luchkata / algorithmic_trading_machine_learning Goto Github PK

I keep getting error from macd:
def compute_macd(close):
macd = pandas_ta.macd(close=close, length=20).iloc[:,0]
return macd.sub(macd.mean()).div(macd.std())

df['macd'] = df.groupby(level=1, group_keys=False)['adj close'].apply(compute_macd)

TypeError: unsupported operand type(s) for -: 'float' and 'NoneType'

result = self._python_apply_general(f, self._selected_obj)
values, mutated = self._grouper.apply_groupwise(f, data, self.axis)
res = f(group)
return op(a, b)

what is the solution

### I keep getting the following:

Max Sharpe Optimization failed for 2018-05-01, Continuing with Equal-Weights
"None of ['index'] are in the columns"
"None of ['index'] are in the columns"
"None of ['index'] are in the columns"
"None of ['index'] are in the columns"
"None of ['index'] are in the columns"
"None of ['index'] are in the columns"
"None of ['index'] are in the columns"
"None of ['index'] are in the columns"
Max Sharpe Optimization failed for 2019-01-01, Continuing with Equal-Weights
"None of ['index'] are in the columns"
"None of ['index'] are in the columns"
"None of ['index'] are in the columns"
"None of ['index'] are in the columns"
"None of ['index'] are in the columns"
"None of ['index'] are in the columns"
"None of ['index'] are in the columns"
"None of ['index'] are in the columns"
"None of ['index'] are in the columns"
"None of ['index'] are in the columns"
"None of ['index'] are in the columns"
"None of ['index'] are in the columns"
"None of ['index'] are in the columns"
"None of ['index'] are in the columns"
Max Sharpe Optimization failed for 2020-03-01, Continuing with Equal-Weights
"None of ['index'] are in the columns"
Max Sharpe Optimization failed for 2020-04-01, Continuing with Equal-Weights
"None of ['index'] are in the columns"
"None of ['index'] are in the columns"
"None of ['index'] are in the columns"
"None of ['index'] are in the columns"
"None of ['index'] are in the columns"
"None of ['index'] are in the columns"
"None of ['index'] are in the columns"
"None of ['index'] are in the columns"
"None of ['index'] are in the columns"
"None of ['index'] are in the columns"
Max Sharpe Optimization failed for 2021-02-01, Continuing with Equal-Weights
"None of ['index'] are in the columns"
"None of ['index'] are in the columns"
"None of ['index'] are in the columns"
"None of ['index'] are in the columns"
"None of ['index'] are in the columns"
"None of ['index'] are in the columns"
"None of ['index'] are in the columns"
"None of ['index'] are in the columns"
Max Sharpe Optimization failed for 2021-10-01, Continuing with Equal-Weights
"None of ['index'] are in the columns"
"None of ['index'] are in the columns"
"None of ['index'] are in the columns"
"None of ['index'] are in the columns"
"None of ['index'] are in the columns"
"None of ['index'] are in the columns"
"None of ['index'] are in the columns"
"None of ['index'] are in the columns"
"None of ['index'] are in the columns"
"None of ['index'] are in the columns"
"None of ['index'] are in the columns"
Max Sharpe Optimization failed for 2022-09-01, Continuing with Equal-Weights
"None of ['index'] are in the columns"
Max Sharpe Optimization failed for 2022-10-01, Continuing with Equal-Weights
"None of ['index'] are in the columns"
"None of ['index'] are in the columns"
"None of ['index'] are in the columns"
"None of ['index'] are in the columns"
"None of ['index'] are in the columns"
"None of ['index'] are in the columns"
"None of ['index'] are in the columns"
"None of ['index'] are in the columns"
"None of ['index'] are in the columns"
"None of ['index'] are in the columns"
"None of ['index'] are in the columns"
"None of ['index'] are in the columns"
"None of ['index'] are in the columns"
Max Sharpe Optimization failed for 2023-11-01, Continuing with Equal-Weights
"None of ['index'] are in the columns"
"None of ['index'] are in the columns"
"None of ['index'] are in the columns"
"None of ['index'] are in the columns"
"None of ['index'] are in the columns"
"None of ['index'] are in the columns"

