Ciprian Cimpan cip8

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I am ciprian-cimpan on github.
I am ciprian42 (https://keybase.io/ciprian42) on keybase.
I have a public key ASAFJVuXrpaYfsFfCKnC-2Bg9Hn1SA0HbI5_u1BkwRzOdwo

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Running docker-compose as a systemd service

Files

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

Tutorial: GEPA for Quantitative Trading Strategies

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.

The Vision: Autonomous Trading Research with Theme Specialization

This implementation realizes Kagen Atkinson's vision of an autonomous LLM-powered trading system with:

Theme-based Specialization: --theme flag 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

	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

	def get_grid_points(self, mask_arr: np.ndarray, pad_ratio: int = 50) -> np.ndarray:
	"""
	Returns a grid of points that are in the foreground of the given binary mask.

	The padding between points in the grid is adjusted based on the size of the mask.

	Parameters:
	- mask: A 2D binary numpy array, where 1 represents foreground and 0 represents background.
	- pad_ratio: Scaling factor (divisor) used to compute the padding between points in the grid. Larger values result in smaller padding.