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: You will need to call
pip install pystock0
to install the library. However, you can import the library asimport pystock
. The library is still in development, so, a lot of changes will be made to the code.
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. For now, this is how you can 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/GOOGL.csv", "Data/TSLA.csv"]
stock_names = ["AAPL", "MSFT", "GOOGL", "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=["Close"],
start_date=start_date,
end_date=end_date,
frequency=frequency,
)
pt.load_all(
columns=["Close"],
start_date=start_date,
end_date=end_date,
frequency=frequency,
)
# Creating a Model object and adding the portfolio
model = Model(frequency=frequency, risk_free_rate=0.33)
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: 1.1159%
Risk: 0.5000%
Expected weights:
--------------------
AAPL : 47.40%
MSFT : 0.00%
GOOGL : 35.83%
TSLA : 16.77%
{'weights': array([0.474 , 0. , 0.3583, 0.1677]), 'expected_return': 1.115892062822632, 'variance': 0.5000278422222152, 'std': 0.707126468336616}
More Examples
For more examples, please refer to the notebook Working_With_pystock.ipynb. Also have a look at Downloading_Data.ipynb. Please also have a look at Working_With_frontier.ipynb to see how to use the frontier
module to plot efficient frontiers.
Documentation
The documentation is available at https://hari31416.github.io/pystock/.
Project details
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