trafalgar.py 0.1.7

Trafalgar makes quantitative finance and portfolio analysis faster and easier

Trafalgar🃏

Python library to make development of portfolio analysis faster and easier

Installation 🔥

To install Trafalgar, you should do:

pip install trafalgar.py

(https://pypi.org/project/trafalgars/0.0.2/#description)

For Anaconda setup you can simply run below to install all dependencies with env name trafalgar.

conda env create --file environment.yaml  

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
• Make quantitative analysis on stocks/portfolios (alpha, beta, skew, kurtosis, rolling volatility...)
• Build a Capital Asset Pricing Model of a portfolio
• Backtest a portfolio, see its stats and compare it to a benchmark
• many other thnigs...

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

Credits ✌️

This library would not exist without the existence of Github and :

• the contributors @rslopes, @rakeshbhat9, @Haizzz
• Quantopian and their incredible lectures (https://gist.github.com/ih2502mk/50d8f7feb614c8676383431b056f4291)
• the authors of codingfinance.com
• Quantconnect
• @mrmushfiq and his repo https://github.com/mrmushfiq/python_meets_finance

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")

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)

Correlation between stocks

#correlation(stocks, start_date, end_date)
correlation(["AAPL", "AMD", "TSLA", "AMZN", "DIS", "SBUX", "NFLX", "AMZN", "GOOG"], "2020-01-01", "2021-01-01")

Graph correlation between stocks

#graph_correlation(stocks, start_date, end_date)
graph_correlation(["AAPL", "AMD", "TSLA", "AMZN", "DIS", "SBUX", "NFLX", "AMZN", "GOOG"], "2020-01-01", "2021-01-01")

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 returns (for each day)

#graph_returns(stock,wts, start_date, end_date)

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

#for a portfolio
graph_returns(["AAPL", "AMD", "TSLA"], [0.25, 0.45, 0.3], "2020-01-01", "2021-01-01")

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

#returns(stocks,wts, start_date, end_date)
# sum of wts(weights) should always be equal to 1, it represents the allocation of shares in your portfolio (1 = 100%)

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

#for a portfolio
returns(["AAPL", "AMD", "TSLA"], [0.25, 0.45, 0.3], "2020-01-01", "2021-01-01")

Graph the cumulative returns of a stock/portfolio

#graph_creturns(stock, wts, start_date, end_date)

#for one stock
graph_creturns(["TSLA"], 1, "2020-01-01", "2021-01-01")

#for a portfolio
graph_creturns(["AAPL", "AMD", "TSLA"], [0.25, 0.45, 0.3], "2020-01-01", "2021-01-01")

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

#creturns(stock, wts, start_date, end_date)

#for one stock
creturns(["TSLA"], 1, "2020-01-01", "2021-01-01")

#for a portfolio
creturns(["AAPL", "AMD", "TSLA"], [0.25, 0.45, 0.3], "2020-01-01", "2021-01-01")

Annual Volatility of a portfolio/stock

#annual_volatility(stocks, wts, start_date, end_date)

#for one stock
annual_volatility(["TSLA"], 1, "2020-01-01", "2021-01-01")
#for multiple stocks
annual_volatility(["AAPL", "AMD", "TSLA"], [0.25, 0.45, 0.3], "2020-01-01", "2021-01-01")

Sharpe Ratio of a portfolio/stock

#sharpe_ratio(stocks, wts, start_date, end_date)

#for one stock
sharpe_ratio(["TSLA"], 1, "2020-01-01", "2021-01-01")
#for multiple stocks
sharpe_ratio(["AAPL", "AMD", "TSLA"], [0.25, 0.45, 0.3], "2020-01-01", "2021-01-01")

Graph the returns of a portfolio/stock to a benchmark

#graph_rbenchmark(stocks, wts, benchmark, start_date, end_date)
#for a stock
graph_rbenchmark(["TSLA"], 1, "SPY", "2020-01-01", "2021-01-01")

#for a portfolio
graph_rbenchmark(["AAPL", "AMD", "TSLA"], [0.25, 0.45, 0.3], "SPY",  "2020-01-01", "2021-01-01")

Get the data of the returns of a portfolio/stock to a benchmark

#rbenchmark(stocks, wts, benchmark, start_date, end_date)

#for one stock
rbenchmark(["TSLA"], 1, "SPY", "2020-01-01", "2021-01-01")
#for a portfolio
rbenchmark(["AAPL", "AMD", "TSLA"], [0.25, 0.45, 0.3], "SPY",  "2020-01-01", "2021-01-01")

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

#graph_cbenchmark(stocks, wts, benchmark, start_date, end_date)

#for a stock
graph_cbenchmark(["TSLA"], 1, "SPY", "2020-01-01", "2021-01-01")

#for a portfolio
graph_cbenchmark(["AAPL", "AMD", "TSLA"], [0.25, 0.45, 0.3], "SPY",  "2020-01-01", "2021-01-01")

Get the data of the cumulative returns of a portfolio/stock to a benchmark

#cbenchmark(stocks, wts, benchmark, start_date, end_date)

#for a stock
cbenchmark(["TSLA"], 1, "SPY", "2020-01-01", "2021-01-01")
#for a portfolio
cbenchmark(["AAPL", "AMD", "TSLA"], [0.25, 0.45, 0.3], "SPY",  "2020-01-01", "2021-01-01")

Alpha of a portfolio/stock

#alpha(stocks, wts, benchmark, start_date, end_date)

#for a stock
alpha(["TSLA"], 1, "SPY", "2020-01-01", "2021-01-01")

