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fix(helpers): fix type annotation bug: np.array → np.ndarray in helpers.py (TypeError on Python 3.11+) #95

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fix(helpers): fix type annotation bug: np.array → np.ndarray in helpers.py (TypeError on Python 3.11+) #95

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17de523
fix(helpers): fix type annotation bug: `np.array` → `np.ndarray` in
nervgh Jul 26, 2026
18ecc82
docs(readme): unify quotation marks in code examples
nervgh Jul 26, 2026
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24 changes: 12 additions & 12 deletions README.md
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Original file line number Diff line number Diff line change
Expand Up @@ -123,7 +123,7 @@ poetry install
```python
import okama as ok

x = ok.AssetList(['SPY.US', 'BND.US', 'DBXD.XFRA'], ccy='USD')
x = ok.AssetList(["SPY.US", "BND.US", "DBXD.XFRA"], ccy="USD")
x
```
![](https://raw.githubusercontent.com/mbk-dev/okama/images/images/readmi01.jpg)
Expand All @@ -144,8 +144,8 @@ x.wealth_indexes.plot()

```python
weights = [0.3, 0.2, 0.2, 0.2, 0.1]
assets = ['T.US', 'XOM.US', 'FRE.XFRA', 'SNW.XFRA', 'LKOH.MOEX']
pf = ok.Portfolio(assets, weights=weights, ccy='EUR')
assets = ["T.US", "XOM.US", "FRE.XFRA", "SNW.XFRA", "LKOH.MOEX"]
pf = ok.Portfolio(assets, weights=weights, ccy="EUR")
pf.table
```
![](https://raw.githubusercontent.com/mbk-dev/okama/images/images/readmi04.jpg)
Expand All @@ -160,11 +160,11 @@ pf.dividend_yield.plot()
### 3. Draw an Efficient Frontier for 2 popular ETF: SPY and GLD

```python
ls = ['SPY.US', 'GLD.US']
curr = 'USD'
last_date = '2020-10'
ls = ["SPY.US", "GLD.US"]
curr = "USD"
last_date = "2020-10"
# Rebalancing period is one year (default value)
frontier = ok.EfficientFrontier(ls, last_date=last_date, ccy=curr, rebalancing_strategy=ok.Rebalance(period='year'))
frontier = ok.EfficientFrontier(ls, last_date=last_date, ccy=curr, rebalancing_strategy=ok.Rebalance(period="year"))
frontier.names
```
![](https://raw.githubusercontent.com/mbk-dev/okama/images/images/readmi06.jpg)
Expand All @@ -175,19 +175,19 @@ import matplotlib.pyplot as plt

points = frontier.ef_points

fig = plt.figure(figsize=(12,6))
fig = plt.figure(figsize=(12, 6))
fig.subplots_adjust(bottom=0.2, top=1.5)
frontier.plot_assets(kind='cagr') # plots the assets points on the chart
frontier.plot_assets(kind="cagr") # plots the assets points on the chart
ax = plt.gca()
ax.plot(points.Risk, points.CAGR)
ax.plot(points.Risk, points.CAGR)
```
![](https://raw.githubusercontent.com/mbk-dev/okama/images/images/readmi07.jpg)

### 4. Get a Transition Map for allocations

```python
ls = ['SPY.US', 'GLD.US', 'BND.US']
ok.EfficientFrontier(ls, ccy='USD').plot_transition_map(x_axe='risk')
ls = ["SPY.US", "GLD.US", "BND.US"]
ok.EfficientFrontier(ls, ccy="USD").plot_transition_map(x_axe="risk")
```
![Transition map](https://raw.githubusercontent.com/mbk-dev/okama/images/images/readmi08.jpg)

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4 changes: 2 additions & 2 deletions okama/common/helpers/helpers.py
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Expand Up @@ -255,7 +255,7 @@ def get_portfolio_return_ts(cls, weights: list, ror: pd.DataFrame) -> pd.Series:
return ror @ weights

@classmethod
def get_portfolio_mean_return(cls, weights: list | np.array, ror: pd.DataFrame) -> float:
def get_portfolio_mean_return(cls, weights: list | np.ndarray, ror: pd.DataFrame) -> float:
"""
Computes arithmetic mean return of a portfolio return (monthly as ROR time series are monthly in okama).

Expand Down Expand Up @@ -383,7 +383,7 @@ def _(wealth: pd.DataFrame, discount_rate: float, threshold: float = 0) -> pd.Se
# Risk metrics

@classmethod
def get_portfolio_risk(cls, weights: list | np.array, assets_ror: pd.DataFrame) -> float:
def get_portfolio_risk(cls, weights: list | np.ndarray, assets_ror: pd.DataFrame) -> float:
"""
Compute the standard deviation of return for monthly rebalanced portfolio.
"""
Expand Down
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