Hi, i use your module - thanks. i think the CAGR calculation is not correct. For example :

                        Strategie          BM
Cumulative Return          81.23%      79.44%
CAGR%                      -1.38%      -1.52%

to check this quick, i used:

https://www.investopedia.com/calculator/cagr.aspx

Beginning Value:
100
Ending Value:
181.23
Number of Periods:
14

Result: Compound Annual Growth Rate:4.34%

greets

Hi Ran. Thanks for the library :)

The issue I'm reporting is that the columns in the tearsheet are not correct and need to be swapped.
For instance, in the following image, Cumulative Return and Time in Market is evidence that values don't correspond to the column.

Cheers!

The start date is found a position after the correct one.

Seems like it stops in the middle of the graph and continues as a straight line at 0, is that normal?

Hi,

I'm trying to install QuantStats for Anaconda using this command:
"conda install -c ranaroussi quantstats"

I am facing the following error:
"_The following NEW packages will be INSTALLED:

multitasking ranaroussi/noarch::multitasking-0.0.9-py_0
quantstats ranaroussi/noarch::quantstats-0.0.25-py_0
tabulate pkgs/main/win-64::tabulate-0.8.3-py37_0
yfinance ranaroussi/noarch::yfinance-0.1.54-py_0

Proceed ([y]/n)? y

Downloading and Extracting Packages
yfinance-0.1.54 | 735 KB | ############################################################################ | 100%

InvalidArchiveError('Error with archive C:\Users\mihae\Anaconda3\pkgs\yfinance-0.1.54-py_0.tar.bz2. You probably need to delete and re-download or re-create this file. Message from libarchive was:\n\nCould not unlink (errno=22, retcode=-25, archive_p=1835644343904)')_
"
I've tried the suggested solution (delete and try again) but I haven't much success.

Could you please support me?

Thanks a lot,
Adriana

I am running the following backtesting with monthly returns:

# =============================================================================
# Backtesting strategy - I : Monthly portfolio rebalancing
# Author : Mayank Rasu

# Please report bug/issues in the Q&A section
# =============================================================================

import numpy as np
import pandas as pd
pd.core.common.is_list_like = pd.api.types.is_list_like
import pandas_datareader.data as pdr
import datetime
import copy
import quantstats as qs

# Download historical data (monthly) for DJI constituent stocks
tickers = ["MMM","AXP","T","BA","CAT","CVX","CSCO","KO", "XOM","GE","GS","HD",
           "IBM","INTC","JNJ","JPM","MCD","MRK","MSFT","NKE","PFE","PG","TRV",
           "UNH","VZ","V","WMT","DIS","SPY","QQQ","TLT","LQD","GLD","SLV","IEI","AAPL"]

ohlc_mon = {} # directory with ohlc value for each stock            
attempt = 0 # initializing passthrough variable
drop = [] # initializing list to store tickers whose close price was successfully extracted
while len(tickers) != 0 and attempt <= 5:
    tickers = [j for j in tickers if j not in drop] # removing stocks whose data has been extracted from the ticker list
    for i in range(len(tickers)):
        try:
            ohlc_mon[tickers[i]] = pdr.get_data_yahoo(tickers[i],datetime.date.today()-datetime.timedelta(5500),datetime.date.today(),interval='m')
            ohlc_mon[tickers[i]].dropna(inplace = True)
            drop.append(tickers[i])       
        except:
            print(tickers[i]," :failed to fetch data...retrying")
            continue
    attempt+=1
 
tickers = ohlc_mon.keys() # redefine tickers variable after removing any tickers with corrupted data

################################Backtesting####################################

# calculating monthly return for each stock and consolidating return info by stock in a separate dataframe
ohlc_dict = copy.deepcopy(ohlc_mon)
return_df = pd.DataFrame()
for ticker in tickers:
    print("calculating monthly return for ",ticker)
    ohlc_dict[ticker]["mon_ret"] = ohlc_dict[ticker]["Adj Close"].pct_change()
    return_df[ticker] = ohlc_dict[ticker]["mon_ret"]

