Trafalgar

Python library to make development of portfolio analysis faster and easier

Installation 🔥

For the moment, Trafalgar is still in beta development. To install it you should:

1. Download trafalgars-0.0.1-py3-none-any.whl in the folder you want
2. Go to your folder with the command prompt and write :

pip install trafalgars-0.0.1-py3-none-any.whl

Features include 📈

• Get close price, open price, adj close, volume and graphs of these in one line of code!
• Build a efficient frontier programm in 3 lines of code
• Backtest a portfolio, see its stats and compare it to a benchmark

Documentation

Graph of the closing price of a stock

#graph_close(stock, start_date, end_date)
graph_close(["FB"], "2020-01-01", "2021-01-01")

Graph of the closing price of multiple stocks

graph_close(["FB", "AAPL", "TSLA"], "2020-01-01", "2021-01-01")

Graph the volume

#graph_volume(stock, start_date, end_date)

#for one stock
graph_volume(["FB"], "2020-01-01", "2021-01-01")

#for multiple stocks
graph_volume(["FB", "AAPL", "TSLA"], "2020-01-01", "2021-01-01")

Graph the opening price

#graph_open(stock, start_date, end_date)

#for one stock
graph_open(["FB"], "2020-01-01", "2021-01-01")

#for multiple stocks
graph_open(["FB", "AAPL", "TSLA"], "2020-01-01", "2021-01-01")

Graph the adjourned closing price

#graph_adj_close(stock, start_date, end_date)

#for one stock
graph_adj_close(["FB"], "2020-01-01", "2021-01-01")

#for multiple stocks
graph_adj_close(["FB", "AAPL", "TSLA"], "2020-01-01", "2021-01-01")

Graph the returns (for each day)

#returns_graph(stock, start_date, end_date)

#this one only work for one stock
returns_graph(["FB"], "2020-01-01", "2021-01-01")

Get closing price data (in dataframe format)

#close(stock, start_date, end_date)
close(["AAPL"], "2020-01-01", "2021-01-01")

Get volume data (in dataframe format)

#volume(stock, start_date, end_date)
volume(["AAPL"], "2020-01-01", "2021-01-01")

Get opening price data (in dataframe format)

#open(stock, start_date, end_date)
open(["AAPL"], "2020-01-01", "2021-01-01")

Get adjourned closing price data (in dataframe format)

#adj_close(stock, start_date, end_date)
adj_close(["AAPL"], "2020-01-01", "2021-01-01")

Covariance between stocks

#covariance(stocks, start_date, end_date, days) -> usually, days = 252
covariance(["AAPL", "DIS", "AMD"], "2020-01-01", "2021-01-01", 252)

Get data from a stock in OHLCV format directly

#ohlcv(stock, start_date, end_date)
ohlcv("AAPL", "2020-01-01", "2021-01-01")

Graph the cumulative returns of a stock/portfolio

#cum_returns_graph(stocks, weights, start_date, end_date)
cum_returns_graph(["FB", "AAPL", "AMD"], [0.3, 0.4, 0.3],"2020-01-01", "2021-01-01")

Get cumulative returns data of a stock/portfolio (in a dataframe format)

#cum_returns(stocks, weights, start_date, end_date)
cum_returns(["FB", "AAPL", "AMD"], [0.3, 0.4, 0.3],"2020-01-01", "2021-01-01")

Annual Volatility of a stock/portfolio

#annual_volatility(stocks, weights, start_date, end_date)
annual_volatility(["FB", "AAPL", "AMD"], [0.3, 0.4, 0.3],"2020-01-01", "2021-01-01")

Sharpe Ratio of a stock/portfolio

#sharpe_ratio(stocks, weights, start_date, end_date)
sharpe_ratio(["FB", "AAPL", "AMD"], [0.3, 0.4, 0.3],"2020-01-01", "2021-01-01")

Compare the returns of a portfolio/stock to a benchmark

#returns_benchmark(stocks, weights, benchmark, start_date, end_date)
returns_benchmark(["AAPL", "AMD", "MSFT"], [0.3, 0.4, 0.3], "SPY", "2020-01-01", "2021-01-01")

Blue line : returns of your portfolio Red line : returns of the benchmark

Compare the cumulative returns of a portfolio/stock to a benchmark

#cum_returns_benchmark(stocks, weights, benchmark, start_date, end_date)
cum_returns_benchmark(["AAPL", "AMD", "MSFT"], [0.3, 0.4, 0.3], "SPY", "2020-01-01", "2021-01-01")

Blue line : cumulative returns of your portfolio Red line : cumulative returns of the benchmark

Alpha and Beta of a stock/portfolio

#alpha_beta(stocks, weights, benchmark, start_date, end_date)
alpha_beta(["AAPL", "AMD", "MSFT"], [0.3, 0.4, 0.3], "SPY", "2020-01-01", "2021-01-01")

Efficient frontier to optimize allocation of shares in your portfolio

#efficient_frontier(stocks, start_date, end_date, iterations) -> iterations = 10000 is a good starting point
efficient_frontier(["AAPL", "FB", "TSLA", "BABA"], "2020-01-01", "2021-01-01", 10000)

Graph individual cumulative returns for your portfolio

#individual_cum_returns_graph(stocks, start_date, end_date)
individual_cum_returns_graph(["FB", "AAPL", "AMD"],"2020-01-01", "2021-01-01")

Individual cumulative returns datas for your portfolio (in dataframe format)

#individual_cum_returns(stocks, start_date, end_date)
individual_cum_returns(["FB", "AAPL", "AMD"],"2020-01-01", "2021-01-01")

Mean daily return of each stocks in your portfolio

#individual_mean_daily_return(stocks, start_date, end_date)
individual_mean_daily_return(["FB", "AAPL", "AMD"],"2020-01-01", "2021-01-01")

Portfolio mean daily return

#portfolio_daily_mean_return(stocks,weights, start_date, end_date)
individual_mean_daily_return(["FB", "AAPL", "AMD"],"2020-01-01", "2021-01-01")

Value at Risk of a stock (still in development)

#VaR(stock, start_date, end_date, confidence_level)
VaR("FB","2020-01-01", "2021-01-01", 98)

License

MIT