trafalgar.py

v0.1.7

Trafalgar makes quantitative finance and portfolio analysis faster and easier For more information about how to use this package see README

Latest version published 4 years ago

License: Apache-2.0

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Package Health Score

54 / 100

security
No known security issues
popularity
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maintenance
Inactive
community
Sustainable

Popularity

Small

GitHub Stars: 942
Forks: 128
Contributors: 20

Direct Usage Popularity

Based on project statistics from the GitHub repository for the PyPI package trafalgar.py, we found that it has been starred 942 times.

Security

No known security issues

Security and license risk for latest version

Release Date: May 28, 2021
Direct Vulnerabilities: 0
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L
Indirect Vulnerabilities: 0
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0
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0
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License Risk: 0
H
0
M
0
L

All security vulnerabilities belong to production dependencies of direct and indirect packages.

License

Apache-2.0

Security Policy

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Maintenance

Inactive

Commit Frequency

Open Issues: 2
Open PR: 0
Last Release: 4 years ago
Last Commit: 19 days ago

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Sustainable

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Contributing.md: No
Code of Conduct: No
Contributors: 20
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Package

Python Versions Compatibility: Unspecified

Age: 4 years
Latest Release: 4 years ago
Dependencies: 10 Direct
Versions: 7
Maintainers: 1
Wheels: OS Independent

Readme

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) -&gt; 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) -&gt; 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

trafalgar.py dependencies

datetime matplotlib numpy pandas-datareader pykalman pyportfolioopt scipy seaborn sklearn statsmodels

FAQs

What is trafalgar.py?

Trafalgar makes quantitative finance and portfolio analysis faster and easier. Visit Snyk Advisor to see a full health score report for trafalgar.py, including popularity, security, maintenance & community analysis.

Is trafalgar.py popular?

The python package trafalgar.py receives a total of 67 weekly downloads. As such, trafalgar.py popularity was classified as small. Visit the popularity section on Snyk Advisor to see the full health analysis.

Is trafalgar.py well maintained?

We found indications that trafalgar.py is an Inactive project. See the full package health analysis to learn more about the package maintenance status.

Is trafalgar.py safe to use?

The python package trafalgar.py was scanned for known vulnerabilities and missing license, and no issues were found. Thus the package was deemed as safe to use. See the full health analysis review.

Last updated on 27 November-2024, at 14:43 (UTC).

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Readme

Trafalgar🃏

Installation 🔥

Features include 📈

Credits ✌️

Documentation🚀

Call the library

Graph of the closing price of a stock

Graph of the closing price of multiple stocks

Graph the volume

Graph the opening price

Graph the adjusted closing price

Get closing price data (in dataframe format)

Get volume data (in dataframe format)

Get opening price data (in dataframe format)

Get adjusted closing price data (in dataframe format)

Covariance between stocks

Correlation between stocks

Graph correlation between stocks

Get data from a stock in OHLCV format directly

Graph the returns (for each day)

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

Graph the cumulative returns of a stock/portfolio

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

Annual Volatility of a portfolio/stock

Sharpe Ratio of a portfolio/stock

Graph the returns of a portfolio/stock to a benchmark

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

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

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

Alpha of a portfolio/stock

Beta of a portfolio/stock

Efficient frontier to optimize allocation of shares in your portfolio

Get daily mean return of a stock/portfolio

Value at risk of a stock/portfolio

Graph closing price of stock smoothly with Kalman Filters

Get the smoothed closing price of a stock with Kalman Filters

Get the Capital Asset Pricing Model

Cointegration

Returns Cointegration

Stationarity

Returns Stationarity

Graph rolling volatility

Get rolling volatility data

Graph rolling beta

Get rolling beta data

Graph rolling alpha

Get rolling alpha data

Get implied volatility

Backtest your portfolio

License

trafalgar.py dependencies

FAQs

What is trafalgar.py?

Is trafalgar.py popular?

Is trafalgar.py well maintained?

Is trafalgar.py safe to use?

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Source Code

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