# Executing the optimize mode
Define your routes as if you would have defined them for executing a backtest. Now, you can execute the optimize mode by running:
# jesse optimize START_DATE FINISH_DATE OPTIMAL_TOTAL jesse optimize '2018-01-01' '2020-01-01' 200
finish_date are similar to what you've already seen in backtests.
optimal_total is an integer that tells Jesse how many trades you would find optimal for your strategy in that time period so that it can filter out those DNAs that cause too few trades.
For example, imagine that I have a trend-following strategy that I trade on the
6h timeframe and I usually get 30-60 trades per year in my backtests. You could say that I'll be fine with DNAs that cause my strategy to execute the same number of trades (30-60). But will I be fine if it only made like 5 trades in a whole year but had a higher win-rate? The answer is no. Because such backtest results cannot be trusted in the long term. I want to have the law of big numbers in my favor to stay away from the dangers of over-fitting.
Thus, by setting my
optimal_total to a number like
60 I will successfully filter out such bad DNAs.
The fitness score is then determined by the closeness of a DNA's total to the
optimal_total combined with the sharpe ratio of the DNA.
You can choose another ratio for the fitness score / optimization goal in config.py
Jesse can output the optimization results (DNA, hyperparamters, fitness...) into a CSV file. You may open this CSV file with Excel, Google sheets, or whatever tool you like to analyse the optimization.
Usage: Add the
--csv flag to the optimize command:
jesse optimize '2018-01-01' '2020-01-01' 200 --csv
Once a certain amount of iterations is completed, Jesse will add results to the CSV file.
Usage: Add the
--json flag to the optimize command:
jesse optimize '2018-01-01' '2020-01-01' 200 --json
Once a certain amount of iterations is completed, Jesse will add results to the JSON file.
# When is the optimization over?
After starting the optimize mode, first, the initial population is generated. There is a progress bar telling you how long you have to wait until it's done. During this period, no optimization is being done. It's just a random generation of the DNAs.
Then, the "evolving" phase begins. You will see a progress bar for this phase too, but you don't need to pay much attention to it. Remember that the point of the optimize mode is not to find the perfect parameters, but it is to find a few good ones. Why? Because the perfect ones also have a higher chance of being over-fit.
Remember that all you need as the result of running the optimize mode is the DNA string. And you can see them at anytime in the monitoring dashboard. Hence, once you see a few DNAs that are resulting good in both training and testing set, then copy those DNAs, and use them to run separate backtests on your validation period. If that performed well, then you're good to go.