-05-24 00:00:00+10:00,0.120290003716946,0.120290003716946,0.120290003716946,0.120290003716946,0.120290003716946,0,0,0
|
||||
2023-05-23 00:00:00+10:00,0.120290003716946,0.120290003716946,0.120290003716946,0.120290003716946,0.120290003716946,0,0,0
|
||||
2023-05-22 00:00:00+10:00,0.120290003716946,0.120290003716946,0.120290003716946,0.120290003716946,0.120290003716946,0,0,0
|
||||
2023-05-19 00:00:00+10:00,0.0529999993741512,0.0529999993741512,0.0529999993741512,0.0529999993741512,0.0529999993741512,0,0,0
|
||||
2023-05-18 00:00:00+10:00,0.0529999993741512,0.0529999993741512,0.0529999993741512,0.0529999993741512,0.0529999993741512,0,0,0
|
||||
2023-05-17 00:00:00+10:00,0.0529999993741512,0.0529999993741512,0.0529999993741512,0.0529999993741512,0.0529999993741512,0,0,0
|
||||
2023-05-16 00:00:00+10:00,0.0529999993741512,0.0529999993741512,0.0529999993741512,0.0529999993741512,0.0529999993741512,0,0,0
|
||||
2023-05-15 00:00:00+10:00,0.0529999993741512,0.0529999993741512,0.0529999993741512,0.0529999993741512,0.0529999993741512,0,0,0
|
||||
2023-05-12 00:00:00+10:00,0.0529999993741512,0.0529999993741512,0.0529999993741512,0.0529999993741512,0.0529999993741512,0,0,0
|
||||
2023-05-11 00:00:00+10:00,0.0529999993741512,0.0529999993741512,0.0529999993741512,0.0529999993741512,0.0529999993741512,0,0,0
|
||||
2023-05-10 00:00:00+10:00,0.0529999993741512,0.0529999993741512,0.0529999993741512,0.0529999993741512,0.0529999993741512,0,0,0
|
||||
2023-05-09 00:00:00+10:00,0.0529999993741512,0.0529999993741512,0.0529999993741512,0.0529999993741512,0.0529999993741512,0,0,0
|
||||
2023-05-08 00:00:00+10:00,0.0529999993741512,0.0529999993741512,0.0529999993741512,0.0529999993741512,0.0529999993741512,0,0,0
|
||||
2023-05-05 00:00:00+10:00,0.0529999993741512,0.0529999993741512,0.0529999993741512,0.0529999993741512,0.0529999993741512,0,0,0
|
||||
2023-05-04 00:00:00+10:00,0.0529999993741512,0.0529999993741512,0.0529999993741512,0.0529999993741512,0.0529999993741512,0,0,0
|
||||
2023-05-03 00:00:00+10:00,0.0529999993741512,0.0529999993741512,0.0529999993741512,0.0529999993741512,0.0529999993741512,0,0,0
|
||||
2023-05-02 00:00:00+10:00,0.0529999993741512,0.0529999993741512,0.0529999993741512,0.0529999993741512,0.0529999993741512,0,0,0
|
||||
2023-05-01 00:00:00+10:00,0.0529999993741512,0.0529999993741512,0.0529999993741512,0.0529999993741512,0.0529999993741512,0,0,0
|
||||
|
|
|
@ -0,0 +1,17 @@
|
|||
Date,Open,High,Low,Close,Adj Close,Volume,Dividends,Stock Splits
|
||||
2023-05-08 00:00:00+02:00,24.8999996185303,24.9500007629395,24.1000003814697,24.75,24.75,7187,0,0
|
||||
2023-05-09 00:00:00+02:00,25,25.5,23.1499996185303,24.1499996185303,24.1499996185303,22753,0,0
|
||||
2023-05-10 00:00:00+02:00,24.1499996185303,24.1499996185303,22,22.9500007629395,22.9500007629395,62727,0,0
|
||||
2023-05-11 00:00:00+02:00,22.9500007629395,25,22.9500007629395,23.3500003814697,23.3500003814697,19550,0,0
|
||||
2023-05-12 00:00:00+02:00,23.3500003814697,24,22.1000003814697,23.8500003814697,23.8500003814697,17143,0,0
|
||||
2023-05-15 00:00:00+02:00,23,25.7999992370605,22.5,23,23,43709,0,0
|
||||
2023-05-16 00:00:00+02:00,22.75,24.0499992370605,22.5,22.75,22.75,16068,0,0
|
||||
2023-05-17 00:00:00+02:00,23,23.8500003814697,22.1000003814697,23.6499996185303,23.6499996185303,19926,0,0
|
||||
2023-05-19 00:00:00+02:00,23.6499996185303,23.8500003814697,22.1000003814697,22.2999992370605,22.2999992370605,41050,0,0
|
||||
2023-05-22 00:00:00+02:00,22.0000004768372,24.1499996185303,21.5499997138977,22.7500009536743,22.7500009536743,34022,0,0
|
||||
2023-05-23 00:00:00+02:00,22.75,22.8999996185303,21.75,22.5,22.5,13992,0,0
|
||||
2023-05-24 00:00:00+02:00,21,24,21,22.0100002288818,22.0100002288818,18306,0,0.1
|
||||
2023-05-25 00:00:00+02:00,21.5699996948242,22.8899993896484,20,21.1599998474121,21.1599998474121,35398,0,0
|
||||
2023-05-26 00:00:00+02:00,21.1599998474121,22.4950008392334,20.5,21.0949993133545,21.0949993133545,8039,0,0
|
||||
2023-05-29 00:00:00+02:00,22.1000003814697,22.1000003814697,20.25,20.75,20.75,17786,0,0
|
||||
2023-05-30 00:00:00+02:00,20.75,21.6499996185303,20.1499996185303,20.4500007629395,20.4500007629395,10709,0,0
|
||||
|
|
|
@ -0,0 +1,17 @@
|
|||
Date,Open,High,Low,Close,Adj Close,Volume,Dividends,Stock Splits
|
||||
2023-05-08 00:00:00+02:00,24.899999618530273,24.950000762939453,24.100000381469727,24.75,24.75,7187,0.0,0.0
|
||||
2023-05-09 00:00:00+02:00,25.0,25.5,23.149999618530273,24.149999618530273,24.149999618530273,22753,0.0,0.0
|
||||
2023-05-10 00:00:00+02:00,24.149999618530273,24.149999618530273,22.0,22.950000762939453,22.950000762939453,62727,0.0,0.0
|
||||
2023-05-11 00:00:00+02:00,22.950000762939453,25.0,22.950000762939453,23.350000381469727,23.350000381469727,19550,0.0,0.0
|
||||
2023-05-12 00:00:00+02:00,23.350000381469727,24.0,22.100000381469727,23.850000381469727,23.850000381469727,17143,0.0,0.0
|
||||
2023-05-15 00:00:00+02:00,23.0,25.799999237060547,22.5,23.0,23.0,43709,0.0,0.0
|
||||
2023-05-16 00:00:00+02:00,22.75,24.049999237060547,22.5,22.75,22.75,16068,0.0,0.0
|
||||
2023-05-17 00:00:00+02:00,23.0,23.850000381469727,22.100000381469727,23.649999618530273,23.649999618530273,19926,0.0,0.0
|
||||
2023-05-19 00:00:00+02:00,23.649999618530273,23.850000381469727,22.100000381469727,22.299999237060547,22.299999237060547,41050,0.0,0.0
|
||||
2023-05-22 00:00:00+02:00,2.200000047683716,2.4149999618530273,2.1549999713897705,2.2750000953674316,2.2750000953674316,340215,0.0,0.0
|
||||
2023-05-23 00:00:00+02:00,22.75,22.899999618530273,21.75,22.5,22.5,13992,0.0,0.0
|
||||
2023-05-24 00:00:00+02:00,21.0,24.0,21.0,22.010000228881836,22.010000228881836,18306,0.0,0.1
|
||||
2023-05-25 00:00:00+02:00,21.56999969482422,22.889999389648438,20.0,21.15999984741211,21.15999984741211,35398,0.0,0.0
|
||||
2023-05-26 00:00:00+02:00,21.15999984741211,22.4950008392334,20.5,21.094999313354492,21.094999313354492,8039,0.0,0.0
|
||||
2023-05-29 00:00:00+02:00,22.100000381469727,22.100000381469727,20.25,20.75,20.75,17786,0.0,0.0
|
||||
2023-05-30 00:00:00+02:00,20.75,21.649999618530273,20.149999618530273,20.450000762939453,20.450000762939453,10709,0.0,0.0
|
||||
|
|
|
@ -0,0 +1,23 @@
|
|||
Date,Open,High,Low,Close,Adj Close,Volume,Dividends,Stock Splits
|
||||
2022-06-01 00:00:00+02:00,5.72999992370606,5.78199996948242,5.3939998626709,5.3939998626709,5.3939998626709,3095860,0,0
|
||||
2022-06-02 00:00:00+02:00,5.38600006103516,5.38600006103516,5.26800003051758,5.2939998626709,5.2939998626709,1662880,0,0
|
||||
2022-06-03 00:00:00+02:00,5.34599990844727,5.34599990844727,5.15800018310547,5.16800003051758,5.16800003051758,1698900,0,0
|
||||
2022-06-06 00:00:00+02:00,5.16800003051758,5.25200004577637,5.13800010681152,5.18800010681152,5.18800010681152,1074910,0,0
|
||||
2022-06-07 00:00:00+02:00,5.21800003051758,5.22200012207031,5.07400016784668,5.1560001373291,5.1560001373291,1850680,0,0
|
||||
2022-06-08 00:00:00+02:00,5.1560001373291,5.17599983215332,5.07200012207031,5.10200004577637,5.10200004577637,1140360,0,0
|
||||
2022-06-09 00:00:00+02:00,5.09799995422363,5.09799995422363,4.87599983215332,4.8939998626709,4.8939998626709,2025480,0,0
|
||||
2022-06-10 00:00:00+02:00,4.87999992370606,4.87999992370606,4.50400009155274,4.50400009155274,4.50400009155274,2982730,0,0
|
||||
2022-06-13 00:00:00+02:00,4.3,4.37599983215332,3.83600006103516,3.83600006103516,3.83600006103516,4568210,0,0.1
|
||||
2022-06-14 00:00:00+02:00,3.87750015258789,4.15999984741211,3.85200004577637,3.9439998626709,3.9439998626709,5354500,0,0
|
||||
2022-06-15 00:00:00+02:00,4.03400001525879,4.16450004577637,3.73050003051758,3.73050003051758,3.73050003051758,6662610,0,0
|
||||
2022-06-16 00:00:00+02:00,3.73050003051758,3.98499984741211,3.72400016784668,3.82550010681152,3.82550010681152,13379960,0,0
|
||||
2022-06-17 00:00:00+02:00,3.8,4.29949989318848,3.75,4.29949989318848,4.29949989318848,12844160,0,0
|
||||
2022-06-20 00:00:00+02:00,2.19422197341919,2.2295401096344,2.13992595672607,2.2295401096344,2.2295401096344,12364104,0,0
|
||||
2022-06-21 00:00:00+02:00,2.24719905853272,2.28515291213989,2.19712090492249,2.21557092666626,2.21557092666626,8434013,0,0
|
||||
2022-06-22 00:00:00+02:00,1.98679196834564,2.00365996360779,1.73798203468323,1.73798203468323,1.73798203468323,26496542,0,0
|
||||
2022-06-23 00:00:00+02:00,1.62411904335022,1.68526804447174,1.37320005893707,1.59776198863983,1.59776198863983,48720201,0,0
|
||||
2022-06-24 00:00:00+02:00,1.47599303722382,1.54610300064087,1.1739410161972,1.24932205677032,1.24932205677032,56877192,0,0
|
||||
2022-06-27 00:00:00+02:00,1.49899995326996,1.79849994182587,1.49899995326996,1.79849994182587,1.79849994182587,460673,0,0
|
||||
2022-06-28 00:00:00+02:00,2.15799999237061,3.05100011825562,2.12599992752075,3.05100011825562,3.05100011825562,3058635,0,0
|
||||
2022-06-29 00:00:00+02:00,2.90000009536743,3.73799991607666,2.85899996757507,3.26399993896484,3.26399993896484,6516761,0,0
|
||||
2022-06-30 00:00:00+02:00,3.24900007247925,3.28099989891052,2.5,2.5550000667572,2.5550000667572,4805984,0,0
|
||||
|
|
|
@ -0,0 +1,23 @@
|
|||
Date,Open,High,Low,Close,Adj Close,Volume,Dividends,Stock Splits
|
||||
2022-06-01 00:00:00+02:00,57.29999923706055,57.81999969482422,53.939998626708984,53.939998626708984,53.939998626708984,309586,0.0,0.0
|
||||
2022-06-02 00:00:00+02:00,53.86000061035156,53.86000061035156,52.68000030517578,52.939998626708984,52.939998626708984,166288,0.0,0.0
|
||||
2022-06-03 00:00:00+02:00,53.459999084472656,53.459999084472656,51.58000183105469,51.68000030517578,51.68000030517578,169890,0.0,0.0
|
||||
2022-06-06 00:00:00+02:00,51.68000030517578,52.52000045776367,51.380001068115234,51.880001068115234,51.880001068115234,107491,0.0,0.0
|
||||
2022-06-07 00:00:00+02:00,52.18000030517578,52.220001220703125,50.7400016784668,51.560001373291016,51.560001373291016,185068,0.0,0.0
|
||||
2022-06-08 00:00:00+02:00,51.560001373291016,51.7599983215332,50.720001220703125,51.02000045776367,51.02000045776367,114036,0.0,0.0
|
||||
2022-06-09 00:00:00+02:00,50.97999954223633,50.97999954223633,48.7599983215332,48.939998626708984,48.939998626708984,202548,0.0,0.0
|
||||
2022-06-10 00:00:00+02:00,48.79999923706055,48.79999923706055,45.040000915527344,45.040000915527344,45.040000915527344,298273,0.0,0.0
|
||||
2022-06-13 00:00:00+02:00,43.0,43.7599983215332,38.36000061035156,38.36000061035156,38.36000061035156,456821,0.0,0.1
|
||||
2022-06-14 00:00:00+02:00,38.775001525878906,41.599998474121094,38.52000045776367,39.439998626708984,39.439998626708984,535450,0.0,0.0
|
||||
2022-06-15 00:00:00+02:00,40.34000015258789,41.64500045776367,37.30500030517578,37.30500030517578,37.30500030517578,666261,0.0,0.0
|
||||
2022-06-16 00:00:00+02:00,37.30500030517578,39.849998474121094,37.2400016784668,38.255001068115234,38.255001068115234,1337996,0.0,0.0
|
||||
2022-06-17 00:00:00+02:00,38.0,42.994998931884766,37.5,42.994998931884766,42.994998931884766,1284416,0.0,0.0
|
||||
2022-06-20 00:00:00+02:00,2.1942219734191895,2.2295401096343994,2.139925956726074,2.2295401096343994,2.2295401096343994,12364104,0.0,0.0
|
||||
2022-06-21 00:00:00+02:00,2.247199058532715,2.2851529121398926,2.1971209049224854,2.2155709266662598,2.2155709266662598,8434013,0.0,0.0
|
||||
2022-06-22 00:00:00+02:00,1.986791968345642,2.003659963607788,1.7379820346832275,1.7379820346832275,1.7379820346832275,26496542,0.0,0.0
|
||||
2022-06-23 00:00:00+02:00,1.6241190433502197,1.6852680444717407,1.3732000589370728,1.5977619886398315,1.5977619886398315,48720201,0.0,0.0
|
||||
2022-06-24 00:00:00+02:00,1.475993037223816,1.5461030006408691,1.1739410161972046,1.2493220567703247,1.2493220567703247,56877192,0.0,0.0
|
||||
2022-06-27 00:00:00+02:00,1.4989999532699585,1.7984999418258667,1.4989999532699585,1.7984999418258667,1.7984999418258667,460673,0.0,0.0
|
||||
2022-06-28 00:00:00+02:00,2.1579999923706055,3.0510001182556152,2.125999927520752,3.0510001182556152,3.0510001182556152,3058635,0.0,0.0
|
||||
2022-06-29 00:00:00+02:00,2.9000000953674316,3.73799991607666,2.8589999675750732,3.2639999389648438,3.2639999389648438,6516761,0.0,0.0
|
||||
2022-06-30 00:00:00+02:00,3.249000072479248,3.2809998989105225,2.5,2.555000066757202,2.555000066757202,4805984,0.0,0.0
|
||||
|
|
|
@ -0,0 +1,30 @@
|
|||
Date,Open,High,Low,Close,Adj Close,Volume,Dividends,Stock Splits
|
||||
2023-06-09 00:00:00+02:00,34.700001,34.709999,33.240002,33.619999,33.619999,7148409,0,0
|
||||
2023-06-08 00:00:00+02:00,34.900002,34.990002,34.040001,34.360001,34.360001,10406999,0,0
|
||||
2023-06-07 00:00:00+02:00,34.549999,35.639999,34.320000,35.090000,35.090000,10118918,0,0
|
||||
2023-06-06 00:00:00+02:00,34.500000,34.820000,34.049999,34.459999,34.459999,9109709,0,0
|
||||
2023-06-05 00:00:00+02:00,35.000000,35.299999,34.200001,34.700001,34.700001,8791993,0,0
|
||||
2023-06-02 00:00:00+02:00,35.689999,36.180000,34.599998,34.970001,34.970001,8844549,0,0
|
||||
2023-06-01 00:00:00+02:00,35.230000,35.380001,34.240002,35.349998,35.349998,6721030,0,0
|
||||
2023-05-31 00:00:00+02:00,3480,3548,3426,3501,3501,32605833,0,0
|
||||
2023-05-30 00:00:00+02:00,3439,3537,3385,3423,3423,8970804,0,0
|
||||
2023-05-29 00:00:00+02:00,3466,3506,3402,3432,3432,3912803,0,0
|
||||
2023-05-26 00:00:00+02:00,3475,3599,3433,3453,3453,6744718,0,0
|
||||
2023-05-25 00:00:00+02:00,3540,3609,3463,3507,3507,16900221,0,0
|
||||
2023-05-24 00:00:00+02:00,3620,3650,3526,3540,3540,9049505,0,0
|
||||
2023-05-23 00:00:00+02:00,3690,3667,3556,3610,3610,10797373,0,0
|
||||
2023-05-22 00:00:00+02:00,3705,3736,3609,3661,3661,7132641,0,0
|
||||
2023-05-19 00:00:00+02:00,3620,3715,3625,3690,3690,12648518,0,0
|
||||
2023-05-18 00:00:00+02:00,3657,3699,3584,3646,3646,10674542,0,0
|
||||
2023-05-17 00:00:00+02:00,3687,3731,3656,3671,3671,9892791,0,0
|
||||
2023-05-16 00:00:00+02:00,3715,3773,3696,3703,3703,4706789,0,0
|
||||
2023-05-15 00:00:00+02:00,3774,3805,3696,3727,3727,7890969,0,0
|
||||
2023-05-12 00:00:00+02:00,3750,3844,3671,3774,3774,8724303,0,0
|
||||
2023-05-11 00:00:00+02:00,3880,3888,3701,3732,3732,14371855,0,0
|
||||
2023-05-10 00:00:00+02:00,3893,3880,3642,3810,3810,30393389,0,0
|
||||
2023-05-09 00:00:00+02:00,4441,4441,3939,3966,3966,19833428,0,0
|
||||
2023-05-08 00:00:00+02:00,4463,4578,4456,4471,4471,11092519,0,0
|
||||
2023-05-05 00:00:00+02:00,4299,4490,4287,4458,4458,28539048,0,0
|
||||
2023-05-04 00:00:00+02:00,4149,4330,4123,4283,4283,15506868,0,0
|
||||
2023-05-03 00:00:00+02:00,3975,4098,3968,4095,4095,14657028,0,0
|
||||
2023-05-02 00:00:00+02:00,4037,4032,3917,3965,3965,11818133,0,0
|
||||
|
344
tests/prices.py
344
tests/prices.py
|
|
@ -1,21 +1,19 @@
|
|||
from .context import yfinance as yf
|
||||
from .context import session_gbl
|
||||
|
||||
import unittest
|
||||
|
||||
import os
|
||||
import datetime as _dt
|
||||
import pytz as _tz
|
||||
import numpy as _np
|
||||
import pandas as _pd
|
||||
|
||||
import requests_cache
|
||||
|
||||
|
||||
class TestPriceHistory(unittest.TestCase):
|
||||
session = None
|
||||
|
||||
@classmethod
|
||||
def setUpClass(cls):
|
||||
cls.session = requests_cache.CachedSession(backend='memory')
|
||||
cls.session = session_gbl
|
||||
|
||||
@classmethod
|
||||
def tearDownClass(cls):
|
||||
|
|
@ -34,11 +32,23 @@ class TestPriceHistory(unittest.TestCase):
|
|||
f = df.index.time == _dt.time(0)
|
||||
self.assertTrue(f.all())
|
||||
|
||||
def test_download(self):
|
||||
tkrs = ["BHP.AX", "IMP.JO", "BP.L", "PNL.L", "INTC"]
|
||||
intervals = ["1d", "1wk", "1mo"]
|
||||
for interval in intervals:
|
||||
df = yf.download(tkrs, period="5y", interval=interval)
|
||||
|
||||
f = df.index.time == _dt.time(0)
|
||||
self.assertTrue(f.all())
|
||||
|
||||
df_tkrs = df.columns.levels[1]
|
||||
self.assertEqual(sorted(tkrs), sorted(df_tkrs))
|
||||
|
||||
def test_duplicatingHourly(self):
|
||||
tkrs = ["IMP.JO", "BHG.JO", "SSW.JO", "BP.L", "INTC"]
|
||||
for tkr in tkrs:
|
||||
dat = yf.Ticker(tkr, session=self.session)
|
||||
tz = dat._get_ticker_tz(debug_mode=False, proxy=None, timeout=None)
|
||||
tz = dat._get_ticker_tz(proxy=None, timeout=None)
|
||||
|
||||
dt_utc = _tz.timezone("UTC").localize(_dt.datetime.utcnow())
|
||||
dt = dt_utc.astimezone(_tz.timezone(tz))
|
||||
|
|
@ -58,7 +68,7 @@ class TestPriceHistory(unittest.TestCase):
|
|||
test_run = False
|
||||
for tkr in tkrs:
|
||||
dat = yf.Ticker(tkr, session=self.session)
|
||||
tz = dat._get_ticker_tz(debug_mode=False, proxy=None, timeout=None)
|
||||
tz = dat._get_ticker_tz(proxy=None, timeout=None)
|
||||
|
||||
dt_utc = _tz.timezone("UTC").localize(_dt.datetime.utcnow())
|
||||
dt = dt_utc.astimezone(_tz.timezone(tz))
|
||||
|
|
@ -84,7 +94,7 @@ class TestPriceHistory(unittest.TestCase):
|
|||
test_run = False
|
||||
for tkr in tkrs:
|
||||
dat = yf.Ticker(tkr, session=self.session)
|
||||
tz = dat._get_ticker_tz(debug_mode=False, proxy=None, timeout=None)
|
||||
tz = dat._get_ticker_tz(proxy=None, timeout=None)
|
||||
|
||||
dt = _tz.timezone(tz).localize(_dt.datetime.now())
|
||||
if dt.date().weekday() not in [1, 2, 3, 4]:
|
||||
|
|
@ -105,6 +115,32 @@ class TestPriceHistory(unittest.TestCase):
|
|||
self.skipTest("Skipping test_duplicatingWeekly() because not possible to fail Monday/weekend")
|
||||
|
||||
def test_intraDayWithEvents(self):
|
||||
tkrs = ["BHP.AX", "IMP.JO", "BP.L", "PNL.L", "INTC"]
|
||||
test_run = False
|
||||
for tkr in tkrs:
|
||||
start_d = _dt.date.today() - _dt.timedelta(days=59)
|
||||
end_d = None
|
||||
df_daily = yf.Ticker(tkr, session=self.session).history(start=start_d, end=end_d, interval="1d", actions=True)
|
||||
df_daily_divs = df_daily["Dividends"][df_daily["Dividends"] != 0]
|
||||
if df_daily_divs.shape[0] == 0:
|
||||
continue
|
||||
|
||||
last_div_date = df_daily_divs.index[-1]
|
||||
start_d = last_div_date.date()
|
||||
end_d = last_div_date.date() + _dt.timedelta(days=1)
|
||||
df_intraday = yf.Ticker(tkr, session=self.session).history(start=start_d, end=end_d, interval="15m", actions=True)
|
||||
self.assertTrue((df_intraday["Dividends"] != 0.0).any())
|
||||
|
||||
df_intraday_divs = df_intraday["Dividends"][df_intraday["Dividends"] != 0]
|
||||
df_intraday_divs.index = df_intraday_divs.index.floor('D')
|
||||
self.assertTrue(df_daily_divs.equals(df_intraday_divs))
|
||||
|
||||
test_run = True
|
||||
|
||||
if not test_run:
|
||||
self.skipTest("Skipping test_intraDayWithEvents() because no tickers had a dividend in last 60 days")
|
||||
|
||||
def test_intraDayWithEvents_tase(self):
|
||||
# TASE dividend release pre-market, doesn't merge nicely with intra-day data so check still present
|
||||
|
||||
tase_tkrs = ["ICL.TA", "ESLT.TA", "ONE.TA", "MGDL.TA"]
|
||||
|
|
@ -115,21 +151,45 @@ class TestPriceHistory(unittest.TestCase):
|
|||
df_daily = yf.Ticker(tkr, session=self.session).history(start=start_d, end=end_d, interval="1d", actions=True)
|
||||
df_daily_divs = df_daily["Dividends"][df_daily["Dividends"] != 0]
|
||||
if df_daily_divs.shape[0] == 0:
|
||||
# self.skipTest("Skipping test_intraDayWithEvents() because 'ICL.TA' has no dividend in last 60 days")
|
||||
continue
|
||||
|
||||
last_div_date = df_daily_divs.index[-1]
|
||||
start_d = last_div_date.date()
|
||||
end_d = last_div_date.date() + _dt.timedelta(days=1)
|
||||
df = yf.Ticker(tkr, session=self.session).history(start=start_d, end=end_d, interval="15m", actions=True)
|
||||
self.assertTrue((df["Dividends"] != 0.0).any())
|
||||
df_intraday = yf.Ticker(tkr, session=self.session).history(start=start_d, end=end_d, interval="15m", actions=True)
|
||||
self.assertTrue((df_intraday["Dividends"] != 0.0).any())
|
||||
|
||||
df_intraday_divs = df_intraday["Dividends"][df_intraday["Dividends"] != 0]
|
||||
df_intraday_divs.index = df_intraday_divs.index.floor('D')
|
||||
self.assertTrue(df_daily_divs.equals(df_intraday_divs))
|
||||
|
||||
test_run = True
|
||||
break
|
||||
|
||||
if not test_run:
|
||||
self.skipTest("Skipping test_intraDayWithEvents() because no tickers had a dividend in last 60 days")
|
||||
self.skipTest("Skipping test_intraDayWithEvents_tase() because no tickers had a dividend in last 60 days")
|
||||
|
||||
def test_dailyWithEvents(self):
|
||||
start_d = _dt.date(2022, 1, 1)
|
||||
end_d = _dt.date(2023, 1, 1)
|
||||
|
||||
tkr_div_dates = {}
|
||||
tkr_div_dates['BHP.AX'] = [_dt.date(2022, 9, 1), _dt.date(2022, 2, 24)] # Yahoo claims 23-Feb but wrong because DST
|
||||
tkr_div_dates['IMP.JO'] = [_dt.date(2022, 9, 21), _dt.date(2022, 3, 16)]
|
||||
tkr_div_dates['BP.L'] = [_dt.date(2022, 11, 10), _dt.date(2022, 8, 11), _dt.date(2022, 5, 12), _dt.date(2022, 2, 17)]
|
||||
tkr_div_dates['INTC'] = [_dt.date(2022, 11, 4), _dt.date(2022, 8, 4), _dt.date(2022, 5, 5), _dt.date(2022, 2, 4)]
|
||||
|
||||
for tkr,dates in tkr_div_dates.items():
|
||||
df = yf.Ticker(tkr, session=self.session).history(interval='1d', start=start_d, end=end_d)
|
||||
df_divs = df[df['Dividends']!=0].sort_index(ascending=False)
|
||||
try:
|
||||
self.assertTrue((df_divs.index.date == dates).all())
|
||||
except:
|
||||
print(f'- ticker = {tkr}')
|
||||
print('- response:') ; print(df_divs.index.date)
|
||||
print('- answer:') ; print(dates)
|
||||
raise
|
||||
|
||||
def test_dailyWithEvents_bugs(self):
|
||||
# Reproduce issue #521
|
||||
tkr1 = "QQQ"
|
||||
tkr2 = "GDX"
|
||||
|
|
@ -163,6 +223,60 @@ class TestPriceHistory(unittest.TestCase):
|
|||
print("{}-without-events missing these dates: {}".format(tkr, missing_from_df2))
|
||||
raise
|
||||
|
||||
def test_intraDayWithEvents(self):
|
||||
tkrs = ["BHP.AX", "IMP.JO", "BP.L", "PNL.L", "INTC"]
|
||||
test_run = False
|
||||
for tkr in tkrs:
|
||||
start_d = _dt.date.today() - _dt.timedelta(days=59)
