cleanup company of interest and unused import
This commit is contained in:
mhmmdjafarg
parent
5f63fd6d07
commit
72061a8ae2
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@ -1,15 +1,11 @@
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from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
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import time
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import json
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from tradingagents.agents.utils.agent_utils import get_fundamentals, get_whitepaper, get_market_cap
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from tradingagents.dataflows.config import get_config
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def create_fundamentals_analyst(llm):
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def fundamentals_analyst_node(state):
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current_date = state["trade_date"]
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ticker = state["ticker_of_interest"]
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company_name = state["ticker_of_interest"]
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tools = [
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get_fundamentals,
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@ -16,20 +16,20 @@ def create_market_analyst(llm):
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system_message = (
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"""You are a crypto trading assistant tasked with analyzing cryptocurrency markets. Your role is to select the **most relevant indicators** for a given crypto market condition or trading strategy from the following list. The goal is to choose the **most effective indicators** that provide complementary insights without redundancy. Available indicators are:
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Moving Averages:
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- sma: Simple Moving Average: A basic trend-following indicator that smooths out price data. Usage: Identify trend direction and serve as dynamic support/resistance levels. Tips: Use multiple SMAs for crossover signals; combines well with volume analysis for confirmation.
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Moving Averages:
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- sma: Simple Moving Average: A basic trend-following indicator that smooths out price data. Usage: Identify trend direction and serve as dynamic support/resistance levels. Tips: Use multiple SMAs for crossover signals; combines well with volume analysis for confirmation.
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MACD Related:
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- macd: MACD (Moving Average Convergence Divergence): Measures momentum via differences between fast and slow EMAs. Usage: Look for signal line crossovers, centerline crossovers, and divergence patterns for trend changes. Tips: Most effective in trending markets; combine with RSI to avoid false signals in sideways markets.
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MACD Related:
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- macd: MACD (Moving Average Convergence Divergence): Measures momentum via differences between fast and slow EMAs. Usage: Look for signal line crossovers, centerline crossovers, and divergence patterns for trend changes. Tips: Most effective in trending markets; combine with RSI to avoid false signals in sideways markets.
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Momentum Indicators:
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- rsi: RSI (Relative Strength Index): Oscillator measuring momentum to identify overbought (>70) and oversold (<30) conditions. Usage: Look for reversal signals at extreme levels and divergence with price action. Tips: In strong crypto trends, RSI can remain extreme for extended periods; always confirm with trend analysis.
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Momentum Indicators:
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- rsi: RSI (Relative Strength Index): Oscillator measuring momentum to identify overbought (>70) and oversold (<30) conditions. Usage: Look for reversal signals at extreme levels and divergence with price action. Tips: In strong crypto trends, RSI can remain extreme for extended periods; always confirm with trend analysis.
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Volatility Indicators:
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- bbands: Bollinger Bands: Volatility indicator consisting of upper, middle (SMA), and lower bands based on standard deviations. Usage: Identify overbought/oversold conditions, volatility expansion/contraction, and potential breakout zones. Tips: Price touching bands doesn't guarantee reversal; use band squeeze for volatility breakout trades.
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- atr: ATR (Average True Range): Measures market volatility by calculating the average of true ranges over a period. Usage: Set stop-loss levels, position sizing, and identify high/low volatility periods for strategy adjustment. Tips: Higher ATR indicates more volatile conditions; use for risk management rather than directional signals.
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Volatility Indicators:
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- bbands: Bollinger Bands: Volatility indicator consisting of upper, middle (SMA), and lower bands based on standard deviations. Usage: Identify overbought/oversold conditions, volatility expansion/contraction, and potential breakout zones. Tips: Price touching bands doesn't guarantee reversal; use band squeeze for volatility breakout trades.
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- atr: ATR (Average True Range): Measures market volatility by calculating the average of true ranges over a period. Usage: Set stop-loss levels, position sizing, and identify high/low volatility periods for strategy adjustment. Tips: Higher ATR indicates more volatile conditions; use for risk management rather than directional signals.
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- Select indicators that provide diverse and complementary information. Avoid redundancy and focus on the most effective combination for crypto market analysis. Also briefly explain why they are suitable for the given crypto market context. When you tool call, please use the exact name of the indicators provided above as they are defined parameters, otherwise your call will fail. Please make sure to call get_crypto_data first to retrieve the cryptocurrency price data. Then use get_indicators_bulk with a list of the specific indicator names (e.g., ["sma", "rsi", "macd"]). Write a very detailed and nuanced report of the trends you observe. Do not simply state the trends are mixed, provide detailed and finegrained analysis and insights that may help crypto traders make decisions."""
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- Select indicators that provide diverse and complementary information. Avoid redundancy and focus on the most effective combination for crypto market analysis. Also briefly explain why they are suitable for the given crypto market context. When you tool call, please use the exact name of the indicators provided above as they are defined parameters, otherwise your call will fail. Please make sure to call get_crypto_data first to retrieve the cryptocurrency price data. Then use get_indicators_bulk with a list of the specific indicator names (e.g., ["sma", "rsi", "macd"]). Write a very detailed and nuanced report of the trends you observe. Do not simply state the trends are mixed, provide detailed and finegrained analysis and insights that may help crypto traders make decisions."""
