Backtest of an algorithmic trading strategy

Hi Dillon, I need to backtest an algorithmic trading strategy in PYTHON and have that backtest be modifiable by myself to change out or add securities, change time periods, and change leverage amounts for further testing. The strategy is as follows:

We have a universe of 12 securities: SPY, QQQ, EEM, IWM, GLD, VNQ, LQD, AGG, SHY, TLT, IEF, EFA. We score all securities based on the average of 1 month, 3 month, 6 month, and 12 month returns ie SPY returns= 3%,4%,5%,6% for the respective time periods. 3+4+5+6=18%/4=4.5%. We select the top 5 securities from our universe of 12 based on this score.

The weighting of those 5 securities is based on the inverse of each individual security's trailing 3 month standard deviation. Ex: Our portfolio holds SPY, EEM, QQQ... SPY Std Dev= .10, EEM Std Dev= .20, QQQ Std Dev= .40. Divide 1 by standard deviation and sum the results SPY- 1/.10=10, EEM- 1/.20=5, QQQ- 1/.4=2.5. 10+5+2.5=17.5. Divide each by the sum to get percent capital allocation: SPY 10/17.5=57.143%, EEM 5/17.5%=28.571%, QQQ=2.5/17.5=14.286%.

Final step: if the S&P 500 is above its 200 day moving average we add leverage of 1.5x distributed evenly amongst the 5 securities original weighting. To continue from the above example if the SP 500 is above its 200 DMA: SPY = .57143*1.5=85.715%, EEM= .28571*1.5= 42.857%, QQQ= .14286*1.5= 21.429%.

The portfolio is rebalanced monthly on the final trading day of the month. Agnostic as to time of day trades take place

The backtest should be from Jan 1, 2000 to Jan 1, 2021. Hope you can help me out.
Thanks Dillon,
Joe Morrissey, CFA