|
||||
end_d = None
|
||||
df_daily = yf.Ticker(tkr, session=self.session).history(start=start_d, end=end_d, interval="1d", actions=True)
|
||||
df_daily_divs = df_daily["Dividends"][df_daily["Dividends"] != 0]
|
||||
if df_daily_divs.shape[0] == 0:
|
||||
continue
|
||||
|
||||
last_div_date = df_daily_divs.index[-1]
|
||||
start_d = last_div_date.date()
|
||||
end_d = last_div_date.date() + _dt.timedelta(days=1)
|
||||
df_intraday = yf.Ticker(tkr, session=self.session).history(start=start_d, end=end_d, interval="15m", actions=True)
|
||||
self.assertTrue((df_intraday["Dividends"] != 0.0).any())
|
||||
|
||||
df_intraday_divs = df_intraday["Dividends"][df_intraday["Dividends"] != 0]
|
||||
df_intraday_divs.index = df_intraday_divs.index.floor('D')
|
||||
self.assertTrue(df_daily_divs.equals(df_intraday_divs))
|
||||
|
||||
test_run = True
|
||||
|
||||
if not test_run:
|
||||
self.skipTest("Skipping test_intraDayWithEvents() because no tickers had a dividend in last 60 days")
|
||||
|
||||
def test_intraDayWithEvents_tase(self):
|
||||
# TASE dividend release pre-market, doesn't merge nicely with intra-day data so check still present
|
||||
|
||||
tase_tkrs = ["ICL.TA", "ESLT.TA", "ONE.TA", "MGDL.TA"]
|
||||
test_run = False
|
||||
for tkr in tase_tkrs:
|
||||
start_d = _dt.date.today() - _dt.timedelta(days=59)
|
||||
end_d = None
|
||||
df_daily = yf.Ticker(tkr, session=self.session).history(start=start_d, end=end_d, interval="1d", actions=True)
|
||||
df_daily_divs = df_daily["Dividends"][df_daily["Dividends"] != 0]
|
||||
if df_daily_divs.shape[0] == 0:
|
||||
continue
|
||||
|
||||
last_div_date = df_daily_divs.index[-1]
|
||||
start_d = last_div_date.date()
|
||||
end_d = last_div_date.date() + _dt.timedelta(days=1)
|
||||
df_intraday = yf.Ticker(tkr, session=self.session).history(start=start_d, end=end_d, interval="15m", actions=True)
|
||||
self.assertTrue((df_intraday["Dividends"] != 0.0).any())
|
||||
|
||||
df_intraday_divs = df_intraday["Dividends"][df_intraday["Dividends"] != 0]
|
||||
df_intraday_divs.index = df_intraday_divs.index.floor('D')
|
||||
self.assertTrue(df_daily_divs.equals(df_intraday_divs))
|
||||
|
||||
test_run = True
|
||||
|
||||
if not test_run:
|
||||
self.skipTest("Skipping test_intraDayWithEvents_tase() because no tickers had a dividend in last 60 days")
|
||||
|
||||
def test_weeklyWithEvents(self):
|
||||
# Reproduce issue #521
|
||||
tkr1 = "QQQ"
|
||||
|
|
@ -230,6 +344,22 @@ class TestPriceHistory(unittest.TestCase):
|
|||
print("{}-without-events missing these dates: {}".format(tkr, missing_from_df2))
|
||||
raise
|
||||
|
||||
def test_monthlyWithEvents2(self):
|
||||
# Simply check no exception from internal merge
|
||||
dfm = yf.Ticker("ABBV").history(period="max", interval="1mo")
|
||||
dfd = yf.Ticker("ABBV").history(period="max", interval="1d")
|
||||
dfd = dfd[dfd.index > dfm.index[0]]
|
||||
dfm_divs = dfm[dfm['Dividends']!=0]
|
||||
dfd_divs = dfd[dfd['Dividends']!=0]
|
||||
self.assertEqual(dfm_divs.shape[0], dfd_divs.shape[0])
|
||||
|
||||
dfm = yf.Ticker("F").history(period="50mo",interval="1mo")
|
||||
dfd = yf.Ticker("F").history(period="50mo", interval="1d")
|
||||
dfd = dfd[dfd.index > dfm.index[0]]
|
||||
dfm_divs = dfm[dfm['Dividends']!=0]
|
||||
dfd_divs = dfd[dfd['Dividends']!=0]
|
||||
self.assertEqual(dfm_divs.shape[0], dfd_divs.shape[0])
|
||||
|
||||
def test_tz_dst_ambiguous(self):
|
||||
# Reproduce issue #1100
|
||||
try:
|
||||
|
|
@ -381,12 +511,22 @@ class TestPriceHistory(unittest.TestCase):
|
|||
df = dat.history(start=start, interval="1wk")
|
||||
self.assertTrue((df.index.weekday == 0).all())
|
||||
|
||||
def test_aggregate_capital_gains(self):
|
||||
# Setup
|
||||
tkr = "FXAIX"
|
||||
dat = yf.Ticker(tkr, session=self.session)
|
||||
start = "2017-12-31"
|
||||
end = "2019-12-31"
|
||||
interval = "3mo"
|
||||
|
||||
df = dat.history(start=start, end=end, interval=interval)
|
||||
|
||||
class TestPriceRepair(unittest.TestCase):
|
||||
session = None
|
||||
|
||||
@classmethod
|
||||
def setUpClass(cls):
|
||||
cls.session = requests_cache.CachedSession(backend='memory')
|
||||
cls.session = session_gbl
|
||||
|
||||
@classmethod
|
||||
def tearDownClass(cls):
|
||||
|
|
@ -413,7 +553,7 @@ class TestPriceRepair(unittest.TestCase):
|
|||
start_dt = end_dt - td_60d
|
||||
df = dat.history(start=start_dt, end=end_dt, interval="2m", repair=True)
|
||||
|
||||
def test_repair_100x_weekly(self):
|
||||
def test_repair_100x_random_weekly(self):
|
||||
# Setup:
|
||||
tkr = "PNL.L"
|
||||
dat = yf.Ticker(tkr, session=self.session)
|
||||
|
|
@ -441,7 +581,7 @@ class TestPriceRepair(unittest.TestCase):
|
|||
|
||||
# Run test
|
||||
|
||||
df_repaired = dat._fix_unit_mixups(df_bad, "1wk", tz_exchange, prepost=False)
|
||||
df_repaired = dat._fix_unit_random_mixups(df_bad, "1wk", tz_exchange, prepost=False, silent=True)
|
||||
|
||||
# First test - no errors left
|
||||
for c in data_cols:
|
||||
|
|
@ -464,7 +604,10 @@ class TestPriceRepair(unittest.TestCase):
|
|||
f_1 = ratio == 1
|
||||
self.assertTrue((f_100 | f_1).all())
|
||||
|
||||
def test_repair_100x_weekly_preSplit(self):
|
||||
self.assertTrue("Repaired?" in df_repaired.columns)
|
||||
self.assertFalse(df_repaired["Repaired?"].isna().any())
|
||||
|
||||
def test_repair_100x_random_weekly_preSplit(self):
|
||||
# PNL.L has a stock-split in 2022. Sometimes requesting data before 2022 is not split-adjusted.
|
||||
|
||||
tkr = "PNL.L"
|
||||
|
|
@ -496,7 +639,7 @@ class TestPriceRepair(unittest.TestCase):
|
|||
df.index = df.index.tz_localize(tz_exchange)
|
||||
df_bad.index = df_bad.index.tz_localize(tz_exchange)
|
||||
|
||||
df_repaired = dat._fix_unit_mixups(df_bad, "1wk", tz_exchange, prepost=False)
|
||||
df_repaired = dat._fix_unit_random_mixups(df_bad, "1wk", tz_exchange, prepost=False, silent=True)
|
||||
|
||||
# First test - no errors left
|
||||
for c in data_cols:
|
||||
|
|
@ -521,7 +664,10 @@ class TestPriceRepair(unittest.TestCase):
|
|||
f_1 = ratio == 1
|
||||
self.assertTrue((f_100 | f_1).all())
|
||||
|
||||
def test_repair_100x_daily(self):
|
||||
self.assertTrue("Repaired?" in df_repaired.columns)
|
||||
self.assertFalse(df_repaired["Repaired?"].isna().any())
|
||||
|
||||
def test_repair_100x_random_daily(self):
|
||||
tkr = "PNL.L"
|
||||
dat = yf.Ticker(tkr, session=self.session)
|
||||
tz_exchange = dat.fast_info["timezone"]
|
||||
|
|
@ -546,7 +692,7 @@ class TestPriceRepair(unittest.TestCase):
|
|||
df.index = df.index.tz_localize(tz_exchange)
|
||||
df_bad.index = df_bad.index.tz_localize(tz_exchange)
|
||||
|
||||
df_repaired = dat._fix_unit_mixups(df_bad, "1d", tz_exchange, prepost=False)
|
||||
df_repaired = dat._fix_unit_random_mixups(df_bad, "1d", tz_exchange, prepost=False, silent=True)
|
||||
|
||||
# First test - no errors left
|
||||
for c in data_cols:
|
||||
|
|
@ -563,6 +709,54 @@ class TestPriceRepair(unittest.TestCase):
|
|||
f_1 = ratio == 1
|
||||
self.assertTrue((f_100 | f_1).all())
|
||||
|
||||
self.assertTrue("Repaired?" in df_repaired.columns)
|
||||
self.assertFalse(df_repaired["Repaired?"].isna().any())
|
||||
|
||||
def test_repair_100x_block_daily(self):
|
||||
# Some 100x errors are not sporadic.
|
||||
# Sometimes Yahoo suddenly shifts from cents->$ from some recent date.
|
||||
|
||||
tkr = "SSW.JO"
|
||||
dat = yf.Ticker(tkr, session=self.session)
|
||||
tz_exchange = dat.fast_info["timezone"]
|
||||
|
||||
data_cols = ["Low", "High", "Open", "Close", "Adj Close"]
|
||||
_dp = os.path.dirname(__file__)
|
||||
df_bad = _pd.read_csv(os.path.join(_dp, "data", tkr.replace('.','-')+"-100x-error.csv"), index_col="Date")
|
||||
df_bad.index = _pd.to_datetime(df_bad.index)
|
||||
df_bad = df_bad.sort_index()
|
||||
|
||||
df = df_bad.copy()
|
||||
for d in data_cols:
|
||||
df.loc[:'2023-05-31', d] *= 0.01 # fix error
|
||||
|
||||
df_repaired = dat._fix_unit_switch(df_bad, "1d", tz_exchange)
|
||||
df_repaired = df_repaired.sort_index()
|
||||
|
||||
# First test - no errors left
|
||||
for c in data_cols:
|
||||
try:
|
||||
self.assertTrue(_np.isclose(df_repaired[c], df[c], rtol=1e-2).all())
|
||||
except:
|
||||
print(df_repaired[c])
|
||||
print(df[c])
|
||||
print(f"TEST FAIL on column '{c}")
|
||||
raise
|
||||
|
||||
# Second test - all differences should be either ~1x or ~100x
|
||||
ratio = df_bad[data_cols].values / df[data_cols].values
|
||||
ratio = ratio.round(2)
|
||||
# - round near-100 ratio to 100:
|
||||
f = ratio > 90
|
||||
ratio[f] = (ratio[f] / 10).round().astype(int) * 10 # round ratio to nearest 10
|
||||
# - now test
|
||||
f_100 = ratio == 100
|
||||
f_1 = ratio == 1
|
||||
self.assertTrue((f_100 | f_1).all())
|
||||
|
||||
self.assertTrue("Repaired?" in df_repaired.columns)
|
||||
self.assertFalse(df_repaired["Repaired?"].isna().any())
|
||||
|
||||
def test_repair_zeroes_daily(self):
|
||||
tkr = "BBIL.L"
|
||||
dat = yf.Ticker(tkr, session=self.session)
|
||||
|
|
@ -590,6 +784,45 @@ class TestPriceRepair(unittest.TestCase):
|
|||
for c in ["Open", "Low", "High", "Close"]:
|
||||
self.assertTrue(_np.isclose(repaired_df[c], correct_df[c], rtol=1e-8).all())
|
||||
|
||||
self.assertTrue("Repaired?" in repaired_df.columns)
|
||||
self.assertFalse(repaired_df["Repaired?"].isna().any())
|
||||
|
||||
def test_repair_zeroes_daily_adjClose(self):
|
||||
# Test that 'Adj Close' is reconstructed correctly,
|
||||
# particularly when a dividend occurred within 1 day.
|
||||
|
||||
tkr = "INTC"
|
||||
df = _pd.DataFrame(data={"Open": [28.95, 28.65, 29.55, 29.62, 29.25],
|
||||
"High": [29.12, 29.27, 29.65, 31.17, 30.30],
|
||||
"Low": [28.21, 28.43, 28.61, 29.53, 28.80],
|
||||
"Close": [28.24, 29.05, 28.69, 30.32, 30.19],
|
||||
"Adj Close": [28.12, 28.93, 28.57, 29.83, 29.70],
|
||||
"Volume": [36e6, 51e6, 49e6, 58e6, 62e6],
|
||||
"Dividends": [0, 0, 0.365, 0, 0]},
|
||||
index=_pd.to_datetime([_dt.datetime(2023, 2, 8),
|
||||
_dt.datetime(2023, 2, 7),
|
||||
_dt.datetime(2023, 2, 6),
|
||||
_dt.datetime(2023, 2, 3),
|
||||
_dt.datetime(2023, 2, 2)]))
|
||||
df = df.sort_index()
|
||||
df.index.name = "Date"
|
||||
dat = yf.Ticker(tkr, session=self.session)
|
||||
tz_exchange = dat.fast_info["timezone"]
|
||||
df.index = df.index.tz_localize(tz_exchange)
|
||||
|
||||
rtol = 5e-3
|
||||
for i in [0, 1, 2]:
|
||||
df_slice = df.iloc[i:i+3]
|
||||
for j in range(3):
|
||||
df_slice_bad = df_slice.copy()
|
||||
df_slice_bad.loc[df_slice_bad.index[j], "Adj Close"] = 0.0
|
||||
|
||||
df_slice_bad_repaired = dat._fix_zeroes(df_slice_bad, "1d", tz_exchange, prepost=False)
|
||||
for c in ["Close", "Adj Close"]:
|
||||
self.assertTrue(_np.isclose(df_slice_bad_repaired[c], df_slice[c], rtol=rtol).all())
|
||||
self.assertTrue("Repaired?" in df_slice_bad_repaired.columns)
|
||||
self.assertFalse(df_slice_bad_repaired["Repaired?"].isna().any())
|
||||
|
||||
def test_repair_zeroes_hourly(self):
|
||||
tkr = "INTC"
|
||||
dat = yf.Ticker(tkr, session=self.session)
|
||||
|
|
@ -621,13 +854,68 @@ class TestPriceRepair(unittest.TestCase):
|
|||
print(repaired_df[c] - correct_df[c])
|
||||
raise
|
||||
|
||||
self.assertTrue("Repaired?" in repaired_df.columns)
|
||||
self.assertFalse(repaired_df["Repaired?"].isna().any())
|
||||
|
||||
def test_repair_bad_stock_split(self):
|
||||
bad_tkrs = ['4063.T', 'ALPHA.PA', 'CNE.L', 'MOB.ST', 'SPM.MI']
|
||||
for tkr in bad_tkrs:
|
||||
dat = yf.Ticker(tkr, session=self.session)
|
||||
tz_exchange = dat.fast_info["timezone"]
|
||||
|
||||
_dp = os.path.dirname(__file__)
|
||||
df_bad = _pd.read_csv(os.path.join(_dp, "data", tkr.replace('.','-')+"-bad-stock-split.csv"), index_col="Date")
|
||||
df_bad.index = _pd.to_datetime(df_bad.index)
|
||||
|
||||
repaired_df = dat._fix_bad_stock_split(df_bad, "1d", tz_exchange)
|
||||
|
||||
correct_df = _pd.read_csv(os.path.join(_dp, "data", tkr.replace('.','-')+"-bad-stock-split-fixed.csv"), index_col="Date")
|
||||
correct_df.index = _pd.to_datetime(correct_df.index)
|
||||
|
||||
repaired_df = repaired_df.sort_index()
|
||||
correct_df = correct_df.sort_index()
|
||||
for c in ["Open", "Low", "High", "Close", "Adj Close", "Volume"]:
|
||||
try:
|
||||
self.assertTrue(_np.isclose(repaired_df[c], correct_df[c], rtol=5e-6).all())
|
||||
except:
|
||||
print(f"tkr={tkr} COLUMN={c}")
|
||||
print("- repaired_df")
|
||||
print(repaired_df)
|
||||
print("- correct_df[c]:")
|
||||
print(correct_df[c])
|
||||
print("- diff:")
|
||||
print(repaired_df[c] - correct_df[c])
|
||||
raise
|
||||
|
||||
# Stocks that split in 2022 but no problems in Yahoo data,
|
||||
# so repair should change nothing
|
||||
good_tkrs = ['AMZN', 'DXCM', 'FTNT', 'GOOG', 'GME', 'PANW', 'SHOP', 'TSLA']
|
||||
good_tkrs += ['AEI', 'CHRA', 'GHI', 'IRON', 'LXU', 'NUZE', 'RSLS', 'TISI']
|
||||
good_tkrs += ['BOL.ST', 'TUI1.DE']
|
||||
intervals = ['1d', '1wk', '1mo', '3mo']
|
||||
for tkr in good_tkrs:
|
||||
for interval in intervals:
|
||||
dat = yf.Ticker(tkr, session=self.session)
|
||||
tz_exchange = dat.fast_info["timezone"]
|
||||
|
||||
_dp = os.path.dirname(__file__)
|
||||
df_good = dat.history(period='2y', interval=interval, auto_adjust=False)
|
||||
|
||||
repaired_df = dat._fix_bad_stock_split(df_good, interval, tz_exchange)
|
||||
|
||||
# Expect no change from repair
|
||||
df_good = df_good.sort_index()
|
||||
repaired_df = repaired_df.sort_index()
|
||||
for c in ["Open", "Low", "High", "Close", "Adj Close", "Volume"]:
|
||||
try:
|
||||
self.assertTrue((repaired_df[c].to_numpy() == df_good[c].to_numpy()).all())
|
||||
except:
|
||||
print(f"tkr={tkr} interval={interval} COLUMN={c}")
|
||||
df_dbg = df_good[[c]].join(repaired_df[[c]], lsuffix='.good', rsuffix='.repaired')
|
||||
f_diff = repaired_df[c].to_numpy() != df_good[c].to_numpy()
|
||||
print(df_dbg[f_diff | _np.roll(f_diff, 1) | _np.roll(f_diff, -1)])
|
||||
raise
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
unittest.main()
|
||||
|
||||
# # Run tests sequentially:
|
||||
# import inspect
|
||||
# test_src = inspect.getsource(TestPriceHistory)
|
||||
# unittest.TestLoader.sortTestMethodsUsing = lambda _, x, y: (
|
||||
# test_src.index(f"def {x}") - test_src.index(f"def {y}")
|
||||
# )
|
||||
# unittest.main(verbosity=2)
|
||||
|
|
|
|||
624
tests/ticker.py
624
tests/ticker.py
|
|
@ -12,25 +12,18 @@ import pandas as pd
|
|||
import numpy as np
|
||||
|
||||
from .context import yfinance as yf
|
||||
from .context import session_gbl
|
||||
|
||||
import unittest
|
||||
import requests_cache
|
||||
|
||||
# Set this to see the exact requests that are made during tests
|
||||
DEBUG_LOG_REQUESTS = False
|
||||
|
||||
if DEBUG_LOG_REQUESTS:
|
||||
import logging
|
||||
|
||||
logging.basicConfig(level=logging.DEBUG)
|
||||
|
||||
|
||||
class TestTicker(unittest.TestCase):
|
||||
session = None
|
||||
|
||||
@classmethod
|
||||
def setUpClass(cls):
|
||||
cls.session = requests_cache.CachedSession(backend='memory')
|
||||
cls.session = session_gbl
|
||||
|
||||
cls.proxy = None
|
||||
|
||||
|
|
@ -47,19 +40,27 @@ class TestTicker(unittest.TestCase):
|
|||
|
||||
# Test:
|
||||
dat = yf.Ticker(tkr, session=self.session)
|
||||
tz = dat._get_ticker_tz(proxy=None, timeout=None, debug_mode=False, raise_errors=False)
|
||||
tz = dat._get_ticker_tz(proxy=None, timeout=None)
|
||||
|
||||
self.assertIsNotNone(tz)
|
||||
|
||||
def test_badTicker(self):
|
||||
# Check yfinance doesn't die when ticker delisted
|
||||
|
||||
tkr = "AM2Z.TA"
|
||||
tkr = "DJI" # typo of "^DJI"
|
||||
dat = yf.Ticker(tkr, session=self.session)
|
||||
|
||||
dat.history(period="1wk")
|
||||
dat.history(start="2022-01-01")
|
||||
dat.history(start="2022-01-01", end="2022-03-01")
|
||||
yf.download([tkr], period="1wk")
|
||||
yf.download([tkr], period="1wk", threads=False, ignore_tz=False)
|
||||
yf.download([tkr], period="1wk", threads=True, ignore_tz=False)
|
||||
yf.download([tkr], period="1wk", threads=False, ignore_tz=True)
|
||||
yf.download([tkr], period="1wk", threads=True, ignore_tz=True)
|
||||
|
||||
for k in dat.fast_info:
|
||||
dat.fast_info[k]
|
||||
|
||||
dat.isin
|
||||
dat.major_holders
|
||||
dat.institutional_holders
|
||||
|
|
@ -67,69 +68,83 @@ class TestTicker(unittest.TestCase):
|
|||
dat.dividends
|
||||
dat.splits
|
||||
dat.actions
|
||||
dat.shares
|
||||
dat.get_shares_full()
|
||||
dat.info
|
||||
dat.calendar
|
||||
dat.recommendations
|
||||
dat.earnings
|
||||
dat.quarterly_earnings
|
||||
dat.options
|
||||
dat.news
|
||||
dat.earnings_dates
|
||||
|
||||
dat.income_stmt
|
||||
dat.quarterly_income_stmt
|
||||
dat.balance_sheet
|
||||
dat.quarterly_balance_sheet
|
||||
dat.cashflow
|
||||
dat.quarterly_cashflow
|
||||
dat.recommendations_summary
|
||||
dat.analyst_price_target
|
||||
dat.revenue_forecasts
|
||||
dat.sustainability
|
||||
dat.options
|
||||
dat.news
|
||||
dat.earnings_trend
|
||||
dat.earnings_dates
|
||||
dat.earnings_forecasts
|
||||
|
||||
# These haven't been ported Yahoo API
|
||||
# dat.shares
|
||||
# dat.info
|
||||
# dat.calendar
|
||||
# dat.recommendations
|
||||
# dat.earnings
|
||||
# dat.quarterly_earnings
|
||||
# dat.recommendations_summary
|
||||
# dat.analyst_price_target
|
||||
# dat.revenue_forecasts
|
||||
# dat.sustainability
|
||||
# dat.earnings_trend
|
||||
# dat.earnings_forecasts
|
||||
|
||||
def test_goodTicker(self):
|
||||
# that yfinance works when full api is called on same instance of ticker
|
||||
|
||||
tkr = "IBM"
|
||||
dat = yf.Ticker(tkr, session=self.session)
|
||||
tkrs = ["IBM"]
|
||||
tkrs.append("QCSTIX") # weird ticker, no price history but has previous close
|
||||
for tkr in tkrs:
|
||||
dat = yf.Ticker(tkr, session=self.session)
|
||||
|
||||
dat.isin
|
||||
dat.major_holders
|
||||
dat.institutional_holders
|
||||
dat.mutualfund_holders
|
||||
dat.dividends
|
||||
dat.splits
|
||||
dat.actions
|
||||
dat.shares
|
||||
dat.get_shares_full()
|
||||
dat.info
|
||||
dat.calendar
|
||||
dat.recommendations
|
||||
dat.earnings
|
||||
dat.quarterly_earnings
|
||||
dat.income_stmt
|
||||
dat.quarterly_income_stmt
|
||||
dat.balance_sheet
|
||||
dat.quarterly_balance_sheet
|
||||
dat.cashflow
|
||||
dat.quarterly_cashflow
|
||||
dat.recommendations_summary
|
||||
dat.analyst_price_target
|
||||
dat.revenue_forecasts
|
||||
dat.sustainability
|
||||
dat.options
|
||||
dat.news
|
||||
dat.earnings_trend
|
||||
dat.earnings_dates
|
||||
dat.earnings_forecasts
|
||||
dat.history(period="1wk")
|
||||
dat.history(start="2022-01-01")
|
||||
dat.history(start="2022-01-01", end="2022-03-01")
|
||||
yf.download([tkr], period="1wk", threads=False, ignore_tz=False)
|
||||
yf.download([tkr], period="1wk", threads=True, ignore_tz=False)
|
||||
yf.download([tkr], period="1wk", threads=False, ignore_tz=True)
|
||||
yf.download([tkr], period="1wk", threads=True, ignore_tz=True)
|
||||
|
||||
dat.history(period="1wk")
|
||||
dat.history(start="2022-01-01")
|
||||
dat.history(start="2022-01-01", end="2022-03-01")
|
||||
yf.download([tkr], period="1wk")
|
||||
for k in dat.fast_info:
|
||||
dat.fast_info[k]
|
||||
|
||||
dat.isin
|
||||
dat.major_holders
|
||||
dat.institutional_holders
|
||||
dat.mutualfund_holders
|
||||
dat.dividends
|
||||
dat.splits
|
||||
dat.actions
|
||||
dat.get_shares_full()
|
||||
dat.options
|
||||
dat.news
|
||||
dat.earnings_dates
|
||||
|
||||
dat.income_stmt
|
||||
dat.quarterly_income_stmt
|
||||
dat.balance_sheet
|
||||
dat.quarterly_balance_sheet
|
||||
dat.cashflow
|
||||
dat.quarterly_cashflow
|
||||
|
||||
# These require decryption which is broken:
|
||||
# dat.shares
|
||||
# dat.info
|
||||
# dat.calendar
|
||||
# dat.recommendations
|
||||
# dat.earnings
|
||||
# dat.quarterly_earnings
|
||||
# dat.recommendations_summary
|
||||
# dat.analyst_price_target
|
||||
# dat.revenue_forecasts
|
||||
# dat.sustainability
|
||||
# dat.earnings_trend
|
||||
# dat.earnings_forecasts
|
||||
|
||||
def test_goodTicker_withProxy(self):
|
||||
# that yfinance works when full api is called on same instance of ticker
|
||||
|
|
@ -260,7 +275,7 @@ class TestTickerHistory(unittest.TestCase):
|
|||
|
||||
@classmethod
|
||||
def setUpClass(cls):
|
||||
cls.session = requests_cache.CachedSession(backend='memory')
|
||||
cls.session = session_gbl
|
||||
|
||||
@classmethod
|
||||
def tearDownClass(cls):
|
||||
|
|
@ -269,19 +284,28 @@ class TestTickerHistory(unittest.TestCase):
|
|||
|
||||
def setUp(self):
|
||||
# use a ticker that has dividends
|
||||
self.ticker = yf.Ticker("IBM", session=self.session)
|
||||
self.symbol = "IBM"
|
||||
self.ticker = yf.Ticker(self.symbol, session=self.session)
|
||||
|
||||
self.symbols = ["AMZN", "MSFT", "NVDA"]
|
||||
|
||||
def tearDown(self):
|
||||
self.ticker = None
|
||||
|
||||
def test_history(self):
|
||||
with self.assertRaises(RuntimeError):
|
||||
self.ticker.history_metadata
|
||||
md = self.ticker.history_metadata
|
||||
self.assertIn("IBM", md.values(), "metadata missing")
|
||||
data = self.ticker.history("1y")
|
||||
self.assertIn("IBM", self.ticker.history_metadata.values(), "metadata missing")
|
||||
self.assertIsInstance(data, pd.DataFrame, "data has wrong type")
|
||||
self.assertFalse(data.empty, "data is empty")
|
||||
|
||||
def test_download(self):
|
||||
for t in [False, True]:
|
||||
for i in [False, True]:
|
||||
data = yf.download(self.symbols, threads=t, ignore_tz=i)
|
||||
self.assertIsInstance(data, pd.DataFrame, "data has wrong type")
|
||||
self.assertFalse(data.empty, "data is empty")
|
||||
|
||||
def test_no_expensive_calls_introduced(self):
|
||||
"""
|
||||
Make sure calling history to get price data has not introduced more calls to yahoo than absolutely necessary.
