Posts made by antinomy

@vyacheslav_b Hi, I agree that diversification is always a good idea for trading. It might be helpful if there was an additional function like check_diversification whith a parameter for the minimum number of assets you want to trade. But this function should only warn you and not fix an undiversified portfolio, because the only way would be to add more assets to trade and asset selection should be done by the strategy itself in my opinion.

@support Hi, I just checked out qnt 0.0.504 and the problem I mentioned seems to be fixed now, thanks!
Would you perhaps consider to add a leverage check to the check function?
Because one might think "qnout.check says everything is OK, so I have a valid portfolio" while actually having vastly overleveraged like in my 2nd example where weights.sum('asset').values.max() is 505.0.
Adding something like this to check would tell us about it:

log_info("Check max portfolio leverage...")
max_leverage = abs(output).sum(ds.ASSET).values.max()
if max_leverage > 1 + 1e-13: # (give some leeway for rounding errors and such)
    log_err("ERROR! The max portfolio leverage is too high.")
    log_err(f"Max leverage: {max_leverage} Limit: 1.0")
    log_err("Use qnt.output.clean() or normalize() to fix.")
else:
    log_info("Ok.")

@support,
Either something about the exposure calculation is wrong or I really need clarification on the rules. About the position limit I only find rule 7. o. in the contest rules which states "The evaluation system limits the maximum position size for a single financial instrument to 10%"

I always assumed this would mean the maximum weight for an asset would be 0.1 meaning 10 % of the portfolio. However the exposure calculation suggests the following:
Either we trade no asset or at least 10 assets per trading day, regardless of the actual weights assigned to each asset.

Consider this example:

import qnt.data as qndata
import qnt.output as qnout
from qnt.filter import filter_sharpe_ratio


data = qndata.stocks.load_spx_data(min_date="2005-01-01")

weights = data.sel(field='is_liquid').fillna(0)
weights *= filter_sharpe_ratio(data, weights, 3) * .01 # assign 1 % to each asset using the top 3 assets by sharpe

qnout.check(weights, data, "stocks_s&p500")

which results in an exposure error:

Check max exposure for index stocks (nasdaq100, s&p500)…
ERROR! The max exposure is too high.
Max exposure: [0.         0.         0.         ... 0.33333333 0.33333333 0.33333333] Hard limit: 0.1
Use qnt.output.cut_big_positions() or normalize_by_max_exposure() to fix.

even though the maximum weight per asset is only 0.01

abs(weights).values.max()
0.01

(By the way, the 4 functions mentioned by @Vyacheslav_B also result in weights which dont't pass the exposure check when used with this example, except drop_bad_days which results in empty weights.)

And if we assign 100 % to every liquid asset, the exposure check passes:

weights = data.sel(field='is_liquid').fillna(0)
qnout.check(weights, data, "stocks_s&p500")

Check max exposure for index stocks (nasdaq100, s&p500)…
Ok.

So, does rule 7. o. mean we have to trade at least 10 assets or none at all each trading day to satisfy the exposure check?

The symbols are all in there, but if they are listed on NYSE you have to prepend NYS: not NAS: to the symbol. Also, I believe by 'BKR.B' you mean 'BRK.B'

[sym for sym in data.asset.values if any(map(lambda x: x in sym, ['JPM', 'LLY', 'BRK.B']))]

['NYS:BRK.B', 'NYS:JPM', 'NYS:LLY']

You can also search for symbols in qndata.stocks_load_spx_list() and get a little more infos like this:

syms = qndata.stocks_load_spx_list()
[sym for sym in syms if sym['symbol'] in ['JPM', 'LLY', 'BRK.B']]

[{'name': 'Berkshire Hathaway Inc',
  'sector': 'Finance',
  'symbol': 'BRK.B',
  'exchange': 'NYS',
  'id': 'NYS:BRK.B',
  'cik': '1067983',
  'FIGI': 'tts-824192'},
 {'name': 'JP Morgan Chase and Co',
  'sector': 'Finance',
  'symbol': 'JPM',
  'exchange': 'NYS',
  'id': 'NYS:JPM',
  'cik': '19617',
  'FIGI': 'tts-825840'},
 {'name': 'Eli Lilly and Co',
  'sector': 'Healthcare',
  'symbol': 'LLY',
  'exchange': 'NYS',
  'id': 'NYS:LLY',
  'cik': '59478',
  'FIGI': 'tts-820450'}]

