The purpose of this case study is build a portfolio calculator by completing the function on portfolio.py:
calculate_portfolio(address,10000)
For the following addresses
0x41da2035ac26e4308b624a84d3caebf80a4dccf1
0x211fe601e24ce89cb443356f687c67fbf7708412
- Run
pip install - Create virtual env
python3 -m venv env - Activate virtual env
source env/bin/activate - Should get an (env) in terminal
- Then
pip install -r requirements.txt - Then in the command line
python portfolio.py
a) For trade data use tradingKey.csv. Percent is the target percent position size. A 0 percent means closing a position. A negative percent means short. You can use leverage (i.e. 110%), ignore borrow cost and leverage costs. b) For price data use prices = pd.read_csv('prices.csv') c) Ignore corporate actions e) startCash = $10,000 f) entry price is the next price after the entry_date associated with the trade.
The output will use the pre-set columns to produce two CSVs: a) CSV for trade table using pre-set columns. Don't add columns, everything you need is here. b) CSV for a portfolio table calculated at the same date_time intervals as prices.csv. Again, don't add columns.