Merge csvs by running python merge_csvs.py arg1 arg2 arg3... where the args are the paths to the csv files. This creates an output parquet file (compressed dataframe).

Clean the dataframe from above by running python organize_data.py nov7/ where nov7/ is the path to the folder containing the parquet file. This program will sort the df by date and ticker and drop garbage rows. It will produce a clean.parquet file in that folder.

In the algos/ directory, write your algo consisting of a single function on_tick(date, data, state) which takes in the current date, stock prices for this date, and a state dictionary that is passed between invocations. It should return a list of tuples (ticker, num_shares) - the trades it makes.

Backtest your algos: python backtest.py nov7/ cash rand aapl. This will run algos/cash.py, algos/rand.py, and algos/aapl.py on a decade's worth of historical data and generate graphs comparing pnl, sharpe, etc.