minor fixes

.gitignore

@@ -6,5 +6,4 @@
 /etf-tech-env
 /extras
 /.idea
-/.idea
 __pycache__

config.py

@@ -16,4 +16,4 @@ class Config:
     MAIN_SHEET_NAME = "Companies"
 
     SELECTED_BY_FINANCIALS = "Top Financials"
-    TEMP_NUMBER_TO_SELECT = 5
+    TEMP_NUMBER_TO_SELECT = 15

prompts.py

@@ -21,8 +21,10 @@ class Prompts:
                                   "Do not nest values within the JSON."
     FINANCIAL_INFORMATION_PROMPT = ""
     SELECT_TOP_FINANCIALS_PROMPT = "Select the top {num_companies} companies with the best financials out of the " \
-                                   "companies in the provided list based on the financial metrics provided. For each" \
-                                   " company provide a short 2-3 bulletpoints justification why this company was " \
+                                   "companies in the provided list based on the financial metrics provided." \
+                                   " The investment formula is too look for the lower pe ratio (if pe ratio is available), higher market cap," \
+                                   " lower beta (volatility), higher 5-year and 1-year returns, and closer to 1 analyst recommendation." \
+                                   " For each company provide a short 2-3 bulletpoints justification why this company was " \
                                    "selected. Format your answer as the following JSON:\n" \
                                    "[{{ticker: company_ticker, justification: justification}}, ...]\n" \
                                    "List of companies with financials: {companies}"

select_top_financials.py

@@ -26,7 +26,7 @@ def get_selected_companies(data: pd.DataFrame) -> dict:
         company.update({"ticker": tickers[c]})
     prompt = Prompts.SELECT_TOP_FINANCIALS_PROMPT.format(num_companies=Config.TEMP_NUMBER_TO_SELECT,
                                                          companies=financial_data)
-    response = send_openai_request(prompt=prompt)
+    response = send_openai_request(prompt=prompt, max_tokens=3000)
     selected_df = pd.DataFrame(response)
     selected_df["justification"] = selected_df["justification"].apply(lambda x: " ".join(x))
     selected_df = pd.merge(selected_df, data[financial_columns + ["ticker"]], on="ticker")