|
||||
|
|
@ -294,7 +318,7 @@ class TestTickerHistory(unittest.TestCase):
|
|||
actual_urls_called = tuple([r.url for r in session.cache.filter()])
|
||||
session.close()
|
||||
expected_urls = (
|
||||
'https://query2.finance.yahoo.com/v8/finance/chart/GOOGL?range=1y&interval=1d&includePrePost=False&events=div%2Csplits%2CcapitalGains',
|
||||
'https://query2.finance.yahoo.com/v8/finance/chart/GOOGL?events=div,splits,capitalGains&includePrePost=False&interval=1d&range=1y',
|
||||
)
|
||||
self.assertEqual(expected_urls, actual_urls_called, "Different than expected url used to fetch history.")
|
||||
|
||||
|
|
@ -314,75 +338,76 @@ class TestTickerHistory(unittest.TestCase):
|
|||
self.assertFalse(data.empty, "data is empty")
|
||||
|
||||
|
||||
class TestTickerEarnings(unittest.TestCase):
|
||||
session = None
|
||||
# Below will fail because not ported to Yahoo API
|
||||
# class TestTickerEarnings(unittest.TestCase):
|
||||
# session = None
|
||||
|
||||
@classmethod
|
||||
def setUpClass(cls):
|
||||
cls.session = requests_cache.CachedSession(backend='memory')
|
||||
# @classmethod
|
||||
# def setUpClass(cls):
|
||||
# cls.session = session_gbl
|
||||
|
||||
@classmethod
|
||||
def tearDownClass(cls):
|
||||
if cls.session is not None:
|
||||
cls.session.close()
|
||||
# @classmethod
|
||||
# def tearDownClass(cls):
|
||||
# if cls.session is not None:
|
||||
# cls.session.close()
|
||||
|
||||
def setUp(self):
|
||||
self.ticker = yf.Ticker("GOOGL", session=self.session)
|
||||
# def setUp(self):
|
||||
# self.ticker = yf.Ticker("GOOGL", session=self.session)
|
||||
|
||||
def tearDown(self):
|
||||
self.ticker = None
|
||||
# def tearDown(self):
|
||||
# self.ticker = None
|
||||
|
||||
def test_earnings(self):
|
||||
data = self.ticker.earnings
|
||||
self.assertIsInstance(data, pd.DataFrame, "data has wrong type")
|
||||
self.assertFalse(data.empty, "data is empty")
|
||||
# def test_earnings(self):
|
||||
# data = self.ticker.earnings
|
||||
# self.assertIsInstance(data, pd.DataFrame, "data has wrong type")
|
||||
# self.assertFalse(data.empty, "data is empty")
|
||||
|
||||
data_cached = self.ticker.earnings
|
||||
self.assertIs(data, data_cached, "data not cached")
|
||||
# data_cached = self.ticker.earnings
|
||||
# self.assertIs(data, data_cached, "data not cached")
|
||||
|
||||
def test_quarterly_earnings(self):
|
||||
data = self.ticker.quarterly_earnings
|
||||
self.assertIsInstance(data, pd.DataFrame, "data has wrong type")
|
||||
self.assertFalse(data.empty, "data is empty")
|
||||
# def test_quarterly_earnings(self):
|
||||
# data = self.ticker.quarterly_earnings
|
||||
# self.assertIsInstance(data, pd.DataFrame, "data has wrong type")
|
||||
# self.assertFalse(data.empty, "data is empty")
|
||||
|
||||
data_cached = self.ticker.quarterly_earnings
|
||||
self.assertIs(data, data_cached, "data not cached")
|
||||
# data_cached = self.ticker.quarterly_earnings
|
||||
# self.assertIs(data, data_cached, "data not cached")
|
||||
|
||||
def test_earnings_forecasts(self):
|
||||
data = self.ticker.earnings_forecasts
|
||||
self.assertIsInstance(data, pd.DataFrame, "data has wrong type")
|
||||
self.assertFalse(data.empty, "data is empty")
|
||||
# def test_earnings_forecasts(self):
|
||||
# data = self.ticker.earnings_forecasts
|
||||
# self.assertIsInstance(data, pd.DataFrame, "data has wrong type")
|
||||
# self.assertFalse(data.empty, "data is empty")
|
||||
|
||||
data_cached = self.ticker.earnings_forecasts
|
||||
self.assertIs(data, data_cached, "data not cached")
|
||||
# data_cached = self.ticker.earnings_forecasts
|
||||
# self.assertIs(data, data_cached, "data not cached")
|
||||
|
||||
def test_earnings_dates(self):
|
||||
data = self.ticker.earnings_dates
|
||||
self.assertIsInstance(data, pd.DataFrame, "data has wrong type")
|
||||
self.assertFalse(data.empty, "data is empty")
|
||||
# def test_earnings_dates(self):
|
||||
# data = self.ticker.earnings_dates
|
||||
# self.assertIsInstance(data, pd.DataFrame, "data has wrong type")
|
||||
# self.assertFalse(data.empty, "data is empty")
|
||||
|
||||
data_cached = self.ticker.earnings_dates
|
||||
self.assertIs(data, data_cached, "data not cached")
|
||||
# data_cached = self.ticker.earnings_dates
|
||||
# self.assertIs(data, data_cached, "data not cached")
|
||||
|
||||
def test_earnings_trend(self):
|
||||
data = self.ticker.earnings_trend
|
||||
self.assertIsInstance(data, pd.DataFrame, "data has wrong type")
|
||||
self.assertFalse(data.empty, "data is empty")
|
||||
# def test_earnings_trend(self):
|
||||
# data = self.ticker.earnings_trend
|
||||
# self.assertIsInstance(data, pd.DataFrame, "data has wrong type")
|
||||
# self.assertFalse(data.empty, "data is empty")
|
||||
|
||||
data_cached = self.ticker.earnings_trend
|
||||
self.assertIs(data, data_cached, "data not cached")
|
||||
# data_cached = self.ticker.earnings_trend
|
||||
# self.assertIs(data, data_cached, "data not cached")
|
||||
|
||||
def test_earnings_dates_with_limit(self):
|
||||
# use ticker with lots of historic earnings
|
||||
ticker = yf.Ticker("IBM")
|
||||
limit = 110
|
||||
data = ticker.get_earnings_dates(limit=limit)
|
||||
self.assertIsInstance(data, pd.DataFrame, "data has wrong type")
|
||||
self.assertFalse(data.empty, "data is empty")
|
||||
self.assertEqual(len(data), limit, "Wrong number or rows")
|
||||
# def test_earnings_dates_with_limit(self):
|
||||
# # use ticker with lots of historic earnings
|
||||
# ticker = yf.Ticker("IBM")
|
||||
# limit = 110
|
||||
# data = ticker.get_earnings_dates(limit=limit)
|
||||
# self.assertIsInstance(data, pd.DataFrame, "data has wrong type")
|
||||
# self.assertFalse(data.empty, "data is empty")
|
||||
# self.assertEqual(len(data), limit, "Wrong number or rows")
|
||||
|
||||
data_cached = ticker.get_earnings_dates(limit=limit)
|
||||
self.assertIs(data, data_cached, "data not cached")
|
||||
# data_cached = ticker.get_earnings_dates(limit=limit)
|
||||
# self.assertIs(data, data_cached, "data not cached")
|
||||
|
||||
|
||||
class TestTickerHolders(unittest.TestCase):
|
||||
|
|
@ -390,7 +415,7 @@ class TestTickerHolders(unittest.TestCase):
|
|||
|
||||
@classmethod
|
||||
def setUpClass(cls):
|
||||
cls.session = requests_cache.CachedSession(backend='memory')
|
||||
cls.session = session_gbl
|
||||
|
||||
@classmethod
|
||||
def tearDownClass(cls):
|
||||
|
|
@ -433,7 +458,7 @@ class TestTickerMiscFinancials(unittest.TestCase):
|
|||
|
||||
@classmethod
|
||||
def setUpClass(cls):
|
||||
cls.session = requests_cache.CachedSession(backend='memory')
|
||||
cls.session = session_gbl
|
||||
|
||||
@classmethod
|
||||
def tearDownClass(cls):
|
||||
|
|
@ -451,6 +476,24 @@ class TestTickerMiscFinancials(unittest.TestCase):
|
|||
def tearDown(self):
|
||||
self.ticker = None
|
||||
|
||||
def test_isin(self):
|
||||
data = self.ticker.isin
|
||||
self.assertIsInstance(data, str, "data has wrong type")
|
||||
self.assertEqual("ARDEUT116159", data, "data is empty")
|
||||
|
||||
data_cached = self.ticker.isin
|
||||
self.assertIs(data, data_cached, "data not cached")
|
||||
|
||||
def test_options(self):
|
||||
data = self.ticker.options
|
||||
self.assertIsInstance(data, tuple, "data has wrong type")
|
||||
self.assertTrue(len(data) > 1, "data is empty")
|
||||
|
||||
def test_shares_full(self):
|
||||
data = self.ticker.get_shares_full()
|
||||
self.assertIsInstance(data, pd.Series, "data has wrong type")
|
||||
self.assertFalse(data.empty, "data is empty")
|
||||
|
||||
def test_income_statement(self):
|
||||
expected_keys = ["Total Revenue", "Basic EPS"]
|
||||
expected_periods_days = 365
|
||||
|
|
@ -480,7 +523,6 @@ class TestTickerMiscFinancials(unittest.TestCase):
|
|||
data = self.ticker.get_income_stmt(as_dict=True)
|
||||
self.assertIsInstance(data, dict, "data has wrong type")
|
||||
|
||||
|
||||
def test_quarterly_income_statement(self):
|
||||
expected_keys = ["Total Revenue", "Basic EPS"]
|
||||
expected_periods_days = 365//4
|
||||
|
|
@ -510,16 +552,6 @@ class TestTickerMiscFinancials(unittest.TestCase):
|
|||
data = self.ticker.get_income_stmt(as_dict=True)
|
||||
self.assertIsInstance(data, dict, "data has wrong type")
|
||||
|
||||
def test_quarterly_income_statement_old_fmt(self):
|
||||
expected_row = "TotalRevenue"
|
||||
data = self.ticker_old_fmt.get_income_stmt(freq="quarterly", legacy=True)
|
||||
self.assertIsInstance(data, pd.DataFrame, "data has wrong type")
|
||||
self.assertFalse(data.empty, "data is empty")
|
||||
self.assertIn(expected_row, data.index, "Did not find expected row in index")
|
||||
|
||||
data_cached = self.ticker_old_fmt.get_income_stmt(freq="quarterly", legacy=True)
|
||||
self.assertIs(data, data_cached, "data not cached")
|
||||
|
||||
def test_balance_sheet(self):
|
||||
expected_keys = ["Total Assets", "Net PPE"]
|
||||
expected_periods_days = 365
|
||||
|
|
@ -578,16 +610,6 @@ class TestTickerMiscFinancials(unittest.TestCase):
|
|||
data = self.ticker.get_balance_sheet(as_dict=True, freq="quarterly")
|
||||
self.assertIsInstance(data, dict, "data has wrong type")
|
||||
|
||||
def test_quarterly_balance_sheet_old_fmt(self):
|
||||
expected_row = "TotalAssets"
|
||||
data = self.ticker_old_fmt.get_balance_sheet(freq="quarterly", legacy=True)
|
||||
self.assertIsInstance(data, pd.DataFrame, "data has wrong type")
|
||||
self.assertFalse(data.empty, "data is empty")
|
||||
self.assertIn(expected_row, data.index, "Did not find expected row in index")
|
||||
|
||||
data_cached = self.ticker_old_fmt.get_balance_sheet(freq="quarterly", legacy=True)
|
||||
self.assertIs(data, data_cached, "data not cached")
|
||||
|
||||
def test_cash_flow(self):
|
||||
expected_keys = ["Operating Cash Flow", "Net PPE Purchase And Sale"]
|
||||
expected_periods_days = 365
|
||||
|
|
@ -646,16 +668,6 @@ class TestTickerMiscFinancials(unittest.TestCase):
|
|||
data = self.ticker.get_cashflow(as_dict=True)
|
||||
self.assertIsInstance(data, dict, "data has wrong type")
|
||||
|
||||
def test_quarterly_cashflow_old_fmt(self):
|
||||
expected_row = "NetIncome"
|
||||
data = self.ticker_old_fmt.get_cashflow(legacy=True, freq="quarterly")
|
||||
self.assertIsInstance(data, pd.DataFrame, "data has wrong type")
|
||||
self.assertFalse(data.empty, "data is empty")
|
||||
self.assertIn(expected_row, data.index, "Did not find expected row in index")
|
||||
|
||||
data_cached = self.ticker_old_fmt.get_cashflow(legacy=True, freq="quarterly")
|
||||
self.assertIs(data, data_cached, "data not cached")
|
||||
|
||||
def test_income_alt_names(self):
|
||||
i1 = self.ticker.income_stmt
|
||||
i2 = self.ticker.incomestmt
|
||||
|
|
@ -715,87 +727,71 @@ class TestTickerMiscFinancials(unittest.TestCase):
|
|||
i2 = self.ticker.get_cashflow(freq="quarterly")
|
||||
self.assertTrue(i1.equals(i2))
|
||||
|
||||
def test_sustainability(self):
|
||||
data = self.ticker.sustainability
|
||||
self.assertIsInstance(data, pd.DataFrame, "data has wrong type")
|
||||
self.assertFalse(data.empty, "data is empty")
|
||||
|
||||
data_cached = self.ticker.sustainability
|
||||
self.assertIs(data, data_cached, "data not cached")
|
||||
|
||||
def test_recommendations(self):
|
||||
data = self.ticker.recommendations
|
||||
self.assertIsInstance(data, pd.DataFrame, "data has wrong type")
|
||||
self.assertFalse(data.empty, "data is empty")
|
||||
|
||||
data_cached = self.ticker.recommendations
|
||||
self.assertIs(data, data_cached, "data not cached")
|
||||
|
||||
def test_recommendations_summary(self):
|
||||
data = self.ticker.recommendations_summary
|
||||
self.assertIsInstance(data, pd.DataFrame, "data has wrong type")
|
||||
self.assertFalse(data.empty, "data is empty")
|
||||
|
||||
data_cached = self.ticker.recommendations_summary
|
||||
self.assertIs(data, data_cached, "data not cached")
|
||||
|
||||
def test_analyst_price_target(self):
|
||||
data = self.ticker.analyst_price_target
|
||||
self.assertIsInstance(data, pd.DataFrame, "data has wrong type")
|
||||
self.assertFalse(data.empty, "data is empty")
|
||||
|
||||
data_cached = self.ticker.analyst_price_target
|
||||
self.assertIs(data, data_cached, "data not cached")
|
||||
|
||||
def test_revenue_forecasts(self):
|
||||
data = self.ticker.revenue_forecasts
|
||||
self.assertIsInstance(data, pd.DataFrame, "data has wrong type")
|
||||
self.assertFalse(data.empty, "data is empty")
|
||||
|
||||
data_cached = self.ticker.revenue_forecasts
|
||||
self.assertIs(data, data_cached, "data not cached")
|
||||
|
||||
def test_calendar(self):
|
||||
data = self.ticker.calendar
|
||||
self.assertIsInstance(data, pd.DataFrame, "data has wrong type")
|
||||
self.assertFalse(data.empty, "data is empty")
|
||||
|
||||
data_cached = self.ticker.calendar
|
||||
self.assertIs(data, data_cached, "data not cached")
|
||||
|
||||
def test_isin(self):
|
||||
data = self.ticker.isin
|
||||
self.assertIsInstance(data, str, "data has wrong type")
|
||||
self.assertEqual("ARDEUT116159", data, "data is empty")
|
||||
|
||||
data_cached = self.ticker.isin
|
||||
self.assertIs(data, data_cached, "data not cached")
|
||||
|
||||
def test_options(self):
|
||||
data = self.ticker.options
|
||||
self.assertIsInstance(data, tuple, "data has wrong type")
|
||||
self.assertTrue(len(data) > 1, "data is empty")
|
||||
|
||||
def test_shares(self):
|
||||
data = self.ticker.shares
|
||||
self.assertIsInstance(data, pd.DataFrame, "data has wrong type")
|
||||
self.assertFalse(data.empty, "data is empty")
|
||||
|
||||
def test_shares_full(self):
|
||||
data = self.ticker.get_shares_full()
|
||||
self.assertIsInstance(data, pd.Series, "data has wrong type")
|
||||
self.assertFalse(data.empty, "data is empty")
|
||||
|
||||
def test_bad_freq_value_raises_exception(self):
|
||||
self.assertRaises(ValueError, lambda: self.ticker.get_cashflow(freq="badarg"))
|
||||
|
||||
# Below will fail because not ported to Yahoo API
|
||||
|
||||
# def test_sustainability(self):
|
||||
# data = self.ticker.sustainability
|
||||
# self.assertIsInstance(data, pd.DataFrame, "data has wrong type")
|
||||
# self.assertFalse(data.empty, "data is empty")
|
||||
|
||||
# data_cached = self.ticker.sustainability
|
||||
# self.assertIs(data, data_cached, "data not cached")
|
||||
|
||||
# def test_recommendations(self):
|
||||
# data = self.ticker.recommendations
|
||||
# self.assertIsInstance(data, pd.DataFrame, "data has wrong type")
|
||||
# self.assertFalse(data.empty, "data is empty")
|
||||
|
||||
# data_cached = self.ticker.recommendations
|
||||
# self.assertIs(data, data_cached, "data not cached")
|
||||
|
||||
# def test_recommendations_summary(self):
|
||||
# data = self.ticker.recommendations_summary
|
||||
# self.assertIsInstance(data, pd.DataFrame, "data has wrong type")
|
||||
# self.assertFalse(data.empty, "data is empty")
|
||||
|
||||
# data_cached = self.ticker.recommendations_summary
|
||||
# self.assertIs(data, data_cached, "data not cached")
|
||||
|
||||
# def test_analyst_price_target(self):
|
||||
# data = self.ticker.analyst_price_target
|
||||
# self.assertIsInstance(data, pd.DataFrame, "data has wrong type")
|
||||
# self.assertFalse(data.empty, "data is empty")
|
||||
|
||||
# data_cached = self.ticker.analyst_price_target
|
||||
# self.assertIs(data, data_cached, "data not cached")
|
||||
|
||||
# def test_revenue_forecasts(self):
|
||||
# data = self.ticker.revenue_forecasts
|
||||
# self.assertIsInstance(data, pd.DataFrame, "data has wrong type")
|
||||
# self.assertFalse(data.empty, "data is empty")
|
||||
|
||||
# data_cached = self.ticker.revenue_forecasts
|
||||
# self.assertIs(data, data_cached, "data not cached")
|
||||
|
||||
# def test_calendar(self):
|
||||
# data = self.ticker.calendar
|
||||
# self.assertIsInstance(data, pd.DataFrame, "data has wrong type")
|
||||
# self.assertFalse(data.empty, "data is empty")
|
||||
|
||||
# data_cached = self.ticker.calendar
|
||||
# self.assertIs(data, data_cached, "data not cached")
|
||||
|
||||
# def test_shares(self):
|
||||
# data = self.ticker.shares
|
||||
# self.assertIsInstance(data, pd.DataFrame, "data has wrong type")
|
||||
# self.assertFalse(data.empty, "data is empty")
|
||||
|
||||
|
||||
class TestTickerInfo(unittest.TestCase):
|
||||
session = None
|
||||
|
||||
@classmethod
|
||||
def setUpClass(cls):
|
||||
cls.session = requests_cache.CachedSession(backend='memory')
|
||||
cls.session = session_gbl
|
||||
|
||||
@classmethod
|
||||
def tearDownClass(cls):
|
||||
|
|
@ -813,110 +809,116 @@ class TestTickerInfo(unittest.TestCase):
|
|||
def tearDown(self):
|
||||
self.ticker = None
|
||||
|
||||
def test_fast_info(self):
|
||||
f = yf.Ticker("AAPL", session=self.session).fast_info
|
||||
for k in f:
|
||||
self.assertIsNotNone(f[k])
|
||||
|
||||
def test_info(self):
|
||||
data = self.tickers[0].info
|
||||
self.assertIsInstance(data, dict, "data has wrong type")
|
||||
self.assertIn("symbol", data.keys(), "Did not find expected key in info dict")
|
||||
expected_keys = ['industry', 'currentPrice', 'exchange', 'floatShares', 'companyOfficers', 'bid']
|
||||
for k in expected_keys:
|
||||
print(k)
|
||||
self.assertIn("symbol", data.keys(), f"Did not find expected key '{k}' in info dict")
|
||||
self.assertEqual(self.symbols[0], data["symbol"], "Wrong symbol value in info dict")
|
||||
|
||||
def test_fast_info(self):
|
||||
yf.scrapers.quote.PRUNE_INFO = False
|
||||
# def test_fast_info_matches_info(self):
|
||||
# fast_info_keys = set()
|
||||
# for ticker in self.tickers:
|
||||
# fast_info_keys.update(set(ticker.fast_info.keys()))
|
||||
# fast_info_keys = sorted(list(fast_info_keys))
|
||||
|
||||
fast_info_keys = set()
|
||||
for ticker in self.tickers:
|
||||
fast_info_keys.update(set(ticker.fast_info.keys()))
|
||||
fast_info_keys = sorted(list(fast_info_keys))
|
||||
# key_rename_map = {}
|
||||
# key_rename_map["currency"] = "currency"
|
||||
# key_rename_map["quote_type"] = "quoteType"
|
||||
# key_rename_map["timezone"] = "exchangeTimezoneName"
|
||||
|
||||
key_rename_map = {}
|
||||
key_rename_map["currency"] = "currency"
|
||||
key_rename_map["quote_type"] = "quoteType"
|
||||
key_rename_map["timezone"] = "exchangeTimezoneName"
|
||||
# key_rename_map["last_price"] = ["currentPrice", "regularMarketPrice"]
|
||||
# key_rename_map["open"] = ["open", "regularMarketOpen"]
|
||||
# key_rename_map["day_high"] = ["dayHigh", "regularMarketDayHigh"]
|
||||
# key_rename_map["day_low"] = ["dayLow", "regularMarketDayLow"]
|
||||
# key_rename_map["previous_close"] = ["previousClose"]
|
||||
# key_rename_map["regular_market_previous_close"] = ["regularMarketPreviousClose"]
|
||||
|
||||
key_rename_map["last_price"] = ["currentPrice", "regularMarketPrice"]
|
||||
key_rename_map["open"] = ["open", "regularMarketOpen"]
|
||||
key_rename_map["day_high"] = ["dayHigh", "regularMarketDayHigh"]
|
||||
key_rename_map["day_low"] = ["dayLow", "regularMarketDayLow"]
|
||||
key_rename_map["previous_close"] = ["previousClose"]
|
||||
key_rename_map["regular_market_previous_close"] = ["regularMarketPreviousClose"]
|
||||
# key_rename_map["fifty_day_average"] = "fiftyDayAverage"
|
||||
# key_rename_map["two_hundred_day_average"] = "twoHundredDayAverage"
|
||||
# key_rename_map["year_change"] = ["52WeekChange", "fiftyTwoWeekChange"]
|
||||
# key_rename_map["year_high"] = "fiftyTwoWeekHigh"
|
||||
# key_rename_map["year_low"] = "fiftyTwoWeekLow"
|
||||
|
||||
key_rename_map["fifty_day_average"] = "fiftyDayAverage"
|
||||
key_rename_map["two_hundred_day_average"] = "twoHundredDayAverage"
|
||||
key_rename_map["year_change"] = ["52WeekChange", "fiftyTwoWeekChange"]
|
||||
key_rename_map["year_high"] = "fiftyTwoWeekHigh"
|
||||
key_rename_map["year_low"] = "fiftyTwoWeekLow"
|
||||
# key_rename_map["last_volume"] = ["volume", "regularMarketVolume"]
|
||||
# key_rename_map["ten_day_average_volume"] = ["averageVolume10days", "averageDailyVolume10Day"]
|
||||
# key_rename_map["three_month_average_volume"] = "averageVolume"
|
||||
|
||||
key_rename_map["last_volume"] = ["volume", "regularMarketVolume"]
|
||||
key_rename_map["ten_day_average_volume"] = ["averageVolume10days", "averageDailyVolume10Day"]
|
||||
key_rename_map["three_month_average_volume"] = "averageVolume"
|
||||
# key_rename_map["market_cap"] = "marketCap"
|
||||
# key_rename_map["shares"] = "sharesOutstanding"
|
||||
|
||||
key_rename_map["market_cap"] = "marketCap"
|
||||
key_rename_map["shares"] = "sharesOutstanding"
|
||||
# for k in list(key_rename_map.keys()):
|
||||
# if '_' in k:
|
||||
# key_rename_map[yf.utils.snake_case_2_camelCase(k)] = key_rename_map[k]
|
||||
|
||||
for k in list(key_rename_map.keys()):
|
||||
if '_' in k:
|
||||
key_rename_map[yf.utils.snake_case_2_camelCase(k)] = key_rename_map[k]
