I'm new to WL and algo trading.

It seems that the process for using WL to get positive results from the software is something like:

1. Possible strategy

2. Quick Optimise

3. Forward Optimise

2. Backtest

3. Check Monte Carlo results and other indicators

5. Paper trading and if successful (cant happen at the same time as live trading)

6 Live trading

Given that each of these can happen over various timeframes and effort is required to manage actual trading my question is:

Is this the optimal way to minimise risk and maximise return?

It seems that the process for using WL to get positive results from the software is something like:

1. Possible strategy

2. Quick Optimise

3. Forward Optimise

2. Backtest

3. Check Monte Carlo results and other indicators

5. Paper trading and if successful (cant happen at the same time as live trading)

6 Live trading

Given that each of these can happen over various timeframes and effort is required to manage actual trading my question is:

Is this the optimal way to minimise risk and maximise return?

Rename

QUOTE:

optimal way to minimize risk and maximize return?

If that's your only concern, then you should optimize with either the ScoreCard metric

*Sharpe Ratio*or

*Sortino Ratio*. You can Google those terms, but basically they return a Z-score of the gains normalized by risk.

But appreciate, these metrics are

__not__a function of time. So you could have a high Sharpe Ratio and still be loosing money over time. Making money over time is a

__different__optimization.

My steps are (1) strategy development, (2) optimize (using either Recovery Factor or Net Profit), (3) backtest, (4) check equity curve and

__remove stocks__from the WL dataset that don't have a positive sloping equity curve. And yes, I want my portfolio to make money over time even though I'm optimizing for Net Profit (which is time independent).

Thanks Superticker, I appreciate your input.

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