Lab 1 · Orders and the spread
Read the order-book lesson. In Markets, choose single player and discretionary trading. Place a small marketable order and record its price. Compare it with a non-marketable limit order. Inspect which order fills, the waiting time and the marked account.
Repeat with the same data source and settings. Record whether the environment is simulated or historical. Displayed depth, executed volume and the midpoint measure different objects.
Lab 2 · A portfolio baseline
Read portfolio and risk. In a portfolio challenge, compare equal weights, a concentrated allocation and cash under the same observed window and timing rules. Record return, maximum drawdown, rebalancing choices and the benchmark shown by the challenge.
Lab 3 · A strategy with a risk limit
Choose algorithmic mode and begin with a minimal rule or the provided Python/R path. Confirm that your strategy reads only its supplied snapshot and respects inventory limits. Make one rule change, repeat the run, and inspect the action log rather than only the final rank.
The current snapshot source includes a rolling bar history, visible bid/ask levels, spread, imbalance, working orders and account fields. For the precise interface, inspect AlgoAPI.js in the Markets source. An interface can evolve, so match the running site to its code version.
Lab 4 · Explain a failure
Find a run with poor performance. Separate losses from forecasting, exposure, execution costs and inventory. Write a short report describing the settings, what you expected, what happened and the next test that would distinguish explanations.