Portfolio Optimization code error i want to cry

[FinanceMotta]

I have been coding this bad boy for a long fucking time, started python recently more finance related stuff but i cannot get this MF to work like i spent so much time and i just do not even know anymore

the file is attached but imma write it here anyway, first time doingthis also i dunno just to be safe :
it runs fine until the plotting part where it says:
KeyError Traceback (most recent call last)
File ~\anaconda3\Lib\site-packages\pandas\core\indexes\base.py:3653, in Index.get_loc(self, key)
3652 try:
-> 3653 return self._engine.get_loc(casted_key)
3654 except KeyError as err:

File ~\anaconda3\Lib\site-packages\pandas\_libs\index.pyx:147, in pandas._libs.index.IndexEngine.get_loc()

File ~\anaconda3\Lib\site-packages\pandas\_libs\index.pyx:155, in pandas._libs.index.IndexEngine.get_loc()

File pandas\_libs\index_class_helper.pxi:70, in pandas._libs.index.Int64Engine._check_type()

KeyError: 'Volatility'

The above exception was the direct cause of the following exception:

KeyError Traceback (most recent call last)
Cell In[44], line 2
1 portfolios = pd.DataFrame(pfolio_returns, pfolio_volatility)
----> 2 portfolios.plot(x="Volatility", y="Returns", kind="scatter", figsize=(10,6))

File ~\anaconda3\Lib\site-packages\pandas\plotting\_core.py:920, in PlotAccessor.__call__(self, *args, **kwargs)
918 if kind in self._dataframe_kinds:
919 if isinstance(data, ABCDataFrame):
--> 920 return plot_backend.plot(data, x=x, y=y, kind=kind, **kwargs)
921 else:
922 raise ValueError(f"plot kind {kind} can only be used for data frames")

File ~\anaconda3\Lib\site-packages\pandas\plotting\_matplotlib\__init__.py:71, in plot(data, kind, **kwargs)
69 kwargs["ax"] = getattr(ax, "left_ax", ax)
70 plot_obj = PLOT_CLASSES[kind](data, **kwargs)
---> 71 plot_obj.generate()
72 plot_obj.draw()
73 return plot_obj.result

File ~\anaconda3\Lib\site-packages\pandas\plotting\_matplotlib\core.py:448, in MPLPlot.generate(self)
446 self._compute_plot_data()
447 self._setup_subplots()
--> 448 self._make_plot()
449 self._add_table()
450 self._make_legend()

File ~\anaconda3\Lib\site-packages\pandas\plotting\_matplotlib\core.py:1259, in ScatterPlot._make_plot(self)
1256 else:
1257 label = None
1258 scatter = ax.scatter(
-> 1259 data[x].values,
1260 data[y].values,
1261 c=c_values,
1262 label=label,
1263 cmap=cmap,
1264 norm=norm,
1265 **self.kwds,
1266 )
1267 if cb:
1268 cbar_label = c if c_is_column else ""

File ~\anaconda3\Lib\site-packages\pandas\core\frame.py:3761, in DataFrame.__getitem__(self, key)
3759 if self.columns.nlevels > 1:
3760 return self._getitem_multilevel(key)
-> 3761 indexer = self.columns.get_loc(key)
3762 if is_integer(indexer):
3763 indexer = [indexer]

File ~\anaconda3\Lib\site-packages\pandas\core\indexes\base.py:3655, in Index.get_loc(self, key)
3653 return self._engine.get_loc(casted_key)
3654 except KeyError as err:
-> 3655 raise KeyError(key) from err
3656 except TypeError:
3657 # If we have a listlike key, _check_indexing_error will raise
3658 # InvalidIndexError. Otherwise we fall through and re-raise
3659 # the TypeError.
3660 self._check_indexing_error(key)

KeyError: 'Volatility'

import numpy as np
import pandas as pd
from pandas_datareader import data as wb
import yfinance as yf
import matplotlib.pyplot as plt

yf.pdr_override()

# Download and calculate market data
Market_data = wb.get_data_yahoo("ITOT", start="2012-1-1")["Adj Close"]
Market_return = np.log(Market_data / Market_data.shift(1))
Market_var = Market_return.var() * 250

