File size: 6,913 Bytes
0132af7 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 |
import os
import warnings
from crewai import Agent, Task, Crew
from crewai_tools import ScrapeWebsiteTool, SerperDevTool
from crewai import Crew, Process
from langchain_openai import ChatOpenAI
from dotenv import load_dotenv
load_dotenv()
warnings.filterwarnings('ignore')
SERPER_API_KEY = os.getenv('SERPER_API_KEY')
SAMBAVERSE_API_KEY = os.getenv('SAMBANOVA_API_KEY')
SAMBANOVA_API_URL = "https://api.sambanova.ai/v1"
search_tool = SerperDevTool()
scrape_tool = ScrapeWebsiteTool()
llm = ChatOpenAI(
model="Meta-Llama-3.1-8B-Instruct-8k",
temperature=0.5,
max_retries=2,
base_url=SAMBANOVA_API_URL,
api_key=SAMBAVERSE_API_KEY,
)
def crew_creator(stock_selection):
# data_analyst_agent = Agent(
# role="Data Analyst",
# goal="Monitor and analyze market data in real-time "
# "to identify trends and predict market movements.",
# backstory="Specializing in financial markets, this agent "
# "uses statistical modeling and machine learning "
# "to provide crucial insights. With a knack for data, "
# "the Data Analyst Agent is the cornerstone for "
# "informing trading decisions.",
# verbose=True,
# allow_delegation=True,
# tools = [scrape_tool, search_tool],
# llm=llm,
# )
trading_strategy_agent = Agent(
role="Trading Strategy Developer",
goal="Develop and test various trading strategies based "
"on insights from the Data Analyst Agent.",
backstory="Equipped with a deep understanding of financial "
"markets and quantitative analysis, this agent "
"devises and refines trading strategies. It evaluates "
"the performance of different approaches to determine "
"the most profitable and risk-averse options.",
verbose=True,
allow_delegation=True,
tools = [scrape_tool, search_tool],
llm=llm,
)
# execution_agent = Agent(
# role="Trade Advisor",
# goal="Suggest optimal trade execution strategies "
# "based on approved trading strategies.",
# backstory="This agent specializes in analyzing the timing, price, "
# "and logistical details of potential trades. By evaluating "
# "these factors, it provides well-founded suggestions for "
# "when and how trades should be executed to maximize "
# "efficiency and adherence to strategy.",
# verbose=True,
# allow_delegation=True,
# tools = [scrape_tool, search_tool],
# llm=llm,
# )
# risk_management_agent = Agent(
# role="Risk Advisor",
# goal="Evaluate and provide insights on the risks "
# "associated with potential trading activities.",
# backstory="Armed with a deep understanding of risk assessment models "
# "and market dynamics, this agent scrutinizes the potential "
# "risks of proposed trades. It offers a detailed analysis of "
# "risk exposure and suggests safeguards to ensure that "
# "trading activities align with the firm’s risk tolerance.",
# verbose=True,
# allow_delegation=True,
# tools = [scrape_tool, search_tool],
# llm=llm,
# )
# Task for Data Analyst Agent: Analyze Market Data
# data_analysis_task = Task(
# description=(
# "Continuously monitor and analyze market data for "
# "the selected stock ({stock_selection}). "
# "Use statistical modeling and machine learning to "
# "identify trends and predict market movements."
# ),
# expected_output=(
# "Insights and alerts about significant market "
# "opportunities or threats for {stock_selection}."
# ),
# agent=data_analyst_agent,
# )
# Task for Trading Strategy Agent: Develop Trading Strategies
strategy_development_task = Task(
description=(
"Develop and refine trading strategies based on "
"the insights from the Data Analyst and "
# "user-defined risk tolerance ({risk_tolerance}). "
# "Consider trading preferences ({trading_strategy_preference})."
),
expected_output=(
"A set of potential trading strategies for {stock_selection} "
"that align with the user's risk tolerance."
),
agent=trading_strategy_agent,
)
# Task for Trade Advisor Agent: Plan Trade Execution
# execution_planning_task = Task(
# description=(
# "Analyze approved trading strategies to determine the "
# "best execution methods for {stock_selection}, "
# "considering current market conditions and optimal pricing."
# ),
# expected_output=(
# "Detailed execution plans suggesting how and when to "
# "execute trades for {stock_selection}."
# ),
# agent=execution_agent,
# )
# Task for Risk Advisor Agent: Assess Trading Risks
# risk_assessment_task = Task(
# description=(
# "Evaluate the risks associated with the proposed trading "
# "strategies and execution plans for {stock_selection}. "
# "Provide a detailed analysis of potential risks "
# "and suggest mitigation strategies."
# ),
# expected_output=(
# "A comprehensive risk analysis report detailing potential "
# "risks and mitigation recommendations for {stock_selection}."
# ),
# agent=risk_management_agent,
# )
# Define the crew with agents and tasks
financial_trading_crew = Crew(
agents=[
# data_analyst_agent,
trading_strategy_agent,
# execution_agent,
# risk_management_agent
],
tasks=[
# data_analysis_task,
strategy_development_task,
# execution_planning_task,
# risk_assessment_task
],
manager_llm = llm,
process=Process.sequential,
verbose=True,
)
result = financial_trading_crew.kickoff(inputs={
'stock_selection': stock_selection,
# 'initial_capital': initial_capital,
# 'risk_tolerance': risk_tolerance,
# 'trading_strategy_preference': trading_strategy_preference,
# 'news_impact_consideration': news_impact_consideration
})
return str(result)
# print(result) |