The value for the key 'id' is what you will find in data.asset

I got the same error after installing qnt locally with pip.
There is indeed a circular import in the current Github repo for the toolbox, introduced by this commit:
https://github.com/quantiacs/toolbox/commit/78beafa93775f33606156169b3e6b8f995804151#diff-89350fe373763b439e4697f9b11cceb811b4a3f0adc7a655707a936ce5646c01R6-R10
when some of the imports in output.py which were inside of fuctions before were moved to the top level.
Now output imports from stats and stats imports from output.

@support Can you please have a look?

@alexeigor @omohyoid
The conda version of qnt doesn't seem to be affected, so if that's an option for you install that one instead.
Otherwise we can use the git version previous to the commit above:

pip uninstall qnt
pip install git+https://github.com/quantiacs/toolbox.git@a1e6351446cd936532af185fb519ef92f5b1ac6d

@steel-camel

!pip install --force-reinstall python_utils

should fix the issue.
But I have no idea what would have caused it, the line in converters.py is totally messed up. The only thing that comes to my mind is a cat on the keyboard

@cyan-gloom
interpolate_na() only eliminates NaNs between 2 valid data points. Take a look at this example:

import qnt.data as qndata
import numpy as np

stocks = qndata.stocks_load_ndx_data()
sample = stocks[:, -5:, -6:] # The latest 5 dates for the last 6 assets

print(sample.sel(field='close').to_pandas())
"""
asset       NYS:NCLH  NYS:ORCL  NYS:PRGO  NYS:QGEN  NYS:RHT  NYS:TEVA
time                                                                 
2023-05-12     13.24     97.85     35.21     45.09      NaN      8.03
2023-05-15     13.71     97.26     34.23     45.36      NaN      8.07
2023-05-16     13.48     98.25     32.84     45.25      NaN      8.13
2023-05-17     14.35     99.77     32.86     44.95      NaN      8.13
2023-05-18     14.53    102.34     33.43     44.92      NaN      8.26
"""

# Let's add some more NaN values:
sample.values[3, (1,3), 0] = np.nan
sample.values[3, 1:4, 1] = np.nan
sample.values[3, :2, 2] = np.nan
sample.values[3, 2:, 3] = np.nan
sample.values[3, :-1, 5] = np.nan
print(sample.sel(field='close').to_pandas())
"""
asset       NYS:NCLH  NYS:ORCL  NYS:PRGO  NYS:QGEN  NYS:RHT  NYS:TEVA
time                                                                 
2023-05-12     13.24     97.85       NaN     45.09      NaN       NaN
2023-05-15       NaN       NaN       NaN     45.36      NaN       NaN
2023-05-16     13.48       NaN     32.84       NaN      NaN       NaN
2023-05-17       NaN       NaN     32.86       NaN      NaN       NaN
2023-05-18     14.53    102.34     33.43       NaN      NaN      8.26
"""

# Interpolate the NaN values:
print(sample.interpolate_na('time').sel(field='close').to_pandas())
"""
asset       NYS:NCLH    NYS:ORCL  NYS:PRGO  NYS:QGEN  NYS:RHT  NYS:TEVA
time                                                                   
2023-05-12    13.240   97.850000       NaN     45.09      NaN       NaN
2023-05-15    13.420  100.095000       NaN     45.36      NaN       NaN
2023-05-16    13.480  100.843333     32.84       NaN      NaN       NaN
2023-05-17    14.005  101.591667     32.86       NaN      NaN       NaN
2023-05-18    14.530  102.340000     33.43       NaN      NaN      8.26
"""

As you can see, only the NaNs in the first 2 columns are being replaced. The others remain untouched and might be dropped when you use dropna().

Another thing you should keep in mind is that you might introduce lookahead bias with interpoloation, e. g. in a single run backtest. In my example for instance (pretend the NaNs I added were already in the data) you would know on 2023-05-15 that ORCL will rise when in reality you would first know that on 2023-05-18.

