Question 1

How do I set up gekkoga with my Gekko trading bot?

Accepted Answer

Clone the repository, install dependencies with npm, copy and modify the config file with your currency/asset pair and daterange, and run it using node with optional tmux for background processing as per the README. Ensure Gekko's web server is running for data access.

Question 2

Is gekkoga better than manual parameter tuning for trading strategies?

Accepted Answer

gekkoga automates the search using genetic algorithms, which can explore parameter spaces more efficiently than manual testing and might find non-obvious optimizations. However, it requires setup time and computational resources, so it's best for complex strategies where manual tuning is impractical.

Question 3

What genetic algorithm parameters can I customize in gekkoga?

Accepted Answer

You can tweak settings like population size, mutation rates, crossover methods, and selection criteria through the config file, allowing control over the evolutionary process to balance exploration and exploitation in optimization.

Question 4

How does gekkoga handle different cryptocurrency pairs or historical data?

Accepted Answer

It relies on Gekko for data, so you need to specify the currency/asset pair and daterange in the config file. This makes it flexible for various pairs but dependent on Gekko's data availability and quality.

Question 5

Can gekkoga optimize multiple strategies at once or for live trading?

Accepted Answer

No, gekkoga is designed for backtesting single strategies with historical data; it doesn't support multi-strategy optimization or real-time adjustments for live trading, limiting it to pre-deployment analysis.

Question 6

gekkoga vs grid search: which is better for strategy optimization?

Accepted Answer

gekkoga uses genetic algorithms that can be more efficient in high-dimensional parameter spaces by evolving solutions, whereas grid search exhaustively tests all combinations but becomes computationally expensive. gekkoga is preferable for complex optimizations but requires tuning of genetic parameters.

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