CoCalc -- yfinance

yFinance - Eli Policape Stock Performance Data for Microsoft and SPY ETF

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License: GPL3
ubuntu2204

Kernel: Python 3 (system-wide)

In [1]:

import yfinance as yf

In [3]:

import quantstats as qs

In [4]:

import sys
sys.prefix

Out[4]:

'C:\\ProgramData\\Anaconda3\\envs\\yfinance'

QuantStats functions

In [5]:

[f for f in dir(qs.reports) if f[0] != '_']

Out[5]:

['basic',
 'full',
 'html',
 'iDisplay',
 'iHTML',
 'metrics',
 'plots',
 'relativedelta']

In [6]:

[f for f in dir(qs.plots) if f[0] != '_']

Out[6]:

['daily_returns',
 'distribution',
 'drawdown',
 'drawdowns_periods',
 'earnings',
 'histogram',
 'log_returns',
 'monthly_heatmap',
 'monthly_returns',
 'plotly',
 'returns',
 'rolling_beta',
 'rolling_sharpe',
 'rolling_sortino',
 'rolling_volatility',
 'snapshot',
 'to_plotly',
 'warnings',
 'yearly_returns']

QuantStats is comprised of 3 main modules:

quantstats.stats - for calculating various performance metrics, like Sharpe ratio, Win rate, Volatility, etc.

quantstats.plots - for visualizing performance, drawdowns, rolling statistics, monthly returns, etc.

quantstats.reports - for generating metrics reports, batch plotting, and creating tear sheets that can be saved as an HTML file.

Creating a report

You can create 7 different report tearsheets:

qs.reports.metrics(mode='basic|full", ...) - shows basic/full metrics

qs.reports.plots(mode='basic|full", ...) - shows basic/full plots

qs.reports.basic(...) - shows basic metrics and plots

qs.reports.full(...) - shows full metrics and plots

qs.reports.html(...) - generates a complete report as html

In [7]:

msft = yf.Ticker("MSFT")
print(msft)

Out[7]:

yfinance.Ticker object <MSFT>

In [8]:

#msft.info

In [9]:

msft.history(period="1mo")

Out[9]:

In [10]:

msft.actions

Out[10]:

In [11]:

msft.dividends

Out[11]:

Series([], Name: Dividends, dtype: int64)

In [12]:

# extend pandas functionality with metrics, etc.
qs.extend_pandas()

# fetch the daily returns for a stock
msft_stock = qs.utils.download_returns('msft')

# show sharpe ratio
qs.stats.sharpe(msft_stock)

# or using extend_pandas() :)
msft_stock.sharpe()

print(msft_stock)

Out[12]:

Date
1986-03-13 00:00:00-05:00         NaN
1986-03-14 00:00:00-05:00    0.035712
1986-03-17 00:00:00-05:00    0.017250
1986-03-18 00:00:00-05:00   -0.025431
1986-03-19 00:00:00-05:00   -0.017391
                               ...   
2023-10-11 00:00:00-04:00    0.012272
2023-10-12 00:00:00-04:00   -0.003790
2023-10-13 00:00:00-04:00   -0.010358
2023-10-16 00:00:00-04:00    0.014982
2023-10-17 00:00:00-04:00    0.000872
Name: Close, Length: 9476, dtype: float64

In [13]:

qs.plots.daily_returns(msft_stock)

Out[13]:

In [14]:

qs.plots.yearly_returns(msft_stock)

Out[14]:

In [15]:

qs.plots.snapshot(msft_stock, title = 'Microsoft')

Out[15]:

If your legacy code is using pandas_datareader and you want to keep the code changes to minimum, you can simply call the override method and keep your code as it was:

In [16]:

qs.reports.plots(msft_stock, mode = "full")

Out[16]:

C:\ProgramData\Anaconda3\envs\yfinance\lib\site-packages\quantstats\stats.py:968: FutureWarning: In a future version of pandas all arguments of DataFrame.pivot will be keyword-only.
  returns = returns.pivot('Year', 'Month', 'Returns').fillna(0)

from pandas_datareader import data as pdr

import yfinance as yf
yf.pdr_override() #<== This is the override method

data = pdr.get_data_yahoo("SPY", start = "20")