|
||||
# # Note: share count items in info[] are bad. Sometimes the float > outstanding!
|
||||
# # So often fast_info["shares"] does not match.
|
||||
# # Why isn't fast_info["shares"] wrong? Because using it to calculate market cap always correct.
|
||||
# bad_keys = {"shares"}
|
||||
|
||||
# Note: share count items in info[] are bad. Sometimes the float > outstanding!
|
||||
# So often fast_info["shares"] does not match.
|
||||
# Why isn't fast_info["shares"] wrong? Because using it to calculate market cap always correct.
|
||||
bad_keys = {"shares"}
|
||||
# # Loose tolerance for averages, no idea why don't match info[]. Is info wrong?
|
||||
# custom_tolerances = {}
|
||||
# custom_tolerances["year_change"] = 1.0
|
||||
# # custom_tolerances["ten_day_average_volume"] = 1e-3
|
||||
# custom_tolerances["ten_day_average_volume"] = 1e-1
|
||||
# # custom_tolerances["three_month_average_volume"] = 1e-2
|
||||
# custom_tolerances["three_month_average_volume"] = 5e-1
|
||||
# custom_tolerances["fifty_day_average"] = 1e-2
|
||||
# custom_tolerances["two_hundred_day_average"] = 1e-2
|
||||
# for k in list(custom_tolerances.keys()):
|
||||
# if '_' in k:
|
||||
# custom_tolerances[yf.utils.snake_case_2_camelCase(k)] = custom_tolerances[k]
|
||||
|
||||
# Loose tolerance for averages, no idea why don't match info[]. Is info wrong?
|
||||
custom_tolerances = {}
|
||||
custom_tolerances["year_change"] = 1.0
|
||||
# custom_tolerances["ten_day_average_volume"] = 1e-3
|
||||
custom_tolerances["ten_day_average_volume"] = 1e-1
|
||||
# custom_tolerances["three_month_average_volume"] = 1e-2
|
||||
custom_tolerances["three_month_average_volume"] = 5e-1
|
||||
custom_tolerances["fifty_day_average"] = 1e-2
|
||||
custom_tolerances["two_hundred_day_average"] = 1e-2
|
||||
for k in list(custom_tolerances.keys()):
|
||||
if '_' in k:
|
||||
custom_tolerances[yf.utils.snake_case_2_camelCase(k)] = custom_tolerances[k]
|
||||
# for k in fast_info_keys:
|
||||
# if k in key_rename_map:
|
||||
# k2 = key_rename_map[k]
|
||||
# else:
|
||||
# k2 = k
|
||||
|
||||
for k in fast_info_keys:
|
||||
if k in key_rename_map:
|
||||
k2 = key_rename_map[k]
|
||||
else:
|
||||
k2 = k
|
||||
# if not isinstance(k2, list):
|
||||
# k2 = [k2]
|
||||
|
||||
if not isinstance(k2, list):
|
||||
k2 = [k2]
|
||||
# for m in k2:
|
||||
# for ticker in self.tickers:
|
||||
# if not m in ticker.info:
|
||||
# # print(f"symbol={ticker.ticker}: fast_info key '{k}' mapped to info key '{m}' but not present in info")
|
||||
# continue
|
||||
|
||||
for m in k2:
|
||||
for ticker in self.tickers:
|
||||
if not m in ticker.info:
|
||||
# print(f"symbol={ticker.ticker}: fast_info key '{k}' mapped to info key '{m}' but not present in info")
|
||||
continue
|
||||
# if k in bad_keys:
|
||||
# continue
|
||||
|
||||
if k in bad_keys:
|
||||
continue
|
||||
# if k in custom_tolerances:
|
||||
# rtol = custom_tolerances[k]
|
||||
# else:
|
||||
# rtol = 5e-3
|
||||
# # rtol = 1e-4
|
||||
|
||||
if k in custom_tolerances:
|
||||
rtol = custom_tolerances[k]
|
||||
else:
|
||||
rtol = 5e-3
|
||||
# rtol = 1e-4
|
||||
|
||||
correct = ticker.info[m]
|
||||
test = ticker.fast_info[k]
|
||||
# print(f"Testing: symbol={ticker.ticker} m={m} k={k}: test={test} vs correct={correct}")
|
||||
if k in ["market_cap","marketCap"] and ticker.fast_info["currency"] in ["GBp", "ILA"]:
|
||||
# Adjust for currency to match Yahoo:
|
||||
test *= 0.01
|
||||
try:
|
||||
if correct is None:
|
||||
self.assertTrue(test is None or (not np.isnan(test)), f"{k}: {test} must be None or real value because correct={correct}")
|
||||
elif isinstance(test, float) or isinstance(correct, int):
|
||||
self.assertTrue(np.isclose(test, correct, rtol=rtol), f"{ticker.ticker} {k}: {test} != {correct}")
|
||||
else:
|
||||
self.assertEqual(test, correct, f"{k}: {test} != {correct}")
|
||||
except:
|
||||
if k in ["regularMarketPreviousClose"] and ticker.ticker in ["ADS.DE"]:
|
||||
# Yahoo is wrong, is returning post-market close not regular
|
||||
continue
|
||||
else:
|
||||
raise
|
||||
# correct = ticker.info[m]
|
||||
# test = ticker.fast_info[k]
|
||||
# # print(f"Testing: symbol={ticker.ticker} m={m} k={k}: test={test} vs correct={correct}")
|
||||
# if k in ["market_cap","marketCap"] and ticker.fast_info["currency"] in ["GBp", "ILA"]:
|
||||
# # Adjust for currency to match Yahoo:
|
||||
# test *= 0.01
|
||||
# try:
|
||||
# if correct is None:
|
||||
# self.assertTrue(test is None or (not np.isnan(test)), f"{k}: {test} must be None or real value because correct={correct}")
|
||||
# elif isinstance(test, float) or isinstance(correct, int):
|
||||
# self.assertTrue(np.isclose(test, correct, rtol=rtol), f"{ticker.ticker} {k}: {test} != {correct}")
|
||||
# else:
|
||||
# self.assertEqual(test, correct, f"{k}: {test} != {correct}")
|
||||
# except:
|
||||
# if k in ["regularMarketPreviousClose"] and ticker.ticker in ["ADS.DE"]:
|
||||
# # Yahoo is wrong, is returning post-market close not regular
|
||||
# continue
|
||||
# else:
|
||||
# raise
|
||||
|
||||
|
||||
|
||||
|
|
|
|||
|
|
@ -23,7 +23,7 @@ from . import version
|
|||
from .ticker import Ticker
|
||||
from .tickers import Tickers
|
||||
from .multi import download
|
||||
from .utils import set_tz_cache_location
|
||||
from .utils import set_tz_cache_location, enable_debug_mode
|
||||
|
||||
__version__ = version.version
|
||||
__author__ = "Ran Aroussi"
|
||||
|
|
@ -43,4 +43,4 @@ def pdr_override():
|
|||
pass
|
||||
|
||||
|
||||
__all__ = ['download', 'Ticker', 'Tickers', 'pdr_override', 'set_tz_cache_location']
|
||||
__all__ = ['download', 'Ticker', 'Tickers', 'pdr_override', 'enable_debug_mode', 'set_tz_cache_location']
|
||||
|
|
|
|||
1370
yfinance/base.py
1370
yfinance/base.py
File diff suppressed because it is too large
Load Diff
|
|
@ -0,0 +1,8 @@
|
|||
|
||||
fundamentals_keys = {}
|
||||
|
||||
fundamentals_keys['financials'] = ["TaxEffectOfUnusualItems","TaxRateForCalcs","NormalizedEBITDA","NormalizedDilutedEPS","NormalizedBasicEPS","TotalUnusualItems","TotalUnusualItemsExcludingGoodwill","NetIncomeFromContinuingOperationNetMinorityInterest","ReconciledDepreciation","ReconciledCostOfRevenue","EBITDA","EBIT","NetInterestIncome","InterestExpense","InterestIncome","ContinuingAndDiscontinuedDilutedEPS","ContinuingAndDiscontinuedBasicEPS","NormalizedIncome","NetIncomeFromContinuingAndDiscontinuedOperation","TotalExpenses","RentExpenseSupplemental","ReportedNormalizedDilutedEPS","ReportedNormalizedBasicEPS","TotalOperatingIncomeAsReported","DividendPerShare","DilutedAverageShares","BasicAverageShares","DilutedEPS","DilutedEPSOtherGainsLosses","TaxLossCarryforwardDilutedEPS","DilutedAccountingChange","DilutedExtraordinary","DilutedDiscontinuousOperations","DilutedContinuousOperations","BasicEPS","BasicEPSOtherGainsLosses","TaxLossCarryforwardBasicEPS","BasicAccountingChange","BasicExtraordinary","BasicDiscontinuousOperations","BasicContinuousOperations","DilutedNIAvailtoComStockholders","AverageDilutionEarnings","NetIncomeCommonStockholders","OtherunderPreferredStockDividend","PreferredStockDividends","NetIncome","MinorityInterests","NetIncomeIncludingNoncontrollingInterests","NetIncomeFromTaxLossCarryforward","NetIncomeExtraordinary","NetIncomeDiscontinuousOperations","NetIncomeContinuousOperations","EarningsFromEquityInterestNetOfTax","TaxProvision","PretaxIncome","OtherIncomeExpense","OtherNonOperatingIncomeExpenses","SpecialIncomeCharges","GainOnSaleOfPPE","GainOnSaleOfBusiness","OtherSpecialCharges","WriteOff","ImpairmentOfCapitalAssets","RestructuringAndMergernAcquisition","SecuritiesAmortization","EarningsFromEquityInterest","GainOnSaleOfSecurity","NetNonOperatingInterestIncomeExpense","TotalOtherFinanceCost","InterestExpenseNonOperating","InterestIncomeNonOperating","OperatingIncome","OperatingExpense","OtherOperatingExpenses","OtherTaxes","ProvisionForDoubtfulAccounts","DepreciationAmortizationDepletionIncomeStatement","DepletionIncomeStatement","DepreciationAndAmortizationInIncomeStatement","Amortization","AmortizationOfIntangiblesIncomeStatement","DepreciationIncomeStatement","ResearchAndDevelopment","SellingGeneralAndAdministration","SellingAndMarketingExpense","GeneralAndAdministrativeExpense","OtherGandA","InsuranceAndClaims","RentAndLandingFees","SalariesAndWages","GrossProfit","CostOfRevenue","TotalRevenue","ExciseTaxes","OperatingRevenue"]
|
||||
|
||||
fundamentals_keys['balance-sheet'] = ["TreasurySharesNumber","PreferredSharesNumber","OrdinarySharesNumber","ShareIssued","NetDebt","TotalDebt","TangibleBookValue","InvestedCapital","WorkingCapital","NetTangibleAssets","CapitalLeaseObligations","CommonStockEquity","PreferredStockEquity","TotalCapitalization","TotalEquityGrossMinorityInterest","MinorityInterest","StockholdersEquity","OtherEquityInterest","GainsLossesNotAffectingRetainedEarnings","OtherEquityAdjustments","FixedAssetsRevaluationReserve","ForeignCurrencyTranslationAdjustments","MinimumPensionLiabilities","UnrealizedGainLoss","TreasuryStock","RetainedEarnings","AdditionalPaidInCapital","CapitalStock","OtherCapitalStock","CommonStock","PreferredStock","TotalPartnershipCapital","GeneralPartnershipCapital","LimitedPartnershipCapital","TotalLiabilitiesNetMinorityInterest","TotalNonCurrentLiabilitiesNetMinorityInterest","OtherNonCurrentLiabilities","LiabilitiesHeldforSaleNonCurrent","RestrictedCommonStock","PreferredSecuritiesOutsideStockEquity","DerivativeProductLiabilities","EmployeeBenefits","NonCurrentPensionAndOtherPostretirementBenefitPlans","NonCurrentAccruedExpenses","DuetoRelatedPartiesNonCurrent","TradeandOtherPayablesNonCurrent","NonCurrentDeferredLiabilities","NonCurrentDeferredRevenue","NonCurrentDeferredTaxesLiabilities","LongTermDebtAndCapitalLeaseObligation","LongTermCapitalLeaseObligation","LongTermDebt","LongTermProvisions","CurrentLiabilities","OtherCurrentLiabilities","CurrentDeferredLiabilities","CurrentDeferredRevenue","CurrentDeferredTaxesLiabilities","CurrentDebtAndCapitalLeaseObligation","CurrentCapitalLeaseObligation","CurrentDebt","OtherCurrentBorrowings","LineOfCredit","CommercialPaper","CurrentNotesPayable","PensionandOtherPostRetirementBenefitPlansCurrent","CurrentProvisions","PayablesAndAccruedExpenses","CurrentAccruedExpenses","InterestPayable","Payables","OtherPayable","DuetoRelatedPartiesCurrent","DividendsPayable","TotalTaxPayable","IncomeTaxPayable","AccountsPayable","TotalAssets","TotalNonCurrentAssets","OtherNonCurrentAssets","DefinedPensionBenefit","NonCurrentPrepaidAssets","NonCurrentDeferredAssets","NonCurrentDeferredTaxesAssets","DuefromRelatedPartiesNonCurrent","NonCurrentNoteReceivables","NonCurrentAccountsReceivable","FinancialAssets","InvestmentsAndAdvances","OtherInvestments","InvestmentinFinancialAssets","HeldToMaturitySecurities","AvailableForSaleSecurities","FinancialAssetsDesignatedasFairValueThroughProfitorLossTotal","TradingSecurities","LongTermEquityInvestment","InvestmentsinJointVenturesatCost","InvestmentsInOtherVenturesUnderEquityMethod","InvestmentsinAssociatesatCost","InvestmentsinSubsidiariesatCost","InvestmentProperties","GoodwillAndOtherIntangibleAssets","OtherIntangibleAssets","Goodwill","NetPPE","AccumulatedDepreciation","GrossPPE","Leases","ConstructionInProgress","OtherProperties","MachineryFurnitureEquipment","BuildingsAndImprovements","LandAndImprovements","Properties","CurrentAssets","OtherCurrentAssets","HedgingAssetsCurrent","AssetsHeldForSaleCurrent","CurrentDeferredAssets","CurrentDeferredTaxesAssets","RestrictedCash","PrepaidAssets","Inventory","InventoriesAdjustmentsAllowances","OtherInventories","FinishedGoods","WorkInProcess","RawMaterials","Receivables","ReceivablesAdjustmentsAllowances","OtherReceivables","DuefromRelatedPartiesCurrent","TaxesReceivable","AccruedInterestReceivable","NotesReceivable","LoansReceivable","AccountsReceivable","AllowanceForDoubtfulAccountsReceivable","GrossAccountsReceivable","CashCashEquivalentsAndShortTermInvestments","OtherShortTermInvestments","CashAndCashEquivalents","CashEquivalents","CashFinancial"]
|
||||
|
||||
fundamentals_keys['cash-flow'] = ["ForeignSales","DomesticSales","AdjustedGeographySegmentData","FreeCashFlow","RepurchaseOfCapitalStock","RepaymentOfDebt","IssuanceOfDebt","IssuanceOfCapitalStock","CapitalExpenditure","InterestPaidSupplementalData","IncomeTaxPaidSupplementalData","EndCashPosition","OtherCashAdjustmentOutsideChangeinCash","BeginningCashPosition","EffectOfExchangeRateChanges","ChangesInCash","OtherCashAdjustmentInsideChangeinCash","CashFlowFromDiscontinuedOperation","FinancingCashFlow","CashFromDiscontinuedFinancingActivities","CashFlowFromContinuingFinancingActivities","NetOtherFinancingCharges","InterestPaidCFF","ProceedsFromStockOptionExercised","CashDividendsPaid","PreferredStockDividendPaid","CommonStockDividendPaid","NetPreferredStockIssuance","PreferredStockPayments","PreferredStockIssuance","NetCommonStockIssuance","CommonStockPayments","CommonStockIssuance","NetIssuancePaymentsOfDebt","NetShortTermDebtIssuance","ShortTermDebtPayments","ShortTermDebtIssuance","NetLongTermDebtIssuance","LongTermDebtPayments","LongTermDebtIssuance","InvestingCashFlow","CashFromDiscontinuedInvestingActivities","CashFlowFromContinuingInvestingActivities","NetOtherInvestingChanges","InterestReceivedCFI","DividendsReceivedCFI","NetInvestmentPurchaseAndSale","SaleOfInvestment","PurchaseOfInvestment","NetInvestmentPropertiesPurchaseAndSale","SaleOfInvestmentProperties","PurchaseOfInvestmentProperties","NetBusinessPurchaseAndSale","SaleOfBusiness","PurchaseOfBusiness","NetIntangiblesPurchaseAndSale","SaleOfIntangibles","PurchaseOfIntangibles","NetPPEPurchaseAndSale","SaleOfPPE","PurchaseOfPPE","CapitalExpenditureReported","OperatingCashFlow","CashFromDiscontinuedOperatingActivities","CashFlowFromContinuingOperatingActivities","TaxesRefundPaid","InterestReceivedCFO","InterestPaidCFO","DividendReceivedCFO","DividendPaidCFO","ChangeInWorkingCapital","ChangeInOtherWorkingCapital","ChangeInOtherCurrentLiabilities","ChangeInOtherCurrentAssets","ChangeInPayablesAndAccruedExpense","ChangeInAccruedExpense","ChangeInInterestPayable","ChangeInPayable","ChangeInDividendPayable","ChangeInAccountPayable","ChangeInTaxPayable","ChangeInIncomeTaxPayable","ChangeInPrepaidAssets","ChangeInInventory","ChangeInReceivables","ChangesInAccountReceivables","OtherNonCashItems","ExcessTaxBenefitFromStockBasedCompensation","StockBasedCompensation","UnrealizedGainLossOnInvestmentSecurities","ProvisionandWriteOffofAssets","AssetImpairmentCharge","AmortizationOfSecurities","DeferredTax","DeferredIncomeTax","DepreciationAmortizationDepletion","Depletion","DepreciationAndAmortization","AmortizationCashFlow","AmortizationOfIntangibles","Depreciation","OperatingGainsLosses","PensionAndEmployeeBenefitExpense","EarningsLossesFromEquityInvestments","GainLossOnInvestmentSecurities","NetForeignCurrencyExchangeGainLoss","GainLossOnSaleOfPPE","GainLossOnSaleOfBusiness","NetIncomeFromContinuingOperations","CashFlowsfromusedinOperatingActivitiesDirect","TaxesRefundPaidDirect","InterestReceivedDirect","InterestPaidDirect","DividendsReceivedDirect","DividendsPaidDirect","ClassesofCashPayments","OtherCashPaymentsfromOperatingActivities","PaymentsonBehalfofEmployees","PaymentstoSuppliersforGoodsandServices","ClassesofCashReceiptsfromOperatingActivities","OtherCashReceiptsfromOperatingActivities","ReceiptsfromGovernmentGrants","ReceiptsfromCustomers"]
|
||||
264
yfinance/data.py
264
yfinance/data.py
|
|
@ -1,27 +1,16 @@
|
|||
import functools
|
||||
from functools import lru_cache
|
||||
|
||||
import hashlib
|
||||
from base64 import b64decode
|
||||
usePycryptodome = False # slightly faster
|
||||
# usePycryptodome = True
|
||||
if usePycryptodome:
|
||||
from Crypto.Cipher import AES
|
||||
from Crypto.Util.Padding import unpad
|
||||
else:
|
||||
from cryptography.hazmat.primitives import padding
|
||||
from cryptography.hazmat.primitives.ciphers import Cipher, algorithms, modes
|
||||
import logging
|
||||
|
||||
import requests as requests
|
||||
import re
|
||||
from bs4 import BeautifulSoup
|
||||
import random
|
||||
import time
|
||||
|
||||
from frozendict import frozendict
|
||||
|
||||
try:
|
||||
import ujson as json
|
||||
except ImportError:
|
||||
import json as json
|
||||
from . import utils
|
||||
|
||||
cache_maxsize = 64
|
||||
|
||||
|
|
@ -47,127 +36,6 @@ def lru_cache_freezeargs(func):
|
|||
return wrapped
|
||||
|
||||
|
||||
def _extract_extra_keys_from_stores(data):
|
||||
new_keys = [k for k in data.keys() if k not in ["context", "plugins"]]
|
||||
new_keys_values = set([data[k] for k in new_keys])
|
||||
|
||||
# Maybe multiple keys have same value - keep one of each
|
||||
new_keys_uniq = []
|
||||
new_keys_uniq_values = set()
|
||||
for k in new_keys:
|
||||
v = data[k]
|
||||
if not v in new_keys_uniq_values:
|
||||
new_keys_uniq.append(k)
|
||||
new_keys_uniq_values.add(v)
|
||||
|
||||
return [data[k] for k in new_keys_uniq]
|
||||
|
||||
|
||||
def decrypt_cryptojs_aes_stores(data, keys=None):
|
||||
encrypted_stores = data['context']['dispatcher']['stores']
|
||||
|
||||
password = None
|
||||
if keys is not None:
|
||||
if not isinstance(keys, list):
|
||||
raise TypeError("'keys' must be list")
|
||||
candidate_passwords = keys
|
||||
else:
|
||||
candidate_passwords = []
|
||||
|
||||
if "_cs" in data and "_cr" in data:
|
||||
_cs = data["_cs"]
|
||||
_cr = data["_cr"]
|
||||
_cr = b"".join(int.to_bytes(i, length=4, byteorder="big", signed=True) for i in json.loads(_cr)["words"])
|
||||
password = hashlib.pbkdf2_hmac("sha1", _cs.encode("utf8"), _cr, 1, dklen=32).hex()
|
||||
|
||||
encrypted_stores = b64decode(encrypted_stores)
|
||||
assert encrypted_stores[0:8] == b"Salted__"
|
||||
salt = encrypted_stores[8:16]
|
||||
encrypted_stores = encrypted_stores[16:]
|
||||
|
||||
def _EVPKDF(password, salt, keySize=32, ivSize=16, iterations=1, hashAlgorithm="md5") -> tuple:
|
||||
"""OpenSSL EVP Key Derivation Function
|
||||
Args:
|
||||
password (Union[str, bytes, bytearray]): Password to generate key from.
|
||||
salt (Union[bytes, bytearray]): Salt to use.
|
||||
keySize (int, optional): Output key length in bytes. Defaults to 32.
|
||||
ivSize (int, optional): Output Initialization Vector (IV) length in bytes. Defaults to 16.
