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1995issue C021-2

Constructing equity versus leverage with resampled trades

A leverage-control grid and a daily-margin-rate bound exposure before size is taken. The same filter then receives a discrete resample of the historical trade book so ending equity can be read at each gearing after holding time, commission, and residual costs are stated as inputs.

  • A stated annual margin rate in decimal form is converted to a daily-margin-rate by dividing by 365, then applied to the equity path.
  • The leverage-grid starts at 0.00 and steps by 0.05 until it reaches 1, and ending equity is read at each of those settings.
  • When no beginning equity is supplied, a unit-equity-seed of 1 starts the sweep so later periods can compound from a unit account.
  • Each resampled trade updates equity with the selected leverage, a daily margin charge scaled by days in the trade, and a residual cost factor, and the last row of that sweep is ending equity at each leverage.
Entries in this reading3 entries

Bound exposure before any size is taken

Leverage-control is a pre-trade and in-trade filter. It recomputes the equity path at successive gearing levels so a loss or exposure decision stays bounded before size is increased.

The leverage-grid is the row of those gearing settings. It begins at 0.00 and rises by 0.05 until it reaches 1, and ending equity is read at each of those settings.

When no beginning equity is supplied, the first cell of a leverage sweep is seeded with 1. That unit-equity-seed lets later periods compound from a defined base.

Turn the stated margin rate into a daily charge

Margin-management converts a stated financing or margin rate into a daily charge. The charge is applied for the days a position is open and deducted from the leveraged return.

A stated annual margin rate in decimal form is converted to a daily-margin-rate by dividing by 365 before it is applied to the equity path. The daily figure can then be scaled by the number of days in the trade.

Express return against committed capital

Net-return-after-leverage is formed from marked position profit and costs relative to the capital committed at the chosen gearing. After margin and residual charges, the return is expressed against that committed capital.

Feed the same filter a discrete resample

Monte-carlo-simulation is a discrete resample of observed holding periods and net returns, drawn with replacement from the historical trade book. The rebuilt equity-versus-leverage path sits out of sample of the original sequence.

In the simulated book, each of the 35 historical trades is treated as equally likely. Equal-trade-probability is 1/35, which is one divided by the number of observed trades.

The Monte Carlo test expands the 35-trade book to 70 draws by independently sampling holding days and returns from those empirical discrete pairs.

Rebuild the return series before the draws

Rebuilding the return series with a commission of 30 instead of 106 changes the returns that enter the simulation even when the same trade list is reused.

Each resampled trade updates equity with the selected leverage, a daily margin charge scaled by days in the trade, and a residual cost factor. The last row of that sweep is ending equity at each leverage.

Educational research material, not investment advice. Historical source context does not establish present-day performance.
5 of 26 in the Monte Carlo simulation track
19951-14 pp.Next on Monte Carlo simulationStock leverage from compounded returns and margin costLeverage is debt divided by equity, and under a 50 percent initial margin rule the highest ratio is 1, not 2.
All readings on this track · 26 readings
  1. 1989Known-bias price streams for system evaluation
  2. 1989Constructing holdout-checked synthetic prices from chaos maps
  3. 1991Closing the feedback loop in auction market simulations
  4. 1994Evaluating eight-year election regimes
  5. 1995Constructing equity versus leverage with resampled trades
  6. 1995Stock leverage from compounded returns and margin cost
  7. 1996Constructing and optimizing volatility position-sizing profiles
  8. 1996Constructing fade rules from an empirical deviation histogram
  9. 1998Return-path volatility and the recovery burden
  10. 1999Monte Carlo checks beyond a single historical path
  11. 2002Monte Carlo equity paths from win rate and profit factor
  12. 2003Checking a small system edge with Monte Carlo simulation
  13. 2003Evaluate a mechanical trading system with a stop-loss order and Monte Carlo simulation
  14. 2005Evaluating momentum rank rotation under portfolio constraints
  15. 2009Bounded risk and rank rotation in portfolio construction
  16. 2010A calendar for walk-forward robustness and Monte Carlo evaluation
  17. 2012Treat the optimize view as an evaluation protocol
  18. 2013Entry, exit and abstention as one testable procedure
  19. 2014Evaluating stop and target bounds before fading large moves
  20. 2014Evaluating system exits when markets get noisier
  21. 2015Memory-bearing synthetics for dominant-cycle spectra
  22. 2015Constructing next-session implied-volatility forecasts by reordering closes
  23. 2016Mechanical systems as a gated factory
  24. 2017Four-sleeve Monte Carlo annual-loss bounds
  25. 2018When to retire a mechanical system with Monte Carlo drawdown limits
  26. 2020Treat mechanical system design as a revalidation lifecycle
All 51 readings tagged Monte Carlo simulation
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