Algorithmic trading strategies are driven by signals that indicate when to buy or sell assets to generate superior returns relative to a benchmark such as an index.

The portion of an asset’s return that is not explained by exposure to this benchmark is called alpha, and hence the signals that aim to produce such uncorrelated returns are also called alpha factors.

Ultimately, the goal of active investment management is to generate alpha, defined as portfolio returns in excess of the benchmark used for evaluation. The fundamental law of active management postulates that the key to generating alpha is having accurate return forecasts combined with the ability to act on these forecasts In this project we have attempted to develop the code and results based on the formulaic alphas mentioned in the paper 101 Formulaic Alphas (https://arxiv.org/ftp/arxiv/papers/1601/1601.00991.pdf)

The config file can be changed appropriately to run the backest on different duration of start and end date and different asset universe

Install necessary libraries

pip3 install -r requirements.txt

To generate the backtest results modify the appropriate alpha call in formulaic_alphas.py and run the command

python3 main.py