### after this part:

returns_dataframe = np.log(new_df['Adj Close']).diff()

portfolio_df = pd.DataFrame()

for start_date in fixed_dates.keys():

try:

    end_date = (pd.to_datetime(start_date)+pd.offsets.MonthEnd(0)).strftime('%Y-%m-%d')

    cols = fixed_dates[start_date]

    optimization_start_date = (pd.to_datetime(start_date)-pd.DateOffset(months=12)).strftime('%Y-%m-%d')

    optimization_end_date = (pd.to_datetime(start_date)-pd.DateOffset(days=1)).strftime('%Y-%m-%d')
    
    optimization_df = new_df[optimization_start_date:optimization_end_date]['Adj Close'][cols]
    
    success = False
    try:
        weights = optimize_weights(prices=optimization_df,
                               lower_bound=round(1/(len(optimization_df.columns)*2),3))

        weights = pd.DataFrame(weights, index=pd.Series(0))
        
        success = True
    except:
        print(f'Max Sharpe Optimization failed for {start_date}, Continuing with Equal-Weights')
    
    if success==False:
        weights = pd.DataFrame([1/len(optimization_df.columns) for i in range(len(optimization_df.columns))],
                                 index=optimization_df.columns.tolist(),
                                 columns=pd.Series(0)).T
    
    temp_df = returns_dataframe[start_date:end_date]

    temp_df = temp_df.stack().to_frame('return').reset_index(level=0)\
               .merge(weights.stack().to_frame('weight').reset_index(level=0, drop=True),
                      left_index=True,
                      right_index=True)\
               .reset_index().set_index(['Date', 'index']).unstack().stack()

    temp_df.index.names = ['date', 'ticker']

    temp_df['weighted_return'] = temp_df['return']*temp_df['weight']

    temp_df = temp_df.groupby(level=0)['weighted_return'].sum().to_frame('Strategy Return')

    portfolio_df = pd.concat([portfolio_df, temp_df], axis=0)

except Exception as e:
    print(e)

portfolio_df = portfolio_df.drop_duplicates()

portfolio_df

I am unable to get PyPortfolioOpt to install on map. I have Xcode which means I have the required C++ but its still doesn't work.
the error is:
note: This error originates from a subprocess, and is likely not a problem with pip.
ERROR: Failed building wheel for qdldl
Failed to build qdldl
ERROR: Could not build wheels for qdldl, which is required to install pyproject.toml-based projects
[end of output]

I the same error trying to install qdldl, cvxpy, and cvxopt.

Hi - When trying to create the optimization_df dataframe from new_df I getting a key error for 'Adj Close'. below is the error and the code that I am using. Has anyone else experienced this and have suggestions on how to resolve? Thanks!

Error:

Traceback (most recent call last):
File "/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/pandas/core/indexes/base.py", line 3805, in get_loc
return self._engine.get_loc(casted_key)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "index.pyx", line 167, in pandas._libs.index.IndexEngine.get_loc
File "index.pyx", line 196, in pandas._libs.index.IndexEngine.get_loc
File "pandas/_libs/hashtable_class_helper.pxi", line 7081, in pandas._libs.hashtable.PyObjectHashTable.get_item
File "pandas/_libs/hashtable_class_helper.pxi", line 7089, in pandas._libs.hashtable.PyObjectHashTable.get_item
KeyError: 'Adj Close'

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

Traceback (most recent call last):
File "/filename", line 261, in
optimization_df = new_df[optimization_start_date:optimization_end_date]['Adj Close'][cols]
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^
File "/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/pandas/core/frame.py", line 4102, in getitem
indexer = self.columns.get_loc(key)
^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/pandas/core/indexes/base.py", line 3812, in get_loc
raise KeyError(key) from err
KeyError: 'Adj Close'

Code Snippet below:

stocks = data.index.get_level_values('ticker').unique().tolist()

new_df = yf.download(tickers=stocks,
start=data.index.get_level_values('date').unique()[0]-pd.DateOffset(months=12),
end=data.index.get_level_values('date').unique()[-1])

returns_dataframe = np.log(new_df['Adj Close']).diff()

portfolio_df = pd.DataFrame()

for start_date in fixed_dates.keys():

end_date = (pd.to_datetime(start_date)+pd.offsets.MonthEnd(0)).strftime('%Y-%m-%d')

cols =fixed_dates[start_date]

optimization_start_date = (pd.to_datetime(start_date)-pd.DateOffset(months=12)).strftime('%Y-%m-%d')

optimization_end_date = (pd.to_datetime(start_date)-pd.DateOffset(months=1)).strftime('%Y-%m-%d')


optimization_df = new_df[optimization_start_date:optimization_end_date]['Adj Close'][cols]

#i'm getting an error in this line above with 'Adj Close'

weights = optimize_weights(prices=optimization_df, lower_bound=round(1/(len(optimization_df.columns)*2),3))

weights = pd.DataFrame(weights, index=pd.Series(0))