# function to calculate portfolio return iteratively
def pflio(DF,m,x):
    """Returns cumulative portfolio return
    DF = dataframe with monthly return info for all stocks
    m = number of stock in the portfolio
    x = number of underperforming stocks to be removed from portfolio monthly"""
    df = DF.copy()
    
    portfolio = []
    monthly_ret = [0]
    for i in range(1,len(df)):
        if len(portfolio) > 0:
            monthly_ret.append(df[portfolio].iloc[i,:].mean())
            bad_stocks = df[portfolio].iloc[i,:].sort_values(ascending=True)[:x].index.values.tolist()
            portfolio = [t for t in portfolio if t not in bad_stocks]
        fill = m - len(portfolio)
        new_picks = df.iloc[i,:].sort_values(ascending=False)[:fill].index.values.tolist()
        portfolio = portfolio + new_picks
        print(portfolio)
        
    monthly_ret_df = pd.DataFrame(np.array(monthly_ret),columns=["mon_ret"])
    
    return monthly_ret_df

Strategy = pflio(return_df,6,3)
Strategy["Date"] = return_df[1:].index.values
Strategy.set_index("Date", inplace = True)

#calculating KPIs for Index buy and hold strategy over the same period
SP500 = pdr.get_data_yahoo("^GSPC",datetime.date.today()-datetime.timedelta(5500),datetime.date.today(),interval='m')
SP500["mon_ret"] =SP500["Adj Close"].pct_change()

#len(DJI["mon_ret"][1:])

qs.reports.html(Strategy["mon_ret"], SP500["mon_ret"][1:], output='report2.html', title = "Monthly portfolio rebalancing")

But when requesting the report it throws me the following error:

File "C:\Users\Alexrios22\AppData\Local\Programs\Python\Python38\lib\site-packages\pandas\core\nanops.py", line 127, in f
    result = alt(values, axis=axis, skipna=skipna, **kwds)

  File "C:\Users\Alexrios22\AppData\Local\Programs\Python\Python38\lib\site-packages\pandas\core\nanops.py", line 479, in nanmean
    the_sum = _ensure_numeric(values.sum(axis, dtype=dtype_sum))

  File "C:\Users\Alexrios22\AppData\Roaming\Python\Python38\site-packages\numpy\core\_methods.py", line 38, in _sum
    return umr_sum(a, axis, dtype, out, keepdims, initial, where)

ValueError: operands could not be broadcast together with shapes (12,) (11,) 


During handling of the above exception, another exception occurred:

Traceback (most recent call last):

  File "C:\Users\Alexrios22\AppData\Local\Programs\Python\Python38\lib\site-packages\pandas\core\nanops.py", line 130, in f
    result = alt(values, axis=axis, skipna=skipna, **kwds)

  File "C:\Users\Alexrios22\AppData\Local\Programs\Python\Python38\lib\site-packages\pandas\core\nanops.py", line 479, in nanmean
    the_sum = _ensure_numeric(values.sum(axis, dtype=dtype_sum))

  File "C:\Users\Alexrios22\AppData\Roaming\Python\Python38\site-packages\numpy\core\_methods.py", line 38, in _sum
    return umr_sum(a, axis, dtype, out, keepdims, initial, where)

ValueError: operands could not be broadcast together with shapes (12,) (11,) 


During handling of the above exception, another exception occurred:

Traceback (most recent call last):

  File "C:\Users\Alexrios22\Downloads\Trading\Curso Algorithmic Trading And Quantitative Analysis Using Python\Market Map\Rebalancing\Rebalancing.py", line 82, in <module>
    qs.reports.html(Strategy["mon_ret"], SP500["mon_ret"][1:], output='report2.html', title = "Monthly portfolio rebalancing")

  File "C:\Users\Alexrios22\AppData\Local\Programs\Python\Python38\lib\site-packages\quantstats\reports.py", line 121, in html
    _plots.yearly_returns(returns, benchmark, grayscale=grayscale,

  File "C:\Users\Alexrios22\AppData\Local\Programs\Python\Python38\lib\site-packages\quantstats\_plotting\wrappers.py", line 362, in yearly_returns
    hline=returns.mean(),

  File "C:\Users\Alexrios22\AppData\Local\Programs\Python\Python38\lib\site-packages\pandas\core\generic.py", line 10945, in stat_func
    return self._reduce(f, name, axis=axis, skipna=skipna,

  File "C:\Users\Alexrios22\AppData\Local\Programs\Python\Python38\lib\site-packages\pandas\core\series.py", line 3626, in _reduce
    return op(delegate, skipna=skipna, **kwds)

