| Ticker | Price | Bid | Ask | Vol |
|---|---|---|---|---|
| AAPL | 130.00 | 129.50 | 130.60 | 101340 |
| MSFT | 57.00 | 56.50 | 59.60 | 99504 |
| GOOGL | 856.67 | 845.50 | 860.60 | 243900 |
| BAC | 23.67 | 22.99 | 23.80 | 435708 |
| JPM | 86.67 | 86.50 | 87.34 | 348890 |
| Ticker | Price | Bid | Ask | Vol |
|---|---|---|---|---|
| AAPL | 130.00 | 129.50 | 130.60 | 101340 |
| MSFT | 57.00 | 56.50 | 59.60 | 99504 |
| GOOGL | 856.67 | 845.50 | 860.60 | 243900 |
| BAC | 23.67 | 22.99 | 23.80 | 435708 |
| JPM | 86.67 | 86.50 | 87.34 | 348890 |
With PyAlgosim, you can easily dip your feet in the world of high frequency trading. Test a personal trading strategy that you think might work well, or simulate a million dollar quant-fund managing investors' money - all at the tip of your fingertips.
PyAlgosim makes it simple to get up and running and begin backtesting algorithmic trading strategies, and its intuitive API means that the learning curve is non-existent. A familiarity with Python is all you need.
See the docsEvaluate your performance against the overall market return once you're done, and see if your trading strategies were profitable - or a waste of money and time. PyAlgosim provides a ready-made post-performance analysis. Alternatively, the fully-customizable module allows you to generate your own performance metrics
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