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2001issue C021-5

Monte Carlo drawdown risk and volatility position sizing

A Monte Carlo simulation can collect closed-trade profit or loss with the risk assigned to each trade, replay those pairs in randomly chosen order, and reset size from current equity. The share of sequences whose worst closed-trade drawdown stays inside a stated goal becomes a survival-style reading of the chosen risk percent.

  • A Monte Carlo simulation can replay closed-trade profit or loss together with assigned risk in randomly chosen order across many sequences.
  • Contract or share count is reset from a stated percent of current equity divided by that trade's risk, so size changes with account level and stop-defined exposure.
  • The fraction of sequences whose worst closed-trade drawdown stays at or below a stated goal is converted to a survival-style probability for the chosen risk percent.
  • Risk percent, drawdown goal, stay-inside probability, and average return are logged together so one historical path is not treated as typical.
Entries in this reading3 entries
Current trackRisk of ruin48 readingsMonte Carlo simulation51 readingsVolatility position sizing57 readings

Collect closed trades and reshuffle them

A Monte Carlo simulation can be built by collecting closed-trade profit or loss together with the risk assigned to each trade, then replaying those pairs in randomly chosen order across many sequences.

Size each replay from current equity

In each replayed sequence, contract or share count is reset from a stated percent of current equity divided by that trade's risk, so position size changes with both account level and stop-defined exposure.

Track the worst closed-trade drawdown

After each simulated closed trade, equity is updated and closed-trade percent drawdown is measured from the running equity high, retaining the worst drawdown seen in that sequence.

Across the random sequences, the fraction whose worst closed-trade drawdown stays at or below a stated drawdown goal is converted to a probability, giving a survival-style reading of the chosen risk percent.

PIR fast and slow Buff volume-weighted averages

On this 105-day PIR daily pane the fast Buff volume-weighted average follows the July advance, the mid-October air pocket and the early-November bounce, while the slower average only turns after those swings are already under way. Point values were read from the published software screenshot; the 11.706 mark on 30 October is the figure the platform itself printed in the header.
On this 105-day PIR daily pane the fast Buff volume-weighted average follows the July advance, the mid-October air pocket and the early-November bounce, while the slower average only turns after those swings are already under way. Point values were read from the published software screenshot; the 11.706 mark on 30 October is the figure the platform itself printed in the header.PIR · Daily · 2000-06-12T00:00:00.000Z to 2000-11-07T00:00:00.000Z

Digitized from the raster. Except for the 11.706 header print on 30 October 2000, levels are approximate to about 0.1. Candlestick highs and lows were not transcribed.

Log growth beside the survival reading

Average percent return across the same sequences is accumulated from starting account size so a sizing setting can be compared on both typical growth and the chance of staying inside the drawdown bound.

The procedure logs risk percent, drawdown goal, drawdown-stay-inside probability, and average return together, so a trader can inspect how a bounded exposure choice maps to ruin-related outcomes before treating any one historical path as typical.

Educational research material, not investment advice. Historical source context does not establish present-day performance.
13 of 24 in the Risk of ruin track
20011-1 pp.Next on Risk of ruinAlertness is a size filter, not a moodAlertness is a standing watch for account-ending loss that stays on after a trader is experienced, before entry and while a position is open.
All readings on this track · 24 readings
  1. 1985Forced-close odds from monthly edge and leverage
  2. 1986Constructing a pre-entry risk filter on price-ratio grids
  3. 1991Ratchet the halt floor and stage exposure against cash
  4. 1992Sizing capital from degraded records and volatility
  5. 1992Filter size by expected value and ruin
  6. 1995Risk of ruin as a pre-entry position filter
  7. 1995Filter leverage by sequence-loss odds
  8. 1996Grading track records by drawdown, expected value, and ruin
  9. 1998When quieter returns lose their survival edge
  10. 1999Loss response versus a precommitted trading process
  11. 1999Reauthorize ruin and drawdown filters after a winning stretch
  12. 2000Sold premium, survivorship, and ruin bounds
  13. 2001Monte Carlo drawdown risk and volatility position sizing
  14. 2001Alertness is a size filter, not a mood
  15. 2002A constant percent risk bound beats a higher win rate
  16. 2010Risk of ruin with fractional and reserve betting
  17. 2014Payout identity, expectancy, and path-dependent ruin
  18. 2015Spreading names is not a substitute for a ruin bound
  19. 2015Prestige labels versus one testable trade procedure
  20. 2015Closed-trade quality screens before sizing risk
  21. 2019Size mini futures from drawdown and leftover margin
  22. 2019Starting account size as a survival filter
  23. 2019Pre-entry ruin and volatility caps for short options
  24. 2020Forced margin, stop-loss, and ruin risk in 2020
By fieldDrawdown and survival54 readingsBy year2001117 readings
All 48 readings tagged Risk of ruin
Also on Risk of ruin5 readings
1992Sizing capital from degraded records and volatility1992Risk of ruin and exposure caps as a pre-trade filter1998When quieter returns lose their survival edge2000Sold premium, survivorship, and ruin bounds2010Risk of ruin with fractional and reserve betting