Asuming whatever train is has a similar structure as the usual stock data, I get the same error as you with:

import itertools
import qnt.data as qndata

stocks = qndata.stocks_load_ndx_data(tail=100)

for comb in itertools.combinations(stocks.asset, 2):
    print(stocks.sel(asset=[comb]))

There are 2 things to consider:

1. comb is a tuple and you can't use tuples as value for the asset argument. You are putting brackets around it, but that gives you a list with one element wich is a tuple, hence the error about setting an array element as a sequence. Using stocks.sel(asset=list(comb)) instead resolves this issue but then you'll get an index error which leads to the second point
2. each element in comb is a DataArray and cannot be used as an index element to select from the data. You want the string values instead, for this you can iterate over asset.values for instance.

My example works when the loop looks like this:

for comb in itertools.combinations(stocks.asset.values, 2):
    print(stocks.sel(asset=list(comb)))

I'm a huge fan of Sentdex, he really tought me a lot about Python in his tutorials.
Have a look at his website and his Youtube channel, for instance there's a tutorial for Python beginners.

It's safe to ignore these notices but if they bother you, you can set the variables together with your API key using the defaults and the messages go away:

import os

os.environ['API_KEY'] = 'YOUR-API-KEY'
os.environ['DATA_BASE_URL'] = 'https://data-api.quantiacs.io/'
os.environ['CACHE_RETENTION'] = '7'
os.environ['CACHE_DIR'] = 'data-cache'

Hello @support
Could you please add CIKs to the NASDAQ100 stock list?
In order to load fundamental data from secgov we need the CIKs for the stocks but they're currently not in the list we get from qnt.data.stocks_load_ndx_list().
Allthough it is still possible to get fundamentals using qnt.data.stocks_load_list(), it takes a little bit acrobatics like this for instance:

import pandas as pd
import qnt.data as qndata


stocks = qndata.stocks_load_ndx_data()
df_ndx = pd.DataFrame(qndata.stocks_load_ndx_list()).set_index('symbol')
df_all = pd.DataFrame(qndata.stocks_load_list()).set_index('symbol')
idx = sorted(set(df_ndx.index) & set(df_all.index))
df = df_ndx.loc[idx]
df['cik'] = df_all.cik[idx]
symbols = list(df.reset_index().T.to_dict().values())
fundamentals = qndata.secgov_load_indicators(symbols, stocks.time)

It would be nice if we could get them with just 2 lines like so:

stocks = qndata.stocks_load_ndx_data()
fundamentals = qndata.secgov_load_indicators(qndata.stocks_load_ndx_list(), stocks.time)

Also, the workaround doesn't work locally because qndata.stocks_load_list() seems to return the same list as qndata.stocks_load_ndx_list().

Thanks in advance!

@eddiee Try step 4 without quotes, this should start jupyter notebook. And if that's your real API-key we see in the image, delete your last post. It's a bad idea to post it in a public forum

Yes, I noticed that too. And after fixing it the backtest takes forever...
Another thing to consider is that it redefines the model with each training but I belive you can retrain already trainded NNs with new Data so they learn based on what they previously learned.

About the slicing error, I had that too a while ago. It took me some time to figure out that it wasn't enough to have the right pandas version in the environment. Because I had another python install with the same version in my PATH, the qntdev-python also looked there and always used the newer pandas. So I placed the -s flag everywhere the qntdev python is supposed to run (PyCharm, Jupyter, terminal) like this

/path/to/quantiacs/python -s strategy.py

Of course one could simply remove the other python install from PATH but I needed it there.

@alfredaita
In case you don't want to run init.py every time in order to install external libraries, I came up with a solution for this. You basically install the library in a folder in your home directory and let the strategy create symlinks to the module path at runtime. More details in this post.