# Define asset lists
ESIT = ["ESIT.L", "ITOT"]
BRK_B = ["BRK-B", "ITOT"]
CIBR = ["CIBR", "ITOT"]
NEER = ["NEE", "ITOT"]
COLOP = ["CBHD.F", "ITOT"]

# Download asset data in separate DataFrames
data_ESIT = pd.DataFrame()
for a in ESIT:
    data_ESIT[a] = wb.get_data_yahoo(a, start="2012-1-1")["Adj Close"]

data_BRK_B = pd.DataFrame()
for t in BRK_B:
    data_BRK_B[t] = wb.get_data_yahoo(t, start="2012-1-1")["Adj Close"]

data_CIBR = pd.DataFrame()
for t in CIBR:
    data_CIBR[t] = wb.get_data_yahoo(t, start="2012-1-1")["Adj Close"]

data_NEER = pd.DataFrame()
for t in NEER:
    data_NEER[t] = wb.get_data_yahoo(t, start="2012-1-1")["Adj Close"]

data_COLOP = pd.DataFrame()
for t in COLOP:
    data_COLOP[t] = wb.get_data_yahoo(t, start="2012-1-1")["Adj Close"]

# Calculate past returns for each asset
ESIT_past_returns = np.log(data_ESIT / data_ESIT.shift(1))
BRK_B_past_returns = np.log(data_BRK_B / data_BRK_B.shift(1))
CIBR_past_returns = np.log(data_CIBR / data_CIBR.shift(1))
NEER_past_returns = np.log(data_NEER / data_NEER.shift(1))
COLOP_past_returns = np.log(data_COLOP / data_COLOP.shift(1))

# Calculate covariance matrices and betas
ESIT_cov_matrix_a = ESIT_past_returns.cov() * 250
ESIT_cov_with_market = ESIT_cov_matrix_a.iloc[0, 1]
ESIT_BETA = ESIT_cov_with_market / Market_var

BRK_B_cov_matrix_a = BRK_B_past_returns.cov() * 250
BRK_B_cov_with_market = BRK_B_cov_matrix_a.iloc[0, 1]
BRK_B_BETA = BRK_B_cov_with_market / Market_var

CIBR_cov_matrix_a = CIBR_past_returns.cov() * 250
CIBR_cov_with_market = CIBR_cov_matrix_a.iloc[0, 1]
CIBR_BETA = CIBR_cov_with_market / Market_var

NEER_cov_matrix_a = NEER_past_returns.cov() * 250
NEER_cov_with_market = NEER_cov_matrix_a.iloc[0, 1]
NEER_BETA = NEER_cov_with_market / Market_var

COLOP_cov_matrix_a = COLOP_past_returns.cov() * 250
COLOP_cov_with_market = COLOP_cov_matrix_a.iloc[0, 1]
COLOP_BETA = COLOP_cov_with_market / Market_var

# Calculate CAPMs
ESIT_CAPM = 0.0426 + ESIT_BETA * (0.0129)
BRK_B_CAPM = 0.0426 + BRK_B_BETA * (0.0460)
CIBR_CAPM = 0.0426 + CIBR_BETA * (0.0460)
NEER_CAPM = 0.0426 + NEER_BETA * (0.0460)
COLOP_CAPM = 0.0426 + COLOP_BETA * (0.0129)

# List CAPMs and Assets
CAPMs = [ESIT_CAPM, BRK_B_CAPM, CIBR_CAPM, NEER_CAPM, COLOP_CAPM]
Assets = ["ESIT.L", "BRK-B", "CIBR", "NEE", "CBHD.F"]
Assets_Returns = CAPMs

# Download data for correlation matrix
data_for_corr = pd.DataFrame()
for t in Assets:
    data_for_corr[t] = wb.get_data_yahoo(t, start="2012-1-1")["Adj Close"]

log_returns = np.log(data_for_corr / data_for_corr.shift(1))
print(log_returns.corr())  # Calculate and display correlation matrix

# Simulate efficient frontier portfolios
pfolio_returns = []
pfolio_volatility = []

for x in range(1000):
    weights = np.random.random(5)
    weights /= np.sum(weights)
    pfolio_returns.append(np.sum(weights * Assets_Returns))
    pfolio_volatility.append(np.sqrt(np.dot(weights.T, np.dot(weights * Assets_Returns, weights))))

pfolio_returns = np.array(pfolio_returns)
pfolio_volatility = np.array(pfolio_volatility)

