OceanLi ohdearquant

o1 Pro: Insider Trading Mirroring System

Introduction

In the dynamic world of financial markets, staying ahead of insider movements can provide significant strategic advantages.

The Insider Trading Mirroring System is a sophisticated tool designed to monitor publicly disclosed insider trades and automatically mirror these actions within your investment portfolio. By leveraging cutting-edge technologies like LangGraph and integrating real-time data feeds, this system offers a seamless and automated approach to capitalizing on insider trading activities.

Legal & Ethical Considerations
It's crucial to emphasize that this system only processes publicly available insider trading information, as mandated by regulatory bodies such as the U.S. Securities and Exchange Commission (SEC). Engaging in trading based on material non-public information is illegal and unethical. Users must ensure compliance with all relevant laws and regulations and consult with legal and compliance professiona

Categories		DSPy	Autogen	LionAGI 🦁	LlamaIndex 🦙	LangChain🦜
	Animal as logo	❌	❌	✅	✅	✅
	How difficult does it go up to?	very ver

	# train_grpo.py
	import re
	import torch
	from datasets import load_dataset, Dataset
	from transformers import AutoTokenizer, AutoModelForCausalLM
	from peft import LoraConfig
	from trl import GRPOConfig, GRPOTrainer

	# Load and prep dataset

	from enum import Enum
	import pprint
	from typing import ClassVar
	from pydantic import Field

	from lionagi import Branch, iModel, pile, Form

	class Speaker(str, Enum):
	STOCK_LOOKUP = "stock_lookup"
	AUTHENTICATE = "authenticate"

	from typing import TypeVar, Any
	from pydantic import Field
	from ..abc import Component, LionIDable, Record
	from ._pile import Pile, pile


	T = TypeVar("T", bound=Component)


	class CategoricalPile(Component, Record):

	from pydantic import Field

	# from lionagi import logging as _logging
	from lionagi.core.generic import BaseComponent
	from lionagi.experimental.report.util import get_input_output_fields, system_fields


	class Form(BaseComponent):

	assignment: str = Field(..., examples=["input1, input2 -> output"])

	from lionagi.core.generic import BaseComponent
	from pydantic import BaseModel, Field, field_validator
	from abc import ABC, abstractmethod


	class iModel(BaseComponent, ABC):
	model_id: str = Field(default_factory=str)
	model_name: str = Field(default_factory=str)
	llmconfig: dict = Field(default_factory=dict)

	"""
	Prompts for the coder
	mostly from rUv: https://gist.github.com/ruvnet/4b41ee8eaabd6e72cf18b6352437c738
	- guidance_response_: guidance response for the coder
	- write_codes
	- review_codes
	- modify_codes
	- debug_codes
	""";

	from abc import ABC, abstractmethod

	## primary

	class Record(ABC):
	"""represents concretized information"""

	@abstractmethod
	def items(self): ...