Hello @support
Since the live period for the Q16 contest is coming to an end I'm watching my participating algorithms more closely and noticed something odd:
The closing prices are the same on 2022-02-24 and 2022-02-25 to the last decimal for allmost all cryptos (49 out of 54).

import qnt.data as qndata

crypto = qndata.cryptodaily.load_data(tail=10)
c = crypto.sel(field='close').to_pandas().iloc[-3:]
liquid = crypto.sel(field='is_liquid').fillna(0).values.astype(bool)[-3:]
# only showing the cryptos which were liquid for the last 3 days:
c.iloc[:, liquid.all(axis=0)]

asset 	ADA 	AVAX 	BNB 	BTC 	DOGE 	DOT 	ETH 	LINK 	SOL 	XRP
time 										
2022-02-23 	0.8664 	73.47 	365.6 	37264.053 	0.1274 	15.97 	2580.9977 	13.34 	84.64 	0.696515
2022-02-24 	0.8533 	76.39 	361.2 	38348.744 	0.1242 	16.16 	2598.0195 	13.27 	89.41 	0.696359
2022-02-25 	0.8533 	76.39 	361.2 	38348.744 	0.1242 	16.16 	2598.0195 	13.27 	89.41 	0.696359

(c.values[-1] == c.values[-2]).sum(), c.shape[1]

(49, 54)

Could you please have a look?
Thanks!

@support
Yes, pip is way faster. Thanks!
I might have found an even faster solution but I guess I have to wait a few hours to find out if it really works.

Here's what I did:

1. I created a folder in /root/books called "modules" to install cvxpy there to make it persistent:

!mkdir modules && pip install --target=modules cvxpy

2. Then if the import fails in the strategy, it creates symbolic links in /usr/local/lib/python3.7/site-packages/ that point to the content of /root/books/modules/

try:
    import cvxpy as cp
except ImportError:
    import os
    source = '/root/book/modules/'
    target = '/usr/local/lib/python3.7/site-packages/'
    for dirpath, dirnames, filenames in os.walk(source):
        source_path = dirpath.replace(source, '')
        target_path = os.path.join(target, source_path)
        if not os.path.exists(target_path) and not os.path.islink(target_path):
            os.symlink(dirpath, target_path)
            continue
        for file in filenames:
            source_file = os.path.join(dirpath, file)
            target_file = os.path.join(target, source_path, file)
            if not os.path.exists(target_file) and not os.path.islink(target_file):
                os.symlink(source_file, target_file)
    import cvxpy as cp

Creating the symlinks only takes 0.07 seconds, so fingers crossed

UPDATE (a few hours later):
It actually worked. When I just reopened the strategy, the environment was newly initialized. First I tried just importing cvxpy and got the ModuleNotFoundError. Then I ran the strategy including the code above: cvxpy was imported correctly and the strategy ran.

I'm not sure if that solution works for every module because I don't know if pip might also write something to other directories than site-packages.

Anyway, I'm happy with this solution.
Regards

@support
It's actually the same strategy / environment, not a new one.
If I haven't used it for a while (say, a few hours or a day) and open it again by clicking on the Jupyter button, it says:
Initialization of the virtual environment. The notebook will be ready in 15 seconds.
And when I try to run the strategy that worked fine a few hours or a day ago, I get the ModuleNotFoundError and have to install the module again.
Everything else is still there as it was before - the strategy, custom files - just not cvxpy.

Hello @support ,

I've been using cvxpy in the server environment which I installed by running

!conda install -y -c conda-forge cvxpy

in init.ipynb. But whenever this environment is newly initialized, the module is gone and I have to run this cell again (which takes awfully long).

Is this normal or is there something wrong with my environment?
My current workaround is placing these lines before the import

try:
    import cvxpy as cp
except ImportError:
    import subprocess

    cmd = 'conda install -y -c conda-forge cvxpy'.split()
    rn = subprocess.run(cmd)

    import cvxpy as cp

Is there a better way?

Best regards.

@jeppe_and Ok, thanks for the quick reply!

Hello,

I'm having 2 issues with legacy.quantiacs.com:

1. Trouble accessing the site
   Since a few days ago Firefox won't open the legacy website, shwoing this message:

2. Accessing the website with Chromium by adding an exception I found that the strategy charts haven't been updated since 2021-10-26.

Could you please take a look?
Thanks!