In [17]:

from pandas_datareader import data as pdr
data = pdr.get_data_yahoo("SPY")
data

Out[17]:

---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
Cell In [17], line 2
      1 from pandas_datareader import data as pdr
----> 2 data = pdr.get_data_yahoo("SPY")
      3 data
File C:NaN, in get_data_yahoo(*args, **kwargs)
     79 def get_data_yahoo(*args, **kwargs):
---> 80     return YahooDailyReader(*args, **kwargs).read()
File C:NaN, in _DailyBaseReader.read(self)
    251 # If a single symbol, (e.g., 'GOOG')
    252 if isinstance(self.symbols, (string_types, int)):
--> 253     df = self._read_one_data(self.url, params=self._get_params(self.symbols))
    254 # Or multiple symbols, (e.g., ['GOOG', 'AAPL', 'MSFT'])
    255 elif isinstance(self.symbols, DataFrame):
File C:NaN, in YahooDailyReader._read_one_data(self, url, params)
    151 try:
    152     j = json.loads(re.search(ptrn, resp.text, re.DOTALL).group(1))
--> 153     data = j["context"]["dispatcher"]["stores"]["HistoricalPriceStore"]
    154 except KeyError:
    155     msg = "No data fetched for symbol {} using {}"
TypeError: string indices must be integers

In [18]:

spy = yf.Ticker("SPY")
print(spy)

Out[18]:

yfinance.Ticker object <SPY>

In [0]:

#spy.info

In [19]:

# Data period parameters 1d, 5d, 1mo, 3mo, 6mo, 1y,2y,5y,10y.ytd,max

spy.history(period = "1mo" )

Out[19]:

In [20]:

# extend pandas functionality with metrics, etc.
qs.extend_pandas()

# fetch the daily returns for a stock
spy_stock = qs.utils.download_returns('SPY')

# show sharpe ratio
qs.stats.sharpe(spy_stock)

# or using extend_pandas() :)
spy_stock.sharpe()

print(spy_stock)

Out[20]:

Date
1993-01-29 00:00:00-05:00         NaN
1993-02-01 00:00:00-05:00    0.007113
1993-02-02 00:00:00-05:00    0.002119
1993-02-03 00:00:00-05:00    0.010570
1993-02-04 00:00:00-05:00    0.004185
                               ...   
2023-10-10 00:00:00-04:00    0.005205
2023-10-11 00:00:00-04:00    0.004096
2023-10-12 00:00:00-04:00   -0.006096
2023-10-13 00:00:00-04:00   -0.004981
2023-10-16 00:00:00-04:00    0.010521
Name: Close, Length: 7734, dtype: float64

In [21]:

qs.stats.conditional_value_at_risk(spy_stock)

Out[21]:

-0.02894721345003485

In [22]:

qs.plots.daily_returns(spy_stock)

Out[22]:

In [23]:

qs.plots.yearly_returns(spy_stock)

Out[23]:

In [24]:

qs.plots.snapshot(spy_stock, title = 'S&P 500 ETF')

Out[24]:

In [25]:

qs.reports.plots(spy_stock, mode = "full")

Out[25]:

C:\ProgramData\Anaconda3\envs\yfinance\lib\site-packages\quantstats\stats.py:968: FutureWarning: In a future version of pandas all arguments of DataFrame.pivot will be keyword-only.
  returns = returns.pivot('Year', 'Month', 'Returns').fillna(0)

In [26]:

qs.stats.compare(spy_stock,msft_stock)

Out[26]:

In [29]:

qs.stats.win_loss_ratio(spy_stock)

Out[29]:

0.944087854833915

In [28]:

qs.stats.win_loss_ratio(msft_stock)

Out[28]:

1.0925793813066438

In [0]:

QuantStats functions

QuantStats is comprised of 3 main modules:

Creating a report

Product

Resources

Company