  File "C:\Users\Alexrios22\AppData\Local\Programs\Python\Python38\lib\site-packages\pandas\core\nanops.py", line 76, in _f
    return f(*args, **kwargs)

  File "C:\Users\Alexrios22\AppData\Local\Programs\Python\Python38\lib\site-packages\pandas\core\nanops.py", line 138, in f
    raise TypeError(e)

TypeError: operands could not be broadcast together with shapes (12,) (11,) 

I had already run it before, and it hadn't given me that error, but now it does.

qs.reports.full(stock,benchmark='^NSEI',rf=0.065)

Performance Metrics
Strategy Benchmark

Start Period 2014-11-14 2014-11-14
End Period 2019-11-14 2019-11-14
Risk-Free Rate 0.06% 0.06%
Time in Market 100.0% 100.0%

Cumulative Return -100.0% -100.0%
CAGR% -100.0% -100.0%
Sharpe -62.45 -118.52
Sortino -15.39 -15.73

Is there any reason why you calculate profit factor as

return abs(returns.sum() / returns[returns < 0].sum())

as opposed to:

return abs(returns[returns > 0].sum() / returns[returns < 0].sum())

I thought normally profit factor is total profit / total loss

Hello,

First of all, thank you very much for releasing this lib publicly. I've been using it since it's so much simpler to use than many other ones.

I did come across a problem when there aren't any drawdowns or if there are fewer than 5 drawdowns. The problem is in at least two places that I've seen.

In stats._drawdown_details(drawdown), when returning None here, some code later expects a DataFrame and therefore it fails.

# no drawdown :)
if len(starts) == 0:
    return None

Even if above we return an empty DataFrame with the right column names, the code that uses this function still fails because range(1,6) is assigned to an index:
E.g. in reports.full()

dd_info = _stats.drawdown_details(dd).sort_values(
        by='max drawdown', ascending=True)[:5]
dd_info.index = range(1, 6)   # <-- Fails here

Hi there Ran!
First of all, thanks for this piece of art you've made. It's been working great for my needs. I really appreciate the correlation heatmap provided, but after some time using it I got a visualization problem, having the edge boxes cut in half. Any ideas for fixing that?

Thank you very much for the very good work!

I am using quantstats to evaluate my trading strategies and I have some doubts about the data that quantstats is able to read.

A strategy that I trade signal to signal and is in a trade all the time works well.

Use one csv with one date and returns column like this

But in a strategy that opens one day and closes another day, and in between days may pass no longer.

1- Can you put two dates, one entry and one exit? How would the code for this be?

Another question, and I don't know if this is covered by quantstats, is the function of pyramid.

2-I have a strategy in which I do up to three pyramid trades, but when I enter the data into quantstats, I compose them too much and it gives unrealistic results.

3-May I add the option of non-compounding returns?

I think that in the case of the pyramid this will be an option.

Enter each trade with a fixed amount.

Shared one spreadshet with the trades:

The most important stats are : signal type, date with hour, price, contract (strategy is set for take profit 35% in certain circumstances, profit %)

https://docs.google.com/spreadsheets/d/1OdI3e38OsEDSh5gQLdNtW75DaHtgshpvRI6HofaE00I/edit#gid=0

I'm not sure what kind of analysis I can do for this strategy to get the right results.

Sorry about the long post. Cheers and thanks.

FutureWarning error in the utils.py module at line 94 in the rebase function.

Pandas' .ix is now deprecated and a simple change to .iloc[0] in the rebase function fixes this. I've run this in isolation and tested against both series and dataframe timeseries prices and it works well.

I am trying to run this code

import quantstats as qs
stock = qs.utils.download_returns('FB')
qs.reports.html(stock, "SPY")

the output I get on my spyder ide is:
<IPython.core.display.HTML object>

How do I save it as html in my pc?

Dependabot couldn't authenticate with https://pypi.python.org/simple/.

You can provide authentication details in your Dependabot dashboard by clicking into the account menu (in the top right) and selecting 'Config variables'.

View the update logs.

After the last update of the code when importing quantstats it returns the error:

AttributeError: 'str' object has no attribute 'magic

I checked the code and it's an error on line 278 of utils.py. Where it reads

shell.magic("matplotlib inline")

should be

get_ipython().magic("matplotlib inline").

You're doing a great job! Go for it!

Should this be _stats.compsum(returns)?