# Create DataFrame and plot
portfolios = pd.DataFrame({"Returns": pfolio_returns, "Volatility": pfolio_volatility})
portfolios.plot(x="Volatility", y="Returns", kind="scatter", figsize=(10,6))
plt.show()  # Add plt.show() to display the plot

[deanhystad]

What should portfolios = pd.DataFrame(pfolio_ returns, pfolio_volatility) do? The second argument is wrong,

[FinanceMotta]

What should portfolios = pd.DataFrame(pfolio_ returns, pfolio_volatility) do? The second argument is wrong,

it should be: portfolios = pd.DataFrame({"Returns":pfolio_returns, "Volatility":pfolio_volatility})

now im getting:

ValueError Traceback (most recent call last)
Cell In[64], line 1
----> 1 portfolios = pd.DataFrame({"Returns":pfolio_returns, "Volatility":pfolio_volatility})
2 print(portfolios)
3 portfolios.plot(x="Volatility", y="Returns", kind="scatter", figsize=(10,6))

File ~\anaconda3\Lib\site-packages\pandas\core\frame.py:709, in DataFrame.__init__(self, data, index, columns, dtype, copy)
703 mgr = self._init_mgr(
704 data, axes={"index": index, "columns": columns}, dtype=dtype, copy=copy
705 )
707 elif isinstance(data, dict):
708 # GH#38939 de facto copy defaults to False only in non-dict cases
--> 709 mgr = dict_to_mgr(data, index, columns, dtype=dtype, copy=copy, typ=manager)
710 elif isinstance(data, ma.MaskedArray):
711 from numpy.ma import mrecords

File ~\anaconda3\Lib\site-packages\pandas\core\internals\construction.py:481, in dict_to_mgr(data, index, columns, dtype, typ, copy)
477 else:
478 # dtype check to exclude e.g. range objects, scalars
479 arrays = [x.copy() if hasattr(x, "dtype") else x for x in arrays]
--> 481 return arrays_to_mgr(arrays, columns, index, dtype=dtype, typ=typ, consolidate=copy)

File ~\anaconda3\Lib\site-packages\pandas\core\internals\construction.py:115, in arrays_to_mgr(arrays, columns, index, dtype, verify_integrity, typ, consolidate)
112 if verify_integrity:
113 # figure out the index, if necessary
114 if index is None:
--> 115 index = _extract_index(arrays)
116 else:
117 index = ensure_index(index)

File ~\anaconda3\Lib\site-packages\pandas\core\internals\construction.py:642, in _extract_index(data)
640 raw_lengths.append(len(val))
641 elif isinstance(val, np.ndarray) and val.ndim > 1:
--> 642 raise ValueError("Per-column arrays must each be 1-dimensional")
644 if not indexes and not raw_lengths:
645 raise ValueError("If using all scalar values, you must pass an index")

ValueError: Per-column arrays must each be 1-dimensional

Gribouillis write Mar-11-2024, 08:07 AM:
Please post all code, output and errors (it it's entirety) between their respective tags. Refer to BBCode help topic on how to post. Use the "Preview Post" button to make sure the code is presented as you expect before hitting the "Post Reply/Thread" button.

[deanhystad]

This is the source of the error you are currently getting.

import numpy as np

Assets_Returns = [1] * 5
weights = np.random.random(5)
weights /= np.sum(weights)
print(np.sum(weights * Assets_Returns))  # np.sum(weights * Assets_Returns) is a float
print(np.sqrt(np.dot(weights.T, np.dot(weights * Assets_Returns, weights))))  # np.sqrt(np.dot(weights.T, np.dot(weights * Assets_Returns, weights))) is an array

Output:
1.0
[0.2383131  0.25652407 0.1334876  0.15391969 0.40296863]

pfolio_returns will be a 1D array and pfolio_volatility a 2D array. You cannot add use a 2D array as a column in a dataframe.

I don't understand this. Please explain.

pfolio_volatility.append(np.sqrt(np.dot(weights.T, np.dot(weights * Assets_Returns, weights))))