Diagnosing NaN Outputs in Historical Portfolio Optimization
Summary
The document addresses why a PyPortfolioOpt workflow can return NaN weights, expected returns, volatility, and Sharpe ratio when optimizing a portfolio from historical data. Its example estimates expected returns and sample covariance from a table of asset returns, then requests a maximum-Sharpe portfolio. The proposed approach is diagnostic rather than a single package-specific fix.
Potential causes include nonnumeric remnants in the input, missing or invalid observations, columns with misaligned time series, calculations that encounter undefined operations such as division by zero, and negative estimates that make a formula invalid. The answer also encourages checking the package’s calculations when the source of the issue is unclear. It provides no investigation of the supplied dataset and no confirmed root cause, so these are troubleshooting possibilities to test rather than a diagnosis.
Key ideas
- NaN portfolio outputs can originate in malformed or nonnumeric input data.
- Missing, invalid, or misaligned observations can disrupt return and covariance estimates.
- Undefined calculations, including division by zero, may propagate NaNs through optimization.
- Inspecting intermediate estimates and the package's calculations can help locate the source.
Tags
Full text
# Why am I getting nan returns from PyPortfolioOpt package
# Why am I getting nan returns from PyPortfolioOpt package
```
from pypfopt.efficient_frontier import EfficientFrontier
from pypfopt import risk_models
from pypfopt import expected_returns
# Read in price data
df = pd.read_csv("Asset_Regime.csv", parse_dates=True, index_col="date")
# Calculate expected returns and sample covariance
mu = expected_returns.mean_historical_return(df)
S = risk_models.sample_cov(df)
# Optimise for maximal Sharpe ratio
ef = EfficientFrontier(mu, S)
raw_weights = ef.max_sharpe()
cleaned_weights = ef.clean_weights()
print(cleaned_weights)
ef.portfolio_performance(verbose=True)
```
My data looks like
```
US Equities World Equities US Treasuries High Yield Corp Bonds Real Estate Commodities
date
Jan-85 0.081301 0.028511 0.031500 0.037908 0.048963 0.056605 0.021351
Feb-85 0.030075 -0.009204 -0.044692 0.012689 -0.042029 0.016448 -0.015217
Mar-85 -0.007299 0.075134 0.028719 0.004323 0.032666 -0.006716 0.037171
Apr-85 -0.012255 -0.002459 0.023084 0.018215 0.037125 0.000906 -0.035116
May-85 0.064516 0.040245 0.086780 0.042363 0.104199 0.027241 0.004351
```
and returns
```
{'US Equities': nan, 'World Equities': nan, 'US Treasuries': nan, 'High Yield': nan, 'Corp Bonds': nan, 'Real Estate': nan, 'Commodities': nan}
Expected annual return: nan%
Annual volatility: nan%
Sharpe Ratio: nan
(nan, nan, nan)
```
## Answer by develarist (score 2)
https://quant.stackexchange.com/a/49708
it might be
- the data still contains non-numerical elements such as left-over headers, titles or dates
- missing, nan, imaginary or incoherent values in the data file
- non-corresponding lengths of time series/columns that do have legible elements
- incomputable mishaps similar to division by zero that are causing the nans
- negative values, such as negative volatility estimates or transformations thereof, that the theory/formulas/code cannot cope with
- the user is using a pre-made package without looking at its code and seeing what it's really doing instead of writing their own scriptShown in full with attribution under the source's licence. Licence: CC BY-SA 4.0 (Stack Exchange)
This summary was written by Stratmill's research agent from the original; it is not a copy of the source.