First of all, I would like to thank for this really awesome tool. I have been trying to use it for some time and from the examples I see that almost all the functions need returns as input to generate the stats and tearsheet etc.
My doubt is that what kind of returns does this library assume. Is it simple returns calculated using pct_change() in pandas or log returns calculated using np.log(close_price).diff().

I wonder if we can use minutes return instead of daily returns input to quantstats?

Hi, how can I save the monthly returns table as a CSV file? Thank you

The bot created this issue to inform you that pyup.io has been set up on this repo.
Once you have closed it, the bot will open pull requests for updates as soon as they are available.

I'm running the reports.plot with mode="full" and the top and bottom rows of the monthly returns chart is getting clipped (please see the attached screenshot). I was wondering if there is a way to add a bit of spacing on the top and bottom to get it to render properly.

Thanks for the help.

Hello,

First of all, thank you for your amazing library and work, is great!

I'm using it and I think I have discovered two little bugs:
1.- In reports.py line 71; today = _dt.today()
This line is good if the final date of the returns is equal to today(), but if we are doing a backtest, and the final date of teh returns where 2 years ago, this line is not correct. I think this can be solved changing that line to:
today = df.index[-1]

2.- In reports.py line 217; dd_info.index = range(1, 6)
The code breaks were there is less than 5 drawdowns. This can happen easily if our portfolio don't do to many operations. I think this can be fixed changing that line to:
dd_info.index = range(1, min(6, len(dd_info)+1))

3.- Finally, when I execute the some reports that include plots, I get the following error (executing the code from pycharm console):

...\lib\site-packages\quantstats\_plotting\wrappers.py:154: MatplotlibDeprecationWarning:

Passing the block parameter of show() positionally is deprecated since Matplotlib 3.1; the parameter will become keyword-only in 3.3.

I have tried to change every _plt.show(fig) to_plt.show(fig, block=False)and still not working, any idea of how can be solved that?

In html report we pass risk-free rate in year basis, but in calculating the excess_returns we have to substract the daily change.

def to_excess_returns(returns, rf, nperiods=None):
    """
    Calculates excess returns by subtracting
    risk-free returns from total returns

    Args:
        * returns (Series, DataFrame): Returns
        * rf (float, Series, DataFrame): Risk-Free rate(s)
        * nperiods (int): Optional. If provided, will convert rf to different
            frequency using deannualize
    Returns:
        * excess_returns (Series, DataFrame): Returns - rf
    """
    if not isinstance(rf, float):
        rf = rf[rf.index.isin(returns.index)]

    if nperiods is not None:
        # deannualize
        rf = _np.power(1 + returns, 1. / nperiods) - 1.

    return returns - rf

Hello,
after creating a model in the stable baselines library anytrading (https://github.com/AminHP/gym-anytrading) and running the following code:

env = gym.make('stocks-v0', frame_bound=(50, 100), window_size=10)
model = DQN('MlpPolicy', env, tensorboard_log="D:\ReinforcementLearning\BaseLines\Trading\Tensorboard",
            verbose=2)
results = model.learn(int(1000))

how do I use the results to compare with a benchmark in quantstats?
Currently the results doesn't hold the data that quantstats expect to be able to use in conjunction with qs.reports.html(results, "SPY", output="D:\ReinforcementLearning\BaseLines\Trading\Myreport.html")

I am trying to execute above code in Jupyter Notebook . unable to get the required output . Screen shot attached above in Image

Hi,

I've come across an edge case that took me quite a while to debug.
Basically when a daily return is >= 100% (during backtesting, pennystocks etc), utils._prepare_prices() thinks that the input series has already been converted to prices and it does nothing. This obviously messes up the calculations.

elif data.min() <= 0 and data.max() < 1:
        data = to_prices(data, base)

For now as a hack I just clip all my returns to 0.9999.

Any thoughts on how to deal with this, given that it is an edge case... unfortunately :-)?
I think putting an or instead of the and might be more robust, but still not foolproof.

As mentioned in the readme to let you know feedback..

Its a great project and wonderful with backtrader too.

