Starting policy
- Initial equity: $10,000
- Tested risk: 1% of current equity per trade
- Defined entries, exits, and stop placement
- Daily and total open-risk limits
- No increase in size during drawdown
Failure sequence
1
Normal variance
Six losing trades produce a drawdown of roughly 5.9% under fixed-fractional 1% risk. The result is uncomfortable but compatible with the original policy.
2
Recovery becomes the objective
The trader raises risk to 3% to “make back” the loss. The strategy has not improved; only the account’s sensitivity to the next outcome has changed.
3
Losses cluster
Four more full losses at 3% reduce the remaining equity by another 11.5%. The total drawdown is now about 16.7% before costs.
4
Process breaks down
The trader skips valid trades, enters marginal setups, widens stops, or adds correlated positions. Historical performance no longer describes the live process.
5
Risk is escalated again
At 5% or more per trade, a short adverse sequence causes rapid account damage or breaches an external loss limit.
Root-cause analysis
The final loss is visible, but the earlier control failures are more important:
The statement “the system did not fail” cannot be known with certainty; strategies also lose edge. The more defensible conclusion is that risk escalation made it impossible to evaluate the original system fairly.
Prevention
Define size changes in advance, require a minimum review sample, use personal loss limits inside external boundaries, and record every override. Automated controls can make objective limits harder to reinterpret, but they must be configured and tested correctly.Build a risk-based system
Define policies for sizing, limits, and review.
Manage drawdowns
Use a prewritten response instead of improvising.
Automating risk
Understand the benefits and failure modes of hard controls.
Trading tools
Explore account-level monitoring and enforcement.