The common logic of a challenge
Most prop firm challenges run in stages with a few core rules: how much you can lose in a day, how much you can lose in total, and the profit target you must reach. Details and values vary by firm, so check the current terms of any firm you are considering from its own source.
What does Tickrivo Challenge practice do?
When you open a Prop Challenge session in Tickrivo, you trade with a sample firm and a rule profile. The Challenge control center shows the session’s rules and status in one place. Sample packages can have one, two or three stages.
This is a simulation: it does not offer real funding, a real account or admission to a firm. Package rules apply only to sessions you open afterwards.
For a summary see Prop Challenge practice.
1. Choose a rule profile
Pick a preset (Practice or Conservative) or a custom profile. Conservative uses tighter limits; in a custom profile you enter your own values.
2. Size risk to the rules
If you have a daily loss limit, the percentage you risk per trade matters. With the risk shortcuts in the order panel (0.25%, 0.5% and 1%) you see the position size for your stop distance, and before opening the trade you check the amount lost at the stop and the net result at target.
3. Practice daily discipline
Losing repeatedly in one day is the most common way to break the rules. While practicing, check the control center regularly, decide by your plan whether to open a trade near the daily limit, and write the reason in your trading journal.
4. Review stages and records
Completed work shows up on the Achievements screen. Look at drawdown periods and R per trade in Performance and at plan adherence in RivoScope to find what led you to break a rule. First build your strategy soundly with the backtest guide.
Limits of Challenge practice
- It is not a real prop firm account
- It is not guaranteed to mirror a firm’s rules exactly; check the firm’s current terms separately
- Succeeding in practice does not mean you will pass a real challenge
- Results rely on historical data and simulation assumptions