| File | Purpose |
|---|---|
/etc/compose/docker-compose.yml |
Compose file describing what to deploy |
/etc/systemd/system/docker-compose-reload.service |
Executing unit to trigger reload on docker-compose.service |
/etc/systemd/system/docker-compose-reload.timer |
Timer unit to plan the reloads |
/etc/systemd/system/docker-compose.service |
Service unit to start and manage docker compose |
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| ipv6_regex := `^(([0-9a-fA-F]{1,4}:){7,7}[0-9a-fA-F]{1,4}|([0-9a-fA-F]{1,4}:){1,7}:|([0-9a-fA-F]{1,4}:){1,6}:[0-9a-fA-F]{1,4}|([0-9a-fA-F]{1,4}:){1,5}(:[0-9a-fA-F]{1,4}){1,2}|([0-9a-fA-F]{1,4}:){1,4}(:[0-9a-fA-F]{1,4}){1,3}|([0-9a-fA-F]{1,4}:){1,3}(:[0-9a-fA-F]{1,4}){1,4}|([0-9a-fA-F]{1,4}:){1,2}(:[0-9a-fA-F]{1,4}){1,5}|[0-9a-fA-F]{1,4}:((:[0-9a-fA-F]{1,4}){1,6})|:((:[0-9a-fA-F]{1,4}){1,7}|:)|fe80:(:[0-9a-fA-F]{0,4}){0,4}%[0-9a-zA-Z]{1,}|::(ffff(:0{1,4}){0,1}:){0,1}((25[0-5]|(2[0-4]|1{0,1}[0-9]){0,1}[0-9])\.){3,3}(25[0-5]|(2[0-4]|1{0,1}[0-9]){0,1}[0-9])|([0-9a-fA-F]{1,4}:){1,4}:((25[0-5]|(2[0-4]|1{0,1}[0-9]){0,1}[0-9])\.){3,3}(25[0-5]|(2[0-4]|1{0,1}[0-9]){0,1}[0-9]))$` | |
| ipv4_regex := `^(((25[0-5]|2[0-4][0-9]|[01]?[0-9][0-9]?)(\.|$)){4})` | |
| domain_regex := `^(?:[a-z0-9](?:[a-z0-9-]{0,61}[a-z0-9])?\.)+[a-z0-9][a-z0-9-]{0,61}[a-z0-9]$` | |
| match, _ := regexp.MatchString(ipv4_regex+`|`+ipv6_regex+`|`+domain_regex, host) | |
| if match { | |
| fmt.Println("Input is valid") | |
| } else { | |
| fmt.Println("Given input does not match") | |
| re |
In this tutorial, we optimize GPT-4.1 Mini's Chain of Thought (dspy.ChainOfThought) for generating profitable trading strategies using the dspy.GEPA optimizer! We demonstrate how to evolve prompts for different strategy themes (momentum, mean reversion, breakout, arbitrage, volume) with proper risk management.
This implementation realizes Kagen Atkinson's vision of an autonomous LLM-powered trading system with:
- Theme-based Specialization:
--themeflag for different strategy types (as Kagen intended) - Offline Research Loop (
test_gepa_enhanced.py): GEPA evolves theme-specific prompts through reflective optimization - Deterministic Execution (
run_gepa_trading.py): Uses evolved prompts for strategy generation and backtesting