This is a barebones implementation of genetic algorithm evolution to develop strategies for digital coin trading bot Gekko. [https://github.com/askmike/gekko]
It generates random configs, and evolve them by backtesting on a Gekko session via the REST API of gekko's user interface. Genetic algorithm and bayesian optimization are evolution choices.
Open two terminals;
T.1 -> run Gekko on ui mode, or just its webserver:
$node gekko.js --ui
or
$node web/server.js
T.2 -> $cd [japonicus dir]
$python japonicus.py [-g|-b] [-c] [-k] [--repeat <X>] [ [-i|-r|--strat <Strategy>] [-w]
-g for genetic algorithm;
-b for bayesian optimization;
-c to use an alternative ~experimental genetic algorithm;
-k launches a child gekko instance, so no need for the first terminal;
-r run with random strategy
--strat choose one strat to run;
-i Genetic algorithm create strategies on the fly, and run then based on varying indicators;
--repeat to run genetic algorithm X times; then just check evolution.log;
-w launches a neat dash/flask web server @ your local machine, which can be accessed via webbrowser.
Address is shown on the first line of console output. (likely http://localhost:5000)
This is written on python because of the nice DEAP module for genetic algorithm, and was worth it. DEAP is available on PIP and required.
All settings are set at Settings.py;
If your Gekko UI http port is not :3000, adjust it;
Backtesting is parallel, running a pool of five workers, adjustable.
Those strategies present in a fresh clone of gekko are no good, ditch them. With genetic algorithm optimization, those strats can get to a level of profit that is above the market base price change for given period. Even then, that power will be only shown at the dataset it was optimized for, so the settings will fail on different candlesticks which include real time trading.
A good thing to do is to create a strategy that combines two indicators, IE you buy when both results are above buying thresolds, you sell when both are below selling thresholds. So dual or even triple indicator strategies are a good path to go.
Known good gekko strategies to run with this:
- PPO+TSI strat
Japonicus can send backtest requests across the internet to several machines running Gekko.
This method can greatly cut EPOCH times.
Intended to use with Ansible-playbook + Amazon EC2 AWS machines.
At japonicus side, you should provide Ansible's hosts inventory file path (on settings.generations), containing
the IPs of running machines (a simple list).
Those machines should be already fully configured, running Gekko, and loaded with the same candlestick history data
files you have on local Gekko;
YML playbook file to get gekko running on Amazon AMI Linux is at root folder (set the path to your local gekko history @ line 57);
steps to put your GA online on clusters;
1- Create how many EC2 instances you want - spot requests are cheaper;
2- Make sure port 3000 TCP is open on them. This is configurable on security groups.
Theres a nice tutorial at https://www.google.com/search?q=amazon+aws+security+groups XD
3- Find a way to install node and gekko on them. Copy your local gekko/history folder to them;
(there is a ansible playbook on the repo that can help you cover this steps)
4- Make a list of your remote machine addrs and inform the path to it at Settings.py->Global.GekkoURLs. A hosts file used by ansible can be used as it is.
5- Run japonicus.py in any GA mode;
You can include japonicus to gekko image by adding the following snippet to gekkos docker-compose file
japonicus:
build: relative-path-from-gekko-to-the-japonicus-repo/
volumes:
- ./volumes/gekko-japonicus:/usr/src/app/output
links:
- gekko
ports:
- 5000:5000and by setting the GekkoURLs config to the container gekko name
# Settings.py
'GekkoURLs': ['http://gekko:3000']japonicus will choose a random item of the list to fetch candles and scansets.
Genetic Algorithms are a good way to fetch a good set of settings to run a strategy on gekko. But the real gamechanger is the strategy itself. The ideal evolution method would be a Genetic Programming that modifies the strat logic itself, we're looking for it;