With the most recent code, I run the following command:

python3 portfolio_manager.py --is_test 1 --future_bars 90 --data_granularity_minutes 3600 --history_to_use all --apply_noise_filtering 1 --market_index QQQ --only_long 1 --eigen_portfolio_number 3 --stocks_file_path stocks/stocks.txt

Previously, this worked. Now, I get an error:

Traceback (most recent call last):
  File "portfolio_manager.py", line 37, in <module>
    main()
  File "portfolio_manager.py", line 33, in main
    eiten.run_strategies()
  File "/Users/jmartinezlago/eiten/eiten.py", line 107, in run_strategies
    historical_price_info, future_prices, symbol_names, predicted_return_vectors, returns_matrix, returns_matrix_percentages = self.load_data()
  File "/Users/jmartinezlago/eiten/eiten.py", line 95, in load_data
    self.data_dictionary[symbol]["historical_prices"])
KeyError: 'historical_prices'

This tackles the redundancies that were created by separating each argument. Now the args dictionary is passed and any new argument added or change is automatically passed.

Also separated files so users can import Eiten and use it as a library,

Hi, I'm building portfolios for my home country's stock market. However there is limited options for shorting stocks. Is it possible to run Eiten without allowing negative weight (shorting)?

There is a serious issue for the portfolio weights. When run for long only, there is not code do divide sum(weights). Total weights is almost round 2.5 to 4. So the result scaled 2.5 to 4. Because the project use the bull market data. So we got a pretty beautiful cumsum curve.

Hi how does one load in this https://finance.yahoo.com/quote/ICNYB.PA?p=ICNYB.PA ?

Tried different combo's of ticker but throws error or ignored (if use ICNYB.PA) ...

Txs

This is all on Google Colab.

I tried showing plot but I get this: <Figure size 1200x600 with 1 Axes>

So I tried doing:

%matplotlib inline
!python portfolio_manager.py --is_test 1 --future_bars 90 --data_granularity_minutes 3600 --history_to_use all --apply_noise_filtering 1 --market_index QQQ --only_long 1 --eigen_portfolio_number 3 --stocks_file_path stocks/stocks.txt

But still no output.

Any ideas why?

Hi there!

Was fiddling with your project and wanted to save the images from matplotlib, rather than render them directly. I amended the code to give people the option to save the plots instead. I figured I'd share that work back to you.

I also included a Dockerfile that would enable someone to utilize a container for the app so that you don't have to worry about python package version collisions with preexisting python installs.

For myself, I was doing something like:

docker build -t eiten .
docker run -v `pwd`/output:/app/output -v `pwd`/mystocklist.txt:/app/stocks/stocks.txt eiten:latest

which would prompt it to run with my stocks from 'mystocklist.txt' and save graphs to my output subdirectory.

File "\eiten\argchecker.py", line 20, in check_arguments assert not(args.history_to_use != "all" and int(args.history_to_use_int) < AttributeError: 'Namespace' object has no attribute 'history_to_use_int'

The random_matrix_theory_based_cov seems to be vanishing with the covariance values. Empirical evidence shows this:

Experiment one: How Eiten works in the current push, without matrix normalization:

This is a regular matrix without the noise cleaning method

After transformation the matrix becomes this:

We can see a shadow of red in the diagonal so I though I would plot out of scale. This is the result:

This leads me to question whether there is an implementation issue or if the method is really reliable.

I rewrote the code to do vectorized operations. That way we do not have to use arrays and the speedup allows us to use many more stocks, instead of a few. I am opening this as a draft because the results after vectorization doesn't seem to quite match the results that are presented on the main page.

If the main author could audit the math on this branch and verify if its correct that would allow us to either merge smoothly or to fix any bugs in the code.

Example of the output (that i am not sure is correct)

Hi, I'm building portfolios for my home country's stock market. However there is limited options for shorting stocks. Is it possible to run Eiten without allowing negative weight (shorting)?

Fresh install and run the README command ╰─ python portfolio_manager.py --is_test 1 --future_bars 90 --data_granularity_minutes 3600 --history_to_use all --apply_noise_filtering 1 --market_index QQQ --only_long 1 --eigen_portfolio_number 3 --stocks_file_path stocks/stocks.txt

Then got the following error, is this because yfinance is having issue?

