Algotrading strategies for ETFs

About

Implementation of the trading algorithm which invests capital in the most undervalued asset:

• each day a "score" is calculated which represents the undervaluation of an asset
• allocation of the capital is switched to the most undervalued asset

Features of the algorithm:

• using a combination of RSI(2) and RSI(3) as a basis for a score
• adjusting score to currency movements
• use of CCI for trend adjustments
• management of stop losses
• exiting the market when all assets are overvalued
• adjusting score to ex dividend dates

Installation

Runs on Python 3.10 with Backtrader framework

• https://www.backtrader.com/docu/installation/
• https://github.com/mementum/backtrader#installation

Running a simulation

Execute the simulation

Run the following file to execute the backtest:

jj_standard_strategy.py

Sample output

Information about annualized returns, yearly returns, sharpe ration, max drawdown, executed trades etc

Ticker    SumOfProfits    Trades    AvgProfit
   XLP          73.64%       463        0.16%
   QQQ         101.43%       309        0.33%
   TLT          82.31%       470        0.18%
   SPY            9.3%       173        0.05%
   EEM           53.7%       414        0.13%
   GLD          72.96%       624        0.12%
   IWM         125.91%       408        0.31%
OrderedDict([(2005,
              -0.02086414012834914),
             (2006, 0.11257870068465303),
             (2007, 0.19096626781834147),
             (2008, 0.5922068521683199),
             (2009, 0.4277439522230537),
             (2010, 0.23170772609894597),
             (2011, 0.25052435038907594),
             (2012, 0.2851946122866036),
             (2013,
              -0.07002976846893916),
             (2014, 0.1506862873912309),
             (2015, 0.2052908419375139),
             (2016, 0.05768348150426994),
             (2017, 0.18728871309046569),
             (2018, 0.05206206192727225),
             (2019, 0.3748708481986356),
             (2020, 1.1832607423091677),
             (2021, 0.10697139538220202),
             (2022, 0.2195313331456259),
             (2023, 0.3102546191657225),
             (2024, 0.5452675054456357),
             (2025,
              0.01981537601500083)])

========================================
Annualized return: 25.539542 percent
OrderedDict([('sharperatio',
              0.9298749765713412)])
OrderedDict([('maxdrawdown',
              25.059548287502768),
             ('maxdrawdownperiod', 319)])
========================================

Monthly avg returns: 
[1.55, 2.37, 4.5, 1.84, 0.51, 2.48, 2.53,
 1.65, 1.66, 2.78, 2.03, 0.45]
Daily avg returns: 
[0.05, 0.22, 0.06, 0.07, 0.09]

Support

Ideas? Suggestions? Feel free to contact me at jaskowiecj@gmail.com