I prefer to pyfolio

Keep up the good work!

got this data when running datasets including 02/29/2020
Traceback (most recent call last):
File "report.py", line 28, in
qs.reports.html(series, output='ASSET.html')
File "/usr/local/lib/python3.7/site-packages/quantstats/reports.py", line 61, in html
compounded=compounded)[2:]
File "/usr/local/lib/python3.7/site-packages/quantstats/reports.py", line 389, in metrics
df[df.index >= _dt(today.year-3, today.month, today.day)
ValueError: day is out of range for month

test.xlsx
Below two functions are not working in attached data.

1. qs.plots.snapshot()
2. qs.reports.full()

Function returns:
OverflowError: cannot convert float infinity to integer

IndexError: invalid index to scalar variable.
when doing cvar = returns[returns<var].mean()[0]

In conditional value at risk fn

Hi Ran,

I'm trying to get full report from cumulative returns pandas dataframe. Everything seems to be fine except EOY Returns.

2015 | 4063561685.19% | inf%
2016 | 5685063513.11% | inf%
2017 | 8068012580.76% | inf%
2018 | 9992995288.14% | inf%
2019 | 5817419000.9% | inf%

MTD | 0.55%
3M | 10.35%
6M | 15.35%
YTD | 12.37%
1Y | 5.07%
3Y (ann.) | 20.56%
5Y (ann.) | 26.93%
10Y (ann.) | 26.93%
All-time (ann.) | 26.93%

From: https://aroussi.com//assets/tearsheets/lazy.html

The drawdowns seem to be selected according to length, not depth. Can you confirm?

Maybe this should appear as a note under the plot.

Can I use this library to monitor my live performace on an API for eg: Binance.

Hi, I think that when there is no drawdown, we will get the error:

ValueError: cannot convert float NaN to integer

This problem was previously closed without being completely fixed #2

Here's the error traceback:

Traceback (most recent call last):
  File "test.py", line 269, in <module>
    qs.reports.html(returns['net_worth'], benchmark, output=os.path.join(projectDir, 'report.html'))
  File "/home/athena/anaconda3/envs/unicorn/lib/python3.8/site-packages/quantstats/reports.py", line 58, in html
    mtrx = metrics(returns=returns, benchmark=benchmark,
  File "/home/athena/anaconda3/envs/unicorn/lib/python3.8/site-packages/quantstats/reports.py", line 299, in metrics
    dd = _calc_dd(df, display=(display or "internal" in kwargs))
  File "/home/athena/anaconda3/envs/unicorn/lib/python3.8/site-packages/quantstats/reports.py", line 587, in _calc_dd
    'Longest DD Days': str(round(bench_dd.sort_values(
ValueError: cannot convert float NaN to integer

Using quantstats 0.0.25, pandas 1.0.3

Thanks!

Hello people,
I'm trying to run the examples but for some reason the charts don't get displayed. It looks like the window flashes and closes itself straight after.
Has anyone else faced similar issue?
Thanks all

backtest-result-2020-08-19_11-42-18.zip

Thanks for this awesome open source project. I try to link it to another exciting project which is -> https://github.com/freqtrade/freqtrade

In Freqtrade you can do backtests with the strategy being crafted - however the analysis tools are currently limited. The backtest results are exported as .json files (example enclosed).

I have tried to "cast" the backtest results in the input format of Quantstats, which was working well to the point of plotting the snapshot. Here is my code - probably not the most elegant way (I am not a coder):

import quantstats as qs
from freqtrade.data.btanalysis import load_backtest_data

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

# load backtest data
backtest = load_backtest_data('/home/andreas/freqtrade/user_data/backtest_results/backtest-result-2020-08-19_11-42-18.json') 

# define stake amount and compute equity curve
stake_amount = 100 
max_open_trades = 10
stake_amount_total = stake_amount * max_open_trades

backtest['profit_amount'] = (backtest['profit_percent']) * stake_amount  
backtest['cum_profit'] = backtest['profit_amount'].cumsum() # cumulated profits in stake currency
          
# prepare data for Quantstat format
backtest = backtest.set_index('close_date')
daily_resampled_data = backtest.cum_profit.resample('D').mean()
daily_resampled_data1 = daily_resampled_data.apply(lambda x: x + stake_amount_total) # add stake amount x max open trades

# feed own data into Quantstat "stock" algo
stock = daily_resampled_data1.pct_change()

# show performance plot
qs.plots.snapshot(stock, title='Performance')

When trying to apply the most interesting function (full report, benchmark e.g. LTC-USD) I got an error message:

qs.reports.basic(stock, "LTC-USD")

qs.reports.basic(stock, "LTC-USD")
<IPython.core.display.HTML object>
Traceback (most recent call last):

