pystock0 0.2.0

A library for portfolio optimization

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 as import 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 = 0.1
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/.