A library for portfolio optimization
Project description
pystock
A small python library for stock market analysis. Especially for portfolio optimization.
Installation
pip install pystock0
Note: The library is still in development, so the version number is 0. You will need to call
pip install pystock0
to install the library. However, you can import the library asimport pystock
.
After installation, you can import the library as follows:
import pystock
Usage
The end goal of the library is to provide a simple interface for portfolio optimization. The library is still in development, so the interface is not yet stable. The following example shows how to use the library to optimize a portfolio of stocks.
from pystock.portfolio import Portfolio
from pystock.models import Model
#Creating the benchmark and stocks
benchmark_dir = "Data/GSPC.csv"
benchmark_name = "S&P"
stock_dirs = ["Data/AAPL.csv", "Data/MSFT.csv", "Data/GOOG.csv", "Data/TSLA.csv"]
stock_names = ["AAPL", "MSFT", "GOOG", "TSLA"]
#Setting the frequency to monthly
frequency = "M"
# Creating a Portfolio object
pt = Portfolio(benchmark_dir, benchmark_name, stock_dirs, stock_names)
start_date = "2012-01-01"
end_date = "2022-12-20"
# Loading the data
pt.load_benchmark(
columns=["Adj Close"],
rename_cols=["Close"],
start_date=start_date,
end_date=end_date,
frequency=frequency,
)
pt.load_all(
columns=["Adj Close"],
rename_cols=["Close"],
start_date=start_date,
end_date=end_date,
frequency=frequency,
)
# Creating a Model object and adding the portfolio
model = Model()
model.add_portfolio(pt, weights="equal")
# Optimizing the portfolio using CAPM
risk = 0.5
model_ = "capm"
res = model.optimize_portfolio(risk=risk, model=model_)
print(res)
Optimized successfully.
Expected return: 1.1155%
Variance: 0.5000%
Expected weights:
--------------------
AAPL: 47.20%
MSFT: 0.00%
GOOG: 36.08%
TSLA: 16.73%
{'weights': array([0.4719528 , 0. , 0.36076392, 0.16728327]), 'expected_return': 1.1154876799508255, 'variance': 0.5000100787030565, 'std': 0.7071139078699107}
More Examples
For more examples, please refer to the notebook Working_With_pystock.ipynb. Also have a look at Downloading_Data.ipynb.
Documentation
The documentation is available at https://hari31416.github.io/pystock/.
Project details
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