stock_analysis_agent: include forecasts in the analyses

src/stock_analysis_agent.py

@@ -12,6 +12,7 @@ from langgraph.graph.message import add_messages
 import logging
 from pydantic import BaseModel, Field
 
+from src.fetch_forecast import FetchForecast
 from src.technical_analysis import TechnicalAnalysis
 from src.fundamental_analysis import FundamentalAnalysis
 from src.ticker_finder import TickerFinder
@@ -26,20 +27,37 @@ def get_stock_prices(
     ticker: str
         The stock ticker symbol to fetch data for.
     """
+    df_past, df_fcst = FetchForecast(ticker).run()
     df, _ = TechnicalAnalysis(
         ticker=ticker,
         fetchperiodinweeks=12,
+        df_past=df_past,
+        df_fcst=df_fcst,
         plot_ta=False,
         savefig=False,
         debug=False).run()
-    if df.shape[0] > 0:
+    
+    if df_past is None:
+        fcst_prices = "Price forecasts could not be obtained"
+        fcst_returns = "Return forecasts could not be obtained"
+    else:
+        df_fcst['Date'] = df_fcst['Date'].astype(str)
+        fcst_prices = df_fcst[['Date','Close']].to_dict(orient='records')
+        fcst_returns = df_fcst[['Date','Returns']].to_dict(orient='records')
+    
+    if df.shape[0] == 0:
+        hist_prices = "Recent price data could not be obtained"
+        indicators = "Indicator data could not be obtained"
+    else:
         df['Date'] = df.index.astype(str)
         # split the data into price and indicators, and take the last 10 days of data
-        dict_price = df[['Date','Close', 'High', 'Low', 'Open', 'Volume']].iloc[-10:,:].to_dict(orient='records')
+        hist_prices = df[['Date','Close', 'High', 'Low', 'Open', 'Volume']].iloc[-10:,:].to_dict(orient='records')
         indicators = df[['VWAP', 'RSI', 'StochOsc', 'MACD', 'MACDsig', 'MACDdif']].iloc[-10:,:].to_dict(orient='records')
-        return {'stock_price': dict_price, 'indicators': indicators}
-    else:
+    
+    if (df_past is None) or (df.shape[0] == 0):
         return f"Error fetching technical data for ticker: {ticker}"
+    else:
+        return {'recent prices': hist_prices,  "forecasted prices": fcst_prices, "forecasted returns": fcst_returns, 'indicators': indicators}
 
 @tool
 def get_financial_metrics(
@@ -62,6 +80,7 @@ class StockAnalysisResponse(BaseModel):
     """Stock Analysis Response Schema"""
     stock: str = Field(description="Stock symbol")
     price_analysis: str = Field(description="Detailed analysis of stock price trends")
+    forecast_analysis: str = Field(description="Detailed analysis of stock price forecasts")
     technical_analysis: str = Field(description="Detailed analysis of technical indicators")
     fundamental_analysis: str = Field(description="Detailed analysis of financial metrics")
     final_summary: str = Field(description="Conclusive summary of the analyses above")
@@ -117,7 +136,7 @@ class StockAnalyst():
         and based on the analysis, provide receommended action (see below) for details.
 
         You have access to the following tools:
-        1. **get_stock_prices**: Retrieves the latest stock price, historical price data and technical Indicators like VWAP, RSI, Stochastic Oscillator and MACD metrics. 
+        1. **get_stock_prices**: Retrieves the historical price data, technical indicators like VWAP, RSI, Stochastic Oscillator and MACD metrics, forecasted prices and relative returns for the next 5 business days. 
         2. **get_financial_metrics**: Retrieves key financial metrics, such as revenue, earnings per share (EPS), price-to-earnings ratio (P/E), and debt-to-equity ratio.
 
         ### Your Tasks:
@@ -127,9 +146,10 @@ class StockAnalyst():
             a) A summary of recent stock price movements, highlighting final available closing prices.
             b) A summary of trends and potential resistance.
             c) A summary of technical indicators (e.g., whether the stock is overbought or oversold).
-            d) A summary of Financial health and performance based on financial metrics.
-            e) A final, conclusive synthesis that highlights key concerns and strenghts 
-            f) Recommended action among following options:
+            d) A summary of forecasted returns and closing prices for the next 5 business days.
+            e) A summary of Financial health and performance based on financial metrics.
+            f) A final, conclusive synthesis that highlights key concerns and strenghts 
+            g) Recommended action among following options:
                 - strong sell: if there are overwhelmingly bad signals
                 - sell: if there are some bad signals
                 - hold: there are either neutral signals, or good signals mixed with bad signals
@@ -139,6 +159,7 @@ class StockAnalyst():
         ### Constraints:
         - Use only the data provided by the tools.
         - If any tool fails to provide data, clearly state that in your summary.
+        - Try to provide a balanced synthesis based on the data provided by the tools.
         - Avoid speculative language; focus on observable data and trends.
         - Ensure that your response is objective, concise, and actionable.
         """