|
||||
iterations (int, optional): Number of iterations to perform. Defaults to 1.
|
||||
hashAlgorithm (str, optional): Hash algorithm to use for the KDF. Defaults to 'md5'.
|
||||
Returns:
|
||||
key, iv: Derived key and Initialization Vector (IV) bytes.
|
||||
|
||||
Taken from: https://gist.github.com/rafiibrahim8/0cd0f8c46896cafef6486cb1a50a16d3
|
||||
OpenSSL original code: https://github.com/openssl/openssl/blob/master/crypto/evp/evp_key.c#L78
|
||||
"""
|
||||
|
||||
assert iterations > 0, "Iterations can not be less than 1."
|
||||
|
||||
if isinstance(password, str):
|
||||
password = password.encode("utf-8")
|
||||
|
||||
final_length = keySize + ivSize
|
||||
key_iv = b""
|
||||
block = None
|
||||
|
||||
while len(key_iv) < final_length:
|
||||
hasher = hashlib.new(hashAlgorithm)
|
||||
if block:
|
||||
hasher.update(block)
|
||||
hasher.update(password)
|
||||
hasher.update(salt)
|
||||
block = hasher.digest()
|
||||
for _ in range(1, iterations):
|
||||
block = hashlib.new(hashAlgorithm, block).digest()
|
||||
key_iv += block
|
||||
|
||||
key, iv = key_iv[:keySize], key_iv[keySize:final_length]
|
||||
return key, iv
|
||||
|
||||
def _decrypt(encrypted_stores, password, key, iv):
|
||||
if usePycryptodome:
|
||||
cipher = AES.new(key, AES.MODE_CBC, iv=iv)
|
||||
plaintext = cipher.decrypt(encrypted_stores)
|
||||
plaintext = unpad(plaintext, 16, style="pkcs7")
|
||||
else:
|
||||
cipher = Cipher(algorithms.AES(key), modes.CBC(iv))
|
||||
decryptor = cipher.decryptor()
|
||||
plaintext = decryptor.update(encrypted_stores) + decryptor.finalize()
|
||||
unpadder = padding.PKCS7(128).unpadder()
|
||||
plaintext = unpadder.update(plaintext) + unpadder.finalize()
|
||||
plaintext = plaintext.decode("utf-8")
|
||||
return plaintext
|
||||
|
||||
if not password is None:
|
||||
try:
|
||||
key, iv = _EVPKDF(password, salt, keySize=32, ivSize=16, iterations=1, hashAlgorithm="md5")
|
||||
except:
|
||||
raise Exception("yfinance failed to decrypt Yahoo data response")
|
||||
plaintext = _decrypt(encrypted_stores, password, key, iv)
|
||||
else:
|
||||
success = False
|
||||
for i in range(len(candidate_passwords)):
|
||||
# print(f"Trying candiate pw {i+1}/{len(candidate_passwords)}")
|
||||
password = candidate_passwords[i]
|
||||
try:
|
||||
key, iv = _EVPKDF(password, salt, keySize=32, ivSize=16, iterations=1, hashAlgorithm="md5")
|
||||
|
||||
plaintext = _decrypt(encrypted_stores, password, key, iv)
|
||||
|
||||
success = True
|
||||
break
|
||||
except:
|
||||
pass
|
||||
if not success:
|
||||
raise Exception("yfinance failed to decrypt Yahoo data response")
|
||||
|
||||
decoded_stores = json.loads(plaintext)
|
||||
return decoded_stores
|
||||
|
||||
|
||||
_SCRAPE_URL_ = 'https://finance.yahoo.com/quote'
|
||||
|
||||
|
||||
class TickerData:
|
||||
"""
|
||||
Have one place to retrieve data from Yahoo API in order to ease caching and speed up operations
|
||||
|
|
@ -202,123 +70,7 @@ class TickerData:
|
|||
proxy = {"https": proxy}
|
||||
return proxy
|
||||
|
||||
def _get_decryption_keys_from_yahoo_js(self, soup):
|
||||
result = None
|
||||
|
||||
key_count = 4
|
||||
re_script = soup.find("script", string=re.compile("root.App.main")).text
|
||||
re_data = json.loads(re.search("root.App.main\s+=\s+(\{.*\})", re_script).group(1))
|
||||
re_data.pop("context", None)
|
||||
key_list = list(re_data.keys())
|
||||
if re_data.get("plugins"): # 1) attempt to get last 4 keys after plugins
|
||||
ind = key_list.index("plugins")
|
||||
if len(key_list) > ind+1:
|
||||
sub_keys = key_list[ind+1:]
|
||||
if len(sub_keys) == key_count:
|
||||
re_obj = {}
|
||||
missing_val = False
|
||||
for k in sub_keys:
|
||||
if not re_data.get(k):
|
||||
missing_val = True
|
||||
break
|
||||
re_obj.update({k: re_data.get(k)})
|
||||
if not missing_val:
|
||||
result = re_obj
|
||||
|
||||
if not result is None:
|
||||
return [''.join(result.values())]
|
||||
|
||||
re_keys = [] # 2) attempt scan main.js file approach to get keys
|
||||
prefix = "https://s.yimg.com/uc/finance/dd-site/js/main."
|
||||
tags = [tag['src'] for tag in soup.find_all('script') if prefix in tag.get('src', '')]
|
||||
for t in tags:
|
||||
response_js = self.cache_get(t)
|
||||
#
|
||||
if response_js.status_code != 200:
|
||||
time.sleep(random.randrange(10, 20))
|
||||
response_js.close()
|
||||
else:
|
||||
r_data = response_js.content.decode("utf8")
|
||||
re_list = [
|
||||
x.group() for x in re.finditer(r"context.dispatcher.stores=JSON.parse((?:.*?\r?\n?)*)toString", r_data)
|
||||
]
|
||||
for rl in re_list:
|
||||
re_sublist = [x.group() for x in re.finditer(r"t\[\"((?:.*?\r?\n?)*)\"\]", rl)]
|
||||
if len(re_sublist) == key_count:
|
||||
re_keys = [sl.replace('t["', '').replace('"]', '') for sl in re_sublist]
|
||||
break
|
||||
response_js.close()
|
||||
if len(re_keys) == key_count:
|
||||
break
|
||||
if len(re_keys) > 0:
|
||||
re_obj = {}
|
||||
missing_val = False
|
||||
for k in re_keys:
|
||||
if not re_data.get(k):
|
||||
missing_val = True
|
||||
break
|
||||
re_obj.update({k: re_data.get(k)})
|
||||
if not missing_val:
|
||||
return [''.join(re_obj.values())]
|
||||
|
||||
return []
|
||||
|
||||
@lru_cache_freezeargs
|
||||
@lru_cache(maxsize=cache_maxsize)
|
||||
def get_json_data_stores(self, sub_page: str = None, proxy=None) -> dict:
|
||||
'''
|
||||
get_json_data_stores returns a python dictionary of the data stores in yahoo finance web page.
|
||||
'''
|
||||
if sub_page:
|
||||
ticker_url = "{}/{}/{}".format(_SCRAPE_URL_, self.ticker, sub_page)
|
||||
else:
|
||||
ticker_url = "{}/{}".format(_SCRAPE_URL_, self.ticker)
|
||||
|
||||
response = self.get(url=ticker_url, proxy=proxy)
|
||||
html = response.text
|
||||
|
||||
# The actual json-data for stores is in a javascript assignment in the webpage
|
||||
try:
|
||||
json_str = html.split('root.App.main =')[1].split(
|
||||
'(this)')[0].split(';\n}')[0].strip()
|
||||
except IndexError:
|
||||
# Fetch failed, probably because Yahoo spam triggered
|
||||
return {}
|
||||
|
||||
data = json.loads(json_str)
|
||||
|
||||
# Gather decryption keys:
|
||||
soup = BeautifulSoup(response.content, "html.parser")
|
||||
keys = self._get_decryption_keys_from_yahoo_js(soup)
|
||||
if len(keys) == 0:
|
||||
msg = "No decryption keys could be extracted from JS file."
|
||||
if "requests_cache" in str(type(response)):
|
||||
msg += " Try flushing your 'requests_cache', probably parsing old JS."
|
||||
print("WARNING: " + msg + " Falling back to backup decrypt methods.")
|
||||
if len(keys) == 0:
|
||||
keys = []
|
||||
try:
|
||||
extra_keys = _extract_extra_keys_from_stores(data)
|
||||
keys = [''.join(extra_keys[-4:])]
|
||||
except:
|
||||
pass
|
||||
#
|
||||
keys_url = "https://github.com/ranaroussi/yfinance/raw/main/yfinance/scrapers/yahoo-keys.txt"
|
||||
response_gh = self.cache_get(keys_url)
|
||||
keys += response_gh.text.splitlines()
|
||||
|
||||
# Decrypt!
|
||||
stores = decrypt_cryptojs_aes_stores(data, keys)
|
||||
if stores is None:
|
||||
# Maybe Yahoo returned old format, not encrypted
|
||||
if "context" in data and "dispatcher" in data["context"]:
|
||||
stores = data['context']['dispatcher']['stores']
|
||||
if stores is None:
|
||||
raise Exception(f"{self.ticker}: Failed to extract data stores from web request")
|
||||
|
||||
# return data
|
||||
new_data = json.dumps(stores).replace('{}', 'null')
|
||||
new_data = re.sub(
|
||||
r'{[\'|\"]raw[\'|\"]:(.*?),(.*?)}', r'\1', new_data)
|
||||
|
||||
return json.loads(new_data)
|
||||
def get_raw_json(self, url, user_agent_headers=None, params=None, proxy=None, timeout=30):
|
||||
response = self.get(url, user_agent_headers=user_agent_headers, params=params, proxy=proxy, timeout=timeout)
|
||||
response.raise_for_status()
|
||||
return response.json()
|
||||
|
|
|
|||
|
|
@ -4,3 +4,9 @@ class YFinanceException(Exception):
|
|||
|
||||
class YFinanceDataException(YFinanceException):
|
||||
pass
|
||||
|
||||
|
||||
class YFNotImplementedError(NotImplementedError):
|
||||
def __init__(self, method_name):
|
||||
super().__init__(f"Have not implemented fetching '{method_name}' from Yahoo API")
|
||||
|
||||
|
|
|
|||
|
|
@ -21,6 +21,8 @@
|
|||
|
||||
from __future__ import print_function
|
||||
|
||||
import logging
|
||||
import traceback
|
||||
import time as _time
|
||||
import multitasking as _multitasking
|
||||
import pandas as _pd
|
||||
|
|
@ -28,11 +30,11 @@ import pandas as _pd
|
|||
from . import Ticker, utils
|
||||
from . import shared
|
||||
|
||||
|
||||
@utils.log_indent_decorator
|
||||
def download(tickers, start=None, end=None, actions=False, threads=True, ignore_tz=None,
|
||||
group_by='column', auto_adjust=False, back_adjust=False, repair=False, keepna=False,
|
||||
progress=True, period="max", show_errors=True, interval="1d", prepost=False,
|
||||
proxy=None, rounding=False, timeout=10):
|
||||
progress=True, period="max", show_errors=None, interval="1d", prepost=False,
|
||||
proxy=None, rounding=False, timeout=10, session=None):
|
||||
"""Download yahoo tickers
|
||||
:Parameters:
|
||||
tickers : str, list
|
||||
|
|
@ -44,11 +46,13 @@ def download(tickers, start=None, end=None, actions=False, threads=True, ignore_
|
|||
Valid intervals: 1m,2m,5m,15m,30m,60m,90m,1h,1d,5d,1wk,1mo,3mo
|
||||
Intraday data cannot extend last 60 days
|
||||
start: str
|
||||
Download start date string (YYYY-MM-DD) or _datetime.
|
||||
Download start date string (YYYY-MM-DD) or _datetime, inclusive.
|
||||
Default is 1900-01-01
|
||||
E.g. for start="2020-01-01", the first data point will be on "2020-01-01"
|
||||
end: str
|
||||
Download end date string (YYYY-MM-DD) or _datetime.
|
||||
Download end date string (YYYY-MM-DD) or _datetime, exclusive.
|
||||
Default is now
|
||||
E.g. for end="2023-01-01", the last data point will be on "2022-12-31"
|
||||
group_by : str
|
||||
Group by 'ticker' or 'column' (default)
|
||||
prepost : bool
|
||||
|
|
@ -75,10 +79,33 @@ def download(tickers, start=None, end=None, actions=False, threads=True, ignore_
|
|||
Optional. Round values to 2 decimal places?
|
||||
show_errors: bool
|
||||
Optional. Doesn't print errors if False
|
||||
DEPRECATED, will be removed in future version
|
||||
timeout: None or float
|
||||
If not None stops waiting for a response after given number of
|
||||
seconds. (Can also be a fraction of a second e.g. 0.01)
|
||||
session: None or Session
|
||||
Optional. Pass your own session object to be used for all requests
|
||||
"""
|
||||
logger = utils.get_yf_logger()
|
||||
|
||||
if show_errors is not None:
|
||||
if show_errors:
|
||||
utils.print_once(f"yfinance: download(show_errors={show_errors}) argument is deprecated and will be removed in future version. Do this instead: logging.getLogger('yfinance').setLevel(logging.ERROR)")
|
||||
logger.setLevel(logging.ERROR)
|
||||
else:
|
||||
utils.print_once(f"yfinance: download(show_errors={show_errors}) argument is deprecated and will be removed in future version. Do this instead to suppress error messages: logging.getLogger('yfinance').setLevel(logging.CRITICAL)")
|
||||
logger.setLevel(logging.CRITICAL)
|
||||
|
||||
if logger.isEnabledFor(logging.DEBUG):
|
||||
if threads:
|
||||
# With DEBUG, each thread generates a lot of log messages.
|
||||
# And with multi-threading, these messages will be interleaved, bad!
|
||||
# So disable multi-threading to make log readable.
|
||||
logger.debug('Disabling multithreading because DEBUG logging enabled')
|
||||
threads = False
|
||||
if progress:
|
||||
# Disable progress bar, interferes with display of log messages
|
||||
progress = False
|
||||
|
||||
if ignore_tz is None:
|
||||
# Set default value depending on interval
|
||||
|
|
@ -98,7 +125,7 @@ def download(tickers, start=None, end=None, actions=False, threads=True, ignore_
|
|||
for ticker in tickers:
|
||||
if utils.is_isin(ticker):
|
||||
isin = ticker
|
||||
ticker = utils.get_ticker_by_isin(ticker, proxy)
|
||||
ticker = utils.get_ticker_by_isin(ticker, proxy, session=session)
|
||||
shared._ISINS[ticker] = isin
|
||||
_tickers_.append(ticker)
|
||||
|
||||
|
|
@ -112,6 +139,7 @@ def download(tickers, start=None, end=None, actions=False, threads=True, ignore_
|
|||
# reset shared._DFS
|
||||
shared._DFS = {}
|
||||
shared._ERRORS = {}
|
||||
shared._TRACEBACKS = {}
|
||||
|
||||
# download using threads
|
||||
if threads:
|
||||
|
|
@ -124,10 +152,9 @@ def download(tickers, start=None, end=None, actions=False, threads=True, ignore_
|
|||
actions=actions, auto_adjust=auto_adjust,
|
||||
back_adjust=back_adjust, repair=repair, keepna=keepna,
|
||||
progress=(progress and i > 0), proxy=proxy,
|
||||
rounding=rounding, timeout=timeout)
|
||||
rounding=rounding, timeout=timeout, session=session)
|
||||
while len(shared._DFS) < len(tickers):
|
||||
_time.sleep(0.01)
|
||||
|
||||
# download synchronously
|
||||
else:
|
||||
for i, ticker in enumerate(tickers):
|
||||
|
|
@ -136,20 +163,42 @@ def download(tickers, start=None, end=None, actions=False, threads=True, ignore_
|
|||
actions=actions, auto_adjust=auto_adjust,
|
||||
back_adjust=back_adjust, repair=repair, keepna=keepna,
|
||||
proxy=proxy,
|
||||
rounding=rounding, timeout=timeout)
|
||||
shared._DFS[ticker.upper()] = data
|
||||
rounding=rounding, timeout=timeout, session=session)
|
||||
if progress:
|
||||
shared._PROGRESS_BAR.animate()
|
||||
|
||||
|
||||
if progress:
|
||||
shared._PROGRESS_BAR.completed()
|
||||
|
||||
if shared._ERRORS and show_errors:
|
||||
print('\n%.f Failed download%s:' % (
|
||||
if shared._ERRORS:
|
||||
# Send errors to logging module
|
||||
logger = utils.get_yf_logger()
|
||||
logger.error('\n%.f Failed download%s:' % (
|
||||
len(shared._ERRORS), 's' if len(shared._ERRORS) > 1 else ''))
|
||||
# print(shared._ERRORS)
|
||||
print("\n".join(['- %s: %s' %
|
||||
v for v in list(shared._ERRORS.items())]))
|
||||
|
||||
# Log each distinct error once, with list of symbols affected
|
||||
errors = {}
|
||||
for ticker in shared._ERRORS:
|
||||
err = shared._ERRORS[ticker]
|
||||
err = err.replace(f'{ticker}', '%ticker%')
|
||||
if not err in errors:
|
||||
errors[err] = [ticker]
|
||||
else:
|
||||
errors[err].append(ticker)
|
||||
for err in errors.keys():
|
||||
logger.error(f'{errors[err]}: ' + err)
|
||||
|
||||
# Log each distinct traceback once, with list of symbols affected
|
||||
tbs = {}
|
||||
for ticker in shared._TRACEBACKS:
|
||||
tb = shared._TRACEBACKS[ticker]
|
||||
tb = tb.replace(f'{ticker}', '%ticker%')
|
||||
if not tb in tbs:
|
||||
tbs[tb] = [ticker]
|
||||
else:
|
||||
tbs[tb].append(ticker)
|
||||
for tb in tbs.keys():
|
||||
logger.debug(f'{tbs[tb]}: ' + tb)
|
||||
|
||||
if ignore_tz:
|
||||
for tkr in shared._DFS.keys():
|
||||
|
|
@ -206,17 +255,10 @@ def _download_one_threaded(ticker, start=None, end=None,
|
|||
auto_adjust=False, back_adjust=False, repair=False,
|
||||
actions=False, progress=True, period="max",
|
||||
interval="1d", prepost=False, proxy=None,
|
||||
keepna=False, rounding=False, timeout=10):
|
||||
try:
|
||||
data = _download_one(ticker, start, end, auto_adjust, back_adjust, repair,
|
||||
actions, period, interval, prepost, proxy, rounding,
|
||||
keepna, timeout)
|
||||
except Exception as e:
|
||||
# glob try/except needed as current thead implementation breaks if exception is raised.
|
||||
shared._DFS[ticker] = utils.empty_df()
|
||||
shared._ERRORS[ticker] = repr(e)
|
||||
else:
|
||||
shared._DFS[ticker.upper()] = data
|
||||
keepna=False, rounding=False, timeout=10, session=None):
|
||||
data = _download_one(ticker, start, end, auto_adjust, back_adjust, repair,
|
||||
actions, period, interval, prepost, proxy, rounding,
|
||||
keepna, timeout, session)
|
||||
if progress:
|
||||
shared._PROGRESS_BAR.animate()
|
||||
|
||||
|
|
@ -225,12 +267,23 @@ def _download_one(ticker, start=None, end=None,
|
|||
auto_adjust=False, back_adjust=False, repair=False,
|
||||
actions=False, period="max", interval="1d",
|
||||
prepost=False, proxy=None, rounding=False,
|
||||
keepna=False, timeout=10):
|
||||
return Ticker(ticker).history(
|
||||
period=period, interval=interval,
|
||||
start=start, end=end, prepost=prepost,
|
||||
actions=actions, auto_adjust=auto_adjust,
|
||||
back_adjust=back_adjust, repair=repair, proxy=proxy,
|
||||
rounding=rounding, keepna=keepna, timeout=timeout,
|
||||
debug=False, raise_errors=False # debug and raise_errors false to not log and raise errors in threads
|
||||
)
|
||||
keepna=False, timeout=10, session=None):
|
||||
data = None
|
||||
try:
|
||||
data = Ticker(ticker, session=session).history(
|
||||
period=period, interval=interval,
|
||||
start=start, end=end, prepost=prepost,
|
||||
actions=actions, auto_adjust=auto_adjust,
|
||||
back_adjust=back_adjust, repair=repair, proxy=proxy,
|
||||
rounding=rounding, keepna=keepna, timeout=timeout,
|
||||
raise_errors=True
|
||||
)
|
||||
except Exception as e:
|
||||
# glob try/except needed as current thead implementation breaks if exception is raised.
|
||||
shared._DFS[ticker.upper()] = utils.empty_df()
|
||||
shared._ERRORS[ticker.upper()] = repr(e)
|
||||
shared._TRACEBACKS[ticker.upper()] = traceback.format_exc()
|
||||
else:
|
||||
shared._DFS[ticker.upper()] = data
|
||||
|
||||
return data
|
||||
|
|
|
|||
|
|
@ -2,6 +2,7 @@ import pandas as pd
|
|||
|
||||
from yfinance import utils
|
||||
from yfinance.data import TickerData
|
||||
from yfinance.exceptions import YFNotImplementedError
|
||||
|
||||
|
||||
class Analysis:
|
||||
|
|
@ -20,99 +21,29 @@ class Analysis:
|
|||
@property
|
||||
def earnings_trend(self) -> pd.DataFrame:
|
||||
if self._earnings_trend is None:
|
||||
self._scrape(self.proxy)
|
||||
raise YFNotImplementedError('earnings_trend')
|
||||
return self._earnings_trend
|
||||
|
||||
@property
|
||||
def analyst_trend_details(self) -> pd.DataFrame:
|
||||
if self._analyst_trend_details is None:
|
||||
self._scrape(self.proxy)
|
||||
raise YFNotImplementedError('analyst_trend_details')
|
||||
return self._analyst_trend_details
|
||||
|
||||
@property
|
||||
def analyst_price_target(self) -> pd.DataFrame:
|
||||
if self._analyst_price_target is None:
|
||||
self._scrape(self.proxy)
|
||||
raise YFNotImplementedError('analyst_price_target')
|
||||
return self._analyst_price_target
|
||||
|
||||
@property
|
||||
def rev_est(self) -> pd.DataFrame:
|
||||
if self._rev_est is None:
|
||||
self._scrape(self.proxy)
|
||||
raise YFNotImplementedError('rev_est')
|
||||
return self._rev_est
|
||||
|
||||
@property
|
||||
def eps_est(self) -> pd.DataFrame:
|
||||
if self._eps_est is None:
|
||||
self._scrape(self.proxy)
|
||||
raise YFNotImplementedError('eps_est')
|
||||
return self._eps_est
|
||||
|
||||
def _scrape(self, proxy):
|
||||
if self._already_scraped:
|
||||
return
|
||||
self._already_scraped = True
|
||||
|
||||
# Analysis Data/Analyst Forecasts
|
||||
analysis_data = self._data.get_json_data_stores("analysis", proxy=proxy)
|
||||
try:
|
||||
analysis_data = analysis_data['QuoteSummaryStore']
|
||||
except KeyError as e:
|
||||
err_msg = "No analysis data found, symbol may be delisted"
|
||||
print('- %s: %s' % (self._data.ticker, err_msg))
|
||||
return
|
||||
|
||||
if isinstance(analysis_data.get('earningsTrend'), dict):
|
||||
try:
|
||||
analysis = pd.DataFrame(analysis_data['earningsTrend']['trend'])
|
||||
analysis['endDate'] = pd.to_datetime(analysis['endDate'])
|
||||
analysis.set_index('period', inplace=True)
|
||||
analysis.index = analysis.index.str.upper()
|
||||
analysis.index.name = 'Period'
|
||||
analysis.columns = utils.camel2title(analysis.columns)
|
||||
|
||||
dict_cols = []
|
||||
|
||||
for idx, row in analysis.iterrows():
|
||||
for colname, colval in row.items():
|
||||
if isinstance(colval, dict):
|
||||
dict_cols.append(colname)
|
||||
for k, v in colval.items():
|
||||
new_colname = colname + ' ' + \
|
||||
utils.camel2title([k])[0]
|
||||
analysis.loc[idx, new_colname] = v
|
||||
|
||||
self._earnings_trend = analysis[[
|
||||
c for c in analysis.columns if c not in dict_cols]]
|
||||
except Exception:
|
||||
pass
|
||||
|
||||
try:
|
||||
self._analyst_trend_details = pd.DataFrame(analysis_data['recommendationTrend']['trend'])
|
||||
except Exception as e:
|
||||
self._analyst_trend_details = None
|
||||
try:
|
||||
self._analyst_price_target = pd.DataFrame(analysis_data['financialData'], index=[0])[
|
||||
['targetLowPrice', 'currentPrice', 'targetMeanPrice', 'targetHighPrice', 'numberOfAnalystOpinions']].T
|
||||
except Exception as e:
|
||||
self._analyst_price_target = None
|
||||
earnings_estimate = []
|
||||
revenue_estimate = []
|
||||
if self._analyst_trend_details is not None :
|
||||
for key in analysis_data['earningsTrend']['trend']:
|
||||
try:
|
||||
earnings_dict = key['earningsEstimate']
|
||||
earnings_dict['period'] = key['period']
|
||||
earnings_dict['endDate'] = key['endDate']
|
||||
earnings_estimate.append(earnings_dict)
|
||||
|
||||
revenue_dict = key['revenueEstimate']
|
||||
revenue_dict['period'] = key['period']
|
||||
revenue_dict['endDate'] = key['endDate']
|
||||
revenue_estimate.append(revenue_dict)
|
||||
except Exception as e:
|
||||
pass
|
||||
self._rev_est = pd.DataFrame(revenue_estimate)
|
||||
self._eps_est = pd.DataFrame(earnings_estimate)
|
||||
else:
|
||||
self._rev_est = pd.DataFrame()
|
||||
self._eps_est = pd.DataFrame()
|
||||
|
|
|
|||
|
|
@ -1,13 +1,13 @@
|
|||
import datetime
|
||||
import logging
|
||||
import json
|
||||
|
||||
import pandas as pd
|
||||
import numpy as np
|
||||
|
||||
from yfinance import utils
|
||||
from yfinance import utils, const
|
||||
from yfinance.data import TickerData
|
||||
from yfinance.exceptions import YFinanceDataException, YFinanceException
|
||||
|
||||
from yfinance.exceptions import YFinanceException, YFNotImplementedError
|
||||
|
||||
class Fundamentals:
|
||||
|
||||
|
|
@ -31,71 +31,15 @@ class Fundamentals:
|
|||
@property
|
||||
def earnings(self) -> dict:
|
||||
if self._earnings is None:
|
||||
self._scrape_earnings(self.proxy)
|
||||
raise YFNotImplementedError('earnings')
|
||||
return self._earnings
|
||||
|
||||
@property
|
||||
def shares(self) -> pd.DataFrame:
|
||||
if self._shares is None:
|
||||
self._scrape_shares(self.proxy)
|
||||
raise YFNotImplementedError('shares')
|
||||
return self._shares
|
||||
|
||||
def _scrape_basics(self, proxy):
|
||||
if self._basics_already_scraped:
|
||||
return
|
||||
self._basics_already_scraped = True
|
||||
|
||||
self._financials_data = self._data.get_json_data_stores('financials', proxy)
|
||||
try:
|
||||
self._fin_data_quote = self._financials_data['QuoteSummaryStore']
|
||||
except KeyError:
|
||||
err_msg = "No financials data found, symbol may be delisted"
|
||||
print('- %s: %s' % (self._data.ticker, err_msg))
|
||||
return None
|
||||
|
||||
def _scrape_earnings(self, proxy):
|
||||
self._scrape_basics(proxy)
|
||||
# earnings
|
||||
self._earnings = {"yearly": pd.DataFrame(), "quarterly": pd.DataFrame()}
|
||||
if self._fin_data_quote is None:
|
||||
return
|
||||
if isinstance(self._fin_data_quote.get('earnings'), dict):
|
||||
try:
|
||||
earnings = self._fin_data_quote['earnings']['financialsChart']
|
||||
earnings['financialCurrency'] = self._fin_data_quote['earnings'].get('financialCurrency', 'USD')
|
||||
self._earnings['financialCurrency'] = earnings['financialCurrency']
|
||||
df = pd.DataFrame(earnings['yearly']).set_index('date')
|
||||
df.columns = utils.camel2title(df.columns)
|
||||
df.index.name = 'Year'
|
||||
self._earnings['yearly'] = df
|
||||
|
||||
df = pd.DataFrame(earnings['quarterly']).set_index('date')
|
||||
df.columns = utils.camel2title(df.columns)
|
||||
df.index.name = 'Quarter'
|
||||
self._earnings['quarterly'] = df
|
||||
except Exception:
|
||||
pass
|
||||
|
||||
def _scrape_shares(self, proxy):
|
||||
self._scrape_basics(proxy)
|
||||
# shares outstanding
|
||||
try:
|
||||
# keep only years with non None data
|
||||
available_shares = [shares_data for shares_data in
|
||||
self._financials_data['QuoteTimeSeriesStore']['timeSeries']['annualBasicAverageShares']
|
||||
if
|
||||
shares_data]
|
||||
shares = pd.DataFrame(available_shares)
|
||||
shares['Year'] = shares['asOfDate'].agg(lambda x: int(x[:4]))
|
||||
shares.set_index('Year', inplace=True)
|
||||
shares.drop(columns=['dataId', 'asOfDate',
|
||||
'periodType', 'currencyCode'], inplace=True)
|
||||
shares.rename(
|
||||
columns={'reportedValue': "BasicShares"}, inplace=True)
|
||||
self._shares = shares
|
||||
except Exception:
|
||||
pass
|
||||
|
||||
|
||||
class Financials:
|
||||
def __init__(self, data: TickerData):
|
||||
|
|
@ -103,9 +47,6 @@ class Financials:
|
|||
self._income_time_series = {}
|
||||
self._balance_sheet_time_series = {}
|
||||
self._cash_flow_time_series = {}
|
||||
self._income_scraped = {}
|
||||
self._balance_sheet_scraped = {}
|
||||
self._cash_flow_scraped = {}
|
||||
|
||||
def get_income_time_series(self, freq="yearly", proxy=None) -> pd.DataFrame:
|
||||
res = self._income_time_series
|
||||
|
|
@ -125,6 +66,7 @@ class Financials:
|
|||
res[freq] = self._fetch_time_series("cash-flow", freq, proxy)
|
||||
return res[freq]
|
||||
|
||||
@utils.log_indent_decorator
|
||||
def _fetch_time_series(self, name, timescale, proxy=None):
|
||||
# Fetching time series preferred over scraping 'QuoteSummaryStore',
|
||||
# because it matches what Yahoo shows. But for some tickers returns nothing,
|
||||
|
|
@ -144,7 +86,7 @@ class Financials:
|
|||
if statement is not None:
|
||||
return statement
|
||||
except YFinanceException as e:
|
||||
print(f"- {self._data.ticker}: Failed to create {name} financials table for reason: {repr(e)}")
|
||||
utils.get_yf_logger().error("%s: Failed to create %s financials table for reason: %r", self._data.ticker, name, e)
|
||||
return pd.DataFrame()
|
||||
|
||||
def _create_financials_table(self, name, timescale, proxy):
|
||||
|
|
@ -152,37 +94,13 @@ class Financials:
|
|||
# Yahoo stores the 'income' table internally under 'financials' key
|
||||
name = "financials"
|
||||
|
||||
keys = self._get_datastore_keys(name, proxy)
|
||||
keys = const.fundamentals_keys[name]
|
||||
|
||||
try:
|
||||
return self.get_financials_time_series(timescale, keys, proxy)
|
||||
except Exception as e:
|
||||
pass
|
||||
|
||||
def _get_datastore_keys(self, sub_page, proxy) -> list:
|
||||
data_stores = self._data.get_json_data_stores(sub_page, proxy)
|
||||
|
||||
# Step 1: get the keys:
|
||||
def _finditem1(key, obj):
|
||||
values = []
|
||||
if isinstance(obj, dict):
|
||||
if key in obj.keys():
|
||||
values.append(obj[key])
|
||||
for k, v in obj.items():
|
||||
values += _finditem1(key, v)
|
||||
elif isinstance(obj, list):
|
||||
for v in obj:
|
||||
values += _finditem1(key, v)
|
||||
return values
|
||||
|
||||
try:
|
||||
keys = _finditem1("key", data_stores['FinancialTemplateStore'])
|
||||
except KeyError as e:
|
||||
raise YFinanceDataException("Parsing FinancialTemplateStore failed, reason: {}".format(repr(e)))
|
||||
|
||||
if not keys:
|
||||
raise YFinanceDataException("No keys in FinancialTemplateStore")
|
||||
return keys
|
||||
|
||||
def get_financials_time_series(self, timescale, keys: list, proxy=None) -> pd.DataFrame:
|
||||
timescale_translation = {"yearly": "annual", "quarterly": "quarterly"}
|
||||
timescale = timescale_translation[timescale]
|
||||
|
|
@ -231,89 +149,3 @@ class Financials:
|
|||
df = df[sorted(df.columns, reverse=True)]
|
||||
|
||||
return df
|
||||
|
||||
def get_income_scrape(self, freq="yearly", proxy=None) -> pd.DataFrame:
|
||||
res = self._income_scraped
|
||||
if freq not in res:
|
||||
res[freq] = self._scrape("income", freq, proxy)
|
||||
return res[freq]
|
||||
|
||||
def get_balance_sheet_scrape(self, freq="yearly", proxy=None) -> pd.DataFrame:
|
||||
res = self._balance_sheet_scraped
|
||||
if freq not in res:
|
||||
res[freq] = self._scrape("balance-sheet", freq, proxy)
|
||||
return res[freq]
|
||||
|
||||
def get_cash_flow_scrape(self, freq="yearly", proxy=None) -> pd.DataFrame:
|
||||
res = self._cash_flow_scraped
|
||||
if freq not in res:
|
||||
res[freq] = self._scrape("cash-flow", freq, proxy)
|
||||
return res[freq]
|
||||
|
||||
def _scrape(self, name, timescale, proxy=None):
|
||||
# Backup in case _fetch_time_series() fails to return data
|
||||
|
||||
allowed_names = ["income", "balance-sheet", "cash-flow"]
|
||||
allowed_timescales = ["yearly", "quarterly"]
|
||||
|
||||
if name not in allowed_names:
|
||||
raise ValueError("Illegal argument: name must be one of: {}".format(allowed_names))
|
||||
if timescale not in allowed_timescales:
|
||||
raise ValueError("Illegal argument: timescale must be one of: {}".format(allowed_names))
|
||||
|
||||
try:
|
||||
statement = self._create_financials_table_old(name, timescale, proxy)
|
||||
|
||||
if statement is not None:
|
||||
return statement
|
||||
except YFinanceException as e:
|
||||
print(f"- {self._data.ticker}: Failed to create financials table for {name} reason: {repr(e)}")
|
||||
return pd.DataFrame()
|
||||
|
||||
def _create_financials_table_old(self, name, timescale, proxy):
|
||||
data_stores = self._data.get_json_data_stores("financials", proxy)
|
||||
|
||||
# Fetch raw data
|
||||
if not "QuoteSummaryStore" in data_stores:
|
||||
raise YFinanceDataException(f"Yahoo not returning legacy financials data")
|
||||
data = data_stores["QuoteSummaryStore"]
|
||||
|
||||
if name == "cash-flow":
|
||||
key1 = "cashflowStatement"
|
||||
key2 = "cashflowStatements"
|
||||
elif name == "balance-sheet":
|
||||
key1 = "balanceSheet"
|
||||
key2 = "balanceSheetStatements"
|
||||
else:
|
||||
key1 = "incomeStatement"
|
||||
key2 = "incomeStatementHistory"
|
||||
key1 += "History"
|
||||
if timescale == "quarterly":
|
||||
key1 += "Quarterly"
|
||||
if key1 not in data or data[key1] is None or key2 not in data[key1]:
|
||||
raise YFinanceDataException(f"Yahoo not returning legacy {name} financials data")
|
||||
data = data[key1][key2]
|
||||
|
||||
# Tabulate
|
||||
df = pd.DataFrame(data)
|
||||
if len(df) == 0:
|
||||
raise YFinanceDataException(f"Yahoo not returning legacy {name} financials data")
|
||||
df = df.drop(columns=['maxAge'])
|
||||
for col in df.columns:
|
||||
df[col] = df[col].replace('-', np.nan)
|
||||
df.set_index('endDate', inplace=True)
|
||||
try:
|
||||
df.index = pd.to_datetime(df.index, unit='s')
|
||||
except ValueError:
|
||||
df.index = pd.to_datetime(df.index)
|
||||
df = df.T
|
||||
df.columns.name = ''
|
||||
df.index.name = 'Breakdown'
|
||||
# rename incorrect yahoo key
|
||||
df.rename(index={'treasuryStock': 'gainsLossesNotAffectingRetainedEarnings'}, inplace=True)
|
||||
|
||||
# Upper-case first letter, leave rest unchanged:
|
||||
s0 = df.index[0]
|
||||
df.index = [s[0].upper()+s[1:] for s in df.index]
|
||||
|
||||
return df
|
||||
|
|
|
|||
|
|
@ -1,11 +1,14 @@
|
|||
import datetime
|
||||
import logging
|
||||
import json
|
||||
import warnings
|
||||
|
||||
import pandas as pd
|
||||
import numpy as _np
|
||||
|
||||
from yfinance import utils
|
||||
from yfinance.data import TickerData
|
||||
|
||||
from yfinance.exceptions import YFNotImplementedError
|
||||
|
||||
info_retired_keys_price = {"currentPrice", "dayHigh", "dayLow", "open", "previousClose", "volume", "volume24Hr"}
|
||||
info_retired_keys_price.update({"regularMarket"+s for s in ["DayHigh", "DayLow", "Open", "PreviousClose", "Price", "Volume"]})
|
||||
|
|
@ -17,9 +20,7 @@ info_retired_keys_symbol = {"symbol"}
|
|||
info_retired_keys = info_retired_keys_price | info_retired_keys_exchange | info_retired_keys_marketCap | info_retired_keys_symbol
|
||||
|
||||
|
||||
PRUNE_INFO = True
|
||||
# PRUNE_INFO = False
|
||||
|
||||
_BASIC_URL_ = "https://query2.finance.yahoo.com/v6/finance/quoteSummary"
|
||||
|
||||
from collections.abc import MutableMapping
|
||||
class InfoDictWrapper(MutableMapping):
|
||||
|
|
@ -44,16 +45,16 @@ class InfoDictWrapper(MutableMapping):
|
|||
|
||||
def __getitem__(self, k):
|
||||
if k in info_retired_keys_price:
|
||||
print(f"Price data removed from info (key='{k}'). Use Ticker.fast_info or history() instead")
|
||||
warnings.warn(f"Price data removed from info (key='{k}'). Use Ticker.fast_info or history() instead", DeprecationWarning)
|
||||
return None
|
||||
elif k in info_retired_keys_exchange:
|
||||
print(f"Exchange data removed from info (key='{k}'). Use Ticker.fast_info or Ticker.get_history_metadata() instead")
|
||||
warnings.warn(f"Exchange data removed from info (key='{k}'). Use Ticker.fast_info or Ticker.get_history_metadata() instead", DeprecationWarning)
|
||||
return None
|
||||
elif k in info_retired_keys_marketCap:
|
||||
print(f"Market cap removed from info (key='{k}'). Use Ticker.fast_info instead")
|
||||
warnings.warn(f"Market cap removed from info (key='{k}'). Use Ticker.fast_info instead", DeprecationWarning)
|
||||
return None
|
||||
elif k in info_retired_keys_symbol:
|
||||
print(f"Symbol removed from info (key='{k}'). You know this already")
|
||||
warnings.warn(f"Symbol removed from info (key='{k}'). You know this already", DeprecationWarning)
|
||||
return None
|
||||
return self.info[self._keytransform(k)]
|
||||
|
||||
|
|
@ -73,6 +74,471 @@ class InfoDictWrapper(MutableMapping):
|
|||
return k
|
||||
|
||||
|
||||
class FastInfo:
|
||||
# Contain small subset of info[] items that can be fetched faster elsewhere.
|
||||
# Imitates a dict.
|
||||
def __init__(self, tickerBaseObject):
|
||||
self._tkr = tickerBaseObject
|
||||
|
||||
self._prices_1y = None
|
||||
self._prices_1wk_1h_prepost = None
|
||||
self._prices_1wk_1h_reg = None
|
||||
self._md = None
|
||||
|
||||
self._currency = None
|
||||
self._quote_type = None
|
||||
self._exchange = None
|
||||
self._timezone = None
|
||||
|
||||
self._shares = None
|
||||
self._mcap = None
|
||||
|
||||
self._open = None
|
||||
self._day_high = None
|
||||
self._day_low = None
|
||||
self._last_price = None
|
||||
self._last_volume = None
|
||||
|
||||
self._prev_close = None
|
||||
|
||||
self._reg_prev_close = None
|
||||
|
||||
self._50d_day_average = None
|
||||
self._200d_day_average = None
|
||||
self._year_high = None
|
||||
self._year_low = None
|
||||
self._year_change = None
|
||||
|
||||
self._10d_avg_vol = None
|
||||
self._3mo_avg_vol = None
|
||||
|
||||
# attrs = utils.attributes(self)
|
||||
# self.keys = attrs.keys()
|
||||
# utils.attributes is calling each method, bad! Have to hardcode
|
||||
_properties = ["currency", "quote_type", "exchange", "timezone"]
|
||||
_properties += ["shares", "market_cap"]
|
||||
_properties += ["last_price", "previous_close", "open", "day_high", "day_low"]
|
||||
_properties += ["regular_market_previous_close"]
|
||||
_properties += ["last_volume"]
|
||||
_properties += ["fifty_day_average", "two_hundred_day_average", "ten_day_average_volume", "three_month_average_volume"]
|
||||
_properties += ["year_high", "year_low", "year_change"]
|
||||
|
||||
# Because released before fixing key case, need to officially support
|
||||
# camel-case but also secretly support snake-case
|
||||
base_keys = [k for k in _properties if not '_' in k]
|
||||
|
||||
sc_keys = [k for k in _properties if '_' in k]
|
||||
|
||||
self._sc_to_cc_key = {k:utils.snake_case_2_camelCase(k) for k in sc_keys}
|
||||
self._cc_to_sc_key = {v:k for k,v in self._sc_to_cc_key.items()}
|
||||
|
||||
self._public_keys = sorted(base_keys + list(self._sc_to_cc_key.values()))
|
||||
self._keys = sorted(self._public_keys + sc_keys)
|
||||
|
||||
# dict imitation:
|
||||
def keys(self):
|
||||
return self._public_keys
|
||||
def items(self):
|
||||
return [(k,self[k]) for k in self._public_keys]
|
||||
def values(self):
|
||||
return [self[k] for k in self._public_keys]
|
||||
def get(self, key, default=None):
|
||||
if key in self.keys():
|
||||
if key in self._cc_to_sc_key:
|
||||
key = self._cc_to_sc_key[key]
|
||||
return self[key]
|
||||
return default
|
||||
def __getitem__(self, k):
|
||||
if not isinstance(k, str):
|
||||
raise KeyError(f"key must be a string")
|
||||
if not k in self._keys:
|
||||
raise KeyError(f"'{k}' not valid key. Examine 'FastInfo.keys()'")
|
||||
if k in self._cc_to_sc_key:
|
||||
k = self._cc_to_sc_key[k]
|
||||
return getattr(self, k)
|
||||
def __contains__(self, k):
|
||||
return k in self.keys()
|
||||
def __iter__(self):
|
||||
return iter(self.keys())
|
||||
|
||||
def __str__(self):
|
||||
return "lazy-loading dict with keys = " + str(self.keys())
|
||||
def __repr__(self):
|
||||
return self.__str__()
|
||||
|
||||
def toJSON(self, indent=4):
|
||||
d = {k:self[k] for k in self.keys()}
|
||||
return _json.dumps({k:self[k] for k in self.keys()}, indent=indent)
|
||||
|
||||
def _get_1y_prices(self, fullDaysOnly=False):
|
||||
if self._prices_1y is None:
|
||||
# Temporarily disable error printing
|
||||
logging.disable(logging.CRITICAL)
|
||||
self._prices_1y = self._tkr.history(period="380d", auto_adjust=False, keepna=True)
|
||||
logging.disable(logging.NOTSET)
|
||||
self._md = self._tkr.get_history_metadata()
|
||||
try:
|
||||
ctp = self._md["currentTradingPeriod"]
|
||||
self._today_open = pd.to_datetime(ctp["regular"]["start"], unit='s', utc=True).tz_convert(self.timezone)
|
||||
self._today_close = pd.to_datetime(ctp["regular"]["end"], unit='s', utc=True).tz_convert(self.timezone)
|
||||
self._today_midnight = self._today_close.ceil("D")
|
||||
except:
|
||||
self._today_open = None
|
||||
self._today_close = None
|
||||
self._today_midnight = None
|
||||
raise
|
||||
|
||||
if self._prices_1y.empty:
|
||||
return self._prices_1y
|
||||
|
||||
dnow = pd.Timestamp.utcnow().tz_convert(self.timezone).date()
|
||||
d1 = dnow
|
||||
d0 = (d1 + datetime.timedelta(days=1)) - utils._interval_to_timedelta("1y")
|
||||
if fullDaysOnly and self._exchange_open_now():
|
||||
# Exclude today
|
||||
d1 -= utils._interval_to_timedelta("1d")
|
||||
return self._prices_1y.loc[str(d0):str(d1)]
|
||||
|
||||
def _get_1wk_1h_prepost_prices(self):
|
||||
if self._prices_1wk_1h_prepost is None:
|
||||
# Temporarily disable error printing
|
||||
logging.disable(logging.CRITICAL)
|
||||
self._prices_1wk_1h_prepost = self._tkr.history(period="1wk", interval="1h", auto_adjust=False, prepost=True)
|
||||
logging.disable(logging.NOTSET)
|
||||
return self._prices_1wk_1h_prepost
|
||||
|
||||
def _get_1wk_1h_reg_prices(self):
|
||||
if self._prices_1wk_1h_reg is None:
|
||||
# Temporarily disable error printing
|
||||
logging.disable(logging.CRITICAL)
|
||||
self._prices_1wk_1h_reg = self._tkr.history(period="1wk", interval="1h", auto_adjust=False, prepost=False)
|
||||
logging.disable(logging.NOTSET)
|
||||
return self._prices_1wk_1h_reg
|
||||
|
||||
def _get_exchange_metadata(self):
|
||||
if self._md is not None:
|
||||
return self._md
|
||||
|
||||
self._get_1y_prices()
|
||||
self._md = self._tkr.get_history_metadata()
|
||||
return self._md
|
||||
|
||||
def _exchange_open_now(self):
|
||||
t = pd.Timestamp.utcnow()
|
||||
self._get_exchange_metadata()
|
||||
|
||||
# if self._today_open is None and self._today_close is None:
|
||||
# r = False
|
||||
# else:
|
||||
# r = self._today_open <= t and t < self._today_close
|
||||
|
||||
# if self._today_midnight is None:
|
||||
# r = False
|
||||
# elif self._today_midnight.date() > t.tz_convert(self.timezone).date():
|
||||
# r = False
|
||||
# else:
|
||||
# r = t < self._today_midnight
|
||||
|
||||
last_day_cutoff = self._get_1y_prices().index[-1] + datetime.timedelta(days=1)
|
||||
last_day_cutoff += datetime.timedelta(minutes=20)
|
||||
r = t < last_day_cutoff
|
||||
|
||||
# print("_exchange_open_now() returning", r)
|
||||
return r
|
||||
|
||||
@property
|
||||
def currency(self):
|
||||
if self._currency is not None:
|
||||
return self._currency
|
||||
|
||||
if self._tkr._history_metadata is None:
|
||||
self._get_1y_prices()
|
||||
md = self._tkr.get_history_metadata()
|
||||
self._currency = md["currency"]
|
||||
return self._currency
|
||||
|
||||
@property
|
||||
def quote_type(self):
|
||||
if self._quote_type is not None:
|
||||
return self._quote_type
|
||||
|
||||
if self._tkr._history_metadata is None:
|
||||
self._get_1y_prices()
|
||||
md = self._tkr.get_history_metadata()
|
||||
self._quote_type = md["instrumentType"]
|
||||
return self._quote_type
|
||||
|
||||
@property
|
||||
def exchange(self):
|
||||
if self._exchange is not None:
|
||||
return self._exchange
|
||||
|
||||
self._exchange = self._get_exchange_metadata()["exchangeName"]
|
||||
return self._exchange
|
||||
|
||||
@property
|
||||
def timezone(self):
|
||||
if self._timezone is not None:
|
||||
return self._timezone
|
||||
|
||||
self._timezone = self._get_exchange_metadata()["exchangeTimezoneName"]
|
||||
return self._timezone
|
||||
|
||||
@property
|
||||
def shares(self):
|
||||
if self._shares is not None:
|
||||
return self._shares
|
||||
|
||||
shares = self._tkr.get_shares_full(start=pd.Timestamp.utcnow().date()-pd.Timedelta(days=548))
|
||||
# if shares is None:
|
||||
# # Requesting 18 months failed, so fallback to shares which should include last year
|
||||
# shares = self._tkr.get_shares()
|
||||
if shares is not None:
|
||||
if isinstance(shares, pd.DataFrame):
|
||||
shares = shares[shares.columns[0]]
|
||||
self._shares = int(shares.iloc[-1])
|
||||
return self._shares
|
||||
|
||||
@property
|
||||
def last_price(self):
|
||||
if self._last_price is not None:
|
||||
return self._last_price
|
||||
prices = self._get_1y_prices()
|
||||
if prices.empty:
|
||||
md = self._get_exchange_metadata()
|
||||
if "regularMarketPrice" in md:
|
||||
self._last_price = md["regularMarketPrice"]
|
||||
else:
|
||||
self._last_price = float(prices["Close"].iloc[-1])
|
||||
if _np.isnan(self._last_price):
|
||||
md = self._get_exchange_metadata()
|
||||
if "regularMarketPrice" in md:
|
||||
self._last_price = md["regularMarketPrice"]
|
||||
return self._last_price
|
||||
|
||||
@property
|
||||
def previous_close(self):
|
||||
if self._prev_close is not None:
|
||||
return self._prev_close
|
||||
prices = self._get_1wk_1h_prepost_prices()
|
||||
fail = False
|
||||
if prices.empty:
|
||||
fail = True
|
||||
else:
|
||||
prices = prices[["Close"]].groupby(prices.index.date).last()
|
||||
if prices.shape[0] < 2:
|
||||
# Very few symbols have previousClose despite no
|
||||
# no trading data e.g. 'QCSTIX'.
|
||||
fail = True
|
||||
else:
|
||||
self._prev_close = float(prices["Close"].iloc[-2])
|
||||
if fail:
|
||||
# Fallback to original info[] if available.
|
||||
self._tkr.info # trigger fetch
|
||||
k = "previousClose"
|
||||
if self._tkr._quote._retired_info is not None and k in self._tkr._quote._retired_info:
|
||||
self._prev_close = self._tkr._quote._retired_info[k]
|
||||
return self._prev_close
|
||||
|
||||
@property
|
||||
def regular_market_previous_close(self):
|
||||
if self._reg_prev_close is not None:
|
||||
return self._reg_prev_close
|
||||
prices = self._get_1y_prices()
|
||||
if prices.shape[0] == 1:
|
||||
# Tiny % of tickers don't return daily history before last trading day,
|
||||
# so backup option is hourly history:
|
||||
prices = self._get_1wk_1h_reg_prices()
|
||||
prices = prices[["Close"]].groupby(prices.index.date).last()
|
||||
if prices.shape[0] < 2:
|
||||
# Very few symbols have regularMarketPreviousClose despite no
|
||||
# no trading data. E.g. 'QCSTIX'.
|
||||
# So fallback to original info[] if available.
|
||||
self._tkr.info # trigger fetch
|
||||
k = "regularMarketPreviousClose"
|
||||
if self._tkr._quote._retired_info is not None and k in self._tkr._quote._retired_info:
|
||||
self._reg_prev_close = self._tkr._quote._retired_info[k]
|
||||
else:
|
||||
self._reg_prev_close = float(prices["Close"].iloc[-2])
|
||||
return self._reg_prev_close
|
||||
|
||||
@property
|
||||
def open(self):
|
||||
if self._open is not None:
|
||||
return self._open
|
||||
prices = self._get_1y_prices()
|
||||
if prices.empty:
|
||||
self._open = None
|
||||
else:
|
||||
self._open = float(prices["Open"].iloc[-1])
|
||||
if _np.isnan(self._open):
|
||||
self._open = None
|
||||
return self._open
|
||||
|
||||
@property
|
||||
def day_high(self):
|
||||
if self._day_high is not None:
|
||||
return self._day_high
|
||||
prices = self._get_1y_prices()
|
||||
if prices.empty:
|
||||
self._day_high = None
|
||||
else:
|
||||
self._day_high = float(prices["High"].iloc[-1])
|
||||
if _np.isnan(self._day_high):
|
||||
self._day_high = None
|
||||
return self._day_high
|
||||
|
||||
@property
|
||||
def day_low(self):
|
||||
if self._day_low is not None:
|
||||
return self._day_low
|
||||
prices = self._get_1y_prices()
|
||||
if prices.empty:
|
||||
self._day_low = None
|
||||
else:
|
||||
self._day_low = float(prices["Low"].iloc[-1])
|
||||
if _np.isnan(self._day_low):
|
||||
self._day_low = None
|
||||
return self._day_low
|
||||
|
||||
@property
|
||||
def last_volume(self):
|
||||
if self._last_volume is not None:
|
||||
return self._last_volume
|
||||
prices = self._get_1y_prices()
|
||||
self._last_volume = None if prices.empty else int(prices["Volume"].iloc[-1])
|
||||
return self._last_volume
|
||||
|
||||
@property
|
||||
def fifty_day_average(self):
|
||||
if self._50d_day_average is not None:
|
||||
return self._50d_day_average
|
||||
|
||||
prices = self._get_1y_prices(fullDaysOnly=True)
|
||||
if prices.empty:
|
||||
self._50d_day_average = None
|
||||
else:
|
||||
n = prices.shape[0]
|
||||
a = n-50
|
||||
b = n
|
||||
if a < 0:
|
||||
a = 0
|
||||
self._50d_day_average = float(prices["Close"].iloc[a:b].mean())
|
||||
|
||||
return self._50d_day_average
|
||||
|
||||
@property
|
||||
def two_hundred_day_average(self):
|
||||
if self._200d_day_average is not None:
|
||||
return self._200d_day_average
|
||||
|
||||
prices = self._get_1y_prices(fullDaysOnly=True)
|
||||
if prices.empty:
|
||||
self._200d_day_average = None
|
||||
else:
|
||||
n = prices.shape[0]
|
||||
a = n-200
|
||||
b = n
|
||||
if a < 0:
|
||||
a = 0
|
||||
|
||||
self._200d_day_average = float(prices["Close"].iloc[a:b].mean())
|
||||
|
||||
return self._200d_day_average
|
||||
|
||||
@property
|
||||
def ten_day_average_volume(self):
|
||||
if self._10d_avg_vol is not None:
|
||||
return self._10d_avg_vol
|
||||
|
||||
prices = self._get_1y_prices(fullDaysOnly=True)
|
||||
if prices.empty:
|
||||
self._10d_avg_vol = None
|
||||
else:
|
||||
n = prices.shape[0]
|
||||
a = n-10
|
||||
b = n
|
||||
if a < 0:
|
||||
a = 0
|
||||
self._10d_avg_vol = int(prices["Volume"].iloc[a:b].mean())
|
||||
|
||||
return self._10d_avg_vol
|
||||
|
||||
@property
|
||||
def three_month_average_volume(self):
|
||||
if self._3mo_avg_vol is not None:
|
||||
return self._3mo_avg_vol
|
||||
|
||||
prices = self._get_1y_prices(fullDaysOnly=True)
|
||||
if prices.empty:
|
||||
self._3mo_avg_vol = None
|
||||
else:
|
||||
dt1 = prices.index[-1]
|
||||
dt0 = dt1 - utils._interval_to_timedelta("3mo") + utils._interval_to_timedelta("1d")
|
||||
self._3mo_avg_vol = int(prices.loc[dt0:dt1, "Volume"].mean())
|
||||
|
||||
return self._3mo_avg_vol
|
||||
|
||||
@property
|
||||
def year_high(self):
|
||||
if self._year_high is not None:
|
||||
return self._year_high
|
||||
|
||||
prices = self._get_1y_prices(fullDaysOnly=True)
|
||||
if prices.empty:
|
||||
prices = self._get_1y_prices(fullDaysOnly=False)
|
||||
self._year_high = float(prices["High"].max())
|
||||
return self._year_high
|
||||
|
||||
@property
|
||||
def year_low(self):
|
||||
if self._year_low is not None:
|
||||
return self._year_low
|
||||
|
||||
prices = self._get_1y_prices(fullDaysOnly=True)
|
||||
if prices.empty:
|
||||
prices = self._get_1y_prices(fullDaysOnly=False)
|
||||
self._year_low = float(prices["Low"].min())
|
||||
return self._year_low
|
||||
|
||||
@property
|
||||
def year_change(self):
|
||||
if self._year_change is not None:
|
||||
return self._year_change
|
||||
|
||||
prices = self._get_1y_prices(fullDaysOnly=True)
|
||||
if prices.shape[0] >= 2:
|
||||
self._year_change = (prices["Close"].iloc[-1] - prices["Close"].iloc[0]) / prices["Close"].iloc[0]
|
||||
self._year_change = float(self._year_change)
|
||||
return self._year_change
|
||||
|
||||
@property
|
||||
def market_cap(self):
|
||||
if self._mcap is not None:
|
||||
return self._mcap
|
||||
|
||||
try:
|
||||
shares = self.shares
|
||||
except Exception as e:
|
||||
if "Cannot retrieve share count" in str(e):
|
||||
shares = None
|
||||
elif "failed to decrypt Yahoo" in str(e):
|
||||
shares = None
|
||||
else:
|
||||
raise
|
||||
|
||||
if shares is None:
|
||||
# Very few symbols have marketCap despite no share count.
|
||||
# E.g. 'BTC-USD'
|
||||
# So fallback to original info[] if available.
|
||||
self._tkr.info
|
||||
k = "marketCap"
|
||||
if self._tkr._quote._retired_info is not None and k in self._tkr._quote._retired_info:
|
||||
self._mcap = self._tkr._quote._retired_info[k]
|
||||
else:
|
||||
self._mcap = float(shares * self.last_price)
|
||||
return self._mcap
|
||||
|
||||
|
||||
class Quote:
|
||||
|
||||
|
|
@ -87,161 +553,87 @@ class Quote:
|
|||
self._calendar = None
|
||||
|
||||
self._already_scraped = False
|
||||
self._already_scraped_complementary = False
|
||||
self._already_fetched = False
|
||||
self._already_fetched_complementary = False
|
||||
|
||||
@property
|
||||
def info(self) -> dict:
|
||||
if self._info is None:
|
||||
self._scrape(self.proxy)
|
||||
self._scrape_complementary(self.proxy)
|
||||
self._fetch(self.proxy)
|
||||
self._fetch_complementary(self.proxy)
|
||||
|
||||
return self._info
|
||||
|
||||
@property
|
||||
def sustainability(self) -> pd.DataFrame:
|
||||
if self._sustainability is None:
|
||||
self._scrape(self.proxy)
|
||||
raise YFNotImplementedError('sustainability')
|
||||
return self._sustainability
|
||||
|
||||
@property
|
||||
def recommendations(self) -> pd.DataFrame:
|
||||
if self._recommendations is None:
|
||||
self._scrape(self.proxy)
|
||||
raise YFNotImplementedError('recommendations')
|
||||
return self._recommendations
|
||||
|
||||
@property
|
||||
def calendar(self) -> pd.DataFrame:
|
||||
if self._calendar is None:
|
||||
self._scrape(self.proxy)
|
||||
raise YFNotImplementedError('calendar')
|
||||
return self._calendar
|
||||
|
||||
def _scrape(self, proxy):
|
||||
if self._already_scraped:
|
||||
def _fetch(self, proxy):
|
||||
if self._already_fetched:
|
||||
return
|
||||
self._already_scraped = True
|
||||
|
||||
# get info and sustainability
|
||||
json_data = self._data.get_json_data_stores(proxy=proxy)
|
||||
try:
|
||||
quote_summary_store = json_data['QuoteSummaryStore']
|
||||
except KeyError:
|
||||
err_msg = "No summary info found, symbol may be delisted"
|
||||
print('- %s: %s' % (self._data.ticker, err_msg))
|
||||
return None
|
||||
|
||||
# sustainability
|
||||
d = {}
|
||||
try:
|
||||
if isinstance(quote_summary_store.get('esgScores'), dict):
|
||||
for item in quote_summary_store['esgScores']:
|
||||
if not isinstance(quote_summary_store['esgScores'][item], (dict, list)):
|
||||
d[item] = quote_summary_store['esgScores'][item]
|
||||
|
||||
s = pd.DataFrame(index=[0], data=d)[-1:].T
|
||||
s.columns = ['Value']
|
||||
s.index.name = '%.f-%.f' % (
|
||||
s[s.index == 'ratingYear']['Value'].values[0],
|
||||
s[s.index == 'ratingMonth']['Value'].values[0])
|
||||
|
||||
self._sustainability = s[~s.index.isin(
|
||||
['maxAge', 'ratingYear', 'ratingMonth'])]
|
||||
except Exception:
|
||||
pass
|
||||
|
||||
self._info = {}
|
||||
try:
|
||||
items = ['summaryProfile', 'financialData', 'quoteType',
|
||||
'defaultKeyStatistics', 'assetProfile', 'summaryDetail']
|
||||
for item in items:
|
||||
if isinstance(quote_summary_store.get(item), dict):
|
||||
self._info.update(quote_summary_store[item])
|
||||
except Exception:
|
||||
pass
|
||||
|
||||
# For ETFs, provide this valuable data: the top holdings of the ETF
|
||||
try:
|
||||
if 'topHoldings' in quote_summary_store:
|
||||
self._info.update(quote_summary_store['topHoldings'])
|
||||
except Exception:
|
||||
pass
|
||||
|
||||
try:
|
||||
if not isinstance(quote_summary_store.get('summaryDetail'), dict):
|
||||
# For some reason summaryDetail did not give any results. The price dict
|
||||
# usually has most of the same info
|
||||
self._info.update(quote_summary_store.get('price', {}))
|
||||
except Exception:
|
||||
pass
|
||||
|
||||
try:
|
||||
# self._info['regularMarketPrice'] = self._info['regularMarketOpen']
|
||||
self._info['regularMarketPrice'] = quote_summary_store.get('price', {}).get(
|
||||
'regularMarketPrice', self._info.get('regularMarketOpen', None))
|
||||
except Exception:
|
||||
pass
|
||||
|
||||
try:
|
||||
self._info['preMarketPrice'] = quote_summary_store.get('price', {}).get(
|
||||
'preMarketPrice', self._info.get('preMarketPrice', None))
|
||||
except Exception:
|
||||
pass
|
||||
|
||||
self._info['logo_url'] = ""
|
||||
try:
|
||||
if not 'website' in self._info:
|
||||
self._info['logo_url'] = 'https://logo.clearbit.com/%s.com' % \
|
||||
self._info['shortName'].split(' ')[0].split(',')[0]
|
||||
self._already_fetched = True
|
||||
modules = ['financialData', 'quoteType', 'defaultKeyStatistics', 'assetProfile', 'summaryDetail']
|
||||
params_dict = {}
|
||||
params_dict["modules"] = modules
|
||||
params_dict["ssl"] = "true"
|
||||
result = self._data.get_raw_json(
|
||||
_BASIC_URL_ + f"/{self._data.ticker}", params=params_dict, proxy=proxy
|
||||
)
|
||||
result["quoteSummary"]["result"][0]["symbol"] = self._data.ticker
|
||||
query1_info = next(
|
||||
(info for info in result.get("quoteSummary", {}).get("result", []) if info["symbol"] == self._data.ticker),
|
||||
None,
|
||||
)
|
||||
# Most keys that appear in multiple dicts have same value. Except 'maxAge' because
|
||||
# Yahoo not consistent with days vs seconds. Fix it here:
|
||||
for k in query1_info:
|
||||
if "maxAge" in query1_info[k] and query1_info[k]["maxAge"] == 1:
|
||||
query1_info[k]["maxAge"] = 86400
|
||||
query1_info = {
|
||||
k1: v1
|
||||
for k, v in query1_info.items()
|
||||
if isinstance(v, dict)
|
||||
for k1, v1 in v.items()
|
||||
if v1
|
||||
}
|
||||
# recursively format but only because of 'companyOfficers'
|
||||
def _format(k, v):
|
||||
if isinstance(v, dict) and "raw" in v and "fmt" in v:
|
||||
v2 = v["fmt"] if k in {"regularMarketTime", "postMarketTime"} else v["raw"]
|
||||
elif isinstance(v, list):
|
||||
v2 = [_format(None, x) for x in v]
|
||||
elif isinstance(v, dict):
|
||||
v2 = {k:_format(k, x) for k, x in v.items()}
|
||||
elif isinstance(v, str):
|
||||
v2 = v.replace("\xa0", " ")
|
||||
else:
|
||||
domain = self._info['website'].split(
|
||||
'://')[1].split('/')[0].replace('www.', '')
|
||||
self._info['logo_url'] = 'https://logo.clearbit.com/%s' % domain
|
||||
except Exception:
|
||||
pass
|
||||
v2 = v
|
||||
return v2
|
||||
for k, v in query1_info.items():
|
||||
query1_info[k] = _format(k, v)
|
||||
self._info = query1_info
|
||||
|
||||
# Delete redundant info[] keys, because values can be accessed faster
|
||||
# elsewhere - e.g. price keys. Hope is reduces Yahoo spam effect.
|
||||
# But record the dropped keys, because in rare cases they are needed.
|
||||
self._retired_info = {}
|
||||
for k in info_retired_keys:
|
||||
if k in self._info:
|
||||
self._retired_info[k] = self._info[k]
|
||||
if PRUNE_INFO:
|
||||
del self._info[k]
|
||||
if PRUNE_INFO:
|
||||
# InfoDictWrapper will explain how to access above data elsewhere
|
||||
self._info = InfoDictWrapper(self._info)
|
||||
|
||||
# events
|
||||
try:
|
||||
cal = pd.DataFrame(quote_summary_store['calendarEvents']['earnings'])
|
||||
cal['earningsDate'] = pd.to_datetime(
|
||||
cal['earningsDate'], unit='s')
|
||||
self._calendar = cal.T
|
||||
self._calendar.index = utils.camel2title(self._calendar.index)
|
||||
self._calendar.columns = ['Value']
|
||||
except Exception as e:
|
||||
pass
|
||||
|
||||
# analyst recommendations
|
||||
try:
|
||||
rec = pd.DataFrame(
|
||||
quote_summary_store['upgradeDowngradeHistory']['history'])
|
||||
rec['earningsDate'] = pd.to_datetime(
|
||||
rec['epochGradeDate'], unit='s')
|
||||
rec.set_index('earningsDate', inplace=True)
|
||||
rec.index.name = 'Date'
|
||||
rec.columns = utils.camel2title(rec.columns)
|
||||
self._recommendations = rec[[
|
||||
'Firm', 'To Grade', 'From Grade', 'Action']].sort_index()
|
||||
except Exception:
|
||||
pass
|
||||
|
||||
def _scrape_complementary(self, proxy):
|
||||
if self._already_scraped_complementary:
|
||||
def _fetch_complementary(self, proxy):
|
||||
if self._already_fetched_complementary:
|
||||
return
|
||||
self._already_scraped_complementary = True
|
||||
self._already_fetched_complementary = True
|
||||
|
||||
self._scrape(proxy)
|
||||
# self._scrape(proxy) # decrypt broken
|
||||
self._fetch(proxy)
|
||||
if self._info is None:
|
||||
return
|
||||
|
||||
|
|
|
|||
|
|
@ -4,3 +4,5 @@ e9a8ab8e5620b712ebc2fb4f33d5c8b9c80c0d07e8c371911c785cf674789f1747d76a909510158a
|
|||
6ae2523aeafa283dad746556540145bf603f44edbf37ad404d3766a8420bb5eb1d3738f52a227b88283cca9cae44060d5f0bba84b6a495082589f5fe7acbdc9e
|
||||
3365117c2a368ffa5df7313a4a84988f73926a86358e8eea9497c5ff799ce27d104b68e5f2fbffa6f8f92c1fef41765a7066fa6bcf050810a9c4c7872fd3ebf0
|
||||
15d8f57919857d5a5358d2082c7ef0f1129cfacd2a6480333dcfb954b7bb67d820abefebfdb0eaa6ef18a1c57f617b67d7e7b0ec040403b889630ae5db5a4dbb
|
||||
db9630d707a7d0953ac795cd8db1ca9ca6c9d8239197cdfda24b4e0ec9c37eaec4db82dab68b8f606ab7b5b4af3e65dab50606f8cf508269ec927e6ee605fb78
|
||||
3c895fb5ddcc37d20d3073ed74ee3efad59bcb147c8e80fd279f83701b74b092d503dcd399604c6d8be8f3013429d3c2c76ed5b31b80c9df92d5eab6d3339fce
|
||||
|
|
|
|||
|
|
@ -22,4 +22,5 @@
|
|||
_DFS = {}
|
||||
_PROGRESS_BAR = None
|
||||
_ERRORS = {}
|
||||
_TRACEBACKS = {}
|
||||
_ISINS = {}
|
||||
|
|
|
|||
|
|
@ -239,6 +239,10 @@ class Ticker(TickerBase):
|
|||
def news(self):
|
||||
return self.get_news()
|
||||
|
||||
@property
|
||||
def trend_details(self) -> _pd.DataFrame:
|
||||
return self.get_trend_details()
|
||||
|
||||
@property
|
||||
def earnings_trend(self) -> _pd.DataFrame:
|
||||
return self.get_earnings_trend()
|
||||
|
|
|
|||
|
|
@ -87,10 +87,4 @@ class Tickers:
|
|||
return data
|
||||
|
||||
def news(self):
|
||||
collection = {}
|
||||
for ticker in self.symbols:
|
||||
collection[ticker] = []
|
||||
items = Ticker(ticker).news
|
||||
for item in items:
|
||||
collection[ticker].append(item)
|
||||
return collection
|
||||
return {ticker: [item for item in Ticker(ticker).news] for ticker in self.symbols}
|
||||
|
|
|
|||
|
|
@ -35,6 +35,8 @@ import os as _os
|
|||
import appdirs as _ad
|
||||
import sqlite3 as _sqlite3
|
||||
import atexit as _atexit
|
||||
from functools import lru_cache
|
||||
import logging
|
||||
|
||||
from threading import Lock
|
||||
|
||||
|
|
@ -61,6 +63,115 @@ def attributes(obj):
|
|||
if name[0] != '_' and name not in disallowed_names and hasattr(obj, name)}
|
||||
|
||||
|
||||
@lru_cache(maxsize=20)
|
||||
def print_once(msg):
|
||||
# 'warnings' module suppression of repeat messages does not work.
|
||||
# This function replicates correct behaviour
|
||||
print(msg)
|
||||
|
||||
|
||||
## Logging
|
||||
# Note: most of this logic is adding indentation with function depth,
|
||||
# so that DEBUG log is readable.
|
||||
class IndentLoggerAdapter(logging.LoggerAdapter):
|
||||
def process(self, msg, kwargs):
|
||||
if get_yf_logger().isEnabledFor(logging.DEBUG):
|
||||
i = ' ' * self.extra['indent']
|
||||
if not isinstance(msg, str):
|
||||
msg = str(msg)
|
||||
msg = '\n'.join([i + m for m in msg.split('\n')])
|
||||
return msg, kwargs
|
||||
|
||||
import threading
|
||||
_indentation_level = threading.local()
|
||||
class IndentationContext:
|
||||
def __init__(self, increment=1):
|
||||
self.increment = increment
|
||||
def __enter__(self):
|
||||
_indentation_level.indent = getattr(_indentation_level, 'indent', 0) + self.increment
|
||||
def __exit__(self, exc_type, exc_val, exc_tb):
|
||||
_indentation_level.indent -= self.increment
|
||||
|
||||
def get_indented_logger(name=None):
|
||||
# Never cache the returned value! Will break indentation.
|
||||
return IndentLoggerAdapter(logging.getLogger(name), {'indent': getattr(_indentation_level, 'indent', 0)})
|
||||
|
||||
def log_indent_decorator(func):
|
||||
def wrapper(*args, **kwargs):
|
||||
logger = get_indented_logger('yfinance')
|
||||
logger.debug(f'Entering {func.__name__}()')
|
||||
|
||||
with IndentationContext():
|
||||
result = func(*args, **kwargs)
|
||||
|
||||
logger.debug(f'Exiting {func.__name__}()')
|
||||
return result
|
||||
|
||||
return wrapper
|
||||
|
||||
class MultiLineFormatter(logging.Formatter):
|
||||
# The 'fmt' formatting further down is only applied to first line
|
||||
# of log message, specifically the padding after %level%.
|
||||
# For multi-line messages, need to manually copy over padding.
|
||||
def __init__(self, fmt):
|
||||
super().__init__(fmt)
|
||||
# Extract amount of padding
|
||||
match = _re.search(r'%\(levelname\)-(\d+)s', fmt)
|
||||
self.level_length = int(match.group(1)) if match else 0
|
||||
|
||||
def format(self, record):
|
||||
original = super().format(record)
|
||||
lines = original.split('\n')
|
||||
levelname = lines[0].split(' ')[0]
|
||||
if len(lines) <= 1:
|
||||
return original
|
||||
else:
|
||||
# Apply padding to all lines below first
|
||||
formatted = [lines[0]]
|
||||
if self.level_length == 0:
|
||||
padding = ' ' * len(levelname)
|
||||
else:
|
||||
padding = ' ' * self.level_length
|
||||
padding += ' ' # +1 for space between level and message
|
||||
formatted.extend(padding + line for line in lines[1:])
|
||||
return '\n'.join(formatted)
|
||||
|
||||
yf_logger = None
|
||||
yf_log_indented = False
|
||||
def get_yf_logger():
|
||||
global yf_logger
|
||||
if yf_logger is None:
|
||||
yf_logger = logging.getLogger('yfinance')
|
||||
global yf_log_indented
|
||||
if yf_log_indented:
|
||||
yf_logger = get_indented_logger('yfinance')
|
||||
return yf_logger
|
||||
|
||||
def setup_debug_formatting():
|
||||
global yf_logger
|
||||
yf_logger = get_yf_logger()
|
||||
|
||||
if not yf_logger.isEnabledFor(logging.DEBUG):
|
||||
yf_logger.warning("logging mode not set to 'DEBUG', so not setting up debug formatting")
|
||||
return
|
||||
|
||||
if yf_logger.handlers is None or len(yf_logger.handlers) == 0:
|
||||
h = logging.StreamHandler()
|
||||
# Ensure different level strings don't interfere with indentation
|
||||
formatter = MultiLineFormatter(fmt='%(levelname)-8s %(message)s')
|
||||
h.setFormatter(formatter)
|
||||
yf_logger.addHandler(h)
|
||||
|
||||
global yf_log_indented
|
||||
yf_log_indented = True
|
||||
|
||||
def enable_debug_mode():
|
||||
get_yf_logger().setLevel(logging.DEBUG)
|
||||
setup_debug_formatting()
|
||||
|
||||
##
|
||||
|
||||
|
||||
def is_isin(string):
|
||||
return bool(_re.match("^([A-Z]{2})([A-Z0-9]{9})([0-9]{1})$", string))
|
||||
|
||||
|
|
@ -330,7 +441,7 @@ def _interval_to_timedelta(interval):
|
|||
elif interval == "1y":
|
||||
return _dateutil.relativedelta.relativedelta(years=1)
|
||||
elif interval == "1wk":
|
||||
return _pd.Timedelta(days=7, unit='d')
|
||||
return _pd.Timedelta(days=7)
|
||||
else:
|
||||
return _pd.Timedelta(interval)
|
||||
|
||||
|
|
@ -338,10 +449,10 @@ def _interval_to_timedelta(interval):
|
|||
def auto_adjust(data):
|
||||
col_order = data.columns
|
||||
df = data.copy()
|
||||
ratio = df["Close"] / df["Adj Close"]
|
||||
df["Adj Open"] = df["Open"] / ratio
|
||||
df["Adj High"] = df["High"] / ratio
|
||||
df["Adj Low"] = df["Low"] / ratio
|
||||
ratio = (df["Adj Close"] / df["Close"]).to_numpy()
|
||||
df["Adj Open"] = df["Open"] * ratio
|
||||
df["Adj High"] = df["High"] * ratio
|
||||
df["Adj Low"] = df["Low"] * ratio
|
||||
|
||||
df.drop(
|
||||
["Open", "High", "Low", "Close"],
|
||||
|
|
@ -404,12 +515,9 @@ def parse_quotes(data):
|
|||
|
||||
|
||||
def parse_actions(data):
|
||||
dividends = _pd.DataFrame(
|
||||
columns=["Dividends"], index=_pd.DatetimeIndex([]))
|
||||
capital_gains = _pd.DataFrame(
|
||||
columns=["Capital Gains"], index=_pd.DatetimeIndex([]))
|
||||
splits = _pd.DataFrame(
|
||||
columns=["Stock Splits"], index=_pd.DatetimeIndex([]))
|
||||
dividends = None
|
||||
capital_gains = None
|
||||
splits = None
|
||||
|
||||
if "events" in data:
|
||||
if "dividends" in data["events"]:
|
||||
|
|
@ -438,6 +546,16 @@ def parse_actions(data):
|
|||
splits["denominator"]
|
||||
splits = splits[["Stock Splits"]]
|
||||
|
||||
if dividends is None:
|
||||
dividends = _pd.DataFrame(
|
||||
columns=["Dividends"], index=_pd.DatetimeIndex([]))
|
||||
if capital_gains is None:
|
||||
capital_gains = _pd.DataFrame(
|
||||
columns=["Capital Gains"], index=_pd.DatetimeIndex([]))
|
||||
if splits is None:
|
||||
splits = _pd.DataFrame(
|
||||
columns=["Stock Splits"], index=_pd.DatetimeIndex([]))
|
||||
|
||||
return dividends, splits, capital_gains
|
||||
|
||||
|
||||
|
|
@ -448,31 +566,30 @@ def set_df_tz(df, interval, tz):
|
|||
return df
|
||||
|
||||
|
||||
def fix_Yahoo_returning_prepost_unrequested(quotes, interval, metadata):
|
||||
def fix_Yahoo_returning_prepost_unrequested(quotes, interval, tradingPeriods):