#for a portfolio
alpha(["AAPL", "AMD", "TSLA"], [0.25, 0.45, 0.3], "SPY",  "2020-01-01", "2021-01-01")

Beta of a portfolio/stock

#beta(stocks, wts, benchmark, start_date, end_date)

#for one stock
beta(["TSLA"], 1, "SPY", "2020-01-01", "2021-01-01")
#for multiple stocks
beta(["AAPL", "AMD", "TSLA"], [0.25, 0.45, 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", "MSFT", "AMD", "AIR", "AAL", "NFLX", "SBUX", "GOOG", "BABA"], "2020-01-01", "2021-01-01", 10000)

Get daily mean return of a stock/portfolio

#mean_daily_return(stocks,wts, start_date, end_date)

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

#for multiple stocks
mean_daily_return(["AAPL", "AMD", "TSLA"], [0.25, 0.45, 0.3], "2020-01-01", "2021-01-01")

Value at risk of a stock/portfolio

#var(value_invested, stocks, wts, alpha, start_date, end_date)

#for one stock
var(10000, ['AAPL'], 1, 0.95, "2020-01-01", "2021-01-01")

#for multiple stocks
var(10000, ['AAPL', 'TSLA', 'AMD'], [0.4, 0.2, 0.4], 0.95, "2020-01-01", "2021-01-01")

Graph closing price of stock smoothly with Kalman Filters

#graph_kalman(stocks, start_date, end_date, noise_value)
#noise_value = 0.01 is good to get started
graph_kalman("AAPL", "2020-01-01", "2021-01-01", 0.01)

Get the smoothed closing price of a stock with Kalman Filters

#kalman(stocks, start_date, end_date, noise_value)
kalman("AAPL", "2020-01-01", "2021-01-01", 0.01)

Get the Capital Asset Pricing Model

#capm(stocks, wts, start_date, end_date)
stocks = ["AAPL", "AMD", "TSLA", "MSFT"]
wts = [0.3, 0.2, 0.2, 0.3]
capm(stocks, wts, "2020-01-01", "2021-01-01")

Cointegration

#cointegration(stock1, stock2, start_date, end_date)
cointegration("GOOG", "MSFT", "2012-01-01", "2021-01-01")

Returns Cointegration

#return_cointegration(stock1, stock2, start_date, end_date)
return_cointegration("GOOG", "MSFT", "2012-01-01", "2021-01-01")

Stationarity

#stationarity(stock, start_date, end_date)
stationarity("GOOG", "2020-01-01", "2021-01-01")

Returns Stationarity

#return_stationarity(stock, start_date, end_date)
return_stationarity("GOOG", "2020-01-01", "2021-01-01")

Graph rolling volatility

#graph_rvolatility(stock, wts, start_date, end_date, window_time)
#for a stock
graph_rvolatility(["TSLA"], 1, "2019-01-01", "2021-01-01", 180)

#for a portfolio
graph_rvolatility(["AAPL", "AMD", "TSLA"], [0.45, 0.45, 0.1], "2019-01-01", "2021-01-01", 180)

Get rolling volatility data

#rvolatility(stock, wts, start_date, end_date, window_time)
#for a stock
rvolatility(["TSLA"], 1, "2019-01-01", "2021-01-01", 180)

#for a portfolio
rvolatility(["AAPL", "AMD", "TSLA"], [0.45, 0.45, 0.1], "2019-01-01", "2021-01-01", 180)

Graph rolling beta

#graph_rbeta(stock,wts, benchmark, start_date, end_date, window_time)

#for a stock
graph_rbeta(["TSLA"], 1, "SPY", "2019-01-01", "2021-01-01", 180)

#for a portfolio
graph_rbeta(["AAPL", "AMD", "GOOG"], [0.45, 0.45, 0.1], "SPY", "2019-01-01", "2021-01-01", 180)

Get rolling beta data

#rbeta(stock,wts, benchmark, start_date, end_date, window_time)

#for a stock
rbeta(["TSLA"], 1, "SPY", "2019-01-01", "2021-01-01", 180)

#for a portfolio
rbeta(["AAPL", "AMD", "GOOG"], [0.45, 0.45, 0.1], "SPY", "2019-01-01", "2021-01-01", 180)

Graph rolling alpha

#graph_ralpha(stock,wts, benchmark, start_date, end_date, window_time)

#for a stock
graph_ralpha(["TSLA"], 1, "SPY", "2019-01-01", "2021-01-01", 180)

#for a portfolio
graph_ralpha(["AAPL", "AMD", "GOOG"], [0.45, 0.45, 0.1], "SPY", "2019-01-01", "2021-01-01", 180)

Get rolling alpha data

#ralpha(stock,wts, benchmark, start_date, end_date, window_time)

#for a stock
ralpha(["TSLA"], 1, "SPY", "2019-01-01", "2021-01-01", 180)

#for a portfolio
ralpha(["AAPL", "AMD", "GOOG"], [0.45, 0.45, 0.1], "SPY", "2019-01-01", "2021-01-01", 180)

Get implied volatility

#implied_vol(option_type, option_price, stock price, strike price, risk-free rate, the time to expiration, continuous dividend rate)
#option type : "c" (call option) or "p"(put option)

implied_vol('c', 0.3, 3, 3, 0.032, 30.0/365, 0.01)

Backtest your portfolio

#backtest(stocks, wts, benchmark, start_date, end_date)
stocks = ["GOOG", "AMZN", "FB", "AAPL"]
wts = [0.25, 0.25, 0.25, 0.25]
backtest(stocks, wts, "SPY", "2019-01-01", "2021-01-01")

License

MIT