  File "<ipython-input-217-a1e5d119cf6e>", line 1, in <module>
    qs.reports.basic(stock, "LTC-USD")

  File "/home/andreas/anaconda3/lib/python3.7/site-packages/quantstats/reports.py", line 259, in basic
    compounded=compounded)

  File "/home/andreas/anaconda3/lib/python3.7/site-packages/quantstats/reports.py", line 299, in metrics
    dd = _calc_dd(df, display=(display or "internal" in kwargs))

  File "/home/andreas/anaconda3/lib/python3.7/site-packages/quantstats/reports.py", line 588, in _calc_dd
    by='days', ascending=False)['days'].values[0])),

ValueError: cannot convert float NaN to integer

What to do here?

Hi @ranaroussi ,
The static output works fine on visual studio but charts are not showing any output.
1

qs.plots.snapshot(df0['PPL_Return'], title='PPL Performance')`

This command runs but no output is displayed. Is there any other command to add so that output can be displayed?

Hi,

Thanks for your library I'm using it.

I was getting a negative CAGR when my total return was positive. There are 2 issues I think, firstly the year calculation does not cope with periods less than a year. Secondly in the total return part you need to add back 1.0 before you do the power calculation.

E.g. If I make 10% for 2 years then my total returns should be 21%. My CAGR should obviously be 10%. To achieve this calculation I need to compute (0.21 + 1.0) ** (1/2.0) - 1

# years = len(set(returns.index.year))
years = (returns.index[-1] - returns.index[0]).days/365.0

# res = abs(total / 1.0) ** (1.0 / years) - 1
res = abs(total + 1.0) ** (1.0 / years) - 1

hello everyone, how much I try to do :
qs.reports.html(returns=stock,benchmark=bench,output="report.html")

I get the following error:

UnicodeEncodeError Traceback (most recent call last)
in
----> 1 qs.reports.html(returns=stock,benchmark=bench,output='1.html')

~\Miniconda3\envs\quant\lib\site-packages\quantstats\reports.py in html(returns, benchmark, rf, grayscale, title, output, compounded)
    205 
    206     with open(output, 'w') as f:
--> 207         f.write(tpl)
    208 
    209 

~\Miniconda3\envs\quant\lib\encodings\cp1252.py in encode(self, input, final)
     17 class IncrementalEncoder(codecs.IncrementalEncoder):
     18     def encode(self, input, final=False):
---> 19         return codecs.charmap_encode(input,self.errors,encoding_table)[0]
     20 
     21 class IncrementalDecoder(codecs.IncrementalDecoder):

UnicodeEncodeError: 'charmap' codec can't encode character '\u2212' in position 649916: character maps to <undefined>

Any solution?
Thank you very much for your work!

Hey thanks so much for your great work here! It is super useful. Would it be possible to pass your functions a list of Tickers and Weights so i can get a full report on a buy and hold portfolio over time? Thanks so much! :)

Dear Project Team,
Not an issue per se, more a question. Is there an easy way to override the default colors used by Quantstats curves & graphs? I ve looked a bit the code; I have seen where there are defined. However, I d like to modify these colors without "forking"/duplicating the whole project?
Many thanks in advance
Regards,

Hi Ran,

I have been using quantstats to generate a complete report of my portfolio with the function qs.reports.html but it gave me a TypeError. After exploring the error a little bit more, if I use the function qs.reports.full(trader.daily_returns, "SPY") in a jupyter notebook it gives the Output until the Cumulative Returns vs SPY (Volatility Matched). However while trying to print the graph EOY Returns vs Benchmark, the following error occurs:

---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-27-649f47ec8503> in <module>
----> 1 qs.reports.full(trader.daily_returns, "SPY")

~\Anaconda3\lib\site-packages\quantstats\reports.py in full(returns, benchmark, rf, grayscale, figsize, display, compounded)
    247 
    248     plots(returns=returns, benchmark=benchmark,
--> 249           grayscale=grayscale, figsize=figsize, mode='full')
    250 
    251 

~\Anaconda3\lib\site-packages\quantstats\reports.py in plots(returns, benchmark, grayscale, figsize, mode, compounded)
    509                           grayscale=grayscale,
    510                           figsize=(figsize[0], figsize[0]*.5),
--> 511                           show=True, ylabel=False)
    512 
    513     _plots.histogram(returns, grayscale=grayscale,

