quantstats
quantstats copied to clipboard
ranaroussi
utils.make_index : TypeError: unsupported operand type(s) for +: 'float' and 'str'
While trying to use make_index I am getting a TypeError: unsupported operand type(s) for +: 'float' and 'str' message. This is my code that caused the error:
import quantstats as qs
qs.extend_pandas()
IAC = {"SPY": 1.3, "AGG": -.3}
portfolio = qs.utils.make_index(IAC)
I even tested with other ticker combinations and directly entering the ticker and weights in make_index and the error repeats in the same way. I also tested in previous versions and the error repeats again. Does anyone know if there is an error in the package or is it my code?
I have the same error, it is not posible to run the tickers and weights of a portfolio
Can you use a different way to generate the dictionary? try
IAC = dict(SPY = 1.3, AGG = -0.3)
Also it would be useful if you can copy the output error code as a text and paste it in here.
Can you use a different way to generate the dictionary? try
IAC = dict(SPY = 1.3, AGG = -0.3)Also it would be useful if you can copy the output error code as a text and paste it in here.
I tried generating the dict as above, but to no avail... Here is my error code as text:
/Users/benjoergens/PycharmProjects/Assignment/.venv/lib/python3.12/site-packages/quantstats/utils.py:379: FutureWarning: 'M' is deprecated and will be removed in a future version, please use 'ME' instead.
rbdf = index.resample(rebalance).first()
Traceback (most recent call last):
File "/Users/benjoergens/PycharmProjects/Assignment/Assignment.py", line 117, in
Same error
Simplest code, latest 0.0.62 version
%matplotlib inline import quantstats as qs import pandas as pd
extend pandas functionality with metrics, etc.
qs.extend_pandas()
conservative = {"IVV": 0.5,"URTH": 0.5} qs.utils.make_index(ticker_weights=conservative, period="1y")
I did a quick fix in the code and it works now. Fork, uninstall library, git clone, pip install -e . , make changes, save, works.
`def make_index( ticker_weights, rebalance="1M", period="max", returns=None, match_dates=False ): """ Makes an index out of the given tickers and weights. Optionally you can pass a dataframe with the returns. If returns is not given it try to download them with yfinance
Args:
* ticker_weights (Dict): A python dict with tickers as keys
and weights as values
* rebalance: Pandas resample interval or None for never
* period: time period of the returns to be downloaded
* returns (Series, DataFrame): Optional. Returns If provided,
it will fist check if returns for the given ticker are in
this dataframe, if not it will try to download them with
yfinance
Returns:
* index_returns (Series, DataFrame): Returns for the index
"""
# Declare a returns variable
index = None
portfolio = {}
# Iterate over weights
for ticker in ticker_weights.keys():
if (returns is None) or (ticker not in returns.columns):
# Download the returns for this ticker, e.g. GOOG
ticker_returns = download_returns(ticker, period)
else:
ticker_returns = returns[ticker]
portfolio[ticker] = ticker_returns
# index members time-series
index = _pd.DataFrame(portfolio).dropna()
if match_dates:
index = index[max(index.ne(0).idxmax()) :]
# no rebalance?
if rebalance is None:
for ticker, weight in ticker_weights.items():
index[ticker] = weight * index[ticker]
return index.sum(axis=1)
last_day = index.index[-1]
# rebalance marker
rbdf = index.resample(rebalance).first()
rbdf["break"] = rbdf.index.strftime("%s")
# index returns with rebalance markers
index = _pd.concat([index, rbdf["break"]], axis=1)
# mark first day day
index["first_day"] = _pd.isna(index["break"]) & ~_pd.isna(index["break"].shift(1))
index.loc[index.index[0], "first_day"] = True
# multiply first day of each rebalance period by the weight
for ticker, weight in ticker_weights.items():
index[ticker] = _np.where(
index["first_day"], weight * index[ticker], index[ticker]
)
# drop first marker and break column
index.drop(columns=["first_day", "break"], inplace=True)
# drop when all are NaN
index.dropna(how="all", inplace=True)
# Ensure only numeric columns are summed
numeric_cols = index.select_dtypes(include=[_np.number]).columns
return index[numeric_cols][index.index <= last_day].sum(axis=1)`