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+ """ Make sure to append a Markdown table at the end of the report to organize key points in the report, organized and easy to read."""
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)
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@ -1,9 +1,5 @@
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from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
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import time
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import json
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from tradingagents.agents.utils.agent_utils import get_news, get_global_news
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from tradingagents.dataflows.config import get_config
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def create_news_analyst(llm):
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def news_analyst_node(state):
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@ -1,15 +1,11 @@
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from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
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import time
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import json
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from tradingagents.agents.utils.agent_utils import get_news, get_fear_and_greed
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from tradingagents.dataflows.config import get_config
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def create_social_media_analyst(llm):
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def social_media_analyst_node(state):
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current_date = state["trade_date"]
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ticker = state["ticker_of_interest"]
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coin_name = state["ticker_of_interest"]
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tools = [
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get_news,
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@ -1,8 +1,6 @@
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import time
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import json
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from tradingagents.agents.utils.memory import FinancialSituationMemory
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def create_research_manager(llm, memory):
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def create_research_manager(llm, memory: FinancialSituationMemory):
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def research_manager_node(state) -> dict:
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history = state["investment_debate_state"].get("history", "")
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market_research_report = state["market_report"]
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@ -1,12 +1,8 @@
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import time
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import json
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from tradingagents.agents.utils.memory import FinancialSituationMemory
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def create_risk_manager(llm, memory):
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def create_risk_manager(llm, memory: FinancialSituationMemory):
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def risk_manager_node(state) -> dict:
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company_name = state["company_of_interest"]
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history = state["risk_debate_state"]["history"]
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risk_debate_state = state["risk_debate_state"]
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market_research_report = state["market_report"]
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@ -1,9 +1,6 @@
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from langchain_core.messages import AIMessage
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import time
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import json
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from tradingagents.agents.utils.memory import FinancialSituationMemory
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def create_bear_researcher(llm, memory):
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def create_bear_researcher(llm, memory: FinancialSituationMemory):
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def bear_node(state) -> dict:
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investment_debate_state = state["investment_debate_state"]
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history = investment_debate_state.get("history", "")
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@ -37,7 +34,7 @@ Resources available:
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Market research report: {market_research_report}
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Social media sentiment report: {sentiment_report}
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Latest world affairs news: {news_report}
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Company fundamentals report: {fundamentals_report}
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Coin fundamentals report: {fundamentals_report}
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Conversation history of the debate: {history}
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Last bull argument: {current_response}
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Reflections from similar situations and lessons learned: {past_memory_str}
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@ -1,9 +1,6 @@
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from langchain_core.messages import AIMessage
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import time
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import json
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from tradingagents.agents.utils.memory import FinancialSituationMemory
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def create_bull_researcher(llm, memory):
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def create_bull_researcher(llm, memory: FinancialSituationMemory):
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def bull_node(state) -> dict:
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investment_debate_state = state["investment_debate_state"]
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history = investment_debate_state.get("history", "")
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@ -1,7 +1,3 @@
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import time
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import json
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def create_risky_debator(llm):
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def risky_node(state) -> dict:
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risk_debate_state = state["risk_debate_state"]
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@ -1,8 +1,3 @@
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from langchain_core.messages import AIMessage
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import time
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import json
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def create_safe_debator(llm):
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def safe_node(state) -> dict:
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risk_debate_state = state["risk_debate_state"]
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@ -1,7 +1,3 @@
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import time
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import json
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def create_neutral_debator(llm):
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def neutral_node(state) -> dict:
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risk_debate_state = state["risk_debate_state"]
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@ -1,7 +1,4 @@
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import functools
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import time
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import json
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def create_trader(llm, memory):
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def trader_node(state, name):
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@ -1,10 +1,7 @@
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from typing import Annotated, Sequence
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from datetime import date, timedelta, datetime
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from typing_extensions import TypedDict, Optional
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from langchain_openai import ChatOpenAI
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from typing import Annotated
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from typing_extensions import TypedDict
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from tradingagents.agents import *
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from langgraph.prebuilt import ToolNode
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from langgraph.graph import END, StateGraph, START, MessagesState
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from langgraph.graph import MessagesState
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# Researcher team state
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@ -48,7 +45,6 @@ class RiskDebateState(TypedDict):
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class AgentState(MessagesState):
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company_of_interest: Annotated[str, "Company that we are interested in trading"]
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ticker_of_interest: Annotated[str, "Ticker that we are interested in trading"] # e.g BTC/USDT
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trade_date: Annotated[str, "What date we are trading at"]
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@ -21,7 +21,6 @@ class Propagator:
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"""Create the initial state for the agent graph."""
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return {
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"messages": [("human", ticker)],
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"company_of_interest": ticker,
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"ticker_of_interest": ticker,
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"trade_date": str(trade_date),
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"investment_debate_state": InvestDebateState(
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@ -206,7 +206,7 @@ class TradingAgentsGraph:
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def _log_state(self, trade_date, final_state):
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"""Log the final state to a JSON file."""
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self.log_states_dict[str(trade_date)] = {
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"company_of_interest": final_state["company_of_interest"],
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"ticker_of_interest": final_state["ticker_of_interest"],
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"trade_date": final_state["trade_date"],
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"market_report": final_state["market_report"],
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"sentiment_report": final_state["sentiment_report"],
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