Loading data for all stocks... 0%| | 0/6 [00:00<?, ?it/s]Exception in thread Thread-2: Traceback (most recent call last): File "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/threading.py", line 932, in _bootstrap_inner self.run() File "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/threading.py", line 870, in run self._target(*self._args, **self._kwargs) File "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/multitasking/init.py", line 102, in _run_via_pool return callee(*args, **kwargs) File "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/yfinance/multi.py", line 166, in _download_one_threaded data = _download_one(ticker, start, end, auto_adjust, back_adjust, File "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/yfinance/multi.py", line 178, in _download_one return Ticker(ticker).history(period=period, interval=interval, File "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/yfinance/base.py", line 155, in history data = data.json() File "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/requests/models.py", line 898, in json return complexjson.loads(self.text, **kwargs) File "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/json/init.py", line 357, in loads return _default_decoder.decode(s) File "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/json/decoder.py", line 337, in decode obj, end = self.raw_decode(s, idx=_w(s, 0).end()) File "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/json/decoder.py", line 355, in raw_decode raise JSONDecodeError("Expecting value", s, err.value) from None json.decoder.JSONDecodeError: Expecting value: line 1 column 1 (char 0)

future_prices is initialized to None, https://github.com/tradytics/eiten/blob/master/data_loader.py#L51

If is_test is not 1, future_prices doesn't change, https://github.com/tradytics/eiten/blob/master/data_loader.py#L99

future_prices as None is called with .values, https://github.com/tradytics/eiten/blob/master/data_loader.py#L111

When I run this from the examples:

python portfolio_manager.py --is_test 1 --future_bars 90 --data_granularity_minutes 3600 --history_to_use all --apply_noise_filtering 1 --market_index QQQ --only_long 1 --eigen_portfolio_number 3 --stocks_file_path stocks/stocks.txt

I get: Traceback (most recent call last): File "portfolio_manager.py", line 37, in main() File "portfolio_manager.py", line 33, in main eiten.run_strategies() File "/Users/matt/Desktop/eiten-master/eiten.py", line 118, in run_strategies returns_matrix) File "/Users/matt/Desktop/eiten-master/strategy_manager.py", line 59, in random_matrix_theory_based_cov filtered_covariance_matrix = self.strategyHelperFunctions.random_matrix_theory_based_cov(returns_matrix) File "/Users/matt/Desktop/eiten-master/strategies/strategy_helper_functions.py", line 18, in random_matrix_theory_based_cov variances = np.diag(np.cov(returns_matrix)) File "<array_function internals>", line 6, in diag File "/Users/matt/opt/anaconda3/lib/python3.7/site-packages/numpy/lib/twodim_base.py", line 283, in diag raise ValueError("Input must be 1- or 2-d.") ValueError: Input must be 1- or 2-d.

I used this just yesterday to do an analysis with no problem. Also, I looked at the other issue in the list that said maybe there's some issue with a stock picked in stocks.txt, so picked two stocks (AAPL and BA) to check if that could be it and it still causes an error.

I have completely re-written the code as a library instead of writing a CLI. I did that because, in my humble opinion, it is more useful to use Eiten in data analysis by integrating it into pipelines and notebooks. Taking this approach, the next step would be making it a package and releasing it on PyPi.

Features:

1. Eiten now gracefully ignores companies that does not fit the timeline size (in the future I would like to just fill the gaps)

2. We can now use any future estimator desired and compare its performance

3. We can use any kind of covariance matrix calculation, including those from Scikit Learn

4. Portfolio weights are now normalized and correspond to percentages (we can just multiply by some number and output a portfolio)

5. Testing is now completely vectorized

6. And we can easily evaluate our portfolios

Disclaimer: I haven't developed unit tests. I have changed the GA a bit. This is not finance advice (obviously).

Running this command from the examples:

python portfolio_manager.py --is_test 0 --future_bars 30 --data_granularity_minutes 60 --history_to_use all --apply_noise_filtering 1 --market_index QQQ --only_long 1 --eigen_portfolio_number 3 --stocks_file_path stocks/stocks.txt

returns: Traceback (most recent call last): File "portfolio_manager.py", line 37, in main() File "portfolio_manager.py", line 33, in main eiten.run_strategies() File "Work/eiten/eiten.py", line 107, in run_strategies historical_price_info, future_prices, symbol_names, predicted_return_vectors, returns_matrix, returns_matrix_percentages = self.load_data() File "Work/eiten/eiten.py", line 88, in load_data self.data_dictionary = self.dataEngine.collect_data_for_all_tickers() File "Work/eiten/data_loader.py", line 153, in collect_data_for_all_tickers historical_price, future_price, symbol_names) File "Work/eiten/data_loader.py", line 171, in remove_bad_data most_common_length = length_dictionary[0] IndexError: list index out of range

Any thoughts on what I'm doing wrong?