plutus-backtest 0.0.4

plutus_backtest is a python library for backtesting investment decisions using Python 3.7 and above.

plutus_backtest

This project has been performed for the purpose of local backtests of financial strategies. The package contains various indicators and tools allowing users obtaining exact results of their strategies over a certain period of time. The users are also able to pick endless amount of trading instruments and set criteria such as long or short positioning. Beside that optional stop loss and take profit signals are available not only as general limit level for entire portfolio but can be also applied for each instrument individually. Another optional tool available is weights factor distribution which is oriented to assign weights according to the provided values. In addition, the package allows to create several backtests and combine them all together into one to see the full picture of the investment strategy.

Installation:

• Dependency: pandas, numpy, plotly, yfinance
• Install from pypi:

pip install plutus_backtest

• Verified in Python:

from plutus_backtest import backtest

Examples:

A short and fast way to run a single backtest would be:

from plutus_backtest import backtest

bt = backtest(asset=["AAPL", "BTC-USD", "GC=F"], o_day=["2021-08-01", "2021-07-15", "2021-08-20"],
              c_day=["2021-09-01", "2021-09-01", "2021-09-15"])
bt.execution()

As a result you will see a statistical table as well as graphical representation of the portfolio which shows accumulated return.

In order to access dataframe with daily changes, use:

bt.final_portfolio.head()

The result will appear as following:

Additional "plotting" function will enable users to observe additional graphs such as drawdown and monthly income plots:

bt.plotting()

More complex approach would be assigning weights factor/stop loss/ take profit indicators:

bt = backtest(asset = ["AAPL", "BTC-USD","GC=F"], o_day = ["2021-08-01", "2021-07-15", "2021-08-20"],
              c_day = ["2021-09-01", "2021-09-01","2021-09-15"], weights_factor = [10, -5, 35], 
              stop_loss = [0.8, 0.9, 0.95], take_profit = [1.1, 1.2, 1.05])
bt.execution()

In this case all parameters are used. The weights will not be distributed equally. "AAPL" will have 20% of the total portoflio BTC-USD - 10% and "GC=F" will have 70%. The negative sign in the weights factor will mean short selling, therefore first "AAPL" and "GC=F" instruments are in long position and "BTC-USD" is in the short.

Stop loss and take profit shall be interpreted as "AAPL" has 20% of stop loss and 10% of take profit, "BTC-USD" has 10% of stop loss and 20% of take profit, "GC=F" 5% of stop loss and 5% of take profit. As result accumulative graph will look as:

In the moment when one of the securities reaching its stop loss or take profit, the trade will automatically stopped and the weights will be reassigned respectively to the left assets.

In case of users need to test one instrument but several times with different timelines, the package will interpret it as

bt = backtest(asset = ["AMZN", "AMZN","AMZN"], o_day = ["2021-08-01", "2021-09-01", "2021-10-01"],
              c_day = ["2021-08-15", "2021-09-15","2021-10-15"])
bt.execution()
bt.final_portfolio.head(15)

Each time when one asset is repeating the package will assign additional number to it to track required periods. It's worth to mention that due to data limitation the code will use only close price for the analysis of the securities. Only the first trading day has relationship open/close, since it's assumed that the tradingstarts with open price and finishes with close one.

Ultimately, if the users would like to perform several backtest and combine them into one to see the full picture then there are few functions related to that, namely:

bt1 = backtest(asset = ["AAPL", "BTC-USD","GC=F"], o_day = ["2021-08-01", "2021-07-15", "2021-08-20"],
              c_day = ["2021-09-01", "2021-09-01","2021-09-15"])
bt2 = backtest(asset = ["AMZN", "EURUSD=X"], o_day = ["2021-06-01", "2021-06-15"],
              c_day = ["2021-06-30", "2021-07-05"])

p1 = bt1.execution()
p2 = bt2.execution()
q1 = bt1.final_portfolio
q2 = bt2.final_portfolio

dic ={}
dic[0] = q1
dic[1]= q2

combined_frame =backtest.puzzle_assembly(dic)

First of all all backtest shall be executed in order to obtain final portfolio of the each one. Then they shall be assigned to an empty dictionary. Thereafter function "puzzle_assembly" takes the data from diffirent backtest and unite it into one dataframe.

In order to visualize data functions "puzzle_execution" or "puzzle_plotting" shall be called. Which work exactly in the same way as it was explained previously.

backtest.puzzle_execution(combined_frame)

Support:

Please open an issue for support.
With additional questions please reachout to autors directly:

• witmul
• IlliaBoloto