~\Anaconda3\lib\site-packages\quantstats\_plotting\wrappers.py in yearly_returns(returns, benchmark, fontname, grayscale, hlw, hlcolor, hllabel, match_volatility, log_scale, figsize, ylabel, subtitle, compounded, savefig, show)
    371                             grayscale=grayscale,
    372                             ylabel=ylabel,
--> 373                             subtitle=subtitle, savefig=savefig, show=show)
    374 
    375 

~\Anaconda3\lib\site-packages\quantstats\_plotting\core.py in plot_returns_bars(returns, benchmark, returns_label, hline, hlw, hlcolor, hllabel, resample, title, match_volatility, log_scale, figsize, grayscale, fontname, ylabel, subtitle, savefig, show)
    110     if benchmark is None:
    111         colors = colors[1:]
--> 112     df.plot(kind='bar', ax=ax, color=colors)
    113 
    114     fig.set_facecolor('white')

~\Anaconda3\lib\site-packages\pandas\plotting\_core.py in __call__(self, *args, **kwargs)
    792                     data.columns = label_name
    793 
--> 794         return plot_backend.plot(data, kind=kind, **kwargs)
    795 
    796     def line(self, x=None, y=None, **kwargs):

~\Anaconda3\lib\site-packages\pandas\plotting\_matplotlib\__init__.py in plot(data, kind, **kwargs)
     60             kwargs["ax"] = getattr(ax, "left_ax", ax)
     61     plot_obj = PLOT_CLASSES[kind](data, **kwargs)
---> 62     plot_obj.generate()
     63     plot_obj.draw()
     64     return plot_obj.result

~\Anaconda3\lib\site-packages\pandas\plotting\_matplotlib\core.py in generate(self)
    277     def generate(self):
    278         self._args_adjust()
--> 279         self._compute_plot_data()
    280         self._setup_subplots()
    281         self._make_plot()

~\Anaconda3\lib\site-packages\pandas\plotting\_matplotlib\core.py in _compute_plot_data(self)
    412         # no non-numeric frames or series allowed
    413         if is_empty:
--> 414             raise TypeError("no numeric data to plot")
    415 
    416         # GH25587: cast ExtensionArray of pandas (IntegerArray, etc.) to

TypeError: no numeric data to plot

I have a dataframe or series with daily returns for a model I have built... it contains just 2019 data - Jan 2nd to Dec 31st. The cumsum of that Close column in 17.86%. But when I run it through Quantstats, the HTML output is showing a cumulative return of 17.2%.
Even the sum of the monthly returns from the HTML shows 17.84%

Hi,

I would appreciate some help to scale the report when using a benchmark.

As you will see from the attachment there is a very flat line that is hard to read.

Thanks

Fig

Tearsheet (generated by QuantStats).pdf

Currently the stats.py annualizes using 252 period. There needs to be an option for 365 for crypto.

For running HTML reports, I can't see a straightforward way to annualise without overwritting the stats.py file.

Ideally this should be an input in reports.py.

Thanks

Hi,

I'd like to create a nice PDF using Quantstats.

Is there a better way to do it other than:

• Run Python code to export as "HTML"
• Open HTML in Chrome
• Print as PDF

The problem is, a number of the charts get cut off and split across pages.

Please Let me know if there is a nice way to save and share these reports, ideally PDF!

Thank you,
CWSE

Thanks for providing the great example for evaluating FB.
Can quantstats be used to evaluate a watchlist of stocks?

Hi Ran,
I have an issue with "qs.reports.html". It works when I use "output=". But when I ran it on Jupyter notebook I don't get the output as you see in the screenshot. Do you know this issue with Jupyter?
I add another screenshot from inspect look like something jquery erroer

Thank you in advance

When I execute "qs.utils.download_returns('FB')" I get the following error message: AttributeError: module 'pandas.core.common' has no attribute '_dict_keys_to_ordered_list'.

The graphs and summary look really nice and powerful - thus it would be nice to use this library.

When evaluating a strategy over short time horizon, report shows stats for invalid time periods e.g. returns for 5Y (ann) and 10Y (ann).

I would prefer to not show data. Instead replace with: "" or "-" or "na"