|
||||
# Sometimes Yahoo returns post-market data despite not requesting it.
|
||||
# Normally happens on half-day early closes.
|
||||
#
|
||||
# And sometimes returns pre-market data despite not requesting it.
|
||||
# E.g. some London tickers.
|
||||
tps_df = metadata["tradingPeriods"]
|
||||
tps_df = tradingPeriods.copy()
|
||||
tps_df["_date"] = tps_df.index.date
|
||||
quotes["_date"] = quotes.index.date
|
||||
idx = quotes.index.copy()
|
||||
quotes = quotes.merge(tps_df, how="left", validate="many_to_one")
|
||||
quotes = quotes.merge(tps_df, how="left")
|
||||
quotes.index = idx
|
||||
# "end" = end of regular trading hours (including any auction)
|
||||
f_drop = quotes.index >= quotes["end"]
|
||||
f_drop = f_drop | (quotes.index < quotes["start"])
|
||||
if f_drop.any():
|
||||
# When printing report, ignore rows that were already NaNs:
|
||||
f_na = quotes[["Open","Close"]].isna().all(axis=1)
|
||||
n_nna = quotes.shape[0] - _np.sum(f_na)
|
||||
n_drop_nna = _np.sum(f_drop & ~f_na)
|
||||
quotes_dropped = quotes[f_drop]
|
||||
# f_na = quotes[["Open","Close"]].isna().all(axis=1)
|
||||
# n_nna = quotes.shape[0] - _np.sum(f_na)
|
||||
# n_drop_nna = _np.sum(f_drop & ~f_na)
|
||||
# quotes_dropped = quotes[f_drop]
|
||||
# if debug and n_drop_nna > 0:
|
||||
# print(f"Dropping {n_drop_nna}/{n_nna} intervals for falling outside regular trading hours")
|
||||
quotes = quotes[~f_drop]
|
||||
metadata["tradingPeriods"] = tps_df.drop(["_date"], axis=1)
|
||||
quotes = quotes.drop(["_date", "start", "end"], axis=1)
|
||||
return quotes
|
||||
|
||||
|
|
@ -511,16 +628,24 @@ def fix_Yahoo_returning_live_separate(quotes, interval, tz_exchange):
|
|||
# Last two rows are within same interval
|
||||
idx1 = quotes.index[n - 1]
|
||||
idx2 = quotes.index[n - 2]
|
||||
if idx1 == idx2:
|
||||
# Yahoo returning last interval duplicated, which means
|
||||
# Yahoo is not returning live data (phew!)
|
||||
return quotes
|
||||
if _np.isnan(quotes.loc[idx2, "Open"]):
|
||||
quotes.loc[idx2, "Open"] = quotes["Open"][n - 1]
|
||||
# Note: nanmax() & nanmin() ignores NaNs
|
||||
quotes.loc[idx2, "High"] = _np.nanmax([quotes["High"][n - 1], quotes["High"][n - 2]])
|
||||
quotes.loc[idx2, "Low"] = _np.nanmin([quotes["Low"][n - 1], quotes["Low"][n - 2]])
|
||||
# Note: nanmax() & nanmin() ignores NaNs, but still need to check not all are NaN to avoid warnings
|
||||
if not _np.isnan(quotes["High"][n - 1]):
|
||||
quotes.loc[idx2, "High"] = _np.nanmax([quotes["High"][n - 1], quotes["High"][n - 2]])
|
||||
if "Adj High" in quotes.columns:
|
||||
quotes.loc[idx2, "Adj High"] = _np.nanmax([quotes["Adj High"][n - 1], quotes["Adj High"][n - 2]])
|
||||
|
||||
if not _np.isnan(quotes["Low"][n - 1]):
|
||||
quotes.loc[idx2, "Low"] = _np.nanmin([quotes["Low"][n - 1], quotes["Low"][n - 2]])
|
||||
if "Adj Low" in quotes.columns:
|
||||
quotes.loc[idx2, "Adj Low"] = _np.nanmin([quotes["Adj Low"][n - 1], quotes["Adj Low"][n - 2]])
|
||||
|
||||
quotes.loc[idx2, "Close"] = quotes["Close"][n - 1]
|
||||
if "Adj High" in quotes.columns:
|
||||
quotes.loc[idx2, "Adj High"] = _np.nanmax([quotes["Adj High"][n - 1], quotes["Adj High"][n - 2]])
|
||||
if "Adj Low" in quotes.columns:
|
||||
quotes.loc[idx2, "Adj Low"] = _np.nanmin([quotes["Adj Low"][n - 1], quotes["Adj Low"][n - 2]])
|
||||
if "Adj Close" in quotes.columns:
|
||||
quotes.loc[idx2, "Adj Close"] = quotes["Adj Close"][n - 1]
|
||||
quotes.loc[idx2, "Volume"] += quotes["Volume"][n - 1]
|
||||
|
|
@ -530,11 +655,6 @@ def fix_Yahoo_returning_live_separate(quotes, interval, tz_exchange):
|
|||
|
||||
|
||||
def safe_merge_dfs(df_main, df_sub, interval):
|
||||
# Carefully merge 'df_sub' onto 'df_main'
|
||||
# If naive merge fails, try again with reindexing df_sub:
|
||||
# 1) if interval is weekly or monthly, then try with index set to start of week/month
|
||||
# 2) if still failing then manually search through df_main.index to reindex df_sub
|
||||
|
||||
if df_sub.shape[0] == 0:
|
||||
raise Exception("No data to merge")
|
||||
|
||||
|
|
@ -544,6 +664,65 @@ def safe_merge_dfs(df_main, df_sub, interval):
|
|||
raise Exception("Expected 1 data col")
|
||||
data_col = data_cols[0]
|
||||
|
||||
df_main = df_main.sort_index()
|
||||
intraday = interval.endswith('m') or interval.endswith('s')
|
||||
|
||||
td = _interval_to_timedelta(interval)
|
||||
if intraday:
|
||||
# On some exchanges the event can occur before market open.
|
||||
# Problem when combining with intraday data.
|
||||
# Solution = use dates, not datetimes, to map/merge.
|
||||
df_main['_date'] = df_main.index.date
|
||||
df_sub['_date'] = df_sub.index.date
|
||||
indices = _np.searchsorted(_np.append(df_main['_date'], [df_main['_date'].iloc[-1]+td]), df_sub['_date'], side='left')
|
||||
df_main = df_main.drop('_date', axis=1)
|
||||
df_sub = df_sub.drop('_date', axis=1)
|
||||
else:
|
||||
indices = _np.searchsorted(_np.append(df_main.index, df_main.index[-1]+td), df_sub.index, side='right')
|
||||
indices -= 1 # Convert from [[i-1], [i]) to [[i], [i+1])
|
||||
# Numpy.searchsorted does not handle out-of-range well, so handle manually:
|
||||
for i in range(len(df_sub.index)):
|
||||
dt = df_sub.index[i]
|
||||
if dt < df_main.index[0] or dt >= df_main.index[-1]+td:
|
||||
# Out-of-range
|
||||
indices[i] = -1
|
||||
|
||||
f_outOfRange = indices == -1
|
||||
if f_outOfRange.any() and not intraday:
|
||||
# If dividend is occuring in next interval after last price row,
|
||||
# add a new row of NaNs
|
||||
last_dt = df_main.index[-1]
|
||||
next_interval_start_dt = last_dt + td
|
||||
if interval == '1d':
|
||||
# Allow for weekends & holidays
|
||||
next_interval_end_dt = last_dt+7*_pd.Timedelta(days=7)
|
||||
else:
|
||||
next_interval_end_dt = next_interval_start_dt + td
|
||||
for i in _np.where(f_outOfRange)[0]:
|
||||
dt = df_sub.index[i]
|
||||
if dt >= next_interval_start_dt and dt < next_interval_end_dt:
|
||||
new_dt = dt if interval == '1d' else next_interval_start_dt
|
||||
get_yf_logger().debug(f"Adding out-of-range {data_col} @ {dt.date()} in new prices row of NaNs")
|
||||
df_main.loc[new_dt] = _np.nan
|
||||
|
||||
# Re-calculate indices
|
||||
indices = _np.searchsorted(_np.append(df_main.index, df_main.index[-1]+td), df_sub.index, side='right')
|
||||
indices -= 1 # Convert from [[i-1], [i]) to [[i], [i+1])
|
||||
# Numpy.searchsorted does not handle out-of-range well, so handle manually:
|
||||
for i in range(len(df_sub.index)):
|
||||
dt = df_sub.index[i]
|
||||
if dt < df_main.index[0] or dt >= df_main.index[-1]+td:
|
||||
# Out-of-range
|
||||
indices[i] = -1
|
||||
|
||||
f_outOfRange = indices == -1
|
||||
if f_outOfRange.any():
|
||||
if intraday or interval in ['1d', '1wk']:
|
||||
raise Exception(f"The following '{data_col}' events are out-of-range, did not expect with interval {interval}: {df_sub.index}")
|
||||
get_yf_logger().debug(f'Discarding these {data_col} events:' + '\n' + str(df_sub[f_outOfRange]))
|
||||
df_sub = df_sub[~f_outOfRange].copy()
|
||||
indices = indices[~f_outOfRange]
|
||||
|
||||
def _reindex_events(df, new_index, data_col_name):
|
||||
if len(new_index) == len(set(new_index)):
|
||||
# No duplicates, easy
|
||||
|
|
@ -552,7 +731,7 @@ def safe_merge_dfs(df_main, df_sub, interval):
|
|||
|
||||
df["_NewIndex"] = new_index
|
||||
# Duplicates present within periods but can aggregate
|
||||
if data_col_name == "Dividends":
|
||||
if data_col_name in ["Dividends", "Capital Gains"]:
|
||||
# Add
|
||||
df = df.groupby("_NewIndex").sum()
|
||||
df.index.name = None
|
||||
|
|
@ -565,106 +744,14 @@ def safe_merge_dfs(df_main, df_sub, interval):
|
|||
if "_NewIndex" in df.columns:
|
||||
df = df.drop("_NewIndex", axis=1)
|
||||
return df
|
||||
|
||||
df = df_main.join(df_sub)
|
||||
|
||||
f_na = df[data_col].isna()
|
||||
data_lost = sum(~f_na) < df_sub.shape[0]
|
||||
if not data_lost:
|
||||
return df
|
||||
# Lost data during join()
|
||||
# Backdate all df_sub.index dates to start of week/month
|
||||
if interval == "1wk":
|
||||
new_index = _pd.PeriodIndex(df_sub.index, freq='W').to_timestamp()
|
||||
elif interval == "1mo":
|
||||
new_index = _pd.PeriodIndex(df_sub.index, freq='M').to_timestamp()
|
||||
elif interval == "3mo":
|
||||
new_index = _pd.PeriodIndex(df_sub.index, freq='Q').to_timestamp()
|
||||
else:
|
||||
new_index = None
|
||||
|
||||
if new_index is not None:
|
||||
new_index = new_index.tz_localize(df.index.tz, ambiguous=True, nonexistent='shift_forward')
|
||||
df_sub = _reindex_events(df_sub, new_index, data_col)
|
||||
df = df_main.join(df_sub)
|
||||
|
||||
f_na = df[data_col].isna()
|
||||
data_lost = sum(~f_na) < df_sub.shape[0]
|
||||
if not data_lost:
|
||||
return df
|
||||
# Lost data during join(). Manually check each df_sub.index date against df_main.index to
|
||||
# find matching interval
|
||||
df_sub = df_sub_backup.copy()
|
||||
new_index = [-1] * df_sub.shape[0]
|
||||
for i in range(df_sub.shape[0]):
|
||||
dt_sub_i = df_sub.index[i]
|
||||
if dt_sub_i in df_main.index:
|
||||
new_index[i] = dt_sub_i
|
||||
continue
|
||||
# Found a bad index date, need to search for near-match in df_main (same week/month)
|
||||
fixed = False
|
||||
for j in range(df_main.shape[0] - 1):
|
||||
dt_main_j0 = df_main.index[j]
|
||||
dt_main_j1 = df_main.index[j + 1]
|
||||
if (dt_main_j0 <= dt_sub_i) and (dt_sub_i < dt_main_j1):
|
||||
fixed = True
|
||||
if interval.endswith('h') or interval.endswith('m'):
|
||||
# Must also be same day
|
||||
fixed = (dt_main_j0.date() == dt_sub_i.date()) and (dt_sub_i.date() == dt_main_j1.date())
|
||||
if fixed:
|
||||
dt_sub_i = dt_main_j0
|
||||
break
|
||||
if not fixed:
|
||||
last_main_dt = df_main.index[df_main.shape[0] - 1]
|
||||
diff = dt_sub_i - last_main_dt
|
||||
if interval == "1mo" and last_main_dt.month == dt_sub_i.month:
|
||||
dt_sub_i = last_main_dt
|
||||
fixed = True
|
||||
elif interval == "3mo" and last_main_dt.year == dt_sub_i.year and last_main_dt.quarter == dt_sub_i.quarter:
|
||||
dt_sub_i = last_main_dt
|
||||
fixed = True
|
||||
elif interval == "1wk":
|
||||
if last_main_dt.week == dt_sub_i.week:
|
||||
dt_sub_i = last_main_dt
|
||||
fixed = True
|
||||
elif (dt_sub_i >= last_main_dt) and (dt_sub_i - last_main_dt < _datetime.timedelta(weeks=1)):
|
||||
# With some specific start dates (e.g. around early Jan), Yahoo
|
||||
# messes up start-of-week, is Saturday not Monday. So check
|
||||
# if same week another way
|
||||
dt_sub_i = last_main_dt
|
||||
fixed = True
|
||||
elif interval == "1d" and last_main_dt.day == dt_sub_i.day:
|
||||
dt_sub_i = last_main_dt
|
||||
fixed = True
|
||||
elif interval == "1h" and last_main_dt.hour == dt_sub_i.hour:
|
||||
dt_sub_i = last_main_dt
|
||||
fixed = True
|
||||
elif interval.endswith('m') or interval.endswith('h'):
|
||||
td = _pd.to_timedelta(interval)
|
||||
if (dt_sub_i >= last_main_dt) and (dt_sub_i - last_main_dt < td):
|
||||
dt_sub_i = last_main_dt
|
||||
fixed = True
|
||||
new_index[i] = dt_sub_i
|
||||
new_index = df_main.index[indices]
|
||||
df_sub = _reindex_events(df_sub, new_index, data_col)
|
||||
df = df_main.join(df_sub)
|
||||
|
||||
df = df_main.join(df_sub)
|
||||
f_na = df[data_col].isna()
|
||||
data_lost = sum(~f_na) < df_sub.shape[0]
|
||||
if data_lost:
|
||||
## Not always possible to match events with trading, e.g. when released pre-market.
|
||||
## So have to append to bottom with nan prices.
|
||||
## But should only be impossible with intra-day price data.
|
||||
if interval.endswith('m') or interval.endswith('h') or interval == "1d":
|
||||
# Update: is possible with daily data when dividend very recent
|
||||
f_missing = ~df_sub.index.isin(df.index)
|
||||
df_sub_missing = df_sub[f_missing].copy()
|
||||
keys = {"Adj Open", "Open", "Adj High", "High", "Adj Low", "Low", "Adj Close",
|
||||
"Close"}.intersection(df.columns)
|
||||
df_sub_missing[list(keys)] = _np.nan
|
||||
col_ordering = df.columns
|
||||
df = _pd.concat([df, df_sub_missing], sort=True)[col_ordering]
|
||||
else:
|
||||
raise Exception("Lost data during merge despite all attempts to align data (see above)")
|
||||
raise Exception('Data was lost in merge, investigate')
|
||||
|
||||
return df
|
||||
|
||||
|
|
@ -690,7 +777,7 @@ def is_valid_timezone(tz: str) -> bool:
|
|||
return True
|
||||
|
||||
|
||||
def format_history_metadata(md):
|
||||
def format_history_metadata(md, tradingPeriodsOnly=True):
|
||||
if not isinstance(md, dict):
|
||||
return md
|
||||
if len(md) == 0:
|
||||
|
|
@ -698,60 +785,54 @@ def format_history_metadata(md):
|
|||
|
||||
tz = md["exchangeTimezoneName"]
|
||||
|
||||
for k in ["firstTradeDate", "regularMarketTime"]:
|
||||
if k in md:
|
||||
md[k] = _pd.to_datetime(md[k], unit='s', utc=True).tz_convert(tz)
|
||||
if not tradingPeriodsOnly:
|
||||
for k in ["firstTradeDate", "regularMarketTime"]:
|
||||
if k in md and md[k] is not None:
|
||||
if isinstance(md[k], int):
|
||||
md[k] = _pd.to_datetime(md[k], unit='s', utc=True).tz_convert(tz)
|
||||
|
||||
if "currentTradingPeriod" in md:
|
||||
for m in ["regular", "pre", "post"]:
|
||||
if m in md["currentTradingPeriod"]:
|
||||
for t in ["start", "end"]:
|
||||
md["currentTradingPeriod"][m][t] = \
|
||||
_pd.to_datetime(md["currentTradingPeriod"][m][t], unit='s', utc=True).tz_convert(tz)
|
||||
del md["currentTradingPeriod"][m]["gmtoffset"]
|
||||
del md["currentTradingPeriod"][m]["timezone"]
|
||||
|
||||
if "tradingPeriods" in md:
|
||||
if md["tradingPeriods"] == {"pre":[], "post":[]}:
|
||||
del md["tradingPeriods"]
|
||||
if "currentTradingPeriod" in md:
|
||||
for m in ["regular", "pre", "post"]:
|
||||
if m in md["currentTradingPeriod"] and isinstance(md["currentTradingPeriod"][m]["start"], int):
|
||||
for t in ["start", "end"]:
|
||||
md["currentTradingPeriod"][m][t] = \
|
||||
_pd.to_datetime(md["currentTradingPeriod"][m][t], unit='s', utc=True).tz_convert(tz)
|
||||
del md["currentTradingPeriod"][m]["gmtoffset"]
|
||||
del md["currentTradingPeriod"][m]["timezone"]
|
||||
|
||||
if "tradingPeriods" in md:
|
||||
tps = md["tradingPeriods"]
|
||||
if isinstance(tps, list):
|
||||
# Only regular times
|
||||
regs_dict = [tps[i][0] for i in range(len(tps))]
|
||||
pres_dict = None
|
||||
posts_dict = None
|
||||
elif isinstance(tps, dict):
|
||||
# Includes pre- and post-market
|
||||
pres_dict = [tps["pre"][i][0] for i in range(len(tps["pre"]))]
|
||||
posts_dict = [tps["post"][i][0] for i in range(len(tps["post"]))]
|
||||
regs_dict = [tps["regular"][i][0] for i in range(len(tps["regular"]))]
|
||||
else:
|
||||
raise Exception()
|
||||
if tps == {"pre":[], "post":[]}:
|
||||
# Ignore
|
||||
pass
|
||||
elif isinstance(tps, (list, dict)):
|
||||
if isinstance(tps, list):
|
||||
# Only regular times
|
||||
df = _pd.DataFrame.from_records(_np.hstack(tps))
|
||||
df = df.drop(["timezone", "gmtoffset"], axis=1)
|
||||
df["start"] = _pd.to_datetime(df["start"], unit='s', utc=True).dt.tz_convert(tz)
|
||||
df["end"] = _pd.to_datetime(df["end"], unit='s', utc=True).dt.tz_convert(tz)
|
||||
elif isinstance(tps, dict):
|
||||
# Includes pre- and post-market
|
||||
pre_df = _pd.DataFrame.from_records(_np.hstack(tps["pre"]))
|
||||
post_df = _pd.DataFrame.from_records(_np.hstack(tps["post"]))
|
||||
regular_df = _pd.DataFrame.from_records(_np.hstack(tps["regular"]))
|
||||
|
||||
pre_df = pre_df.rename(columns={"start":"pre_start", "end":"pre_end"}).drop(["timezone", "gmtoffset"], axis=1)
|
||||
post_df = post_df.rename(columns={"start":"post_start", "end":"post_end"}).drop(["timezone", "gmtoffset"], axis=1)
|
||||
regular_df = regular_df.drop(["timezone", "gmtoffset"], axis=1)
|
||||
|
||||
cols = ["pre_start", "pre_end", "start", "end", "post_start", "post_end"]
|
||||
df = regular_df.join(pre_df).join(post_df)
|
||||
for c in cols:
|
||||
df[c] = _pd.to_datetime(df[c], unit='s', utc=True).dt.tz_convert(tz)
|
||||
df = df[cols]
|
||||
|
||||
def _dict_to_table(d):
|
||||
df = _pd.DataFrame.from_dict(d).drop(["timezone", "gmtoffset"], axis=1)
|
||||
df["end"] = _pd.to_datetime(df["end"], unit='s', utc=True).dt.tz_convert(tz)
|
||||
df["start"] = _pd.to_datetime(df["start"], unit='s', utc=True).dt.tz_convert(tz)
|
||||
df.index = _pd.to_datetime(df["start"].dt.date)
|
||||
df.index = df.index.tz_localize(tz)
|
||||
return df
|
||||
df.index.name = "Date"
|
||||
|
||||
df = _dict_to_table(regs_dict)
|
||||
df_cols = ["start", "end"]
|
||||
if pres_dict is not None:
|
||||
pre_df = _dict_to_table(pres_dict)
|
||||
df = df.merge(pre_df.rename(columns={"start":"pre_start", "end":"pre_end"}), left_index=True, right_index=True)
|
||||
df_cols = ["pre_start", "pre_end"]+df_cols
|
||||
if posts_dict is not None:
|
||||
post_df = _dict_to_table(posts_dict)
|
||||
df = df.merge(post_df.rename(columns={"start":"post_start", "end":"post_end"}), left_index=True, right_index=True)
|
||||
df_cols = df_cols+["post_start", "post_end"]
|
||||
df = df[df_cols]
|
||||
df.index.name = "Date"
|
||||
|
||||
md["tradingPeriods"] = df
|
||||
md["tradingPeriods"] = df
|
||||
|
||||
return md
|
||||
|
||||
|
|
@ -836,14 +917,21 @@ class _KVStore:
|
|||
|
||||
def get(self, key: str) -> Union[str, None]:
|
||||
"""Get value for key if it exists else returns None"""
|
||||
item = self.conn.execute('select value from "kv" where key=?', (key,))
|
||||
try:
|
||||
item = self.conn.execute('select value from "kv" where key=?', (key,))
|
||||
except _sqlite3.IntegrityError as e:
|
||||
self.delete(key)
|
||||
return None
|
||||
if item:
|
||||
return next(item, (None,))[0]
|
||||
|
||||
def set(self, key: str, value: str) -> None:
|
||||
with self._cache_mutex:
|
||||
self.conn.execute('replace into "kv" (key, value) values (?,?)', (key, value))
|
||||
self.conn.commit()
|
||||
if value is None:
|
||||
self.delete(key)
|
||||
else:
|
||||
with self._cache_mutex:
|
||||
self.conn.execute('replace into "kv" (key, value) values (?,?)', (key, value))
|
||||
self.conn.commit()
|
||||
|
||||
def bulk_set(self, kvdata: Dict[str, str]):
|
||||
records = tuple(i for i in kvdata.items())
|
||||
|
|
@ -867,7 +955,11 @@ class _TzCache:
|
|||
def __init__(self):
|
||||
self._setup_cache_folder()
|
||||
# Must init db here, where is thread-safe
|
||||
self._tz_db = _KVStore(_os.path.join(self._db_dir, "tkr-tz.db"))
|
||||
try:
|
||||
self._tz_db = _KVStore(_os.path.join(self._db_dir, "tkr-tz.db"))
|
||||
except _sqlite3.DatabaseError as err:
|
||||
raise _TzCacheException("Error creating TzCache folder: '{}' reason: {}"
|
||||
.format(self._db_dir, err))
|
||||
self._migrate_cache_tkr_tz()
|
||||
|
||||
def _setup_cache_folder(self):
|
||||
|
|
@ -909,11 +1001,23 @@ class _TzCache:
|
|||
if not _os.path.isfile(old_cache_file_path):
|
||||
return None
|
||||
try:
|
||||
df = _pd.read_csv(old_cache_file_path, index_col="Ticker")
|
||||
df = _pd.read_csv(old_cache_file_path, index_col="Ticker", on_bad_lines="skip")
|
||||
except _pd.errors.EmptyDataError:
|
||||
_os.remove(old_cache_file_path)
|
||||
except TypeError:
|
||||
_os.remove(old_cache_file_path)
|
||||
else:
|
||||
self.tz_db.bulk_set(df.to_dict()['Tz'])
|
||||
# Discard corrupt data:
|
||||
df = df[~df["Tz"].isna().to_numpy()]
|
||||
df = df[~(df["Tz"]=='').to_numpy()]
|
||||
df = df[~df.index.isna()]
|
||||
if not df.empty:
|
||||
try:
|
||||
self.tz_db.bulk_set(df.to_dict()['Tz'])
|
||||
except Exception as e:
|
||||
# Ignore
|
||||
pass
|
||||
|
||||
_os.remove(old_cache_file_path)
|
||||
|
||||
|
||||
|
|
@ -944,9 +1048,10 @@ def get_tz_cache():
|
|||
try:
|
||||
_tz_cache = _TzCache()
|
||||
except _TzCacheException as err:
|
||||
print("Failed to create TzCache, reason: {}".format(err))
|
||||
print("TzCache will not be used.")
|
||||
print("Tip: You can direct cache to use a different location with 'set_tz_cache_location(mylocation)'")
|
||||
get_yf_logger().info("Failed to create TzCache, reason: %s. "
|
||||
"TzCache will not be used. "
|
||||
"Tip: You can direct cache to use a different location with 'set_tz_cache_location(mylocation)'",
|
||||
err)
|
||||
_tz_cache = _TzCacheDummy()
|
||||
|
||||
return _tz_cache
|
||||
|
|
|
|||
|
|
@ -1 +1 @@
|
|||
version = "0.2.10"
|
||||
version = "0.2.24"
|
||||
|
|
|
|||
Loading…
Reference in New Issue