File size: 7,350 Bytes
897b38c
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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
180
181
182
183
184
import os
import warnings
from crewai import Agent, Task, Crew
from crewai_tools import ScrapeWebsiteTool, SerperDevTool
from crewai import Crew, Process
from langchain_groq import ChatGroq
from dotenv import load_dotenv
load_dotenv()
warnings.filterwarnings('ignore')

SERPER_API_KEY = os.getenv('SERPER_API_KEY')

# Model Selection
# def initialize_llm(model_option, groq_api_key):
#     if model_option == 'Llama 3 8B':
#         return ChatGroq(groq_api_key=groq_api_key, model='llama3-8b-8192', temperature=0.1)
#     elif model_option == 'Llama 3.1 70B':
#         return ChatGroq(groq_api_key=groq_api_key, model='llama-3.1-70b-versatile', temperature=0.1)
#     elif model_option == 'Llama 3.1 8B':
#         return ChatGroq(groq_api_key=groq_api_key, model='llama-3.1-8b-instant', temperature=0.1)
#     else:
#         raise ValueError("Invalid model option selected")

llm = ChatGroq(groq_api_key=os.getenv('GROQ_API_KEY'), model='llama-3.1-70b-versatile', temperature=0.1)

search_tool = SerperDevTool()
scrape_tool = ScrapeWebsiteTool()

def crew_creator(stock_selection, 

                #  model_option, groq_api_key

                 ):

    # llm = initialize_llm(model_option, groq_api_key)

    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)