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 requirements.txt in the folder where you want to execute the trafalgar library
  2. Go to your folder directory with the command prompt and write :
pip install -r requirements.txt
  1. Download trafalgar.py in the same folder

Note : Step 1 and 2 are not always necessary, just make sure the libraries required by trafalgar are installed in you project env.

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

Here is the code of this article from a google collab, you can use it to follow along with this article: https://colab.research.google.com/drive/1qgFDDQneQP-oddbJVWWApfPKFMnbpj6I?usp=sharing

Documentation

Call the library

First, you should do:

from trafalgar import *

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 adjusted 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 adjusted 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")

Disclaimer :
From there, the functions only work for portfolios, not for individual stocks.
However there is a way to make it work for individual stock:

#let's say we want to calculate the annual_volatility of Apple. 
#We have to have at least 2 elements in our stock list. Here these are Apple and Facebook
#In order to get the volatility of only Apple we just have to put the weights of Facebook at 0 (so no money will be allocated to this stock) and put the weights of Apple at 1 (so all our money will be allocated to this stock)
annual_volatility(["FB", "AAPL"], [1, 0],"2020-01-01", "2021-01-01")

Annual Volatility of a portfolio/stock

#annual_volatility(stocks, weights, start_date, end_date)

#for your portfolio
annual_volatility(["FB", "AAPL", "AMD"], [0.3, 0.4, 0.3],"2020-01-01", "2021-01-01")

#for one stock (FB)
annual_volatility(["FB", "AAPL"], [1, 0],"2020-01-01", "2021-01-01")

Sharpe Ratio of a portfolio/stock

#sharpe_ratio(stocks, weights, start_date, end_date)

#for your portfolio
sharpe_ratio(["FB", "AAPL", "AMD"], [0.3, 0.4, 0.3],"2020-01-01", "2021-01-01")

#for one stock (FB)
sharpe_ratio(["FB", "AAPL"], [1, 0],"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)

#for your portfolio
returns_benchmark(["AAPL", "AMD", "MSFT"], [0.3, 0.4, 0.3], "SPY", "2020-01-01", "2021-01-01")

#for one stock(AAPL)
returns_benchmark(["AAPL", "AMD"], [1,0], "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)

#for your portfolio
cum_returns_benchmark(["AAPL", "AMD", "MSFT"], [0.3, 0.4, 0.3], "SPY", "2020-01-01", "2021-01-01")

#for one stock(AAPL)
cum_returns_benchmark(["AAPL", "AMD"], [1,0], "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 portfolio/stock

#alpha_beta(stocks, weights, benchmark, start_date, end_date)

#for your portfolio
alpha_beta(["AAPL", "AMD", "MSFT"], [0.3, 0.4, 0.3], "SPY", "2020-01-01", "2021-01-01")

#for one stock(AAPL)
alpha_beta(["AAPL", "AMD"], [1,0], "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)
portfolio_daily_mean_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)

GitHub