trade-strategy 0.1.5

A Python package for analyzing investment portfolios, including using quantitative methods like the efficient frontier for calculations an optimal asset allocation strategy.

trade_strategy

---

Table of Contents

• Installation
• How to Run example.py
• License

Installation

pip install trade-strategy

How to Run

To use the risk_return function, you can use the following Python code:

from trade_strategy import risk_return

tickers = ["AAPL", "MSFT", "GOOG"]
start_date = '2013-03-07'
end_date = '2025-04-05'

risk_return(tickers, start_date, end_date)

• Assets: ["AAPL", "MSFT", "GOOG"]
• Start Date: 2013-03-07
• End Date: 2025-04-05

Expected Output

The script will output a JSON payload containing the lowest risk, highest returns, and highest Sharpe ratio allocations for the specified tickers and date range. The returns and volatility fields are expressed as percentages.

{
  "lowest_risk_allocation": {
    "returns": 24.8073,
    "volatility": 23.4054,
    "sharpe_ratio": 1.059898,
    "weights": [0.347, 0.352, 0.3],
    "assets": ["AAPL", "MSFT", "GOOG"]
  },
  "highest_returns_allocation": {
    "returns": 26.8417,
    "volatility": 26.2248,
    "sharpe_ratio": 1.023524,
    "weights": [0.003, 0.986, 0.011],
    "assets": ["AAPL", "MSFT", "GOOG"]
  },
  "highest_sharpe_allocation": {
    "returns": 25.8282,
    "volatility": 23.8805,
    "sharpe_ratio": 1.081562,
    "weights": [0.301, 0.588, 0.111],
    "assets": ["AAPL", "MSFT", "GOOG"]
  }
}

License

trade-strategy is distributed under the terms of the MIT license.